Arts, Faculty of
Psychology, Department of
DSpace
UBCV
Alaifan, Nada
Doctor of Philosophy - PhD
University of British Columbia
The assumption that memory is superior for emotional events is widespread and appears in most introductory psychology textbooks as well as in the popular media. However, the evidence from episodic memory research remains mixed, which leaves uncertainty as to how emotions influence memory and when emotional enhancement effects are likely to occur. My dissertation consisted of a meta-analysis along with three large-scale experiments, using memory tests that varied by the extent to which they relied on integrative/environmentally-driven versus elaborative/self-initiated processing (Craik, 1983,1986): Experiment 1 focused on old/new recognition of valenced and neutral pictures, Experiment 2 used associative recognition of randomly-paired valenced and neutral pictures, and Experiment 3 used free recall to assess memory for valenced and neutral pictures. The main findings from my meta-analysis and from my three large-scale experiments, validate the common belief that emotional events are remembered better than neutral ones, validating also more specific assumptions that the emotional enhancement effect is larger for negative than for positive pictures, larger in free recall than in recognition memory, and also larger after a long retention interval than a short one. My meta-analysis and experimental work were guided by a novel theoretical account I call Emotional Events Interrupt-Processing Augment (EEI/PA) theory. The theory provides a cognitive-processing explanation of how valence influences episodic memory for emotional events. The EEI/PA theory predicted the main findings from my meta-analysis, as well as those of my experiments, all of which tested the EEI/PAâs core assumption: that valence influences encoding-phase accommodative/elaborative processing, but not assimilative/integrative processing. My dissertation demonstrates that the EEI/PA theory can completely explain the findings based on the influence of valence, like arousal-based theories have been used to explain similar kinds of findings. My dissertation further highlights the need for an integrated account that identifies the unique contributions of both valence and arousal to the way in which we remember emotional events.
VALENCE INFLUENCES ON EPISODIC MEMORY: A COGNITIVE PROCESSING ACCOUNT by Nada Alaifan A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Psychology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) October 2025 © Nada Alaifan, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Valence Influences on Episodic Memory: A cognitive Processing Account submitted by Nada Alaifan in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Psychology Examining Committee: Peter Graf, Professor, Psychology, UBC Supervisor Daniela Palombo, Associate Professor, Psychology, UBC Supervisory Committee Member Joelle LeMoult, Associate Professor, Psychology, UBC Supervisory Committee Member Nancy Sin, Associate Professor, Psychology, UBC University Examiner Yann Cornil, Associate Professor, Sauder School of Business, UBC University Examiner Kimberly Chiew, Associate Professor, Psychology, University of Denver External Examiner iii Abstract The assumption that memory is superior for emotional events is widespread and appears in most introductory psychology textbooks as well as in the popular media. However, the evidence from episodic memory research remains mixed, which leaves uncertainty as to how emotions influence memory and when emotional enhancement effects are likely to occur. My dissertation consisted of a meta-analysis along with three large-scale experiments, using memory tests that varied by the extent to which they relied on integrative/environmentally-driven versus elaborative/self-initiated processing (Craik, 1983,1986): Experiment 1 focused on old/new recognition of valenced and neutral pictures, Experiment 2 used associative recognition of randomly-paired valenced and neutral pictures, and Experiment 3 used free recall to assess memory for valenced and neutral pictures. The main findings from my meta-analysis and from my three large-scale experiments, validate the common belief that emotional events are remembered better than neutral ones, validating also more specific assumptions that the emotional enhancement effect is larger for negative than for positive pictures, larger in free recall than in recognition memory, and also larger after a long retention interval than a short one. My meta-analysis and experimental work were guided by a novel theoretical account I call Emotional Events Interrupt-Processing Augment (EEI/PA) theory. The theory provides a cognitive-processing explanation of how valence influences episodic memory for emotional events. The EEI/PA theory predicted the main findings from my meta-analysis, as well as those of my experiments, all of which tested the EEI/PAâs core assumption: that valence influences encoding-phase accommodative/elaborative processing, but not assimilative/integrative processing. My dissertation demonstrates that the EEI/PA theory can completely explain the findings based on the influence of valence, just like arousal-based theories have been used to explain similar kinds of findings. My dissertation further highlights the need for an integrated account that identifies the unique contributions of both valence and arousal to the way in which we remember emotional events. iv Lay Summary My dissertation validates the common beliefs that memory for emotional events, like a car accident or a wedding celebration, is better than for neutral events, with negative events being better remembered than positive ones. It also supports other specific assumptions about memory, such as the memory advantage for emotional events being greater when memory is tested using a free recall rather than a recognition test, and the tendency for emotional memories to last longer than neutral ones. My dissertation further provides a novel explanation of these findings by focusing on the cognitive processing associated with the valence of events. The next step for this line of research is to integrate both valence and arousal perspectives to develop a better understanding of how emotional events influence memory. v Preface This dissertation was prepared according to the requirements for a PhD thesis of the University of British Columbia, Faculty of Graduate and Postdoctoral Studies. This document is an original intellectual product of the author, Nada Alaifan. All of the work in the present document was conducted in the Memory and Cognition Lab at the University of British Columbia, where Peter Graf is the principal Investigator. Nada is the author of the content of this dissertation. She conducted and/or supervised all data collection, and she programmed all experiments, and analyzed all data for all work reported herein. Peter Graf, assisted with project design and conceptualization, supervised data analyses, and suggested edits to the writing. Generative AI (ChatGPT) was used solely for occasional editorial purposes (e.g., grammar checking). The experiments reported in this dissertation were approved by the University of British Columbiaâs Behavioural Research Ethics Board, Certificate number H03-80566. A version of Chapter 2 has been prepared to be submitted for publication. Nada Alaifan is the lead author and principal investigator responsible for the conceptualization, the methodology (searching databases, screening and assessing studies for inclusion, developing the coding protocol, coding variables), the data-analysis and for writing the manuscript. Peter Graf supervised the project, contributed to conceptualization and methodological decisions, and reviewed and suggested editing to the manuscript. vi Table of Contents Abstract ......................................................................................................................................................... iii Lay Summary ................................................................................................................................................ iv Preface ........................................................................................................................................................... v Table of Contents ......................................................................................................................................... vi List of Tables ................................................................................................................................................ ix List of Figures ............................................................................................................................................... x Acknowledgments ...................................................................................................................................... xii Dedication ................................................................................................................................................... xiv Chapter 1: Episodic Memory for Emotional Events ................................................................................... 1 1.1: Status Quo of Research on Episodic Memory for Valenced Events .................................................... 5 1.2: Theoretical Accounts of Emotional Enhancement Effects in Episodic Memory ................................... 6 1.3: Sex Differences in Memory Performance for Emotional Events ........................................................ 14 1.4: Overview of Dissertation .................................................................................................................... 15 Chapter 2: Recall and Recognition of Valenced Pictures: A Meta-Analysis ......................................... 17 2.1: Method ............................................................................................................................................... 28 2.1.1: Inclusion and Exclusion Criteria ................................................................................................. 29 2.1.2: Variables Coding Procedure ...................................................................................................... 32 2.1.3: Effect size calculations ............................................................................................................... 34 2.1.4: Overview of data analysis .......................................................................................................... 35 2.1.5: Analysis model ........................................................................................................................... 36 2.1.6: Assessing heterogeneity of effect sizes ..................................................................................... 36 2.1.7: Assessing publication bias ......................................................................................................... 37 2.2: Results ............................................................................................................................................... 37 2.2.1: Records, Experiments and Participants ..................................................................................... 37 2.2.2: Memory for Positive and Negative Pictures on Recall and Recognition Tests ........................... 39 2.2.3: Moderators of the Emotional Enhancement Effect ..................................................................... 40 vii 2.2.4: Risk of Publication Bias .............................................................................................................. 42 2.3: Discussion ......................................................................................................................................... 46 2.3.1: External and Internal Validity ...................................................................................................... 46 2.3.2: Theoretical Implications .............................................................................................................. 49 2.3.3: Conclusion .................................................................................................................................. 51 Chapter 3: The Influence of Valence on Picture Recognition ................................................................. 52 3.1: Method ............................................................................................................................................... 55 3.1.1: Participants and Design ............................................................................................................. 55 3.1.2: Materials ..................................................................................................................................... 55 3.1.3: Procedure ................................................................................................................................... 59 3.2: Results ............................................................................................................................................... 62 3.2.1: Data Preparation ........................................................................................................................ 63 3.2.2: Encoding Phase ......................................................................................................................... 63 3.2.3: Testing Phase ............................................................................................................................ 66 3.3: Discussion ......................................................................................................................................... 73 Chapter 4: The Influence of Valence on Associative Recognition of Picture Pairs .............................. 78 4.1: Method ............................................................................................................................................... 82 4.1.1: Participants and Design ............................................................................................................. 82 4.1.2: Materials ..................................................................................................................................... 83 4.1.3: Procedure ................................................................................................................................... 85 4.2: Results ............................................................................................................................................... 88 4.2.1: Data Preparation ........................................................................................................................ 88 4.2.2: Encoding Phase ......................................................................................................................... 89 4.2.3: Testing Phase ............................................................................................................................ 93 4.3: Discussion ......................................................................................................................................... 97 Chapter 5: The Influence of Valence on Free Recall .............................................................................. 101 5.1: Method ............................................................................................................................................. 104 5.1.1: Participants and design ............................................................................................................ 104 5.1.2: Materials ................................................................................................................................... 105 viii 5.1.3: Procedure ................................................................................................................................. 107 5.1.4: Picture Recollection Scoring .................................................................................................... 110 5.2: Results ............................................................................................................................................. 110 5.2.1: Data Preparation ...................................................................................................................... 111 5.2.2: Encoding Phase ....................................................................................................................... 112 5.2.3: Testing Phase .......................................................................................................................... 115 5.3: Discussion ....................................................................................................................................... 123 Chapter 6: General Discussion ................................................................................................................ 129 6.1: Main Findings and Implications ....................................................................................................... 130 6.2: Limitations ........................................................................................................................................ 134 6.3: Conclusion: ...................................................................................................................................... 136 References ................................................................................................................................................. 138 Appendices ................................................................................................................................................ 207 Appendix 1. Searches carried out in databases including individual searches of search terms and the combinations of searches used for records selection. ............................................................................ 207 Appendix 2. Standardized data coding manual used to code the data by defining each variable and providing the precise steps for coding it. ................................................................................................ 210 Appendix 3. Summary of the experiments included in the meta-analysis. ............................................. 221 Appendix 4. Protocol of Transcription, Scoring of Audio Recordings of Memory for Emotional Pictures ................................................................................................................................................................ 227 Appendix 5. Transitions in Recall ........................................................................................................... 230 ix List of Tables Table 2.1. Effect Sizes (Hedges g) Corresponding to Moderators of Recall and Recognition of Valenced Pictures ......................................................................................................................................................... 44 Table 2.2. Effect Size (Hedges g) for Analysis of Potential Moderators of Recall and Recognition of Valenced Pictures ......................................................................................................................................... 45 Table 3.1. Valence and Arousal Ratings Used for Selecting Pictures and for the Selected Pictures â for the Negative, Neutral, and Positive Pictures Used in Experiment 1 ................................................................... 57 Table 3.2. Recognition Decision Times on Hits and False Alarms for Negative, Neutral, and Positive Pictures ......................................................................................................................................................... 70 Table 4.1. Valence and Arousal Ratings for Selecting Criteria and for the Selected Pictures for Negative, Neutral, and Positive Pictures Used in Experiment 2 .................................................................................... 83 Table 4.2. Hits and False Alarms for each Picture Pair Valence Condition and Test-Pair Type ................... 94 Table 4.3. The Means Median Decision Times for Negative, Neutral, and Positive Picture Pairs ................ 95 Table 5.1. Valence and Arousal Ratings for Selecting Pictures and for the Selected Pictures â for the Negative, Neutral, and Positive Pictures Used in Experiment 3 ................................................................. 106 x List of Figures Figure 1.1. Events and Processes Assumed to be Required for the Occurrence of Arousal and Valence Responses According to the EEI Theory ........................................................................................................ 8 Figure 2.1. Events and Processes Assumed to be Required for Arousal and Valence Responses According to the EEI Theory .......................................................................................................................................... 23 Figure 2.2. A Flowchart Showing the Steps Used for Identifying and Selecting Records for Inclusion in the Meta-Analysis ................................................................................................................................................ 31 Figure 2.3. Distribution of Records by Year of Publication ............................................................................ 38 Figure 2.4. A Funnel Plot of Overall Effect Size Estimates ........................................................................... 43 Figure 3.1. The Sequence of Specific Events with their Duration on a Single Trial in the Encoding Phase and Testing Phase of Experiment 1 .............................................................................................................. 60 Figure 3.2. Mean Attractiveness Ratings (left panel) and Mean Repulsiveness Ratings (right panel) for Negative, Neutral, and Positive Pictures ....................................................................................................... 65 Figure 3.3. Mean of Median Valence Rating Times for Negative, Neutral, and Positive Pictures ................ 66 Figure 3.4. Mean Proportion of Hits (left panel) and False Alarms (right panel) for Negative, Neutral, and Positive Pictures ............................................................................................................................................ 67 Figure 3.5. Mean Corrected Recognition for Negative, Neutral, and Positive Pictures for All Participants (left panel) and for the Middle 50% of Participants (right panel) .......................................................................... 69 Figure 3.6. Mean Proportion of Hits (left panel) and False Alarms (right panel) on the Arousal-Controlled Negative, Neutral, and Positive Picture Subsets ........................................................................................... 71 Figure 3.7. Mean Proportion Corrected Recognition for the Arousal-Controlled Negative and Positive Picture Subsets ............................................................................................................................................. 72 Figure 4.1. The Sequence of Specific Events with their Duration on a Single Trial in the Encoding Phase and in the Testing Phase of Experiment 2 .................................................................................................... 86 Figure 4.2. Mean Association-Difficulty Ratings for Negative, Neutral, and Positive Picture Pairs ............... 90 xi Figure 4.3. Mean of Median Association-Difficulty Rating Times for Negative, Neutral, and Positive Picture Pairs .............................................................................................................................................................. 92 Figure 4.4. Mean Corrected Associative-Recognition Performance for Negative, Neutral, and Positive Picture Pairs .................................................................................................................................................. 97 Figure 5.1. The Sequence of Events with their Duration on a Single Trial in the Encoding Phase of Experiment 3 ............................................................................................................................................... 108 Figure 5.2. Mean Pleasantness Ratings for Negative, Neutral, and Positive Pictures ................................ 113 Figure 5.3. Mean of Median Valence Rating Times for Negative, Neutral, and Positive Pictures .............. 114 Figure 5.4. Mean Proportion Recalled for Negative, Neutral, and Positive Pictures ................................... 116 Figure 5.5. Mean Proportion of Pictures Recalled from each Valence Condition in 1st Segment (left panel) and 3rd Segment of the Recall Protocols (right panel) ............................................................................... 118 Figure 5.6. Mean Proportion of Negative, Neutral, and Positive Pictures Recalled at each Output Position .................................................................................................................................................................... 119 Figure 5.7. Mean Proportion for Negative, Neutral, and Positive Pictures Recalled at each Output Position .................................................................................................................................................................... 120 Figure 5.8. Mean Proportion of Three Types of Transitions in Free RecallâNegative-Negative, Neutral-Neutral, and Positive-Positiveâas a Function of Recall Output Position ................................................... 122 xii Acknowledgments This has been a journey of self-doubt, despair, determination, and eventual growth. I would have never reached this destination without the many people who have encouraged, uplifted, and nurtured me along the way. First, I would like to express my deepest appreciation and gratitude to my supervisor Dr. Peter Graf for all his invaluable guidance, generosity with his time, and persevering support throughout my doctoral studies. He taught me to think with rigor and to challenge even the most well-grounded assumptions. Peter, thank you for pushing me to reach further than I thought possible and for preparing me to pursue a lifetime of academia. I appreciate your insights, patience, wisdom and the valuable life lessons you shared. I would like to extend my sincere gratitude to my committee members, Dr. Daniela Palombo and Dr. Joelle LeMoult for their suggestions and insights that strengthened this research. I owe a special thanks to Daniela, for her wholehearted support throughout this process, for including me as a part of her lab community, and for providing me with a nurturing space where I could flourish. Daniela, I am truly grateful that our paths crossed. I will always value our thoughtful discussions about memory and emotion, and the conversations about equity, diversity, and inclusion. Thank you for being there whenever I needed you. I am also thankful to Joelle for her thought-provoking questions that helped me to see my research from a different perspective. Furthermore, I wish to thank my university examiners, Drs. Yann Cornil and Nancy Sin, for their thoughtful questions and the invaluable insights shared during the examination. It was a privilege to have you both as part of this special milestone in my academic journey. A huge thank you to my lab mates: Natasha and Anna for their friendship, constant encouragement, and compassion that kept me going through this daunting degree. I would also like to express my deep gratitude to Nancy for her endless support and encouragement throughout every stage of this journey and for consistently encouraging me to believe in myself when things seemed impossible. xiii Special thanks also go out to my incredible research assistants for their enthusiasm and dedication to the project. Additionally, I would also like to thank the members of the Memory and Imagination Lab; It has been a great pleasure getting to know each of you, and Iâve truly enjoyed the time we spent together and the ideas we shared along the way. My grateful heart goes out to my dear husband Abdullah; thank you for being by my side and for taking care of me and our kids during the times when I couldn't hold myself together. I deeply appreciate your unending patience and the sacrifices you made so I could pursue this opportunity. Sharing this life with you is a true blessing. To my beloved kids, Deemah, Meshari, and Aaliyah, the joys of my life, thank you for the boundless love, hugs, and inspiration you bring. Forgive me for the moments I wasnât there, the games and stories I missed and the adventures I postponed. May this work remind you that dedication to your dreams requires sacrifice, but that my love for you has never wavered. I am forever grateful to my devoted mother, Nora, and my late father, Mohammed (in loving memory), for being enduring role models with their resilience in the face of adversity, for cultivating in me a lifelong love of learning, and for nurturing my ability to maintain moral fortitude no matter what the circumstances. I am your ever-loving daughter, forever inspired and standing strong because of you. I have to mention, too, my siblings for their unwavering love and continuous support during the many years I was away. Thank you for believing in me, and for always reminding me that I am capable. I would also like to extend my gratitude to friends, near and far, for their enduring friendship and generosity of spirit Finally, I acknowledge with gratitude the Ministry of Education and King Saud University in Saudi Arabia and the Saudi Arabian Cultural Bureau in Canada for their patronage and the generous doctoral scholarship. I also acknowledge with respect that my time in this program unfolded on the unceded, traditional, and ancestral lands of the xÊ·mÉÎ¸kÊ·Éy ÌÉm (Musqueam) First Nations. I am grateful for the opportunity to live, work, and learn on this land as an uninvited guest. xiv n Dedication To my anchor, my soulmate, and dear sister, May your genuine care and unrelenting support have carried me through every step of this path. I couldnât have made it this far without you. 1 Chapter 1: Episodic Memory for Emotional Events Emotional events like a graduation celebration or a funeral service tend to leave lasting memories, whereas routine events like taking a shower or commuting to work on a particular day are often quickly forgotten. This phenomenonâbetter memory for emotional events than for non-emotional or affectively neutral eventsâis called an emotional enhancement effect. While evidence of an emotional enhancement effect is firmly established in research on autobiographical memory (Amemiya & Sekiguchi, 2006; Daum et al., 1996; Lanciano & Curci, 2011; Berntsen & Rubin, 2002; Brown & Kulik, 1977; D'Argembeau et al., 2003; St. Jacques & Levine, 2007), the evidence from episodic memory research is mixed, with some studies revealing enhanced memory for emotional events (e.g., Bradley et al., 1992; Davidson & Vanegas, 2015; Dolcos & Cabeza, 2002; Kensinger et al., 2006; Sharot & Phelps, 2004) and others showing no such enhancement (e.g., Bergmann et al., 2012; Sharot & Yonelinas, 2008) or even showing worse memory for emotional than for neutral events (e.g., Bessette-Symons, 2018; Bowen & Spaniol, 2011; Wang, 2014). The goals of my dissertation were to shed light on the research status quo, to clarify the circumstances under which emotional events show an advantage in episodic memory, and also to increase theoretical understanding of the findings. Episodic memory has received a variety of definitions since Tulving (1972) introduced the concept, and yet its usefulness continues to be questioned (Addis & Szpunar, 2024). In view of this state of affairs, for my dissertation, I relied on functionalism (James, 1890) to define episodic memory as the ability we use to form and retrieve certain kinds of to-be-remembered (TBR) events in a conscious manner. More precisely, I use episodic memory for situations in which TBR events are provided or specified, for example, by an experimenter, a teacher, a textbook, or an instruction manual, and where remembrance is required at a particular place and time in response to a recall or recognition test. 2 In a functional definition, episodic memory differs from autobiographical memory only in one respect: In how the TBR events are specified. For episodic memory, the TBR events are always specified by an external agent such as an experimenter or a teacher or an instruction manual, whereas for autobiographical memory, the selection of the TBR eventsâwhat is chosen for encoding into memoryâis decided by the rememberer. In episodic memory research, encoding of TBR events is commonly guided by an experimenterâs instructions, whereas in autobiographical memory research, encoding is guided by the remembererâs momentary needs, interests, beliefs and biases (Bradley et al., 1992; Tulving, 1972, 1983). I chose my dissertation focus, in part, to answer questions about the empirical evidence on emotional enhancements in episodic memory performance, but more importantly also to investigate theoretical claims about episodic memory for emotional events. My work was guided by basic assumptions about episodic memory like those stemming from levels of processing (Craik & Lockhart, 1972; Craik, 1983,1986; Fisher & Craik, 1977), from transfer appropriate processing (Morris et al.,1977; Tulving & Thomson, 1973) and from analyses of the processing differences that underlie recall and recognition test performance (Anderson & Bower, 1972; Watkins & Gardiner, 1979; Mandler, 1980a; Jacoby, 1991; Craik, 1983, 1986; Craik and McDowd, 1987; Kintsch, 1970; Yonelinas, 2002). However, none of the assumptions listed above is about emotional events, and for this reason, I also relied on more specific speculation about how valence affects episodic memory, including a proposal by Bowen, Kark and Kensinger (2018), which they called Negative Emotional Valence Enhances Recapitulation (NEVER) model. And for a more cognitive perspective, I relied primarily on theoretical claims that have been advanced to explain how emotional events trigger arousal and valence responses (e.g. MacDowell & Mandler, 1989; Mandler, 1980b,1982, 1990, 1992; cf. Whittlesea & Williams, 1998, 2000, 2001a, 2001b), as well as on more familiar theoretical speculations about how the cognitive system responds to emotional events (Clore & Schnall, 3 2005; Clore et al., 2001; Fredrickson, 1998, 2001, 2004; Fredrickson & Branigan, 2005; Gasper & Clore, 2002; Schwarz & Clore, 1983). Emotional eventsâdefined as any stimuli or situation that elicits emotional responsesâcome in many different forms. Emotional events can be natural phenomena like a thunderstorm or a rainbow, or they can be human creations like an inspiring speech or work of art. Such events can be short lasting like a scream in the night or long enduring like a war or global pandemic, planned like a wedding ceremony or birthday celebration, or unexpected like a car accident or an earthquake. Emotional events can be experienced directly as when playing with a fluffy kitten or indirectly as when viewing a video recording or a picture of a playful kitten. A recent meta-analysis by Pereira et al. (2023) suggests approximately 46% of the experiments pertaining to valence and arousal employed picture stimuli, and for this reason, my dissertation also focused on memory for emotional pictures. The extensive use of pictures in previous research on memory for emotional events is likely due to several factors. Perhaps the most important is that pictures can be used to capture a wide variety of events including celebrations, sunsets on the beach, children or animals at play, traffic accidents or natural disasters. The extensive use of picture stimuli is also likely due to their effectiveness for inducing strong and lasting emotions such as fear and disgust or happiness and joy (Bernat et al., 2006; BoÄa et al., 2023; Uhrig et al., 2016; Lench et al., 2011; Mikels et al., 2005; Siedlecka & Denson, 2019). Using pictures in memory experiments also allows the experimenter to manipulate the properties (e.g., making them colored vs. grayscale, large or small), making it easy to control variables related to encoding them into memory (e.g. time available for encoding). Another major factor for the widespread use of pictures is likely to be the availability of databases with extensive normative data on pertinent attributes of large sets of pictures (e.g., the International Affective Picture System (IAPS) by Lang et al., 2008; the Nencki Affective Picture System (NAPS) by Marchewka et al., 2014). 4 Emotional events are commonly categorized according to the subjective responses they elicit, the most prominent of which are valence and arousal. Valence refers to an eventâs propensity to elicit a negative or positive subjective response (Lang et al., 1993; Russell, 1980; Russell, 2003). Encountering a thunderstorm while driving at night may elicit negative feelings, such as fear and anxiety, while a birthday party tends to elicit positive feelings, such as happiness and joy. Arousal refers to an eventâs propensity to elicit physiological changes, either by energizing the body (e.g., increasing heart rate or blood pressure) or by calming it (e.g., decreasing heart rate and breathing rates) (Barrett & Russell, 1999; Russell, 1980; Steriade, 1996). A roller coaster ride tends to be exciting and agitating, while an event like a sunset on the beach tends to be calming and tension releasing. My dissertation focused on valence, primarily because it is a fundamental attribute of emotional experience, and also because its influence on episodic memory is still poorly understood, despite emerging evidence that valence produces a distinct effect on memory performance (Bowen & Kensinger, 2017a; Charles et al., 2003; Kark & Kensinger, 2015; Keightley et al., 2011; Kensinger & Schacter, 2006; Robinson-Riegler & Winton,1996; for review see Williams et al., 2022). My dissertation also focused on valence because it is more prominent than arousal in our experience of emotional events (Barrett, 2006). According to Russell (1991), valence words are present in the emotional lexicons of most cultures, and in some cultures it is the only attribute identified. In emotional development, valence is typically the first attribute children acquire and use to categorize emotions (e.g., good vs. bad) (Nook et al., 2017; Widen, 2013). In daily life, we tend to emphasize valence when reporting our feelings (e.g., âI feel awfulâ) or when talking about our emotional experiences and memories (e.g., âI received bad newsâ), and research also has shown that people prioritize valence information when reporting their affective state (Feldman, 1995). Valence also tends to stand out when we search our memories (e.g., When was I this happy? Have I ever been this scared?) (Schulkind & Woldorf, 2005; 5 Sheldon & Donahue, 2017), and it is known to guide both intuitive and deliberate daily life decision making (e.g., Will this make me feel good or bad?) (Carruthers, 2018; Gilbert & Wilson, 2005; Yao et al., 2019). 1.1: Status Quo of Research on Episodic Memory for Valenced Events As part of my dissertation, I conducted the meta-analysis, reported in Chapter 2, to gain a better understanding of what is currently known about episodic memory for valenced events. This analysis included experiments that used pictures as TBR stimuli, that had healthy young adults as participants, and that reported results from recognition and free recall memory tests. The focus of the meta-analysis was on variables known or suspected to influence episodic memory such as picture valence type (negative/positive vs. neutral), type of memory test (recognition vs. free recall), duration of the retention interval (short vs. long), and sex of participants (male vs female). The meta-analysis highlights the enormous method variability in previous research. Across studies, there is a wide range in the number of TBR pictures used, from as few as 22 pictures (e.g., Zarubin et al., 2020) to as many as 450 pictures (e.g., Pais-Vieira et al., 2016). Pictures were chosen from diverse sources such as from Flicker and Google Images as well as from standardized picture databases (e.g., IAPS and NAPS). The number of participants across studies also varied from as few as 10 participants (e.g., Hamann et al., 1999) to as many as 646 participants (e.g., Schu Ìmann et al., 2018). The studies varied in the amount of time allowed for viewing pictures in the encoding phase, ranging from 250 milliseconds per picture (e.g., Harris & Pashler, 2005) to 10 seconds per picture (e.g., Kugler & Kuhbandner, 2015; Xing & Isaacowitz, 2006), and there also were differences in the instructions for encoding (e.g., no instructions or instructions to focus on perceptual or semantic properties of pictures). This variability in the methods is the most likely contributor to the current mixed findings on episodic memory for valenced pictures. 6 The main findings from my meta-analysis, which included 152 experiments and data from 12,227 young adult participants, were as follows: a medium-to-large emotional enhancement effect on episodic memory performance, an emotional enhancement effect that was larger for negative than positive pictures, larger when memory was assessed with recall rather than with recognition tests, and also larger after a long rather than short retention interval. 1.2: Theoretical Accounts of Emotional Enhancement Effects in Episodic Memory Both valence and arousal are recognized as important attributes of emotional experiences, and yet, only arousal is typically invoked in theoretical accounts of emotional enhancement effects in episodic memory (e.g., Cahill & McGaugh, 1996; Crowley et al., 2019; Clewett & Murty, 2019; Hamann, 2001; Mather & Sutherland, 2011; McGaugh, 2000, 2004; Storbeck & Clore, 2008; Talmi, 2013). However, the NEVER model (Bowen et al., 2018) stands apart from such efforts by focusing on influences specifically due to valence. NEVER is a model of the neural/brain activity that accompanies the encoding and recollection of valenced and non-valenced events, and as such, it does not make assumptions about specific types of cognitive processes that yield enhancement effects in memory. To fill this gap , I turned to a theory that is rarely cited in the contemporary literature by Mandler and colleagues (Mandler, 1980b, 1982, 1990, 1992; MacDowell & Mandler, 1989; see also Whittlesea & Williams, 1998, 2000, 2001a, 2001b), which postulates a set of specific processing assumptions for explaining how emotional events cause arousal and valence responses. I used this theory in conjunction with several basic assumptions about the processing requirements of different encoding and retrieval tasks (Craik, 1983, 1986; Craik & McDowd, 1987; Morris et al., 1977; Tulving & Thomson, 1973; Yonelinas, 2002) to guide my dissertation research. NEVER (Bowen et al., 2018) posits that compared to non-valenced and positive events, negative events are associated with enhanced sensory-perceptual processing, and that this enhanced processing 7 occurs at the time of encoding and also at the time of memory retrieval. The model further suggests a lesser degree of enhanced processing occurs for positive events compared to neutral events. The NEVER model (Bowen et al., 2018) is a good fit for two main findings from the meta-analysis in Chapter 2: the occurrence of an emotional enhancement effect in episodic memory, and also the larger enhancement effect for negative than for positive events. However, for a cognitive understanding of what gives rise to these effects, what is responsible for the enhanced processing of valenced events postulated by the NEVER model, I turned to Mandlerâs theory, which originally called the discrepancy/evaluation theory, but I prefer the more descriptive label, Emotional Events Interrupt (EEI) theory (Mandler, 1980b, 1982, 1990; MacDowell & Mandler, 1989). The EEI theory makes several assumptions which are depicted in Figure 1.1. First, it assumes the cognitive system functions with knowledge structures called schemas (Bartlett, 1932; Evans, 1967; Mandler, 1984; Rumelhart, 1980; Rumelhart & McClelland, 1985; Schmidt, 1975). Schemas are used for building a mental model of the world (i.e. they specify what are typical parts of a given situation and what is likely to happen), and they allow us to interact with the situation in an effective manner (Bartlett, 1932; Ghosh & Gilboa, 2014; Rumelhart, 1980; Rumelhart & McClelland, 1985; Rumelhart & Ortony, 1977; Piaget, 1952; 1954). Second, the EEI theory assumes when an event happens which was not anticipated by the cognitive system, this creates an interruption in normal or routine processing and triggers an arousal/attention response. Third, the theory posits that an event is perceived as positive or negative depending on how the cognitive system copes with the processing interruption. If the current event is similar to the anticipated event, there is no processing interruption, the system assimilates the event into the current knowledge structure, and the EEI theory postulates for this case that the event will be experienced as neutral or mildly positive. By contrast, if the system is unable to assimilate the event into the currently active schemas, the cognitive system attempts to accommodate the schema, switch to a 8 different schema or create a new schema, and if these efforts succeed, the event will be experienced more intensely and positively. Finally, if the system is unable to accommodate the current event, the EEI theory posits that the event will be experienced negatively, with the intensity of the emotion correlating with the amount of effort invested to accommodate the interruption in processing. Figure 1.1. Events and Processes Assumed to be Required for the Occurrence of Arousal and Valence Responses According to the EEI Theory Note. Arousal is triggered when there is an interruption in routine processing, where the activated schemas are inconsistent with the current event; the magnitude of arousal determined by the amount of accommodative processing required. Valence is determined by the success or failure of the accommodative processing. 9 The core concepts of the EEI theoryâschemas, assimilation and accommodationâmap directly onto critical concepts invoked to explain the functioning of episodic memory. A schema is a type of memory representation (Bartlett, 1932; Bobrow & Norman, 1975; Mandler, 1984; Rumelhart, 1980; Rumelhart & McClelland, 1985; Rumelhart & Ortony, 1977), and the processes of assimilation and accommodation pertain to the integration and elaboration processes by which these representations are thought to be changed by experience and learning (Anderson & Bower,1972; Anderson & Reder, 1979; Craik & Lockhart, 1972; Klein & Kihlstrom,1986; Macken & Hampson,1993; Mandler, 1979, 1980b, 1992). Assimilation is said to occur when understanding of and response to events and ideas is managed in a routine manner by means of existing representations/schemas; accommodation is said to occur when understanding of and response to events and ideas requires modifying existing representations or creating entirely new representations (Bless & Fiedler, 2006; Gallagher & Reid, 1981; Koffka, 1935; Piaget, 1952; 1976; Von Glasersfeld, 1995). In theoretical accounts of episodic memory, when a representation is activated in the course of assimilating an event or idea, this has the effect of increasing its integration, meaning it builds up the internal cohesion of the representation or the strength of the associations among its components (Mandler, 1980a; Mandler et al., 1986; Mandler et al., 1969; Rumelhart, 1980; Rumelhart & Ortony, 1977). In turn, the degree of integration of a representation is assumed to determineâamong other thingsâthe familiarity we experience when subsequently encountering the represented event, and familiarity is known to be a basis for making recognition decisions (Anderson & Bower, 1972; Graf & Mandler, 1984; Mandler, 1979, 1980b; Mandler et al., 1986; Yonelinas, 2002). When accommodation is required for an understanding of and responding to events and ideas, this is thought to involve creating new associations inside an activated representation or between it and other representations. This process of creating new associations is called elaboration (Anderson & Reder, 1979; Jacoby et al., 1979; Mandler, 1979, 1980b; Slamecka & Graf, 1978) and it is assumed to provideâamong other thingsâa basis for searching, accessing and retrieving information from memory, and as such it is critical for performance on recall tests 10 (Bower, 1970; Einstein & Hunt, 1980; Craik & Tulving, 1975; Mandler, 2002; Mandler et al., 1969; Tulving, 1962; Tulving & Pearlstone, 1966). To gain further insight into the integrative and elaborative processing of emotional events, I looked to the Affect-as-Information hypothesis (Clore et al., 2001; Gasper & Clore, 2002; Schwarz & Clore, 1983) and to the Broaden and Build theory (Fredrickson, 1998, 2001, 2004), both of which make specific claims about the sensory-perceptual processing of emotional versus non-emotional events. Despite critical differences between them, both assume that emotional events, are specialâthey stand outâby virtue of arousing the cognitive system and by triggering processing that is more controlled (i.e., less automatic) than it would be for non-emotional events (cf. Kahneman,1973; Oberauer, 2019; Schneider & Chein, 2003; Meinhardt & Pekrun, 2003; Norman,1986; Schneider & Shiffrin, 1977; Schneider et al., 1982). Both the Affect-as-Information hypothesis and the Broaden and Build theory assume that positive events and negative events elicit unique types of sensory-perceptual processing. Specifically, they assume negative events engage the analytic and focused processing associated with problem solving or with avoiding or escaping potentially harmful situations, whereas positive events engage processing typically associated with the exploration of situations that are safe and potentially rewarding (Clore et al., 2001; Fredrickson, 1998, 2001, 2004; Gasper & Clore, 2002; Schwarz & Clore, 1983). Therefore, in the context of the preceding paragraphs, I regard the Affect-as-Information and the Broaden and Build as being consistent with the claim that, ceteris paribus, non-emotional events are processed in a routine manner, automatically, and this affects only the integration of their memory representations. By contrast, valenced events are processed elaboratively, and both Affect-as-Information and Broaden and Build postulate that different types of elaborative processing are recruited for positive and negative events. I postulate further that the amount of elaborative processing differs between negative and positive events, because for negative events, a larger amount of such processing is necessary or obligatory for the purpose of problem solving or 11 for avoiding or escaping from danger (Baumeister et al., 2001; Christianson, 1992; Mandler, 1982; Peeters & Czapinski, 1990; Rozin & Royzman, 2010; Abado et al., 2020; Taylor; 1991). Positive events, by contrast, are likely to receive less elaboration because much of this type of processing is optional (i.e. not required when responding to the event) (Bless & Fiedler, 2006; Brown et al., 1998; Gasper & Clore, 2002; Schwarz et al., 1991; Storbeck & Clore, 2005; Taylor; 1991), and people tend to minimize optional processing because it requires attentional resources (Gigerenzer & Gaissmaier, 2011; Gigerenzer et al., 2000; Kahneman,1973, Richardson, 2017). By this line of reasoning, it is possible to construct a straightforward processing explanation for the two main findings from the meta-analysis reported in Chapter 2: the occurrence of an emotional enhancement effect in episodic memory, and the larger enhancement effect for negative over positive events. Specifically, it follows that the overall enhancement effect likely occurs because at the time of encoding, valenced events receive more accommodative/elaborative processing than non-valenced events, and the larger enhancement effect for negative over positive events likely occurs because of the greater amount of accommodative/elaborative processing that is obligatory for responding to negative events (Carretié, 2014; Christianson, 1992; Schmidt & Saari, 2007; Vuilleumier et al., 2003; Williams et al., 1996; cf. Garner, 1974; Jones et al., 2010; Melara & Mounts, 1993; Miller, 1987). The larger enhancement effect for negative, compared to positive events, also may occur due to the processing that give rise to the Zeigarnik effect (Zeigarnik, 1938). According to the EEI theory, negative emotions occur in response to unexpected events that cannot be successfully accommodated, and therefore, it may be argued that such events create the psychological equivalent of an unfinished task. The phenomenon of improved memory for unfinished tasks is called the Zeigarnik effect (Baddeley, 1963; Beike & Wirth-Beaumont, 2005; Ergen & Gülgöz, 2023; Seifert & Patalano, 1991; Syrek et al., 2017) is thought to 12 occur because such tasks create cognitive tensions which boosts their memorability (cf. Hull,1943, 1952; Geen & Gange, 1977). The meta-analysis in Chapter 2 also revealed a substantially larger emotional enhancement effect on recall tests than on recognition tests. To accommodate this finding, I augmented my budding EEI theory, inspired explanatory framework, with two additional fundamental claims about the functioning of episodic memory. The first of these is that performance on episodic memory tests is determined by the degree of overlap in processing carried out at the time of encoding and retrieval. This claim is the core idea of what is known as transfer-appropriate processing (Fisher & Craik, 1977; Morris et al., 1977); it also is at the heart of the encoding-specificity principle (Tulving & Thomson, 1973) and it is consistent with Hebbâs mantra that neurons that fire together wire together (Hebb, 1949). The second basic claim is that free recall and recognition test performance are dependent on different processes (Anderson & Bower, 1972; Jacoby, 1991; Kintsch, 1970; Mandler, 1980a; Watkins & Gardiner, 1979; Yonelinas, 2002). Mandler (1980a) and others have emphasized that recognition test performance depends more on integrative encoding-phase processing while recall test performance is more dependent on prior elaborative processing (Anderson & Bower,1972; Jacoby, 1991; Mandler et. al., 1969; Yonelinas, 2002). Craik (1983, 1986; Craik & McDowd, 1987) offered a more detailed account focused on the processing overlap between encoding and retrieval. He characterized the recall-recognition difference by distinguishing between processing that is initiated and guided by events in the environment versus initiated and guided by the rememberer. He called these, respectively, environmentally-driven processing and self-initiated processing. Craik postulated that environmentally-driven processing and self-initiated processing are the end points of a continuum, and that recognition test performance is primarily dependent on the former while recall test performance primarily requires the latter. Consistent with this understanding, the finding of a substantially larger emotional enhancement effect on recall than recognition tests leads to the novel insight that valence has a larger 13 influence on self-initiated elaborative processing than on environmentally-driven integrative processing (cf. Anderson & Reder, 1979; Danckert & Craik, 2013; Klein & Kihlstrom, 1986; Slamecka & Graf, 1978; Tulving, 1962; Tulving & Pearlstone, 1966; Tulving & Thomson, 1973). In summary, my dissertation research on emotional enhancement effects in episodic memory was guided and motivated, on one hand, by the neuroscience evidence summarized by the NEVER model (Bowen et al., 2018), and on the other hand, by foundational insights from cognitive psychology literature. The first of these insights is that arousal and valence responses to emotional events are triggered by interruptions or disruptions in routine processing (Mandler, 1980b, 1982, 1990), and that the intensity of emotional responses and the valence of these responses are determined by how the cognitive system evaluates and copes with the interruption (Clore & Schnall, 2019; Clore & Ortony, 2008; Fredrickson, 1998, 2001, 2004; Fredrickson & Branigan, 2005; Mandler, 1980b, 1982, 1990). Second insight: Non-emotional events tend to be handled routinely by processes that have become automatized and thus influence only the integration of memory representation. By contrast, emotional events are handled non-automatically via accommodative/elaborative processes that demands attentional resources. These processes are required for resolving interruptions in routine processing, and this elaborative processing involves creating new associations among memory representation which are known to facilitate remembering. Finally, I speculate that more accommodative/elaborative processing occurs for negative than positive events because more of this type of processing is obligatory for the former. In short, my cognitive processing augmentation of the EEI theoryâhenceforth the EEI/PA theoryâprovides a coherent account of emotional enhancement effects in episodic memory and for the main findings from the meta-analysis in Chapter 2, and for these reasons, I relied on it to motivate and guide all of the research reported later in this dissertation. 14 1.3: Sex1 Differences in Memory Performance for Emotional Events While the primary focus of my dissertation was to understand the cognitive mechanism underlying emotional enhancements in episodic memory, a secondary focus was potential sex differences in memory for valenced events. Such sex differences are not directly predicted by the EEI/PA theory, but they are assumed to result from processing differences (i.e., elaboration) that could be readily accommodated by the theory. Two prominent hypotheses have been proposed regarding sex differences in episodic memory for emotional events, and both attribute such differences to nature (e.g., brain structure or function, and sex hormones) and to nurture (e.g., gender roles, socialization, and upbringing). The cognitive style hypothesis (Seidlitz & Diener, 1998) argues that females engage in more elaborative processing during encoding (Seidlitz & Diener, 1998; Stoet, 2010; Meyers-Levy, 1989; Wirth et al., 2007), which leads to overall memory advantages for females. This hypothesis is supported by research showing that females consistently outperform males on episodic memory tasks across wide variety of stimuli including names, faces, words, objects (for a meta-analysis, see Asperholm et al., 2019). By contrast, the emotional intensity hypothesis postulates that females experience emotional events with more intensity (Fujita et al., 1991) and have an advantage in processing emotional stimuli (Grossman & Wood, 1993; Bauer et al., 2003; Fivush et al., 2003), which in turn leads to a memory advantage for emotional events. This hypothesis is supported by research showing that females exhibit higher physiological activity when experiencing emotions 1 The label âsexâ is often used interchangeably with âgenderâ. In psychological research, âsexâ is a biological construct, referring to the binary division of human beings into âmalesâ and âfemalesâ based on biological physiological characteristics such as chromosomes, hormones, and reproductive anatomy. In contrast, âgenderâ is a cultural and social construct that extends beyond labelling individuals as âmenâ or âwomenâ based on their biological attributes as it includes a wide range of expectations, behaviours, roles and responsibilities that societies associate with individuals based on their perceived sex. Throughout my dissertation, I used the term âsexâ to refer to âmalesâ and âfemalesâ in terms of biology consistent with terminology commonly used in cognitive research. 15 (Chentsova-Dutton & Tsai, 2007) and when viewing emotional pictures (Bradley et al., 2001; Lithari et al., 2010). While there is evidence that females have better memory for emotional events (Bloise & Johnson, 2007; Cahill & Van Stegeren, 2003; Gasbarri et al., 2007), the evidence for sex differences in the emotional enhancement effectâwhether such differences are the same for emotional and neutral eventsâremains unclear. While some studies have shown that sex differences have a greater effect in memory for emotional over neutral events (Canli et al., 2002; Arnone et al., 2011; Kim & Hamann, 2012), others have shown no difference between the sexes (Bloise & Johnson, 2007; Hsu et al., 2018; Spalek et al., 2015; Stewardson et al., 2023). My meta-analysis in Chapter 2 revealed no evidence of sex differences in the emotional enhancement effect. Nevertheless, in light of the method variability in previous research, I included sex as a variable in my dissertation research to examine its potential influence in my experiments, each of which had a large number of participants and large sets of well-normed TBR pictures. Specifically, my research sought to examine whether sex differences exist in memory for emotional events, and if so, whether these differences remain consistent across both neutral and emotional events, as proposed by the cognitive hypothesis, or whether they are more pronounced for emotional events than neutral events, as proposed by the emotional intensity hypothesis. 1.4: Overview of Dissertation My dissertation was inspired and guided by the EEI/PA theory, a cognitive-processing account of how valence influences episodic memory for emotional events. My dissertation is arranged into 6 chapters including this Introduction and the aforementioned meta-analysis presented in Chapter 2. Chapter 3 reports my first experiment which aimed to test the core assumption of the EEI/PA theoryâvalence only influences 16 encoding-phase accommodative/elaborative processing, but not assimilative/integrative processingâthrough high-powered study with a large number of participants and a large number of well-normed standardized pictures. In this experiment, I used a typical old/new recognition test for assessing episodic memory for valenced and neutral pictures, knowing that recognition tests primarily rely on study-test overlaps in integrative/environmentally-driven processing. Chapter 4 reports Experiment 2, another high-powered study, which used an associative recognition test with randomly-paired valenced and neutral pictures. In this experiment, I used an associative recognition test for assessing episodic memory for newly learned associations, knowing that it is more heavily dependent on study-test overlaps in elaborative processing compared to an old/new recognition test. Chapter 5 reports the final experiment, another high-powered study, which used a free recall memory test. Free recall test performance is known to depend heavily on study-test overlaps in elaborative/self-initiated processing, thus making it ideally suitable for testing the core assumption of the EEI/PA theory that valence only affects accommodative/elaborative processing. A free recall test also allowed me to explore, whether and how, valence is strategically used to search memory during retrieval. Chapter 6 is composed of the general discussion. In this chapter, I summarized the main findings and their implications. Among the theoretical implications of my dissertation is the novel insight that valence has a distinct influence on elaborative/self-initiated processing but not on integrative/environmentally-driven processing, and that valence is critical for episodic remembering in the long term. 17 Chapter 2: Recall and Recognition of Valenced Pictures: A Meta-Analysis2 People are more likely to recollect a car accident or a wedding celebration over something routine and mundane such as eating scrambled eggs with toast for breakfast last week on Sunday. The research evidence supporting this claim, that memory is enhanced for emotional events compared to neutral events, is fairly conclusive for autobiographical memory (Amemiya & Sekiguchi, 2006; Daum et al., 1996; Lanciano & Curci, 2011). However, the results from pertinent investigations focused on episodic memory remain unclear, with some studies showing superior recollection of emotional events (Bradley et al., 1992; Davidson & Vanegas, 2015; Kensinger et al., 2006), and others showing equivalent recollection for emotional and neutral events (Bergmann et al., 2012; Sharot & Yonelinas, 2008). To prompt a closer examination into these mixed results, we carried out a meta-analysis that focused on the influence of key variables known to be major determiners of episodic memory test performance. An equally important goal was to examine predictions concerning episodic memory for emotional events based on a novel theoretical account that specifies how the cognitive system processes emotional events and creates our arousal and valence responses to them, as well as our episodic memory for them. When defined according to their distinct functions, episodic and autobiographical memory are similar in many respects but fundamentally different in others. Both are concerned with remembering events, observations, happenings, experiences, etc.; both are about the ability to bring back into the conscious mind events from the past; and in both cases, recollection is autonoeticâsubjectively experienced as being part of the personal past (Tulving, 1972, 1983). Investigations of both memory types have also used the same method phases: a study/learning phase, followed by a retention phase, and lastly 2 This chapter is a manuscript that has been prepared for submission. As such, some of the content discussed at length in Chapter 1 is summarized again in the introduction of this manuscript. Because this research was submitted as a collaboration between N. Alaifan and P. Graf, the pronoun âweâ is used. 18 a testing/recollecting phase. The latter two phases need not (and frequently do not) differ between investigations of episodic and autobiographical memory; for both cases, the retention interval can be brief (e.g., seconds or minutes) or long lasting (e.g., hours, days, weeks, months) and event recollection may be assessed with the exact same instruments (e.g., with free or cued recall tests or with recognition tests). By contrast, investigations of episodic and autobiographical memory differ critically by virtue of who selects the events that are to be learned and remembered. For autobiographical memory, the learners select what events they will commit to memory based on their momentary or enduring wants, needs and interests, whereas for episodic memory, someone else (like an experimenter, a teacher, or an instruction manual) determines what is to be learned and remembered. Consistent with this defining difference in memory input selection, autobiographical recollections are more likely to be personal and self-related, and they tend to be subjectively re-lived in the context of the personal past. By contrast, episodic recollections tend to be re-experienced in the temporal, spatial and semantic context in which the recollected events had initially occurred (Bradley et al., 1992; Tulving, 1972, 1983; Cabeza et al., 2004; Roediger & Marsh, 2003). We restricted this meta-analysis to investigations of episodic memory, in part to focus on the study phase variables that typically are not controlled or manipulated in autobiographical memory research. The most important aspect of these variables are the specific properties of the emotional and non-emotional events that are to-be-remembered by research participants, the context in which these events are presented, and the study tasks or strategies participants employ for memory encoding. Any kind of stimulus that elicits an affective response is an emotional event. Such events may be brief (e.g., a first kiss; a dogâs snarl) or long-lasting (e.g., a graduation ceremony; a pandemic); they may be human creations (e.g., a piano concerto; a birthday party) or something offered by nature (e.g., a summer sunset; the devastation caused by an earthquake); and we may experience them either first-hand, for example, while at the beach on a warm summer evening, or second hand via recordings, pictures, texts, 19 etc. Commensurate with the abundant variety of emotional events, it is not surprising that researchers have used a wide range of stimuli for laboratory investigations including pictures, words, stories, movies, and songs (Bradley et al., 1992; Cahill & McGaugh, 1995; Phelps et al., 1997; Subramanian et al., 2014). Of these, pictures have been used most in episodic memory research probably because of their versatility. Pictures can be used for representing real-world events, human interactions, facial expressions, animals, landscapes, objects, for instance, and with pictures, it is relatively easy to manipulate variables known to affect episodic memory performance (e.g., exposure duration, memory encoding strategies). Another likely key reason for the use of pictures in research is the existence of large repositories that provide normative data on critical properties such as each pictureâs perceived valence, arousal and motivational intensity (e.g., the International Affective Picture System (IAPS) by Lang, Bradley, & Cuthbert, 2008; the Nencki Affective Picture System (NAPS) by Marchewka, 2014). Only investigations with pictures as the to-be-remembered stimuli provided data for this meta-analysis. We made this choice because previous research with picture stimuli has produced sufficient data for a meta-analysis, and also because we wished to investigate episodic memory influences due to encoding phase variables commonly employed with pictures (e.g., type of encoding task; exposure duration) but not used or used differently for encoding other emotion-eliciting stimuli such as stories or songs. Emotional events are commonly distinguished according to the responses they elicit in people. Events such as a playful kitten or a happy baby tend to elicit positive feelings like joy or happiness, whereas others like a snarling dog or a festering wound trigger negative feelings like fear or revulsion. Events like these differ by valence or their propensity to trigger positive or negative feelings (e.g., Cacioppo & Berntson, 1994; Charland, 2005; Russell & Barrett, 1999; Watson & Tellegen, 1985). Arousal is another variable commonly that used for categorizing emotional events, according to their power to excite and 20 agitate people (e.g., a violent car crash) or to calm and relax them (e.g., soft music) (Barrett & Russell, 1999; Kensinger, 2004; Kensinger & Schacter, 2006; Lang, Greenwald, Bradley, & Hamm, 1993). Emotional events have other properties such as motivational intensity (Gable & Harmon-Jones, 2008), but only valence and arousal have so far received sufficiently extensive investigation to permit an informative meta-analysis. A major purpose of our meta-analysis was to fill the lacuna in the literature concerning the influence of valence on episodic memory performance. Previous meta-analyses focused on related influences, for example, by asking how participantsâ affective state (e.g., depressed or not; sad or happy) affects episodic memory for emotional events (Matt, Vázquez & Campbell, 1992). The results showed that compared to participants with depression, non-depressed participants recalled more positive than negative events. Two more meta-analyses on the general topic of episodic memory for emotional events (Lipinska et al., 2019; Schäfer et al. 2020) searched for specific effects caused by sleeping during the retention interval, and the results showed that whether participants slept or stayed awake during the retention interval, memory for emotional events was superior to that for neutral events, and there was no specific effect on memory due to sleep manipulation for the retention interval. Finally, a recent meta-analysis by Pereira, et al., (2023) investigated the effects of valence and arousal on source memory, and the findings showed that source memory was impaired for valenced events compared to neutral events. By contrast to these meta-analyses, ours differs from Pereira et al.âs (2023) by focusing on item memory rather than source memory, and it differs from the other listed meta-analyses by focusing on item-memory in the absence of influences from factors such as the participantsâ affective state or sleep/wake state. A second major purpose of this meta-analysis was to test predictions about episodic memory for valenced events, and specifically about performance differences between positive and negative events that are matched on arousal. We derived specific predictions from a number of theories that speak to valence 21 influences on episodic memory, especially the Broaden and Build theory (Fredrickson, 1998, 2001, 2004), the Negative Emotional Valence Enhances Recapitulation (NEVER) model (Bowen, Kark & Kensinger 2018), and a theoryâhere referred to as the Emotional Events Interrupt (EEI) theoryâinspired by Mandler and colleagues (Mandler, 1980b, 1982, 1990; MacDowell & Mandler, 1989). NEVER (Bowen et al., 2018) is a model of the neural/brain activity that accompanies the encoding and recollection of valenced and non-valenced events. It posits that compared to non-valenced and positive events, negative events are associated with enhanced sensory-perceptual processing, and this enhanced processing occurs at the time of encoding as well as at the time of memory retrieval. The model also posits a lesser amount of enhanced processing occurs for positive events compared to negative events. Therefore, based on the NEVER model, we predicted our meta-analysis would show an emotional enhancement effect in episodic memory, and we expected this enhancement effect to be larger for negative than positive pictures. We made the same predication plus two additional predictions about the results of this meta-analysis based on the EEI theory, which we augmented with several assumptions about the processing requirements of different episodic memory encoding and retrieval tasks (Craik, 1983, 1986; Craik & McDowd, 1987; Morris, Bransford, & Franks, 1977; Tulving & Thomson, 1973; Yonelinas, 2002), and with insights into how the cognitive system responds to emotional events (Clore & Schnall, 2005; Clore et al., 2001; Fredrickson, 1998, 2001, 2004; Fredrickson & Branigan, 2005; Gasper & Clore, 2002; Schwarz & Clore, 1983). 22 Consistent with the view that emotions are a byproduct of the bodyâs allostatic control (McEwen, 2003; Shaffer et al., 2022; Sterling & Eyer, 1988), the EEI theory postulates that emotional events interrupt routine perceptual-cognitive processing and trigger a cascade of compensatory activities that create peopleâs arousal and valence responses to them (Mandler, 1980b, 1982, 1990; MacDowell & Mandler, 1989). The theory, sketched in Figure 2.1, has three core assumptions. First, it assumes that the cognitive system operates by means of knowledge structures called schemas (Bartlett, 1932; Evans, 1967; Mandler, 1984; Rumelhart, 1980; Rumelhart & McClelland, 1985; Schmidt, 1975). By means of schemas, the system builds and maintains a mental model of the ambient environment; it anticipates what is likely included in the current environment and what is likely to happen (Bartlett, 1932; Ghosh & Gilboa, 2014; Rumelhart, 1980; Rumelhart & McClelland, 1985; Rumelhart & Ortony, 1977; Piaget, 1952; 1954; Shaffer et al., 2022). Second, the EEI theory assumes that when a stimulus event occurs which was not anticipated by the system, this creates an interruption in routine processing, triggers an arousal response and switches the system to attentive/controlled processing. As emotional events are encountered less frequently than non-emotional events, emotional events typically are not anticipated by the cognitive system and thus they interrupt routine processing. Third, the EEI theory assumes that a stimulus event is perceived as positive or negative depending on how the cognitive system copes with the processing interruption. If the stimulus event is the same or similar to something anticipated, the system assimilates it into its current schemas, and the EEI theory postulates for this case that the event will be experienced as neutral or mildly positive. By contrast, if the cognitive system is unable to assimilate the event into the currently active schema, it will attempt to accommodate the schema, switch to a different or construct a new schema, and if these efforts succeed, the stimulus event will be experienced positively. Finally, if the cognitive system is unable to accommodate the stimulus event, it will be experienced negatively. The theory posits that for each of these cases, the intensity of the experienced emotion correlates with the amount of effort invested in accommodative processing for coping with the interruption in routine processing. 23 Figure 2.1. Events and Processes Assumed to be Required for Arousal and Valence Responses According to the EEI Theory Note. Arousal is triggered when there is an interruption in routine processing, where the activated schemas are inconsistent with the current event, and its magnitude is determined by the amount of accommodative processing that is required. Valence is determined by the success or failure of the accommodative processing. The core concepts of the EEI theory, that of schemas, assimilation, and accommodation, map directly onto concepts that have been invoked to explain how episodic memory functions. A schema is a specific type of mental representation (Bartlett, 1932; Bobrow & Norman, 1975; Mandler, 1984; Rumelhart, 1980; Rumelhart & Ortony, 1977), and the processes of assimilation and accommodation map directly onto the processes of integration and elaboration that have been used to explain how mental representations 24 are changed by experience and learning (Anderson & Bower, 1972; Anderson & Reder, 1979; Craik & Lockhart, 1972; Klein & Kihlstrom, 1986; Macken & Hampson, 1993; Mandler, 1979, 1980a, 1992). Assimilation occurs when an understanding of and responding to a stimulus event is managed in a routine manner by means of the currently active schemas (Piaget, 1952; 1976; Bless & Fiedler, 2006; Gallagher & Reid, 1981; Von Glasersfeld, 1995). In theoretical accounts of episodic memory, processing that is routineâalso referred to as automaticâdoes not create long-lasting memories (Craik & Lockhart, 1972; Hasher & Zacks, 1979). Instead, it is postulated that when a representation is used for routine processing, it is activated, and activating a representation increases its integration, defined as its internal cohesion or the strength of the associations among its components (Koffka, 1935; Mandler, 1980a; Mandler et al., 1986; Mandler et al., 1969; Rumelhart, 1980; Rumelhart & Ortony, 1977). A representationâs degree of integration is directly associated withâamong other thingsâthe familiarity we experience when subsequently encountering the event, and this feeling of familiarity is a basis for making recognition decisions (Anderson & Bower, 1972; Graf & Mandler, 1984; Mandler, 1979, 1980a; Mandler et al., 1986; Yonelinas, 2002). Consequently, by this processing augmentation of Mandlerâs EEI theory, we expect that unless additional processing is required to comply with an encoding task assigned to the participants in an experiment, episodic memory for non-emotional events will be relatively poor and supported only by familiarity-based recognition. Accommodation occurs when an understanding of and responding to a current event requires modifying an existing schema or representation, switching to a different schema or creating a new representation. In the language of memory theories, accommodation involves making new associations inside an activated representation or between it and other representations, and the process of new association-making is called elaboration (Anderson & Reder, 1979; Jacoby et al., 1979; Mandler, 1979, 1980a; Slamecka & Graf, 1978). Encoding-phase elaborative processing is thought to provideâamong 25 other things âa basis for subsequently searching in or accessing and retrieving information from memory, and as such it is critical for performance on recall tests (Bower, 1970; Craik, 1986; Einstein & Hunt, 1980; Craik & Tulving, 1975; Mandler, 2002; Mandler et al., 1969; Tulving, 1962; Tulving & Pearlstone, 1966). Consequently, by augmenting Mandlerâs EEI theory with this consideration of memory processing, because emotional events interrupt routine processing and trigger the controlled elaborative encoding-phase processing which is known to boost episodic memory test performance (e.g., Craik & Lockhart, 1972; Hasher & Zacks, 1979; Kahneman,1973), we expected our meta-analysis to reveal an emotional enhancement effect in episodic memory for valenced compared to non-valenced pictures. Our augmented EEI theory is consistent with insights from the Affect-as-Information hypothesis (Clore et al., 2001; Schwarz & Clore, 1983, 1988) and from the Broaden and Build theory (Fredrickson, 1998, 2001, 2004). Despite critical differences between these theories, both are alike in assuming that emotional events differ from non-emotional events because they arouse the cognitive system and engage processing that is more controlled and strategic (cf. Kahneman,1973; Oberauer, 2019; Schneider & Chein, 2003; Meinhardt & Pekrun, 2003; Norman,1986; Schneider & Shiffrin, 1977). This assumption mirrors Mandlerâs claim that emotional events interrupt routine processing and engage accommodative processing. More importantly, both Affect-as-Information and Broaden and Build augment the EEI theory by postulating that positive and negative events elicit different types of controlled elaborative processing. Specifically, they assume negative events engage the analytic and focused processing associated with problem solving or with avoiding or escaping from potentially harmful situations, whereas positive events engage processing typically associated with exploring situations that are safe and potentially rewarding (Clore et al., 2001; Fredrickson, 1998, 2001, 2004; Schwarz & Clore, 1983, 1988). We combined this characterization of the processing engaged for negative and positive events with Mandlerâs proposal that the valence of our feelings about events is determined, respectively, by 26 accommodative processing which either failed or succeeded, and thus predicted our meta-analysis would show a larger emotional enhancement effect in episodic memory for negative than positive pictures. This prediction is also consistent with the Zeigarnik effect (Zeigarnik, 1938), which is defined as better memory for unfinished (i.e., failed) versus finished tasks (Baddeley, 1963, Beike & Wirth-Beaumont, 2005; Ergen & Gülgöz, 2023; Seifert & Patalano, 1991; Syrek et al., 2017). The Zeigarnik effect is thought to occur because unfinished tasks create cognitive tensions which in turn boost memory in some manner (Hull, 1943, 1952) Our processing augmentation of the EEI theory predicts the same outcome. Consistent with the proposal that the valence of our feelings about events is determined, respectively, by accommodative processing which either failed or succeeded, it followsâceteris paribusâthat more processing is required for negative than positive events. After all, if the current accommodative attempt results in success, no further accommodative processing is necessary; however, at least one (or perhaps many more) more accommodative attempt is necessary for a failure (e.g., Baumeister, Bratslavsky, Finkenauer & Vohs, 2001; Christianson, 1992; Mandler, 1982; Peeters & Czapinski, 1990; Rozin & Royzman, 200; Abado et al., 2020; Bless & Fiedler, 2006; Brown et al., 1998; Gasper & Clore, 2002; Schwarz et al., 1991; Storbeck & Clore, 2005; Taylor; 1991). Because elaborative processing requires attentional resources, and because the human cognitive system is configured to conserve such resources (Gigerenzer & Gaissmaier, 2011; Gigerenzer et al., 2000; Kahneman, 1973; Richardson, 2017), we assume participants in a memory experiment will carry out only the processing that is obligatory for a task. Thus, for these reasons, we expected the meta-analysis to show a larger emotional enhancement effect in episodic memory for negative than positive pictures because the former require more obligatory accommodative/elaborative processing. 27 We also predicted the meta-analysis would show a larger emotional enhancement effect in recall than recognition test performance, as well as a larger emotional enhancement effect when the memory assessment is made after a delay rather than immediately after study. A larger effect in recall than in recognition follows, on the one hand, from the EEI theoryâs assumption that the valence we feel about an event is determined entirely by whether the cognitive systemâs accommodative/elaborative processing has failed or succeeded, and that valence is associated only with accommodative/elaborative processing, not with assimilative/integrative processing. It follows, on the other hand, because recall performance depends primarily on study-test overlaps in elaborative processing, while recognition depends more heavily on overlaps in integrative processing (Craik, 1986; Anderson & Bower, 1972; Mandler, 1980a; Watkins & Gardiner, 1979; Yonelinas, 2002). The expectation of a larger emotional enhancement effect with delayed compared to immediate memory testing is consistent with theorizing that elaborative processing leads to longer-lasting memory traces (Craik & Lockhart, 1972; Reder 1980; Pressley, 1982), and further confirmed by research showing larger influences due to elaborative processing on delayed versus immediate memory tests (Bartsch et al., 2018; Bartsch & Oberauer, 2021; Loaiza et al., 2011; Schacter & Graf, 1986) Finally, although not predicted by our processing-augmented form of EEI theoryâhenceforth called the EEI/PA theoryâwe also used the meta-analysis to seek reliable evidence of a sex difference in episodic memory for valenced pictures. Sex differences in episodic memory have been found for a wide variety of emotionally neutral stimuli including names, faces, words, objects (Andreano & Cahill, 2009; Asperholm et al., 2019; Borges & Vaughn, 1977; Herlitz et al., 1997; Herlitz & Yonker, 2002, Herlitz et al., 2013; Kramer et al., 2003; Kramer et al., 1997; Voyer et al., 2007). By contrast, far less research has focused on sex differences in memory for emotional events, and more specifically, on whether such differences are the same for emotional and neutral events. The results currently available are inconclusive: Some studies have shown sex differences are larger in memory for emotional than for neutral events 28 (Bloise & Johnson, 2007; Cahill & Van Stegeren, 2003; Canli et al., 2002; Gasbarri et al., 2007), whereas others have failed to show such an interaction effect (Cahill et al., 2004). Research on sex differences in episodic memory for emotional events is motivated by speculations about sex differences in brain structure and function (Nyberg et al., 2000; Ragland et al., 2000; Subramaniapillai et al., 2019) and in hormones (Conner et al., 2020; Conner et al., 2020; Jacobs & Goldstein, 2018; Karalija et al., 2021; Postma et al., 1999; Rentz et al., 2017) all of which may influence how females and males process and respond to neutral and emotional events. Research has shown that emotional events activate different brain regions in females and males (Stevens & Hamann, 2012; Filkowski et al., 2017; Wager et al., 2003), and that compared to males, females show higher physiological activity when experiencing emotions (Chentsova-Dutton & Tsai, 2007) and when viewing emotional pictures (Bradley et al., 2001; Lithari et al., 2010). Consistent with such speculations and evidence, we expected our meta-analysis to reveal a larger valenced-based memory enhancement effect in female than in male research participants. In summary, we carried out a meta-analysis of research on one specific kind of memoryâepisodic memoryâfor one type of to-be-remembered events, in particular, valenced and neutral pictures, in order to ask and answer specific questions about influences due to the processes or strategies used for memory encoding, due to the tests used for assessing memory, due to the length of the retention interval and due to the participantsâ sex. We used the NEVER model (Bowen et al., 2018), and the EEI/PA theory to predict the meta-analysis results pertaining to each of these influences, except for the last. 2.1: Method We used the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines (PRISMA; Page et al., 2021) for this meta-analysis. On May 1st, 2020 and again on October 10th, 2023, we 29 searched the PsycInfo and Medline databases using the terms, in the manner listed, in Appendix 1. As shown in Figure 2.2, our searches yielded a total of 7812 records, with 4226 and 3586 from PsycInfo and Medline, respectively. Of these records, 2267 were duplicates, leaving 5542 unique records. Next, we examined the remaining records for meeting the inclusion and exclusion criteria described as follow, with the help of the Covidence systematic review software (Covidence, 2020): In Phase 1, we screened titles and abstracts, and excluded a further 4962 records that failed our criteria, and in Phase 2, we examined the full text of the remaining 583 records and identified 123 eligible records. 2.1.1: Inclusion and Exclusion Criteria 2.1.1.1: Publication type. To be included, a record had to be a complete publication of empirical quantitative experimental research, published in English, in a peer-reviewed journal. We excluded records that were reviews, meta-analyses, editorials, letters, conference contributions, commentaries or book chapters. 2.1.1.2: Participants. Records were included if the research participants were healthy young adults described as free of cognitive, physical health conditions, or emotional complaints. For records pertaining primarily to participants from special populations (e.g., elderly people, people with anxiety disorders), we accepted for inclusion only the data from healthy young adult control group participants. 2.1.1.3: Encoding manipulation. Records were included if they concerned episodic memory for valenced pictures that were studied with participantsâ full attention, typically under the guidance of a specific study task (e.g., by requiring participants to answer a question about the meaning or appearance of each picture). We excluded records from experiments that focused on other issues, such as the effects produced by directed forgetting instruction or effects induced by the administration of drugs, alcohol, music or stress. We 30 included only the control group data from studies which focused on the memory effects of interventions administered during the retention interval (e.g., sleep). 2.1.1.4: Picture valence. To be included, a record had to report separately participantsâ episodic memory for positively valenced versus neutral pictures, or negatively valenced versus neutral pictures, or it had to provide data pertaining to both of these differences. We excluded experiments which only reported a combined score for positively and negatively valenced vs neutral pictures. 2.1.1.5: Picture set composition and arrangement. We included records with experiments on episodic memory for pictures that were not specifically related to each other. Excluded were records reporting experiments on memory for a single specific type of pictures (e.g., faces), or with pictures that were arranged to tell a story, or with pictures accompanied by a written or spoken narrative, or with pictures that were changed in some manner (e.g., from picture to line drawing) between the study and test phase. 2.1.1.6: Memory type and memory test type. We included records reporting research on episodic memory, and excluded records on other forms of memory (e.g., autobiographical memory, implicit memory or prospective memory). To be included, episodic memory had to be assessed either with a straightforward old/new recognition test or a recall test, and the record had to provide the relevant performance data, such as the average number or the proportion of both hits and false alarms, or the proportion of hits minus false alarms. For records that reported multiple successive measurement for the same participant sample (e.g., recognition followed by free recall, testing immediately and after a delay), we only included data from the first administered test. We excluded records where recognition testing required something other than making a simple yes/no or old/new decision about each item, for example, if it required choosing among new/know/remember. 31 Figure 2.2. A Flowchart Showing the Steps Used for Identifying and Selecting Records for Inclusion in the Meta-Analysis 32 2.1.2: Variables Coding Procedure For each eligible record and for each experiment, we recorded several characteristics and outcomes, using a manual, developed by the first author, which is available on Appendix 2. The manual, which defines each variable and describes the precise steps for coding procedure, was used by the first author and by trained research assistants to code all records, and they achieved an inter-rater agreement of 97%. Disagreements between the coders were resolved by discussion. 2.1.2.1: Record information. For each record, we recorded the authorâs name(s), the year of publication, a unique identifier for each experiment reported in the same article (1st, 2nd, etc.), and the number of effect sizes computed for each experiment. 2.1.2.2: Participant characteristics. For each experiment, we recorded the country of research participation, the population from which participants were sampled (e.g., university students, community living young adults), the sample size, the sex of the sample or sub-sample (males, females, mixed sample), the proportion of females and males and the age of participants. 2.1.2.3: Experiment characteristics. For each experiment, we recorded the experimental design, that is, whether the critical variable of interestâthe picture valenceâwas manipulated within subjects or between subjects. We also recorded the picture valence types that were investigated (e.g., positive vs neutral; negative vs neutral), the type of encoding phase activity (i.e., whether the encoding task required semantic vs. perceptual encoding, or was not constrained), whether encoding did or did not focus on the valence of each picture (e.g., valence vs not-valence focused encoding), and the duration of the retention interval (i.e., whether it was shortâfrom a few minutes to one hour in duration, or longâ24 hours or longer). 33 We were also interested and coded other variables known to affect performance on episodic memory tests, including whether participants were or were not made aware that their memory for the pictures would be assessed (Noldy et al., 1990; Popov & Dames, 2023), the number of pictures that had to be remembered (Cary & Reder, 2003; Nairne & Neath, 2001), whether memory encoding was timed or self-paced by participants, the duration for which each to-be-remembered picture was available for encoding (Calvo et al., 2007; Christianson et al., 1991), and whether pictures from each valence condition were studied in the same block or the pictures from the different valence conditions were selected and shown randomly. For exploratory purposes, we also coded the content of the to-be-remembered pictures (i.e., whether they were of people, animals, faces, objects, landscapes, or mixed etc.), the source from which the pictures were selected (i.e., from a standardized data bases or other), and any other picture characteristics that were reported (e.g., arousal ratings). 2.1.2.4: Outcome variables. We recorded all data needed for computing the effect size pertaining to each valence condition (i.e., positive vs neutral or negative vs neutral), and whether memory was assessed with a free recall test or a recognition test. For free recall, we recorded the proportion of correctly recollected pictures in each valence condition and the associated standard deviation. Because the rate of false alarms tends to be negligibly small on free recall tests (Choi et al., 2017; Talmi, McGarry & Moscovitch, 2007; Tomaszczyk & Fernandes, 2013) and thus is rarely reported, we did not record this aspect of performance. For recognition, we recorded the proportion and standard deviation of hitsâdefined as the proportion of studied pictures that were correctly recognized as having been studied, the proportion and standard deviation of false alarmsâdefined as the proportion of non-studied or distractor pictures falsely recognized as having been studied, and the proportion and standard deviation of corrected recognitionâdefined as the difference between the proportion of hits and the proportion of false alarms. 34 For outcomes that were reported only in graphic form, we used the WebPlot Digitizer software version 4.4 (Rohatgi, 2021) to extract numerical values for performance means and standard deviations from graphs. If the standard deviation was not reported for a condition, we used other reported indices of variability (e.g., the standard error or the 95% Confidence Interval) to estimate the missing standard deviation. If none of these variability indices was reported, we used the pooled standard deviation from the dataset to estimate the missing standard deviation. For 63.3% of experiments, all data required to calculate the effect sizes (i.e., Hedgesâ g) were directly available (i.e., in a text or a table) in the record. For a further 30.7%, we had to estimate these data from graphs. For the remaining 6% of experiments, no index of variability was provided. 2.1.3: Effect size calculations Consistent with our research goals, we calculated two effect sizes where possible, one for positive picturesâdefined by the memory advantage for positively valenced versus neutral picturesâand the other for negative picturesâdefined by the memory advantage for negative versus neutral pictures. For calculation, we used the standardized mean differences method for Cohenâs d, and then converted those to Hedgesâ g, to correct for small sample bias (Hedges, 1981; Hedges & Olkin, 1985). Hedgesâ g can be interpreted by the same criteria as Cohenâs d: d ~ 0.2 represents a small effect; d ~ 0.5 represents a medium effect; d ~ 0.8 represents a large effect (Cohen, 1988). Cohenâs d and Hedgesâ g are computed differently for data from between versus within subjects experiments (Cumming, 2013; Cumming & Calin-Jageman, 2016; Lakens, 2013). For a between-subject design, we calculated Cohenâs d with the formula: 35 ð¶ðâðð!ð ð" = ð#+++ â ð$+++-(ð# â 1)ðð·# $ + (ð$ â 1)ðð·$$ð# + ð$ â 2 Where ð#+++ ððð ð$+++ denote the mean memory performance for the two conditions, respectively, for valenced pictures, either negative or positive, and neutral pictures; ð# ððð ð$ denote the sample sizes for the two conditions; and SD1 and SD2 denote their standard deviations. For within-subject design experiments, we calculated Cohenâs d as follows: ð¶ðâðð!ð ð&' = ð()**ðð·# + ðð·$2 Where ð()** denotes the difference in means of memory performance between valenced pictures, either negative or positive, and neutral pictures; SD1 and SD2 denote the standard deviations of the memory test performance for each condition in a within-subject design. For Hedgesâ g we used this formula for data from between-subject design experiments: ð»ððððð ð = ð¶ðâðð!ð ð Ã :1 â +, (.!/."0#)0# ;. Where ð# ððð ð$ denote the sample size of the participants in the valenced and neutral conditions respectively. We calculated Hedgesâ g data from within-subject experiments with the following formula: ð»ððððð ð = ð¶ðâðð!ð ð Ã :1 â +, (.0#)0#;. 2.1.4: Overview of data analysis We used the effect size data in a first set of analyses to answer broad questions, such as: Is there an emotional enhancement effect due to valence in episodic memory? If so, how large is it? Is this effect the same size for positive and negative valence? In addition, is the memory enhancement due to valence 36 the same size on recall and recognition tests? How are recall and recognition of valenced pictures influenced by well-known moderator variables like type of encoding phase activity (Craik & Lockhart, 1972; Slamecka & Graf, 1978) or retention interval duration (Runquist, 1983; Slamecka & McElree, 1983)? For all analysis, we use the JASP meta-analysis module software version 0.14 (JASP Team, 2021). 2.1.5: Analysis model We used a random-effects meta-analytic model for estimating the pooled effect sizes for all analyses (DerSimonian & Laird, 1986). This model takes into account additional heterogeneity beyond the sampling error (Borenstein, Hedges, Higgins, & Rothstein, 2009; Cumming, 2013). 2.1.6: Assessing heterogeneity of effect sizes We used the standard Cochranâs Q and I2 tests to estimate the heterogeneity of effect sizes. Cochranâs Q tests whether the heterogeneity between experiments is significant based on the null hypothesis that all experiment-level effect sizes evaluate the same effect (i.e., effect sizes are homogeneous). Cochranâs Q is calculated as the sum of weighted squared differences between the experiment-level effect size and the pooled effect size. A significant p-value indicates heterogeneity between experiments (Cochran, 1954). The I2 test quantifies the percentage of heterogeneity between experiments. The I2 test was calculated as the ratio of variance attributed to heterogeneity between experiments to the total variance. Values of I2 at 25%, 50% and 75% are graded as indicating a low, moderate, and high degree of heterogeneity, respectively (Higgins & Thompson, 2002; Higgins et al., 2003). 37 2.1.7: Assessing publication bias We used two steps to check for publication bias. First, we used a funnel plot for a visual assessment. For the funnel plot, we plotted effect sizes from each experiment, represented by a standardized mean difference against their corresponding standard error as a measure of study precision (Sterne & Egger, 2001). In the absence of publication bias, the effect sizes with high precision (i.e., smaller standard error) are plotted at the top of the funnel plot close to the pooled effect (i.e., the vertical line in the middle of the symmetry) and effect sizes with low precision (i.e., larger standard error) are plotted at the bottom of the plot and spread evenly around the pooled effect, producing a classic funnel shape distribution. The presence of publication bias can be visually detected if the distribution of effect sizes is asymmetrical, indicating missing effect sizes related to unpublished experiments because of non-significant results (Egger et al., 1997; Macaskill et al., 2001; Sterne & Egger, 2001). In a second step to check for publication bias, we used Eggersâs regression test (Egger et al., 1997) to statistically assess the asymmetry of the funnel plot. 2.2: Results 2.2.1: Records, Experiments and Participants We report results from 123 records, published between 1997 and 2023. As shown in Figure 2, research publications peaked between 2011-2015, perhaps because of the publication of the picture databases that furnished the stimuli for a majority of experiments on valence and episodic memory (Dan-Glauser & Scherer, 2011; Lang et al., 2008; Marchewka et al., 2014). A complete table with the pertinent characteristics of each record is provided in Appendix 3. A significant majority of experiments, 69.07%, made use of pictures from the International Affective Picture System (IAPS) (Lang et al., 2008). Of the other experiments, 1.32% used picture from the Nencki Affective Picture set (NAPS) (Marchewka et al., 2014), 0.66% used pictures from the Hemera photo-objects 38 set (Hemera Technologies, 2002), 0.66% used pictures from the Chinese Affective Pictures System (Bai et al., 2005), 0.66% used pictures from Developmental Affective Photo System (DAPS) (Cordon et al., 2013), and the remainder, 5.27%, used in-house normed pictures from the Internet or from free online digital photos databases (e.g., www.freedigitalphotos.net). A substantial portion of experiments, 19.07%, used pictures from multiple sources, and for 3.29% of the experiments no picture source information was provided. Figure 2.3. Distribution of Records by Year of Publication The experiments also varied substantially in the number of pictures participants had to remember and in how long each picture was displayed for the purpose of studying it. The median number of pictures to-be-remembered was 72 [IQR, 45 - 137.25 pictures], ranging from a low of 22 pictures (Zarubin et al., 2020) to a high of 450 pictures (Pais-Vieira et al., 2016). The median amount of time available for studying each picture was 2500 milliseconds [IQR, 2000-4000 msec], with a mode of 2000 milliseconds, and with 02468101214199719981999200020012002200320042005200620072008200920102011201220132014201520162017201820192020202120222023Research Records (%)Publication Year 39 study time ranging from 250 milliseconds per picture (Harris & Pashler, 2005) to 10 seconds per picture (Kugler & Kuhbandner, 2015; Xing & Isaacowitz, 2006). In total, we collected data from 152 experiments, 63.8% of which examined episodic memory for both negative and positive pictures, 33.6% investigated only negative pictures, and 2.6% investigated only positive pictures. Of these experiments, 44.74% assessed episodic memory with a recognition test; the other 55.26% used a recall test. Experiments varied widely in their number of participants. They had a median of 34 participants, with an IQR of 24 â 57 participants. A total of 12,227 participants contributed data. They had a weighted mean age of 21.78 years (SD= 3.78), and well over halfâ64.87%âwere females. The majority of experimentsâ 59.9%ârecruited participants from a university student pool; 11.8% recruited from the community; 13.8% recruited from both university and community; and for 14.5% the source of participants was not specified. The research originated as follows: 58.6% from North America, 33.6% from Europe, 5.2% from Asia, and 2.6% from Australia. 2.2.2: Memory for Positive and Negative Pictures on Recall and Recognition Tests We first asked this question: Is there an overall emotional enhancement effect in episodic memory, which occurs for both negative and positive pictures and on both recall and recognition tests. We used all 434 effect sizes (ES), each representing the difference in episodic memory for valenced pictures, either positive or negative, versus neutral pictures. The results show a significant emotional enhancement effect, with Hedgesâ g = 0.72 (95%CI [0.65, 0.78], p < .001), and this effect was associated with significant heterogeneity: Q (433) = 5181.70 p <.001, I2 = 91.64%. 40 We used Cookâs distance statistic and identified three outliers in the effect size set, with one each in Abercrombie et al., (2008), Arnone et al., (2011) and Charles et al., (2003). An analysis without these outliers showed a smaller but still significant emotional enhancement effect, with Hedgesâ g = 0.70 (95%CI [0.64, 0.76], p < .001), with significant heterogeneity: Q (430) = 5023.14 p <.001, I2 = 91.44 %. Due to their relatively small influence on the analysis results, we retained the outliers in subsequent analyses. In a second overall analysis we asked whether the emotional enhancement effect in episodic memory is the same for positive and negative pictures. For this analysis, we also used all 434 effect sizes, and the results showed a significant difference in the emotional enhancement effect for negative and positive pictures. The enhancement effect for negative pictures (k= 266) was much larger (Hedgesâ g = .82, 95%CI [0.73, .91], p < .001), than for positive pictures (k= 172) (Hedgesâ g = 0.57, 95%CI [0.47, 0.67], p < .001). In two final planned analyses, we investigated differences in the emotional enhancement effect in episodic memory when it was assessed with a recall versus recognition test. For these analyses, we used all 269 and 165 effect sizes, respectively, for recall and recognition. The results showed a much larger emotional enhancement effect for recall, with Hedgesâ g = .99 (95%CI [0.93, 1.07], p < .001), than for recognition, with Hedgesâ g = 0.24 (95%CI [0.17, 0.31], p < .001). The enhancement effect was associated with more heterogeneity for recall, Q (268) = 3069.91 p <.001, I2 = 91.27%, than for recognition, Q (164) = 616.62 p <.001, I2 = 73.40%, but with sufficient heterogeneity in both cases to justify planned follow-up moderator analyses (Borenstein et al., 2009). 2.2.3: Moderators of the Emotional Enhancement Effect In separate analyses of the emotional enhancement effect on recall and recognition tests, we examined the influence of four primary moderators: picture valence (positive, negative), retention interval 41 (short, long), study phase activity (perceptual, semantic), and sex of participants (male, female). The results of these analyses are summarized in Table 2.1 and Table 2.2. The recall results show a much larger emotional enhancement effect for negative than positive pictures, with Hedgesâ g = 1.10 (95% CI [1.01, 1.20], p < .001) and Hedgesâ g = 0.84 (95% CI [0.72, 0.96], p < .001), respectively. Recognition shows the same pattern, albeit with much smaller though still significant emotional enhancement effects, for both negative pictures, with Hedgesâ g = 0.30 (95% CI [0.21, 0.39], p < .001) and for positive pictures, with Hedgesâ g = 0.14 (95% CI [0.03, 0.25], p < .001). The results pertaining to participantsâ sex show no significant influences either in recall or recognition test performance. An analysis of the influence due to retention interval duration showed substantial emotional enhancement effects occurred when memory was assessed immediately after the study phase, with Hedgeâs g = 0.97 (95% CI [0.90, 1.05], p < .001) for recall and Hedgesâ g = 0.12 (95% CI 0.03, 0.21], p < .001) for recognition, and it also showed both of these effects were larger when memory was assessed after a delay, with Hedgesâ g = 1.25 (95% CI [0.94, 1.57], p < .001) for recall, Hedgesâ g = 0.37 (95% CI [0.27, 0.48], p < .001) for recognition. Further analyses examined influences due to study phase activity, and they showed different effects on recall and recognition test performance. Recall test performance showed a larger emotional enhancement effect following a perceptual versus semantic study phase activity, with Hedgesâ g = 1.38 (95% CI [1.15, 1.61], p < .001) and Hedgesâ g = 0.90 (95% CI [0.82, 0.99], p < .001), respectively. By contrast, for recognition test performance, the emotional enhancement effect was not significant and is not influenced by perceptual versus semantic study phase activities. 42 We also carried out exploratory analyses for influences due other variables that would be expected to affect episodic memory test performance: Awareness at the time of study that memory would be assessed; the number of pictures to be remembered; the amount of time available for studying each picture in the study phase; and whether the study phase activity focused on picture valence. The results of these analyses are summarized in Table 2. The display time variable showed a different effect on recall and recognition; specifically, the effect of valence in recall was significantly larger with a display time < 2500 milliseconds (Hedgesâ g = 1.22, 95% CI [1.07, 1.38], p < .001) compared to >= 2500 milliseconds (Hedgesâ g = 0.88, 95% CI [0.79, 0.97], p < .001). The opposite pattern occurred in recognition, where the effect was larger with a display time >= 2500 milliseconds (Hedgesâ g = 0.35, 95% CI [0.24, 0.46], p = .002) compared to <2500 milliseconds (Hedgesâ g = 0.13, 95% CI [0.03, 0.21], p < .002. Awareness of test had an influence only on recognition. The effect of valence was significantly larger when participants were test unaware (Hedgesâ g = 0.36, 95% CI [0.28, 0.44], p < .001) than when they were aware (Hedgesâ g = -0.04, 95% CI [-0.16, 0.08], p 0.463). 2.2.4: Risk of Publication Bias We used a funnel plot and Eggerâs test (Egger et al., 1997) test to check for publication biases in the data. The funnel plot in Figure 2.3 shows a skew to the right, suggesting that we are missing small studies with no or small emotional enhancement effects. Eggerâs test confirmed this observation by a significant amount of asymmetry in the data (z = 3.23, p = 0.001). 43 Figure 2.4. A Funnel Plot of Overall Effect Size Estimates Note. The horizontal axis represents the magnitude of effects (Hedges g) with positive values indicating an emotional enhancement effect and negative values indicating neutral enhancement effect. The vertical axis represents the inverse standard error of the effect size and is a measure of precision. The dots in the figure represent effect sizes included in the analysis, and the vertical line and the diagonal dashed lines illustrate the overall effect and the 95% confidence interval, respectively. 44 Table 2.1. Effect Sizes (Hedges g) Corresponding to Moderators of Recall and Recognition of Valenced Pictures Primary Moderators K g 95% CI QBetween P-value Lower Upper Recall Valence type 269 0.999 0.925 1.073 11.313 0.001 Negative 165 1.102 1.007 1.197 Positive 104 0.840 0.719 0.961 Sex 95 0.945 0.822 1.067 0.744 0.388 Females 50 0.897 0.715 1.079 Males 45 0.997 0.836 1.159 Retention Interval 269 0.999 0.925 1.073 4.136 0.042 Short 241 0.973 0.896 1.050 Long 28 1.251 0.938 1.565 Encoding Activity 175 0.966 0.886 1.047 15.806 0.001 Semantic 150 0.902 0.816 0.988 Perceptual 25 1.381 1.154 1.608 Recognition Valence type 165 0.235 0.165 0.305 4.987 0.026 Negative 97 0.301 0.212 0.390 Positive 68 0.141 0.028 0.254 Sex 28 0.342 0.190 0.493 0.093 0.761 Females 17 0.362 0.161 0.562 Males 11 0.309 0.072 0.546 Retention Interval 165 0.235 0.165 0.305 12.543 0.001 Short 88 0.121 0.031 0.212 Long 77 0.373 0.267 0.479 Encoding Activity 117 0.192 0.109 0.274 0.364 0.546 Semantic 106 0.184 0.095 0.274 Perceptual 11 0.187 0.065 0.309 Note. K = number of effect sizes; g = Hedgeâs g; 95% CI = the 95% confidence interval; QBetween = heterogeneity that was accounted for by the moderators; p = the p-value associated with QBetween. A significant p-value (< .05) indicate that moderator explained a significant amount of heterogeneity. The shaded rows show the overall summary for each moderator. The rows show the results of each level of the preceding moderator. 45 Table 2.2. Effect Size (Hedges g) for Analysis of Potential Moderators of Recall and Recognition of Valenced Pictures Exploratory Moderators K g 95% CI QBetween p-value Lower Upper Recall Awareness of test 236 0.994 0.916 1.073 0.716 0.397 Aware 62 0.957 0.760 1.153 Non-Aware 174 1.009 0.927 1.091 Number of to-be remembered Pictures 269 0.999 0.925 1.073 0.742 0.389 < 72 Pictures/few 177 0.985 0.878 1.092 >= 72 pictures/many 92 1.042 0.945 1.139 Display duration 265 0.979 0.905 1.053 16.384 < 0.001 < 2500 Milliseconds 81 1.223 1.070 1.376 >= 2500 Millisecond 184 0.881 0.794 0.968 Emotionality of encoding 140 0.902 0.816 0.988 0.105 0.746 Emotional Focus 130 0.908 0.817 0.999 Non-Emotional 20 .869 0.592 1.146 Recognition Awareness of test 125 0.197 0.118 0.275 32.885 < 0.001 Aware 51 -0.038 -0.158 0.082 Non-Aware 74 0.359 0.276 0.442 Number of to-be remembered Pictures 165 0.235 0.165 0.305 0.115 0.735 < 72 Pictures 32 0.238 0.117 0.359 >= 72 pictures 129 0.230 0.148 0.312 Display duration 165 0.235 0.165 0.305 9.393 0.002 < 2500 Milliseconds 81 0.126 0.033 0.219 >= 2500 Millisecond 84 0.350 0.240 0.459 Emotionality of encoding 109 0.181 0.095 0.267 .247 0.619 Emotional Focus 68 0.167 0.051 0.283 Non-Emotional 41 0.207 0.084 0.329 Note. K: number of effect sizes; g: Hedgeâs g; 95% CI: the 95% confidence interval; QBetween: heterogeneity that was accounted for by the levels of the moderator (i.e., subgroups); p: the p-value associated with QBetween, A significant p-value (< .05) indicate that moderator explained a significant amount of heterogeneity The shaded rows show the overall summary for each moderator. The consecutive rows show the results of each level of the preceding moderator. 46 2.3: Discussion A major purpose of this meta-analysis was to answer this question concerning the influence of valence on episodic memory: Does valence enhance episodic remembering, and if so, is the memory enhancement the same for negative and positive events, on recall and recognition tests, and with immediate as well as a delayed testing? We also asked if the memory enhancement is the same for male and female research participants. Supported by data from 152 experiments and over 12,000 participants, we view the meta-analysis results as answering these questions conclusively, as firmly establishing that episodic memory is better for valenced events compared to non-valenced/neutral events, that the memory enhancement due to valence is substantially larger for negative than positive events, that it is substantially larger when memory is assessed with recall tests compared to recognition tests and also larger when memory is assessed after a delay rather than immediately after study, and finally that the memory enhancement is the same for male and female rememberers. 2.3.1: External and Internal Validity These affirmations about the influence of valence on episodic memory must be considered within the limited scope of our meta-analysis, however. Our meta-analysis was restricted to laboratory research on healthy research participantsâ recollection of valenced and non-valenced pictures. As noted in the Introduction of this chapter, picture stimuli are used most extensively in laboratory research on episodic memory for emotional events, but they clearly do not exhaust the full range of events that elicit emotions. It seems likely that other types of emotional events, for example, a song, a dance or a birthday party, would elicit more intense and longer-lasting emotions. Moreover, when pictures are used in research, they are selected to comply with research ethics requirements, which typically means valence and arousal ratings are held up to a modest level so as not to elicit strong emotional discomfort or other potentially adverse events. Nevertheless, consistent with these provisos, because pictures are used most commonly for 47 research on episodic memory for valenced events, we are confident our assertions about the episodic recollection of valenced events validly characterize the current state of laboratory research findings on this topic. Even so, we recognize these assertions may not generalize to the episodic recollection of other emotional events especially not to those that elicit more intense and enduring emotions. The external validity of our assertions about episodic memory for valenced events is also constrained by the meta-analysisâ focus on laboratory research. For the experiments contributing data for this meta-analysis, participants were required to study and recollect a median of 72 pictures, with one half or one third of them typically being valenced pictures. This number far exceeds the density of emotional events in anyoneâs daily life. This density of emotional events leaves no time for dwelling on the emotions elicited by individual events, and emotional responses are likely altered by encountering different kinds of emotional events (positive, negative, neutral) in close proximity to each other. For reasons like these, we urge extreme caution when generalizing our assertions about the recollection of valenced events to everyday settings. We urge further caution because the meta-analysis yielded data primarily from healthy young adult research participants (our data came from participants with a mean age of 21.8 years). Children tend to respond to emotional events more strongly and differently (e.g., with actions rather than words) than young adults (Chaplin & Aldao, 2013; Gregory et al., 1996), and similarly, there are substantial differences between young and older adults when responding to emotional events (e.g., Charles et al., 2003; Keil & Freund, 2009; Grühn D, Scheibe, 2008). Differences in perception, habits, and experience of emotions are likely to affect episodic recollection, and for this reason, it is clear that follow-up research is required before making assertions about episodic memory for valenced events that pertain to the full human lifespan. 48 The scope of our meta-analysis also limits the internal validity of the assertions that are justified by the findings, because although we used valence as a selection criterion, we found differences in both picture valence and arousal ratings. Specifically, after we aggregated the valence and arousal ratings into proportion scores, we found the following mean valence ratings: for negative pictures M = .31 (SD = .04), for neutral pictures M = .57 (SD = .05), and for positive pictures M = .80 (SD = .05), and the following mean arousal ratings: for negative pictures M = .63 (SD = .10), for neutral pictures M = .36 (SD = .10), and for positive pictures M = .57 (SD = .11). These means underscore that in the studies that provided the data for our meta-analysis, the valence manipulation was about equally strong for the positive and negative pictures, as defined by their approximately equal distance from the valence ratings of neutral pictures: M = |.23| for positive vs neutral pictures, and M= |.26| for negative vs neutral pictures. The means also clearly show a similar difference in the arousal ratings of valenced pictures (.60) versus the neutral pictures (.36). As a consequence, a core finding of our meta-analysisâthe overall emotional enhancement in episodic memory performanceâcould be attributed either to picture valence or arousal. This confounding of valence and arousal is not a flaw in our research, however; it is inherent in research that requires strong valence manipulations and comparisons between neutral and valenced pictures. Is arousal also a potential confounding variable in the meta-analysis finding of a larger emotional enhancement effect for negative over positive pictures? Unfortunately, the answer also is affirmative. The goal of the meta-analysis was to examine episodic memory influences due to valence alone, in the absence of influences due to arousal. To rule out arousal as a potential confounding variable, we inspected the arousal control methods used in those studies included in our meta-analysis which had both negative and positive picture conditions. For these studies, we found 71.8% reported no differences in arousal between the positive and negative picture sets, 16.3% reported that arousal was not controlled between these picture sets, and the remaining 12.2% provided no information about arousal. In addition, the mean arousal 49 ratings reported in the preceding paragraph showed a difference in the strength of the arousal manipulation between positive and negative pictures; specifically, the arousal difference was .21 for positive vs neutral pictures, and .27 for negative vs neutral pictures. Does an arousal confound also potentially mar the interpretation of the meta-analysis findings of a larger emotional enhancement effect on recall than on recognition tests, and with delayed rather than immediate recollection? We propose a no answer for the first finding, which is sufficiently explained by processing differences between recall and recognition tests that are well established from research on episodic memory (Anderson & Bower, 1972; Watkins & Gardiner, 1979; Mandler, 1980a; Jacoby, 1991; Craik, 1983,1986; Craik and McDowd, 1987; Kintsch, 1970; Yonelinas, 2002). However, considering the arousal difference between negative and positive pictures in the meta-analysis data, we cannot rule it out as a potential confound for the second finding especially because arousal has been theorized to promote memory consolidation and to result in a larger memory enhancement on delayed versus immediate tests (e.g., Cahill & McGaugh, 1996; LaBar & Phelps, 1998; McGaugh, 2000, 2004; Ritchey, Dolcos, & Cabeza, 2008; Sharot & Phelps, 2004; B. Wang, 2014). 2.3.2: Theoretical Implications The primary theoretical implication of this meta-analysis is that several basic aspects of episodic memory for valenced events are explainable by theories like the NEVER model (Bowen et al., 2018) and the EEI/PA theory, that is, by theories that assign a causal role to valence but no such role to arousal. We draw this implication from the fact that all basic findings of this meta-analysisâthe memory enhancement for valenced compared to non-valenced events, and the larger memory enhancement for negative compared to positive events, for recall compared recognition tests, and for delayed compared to immediate testsâwere correctly predicted by the NEVER model and/or the EEI/PA theory. These predictions held true even though, as acknowledged and documented in the preceding paragraphs, in the research data used for 50 this meta-analysis, the valence-neutral manipulation as well as the positive-negative valence manipulation, were confounded with arousal. Theoretical accounts of episodic memory for emotional events that assign a causal role only to one of the core attributes of emotional events (e.g., valence, arousal, motivational intensity) are not novel; what is noteworthy is that in contrast to the NEVERâs and the EEI/PAâs focus on the role of valence, the majority of current theories focus exclusively on causal influences linked to arousal (e.g., Cahill & McGaugh, 1996; Crowley et al., 2019; Clewett & Murty, 2019; Mather & Sutherland, 2011; McGaugh, 2000, 2004; Storbeck & Clore, 2008). Although there exist important differences among contemporary arousal theories of episodic memory for emotional events, they subscribe to a version of the claim that emotional events enhance memory because they activate the basolateral amygdala and the hippocampal formation which in turn are assumed to facilitate episodic memory encoding, consolidation, and retrieval. As we anticipated earlier, because the arousal and valence variables were confounded with each other in the data used for this meta-analysis, a number of findings from the meta-analysisâespecially the overall enhancement effect for emotional events, the larger effect with negative than positive pictures and the larger effect with delayed than with immediate testingâare explainable by both valence-only theories (NEVER; EEI/PA) and arousal-only theories (e.g., Cahill & McGaugh, 1996; Crowley et al., 2019; Clewett & Murty, 2019; Mather & Sutherland, 2011; McGaugh, 2000; 2004; Storbeck & Clore, 2008). This surfeit of explanatory power is not desirable, and it clashes with scienceâs parsimony dictum. Thus, we recommend the next step in theorizing about episodic memory for emotional events should be to identify the distinct contributionsâif anyâmade to episodic recollection by valence and arousal. What outcome would you expect for an experiment in which the to-be-remembered events are positive or negative, crossed with low and high arousal? Our intuition suggests a larger memory 51 enhancement effect for negative compared to positive events, and it also suggests that for both positive and negative events, those with higher arousal ratings would be remembered better. The current version of the EEI/PA theory would not predict any effect due to arousal, and to the best of our knowledge, current arousal theories canât explain memory differences between positive and negative events that donât differ in arousal. A puzzle we see in our future research is figuring out a specific memory-modulating role to arousal in a cognitive processing theory like the EEI/PA theory. 2.3.3: Conclusion This meta-analysis validates widespread assumptions about episodic memory for emotional events, specifically, that episodic memory is better for valenced events compared to non-valenced/neutral events; that the memory enhancement due to valence is substantially larger for negative than positive events, larger when memory is assessed with recall tests compared to recognition tests and also larger when memory is assessed after a delay rather than immediately after study; and finally that the memory enhancement is the same for male and female rememberers. Equally important, this meta-analysis underscores that the core findings from episodic memory for emotional events are explainable by theoretical frameworks that focus entirely on the causal role of valence, in stark contrast to existing theoretical frameworks that have focused entirely on the episodic memory effects assumed to be produced by arousal. 52 Chapter 3: The Influence of Valence on Picture Recognition A primary goal of my dissertation was to examine theoretical predictions derived from the EEI/PA theory about valence-based emotional enhancement effects in episodic memory. However, the starting point for my laboratory research was to document the basic phenomenon, an emotional enhancement effect for valenced pictures in episodic memory, and to determine the magnitude of this effect for both negative and positive pictures. To conduct a robust test of the theoretical assumptions proposed by the EEI/PA theory, I designed a powerful experiment that addressed methodological limitations in previous research included in the meta-analysis, which may have contributed to the inconsistencies in the existing findings. Specifically, while previous studies had a median sample size of 34 participants and used a median of 72 to-be-remembered pictures, my Experiment 1 had 160 participants, and they were required to remember 180 pictures. This larger sample and picture sets allowed for greater generalizability of the findings. Although the meta-analysis in Chapter 2 revealed a larger emotional enhancement effect in recall than in recognition test performance, Experiment 1 used a recognition test primarily because of the methodological advantages of this method for investigating episodic memory. A recognition test for pictures is relatively easy to implement on a computer, and with recognition it is easy to automate performance scoring to yield reliable response accuracy and speed data. An additional advantage is that recognition performance tends to remain well above chance even when using a large set of to-be-remembered pictures (Brady et al., 2008; Jenkins et al., 2018; Shepard, 1967; Standing et al.,1970; Standing, 1973). The type of the test used for assessing episodic memory has a significant influence on performance, and even recognition and recallâthe most used testsâtend to yield different outcomes. Recognition tests are easier and frequently yield higher memory performance than recall tests (Fischer, 53 1909; McDougall, 1904; Srivastava & Vul, 2017), but a more fundamental difference between these test types is not how easy or difficult they are; it is their dependence on different memory retrieval processes. The retrieval processes assumed to be engaged for recognition and recall have been characterized in a variety of different ways (e.g., Jacoby, 1991; Mandler et al., 1969; Yonelinas, 2002; Kintsch, 1970; Anderson & Bower, 1972). In a particularly compelling characterization, Craik (1983, 1986) postulated a continuum with all memory tests arranged according to the extent to which they depend on what he called environmentally-driven versus self-initiated processing. Environmentally-driven refers to the habitual, automatic, and integrative processing that is guided by stimuli and cues available in the environment. By contrast, self-initiated refers to the controlled, attention-demanding, and elaborative processing that is guided by internal constraints and cues such as the remembererâs assumptions, knowledge, and strategies. Craik postulated that environmentally-driven processing and self-initiated processing represent the end points of a continuum, and that recognition test performance depends more heavily on the former while recall tests depend more heavily on the latter. While each test type heavily depends on either environmentally-driven or self-initiated processing, it also involves the other to some extent, with their relative contributions varying based on the demands of the memory test. Consistent with the findings from the meta-analysis in Chapter 2, Craikâs (1986) description of the retrieval processes engaged for recognition performance and with the EEI/PA theory, I hypothesized Experiment 1 would show a small emotional enhancement effect in recognition test performance. According to the EEI/PA theory, valence has an influence only on the accommodative/elaborative processing of events. And because recognition test performance depends heavily on assimilative/integrative (i.e., environmentally-driven) processing and only minimally on elaborative (i.e., self-initiated) processing (Craik, 1983, 1986), I expected recognition test performance to reveal only a slight emotional enhancement effect. 54 Building further on the findings from my meta-analysis and the prediction of the EEI/PA theory, I also hypothesized that the emotional enhancement effect would be larger for negative pictures than for positive ones. According to the EEI/PA theory, negative events involve more accommodative/elaborative processing than positive eventsâfor the reasons articulated in Chapter 1âthus giving rise to a larger emotional enhancement effect for negative than for positive pictures. Although Experiment 1 was designed to focus on valence influences on episodic memory for negative and positive pictures, it also re-examined potential influences due to arousal. Several theories maintain that emotional enhancement effects in memory occur because of arousal (Bradley et al., 1992; Bradley, 2014; Hamann, 2001; Mather & Sutherland, 2011; McGaugh, 2006; Talmi, 2013), and more importantly, some accounts suggest a larger emotional enhancement effect occurs for negative than positive events because negative events are more arousing than positive events (Baumeister et al., 2001; Clewett & Murty, 2019; Kensinger & Schacter, 2008; Rozin & Royzman, 2001; Vaish et al., 2008). NEVER (Bowen et al., 2018), the model introduced in Chapter 1, stands apart from other extant theories by claiming that a difference between negative and positive events may occur solely due to valence, and that a larger enhancement effects for negative than positive pictures may occur even if arousal is equated between negative and positive events. The NEVER model predicts this outcome by claiming that negative events receive more extensive sensory-perceptual processing than positive events, giving rise to a larger emotional enhancement effect for negative than for positive pictures. For a closer examination of this claim about the role of valence versus arousal, Experiment 1 included subsets of negative and positive pictures with matching normative arousal ratings. When using large sets of negative and positive pictures, a perfect matching of arousal is not possible between them. However, such a match is possible for smaller set of pictures. Therefore, Experiment 1 included small 55 subsets of pictures with arousal levels equated between negative and positive pictures. Consistent with the NEVER model and the EEI/PA theory, I expected a larger emotional enhancement effect for negative than positive pictures even in this arousal-controlled subset of pictures. 3.1: Method 3.1.1: Participants and Design The participants were 160 undergraduate students (81 self-identified as females and 79 self-identified as males) from the University of British Columbia. All had normal or corrected-to-normal vision. Participants were recruited from the Psychology Department Human Subject Pool, and all received partial course credit for their participation in the study, which was approved by the University of British Columbia Behavioral Research Ethics Board. The sample size was determined based on an analysis using the G*Power computer software (Version 3.1.9.6; Faul et al., 2007). The analysis showed that to detect an interaction effect between the memory enhancement due to picture valence and the sex of participants, a minimum of (134) participants, 67 participants per sex group, is required to detect a small effect (d = 0.2), with a power equal to 0.8 and an alpha set to 0.05 (Cohen, 1988). Two female participants withdrew from the study, one because she was repulsed by some of the negative picture stimuli; the other did not provide a reason. The experiment used a mixed subject design, with picture valence manipulated within subjects with three levels: negative, neutral, and positive. 3.1.2: Materials 3.1.2.1: Pictures A total of 360 colored picturesâ120 negative, 120 neutral, and 120 positiveâwere selected from the Nencki Affective Picture System (NAPS) database (Marchewka et al., 2014). The NAPS database is 56 one of the most extensively used databases which holds 1356 high-resolution pictures, each accompanied by standardized visual properties (e.g. brightness, contrast). In the database, each picture comes with normative data on valence and arousal, collected via 9-point scales where 1 indicates very negative and 9 indicates very positive, and similarly where 1 indicates relaxed (low arousal) and 9 indicates high arousal. The data base lists separate norms for men and for women, as well as for the combined group with both sexes (men and women). These properties and normative data enable a more controlled selection of pictures. For this study, pictures were selected based on the combined normative data for valence and arousal. The picture selection criteria and the normative data on valence and arousal for the selected pictures are shown in Table 3.1. The three picture setsâ120 negative, 120 neutral, and 120 positiveâwere significantly different from each other on their normative valence ratings. The selected pictures depicted different themes or objects such as people, human interaction, animals, landscapes, or urban settings; these themes were similar across the valence sets. Each picture was saved as a 480 x 360 pixel .jpeg file, and used in the experiment in the pertinent portrait or landscape orientation. The data in Table 3.1 show that for the selected picture stimuli, valence was negatively correlated with arousal. This result is due to the fact that the NAPS database is not large enough for the purpose of matching arousal of as many as 120 strongly positive pictures and 120 strongly negative pictures. However, because I wished to examine the influence due to valence independently of arousal, I created subsets of 26 positive and 26 negative pictures in such a manner as to equate them on arousal. Table 3.1 has the normative data for these subsets, which differ significantly on valence ratings, but not on arousal. The 120 pictures for each negative and positive valence condition were pseudo-randomly arranged into subsets of 60 in such a way that each subset included 13 arousal-matched pictures that were 57 intermixed within the full study lists. I also created two subsets with 60 neutral pictures. Then we used these subsets to create two lists of 180 pictures, List A and B, each with 60 pictures from each valence condition. These lists were administered in a counterbalanced manner across research participants in the study phase of the experiment, as well as for use as non-studied distractors for recognition testing. Table 3.1. Valence and Arousal Ratings Used for Selecting Pictures and for the Selected Pictures â for the Negative, Neutral, and Positive Pictures Used in Experiment 1 Valence of picture sets Picture Selection Criteria Properties of Selected Pictures Arousal-non-matched (94) Arousal-matched (26) Valence Arousal Valence Arousal Valence Arousal Range Range Mean 95% CI Mean 95% CI Mean 95% CI Mean 95% CI Negative 2.25 â 3.25 4.5 â7.00 2.49 2.43-2.55 6.81 6.73-6.90 3.24 3.18-3.32 5.73 5.65- 5.80 Neutral 4.75 â 5.25 5.03 4.98-5.08 4.89 4.86- 4.92 Positive 7.25 â 8.00 7.41 7.35- 7.47 4.63 4.56- 4.70 7.34 7.25-7.43 5.60 5.48-5.71 3.1.2.2: Self-Report Questionnaires 3.1.2.2.1: Personality Inventory The NEO Five Factor Inventory (NEO-FFI; Costa & McCrae, 1989) was used for assessing personality. The NEO is a self-report questionnaire for measuring five personality domains: Neuroticism, Extraversion, Openness to experience, Agreeableness, and Conscientiousness. The questionnaire consists of 60 items (e.g., I like to have a lot of people around me; I strive for excellence in everything I do; I am seldom sad or depressed), and participants are required to rate each item on a 5-point scale (from 1 Strongly Disagree to 5 Strongly Agree). The NEO-FFI personality dimensions are moderately correlated with other tests that measure the same dimensions (e.g., HEXACO-Personality Inventory (HEXACO-PI; 58 Wakabayashi, 2014). The reliability of the NEO-FFI (i.e., the correlation between self and observer rating) ranges from .47 for Extraversion to .68 for Agreeableness (Robins et al., 2001), and the test-retest reliability after two weeks is: .90 for Conscientiousness; .89 for Neuroticism, Extraversion, and Agreeableness; .88 for Openness to experience (Foltz et al.,1997). 3.1.2.2.2: Gender Scale I The Traditional Masculinity-Femininity scale (TMF; Kachel et al., 2016) is a self-assessment instrument for measuring participantsâ gender-role self-concept. The scale consists of six items (e.g., Ideally, I would like to beâ¦; traditionally, my attitudes and beliefs would be considered asâ¦), each to be rated on a 7-point scale, with its end-points marked 1 totally masculine and 7 totally feminine. TMF has a high-level of internal consistency (Cronbach's a= .94), and the scale scores are moderately correlated with other gender-rating scales, such as Bemâs Sex Role Inventory (BSRI) (Bem, 1974) and the Gender Role Behavior Scale (GRBS) (Kerr & Holden, 1996). Test-retest reliability for the TMF after one year is 0.75. (Kachel et al., 2016). 3.1.2.2.3: Gender Scale II The Gender Role Behavior Scale (GRBS; Athenstaedt, 2003) was administered for measuring stereotypical feminine and masculine behaviors. The GRBS has two independent subscales: a feminine behavior scale (FBehav) and a masculine behavior scale (MBehav), covering a wide variety of everyday behaviors (e.g., go sailing, do extreme sports, cook, put on make-up). It consists of 52 items, each rated on a 7-point scale (from 1 not at all typical to 7 very typical). The internal consistency of the components of the scale are as follows: FBehav: women α = .83 and men α = .83; MBehav: women α = .83 and men α = .84. 59 3.1.3: Procedure Participants were tested individually in a quiet laboratory room equipped with a desktop computer. The experiment had an encoding phase followed by a testing phase. Both phases were controlled by a desktop computer running E-Prime version 2.0 (Psychology Software Tool, Inc). The experiment required a single session lasting approximately one hour. 3.1.3.1: Encoding Phase The encoding phase had 180 trials, with 60 trials each displaying negative, neutral, and positive pictures. Each trial involved the sequence of events shown in Figure (3.1). It started with a fixation cross for 250 ms, to direct participantsâ attention to the center of the computer screen where all pictures were displayed. This was followed by the display of a visual pattern mask for 50 ms, and then the display of a picture together with a question and a rating scale. The trial ended with the display of a blank grey screen for 1500 ms. The picture displayed for each trial was selected randomly and without replacement from the three valence sets, and each remained on the screen until the participant entered a response, and on the screen, each display measured 13.5 x 9.5 cm for landscape and 10.2 x 13.5 cm portrait, respectively. The participants completed the following tasks. Each picture display asked the question: "How [attractive or repulsive] is this picture?" and it provided a 6-point scale, with its end-points marked 1 âNot [attractive or repulsive] at allâ to 6 âextremely [attractive or repulsive]â, written in 36-point black Tahoma font. Each participant was asked only one question version (e.g. about attractiveness or repulsiveness); the question versions were counterbalanced across participants. Participants were instructed to âexperience the feelings and thoughts evoked by each displayed pictureâ, and they were asked to rate each picture according to the scale with instruction to give a rating which captures âyour first impression and gut reactionâ. Participants were encouraged to âmake rating as fast and accurately as possibleâ. Participants received beeps (i.e., a relatively soft 300 ms pure tone) as feedback if their ratings deviated substantially 60 from the valence ratings in the data base. This feedback was intended to maintain attention throughout the long series of pictures and to reduce random ratings of pictures. Each picture display, along with the question and rating scale, remained on the screen until the participant entered a rating on the keyboard number pad, and then the picture display and the scale disappeared, and the next trial began. The main reason for the picture rating task was to increase participantsâ engagement with each picture, and to use the ratings as a measure of participantsâ emotional response as well as to verify the valence manipulation. Figure 3.1. The Sequence of Specific Events with their Duration on a Single Trial in the Encoding Phase and Testing Phase of Experiment 1 3.1.3.2: Testing Phase The testing phase involved 360 trials presented in a random order, each trial maintained the same event sequence as used for the encoding trials, as shown in Figure (3.1). In the testing phase, half of the Blank gray screen (1500 ms)+ Fixation cross (500 ms)Mask (50 ms)Picture display + rating scale (until response)Time+Is this picture Old or New? << Old New >>How attractive?1 2 3 4 5 6 Not at all Extremely Encoding Phase Recognition Testing Phase 61 trials were used for displaying old pictures (i.e., pictures rated during the preceding encoding phase), and the other half was used for showing new pictures (i.e., pictures not shown during the encoding phase). Each picture display was centered on the screen and was accompanied with a question: âIs this picture old or new?â in 36-point black/maroon Tahoma font. For each picture, the participantâs task was to make an Old/New decision, determining whether it was old (i.e., previously seen during encoding phase) or new (i.e., novel and shown for the first time) by pressing the left-arrow key for old and the right-arrow for new on the keyboard. Participants were encouraged to make their decisions âas fast and accurately as possibleâ. Each picture display, along with the question, remained until the participant made a decision, then the next trial began. Participants received beeps (i.e., a relatively soft 300 ms pure tone) as feedback for incorrect decisions. 3.1.3.3: Experiment Flow Participants were tested by a female experimenter, one at a time, in a quiet laboratory room. All participants provided informed written consent at the beginning of the experiment. Participants were presented with instructions orally by the experimenter as well as written on the screen, and participants were encouraged to ask questions to ensure they understood the instructions and experimental procedure. To ensure participants understood the instructions for the encoding phase, six practice encoding trials were first administered, using two extra pictures from each valence condition. The practice then followed the 180 encoding phase trials. Each trial was as described above, and participants were encouraged to ask questions immediately after the practice trials. All participants were informed that their memory for the displayed pictures would be tested after the encoding phase. Immediately after the encoding phase, the participants performed an irrelevant cognitive task (i.e., a continuous recognition memory task) on the computer, and also completed NEO-FFI, GRBS, TMF in a 62 paper-and-pencil format. These instruments were used mainly as filler tasks, for the purpose of separating the encoding and testing phases of the experiment. These instruments were also used to explore potential associations between personality traits, gender identity, and memory for emotional events. However, no analyses regarding such associations between individual differences and memory performance are reported in this dissertation. The average duration between encoding and testingâthe retention intervalâwas approximately 25 minutes. After the testing phase, a funnel debriefing interview was employed to collect information on participantâs experience, thoughts, and observations. During the funnel debriefing, participants were first asked a few open-ended questions, then more specific questions about learning and remembering valenced pictures. Then the participants completed on online debriefing survey required for compliance with UBC ethics guidelines. Finally, they were thanked for their participation. 3.2: Results The main goal of this experiment was to examine whether there is an emotional enhancement effect in episodic recognition memory and to determine whether this effect is different for negative and positive pictures. In addition, this experiment aimed to explore the influence of participantsâ sex on recognition memory. The main independent variables were the valence of the pictures (negative, neutral, and positive) and the sex of participants (male and female), and the dependent variables were recognition memory performance and recognition decision times (in milliseconds). Also of interest were the picture valence ratings and the time required for making valence ratings (in milliseconds). I used the 95% confidence interval to assess differences among means, and when further analyses were required, I used repeated measures analysis of variance (ANOVA). The Greenhouse-Geisser 63 correction was used in cases that violated the sphericity assumption, and the Bonferroni correction was used for multiple comparisons. F ratios and partial eta-squared (η2) are reported only for significant results. 3.2.1: Data Preparation All data were screened for outliers using participantâs median picture valence rating times and recognition decision times. The picture valence rating times showed three participants had medians of 3427 ms, 3604 ms, and 3628 ms, respectivelyâmore than three standard deviations above the mean of all participantsâ median time of 1730 ms. Outliers were interpreted as evidence of participants doing the rating task in a manner that deviated from instructions, and thus, their data were not included in any further analysis. No further outliers were identified. The results reported below are based on the data for the remaining 155 participants (78 females and 77 males). I checked recognition performance for ceiling and floor effects. Participants achieved a mean hit rateâdefined as the proportion of studied pictures that were correctly recognized as having been studiedâof .85 (95% CI [.84, .86]), .85 (95% CI [.83, .86]), and 85 (95% CI [.84, .87]), respectively, for negative, neutral, and positive pictures. The false alarms rateâdefined as the proportion of non-studied or distractor pictures falsely recognized as having been studiedâwas .11 (95% CI [.09, .12]), .07 (95% CI [.06, .08]), and .08 (95% CI [.07, .09]) for negative, neutral, and positive pictures respectively. These findings indicate that recognition memory performance was not marred by either ceiling or floor effects. 3.2.2: Encoding Phase I examined encoding phase performance to validate the valence manipulation, to verify that participants had completed tasks as instructed, and to explore differences in the ratings on the attractiveness and repulsiveness scales. 64 3.2.2.1: Valence Ratings Participantsâ mean ratings of the pictures in each valence condition are shown in Figure 3.2; the left panel shows the mean attractiveness ratings, and the right panel shows the mean repulsiveness ratings. For both panels, the ratings shows that participants rated pictures differently based on their valence. On the attractiveness scale, participants rated positives pictures (M = 4.64, 95% CI [4.51, 4.77]) as significantly more attractive than neutral ones (M = 2.91, 95% CI [2.80, 3.01]), which in turn were rated significantly more attractive than negative pictures (M = 1.50, 95% CI [1.42, 1.58]). Conversely, on the repulsiveness scale participants rated positive pictures (M = 1.66, 95% CI [1.53, 1.79]) significantly less repulsive than neutral pictures (M = 2.57, 95% CI [2.43, 2.71]), which were rated significantly less repulsive than negative pictures (M = 4.71, 95% CI [4.54, 4.88]). The picture rating data show participants perceived the pictures as expected on both the attractiveness and repulsiveness scale and in a manner consistent with the NAPS norms (Marchewka et al., 2014). I interpret these data as evidence that the valence manipulation was sufficiently powerful. Although these picture ratings are roughly consistent with the valence norms from the NAPS (Marchewka et al., 2014), they show that on both scales, neutral pictures were rated lower than the scale midpoint of 3.5, despite the fact that I selected these pictures to match the mid-point of the rating scale used in the NAPS norms. One reason for these lower ratings is that participants might have perceived the pictures as dated or trite, and for this reason, they anchored their ratings below the midpoint on both the attractiveness and repulsiveness scale. 65 Figure 3.2. Mean Attractiveness Ratings (left panel) and Mean Repulsiveness Ratings (right panel) for Negative, Neutral, and Positive Pictures Note. Error bars represent the 95% confidence intervals. 3.2.2.2: Valence Rating Times The means of the median valence rating times, measured from the display onset of each picture on the screen to entering a rating on the computer keyboard, are shown in Figure 3.3. Because there were only small differences in the rating times from the attractiveness and repulsiveness scale, I combined the results from both scales for Figure 3.3. The figure shows participants made ratings faster for both negative pictures (M = 1576 ms, 95% CI [1488 ms, 1664 ms]) and positive pictures (M =1633 ms, 95% CI [1562 ms, 1705 ms]), which did not significantly differ from each other -- than for neutral pictures (M = 1877 ms, 95% CI [1793 ms, 1962 ms]). 123456Negative Neutral Positive Attractiveness (1-6)123456Negative Neutral Positive Repulsivness (1-6)Picture Valence 66 Figure 3.3. Mean of Median Valence Rating Times for Negative, Neutral, and Positive Pictures Note. Error bars represent the 95% confidence intervals. The finding that valenced pictures were rated faster than neutral pictures is consistent with previous research (e.g., Scott et al., 2009). A potential reason for this finding is that only two scale-points may need to be considered when rating negative or positive pictures (at either end of the scale) whereas more scale-points may need considering for rating neutral pictures (around the middle of the scale), and thus more time is required for rating them. 3.2.3: Testing Phase I examined the speed and accuracy of recognition test hits and false alarms for negative, neutral, and positive pictures. 4006008001000120014001600180020002200Negative Neutral PositiveValence Rating Time (ms)Picture Valence 67 3.2.3.1: Recognition Hits and False Alarms The mean proportions of hits and false alarms are shown in Figure 3.4, with hits and false alarms in the left and right panel respectively. The hit means are about the same for negative, neutral and positive pictures, though there are small differences due to participantsâ sex. This was partially confirmed by an ANOVA with valence (negative, neutral, and positive) as a within-subject variable and sex (females and males) as a between-subjects variable. The results showed no significant main or interaction effects, all pâs > .05. A different pattern is evident in the right panel of Figure 3.4: the false alarm rate is higher for negative pictures compared to neutral and positive pictures. An ANOVA revealed a significant main effect for valence, F (2,153) = 45.18, p < .001, η2 = .228, with no other significant main or interaction effects, both pâs > .05. Follow-up pairwise comparisons showed the false alarm rate for negative pictures (M = .106, 95% CI [.092, .120]) was significantly higher than it was for both neutral pictures (M = .068, 95% CI [.056, .079]) and positive pictures (M = .076, 95% [CI .066, .087]). Figure 3.4. Mean Proportion of Hits (left panel) and False Alarms (right panel) for Negative, Neutral, and Positive Pictures Note. Error bars represent the 95% confidence intervals. 0.000.050.100.150.200.250.30Negative Neutral PositiveProportion of False AlarmsPicture Valence 0.000.100.200.300.400.500.600.700.800.901.00Negative Neutral PositiveProportion of Hits 68 3.2.3.2: Corrected Recognition In order to accommodate the differences in the false alarms across the picture valence conditions, I calculated a corrected recognition scoreâdefined as the proportion of hits minus false alarms, for each participant and for each picture valence condition. The corrected recognition means, in the left panel of Figure 3.5, show small differences across the picture valence conditions and small differences due to participantsâ sex. This was partially confirmed by an ANOVA, with the same variables as used for the hits and false alarms; it revealed a significant main effect for valence, F (2,153) = 14.78, p < .001, η2 = .088, but no other significant main or interaction effects, both pâs >.05. Follow-up pairwise comparisons revealed corrected recognition for negative pictures (M = .742, 95% CI [.721, .764]) was significantly lower than for both neutral pictures (M = .780, 95% CI [.758, .802]), p < .001 or positive pictures (M = .779, 95% CI [.756, .802]), p < .001, with no significant difference between positive and neutral pictures, p >.05. Because I was surprised by the results in Figure 3.4, I explored whether the same performance pattern holds for participants without exceptionally good or poor memory. For this reason, I re-examined corrected recognition, but this time only with the data from those 50% of participants in the middle of the entire sampleâs performance distribution. To that end, I calculated the 1st quartile (25th percentile) and 3rd quartile (75th percentile) of average performance (combined across the three valence conditions) for males and females. I then excluded the data below Q1 and above Q3 and retained only the data between them. Because some participants had the same average performance at the quartile boundaries (Q1 and Q2), I ended up with a sample of 80 participants (39 females and 41 males), slightly larger than 50% of the total sample of 155 participants. The results from this sample are shown in the right panel of Figure 3.5. This figure shows a similar pattern to the whole sample, but an ANOVA revealed a significant sex effect, F (1,79) = 9.18, p = .003, η2 = .105, which was not present in the whole sample, along with a significant main effect for valence, F (2,78) = 7.24, p = .001, η2 = .085. The sex by valence interaction was not significant, p 69 >.05. Follow-up pairwise comparisons showed recognition of negative pictures (M = .765, 95% CI [.750, .779]) was significantly lower than recognition of neutral (M = .796, 95% CI [.781, .812]), p = .011, or positive pictures (M = .801, 95% CI [.785, .817]), p = .013. The memory performance of male participants (M = .802, 95% CI [.789, .815]), p = .011, was higher than that of female participants (M = .773, 95% CI [.756, .787]), but this outcome should be interpreted with caution in view of the relatively small sample size of only 80 participants. Figure 3.5. Mean Corrected Recognition for Negative, Neutral, and Positive Pictures for All Participants (left panel) and for the Middle 50% of Participants (right panel) Note. Error bars represent the 95% confidence intervals. 3.2.3.3: Recognition Decision Times The mean recognition decision times, measured from the onset of a picture on the display screen to when participants entered their Old/New decision on the computer keyboard, are summarized in Table 3.2. Because there were no sex differences in decision times, I combined the data from both sexes. The 0.500.550.600.650.700.750.800.850.900.951.00Negative Neutral PositiveProportion Corrected RecognitionPicture Valence 0.500.550.600.650.700.750.800.850.900.951.00Negative Neutral PositiveProportion Corrected Recognition 70 means show participants were faster to make correct old decisions about previously studied pictures (hits) than to make incorrect old decisions about non-studied pictures (false alarms) in all picture valence conditions. The means also show that decision times for hits were significantly slower for negative and positive pictures compared to neutral pictures, whereas there were no differences in the decision times for false alarms among the picture valence conditions. I postulate that the faster decision time for hits compared to false alarms is due participantsâ greater familiarity with studied stimuli (Malmberg, 2008; Sikström, 2004; Weidemann & Kahana, 2016; Yonelinas, 2002). In contrast, it is likely false alarm decisions were made more slowly because they involve more memory searching and greater reliance on constructive/elaborative retrieval processes (Walla et al., 2000). Table 3.2. Recognition Decision Times on Hits and False Alarms for Negative, Neutral, and Positive Pictures Valence of Picture Sets Recognition Decision Time (ms) Hits False Alarms Mean 95% CI Mean 95% CI Negative 1046.28 1017.38 - 1075.18 1464.18 1360.31 - 1568.04 Neutral 1004.27 977.65 - 1030.88 1458.72 1327.15 - 1590.30 Positive 1030.13 1003.08 - 1057.19 1414.24 1303.24 - 1525.24 3.2.3.4: Recognition of Arousal-controlled Pictures To explore possible confounding influences due to arousal on recognition, Experiment 1 included a subset of negative and positive pictures with closely matched normative arousal ratings, but because these subsets were small, the results must be considered with caution. Performance on these pictures is shown in Figure 3.6, with hits in the left panel and false alarms in the right panel. The figure shows small 71 differences in the hits and in the false alarms. An ANOVA of the hits, appropriately adjusted for a significant Mauchly sphericity test (Ï2 (2) = 8.24, p = .016), revealed a significant main effect for valence, F (2,153) = 9.248, p < .001, η2 = .057, but not for sex or for the sex by valence interaction, both pâs >.05. Follow-up pairwise comparison showed hits for negative pictures (M = .810, 95% CI [.789, .831]) was significantly lower than for neutral pictures (M = .847, 95% CI [.831, .864]), p = .008 or positive pictures (M = .874, 95% CI [.821, .861]), p =.010. Hits for positive and neutral pictures were not significantly different, p > .05. The same kind of ANOVA with a Greenhouse-Geisser correction was also used to analyze the false alarm data, and the results showed no significant main or interaction effects, all pâs >.05. Figure 3.6. Mean Proportion of Hits (left panel) and False Alarms (right panel) on the Arousal-Controlled Negative, Neutral, and Positive Picture Subsets Note. Error bars represent the 95% confidence intervals. In the light of the differences in hit rates across picture valence conditions, and for consistency with how I summarized recognition performance in the preceding section, I calculated corrected recognition 0.000.050.100.150.200.250.30Negative Neutral PositiveMean Proportion of False AlarmsPicture Valence 0.000.100.200.300.400.500.600.700.800.901.00Negative Neutral PositiveMean Proportion of Hits 72 scores in the manner described above. The results, in Figure 3.7, show the same pattern as Figure 3.5: no overall emotional enhancement effect and no influence due to sex. An ANOVA exactly as used for the hits and false alarms, with the Greenhouse-Geisser correction, revealed a significant main effect of valence, F (2,153) = 5.29 p = .008, η2 = .033, but no significant main effect for sex, and no sex by valence interaction, both pâs > .05. Follow-up pairwise comparisons showed worse recognition for negative pictures (M = .741, 95% CI [.712, .770]) compared to neutral pictures (M = .780, 95% CI [.758, .802]), p < .001, but negative and positive pictures were not significantly different, p > .05. There was no significant difference between positive and neutral pictures, p > .05. Figure 3.7. Mean Proportion Corrected Recognition for the Arousal-Controlled Negative and Positive Picture Subsets Note. Error bars represent the 95% confidence intervals. The absence of sex differences in recognition memory is inconsistent with the evidence from the autobiographical and episodic memory research showing that females outperform males (Asperholm et al., 0.500.550.600.650.700.750.800.850.900.951.00Negative Neutral PositiveProportion Corrected RecognitionPicture ValenceFemales Males 73 2019; Bloise & Johnson, 2007; Canli et al., 2002; Davis, 1999; Gasbarri et al, 2007; Seidlitz & Diener, 1998; Wang, 2013). This memory advantage, according to the cognitive style hypothesis (Seidlitz & Diener, 1998), is argued to arise from a tendency of females to engage in more elaborative processing during encoding (Seidlitz & Diener, 1998; Meyers-Levy, 1989; Wirth et al., 2007). However, this experiment used a recognition memory test, which is known to depend primarily on study-test overlaps in integrative/environmentally-driven processing, and it may have prevented the occurrent of the sex difference which is assumed to be dependent on elaborative/self-initiated processing. Accordingly, sex differences are more likely to occur on tasks that require elaborative processing, such as free recall tests (Craik, 1983, 1986). 3.3: Discussion The main goal of Experiment 1 was to test the assumption that valence influences memory performance via study-test overlaps in accommodative/elaborative processing. On an old/new recognition memory test, I expected only a small emotional enhancement effect for valenced pictures, on the assumption that performance on such tests depends only to a limited extent on accommodative/elaborative processing overlaps (Craik, 1983, 1986). In contrast to this expectation, the results showed no evidence of an overall emotional enhancement effect in recognition hits, either for the entire set of to-be-remembered pictures nor for the arousal-control subset, and the false alarm rate was higher for negative pictures compared to both positive and neutral ones. The finding of a higher false alarms rate for negative pictures is consistent with previous studies (Bessette-Symons, 2018; Hourihan, 2020; Hourihan & Bursey, 2017; MacMillan et al., 2022; Maratos et al., 2000; Windmann & Kutas, 2001). This finding suggests false alarm decisions are made by partially relying on elaborative processing. Recognition performance is known to be flexible, and the balance between relying on integrative/environmentally-driven versus elaborative/self-initiated processing is assumed to shift 74 depending on task demands (Craik, 1983, 1986). Participants may have shifted this balance in favor of elaborative processing, because new pictures donât evoke the sense of familiarity or âoldnessâ that tends to occur with old pictures. It is likely that when faced with less familiar information for decision making, participants relied more on elaborative processing, for example, by searching their memory for pertinent cues (e.g., âDid this picture make me feel something during encoding?â or âWhat was the context in which I saw this picture before?â) (cf. Walla et al., 2000). This speculation is supported by the false alarm decision times (see Table 3.2), which show participants were much slower to make false alarm decisions than they were to make hit decisions, because elaborative processing is slow. The higher false alarms rate for negative pictures may also be explained by either the difference between negative and positive pictures in semantic/thematic relatedness or by arousal. Negative pictures, compared to positive and neutral pictures, tend to be highly related and often share common themes (e.g., accidents, injuries), which makes them more similar to each other (Dougal & Rotello, 2007; Maratos et al., 2000; McNeely et al., 2004; Windmann & Kutas, 2001; Talmi, 2013). Thus, it is possible that the greater thematic similarity among pictures brought about the higher rate of false alarms (Bookbinder & Brainerd, 2016; Johnson & Raye 1998; Maratos et al., 2000; Roediger & McDermott, 1995; Robinson & Roediger, 1997). The more false alarms with negative pictures may also be attributed to arousal. A great deal of evidence suggests arousing events are remembered better than non-arousing events (Bradley et al., 1992; Cahill & McGaugh, 1995, 1998; Hamann, 2001; LaBar & Phelps, 1998; Mather & Sutherland, 2011; McGaugh, 2006), and more importantly, arousal can even have the opposite effect on false alarms (Boywitt, 2015; Corson & Verrier, 2007; Mirandola & Toffalini, 2016). In my experiment, the negative pictures had higher arousal ratings than the positive pictures. It is possible the participants may have avoided or curtailed looking at the highly arousing pictures (because they were ugly or gross), which would 75 have resulted in poorer encoding and difficulty in distinguishing between old and new pictures at the time of testing. Support for this suggestion comes from debriefing the participants, several of whom reported that some negative pictures were disturbing. This suggestion is also supported by the results from arousal-controlled pictures, specifically the finding that their false alarm rate was the same across the valence conditions The absence of an overall emotional enhancement effect in hits was unexpected, suggesting that recognition decisions were made not as anticipated (i.e., involving at least partially on elaborative/self-initiated retrieval processing), but rather that participants may have relied entirely on integrative/environmentally-driven retrieval processing. Craik (1983, 1986) postulated that recognition performance relies on a combination of environmentally-driven processing and self-initiated processing. However, recognition testing is known to be flexible, and the extent to which performance depends on environmentally-driven versus self-initiated processes is known to vary depending on task demands (e.g., old/new recognition vs. forced choice decisions) (Bastin & Van der Linden, 2003; Migo et al., 2009) or retention interval duration (i.e., short vs. long delay) (Yonelinas & Levy, 2002). In my experiment, it seems participants relied entirely on integrative/environmentally-driven retrieval processing which â¦. One possible reason participants did not use elaborative/self-initiated processing for making recognition decisions in Experiment 1 is that this kind of processing was not necessary for achieving a satisfactorily high level of performance. Elaborative processing is effortful, demanding attentional resources, and the human cognitive system is configured to conserve these resources as much as possible, and thus whenever possible privileges integrative/environmentally-driven processing (Gigerenzer & Goldstein, 1996; Kahneman,1973; Simon, 1956). In my experiment, participants may have made their recognition decisions supported only by integrative/environmentally-driven processing, which is thought to 76 create the sense of familiarity one feels for old items, and which seems to have been sufficient in achieving a subjectively-satisfactory hit rate of about 85% in all valenced conditions. Another potential explanation for relying primarily on integrative/environmentally-driven processing in recognition is the extensive length of the picture test lists. Increasing the length of a test-list makes a test more demanding (Dennis & Humphreys, 2001; Underwood, 1978). I used a large numbers of pictures in my experiment, to prevent ceiling effects and enhance the generalizability of the results, but this may have encouraged participants to rely solely on integrative/environmentally-driven memory retrieval processes. Given the number of decisions participants needed to make (360 trials), perhaps they opted for integrative/environmentally-driven processing to prioritize task completion. During debriefing, many participants commented that the task was long and time-consuming because of the large number of pictures they had to rate and make decisions on. To conclude, my experiment, surprisingly, did not demonstrate the predicted emotional enhancement in memory using a recognition memory test. This finding suggests not all recognition tests are created equal in demonstrating an emotional enhancement effect on episodic memory. In my view, the lesson taught by Experiment 1 is that an emotional enhancement effect may not occur because recognition tests are flexible and do not always draw on the combination of environmentally-driven and self-initiated processes described by Craik (1983, 1986). Instead, recognition performance may be based entirely on environmentally-driven processes, and if this manner of performing the task is successful, no emotional enhancement is expected. The fact that recognition decisions can be made either with or without contributions from self-initiated processing augments our understanding of the mixed results in the literature, where emotional enhancement effect occurs, perhaps only when both processes contribute to memory performance and not when performance relies solely on environmentally-driven processes. My recommendation is that future research using recognition to test memory for emotional events should place 77 emphasis on tasks that require more self-initiated retrieval processing. For example, researchers may use specific recognition paradigms such as a remember/know procedure, increase the difficulty of recognition task to the extent that self-initiated processing is required, or test memory after a long retention interval. 78 Chapter 4: The Influence of Valence on Associative Recognition of Picture Pairs Experiment 2 was a powerful experiment designed to test specific predictions about another well-known effect in episodic memory for valenced materials: impaired recognition of newly formed associations. Research has shown that recognition of newly formed associations between randomly-paired neutral events tends to be higher than recognition of such associations between valenced events (e.g., Caplan et al., 2019; Madan et al., 2017; Murray & Kensinger, 2012; Nashiro & Mather, 2011; Stewardson et al., 2022; but see Chiu et al., 2013), an outcome that is predicted by several extant theories such as the Object-Based Binding Framework (Mather, 2007), the Arousal-Biased Competition in Perception and Memory (ABC) theory (Mather & Sutherland, 2011), the Broaden and Build theory (Fredrickson, 1998, 2001, 2004), and also the EEI/PA theory. Despite the common assumption that valence impairs memory for association, the evidence regarding the influence of valence on associative recognition remains unclear. A substantial body of research has shown that negative valence, compared to neutral valence, impairs memory for associations (e.g., Caplan et al., 2019; Bisby & Burgess, 2014; Madan et al., 2017; Murray & Kensinger, 2012; Pierce & Kensinger, 2011; Stewardson et al., 2022), but some studies have shown that negative valence has no influence on or even enhances memory for associations (e.g., Benjamin et al., 2012; Guillet & Arndt, 2009; Nie & Jiang, 2021). Similarly, findings regarding the influence of positive valence remain inconclusive, with a few studies revealing an impairment effect (e.g., Nashiro & Mather, 2011), while others show no effect or an enhancement effect on associative memory (e.g., Luck et al., 2014; Murray & Kensinger, 2012; Naveh-Benjamin et al., 2012; Nie & Jiang, 2021; Nie et al., 2021; Smith et al., 2004). Experiment 2 was inspired, in part, by the failure to find an emotional enhancement effect in Experiment 1, which I interpreted as being consistent with the fact that recognition test performance is 79 flexible and may be driven entirely by environmentally-driven processing (Craik, 1983, 1986), or by what others have called familiarity-based decision making (Anderson & Bower, 1972; Jacoby, 1991; Mandler et al., 1969; Yonelinas, 2002; Kintsch, 1970). In Craikâs (1983, 1986) view, recognition is generally assumed to depend on a combination of environmentally-driven and self-initiated elaborative processing, but the relative contribution of these two types of processing is determined by many variables, including the fact that self-initiated processing demands attentional resources, and such resources are expended only if required for achieving a satisfactory performance on a task (Gigerenzer & Goldstein, 1996; Simon, 1956). According to the EEI/PA theory, episodic memory influences due to valence are expected only to the extent that encoding and retrieval engage accommodative/elaborative processing, and for this reason, participants in Experiment 2 were given an encoding task and a memory retrieval task that required substantial elaborative processing. Specifically, participants worked with pictures that were randomly-paired, and in the study phase, they were required to create a meaningful association between the two pictures in each pair, while in the test phase, their task was to decide which picture pairs were old (i.e., presented together at study) versus new (i.e., not presented together at study). My Experiment 2 differed from the methods commonly used for investigating associative memory by virtue of the encoding task assigned to participants and by the type of valenced pairs that were to be remembered. Unlike some previous investigations where no specific instructions were provided for making associations between paired stimuli (e.g., Bergmann et al., 2012; Gerver et al., 2020; Madan et al., 2012; Nashiro et al., 2013; Luck et al., 2014), my experiment used a study task that emphasized elaborative processing of paired pictures, an activity which has been shown to enhance associative memory performance for both neutral and emotional stimuli (Buchsbaum et al., 2024; Bower & Winzenz,1970; Jaeger et al., 2024; Graf & Schacter, 1985, 1989; Murray & Kensinger, 2012; Murray & Kensinger, 2014; Nashiro & Mather, 2011). In order to emphasize such elaborative/self-initiated processing, the task 80 specifically asked participants to form a meaningful connection between each pair. My experiment also differed from previous research by using different types of valenced picture pairs. Whereas a majority of previous studies typically used pairs of mixed-valence (e.g., neutral-negative; neutral-positive) (e.g., Luck et al., 2014; Murray & Kensinger, 2012, 2014; Nashiro et al., 2013), I used pairs of pictures with the same valence (i.e., negative-negative; positive-positive; neutral-neutral) (see also Bisby & Burgess, 2014; Madan et al., 2012; Maddox et al., 2012; Pierce & Kensinger, 2011). My decision to use pairs of pictures with the same valence (e.g., negative-negative) rather than with mixed-valence (e.g., neutral-negative) was intended to induce a stronger valence manipulation and was informed by both theoretical assumptions and empirical evidence. Based on the EEI/PA assumption that valenced pictures capture attentional resources and draw them away from other activitiesâsuch as making an association between themâI predicted that pairing two pictures with the same valence would lead to a larger impairment in associative recognition because more attentional resources would be devoted to processing each of the valenced pictures within a pair relative to mixed-valence pairs, wherein attentional resources would focus only on the one valenced picture in the pair. This prediction is consistent with evidence from previous research (e.g., Madan et al., 2012; Madan et al., 2017), which has shown that impairments in associative memory are more pronounced for same-valence pairs than for mixed-valence pairs. For selecting pictures for this experiment, I aimed to manipulate valence while matching the arousal levels between the negative and positive pictures; however, despite my efforts, matching arousal was not possible due to the need for achieving additional selection criteria: maintaining thematic variety between picture pairs. The experiment involved creating picture pairs with the same valence, but drawn from distinct thematic categories to avoid pairing two pictures of same theme (e.g., two car accidents, two 81 weddings). This criterion contributed to the difference in arousal ratings between negative and positive pictures, with negative pictures having higher arousal ratings than positive pictures. Several theories are available to predict the results from Experiment 2, including the Object-Based Binding framework (Mather, 2007), the ABC theory (Mather & Sutherland, 2011), the Broaden and Build theory (Fredrickson, 1998, 2001, 2004), and the EEI/PA theory. The first two of these are arousal-based theories, and despite important differences between them, they both suggest that arousing stimuli capture attention and draw cognitive resources to them, thus leaving fewer resources for other activities such as creating new associations between paired pictures. Therefore, in the context of my experiment, both arousal-based theories predict a large impairment in associative recognition for negative picture pairs relative to both positive and neutral picture pairs, which were expected to produce a comparable performance. The Broaden and Build theory (Fredrickson, 1998, 2001, 2004) and the EEI/PA theory are valence-based theories, both of which suggest that negative and positive events have different influences on cognitive processing and memory performance. The Broaden and Build theory (Fredrickson, 1998, 2001, 2004) posits that negative pictures narrow the focus of attention to cues depicted in the pictures in order to facilitate responses to potential threats, leaving fewer attentional resources for other processes, such as the creation of associations between the paired pictures. This process of resource shifting aligns with arousal-based theories (Mather, 2007; Mather & Sutherland, 2011). By contrast, positive pictures broaden attention, allowing for more flexible processing and exploration of their content. Thus, compared to negative pairs, more attentional resources may be available for creating associations between positive pictures, but these resources would still be fewer than those available for creating associations between neutral pictures because processing neutral pictures themselves primarily engage default and automatic processing that demand fewer cognitive resources. In the context of my experiment, the Broaden and Build theory predicts 82 that associative recognition will be highly impaired for negative pairs and slightly impaired for positive pairs, when compared to neutral picture pairs. The EEI/PA theory asserts that perceptual-cognitive processing is interrupted by emotional events, prompting the cognitive system to engage in accommodative/elaborative processing to resolve the disruption. According to this theory, valenced picture pairs require accommodative/elaborative processing, with more such processing obligatory for negative pictures than for positive ones, and any such processing leaves fewer attentional resources for forging associations between valenced paired pictures compared to neutral ones. Thus, similar the Broaden and Build theory, the EEI/PA theory predicts an emotional impairment effect in associative memory for both negative and positive picture pairs relative to neutral ones, with the impairment being larger for negative than for positive picture pairs. 4.1: Method 4.1.1: Participants and Design The participants were 184 undergraduate students (91 self-identified as females and 93 self-identified as males) from the University of British Columbia. All had normal or corrected-to-normal vision. Participants were recruited from the Psychology Department Human Subject Pool, and all received partial course credit for their participation in the study, which was approved by the University of British Columbia Behavioral Research Ethics Board. The sample size was determined based on an a priori analysis using the G*Power computer software (Version 3.1.9.6; Faul, Erdfelder, Lang, & Buchner, 2007). The analysis showed that in order to detect an interaction effect between the memory enhancement due to picture pair valence and the sex of participants, a minimum of (178) participants, 89 participants per sex group, is required to detect a small effect (d = 0.2), with a power equal to 0.9 and an alpha set to 0.05 (Cohen, 1988). Two participants 83 withdrew from the study, one female because she was repulsed by some of the negative picture stimuli and one male who reported being color blind. The experiment used a mixed subject design, with picture-pair valence manipulated within subjects with three levels: negative, neutral, and positive. 4.1.2: Materials 4.1.2.1: Pictures A total of 210 colored picturesâ70 negative, 70 neutral and 70 positiveâwere selected from the Nencki Affective Picture System (NAPS) (Marchewka et al., 2014). The picture selection criteria and the normative data on valence and arousal for the selected pictures are shown in Table 4.1. The data show that the three sets of selected picturesânegative, neutral, and positiveâdiffered significantly from each other on their normative valence ratings. The selected pictures depicted different themes or objects such as people, human interactions, animals, landscapes, or urban settings, and the picture themes were similar across the picture sets. Each picture was saved as a 480 x 360 pixel .jpeg file, and used in the experiment either in portrait or landscape orientation. Table 4.1. Valence and Arousal Ratings for Selecting Criteria and for the Selected Pictures for Negative, Neutral, and Positive Pictures Used in Experiment 2 Valence of Picture Sets Picture Selection Criteria Properties of Selected Pictures Valence Arousal Valence Arousal Range Range Mean 95% CI Mean 95% CI Negative 2.50 â 3.25 4.5 â 7.00 2.57 2.49 - 2.64 6.71 6.60 - 6.82 Neutral 4.75 â 5.25 5.06 5.0 - 5.12 4.91 4.87 - 4.97 Positive 7.00 â 8.00 7.42 7.36 â 7.49 4.95 4.81 - 5.08 84 The 70 pictures from each valence set were arranged into seven subsets each with 10 pictures, in a pseudorandom manner intended to ensure comparable representations of picture themes across the subsets. Six of the subsets from each valence set were randomly selected for creating picture pairs used in the study list while the seventh subset was reserved to make old-new/novel pairings in the recognition test list. All seven subsets were used for the recognition test list. For the study phase, I created two study lists, A and B, each with 90 picture pairs. The 60 pictures from each valence set were arranged into 30 pairs in a pseudorandom manner that avoided creating pairs with two pictures of the same theme. For the testing phase, I created three versions of an associative recognition test for each of the two study lists. Each recognition test included a total of 90 picture pairs, with 30 pairs each from the study list from each valence condition. For each valence condition, 10 picture pairs were displayed exactly as shown in the study list (herein called the âIntact pairsâ). A further 10 picture pairs were re-paired between study and testing, meaning that each pair showed two pictures that had appeared in the study list but as part of different pairs (herein called âre-paired pairsâ). A final 10 picture pairs were used for creating 10 old-new pairs, each of which included a picture from a studied pair matched with a picture from the subset of 10 (subset 7) which had not been presented for study. For the old-new pairs, five had an old picture shown on the left side of a studied pair while the others had the old picture shown on the right side of a studied pair. Three versions of the recognition test were required in order to counterbalance the use of the study list picture sets in the three recognition test pair type conditions (i.e., intact, re-paired, and old-new). 4.1.2.2: Self-Report Questionnaires I used the NEO Five Factor Inventory (NEO-FFI; Costa & McCrae, 1989) and the Traditional Masculinity-Femininity scale (TMF; Kachel et al., 2016) that were utilized in Experiment 1. 85 4.1.3: Procedure Participants were tested individually in a quiet laboratory room equipped with a desktop computer. The experiment had an encoding phase followed by a testing phase. Both phases were controlled by a desktop computer running E-Prime version 2.0 (Psychology Software Tool, Inc). The experiment required a single session lasting approximately one hour. 4.1.3.1: Encoding Phase The encoding phase required 90 trials, with 30 trials respectively for displaying negative, neutral, and positive picture pairs. The precise sequence of events for each trial is shown in Figure (4.1). Each trial started with the display of a fixation cross for 500 ms, in order to direct participantsâ attention to the center of the computer screen where all picture pairs were displayed. This was followed by the display of a picture pair together with an encoding question and a rating scale, and the trial ended with the display of blank grey screen for 1500 ms. The picture pair displayed on each trial was selected randomly from the study list and without replacement from the three valence sets. All trial events were displayed in the center of the computer monitor against a grey background. Picture pairs, each sized 13.5 x 10 cm in landscape and 10.2 x 13.5 cm in portrait mode, were displayed on the screen side by side with a 2.8 cm gap between them. Each picture pair display was accompanied with a texted question asking, âHow difficult [is it] to connect the two pictures?â together with a 6-point scale with its end-points marked 1 âVery easyâ to 6 âVery difficultâ, written in 20-point black Tahoma font. The instructions required participants to âthink of a meaningful connection between the two pictures in each pairâ, and then to rate how difficult it was to connect the paired pictures. The purpose of the task was to encourage elaborative/ self-initiated processing of the paired pictures. 86 Each picture display with its question and rating scale remained on the screen until the participant entered a rating on the keyboard number pad. Then, the picture pair and scale disappeared, a blank screen was shown for 1500 ms, and the next trial began. Figure 4.1. The Sequence of Specific Events with their Duration on a Single Trial in the Encoding Phase and in the Testing Phase of Experiment 2 4.1.3.2: Testing Phase The testing phase involved 90 trials each with the event sequence shown on the right side of Figure 4.1. On each trial, a picture pair was displayed together with a Yes-No question in 18-point black Tahoma font asking: âIs this the same pair [you saw] earlier?â Participants responded by pressing the right-arrow key for Yes and left-arrow for No on the computer keyboard. Participants were instructed to âMake decision carefullyâ. They were informed that some picture pairs were identical to pairs displayed in the study phase, that some would consists of two old pictures that had not appeared together in the study phase, and that some would consist of a mix of old and new pictures. Participants were instructed to Blank gray screen (1500 ms)+ Fixation cross (500 ms)Picture display + rating scale (until response)Encoding Phase Associative Memory Testing PhaseTimeHow difficult to connect?1 2 3 4 5 6 Very easy Very difficult+Same pair as earlier?<< No Yes >>Intact pairRe-paired pairOld/new pair 87 respond Yes only if the same two pictures were paired with each other in the encoding phase of the experiment. Each picture-pair display, along with the question remained until the participant made a decision, then the next trial began. 4.1.3.3: Experiment Flow Participants were tested by a female experimenter, one at a time, in a quiet laboratory room. All participants provided informed written consent at the beginning of the experiment. Participants were provided with all tasks' instructions orally by the experimenter as well as in written form, and they were encouraged to ask questions. To ensure participants understood the instructions for the encoding phase, three practice encoding trials were administered, one with an extra picture pair from each valence condition. Each of these trials was as described above except participants were encouraged to ask questions. Participants were informed that their memory for the displayed picture pairs would be tested after the encoding phase. Immediately after the encoding phase, participants completed NEO-FFI, TMF in paper-and-pencil format. These instruments were used mainly as filler tasks, for the purpose of separating the encoding and testing phases of the experiment. These instruments also served to explore potential associations between personality traits, gender identity, and memory performance. However, no analyses regarding potential associations between individual differences and memory performance are reported in this dissertation. The average duration of the retention interval was approximately 10 minutes. Immediately after the testing phase, a funnel debriefing interview was employed to collect information on participantâs experiences, thoughts, and observations. During the funnel debriefing, participants were first asked a few open-ended questions, and then more specific questions about learning 88 and remembering valenced picture pairs. Then the participants completed on online debriefing survey required for compliance with UBC ethics guidelines. Finally, they were thanked for their participation. 4.2: Results The main goal of this experiment was to examine theoretical predictions about whether there is an emotional impairment effect in episodic associative recognition memory for valence compared to neutral picture pairs, and to determine whether this impairment effect is different for negative and positive picture pairs. In addition, this experiment aimed to explore the influence of participantsâ sex on associative recognition memory. The main independent variables were the valence of the picture pairs and the sex of participants, and the dependent variables were the associative recognition decisions and the decision times. Also of interest were participantsâ encoding-phase association-difficulty ratings and the time spent on making these ratings. I used the 95% confidence interval to assess differences among means, and when further analyses were required, I used repeated measures analysis of variance (ANOVA), with follow-up comparisons as required. The Greenhouse-Geisser correction was used in cases that violated the sphericity assumption, and the Bonferroni correction was used for multiple comparisons. F ratios and partial eta-squared (η2) are reported only for significant results. 4.2.1: Data Preparation All data were screened for outliers using the participantâs encoding-phase median association-difficulty rating times and the association-recognition decision times. These times showed four participants had a median of association-difficulty rating time of 20860 ms, 20997 ms, 22969.5 ms, and 23183 ms, respectively, more than three standards deviations above the mean of all participantsâ median times of 6966 ms. The associative-recognition decision times showed two participants had a median time of 3858 89 ms and 3990 ms, respectively, more than three standards deviations above the mean of all participantsâ median time of 1738 ms. Outliers were interpreted as evidence of participants doing the rating task in a manner that deviated from instructions, and thus, their data were not included in any further analysis. The results reported below are based on the data from the remaining 177 participants (87 females and 90 males). 4.2.2: Encoding Phase I examined encoding phase performance to assess whether the participants completed the task as instructed, and to explore the influence of valence on the subjective difficulty of creating an association between the two pictures in each pair. 4.2.2.1: Association-Difficulty Ratings Participantsâ mean ratings of the difficulty of creating associations for the pairs in each valence condition are shown in Figure 4.2. The findings show participants found it easier to create a meaningful association between paired negative pictures (M = 2.99, 95% CI [2.89 to 3.1]) and paired positive pictures (M = 3.25, 95% CI [3.16 to 3.34]) than between neutral pictures (M = 3.72, 95% CI [3.63 to 3.81]). The figure also suggests it was easier to make an association between two negative pictures than between two positive pictures. These findings are consistent with those of Murray and Kensinger (2012), who found that neutral pairs were more difficult to associate; specifically, their findings showed that associative performance improved when participants were given longer time (6 seconds) compared to a shorter one (2 seconds). 90 Figure 4.2. Mean Association-Difficulty Ratings for Negative, Neutral, and Positive Picture Pairs Note. Error bars represent the 95% confidence intervals A possible explanation for why my participants might have found it easier to associate valenced pictures may come from the greater similarity among valenced pictures in their thematic content; greater similarity may have afforded more meaningful relationships between paired pictures compared to neutral pairs (cf. Gibson, 1979). It has been argued that similarity in thematic content is a potential confound in studies using valenced and neutral pictures (Buchanan et al., 2006; Talmi & Moscovitch, 2004; Talmi, Luk, McGarry, et al, 2007; Talmi, 2013; Maratos et al., 2000). It is possible that valenced picture pairs, compared to neutral ones, shared common themes or topics (e.g., accidents, injuries, parties, food), which may have facilitated the creation of meaningful associations between two valenced pictures. However, this may have led to the creation of similar and less distinctive associations across valenced pairs. Another related explanation is that valence itself as an emotional dimension may have facilitated the creation of meaningful associations by suggesting a relatively narrow range of possible associations 1.01.52.02.53.03.54.04.5Negative Neutral PositiveDifficulty of Association (1-6)Picture Pair Valence 91 that fall into broad categories of âbadâ or âgoodâ. For example, a picture of âan injured animalâ and âa crying childâ both evoke sadness while âa beachâ and âice creamâ evoke happiness. This emotional overlap could lead in the creation of more common and similar associations across valenced pairs. In contrast, neutral pairs likely broaden the range of possible associations because they lack a shared emotional theme, which leads participants to focus on properties unique to each pair in order to create associations. During debriefing, many participants reported that valenced pairs were easier to associate because their emotionality helped in creating meaningful connections and narratives (see Miall, 2011), whereas for neutral pairs, they relied on perceptual features available in the pictures (e.g., color, shape) for associating them. 4.2.2.2: Association-Difficulty Rating Times I examined the association-difficulty rating timesâdefined as the duration from the onset of each picture pair display to the participantâs entry of a rating on the keyboard. The means of participantsâ median association-difficulty rating times for negative, neutral, and positive picture pairs are shown in Figure 4.3. The findings indicated participants spent about the same amount of time imagining/creating associations for negative 7113.48 ms (95% CI [6483.94 ms to 7743.03 ms]), neutral 6985.29 ms (95% CI [6429.43 ms to 7541.15 ms]), and positive picture pairs 6894.44 ms (95% CI [6327.18 ms to 7461.70 ms]). The fact that participants spent about seven seconds to make an association per picture pair aligns with previous research showing a similar amount of time was needed to associate both emotional and neutral pairs when encoding was self-paced (e.g., Zimmerman & Kelley, 2010). The finding of roughly similar times for making associations across all valence conditions is surprising, given that participants rated associating neutral pictures as more difficult than associating valenced pictures (see Figure 4.2); one would expect the more difficult associations to take more time. This finding is consistent with those of 92 Bastin et al. (2013, Exp.1) which also showed no difference in encoding time between associations rated as easy versus difficult to create. One possible explanation for this finding is that participants may have allocated their processing time differently depending on the valence of picture pairs. For valenced pairs, more time may have been devoted to processing the pictures themselvesâconsistent with the EEI/PA theory stipulated need for accommodative/elaborative processingâthan to creating associations between them. In contrast, for neutral pairs, less time may have been spent processing the pictures, with more time spent creating the associations between them. Figure 4.3. Mean of Median Association-Difficulty Rating Times for Negative, Neutral, and Positive Picture Pairs Note. Error bars represent the 95% confidence intervals. 20003000400050006000700080009000Negative Neutral PositiveAssociation-Difficulty Rating Time (ms)Picture Pair Valence 93 4.2.3: Testing Phase I used performance on the associative recognition test including the hits, false alarms, and time spent on making recognition decisions about the different picture pairs (intact, re-paired, and old/new), and I examined them for influences due to picture pair valence and participant sex. 4.2.3.1: Associative-Recognition Hits and False Alarms On the associative recognition test, hits refers to the proportion of yes responses to intact picture pairs, whereas false alarms refers to the proportion of yes responses to re-paired or old/new picture pairs. The hits and false alarms for each pair type, for each valence condition and for both sexes are shown in Table 4.2. Performance on old/new picture pairs is included here only for completeness as old/new pairs were used as a control, and performance on these pairs is not directly pertinent to memory for newly-formed associations between paired pictures. The means in Table 4.2 shown hits are lower for both negative and positive picture pairs compared to neutral picture pairs, while false alarms are higher for negative picture pairs compared to both neutral and positive picture pairs. These observations were confirmed by the results of mixed ANOVA, with valence of picture pairs as a within-subject variable. The hits revealed a significant main effect for valence, F (2,175) = 9.82, p < .001, η2 = .053, and a sex by valence interaction, F (2,175) = 5.17, p = .006, η2 = .029. The same kind of ANOVA on false alarms, appropriately adjusted for a significant Mauchly sphericity test (Ï2 (2) = 14.40, p = .001), revealed a significant main effect for valence, F (2,175) = 31.07, p < .001, η2 = .151, and a sex by valence interaction, F (2,175) = 3.65, p = .030, η2 = .020. There was no sex main effect on either the hits or false alarms, both pâs > .10. In the view of the interaction effect between hits and false alarms, I computed corrected associative-recognition scores for representing the combination of both hits and false alarms. 94 Table 4.2. Hits and False Alarms for each Picture Pair Valence Condition and Test-Pair Type Valence of Picture pairs Test Pair Type Measure Females Males Total Sample Mean 95% CI Mean 95% CI Mean 95% CI Negative Intact Hits 0.79 0.75 - 0.83 0.84 0.81 - 0.88 0.82 0.79 - 0.84 Re-Paired False Alarms 0.18 0.14 - 0.21 0.21 0.17 - 0.25 0.19 0.17 - 0.22 Old/New False Alarms 0.06 0.03 - 0.08 0.05 0.03 - 0.08 0.06 0.04 - 0.07 Neutral Intact Hits 0.85 0.82 - 0.89 0.88 0.85 - 0.92 0.87 0.84 - 0.89 Re-Paired False Alarms 0.08 0.05 - 0.11 0.13 0.10 - 0.16 0.11 0.08 - 0.13 Old/New False Alarms 0.04 0.02 - 0.06 0.04 0.02 - 0.06 0.04 0.03 - 0.05 Positive Intact Hits 0.85 0.81 - 0.88 0.82 0.79 - 0.86 0.84 0.81 - 0.86 Re-Paired False Alarms 0.14 0.11 - 0.17 0.13 0.10 - 0.16 0.13 0.11 - 0.15 Old/New False Alarms 0.07 0.04 - 0.09 0.07 0.04 - 0.10 0.07 0.05 - 0.09 4.2.3.2: Association-Recognition Decision Times The mean associative-recognition decision times, measured from the onset of a picture pair on the display screen to when participants entered their Yes/No decision on the computer keyboard, are summarized in Table 4.3. Because there were no sex differences in decision times, I combined the data from both sexes. The means in the table reveal that participants were faster to make hit decisions than false-alarm decisions in all valence conditions. The means also show that decision times for hits are significantly longer for negative and positive picture pairs compared to neutral pairs, with hits on negative picture pairs being slower than for the positive pairs. One possible explanation for the faster decision time for hits compared to false alarms is the confirmation bias toward detecting the designated targets (i.e., intact pairs) in the test list (cf. Chun & Wolfe, 95 1996; Krueger & Shapiro,1980). During the testing phase, participants are inclined to detect evidence that confirms the previously created associations, so when intact picture pairs are presented, they confirm these expectations, which leads to a rapid response compared to the other pairs that violate expectations. Table 4.3. The Means Median Decision Times for Negative, Neutral, and Positive Picture Pairs Valence of Picture Pairs Pair Type Measure Recognition Decision Times Mean 95% CI Negative Intact Hits 1925.73 1832.07 - 2019.39 Re-Paired False Alarms 3244.63 2823.63 - 3665.63 Old/New False Alarms 2336.46 1931.85 - 2741.08 Neutral Intact Hits 1562.08 1496.14 - 1628.01 Re-Paired False Alarms 2468.74 2107.69 - 2829.79 Old/New False Alarms 2683.56 2059.65 - 3307.47 Positive Intact Hits 1725.81 1655.82 - 1795.80 Re-Paired False Alarms 3121.71 2592.49 - 3650.93 Old/New False Alarms 2907.71 2274.60 - 3540.83 4.2.3.3: Corrected Associative-Recognition: Considering the differences among the picture pair valence conditions in the mean proportions of hits and re-paired false alarms, I calculated a corrected associative-recognition scoreâdefined as the proportion of hits minus the proportion of re-paired false alarmsâfor each participant and for each picture pair valence condition. The means of the corrected scores for each picture pair valence condition are shown in Figure 4.4. The results show evidence of an emotional impairment effect, with lower performance for both negative and positive picture pairs compared to neutral picture pairs. This observation was confirmed by an ANOVA, with the same variables used for the hits and false alarms, which revealed a 96 significant main effect for valence, F (2,175) = 36.44, p < .001, η2 = .172, but no other significant main or interaction effects (both pâs >.05). Follow-up pairwise comparisons revealed corrected recognition for negative picture pairs (M = .624, 95% CI [.586, .661]) and positive picture pairs (M = .704, 95% CI .667, .740]) was significantly lower than correct recognition of neutral picture pairs (M = .763, 95% CI [.727, .800]), p < .001. The difference between negative and positive picture pairs was also significant, p <.001. The absence of sex differences in associative recognition performance contradicts my expectation informed by the cognitive style hypothesis (Seidlitz & Diener, 1998), which suggests females engage in more elaborative processing during encoding than males (Seidlitz & Diener, 1998; Stoet, 2010; Meyers-Levy, 1989; Wirth et al., 2007). This also contradicts the findings of a large-scale study (> 500 participants) by Stewardson et al. (2022), which revealed females outperformed males in associative memory for both neutral and emotional picture pairs. One possible explanation for the absence of sex differences in my experiment may come from the specific demands of my encoding task. The female advantage in elaboration might not have shown because the task emphasized elaborative processing, and because of this emphasis, sexes engaged in a similar level of elaboration and therefore performed comparably, unlike in Stewardson et al (2022) where participants were not given such instructions. 97 Figure 4.4. Mean Corrected Associative-Recognition Performance for Negative, Neutral, and Positive Picture Pairs Note. Error bars represent the 95% confidence intervals. 4.3: Discussion The main goal of Experiment 2 was to test predictions derived from a number of established theories (Fredrickson, 1998, 2001, 2004; Mather, 2007; Mather & Sutherland, 2011) as well as from the EEI/PA theory: that emotional valence leads to an impairment effect in associative recognition. The main findings of Experiment 2 showed the expected impairment effect in associative recognition for both negative and positive picture pairs compared to neutral ones, with a larger impairment effect for negative than for positive picture pairs. The impaired associative recognition for negative and positive pictures pairs compared to neutral ones replicates and extends existing results in associative memory. This outcome is consistent with 0.500.550.600.650.700.750.800.850.900.951.00Negative Neutral PositiveProportion Corrected Associative-RecognitionPicture Pair ValenceFemales Males 98 previous research that used a variety of different paradigms for assessing associative recognition (e.g., Bisby & Burgess, 2014; Caplan et al., 2019; Chiu et al., 2013; Madan et al., 2017; Murray & Kensinger, 2012; Nashiro & Mather, 2011; Pierce & Kensinger, 2011; Stewardson et al., 2022). By demonstrating a memory impairment even under encoding conditions that emphasize and prioritize substantial elaborative processing of the associations between paired pictures, my findings add to the few studies that do emphasize encoding-phase elaborative processing (e.g., Bisby & Burgess; 2014; Ventura-Bort et al., 2016; Naveh-Benjamin et al., 2012), as well as to those showing that an impairment occurs even when paired pictures are of the same valence (e.g., Bisby & Burgess, 2014; Madan et al., 2012; Maddox et al., 2012; Nie et al., 2021; Onoda et al., 2009; Pierce & Kensinger, 2011; Zimmerman & Kelley, 2010). The finding of associative-recognition impairments for both negative and positive picture pairs, despite the arousal levels of positive pictures matching those of the neutral ones, contradicts the predictions of both the Object-Based Binding framework (Mather, 2007) and the ABC theory (Mather & Sutherland, 2011). Both theories posit that arousing pictures draw attentional resources toward the content of the pictures themselves, leaving fewer of attentional resources available for other activities such as creating associations between paired pictures. Within this associative recognition task, both theories predicted impaired associative recognition for negative pairs due to their higher arousal level relative to neutral pictures. However, neither theory anticipated the impairment observed for positive picture pairs, whose arousal levels were matched to those of the neutral ones. Given that arousal was not equated between the negative and positive pictures in Experiment 2, one might argue that it may represent a potential confounding variable when interpreting the findings in the context of arousal-based theories. The fact that negative pictures had higher arousal ratings than positive ones raise the possible interpretation that the larger impairment in associative recognition observed for negative pairs, compared to positive ones, may have been driven by the difference in the arousal levels 99 rather than by valence alone. However, both theories are challenged by the finding of impairment in associative recognition for positive picture pairs compared to neutral ones despite both having the same arousal ratings. If arousal is the primary determinant of memory performance, as suggested by these theories, then association recognition performance for both positive and neutral picture pairs should have been comparable, which was not the case. This discrepancy calls for an alternative explanation that focuses on the influence of valence in memory performance. The Broaden and Build theory (Fredrickson, 1998, 2001, 2004) and the EEI/PA theory provide a valence-based explanation that accounts for the findings of associative recognition impairments occurring for both negative and positive pictures pairs, with a larger impairment for negative relative to positive pairs. The Broaden and Build theory (Fredrickson, 1998, 2001, 2004) attributes the larger impairment for negative pairs relative to positive ones to the assumption that negative pictures narrow the focus of attention toward potential threat-relevant cues, reducing resources available for making associations between the pairs. In contrast, positive pictures broaden the attention, allowing for more flexible processing, and thus, preserves more resources for associative processing compared to negative pairsâthough fewer than neutral pairs. The EEI/PA theory makes the same prediction as the Broaden and Build theory, but extends it by focusing on the cognitive processes accounting for these effects. The EEI/PA theory attributes the associative memory impairment for valenced pictures pairs to the increased engagement of accommodative/elaborative processing, which reduces resources available for creating associations between paired pictures. The larger impairment for negative pairs is attributed to the assumption that negative pictures demand more obligatory accommodative/elaborative processing than positive pictures. To conclude, Experiment 2 demonstrated the emotional impairment effect for valenced picture pairs, as predicted by the EEI/PA theory. The EEI/PA theory offers an explanation of memory performance for emotional events that emphasizes the cognitive processes that elicit both valence and arousal, with the 100 assumption that valence, not arousal, is the primary determinant of memory performance. According to this theory, the valence of an experienced event is determined by the success (in the case of positive events) or failure (in the case of negative events) of the accommodative/elaborative processing, with such processing being more obligatory for negative events. Contrary to arousal-based theories, the EEI/PA theory posits that arousal is not the primary determinant of memory performance, but rather a consequence of the accommodative/elaborative processing, with its magnitude reflecting the amount of such processing involved. 101 Chapter 5: The Influence of Valence on Free Recall This chapter reports my final experiment that further examined the core assumption of the EEI/PA theory: that the valence of to-be-remembered events only influences the extent of their encoding-phase engages accommodative/elaborative processing, but not their assimilative/integrative processing. To examine this assumption, I used valenced and neutral pictures and assessed memory with an old/new picture recognition test in Experiment 1, and for Experiment 2, I used randomly-paired pictures as the to-be-remembered events and assessed episodic memory with an associative recognition test. Old/new recognition test performance is known to be less dependent on encoding-phase elaborative processing compared to associative recognition test performance (Giovanello et al., 2003; Turriziani et al., 2004; Yonelinas, 2002), and more importantly, all recognition tests tend to be less dependent on this kind of processing than cued and free recall tests (Anderson & Bower, 1972; Craik, 1983, 1986; Jacoby, 1991; Mandler et al., 1969; Yonelinas, 2002; Kintsch, 1970). Therefore, for a stronger test of the core assumption that valence affects only encoding-phase accommodative/elaborative processing, I used a free recall test in Experiment 3. I also used a free recall test because it is able to reveal strategic influences on retrieval that are afforded by encoding-phase elaborative processing (Bailey et al., 2008; Benjamin, 2007; Bousfield, 1953; Eagle, 1967; Craik, 2002; Craik & Tulving, 1975; Cooke et al., 1986; Engelkamp & Zimmer, 2002; Fabiani & Donchin, 1995; Klein & Kihlstrom, 1986; Unsworth, 2015; Unsworth et al., 2019; Mandler, 2002; McDaniel & Kearney,1984; Tulving, 1962; Waters & McAlaster, 1983). A critical difference between recognition and recall tests is that for the former, rememberers are provided with cuesâtypically to-be-remembered events from the encoding phase together with similar non-studied events called distractorsâabout which they need to make decisions. To make such decisions, the rememberer is assumed to search their memory for associations between cues and the encoding-phase context (Anderson & Bower, 1972; Jacoby, 1991; Mandler, 1980a; Mandler et al.,1969 Yonelinas, 2002). 102 By contrast, no such cues are provided for free recall tests and this, radically alters memory retrieval. For recollecting an encoding phase event on a free recall test, the rememberer uses the test instructions as a guide for mentally traveling back in time, and more particularly, to re-create in their conscious mind the encoding-phase context (e.g., time, place, encoding phase activities), and they use this as a cue for retrieval (Tulving, 1962; Tulving, 1983; Tulving, 2002). Craik (1983, 1986) used self-initiated processing as a label for this type of re-creative elaborative processing. In Craikâs organization of episodic memory tests, the free recall test is the most dependent on self-initiated processing, and this is my reasoning for using a free recall test in Experiment 3. The findings of the meta-analysis reported in Chapter 2 supports the assumption from the EEI/PA theory that free recall is more heavily dependent on encoding-phase elaborative processing compared to recognition tests. Specifically, the meta-analysis revealed a substantially larger emotional enhancement effect on free recall (Hedgesâ g = .99) compared to recognition (Hedgesâ g = 0.24). Free recall tests get their distinct name from the fact that the rememberer is free, that is, not constrained in any way when retrieving events from memory. As a consequence, any pattern or regularity in which to-be-remembered events are recollected provides clues about the strategy used for accessing memory. For example, if a rememberer in Experiment 3 were to recollect a group of negative pictures, followed by a group of positive pictures, one might infer they were using a valence-guided retrieval strategy. Previous research on strategic influences in episodic memory retrieval has investigated free recall clustersâdefined by the consecutive recollection of to-be-remembered events from the same semantic category or from the same portion of the list presented for study (i.e., list beginning; list ending), or by differences in the time elapsed between consecutively recollected events (Bousfield, 1953,1966; Bair et al., 2023; Cooke et al., 1986; Delis et al., 2010; Fiedler & Welpe, 2010; Frender & Doubilet, 1974; Kahana, 1996; Klein & Kihlstrom, 1986; Raaijmakers & Shiffrin, 1981; Roenker et al., 1971; Stricker et al., 2002; 103 Tulving, 1962; Miller et al., 2013; Puff, 1979; Polyn et al., 2009; Wixted & Rohrer, 1994). Another commonly used investigative method focuses on trial-by-trial transitions in recall (e.g., recalling a positive event on Trial n, followed by recalling a positive event on Trial n+1?) (Howard & Kahana, 2002; Kahana, 1996; Roenker, Thompson & Brown, 1971), which would be influenced by the rememberersâ use of a win-stay lose-shift strategy (Cassotti et al., 2011; Forder & Dyson, 2016; Nowak & Sigmund, 1993). This type of strategy predicts that after successfully recalling an item from a specific cluster, the rememberer will continue recollecting from the same cluster. Thus, by using a free recall test in Experiment 3, I was able to investigate if valence influences the strategies that guide retrieval from memory. My predictions for the outcomes of Experiment 3 were informed by the NEVER model (Bowen et al., 2018) and the EEI/PA theory. According to the NEVER model (Bowen et al., 2018), negative eventsâcompared to positive eventsâare associated with enhanced sensory and perceptual processing, and therefore, the NEVER model predicts a larger enhancement effect for negative than for positive pictures. The EEI/PA theory complements the NEVER model by stipulating the specific sensory-perceptual processes the cognitive system engages for negative and positive events, arguing that valence has an influence only on the accommodative/elaborative processing of such events, with negative events involving more accommodative/elaborative processing than positive events. Given that free recall performance is heavily dependent on elaborative processing, I predicted an overall large emotional enhancement effect in free recall performance. In agreement with the NEVER model (Bowen et al., 2018), I also predicted that the emotional enhancement effect would be larger for negative than for positive pictures. I also predicted valence would be used strategically for memory retrieval. Previous research has shown that rememberers use valence as an organizing strategy when recollecting to-be-remembered events (Cohen & Kahana, 2022; Long et al., 2015; Siddiqui & Unswort, 2011, Talmi, Luk, McGarry & 104 Moscovitch, 2007; Talmi et al., 2019; Gomes et al., 2013). Consistent with such evidence, I hypothesized that valenced pictures would be recalled earlier than neutral ones, and that transitions in recall would more frequently occur between negative-to-negative pictures or positive-to-positive pictures than between neutral-to-neutral pictures. A final purpose of Experiment 3 was to examined sex differences in episodic memory. The extant evidence shows females often outperform male participants in recollecting a wide variety of stimuli (Borges & Vaughn, 1977; Bloise & Johnson, 2007; Cahill & Van Stegeren, 2003; Gasbarri et al., 2007; Herlitz et al., 1997; Voyer et al., 2007; Herlitz & Yonker, 2002, Asperholm et al., 2019). Research has shown that emotional events activate different brain regions in females and males (Stevens & Hamann, 2012; Filkowski et al., 2017; Wager et al., 2003), and that compared to males, females show higher physiological activity when experiencing emotions (Chentsova-Dutton & Tsai, 2007) and when viewing emotional pictures (Bradley et al., 2001; Lithari et al., 2010). Research has also shown that females tend to subjectively experience emotions at a higher intensity (Bloise et al., 1999; Fujita et al., 1991), to show more facial expressions (Chentsova-Dutton & Tsai, 2007; Kring & Gordon, 1998), to produce more emotional wording when describing life events (Bauer, Stennes, & Haight, 2003). Consistent with such speculations and evidence, I hypothesized that female participants would demonstrate a larger emotional enhancement effect in memory than male participants. 5.1: Method 5.1.1: Participants and design The participants were 188 undergraduate students (96 self-identified as females, and 92 self-identified as males) from the University of British Columbia. Participants were aged 18-21 years, and all had normal or corrected-to-normal vision. Participants were recruited from the Psychology Department 105 Human Subject Pool, and all received partial course credit for their participation in the study, which was approved by the University of British Columbia Behavioral Research Ethics Board. The sample size was determined based on an a priori analysis using the G*Power computer software (Version 3.1.9.6; Faul, Erdfelder, Lang, & Buchner, 2007). The analysis showed that for detecting an interaction effect between the memory enhancement due to picture valence and the sex of participants, a minimum of (178) participants, 89 participants per sex group, is required for a small effect (d =0.2), with a power equal to 0.9 and an alpha set to 0.05 (Cohen, 1988). The experiment used a mixed subject design with valence manipulated within subject with three levels: negative, neutral and positive. 5.1.2: Materials 5.1.2.1: Pictures A total of 60 colored picturesâ20 negative, 20 neutral, and 20 positiveâwere selected from the Nencki Affective Picture System (NAPS) database (Marchewka et al., 2014). The picture selection criteria and the normative data on valence and arousal for the selected pictures are shown in Table 5.1. The data show that the three sets of selected picturesânegative, neutral, and positiveâdiffered significantly from each other on their normative valence ratings. The data also show that the positive and negative picture sets were not different from each other in terms of their normative arousal ratings, but both of these picture sets had a higher mean arousal rating than the neutral pictures. The selected pictures depicted different themes and objects such as people, human interactions, animals, landscapes, or urban settings, and the picture themes were similar across the picture sets. Each picture was saved as a 480 x 360 pixel .jpeg file, and used in the experiment either in portrait or landscape orientation. An additional picture selection criterion was that each had to have a unique label (e.g., a monkey or a scuba diver) or identifying descriptive phrase (e.g., monkey in a cage, a scuba diver exploring 106 underwater creatures). Such identifiers were required for assessing memory for the pictures with a free recall test. I used an iterative pilot testing procedure for finding pictures with unique identifiers. Table 5.1. Valence and Arousal Ratings for Selecting Pictures and for the Selected Pictures â for the Negative, Neutral, and Positive Pictures Used in Experiment 3 Valence of picture sets Picture Selection Criteria Properties of selected pictures Valence Arousal Valence Arousal Range Range M 95% CI M 95% CI Negative 3.00 â 3.75 4.5 â 6.00 3.25 3.164 to 3.335 5.80 5.689 to 5.911 Neutral 4.75 â 5.25 5.00 4.993 to 5.067 4.95 4.44 to 5.02 Positive 7.25 â 8.00 7.36 7.256 to 7.464 5.59 5.389 to 5.791 5.1.2.2: Self-Report Questionnaires In addition to the NEO Five Factor Inventory (NEO-FFI) (Costa & McCrae, 1989) and the Traditional Masculinity-Femininity scale (TMF) (Kachel et al., 2016) that were reported in Experiment 1, I also required participants to complete the North American Adult Reading Test (NAART; Blair & Spreen, 1989). The NAART consists of 61 irregular English rare words (e.g., impugn; paradigm), and participants are required to read them out-loud. The number of words pronounced incorrectly is recorded, and higher scores indicate lower verbal ability. The validity coefficient, measured by the correlation between the NAART and the Wechsler Adult Intelligence Scale WAIS-R Vocabulary raw scores (Wechsler, 1955) is .75. The interrater reliability (e.g., correlation between scores obtained from two different scorers) is .93 (Uttl, 2002). 107 5.1.3: Procedure Participants were tested individually in a quiet room equipped with a desktop computer. The experiment had an encoding phase followed by a testing phase, and the encoding phase was controlled by a desktop computer running E-Prime version 2.0 (Psychology Software Tool, Inc). The entire experiment was completed in a single session lasting approximately 45 minutes. 5.1.3.1: Encoding Phase The encoding phase had 60 trials, with 20 trials each for displaying positive, negative, and neutral pictures. The precise sequence of events for each trial is shown in Figure (5.1). Each trial started with the display of a fixation cross for 500 ms, to direct participantsâ attention to the center of the computer screen, where all pictures were displayed. This was followed by the display of a visual pattern mask for 50 ms, and then the display of a picture together with a question and a rating scale. The trial ended with the display of blank grey screen for 1500 ms. The picture displayed for each trial was selected randomly and without replacement from one of the three valence sets, and each remained on the screen until the participant entered a response. Participants were seated about 60 cm in front of the computer screen. All trial events were centered on the computer monitor and displayed against a grey background. The picture displays measured 13.5 x 10 cm in landscape mode and 10.2 x 13.5 cm in portrait mode. Each picture display was accompanied with a question asking, âHow does this picture make you feel?â together with a 6-point scale with its end-points marked 1 âvery unpleasantâ to 6 âvery pleasantâ, written in 30-point black Tahoma font. Participants were instructed to attend to âthe feelings and thoughts evoked by each displayed pictureâ and to rate each picture, with instruction to give a rating which captures âyour first impression and gut reactionâ. Participants were encouraged to âmake rating as fast and accurately as possibleâ. The purpose of the 108 picture rating task was to increase participantsâ engagement with each picture, as well as to verify the effectiveness of the valence manipulation. Figure 5.1. The Sequence of Events with their Duration on a Single Trial in the Encoding Phase of Experiment 3 5.1.3.2: Testing Phase The testing phase employed a free recall test for assessing memory of the pictures with a free recall test. For this test, participants were required to recall orally and to the best of their ability as many pictures as they could, in any order they liked, while taking as much time as needed. Participants were instructed by the experimenter âto provide a clear and detailed description for each picture to enable the experimenter identify exactly which picture is being recollected, and to distinguish this picture from among all other studied picturesâ. Participantsâ recollections were recorded with an Olympus DS-40 Digital Voice Recorder. +How does it make you feel?1 2 3 4 5 6 Very unpleasant Very pleasantFixation cross (500 ms)Mask (50 ms)Picture display + rating scale (until response)Blank gray screen (1500 ms)Time 109 To encourage picture recollection, participants were prompted during the testing phase with statements like: âtake your time and try to remember a bit moreâ. Prompts were given when the participant indicated they could not recall any additional images (e.g., âI think this is all I could rememberâ) or if the participant paused for about 30 seconds without recollecting anything. Participants were encouraged to elaborate on their picture descriptions if their descriptions were too general (e.g., âthere was a picture of kidsâ) and not sufficient for identifying a picture from the studied set. In such cases, participants were prompted with instructions like: âCould you please provide a more detailed description of this picture.â I used a one-minute rule for ending picture recollection; that is, if no additional pictures were recollected for one minute after the second recall-encouraging prompt was provided, the testing phase ended. Across all participants, the average amount of time required for recollection was about 10 minutes. 5.1.3.3: Experiment Flow Participants were tested by a female experimenter, one at a time, in a small laboratory room. All participants provided informed written consent at the beginning of the experiment. All tasks' instructions were provided orally by the experimenter as well as in written form on a computer monitor, and participants were encouraged to ask questions. To ensure that participants understood the instructions for the encoding phase, three practice trials were administered, one for each valence condition. Each of these trials was as described above, except participants were encouraged to ask questions. Participants were informed that their memory for the displayed pictures would be tested after the encoding phase. Immediately after the encoding phase, the participants completed NEO-FFI, TFM in paper and pencil format, and NAART orally. These instruments were used mainly as filler tasks, for the purpose of 110 separating the encoding and testing phases. These instruments also were added to explore potential associations between personality traits, gender identity, and English proficiency and verbal ability with free recall performance for emotional events. However, no analyses regarding potential associations between individual differences and memory performance are reported in this dissertation. The average duration between the encoding and testing phaseâthe retention intervalâwas approximately 15-20 minutes. After the testing phase, a funnel debriefing interview was employed to collect information on participantsâ experience, thoughts, and observations. During the funnel debriefing, participants were first asked a few open-ended questions, then more specific questions about learning and remembering valenced pictures. Finally, the participants completed an on online debriefing survey required for compliance with UBC ethics guidelines. Finally, they were thanked for their participation. 5.1.4: Picture Recollection Scoring The audio recordings from the testing phase were transcribed verbatim by the same experimenter who tested the same participant, and the transcriptions were then scored according to the scoring protocol in Appendix 4. This protocol identifies the precise steps used for scoring all picture recollections. Recollection accuracy was determined by whether a picture description provided by a participant was a unique match for one of the studied pictures. Any description that failed to match one of the studied pictures was scored as an intrusion. For each participant, I recorded the total of all free recall intrusions, the total of all accurately recalled pictures together with each pictureâs output position in the recall protocol (i.e., first, second, third). 5.2: Results The main goal of this experiment was to examine the prediction of the EEI/PA theory that there is an emotional enhancement effect in free recall, and that this enhancement effect is larger for negative than 111 for positive pictures. The main independent variables were the valence of the pictures and the sex of participants, and the dependent variables were correctly recalled pictures and recall intrusions, together with the picture valence ratings and the time required for making valence ratings. I used the 95% confidence interval to assess differences among means, and when further analyses were required, I used repeated measures analysis of variance (ANOVA), with follow-up comparisons as required. The Greenhouse-Geisser correction was used in cases that violated the ANOVA sphericity assumption, and the Bonferroni correction was used to adjust for multiple comparisons. F ratios and partial eta-squared (η2) are reported only for significant results. 5.2.1: Data Preparation All data were screened for outliers using participantsâ median picture valence rating times. These times showed four participants with medians of 8047 ms, 8534 ms, 8147 ms and 13141 ms, respectively, more than three standard deviations above the mean of all participantsâ median rating times of 2921 ms. Outliers were interpreted as evidence of participants doing the rating task in a manner that deviated from instructions, and thus, their data were not included in any further analysis. The data from two additional participants also could not be used, in one case because of a recording failure and in another case because the experimenter failed to adhere to the free recall protocol. The results reported below are based on the data from the remaining 182 participants (92 females and 90 males). To verify the scoring reliability, a random subset of 20% of transcribed recall protocols were scored by two research assistants. An interclass correlation analysis yielded an r = .99, revealing excellent agreement between the scorers. Discrepancies between the scorers were few and all were resolved by discussion between the scorers. 112 I checked recall performance for ceiling or floor effects. Recall performance averaged .36 (95% CI [.34, .38]), .26 (95% CI [.24, .28]), and .33 (95% CI [.31, .35]), respectively, for negative, neutral and positive pictures, indicating that performance was not marred by either ceiling or floor effects. 5.2.2: Encoding Phase I examined encoding phase performance to validate the valence manipulation and to verify participants completed the task as instructed. 5.2.2.1: Valence Ratings Participantsâ mean ratings of the pictures in each valence condition are displayed in Figure 5.2. They show participantsâ ratings were sensitive to picture valence manipulation; positive pictures were rated significantly higher (M = 4.47, 95% CI [4.38, 4.55]) than neutral ones (M = 2.93, 95% CI [2.87, 3.00]), which in turn were rated significantly higher than negative pictures (M = 1.79, 95% CI [1.72, 1.85]). The pleasantness ratings have the same pattern as the attractiveness and repulsiveness ratings in Experiment 1 (see Figure 3.2). This outcome is interesting given that different rating scales were used in the two experiments. In Experiment 1, I used a unipolar scale with its end-points marked, âNot [attractive or repulsive] at allâ and âvery [attractive or repulsive]â, respectively, whereas in Experiment 3, I used a bipolar scale with its end-points marked âvery unpleasantâ and âvery pleasantâ, respectively, consistent with the rating scale used for the NAPS norming study (Marchewka et al., 2014). Thus, the similarity in the valence ratings from Experiment 1 and 3 suggests that the type of rating scale used didnât exert a major influence on the valence ratings, and it rules out the possibility that the lower-than-expected ratings for the neutral pictures in Experiment 1 were due to the use of a unipolar scale. Another point to consider is that using different labels for the picture valence rating scaleââattractivenessâ in Experiment 1 and âpleasantnessâ in Experiment 3âlikely doesnât change participantsâ 113 valence ratings, as such terms tap into the same underlying dimension of emotion. For example, positive valence includes constructs such as joy, pleasantness, likeability, which all reflect the positivity of the experience (Campos, et al., 2013; Fredrickson et al., 2008; Pereira, 2000). Figure 5.2. Mean Pleasantness Ratings for Negative, Neutral, and Positive Pictures Note. Error bars represent the 95% confidence intervals. 5.2.2.2: Valence Rating Times The means of the median valence rating times, measured from the display onset of each picture on the screen to the entry of a rating on the computer keyboard, are shown in Figure 5.3. The figure shows participants made ratings significantly faster for negative pictures (M = 2602 ms, 95% CI [2446 ms, 2758 ms]) compared to both positive (M = 2850 ms, 95% CI [2678 ms, 3022 ms]) and neutral pictures (M = 2971 ms, 95% CI [2803 ms, 3138 ms]), with no significant difference between the latter two. 123456Negative Neutral PositiveMean Pleasantness Rating (1-6)Picture Valence 114 Figure 5.3. Mean of Median Valence Rating Times for Negative, Neutral, and Positive Pictures Note. Error bars represent the 95% confidence intervals. The rating times were significantly longer in this experiment (2921 ms) compared to Experiment 1 (1730 ms) (see Figure 3.3), most likely due to the change in the instruction given for making valence ratings. Rather than simply rating each pictureâs valence as instructed in Experiment 1, in Experiment 3, the instructions urged participants to reflect on âhow each picture makes you feelâ before rating it. It is also possible that participants took longer to make valence ratings in this experiment compared to Experiment 1 because they were informed about the need for having to recall the pictures, unlike in Experiment 1, where they were only required to make old/new recognition decisions. It is noteworthy that the same pattern of results, with neutral pictures requiring more time than negative pictures, occurred despite the substantial difference in the overall rating times between the experiments. 150020002500300035004000Negative Neutral PositiveValence Rating Times (ms)Picture Valence 115 5.2.3: Testing Phase I examined the free recall protocols for negative, neutral, and positive pictures, and also explored the strategies participants used for retrieval. 5.2.3.1: Free Recall Participants recalled an average of 18.9 pictures, with a range from only 5 to 39 pictures. The recall means, in Figure 5.4, show an emotional enhancement effect, with higher recall of valenced pictures than neutral pictures, and there also was a sex effect, with females outperforming males in all picture valence conditions. These observations were confirmed by an ANOVA which revealed a significant main effect for valence, F (2,360) = 51.30, p < .001, η2 = .222, and for sex, F (2,180) = 6.09, p =.015, η2 = .033. The sex by valence interaction was not significant, p >.05. Follow-up pairwise comparisons revealed that recall of negative pictures was significantly higher (M = .361, 95% CI [.341, .382]) than recall of positive pictures (M = .333, 95% CI [.312, .353]), p = .023, which in turn was higher than recall of neutral pictures (M = .258, 95% CI [.240, .276]), p < .001, with significant difference between negative and neutral pictures, p < .001. Female participantsâ recall performance (M = .337, 95% CI [.315, .359]) was significantly higher than that of male participants (M = .298, 95% CI [.275, .320]), p = .015. An analysis of intrusions in recallâdefined as incorrect recollections of pictures that had not been studiedâshowed means of .122, .176, and .149 for negative, neutral, and positive pictures respectively. Due to these low intrusion rates, there was no need for correcting the recollection data reported above. 116 Figure 5.4. Mean Proportion Recalled for Negative, Neutral, and Positive Pictures Note. Error bars represent the 95% confidence intervals. In an exploratory analysis, I examined whether spending more time describing the pictures might have had a confounding influence on recall. For this purpose, I counted the number of words used by participants for recalling each picture, herein called the word-length of the recall protocols. The word-length of the recall protocols was similar in all picture valence conditions, though with participants using more words for recalling negative pictures (M = 22.58 words, 95% CI [20.59, 24.57]) and positive pictures (M = 21.58 words, 95% CI [19.66, 23.50]) compared to neutral pictures (M = 19.49 words, 95% CI [17.68, 21.29]). 5.2.3.2: Strategic Influences on Free Recall There are many different ways to explore strategic influences on free recall, with each method focusing on specific aspects of how participants go about retrieving information from memory (Bousfield, 1953; Cooke et al., 1986; Roenker, Thompson & Brown,1971; Miller et al., 2013). I examined influences on 00.050.10.150.20.250.30.350.40.450.5Negative Neutral PositiveProportion of Pictures RecalledPicture ValenceFemales Males 117 the order of recalling pictures from the different valence conditions, and transitions in recalling successive pictures from the same valence condition (e.g., negative-negative; neutral-neutral) versus recalling from different valence conditions (e.g., negative-positive; neutral-negative). 5.2.3.2.1: Picture Recall Order To analyze the order in which pictures were recalled from the different valence conditions, I first divided each recall protocol (i.e., all pictures recalled by each participant in the order in which they were recalled) into three segments: 1st third, 2nd third, and 3rd third. Then, I computed the average of the proportion of pictures recalled from each valence condition in the 1st, 2nd, and 3rd segment. Figure 5.5 shows the mean proportion of negative, neutral, and positive pictures in the 1st segment of the recall protocols (left panel) and in the 3rd segment of the recall protocols (right panel). The means in the left panel show that participants recalled more negative and positive pictures compared to neutral ones, whereas the means in the right panel show only small differences among the picture valence conditions with a slight increase in the recall of neutral pictures. These observations were confirmed by ANOVAs. The 1st-recall segment revealed a significant main effect for valence, F (2,360) = 11.34, p < .001, η2 = .059, with no other significant main or interaction effects, both pâs > .05. Follow-up pairwise comparisons revealed recall of negative pictures (M = .364, 95% CI [.340, .388]) and positive pictures (M = .357, 95% CI [.329, .384]) was significantly higher than recall of neutral pictures (M = .267, 95% CI [.237, .297]), both pâs < .001, with no difference between positive and negative pictures, p > .05. By contrast, the ANOVA of the 3rd recall segment revealed no significant main or interaction effects, all pâs > .05. 118 Figure 5.5. Mean Proportion of Pictures Recalled from each Valence Condition in 1st Segment (left panel) and 3rd Segment of the Recall Protocols (right panel) Note. Error bars represent the 95% confidence intervals. For a different perspective on the results in Figure 5.5 where every participant contributed to the 1st and 3rd segments, I computed the proportion of negative, neutral, and positive pictures reported at each recall output position, from the first to the last picture recalled as shown in Figure 5.6 [For this analysis, the number of participants contributing to performance decreases across the recall output positions. In the figure, the vertical dashed-lines represent cut-off points that show performance with contributions to recall from at least 75% of all participants, from at least 50% of all participants, and from at least 25% of all participants, respectively.] The means show that at the beginning of recall, participants tended to retrieve more negative and positive pictures compared to neutral pictures; however, this preference for recollecting valenced pictures was absent in later portions of the recall protocols. 00.050.10.150.20.250.30.350.40.450.5Negative Neutral PositiveRecall in 3rd Segment (Prop)00.050.10.150.20.250.30.350.40.450.5Negative Neutral PositiveRecall in 1st Segment (Prop)Picture Valence 119 Figure 5.6. Mean Proportion of Negative, Neutral, and Positive Pictures Recalled at each Output Position Note. The vertical dashed lines are cut-off points, to the right of which additional contributions to recall performance were received from less than 75%, less than 50% and less than 25% of participants, respectively. Figure 5.6 is a common method for visualizing changes in the strategy used for guiding retrieval from different valence conditions across recall output positions (Talmi, Luk, McGarry & Moscovitch, 2007; Talmi et al., 2019). However, this method confounds recall strategy changes with declines in performance that always occur in the course of recall sessions. In the present study, 182 participants contributed to recall in output position 1 whereas only 42 participants contributed to recall in output position 24. To manage this confound, I adjusted the recall scores based the number of participants who contributed to recall at each output position and replotted the adjusted proportions in Figure 5.7. For this figure, I did not track female and male participantsâ recall because the preceding analysis showed no evidence of a sex 0.000.050.100.150.200.250.300.350.400.450.501 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39Proportion of Pictures Recalled Recall Output PositionNegative Positive Neutral50% 25% 75% 120 0.000.050.100.150.200.250.300.350.400.450.501 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Proportion of Pictures RecalledRecall Output PositionNegative Positive Neutraldifference, and I only included data up to recall output position 24. In the figure, the red-horizontal line represents the chance level for retrieving a picture from a specific valence condition, and the means show that at the beginning of recall, recollection of negative and positive pictures is above the chance level, while the recollection of neutral pictures is below the chance level. This suggests that participants had a preference for recalling valenced pictures early in the recall session. However, out beyond position 14, this preference seems to have disappeared, and retrieval from all picture valence conditions hovers around chance. Figure 5.7. Mean Proportion for Negative, Neutral, and Positive Pictures Recalled at each Output Position Note. The vertical dashed lines are cut-off points, to the right of which additional contributions to recall performance were received from less than 75%, less than 50% and less than 25% of participants, respectively. 75% 50% 25% 121 5.2.3.3.2: Valence-specific Transitions in Recall I also used the recall protocol data for exploring transitions in recall, with transition defined as the likelihood of recalling a picture from the same or a different valence condition immediately after recalling a picture from one of the valence conditions. These transitions may indicate the participantsâ use of a win-stay, lose-shift strategy (Cassotti et al., 2011; Forder & Dyson, 2016; Nowak & Sigmund, 1993; Ritchey et al., 2022), particularly for positive and negative pictures compared to neutral ones. By a win-stay strategy, after easily (or relatively easily) recollecting a negative picture, a participant would be likely to try again and recollect another negative picture. By a lose-shift strategy, participants are likely to shift away from the currently picture-valence condition (e.g., negative) and attempt on the next trial to recollect a picture from one of the other valence conditions. There are different ways to explore valence-specific transitions in recall. One method is to compute the frequency of specific transition types in recall (i.e., from the 1st picture recalled to the 2nd; from the 2nd to the 3rd, etc.) (cf. Bousfield, 1953). Figure 5.8 shows the proportion of three types of recall transitionânegative-negative, positive-positive, neutral-neutralâup to the 24th output positions. The findings in Figure 5.10 show a different pattern of transitions in the initial recall output positions versus in later output position. Specifically, in the first dozen output positions, recall of a valence picture tended to be followed by recall of a picture from the same valence condition (i.e., negative was followed by negative and positive was followed by positive), and these transitions were more frequent than neutral to neutral transitions. This observation was confirmed by an ANOVA with valence (negative, neutral, and positive) and the transition types as a within-subject variables. For this analysis, I used data from those 148 participants who recalled at least 14 pictures, and for each of them, I computed the average proportion of each transition type on the first five transitions (i.e., T1/2 to T5/6) and their average on transitions T9/10 to T13/14. The results revealed a significant main effect for valence, F (2,294) = 20.72, p < .001, η2 = .124, 122 with an interaction that was marginal, F (2,294) = 2.64, p =.077, η2 = .018. The first five transitions for neutral valence (M = .318, 95% CI [.227, .409]) was significantly lower than last five transitions (M = .527, 95% CI [.394, .660]). Figure 5.8. Mean Proportion of Three Types of Transitions in Free RecallâNegative-Negative, Neutral-Neutral, and Positive-Positiveâas a Function of Recall Output Position Note. The vertical dashed lines are cut-off points, to the right of which additional contributions to recall performance were received from less than 75%, less than 50% and less than 25% of participants, respectively. Another similar method involves calculating the total proportion of all nine possible transition types (e.g., positive-positive, positive-neutral, positive-negative, etc.) in recall performance (Long et al., 2015; Siddiqui & Unswort, 2011). The results of this kind of analysis, presented in Appendix 5, suggest that 00.020.040.060.080.10.120.140.160.180.20.220.24T 1/2T 2/3T 3/4T 4/5T 5/6T 6/7T 7/8T 8/9T 9/10T 10/11T 11/12T 12/13T 13/14T 14/15T 15/16T 16/17T 17/18T 18/19T 19/20T 20/21T 21/22T 22/23T 23/24Type of Valence TransitionsRecall Output TransitionNEG to NEG NEU to NEU POS to POS75% 50% 25% 123 compared to neutral pictures, when participants recalled a valenced picture, either negative or positive, they were more likely to subsequently recall another picture from the same valence condition. 5.3: Discussion The main goal of Experiment 3 was to test the EEI/PA theory assumption that emotional valence influences only encoding-phase accommodative/elaborative processing. I used a free recall test to examine this assumption, as it depends heavily on elaborative processing, and I predicted valence would enhance free recall performance. The experiment also investigated whether valence influences the retrieval strategies. The findings of Experiment 3 revealed an overall emotional enhancement effect in free recall, with a larger enhancement effect for negative compared to positive pictures. The findings also showed that valenced pictures were recollected earlier and in a more organized manner than neutral pictures. In addition, the results showed sex differences in free recall for both valenced and neutral pictures. The pronounced emotional enhancement effect in free recall for both negative and positive pictures supports the core assumption of the EEI/PA theory and is consistent with the well documented evidence for such emotional enhancements in the memory literature (Buchanan, 2007; Kensinger & Schacter, 2008; Kensinger & Ford, 2020; Yonelinas & Ritchey, 2015). The EEI/PA theory posits that valence affects only encoding-phase accommodative/elaborative processing. Given that free recall test performance is heavily dependent on encoding-retrieval phase overlaps in elaborative processing (Craik, 1983, 1986), it is more likely to detect valence-related effects occurring at encoding. Several other valence-based theoretical accounts are also possible for the main recall results from Experiment 3. A familiar one of these, founded on the Broaden and Build model (Fredrickson, 1998, 2001, 2004), is that the effect occurs because the cognitive system is by nature configured to process valenced events in a more attentive manner compared to how we process emotionally neutral events. The model 124 posits that compared to neutral events, positive events broaden the scope of attention in the service of curiosity-driven elaborative processing, whereas negative events narrow the scope of attention and activate safety-driven search and escape activities. Thus, although Broaden and Build is not a theory about memory, it anticipates an emotional enhancement effect in episodic memory because attentive processing is known to enhance memory storage and retention compared to processing that is non-attentive, also called automatic or habitual (Craik et al., 1996; Craik & Levy, 1976). An emotional enhancement effect in episodic memory may also be attributed to event distinctiveness. Events are distinctive by having properties that set them apart from others (e.g., sharing only a few features with other events), and episodic memory research suggests that such events draw more attention and receive more extensive memory encoding compared to non-distinctive events (Cohen et al., 1990; Eysenck & Eysenck 1979; Hunt, 995; Hunt & McDaniel, 1983; Schmidt, 1991). By virtue of being affectively positive or negative, valenced events are different from neutral events. In the context of daily life, valenced events are relatively rare and special, and this distinguishing property may ensure their enhanced memory encoding (Schmidt, 1991; Talmi, Luk, McGarry, & Moscovitch, 2007; Talmi, 2013). The finding of a larger enhancement effect in memory for negative than for positive pictures confirms the prediction of both the NEVER model (Bowen et al., 2018) and the EEI/PA theory, and also aligns with the finding of my meta-analysis. The NEVER model attributes the larger enhancement effect for negative relative to positive pictures to the increased sensory-perceptual processing for negative pictures compared to positive ones. The EEI/PA theory further complements this explanation by proposing that negative pictures elicit more extensive and obligatory use of accommodative/elaborative processes than do positive pictures. 125 Other theoretical perspectives have been proposed to explain the enhanced memory for negative over positive events. From an evolutionary perspective, enhanced memory for emotional events may serve as a survival mechanism (Baumeister et al., 2001). Humans are predisposed to attend and remember both negative (e.g., encountering a cliff or facing a predator) and positive events (e.g., finding shelter or source of fresh water), yet negative events may have a more significant influence due to their relevance to our survival and well-being (e.g., serious injury and death) (Baumeister et al., 2001; Rozin & Royzman, 2001; Vaish et al., 2008). Such assumptions are supported by related research that showed enhanced memory for survival-relevant information (Nairne et al., 2008; Pratto & John, 1991). My findings from Experiment 3âbetter memory for negative pictures than for positive onesâcannot be explained by arousal-based accounts and thus the highlights the need for valence-based accounts. Researchers often argue that memory advantage of negative over positive events is due to negative events being more arousing than positive ones (Baumeister et al., 2001; Clewett & Murty, 2019; Rozin & Royzman, 2001; Vaish et al., 2008). However, this argument is not pertinent to my findings because in Experiment 3, the arousal ratings were the same for the negative and positive picture sets. As such, valence-based accounts (e.g., the EEI/PA theory; NEVER; Bowen et al., 2018) offer a more compelling explanation for the memory advantage of negative events. These findings help clarify a gap in the extant theories of memory for emotion, which have predominantly focused on the role of arousal, and underscore the distinct influence of valence on memory. That participants tended to recall valenced pictures earlier than neutral ones, and were more likely to recall another picture of the same valence after recalling valenced picture, provides evidence of a strategic influence due to valence on free recall. Research on the strategic influence of emotion is relatively limited, with some studies revealing evidence of valence in output order (Talmi, Luk, McGarry & Moscovitch, 2007; Talmi et al., 2019), and clustering in free recall (Barber et al., 2017; Long et al., 2015; 126 Siddiqui & Unswort, 2011; Gomes et al., 2013), while others have found no such effect neither in output order (Talmi, Schimmack, et al., 2007) nor in clustering (Barnacle et al., 2016; Manning & Julian,1975; Zimmerman & Kelley, 2010). Among studies that examined strategic influence due to valence, only one study by Barber et al. (2017) has explored clustering in the recall of both negative and positive pictures. One plausible explanation for the strategic influence of valence on free recall is that participants may have used valence as a cue to guide their recollection. Participants were made aware of the valence categories during encoding, as they were required to rate the valence of each picture, so it is possible that they use their ratings to guide their memory search. Given that emotional pictures fall within distinct categories (e.g., pleasant or unpleasant) whereas neutral pictures fall into a more undefined category, participants were more likely to use valence as a retrieval cue, which led to a valence clustering effect. Upon debriefing my participants about how they went about remembering the pictures, participants reported using a number of strategies, including grouping pictures by categories (e.g., animals, activities, food). However, the most commonly reported strategy was using valence to guide recall. Many participants mentioned that they recalled pictures by grouping them based on the emotional response they elicited such as how they felt when viewing pictures, or how they had rated them. This explanation aligns, to some extent, with the emotional Context Maintenance and Retrieval (eCMR) Model (Talmi et al., 2019). According to the eCMR model, emotion functions as a contextual dimension that influences both encoding and retrieval processes. During encoding, emotional events are encoded within their emotional context. During retrieval, recalling an emotional event (e.g., negative picture) reinstates the emotional context associated with it, and thus facilitates recalling further emotional events within the same context. 127 The findings also showed that female participants outperformed male participants in recalling both valenced and neutral pictures, suggesting that these differences are not specific to memory for emotional events. This finding is in line with those studies showing that females consistently outperform males in the episodic memory for a wide variety of stimuli, including names, faces, words, and objects (Asperholm et al., 2019), as well as for emotional events (Bloise & Johnson, 2007; Cahill & Van Stegeren, 2003; Canli et al., 2002; Gasbarri et al., 2007). The observed sex differences in memory can be explained by the cognitive style hypothesis (Seidlitz & Diener, 1998), which suggests that the female memory advantage is due to engaging in more elaborative processing and encoding of events with more details (Seidlitz & Diener, 1998; Meyers-Levy, 1989; Wirth et al., 2007). For example, females, when compared to males, tend to attend to more peripheral information (Stoet, 2010), and make more exploratory fixations when processing visual stimuli (Mercer Moss, Baddeley, & Canagarajah, 2012). Another possible explanation for the female memory advantage is their superior verbal ability (Hirnstein et al., 2023; Hyde & Linn, 1988), which may contribute to their higher tendency to use semantic organizational strategies during retrieval compared to males (Andreano & Cahill, 2009; Kramer et al., 1997). To conclude, Experiment 3 provided evidence of an emotional enhancement effect in memory for valenced pictures, with a larger effect for negative pictures than for positive ones, consistent with the predictions of both the NEVER model and the EEI/PA theory. The findings support the assumption that valence influences accommodative/elaborative processing and confers a memory performance advantage under testing conditions that rely on study-test phase overlaps in elaborative processing. The larger enhancement for negative compared to positive pictures, despite arousal levels being equated, can only be explained by valence-based theories such as the NEVER model and the EEI/PA theory. Specifically, the NEVER model claims that enhanced sensory-perceptual processing for negative events persists even 128 when arousal is equated. The EEI/PA theory clarifies the cognitive mechanism by suggesting that the effect arises from a more obligatory involvement of elaborative processing for negative pictures compared to positive pictures. Further research should examine whether the larger enhancement effect for negative events persists when arousal is equated or manipulated orthogonally across negative and positive events. 129 Chapter 6: General Discussion Memory is superior for emotional than for neutral events is a common assumption which can be found in most introductory psychology textbooks as well as in the popular media, but the evidence from episodic memory research provides, at best, mixed support for this assumption (e.g., Kensinger & Schacter, 2008; Chiu et al., 2013). My dissertation sought to clarify the available evidence, and also to clarify theoretical models available for explaining why memory might be different for emotional and non-emotional/neutral events. As a starting point for my research, I completed a meta-analysis on influences in episodic memory created by valenced and neutral pictures, reported in Chapter 2 of this dissertation. Chapters 3, 4 and 5 report on my three experiments, respectively: Experiment 1 on old/new recognition of valenced and neutral pictures, Experiment 2 on associative recognition of randomly-paired valenced and neutral pictures, and Experiment 3 on free recall of valenced and neutral pictures. The meta-analysis and my experimental work were inspired and guided by the EEI/PA theory, a novel cognitive-processing account that I developed over the course of my work to explain how valence influences episodic memory for emotional events. The EEI/PA theory predicted the main findings from my meta-analysis, as well as those of my three large-scale experiments. The EEI/PA theory provides a valence-based account of memory for emotional events by identifying the encoding-phase sensory-perceptual processes through which valence influences episodic memory. According to this theory, our cognitive system is guided by knowledge structures known as schemas, which help us navigate the environment by predicting what to expect in a given situation and to interact with it in an effective manner. The EEI/PA theory posits that when a current event matches our expectations, it will be assimilated into the active schemas. However, when an unexpected event is encountered, it interrupts routine processing and prompts the cognitive system to engage in accommodative/elaborative processing in an effort to resolve the interruption. Whether an event is 130 experienced as positive or negative depends on the outcome of this processing. Successful accommodation leads to positive emotions, and failed accommodation leads to negative emotions. The theory also posits that processing negative events is obligatory and demands more attentional resources than is the case with positive events. 6.1: Main Findings and Implications The meta-analysis showed an overall emotional enhancement effect in episodic memory, with this enhancement effect being larger for negative than for positive pictures, larger on recall than recognition memory tests, and more pronounced when retention was assessed after a long delay than it was after a short delay. Based on the EEI/PA theory, the emotional enhancement effect in episodic memory for valenced pictures occurs because valenced pictures receive more accommodative/elaborative processing during encoding compared to neutral pictures, which are encoded by routine assimilative/integrative processing. The larger enhancement effect for negative relative to positive pictures occurs because negative pictures demand more accommodative/elaborative processing, which is obligatory for responding to negative events but not for positive events. Free recall yields a larger emotional enhancement effect than recognition because free recall primarily relies on the study-test phase overlap in elaborative/self-initiated processing, which is exactly the kind of processing required for encoding valenced pictures. The larger emotional enhancement effect after a longer delay occurs because over time memory retrieval begins to rely more heavily on elaborative processing, whereas the assimilative/integrative processing that benefit memory on immediate tests tend to fade relatively quickly (Craik & Lockhart, 1972; Reder 1980; Pressley 1982). To the best of my knowledge, this meta-analysis is the first in the field to synthesize and reconcile inconsistencies within the existing literature on emotional enhancements in episodic memory for events. The findings provide solid evidence for this enhancement effect and quantify its magnitude. My meta- 131 analysis provides methodological insights into the best experimental methods for examining the influence of valence (using recognition vs. free recall tests), including guidance on determining the number of participants required for well-powered experiments. Experiment 1, in contrast to the predictions which motivated the study, showed no evidence of an emotional enhancement effect in old/new recognition test performance. I interpreted this finding as consistent with the Craikâs view (1983, 1986) that while recognition performance involves a combination of environmentally-driven and self-initiated processes, it is flexible and can rely entirely on environmentally-driven processing. If this was the case, the emotional enhancement effect would not be expected, because according to the EEI/PA theory, valence influences only the encoding-phase accommodative/elaborative processing, but not integrative/environmentally-driven processing. Experiment 2 showed an emotional impairment effect in associative recognition memory of randomly-paired valenced and neutral pictures, with the impairment being larger for negative than positive pairs. This impairment effect was predicted by the EEI/PA theory due to valenced pictures requiring more accommodative/elaborative processing during encoding, compared to neutral pictures, which leaves fewer attentional resources for creating associations between valenced paired pictures. The larger impairment for negative than for positive pairs may be due to the greater obligatory processing demands of negative pictures, which leaves even fewer resources required for associating the pictures. Experiment 3 demonstrated an emotional enhancement effect in free recall, with a larger enhancement for negative than positive pictures. This outcome was predicted by the EEI/PA theory which postulates performance on free recall tests is primarily determined by study-test phase overlaps in elaborative processing, and assumes that such processing is influence by the valence of to-be-remembered events. Experiment 3 also provided evidence of a strategic valence-related influence on 132 retrieval, which suggests participants were using valence as a cue for retrieval, for example, using valence to prioritize the kind of picture to recall first. Taken together, these findings suggest the influence of valence on episodic memory is not uniform but varies depending on the cognitive processes engaged during both encoding and retrieval. In my experiments, I examined the valence influence on episodic memory using memory tests that varied in the extent in which they rely on study-test overlaps in elaborative/self-initiated processing. The findings revealed that an emotional enhancement effect occurs under retrieval conditions that depend on elaborative/self-initiated processing, but not under conditions which depend entirely on integrative/environmentally-driven processing. The meta-analysis finding of a larger enhancement effect for free recall compared to recognition memory further supports this assumption: that the influence due to valence on episodic memory depends on the extent to which retrieval involves self-initiated processing. My research highlights that a complete theory of memory for emotional events should account for the variability in memory performance due to valence that arises in both encoding and retrieval conditions. While some extant theories of memory for emotional events that consider encoding and/or retrieval processing (e.g., the mediation theory; Talmi, 2013; the ABC theory; Mather & Sutherland, 2011; eCMR; Talmi et al., 2019) help explain some of the research findings (e.g., emotional enhancement and impairment effect, strategic influence of valence), none of them, to the best of my knowledge, provides a complete cognitive-processing account of how valence influences both encoding and retrieval processing. The EEI/PA theory can be seen a helpful step towards a more comprehensive account, due to its focus on valenceâs influence on cognitive processing that involves during both encoding and retrieval. My research further underscores a distinct influence of valence on episodic memory. In Experiment 3, a larger emotional enhancement effect was found for negative pictures than for positive pictures, despite arousal being matched between the negative and positive picture sets. Similarly, Experiment 2 133 demonstrated an emotional impairment in memory for positive picture pairs compared to neutral pairs, despite arousal ratings matching between the two picture sets. Such findings, challenge the arousal-based theories (e.g., Cahill & McGaugh, 1996; Crowley et al., 2019; Clewett & Murty, 2019; Mather & Sutherland, 2011; McGaugh, 2000, 2004; Storbeck & Clore, 2008; Talmi, 2013), which attribute the enhancement effect to arousalâs influence, and instead call for an alternative explanation that focuses on the influence of valence in memory performance. The dominant explanation of emotional enhancement often attributes the effect to arousalâs influence. According to these accounts, the emotional enhancement effect occurs not because of valence, but because emotional events trigger amygdala-mediated arousal, and this arousal prioritizes the memory processing of those events (Cahill & McGaugh, 1996; Crowley et al., 2019; Hamann, 2001; McGaugh, 2000, 2004; Storbeck & Clore, 2008). In particular, with regard to valence, some accounts suggest a larger emotional enhancement effect occurs for negative than for positive events because negative events are typically more arousing than positive ones (Baumeister et al., 2001; Clewett & Murty, 2019; Rozin & Royzman, 2001; Vaish et al., 2008). To my knowledge, until relatively recently when Bowen et al., (2018) proposed the NEVER model, there was no theory specifically focused on how valence influences episodic memory. The NEVER model explains the memory advantage of negative compared to positive events by claiming that negative events are associated with enhanced sensory-perceptual processing even if arousal ratings are equated between positive and negative events. However, as with most arousal-based accounts, The NEVER model is a neuroscience model and thus does not make any assumptions about the specific types of cognitive processes that yield emotional enhancement effects in memory. To the best of my knowledge, the EEI/PA theory is the first theoretical attempt to clarify the cognitive processes that give rise to the effects of both valence and arousal. As such, the theory provides researchers in the field with a novel perspective in 134 understanding memory for emotional events and with a framework for further examination of the role of valence in episodic memory theory across a broader range of contexts, including different encoding activities, different stimulus types (e.g., words, narratives), and different retrieval methods (e.g., cued recall). 6.2: Limitations My dissertation has number of limitations. A significant limitation of my research was that the influence of valence and arousal were confounded, which constrains the conclusions that can be drawn about the specific influence of valence on memory performance. Although valence was the variable of interest for the selection of pictures, valenced pictures differed from neutral ones not only by virtue of being affectively positive or negative, but also by being more arousing pictures. Across the studies included in the meta-analysis, both positive and negative pictures were rated as more arousing than neutral pictures, with about 70% of the experiments reporting that arousal was matched between negative and positive pictures. Therefore, the evidence of the overall emotional enhancement effect and of the larger effect for negative compared to positive pictures can both be attributed to valence and/or arousal as their influences are confounded in the data. Such confounding extends to the finding of an overall emotional enhancement effect in Experiment 3, where both negative and positive pictures had higher arousal than neutral pictures, as well as to the finding of a larger impairment for negative picture pairs compared to positive ones found in Experiment 2, where negative pairs had higher arousal ratings than positive pairs. These findings do not permit an unambiguous explanation of valence-specific influence; instead, both valence-based and arousal-based theories provide competing accounts for the same findings. The confounding of arousal in the findings underscores the need for developing a memory theory that integrates the effects of both valence and arousal to explain memory for emotional events. Further research is needed to examine whether the 135 differences in effect between negative and positive events would hold when arousal and valence are orthogonally manipulated. An additional limitation is that negative and positive emotions induced from pictures may not elicit the same kinds of emotions experienced in actual situations. Emotions evoked by pictures differ from those evoked by dynamic, real-life situations where events unfold over time. For example, witnessing a car accident would elicit more intense and longer-enduring emotions than viewing a picture of a car accident. Emotional experiences in real-life events have been shown to involve self-referential and multi-sensory processing (e.g., sensory, spatial) (Cabeza et al., 2004; McDermott et al., 2009) and their memories tend to be more detailed and durable than those of laboratory stimuli (Ceccato et al., 2024; Yuille & Cutshall, 1986; Schöne et al., 2019). Moreover, to comply with minimal risk ethics, the selected pictures had moderate levels of arousal. Therefore, the findings based on emotions evoked by picture stimuli should be interpreted with caution when generalizing to everyday emotional events that elicit more intense and longer-enduring emotions. Cautious should also be taken when generalizing the findings to other stimuli such as words, faces, stories, and movies, as memory performance varies as a function of stimulus type (Ally & Budson, 2007; Ceccato et al., 2024; Kensinger & Corkin, 2003; Oates & Reder, 2011; Mintzer & Snodgrass, 1999; Nelson et al., 1976; Paivio & Csapo, 1973; Stenberg, 2006). Another potential limitation in my research is that memory performance was assessed in laboratory settings, which further constrains the generalizability of the findings to memory of real-life events. While laboratory settings allow for precise control over stimuli and encoding/retrieval manipulations, they may limit ecological validity (McDermott et al., 2009). In my experiments, participants were presented with a large number of valenced pictures within a short period of time. The brief exposure to each picture may have restricted the intensity of emotions experienced, compared to those experienced in the real world, with events taking place over a period of time, allowing for longer time dwell on the evoked emotions. Moreover, 136 participants tend to be familiar with laboratory tasks and procedures, and they are often motivated by receiving course credits in exchange for participation (Landers & Behrend, 2015), which may influence their engagement and performance (Bowen & Kensinger, 2017b). A further potential limitation of my research stems from the sample population. My participants were young undergraduate students, which represents a narrow and fairly homogeneous subset of the global population (Gallander et al., 2001; Henry, 2008; Novielli et al., 2023; Sears, 1986). Undergraduate students tend to share similar demographic characteristics and often come from a WEIRD (Western, Educated, Industrialized, Rich, and Democratic) population (Arnett, 2016; Henrich et al., 2010; Walpole, 2003). They have also been shown to possess higher levels of cognitive skills compared to the general population (Sears, 1986). Therefore, the findings of my experiments may have constrained generalizability to different populations varying in age, cultural background, socio-economic status, and education. For example, research shows that older people tend to demonstrate a positivity bias in memory compared to young adults, who tend to demonstrate more negativity bias (e.g., Charles et al., 2003). This difference can be explained by the socio-emotional selectivity theory, which suggests that as people age, they prioritize emotionally valuable goals, and thus allocate more cognitive and behavioural resources to processing positive information in order to reach these goals (Carstensen, 1992; Carstensen et al., 2003). 6.3: Conclusion: The claim that memory is superior for emotional events is widespread; less clear, though, is how emotional events influence memory. Both valence and arousal are core attributes of emotional experiences, yet most theoretical accounts emphasize the role of arousal as a main determinant of memory for emotional events (e.g., Cahill & McGaugh, 1996; Crowley et al., 2019; Clewett & Murty, 2019; Mather & Sutherland, 2011; McGaugh, 2000, 2004; Storbeck & Clore, 2008; Talmi, 2013), while relatively little effort has been devoted to understand the influence of valence (Bowen et al., 2018; Williams et al., 2022). 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Journal of Memory and Language, 62(3), 240-253. https://doi.org/10.1016/j.jml.2009.11.004 207 Appendices Appendix 1. Searches carried out in databases including individual searches of search terms and the combinations of searches used for records selection. Database Search Phases Search ID # Search Terms Number of records Hits by date PsycINFO (1887 - present) Phase 1: Terms are searched using a combination of keywords and subject headings Search 1 (S1) TI ( (emotion* or affect* or valence* or arous* or negative or positive or neutral)N0(Picture* or image* or stimuli or item* or scene* or material* or photo* or content or object* or event*) ) OR AB ( (emotion* or affect* or valence* or arous* or negative or positive or neutral)N0(Picture* or image* or stimuli or item* or scene* or material* or photo* or content or object* or event*) ) OR KW ( (emotion* or affect* or valence* or arous* or negative or positive or neutral)N0(Picture* or image* or stimuli or item* or scene* or material* or photo* or content or object* or event*) ) May 2020: 3631 October 2023: 4226 Search 2 (S2) TI ( ( emotion* or affect* or valence* or arous* ) ) OR AB ( ( emotion* or affect* or valence* or arous* ) ) OR KW ( ( emotion* or affect* or valence* or arous* ) ) Search 3 (S3) DE "Emotions" OR DE "Emotional Content" OR DE "Emotional States" OR DE "Negative Emotions" OR DE "Positive Emotions" OR DE "Affective Valence" Search 4 (S4) DE "Physiological Arousal" Search 5 (S5) TI memor* OR AB memor* OR KW memor* Search 6 (S6) DE "Memory" OR DE "Episodic Memory" OR DE "Long Term Memory" OR DE "Memory Consolidation" OR DE "Memory Decay" OR DE "Memory Trace" OR DE "Cued Recall" OR DE "Free Recall" OR DE "Recall (Learning)" OR DE "Retention" 208 Database Search Phases Search ID # Search Terms Number of records Hits by date Phase 2: combined searches using Boolean operators Search 7 (S7) S2 OR S3 OR S4 Search 8 (S8) S5 OR S6 Search 9 (S9) S1 AND S7 AND S8 MEDLINE ovid (1964- present) Phase 1: Terms are searched using a combination of keywords and subject headings Search 1 (1) ((emotion* or affect* or valence* or arous* or negative or positive or neutral) adj1 (Picture* or image* or stimuli or item* or scene* or event* or material* or photo* or content or object*)).ti,ab,kw. May 2020: 3000 October 2023: 3586 Search 2 (2) (emotion* or affect* or valence* or arous*).ti,ab,kw. Search 3 (3) Arousal/ Search 4 (4) emotions/ or affect/ Search 5 (5) memory/ or memory, episodic/ or memory, long-term/ or mental recall/ or "recognition (psychology)"/ or "retention (psychology)"/ 209 Database Search Phases Search ID # Search Terms Number of records Hits by date Search 6 (6) memor*.ti,ab,kw. Phase 2: combined searches using Boolean operators Search 7 (7) 2 OR 3 OR 4 Search 8 (8) 5 OR 6 Search 9 (9) 1 AND 7 AND 8 Note. TI: title, KW: author keywords, AB: abstract, DE: subject headings in psycINFO database, /: subject headings in MEDLINE ovid database, OR: retrieves records that contains at least one of search terms, AND: retrieves records that include all search terms 210 Appendix 2. Standardized data coding manual used to code the data by defining each variable and providing the precise steps for coding it. Coding Manual Preparation for data coding: - Coding process involves coding manual, data extraction Excel spreadsheet template, and the included records (i.e., articles) that might include multiple experiments. - This manual contains a list of the variables to be recorded for the included experiments in the data extraction Excel spread sheet. - The manual outlines various types of variables arranged into five main categories. - The manual outlines the name and definition of each variable, its corresponding label in the Excel spreadsheet and provides guidelines on how to record it into the Excel spreadsheet. - Read the coding manual thoroughly to ensure clear understanding of the variables to be recorded for each experiment. - Utilize the provided Excel spreadsheet for recording data. This spreadsheet comprises columns for all variables outlined in this manual. Each subset of variables is distinguished by a different color. - Scan each record (i.e., article) to determine the number of conducted experiments. - For each experiment, identify the relevant information for each listed variable in this manual (e.g., number of participants, their age), then record the information of each variable in proper column in the Excel spreadsheet. - One experiment could occupy one or two rows of the Excel spreadsheet depending on how many valence types were manipulated (e.g., negative and positive). If the experiment manipulated one valence condition (negative or positive) and compare it to neutral condition, one row of the Excel spreadsheet will be needed to fill out the required information. However, if the experiment manipulated two valence conditions (negative and positive), then two rows of the Excel spreadsheet will be needed: one row for the data for each valence condition. - Add your comments about each experiment, if you have any, in the comment column. Identification Information This section includes the variables related to identifying each article. Any information not available in the article should be recorded as Not Reported (NR) Author Name IdentInfo_Author Record the name/names of the authors of the article. Publication Year IdentInfo_PubYear Record the year in which the article was published. Citation This coding manual is developed to facilitate systematic and accurate data coding for the experiments included in the meta-analysis of episodic memory for emotional events. 211 IdentInfo_Citation Record citation of the article as follows: One author Last name (Year) Two authors Last name & Last name (Year) Three+ Authors Last name et al. (Year) APA Citation IdentInfo_APACitation Record the full APA citation of the article. Digital Object Identifier DOI IdentInfo_DOI Record DOI of the article. Record not reported (NR) if DOI canât be found for the article. Correspondence IdentInfo_Correspond Record the corresponding authorâs email who takes primary responsibility for communication with the journal during the manuscript submission, peer review, and publication process. Record not reported (NR) if correspondence is not reported. Experiment Identifier IdentInfo_ExpID Record a unique number for the different experiments published in the same article (1st, 2nd, etc.) Effect Size Identifier IdentInfo_ES_ID Record a unique number for each effect sizes in the dataset (1,2,3 etc.) Valence Manipulated in the Experiment IdentInfo_ValType Report the valence type that was manipulated in the experiment. Valence Instruction Code Negative Classify a valence type as negative if the experiment focused on memory for negatively valenced pictures Neg Positive Classify a valence type as positive if the experiment focused on memory for positively valenced pictures Pos Negative and positive Classify a valence type as negative and positive if the experiment focused on memory for negatively and positively valenced pictures Neg-Pos Participant, Design Characteristics, and Country This section includes the variables related to participant population, sample size, study design, and the country in which data were collected. Any characteristic not described in the Experiment should be listed as Not Reported (NR) 212 Population PartChar_Population Record the specific group from which the individuals participating in the experiment were drawn. Population Type Instruction Code University students Classify a population as a university student if the recruited participants were either undergraduate or graduate students. UNS Community living Individuals Classify a population as a community living if the participants were recruited from general community by advertisement posters or newspaper. CLI University students & Community living Individuals Classify a population as a mixed if the participants were either undergraduate, graduate students, or recruited from general community by advertisement posters or newspaper. Mixed Sample size_ N PartChar_Total_N Record the total number of participants included in the experiment or relevant condition. If the sample size of the experiment divided into different conditions, and there is a fraction in N of each condition (e.g., 21.5 people), consider one of the following solutions: Figure out from relevant data (e.g., F or t-tests) the exact number of participants in the relevant condition. If the above data are not available, round the fraction to the nearest full number (e.g., 21.5 ~ 22 people) Sample size_ N included in the analysis. PartChar_Total_N_Ana Record the total number of participants completed the experiment and included in the analysis. N Female F_N Record the total number of female participants included in the experiment or relevant condition. N Male M_N Record the total number of male participants included in the relevant experiment or condition. Female Proportion PartChar_F_Prop Record the proportion of female participants included in the sample. Mean Age of Participants PartChar_Mean_Age Record the mean age of participants in years. Standard Deviation of Age PartChar_SD_Age Record the standard deviation of the age of participants. 213 Age Range PartChar_Rang_Age Record the age range of participants in years indicating the upper and lower ages. Design Design_Char Record whether the valence was manipulated within-subject or between-subjects. Design Instruction Code Within-Subject Classify an experiment as within-subject design (repeated measures) if the same participants were tested in all the experimental conditions (in this case negative, neutral, and positive pictures) WS Between-Subjects Classify an experiment as between-subjects design if the participants of an experiment are assigned to different conditions, with each group experiencing only one type of valence (e.g., negative, neutral, or positive) BS Country PMdr_Country Record the country where the data were collected (based on the community where participants recruited or according to the party that gave the approval of the study). Memory Test Performance Variables This section includes the variables related to the memory test type, and data required for effect size calculations for recall and recognition in each valence condition. If data for any variable is not provided, it should be listed as Not Reported (NR). Memory Test Type PrimMdr_MemTestType Record measures used to assess memory for pictures. Memory test type Instruction Code Recognition Memory Test Classify a memory test as a recognition if the memory for pictures was assessed by recognition test RECOG Picture Recall Memory Test Classify a memory test as a picture recall if the memory for pictures was assessed by recognition test RECAL Variables Related to Recognition Memory Test Performance: Proportion of Hits for Valenced Pictures Velenced_Hits_Prop 214 Record the proportion of valenced studied pictures (negative OR positive) that were correctly recognized as having been studied. In case the paper provided hits data as a number of studies pictures correctly recognized, convert the number to the proportions as follow: # of hits in the condition of interest (negative or positive) / Total # of studied pictures (old) of the same condition (negative or positive). Standard Deviation of Hits for Valenced Pictures Velenced_Hits_SD Record the standard deviation of the proportion of valenced studied pictures (negative OR positive) that were correctly recognized as having been studied. Record standard error (SE), or confidence interval (CI) in the proper columns, if they are the only estimates reported; they will be converted to standard error. Proportion of False Alarms for Valenced Pictures Velenced_FA_Prop Record the proportion of valenced (negative OR positive) of non-studied or distractor pictures falsely recognized as having been studied. In case the paper provided false alarms data as a number of falsely recognized new pictures as studied (old), convert the number to the proportions as follow: False alarms # of false alarms in the condition of interest (negative or positive) / Total # of new pictures of the same condition (negative or positive). Standard Deviation of False Alarms for Valenced Pictures Velenced_FA_SD Record the standard deviation of the proportion of valenced non-studied or distractor pictures (negative OR positive) falsely recognized as having been studied. Record standard error (SE), or confidence interval (CI) in the proper columns, if they are the only estimates reported; they will be converted to standard error. Proportion of Corrected Recognition: Hits minus Fales Alarms for Valenced Pictures Valenced_FA-Hits_Prop Record the difference between the proportion of hits and the proportion of false alarms for valenced pictures (negative OR positive) as reported in the paper. If not provided, a calculation formula will be applied to obtain the corrected recognition from data reported for Hits and False alarms. Standard Deviation of Hits Minus Fales Alarms for Valenced Pictures Valenced_Hits-FA_SD Record the standard deviation of the difference between the proportion of hits and the proportion of false alarms for valenced pictures (negative OR positive). Record standard error (SE), or confidence interval (CI) in the proper columns, if they are the only estimates reported; they will be converted to standard error. Proportion of Hits for Neutral Pictures Neut_Hits_Prop Record the proportion of neutral studied pictures that were correctly recognized as having been studied. In case the paper provided hits data as a number of studies pictures correctly recognized, convert the number to the proportions as follow: # of hits in the neutral condition / Total # of studied pictures (old) of neutral condition Standard Deviation of Hits for Neutral Pictures Neut_Hits_SD 215 Record the standard deviation of the proportion of neutral studied pictures that were correctly recognized as having been studied. Record standard error (SE), or confidence interval (CI) in the proper columns, if they are the only estimates reported; they will be converted to standard error. Proportion of False Alarms for Neutral Pictures Neut_FA_Prop Record the proportion of neutral non-studied or distractor pictures falsely recognized as having been studied. In case the paper provided false alarms data as a number of falsely recognized new pictures as studied (old), convert the number to the proportions as follow: False alarms # of false alarms in the neutral condition / Total # of new pictures of neutral condition. Standard Deviation of False Alarms for Neutral Pictures Neut_FA_SD Record the standard deviation of the proportion of neutral non-studied or distractor pictures falsely recognized as having been studied. Record standard error (SE), or confidence interval (CI) in the proper columns, if they are the only estimates reported; they will be converted to standard error. Proportion of Corrected Recognition: Hits Minus Fales Alarms for Neutral Pictures Neut_Hits-FA_Prop Record the difference between the proportion of hits and the proportion of false alarms for neutral pictures as reported in the paper. If not provided, a calculation formula will be applied to obtain the corrected recognition from data reported for Hits and False alarms. Standard Deviation of Hits Minus Fales Alarms for Neutral Pictures Neut_Hits-FA_SD Record the standard deviation of the difference between the proportion of hits and the proportion of false alarms for neutral pictures. Record standard error (SE), or confidence interval (CI) in the proper columns, if they are the only estimates reported; they will be converted to standard error. Variables Related to Recall Memory Test Performance: Proportion of Correctly Recalled Valenced Pictures Velenced_Correct_Prop Record the proportion of valenced studied pictures (negative OR positive) that were correctly recollected. -If the paper provided the number of pictures correctly recalled, convert the number to a proportion as follow: # of correctly recalled pictures of the condition of interest (negative or positive) / Total # of studied pictures of the same condition (negative or positive). -If the paper provided the percentage of correctly recalled pictures, covert it to the proportion by dividing it by 100. Standard Deviation of Correctly Recalled Valenced Pictures Velenced_Correct_SD Record the standard deviation of the proportion of valenced studied pictures (negative OR positive) that were correctly recollected. Record standard error (SE), or confidence interval (CI) in the proper columns, if they are the only estimates reported; they will be converted to standard error. Proportion of Correctly Recalled Neutral Pictures Neut_Correct_Prop Record the proportion of neutral studied pictures that were correctly recollected. -If the paper provided the number of pictures correctly recalled, convert the number to a proportion as follow: # of correctly recalled pictures of the neutral condition / Total # of studied pictures of the neutral condition. -If the paper provided the percentage of correctly recalled pictures, covert it to the proportion by dividing it by 100. 216 Standard Deviation of Correct Neutral Pictures Neut_Correct_SD Record the standard deviation of the proportion of neutral studied pictures that were correctly recollected. Record standard error (SE), or confidence interval (CI) in the proper columns, if they are the only estimates reported; they will be converted to standard error. Source of Data Data_Source Record method of data extraction Source Instruction Code Text Classify as Text if the data were obtained from the text TXT Table Classify as Table if the data were obtained from the table TBL Figure Classify as Figure if the data were obtained from the figure using the WebPlotDigitizer software FGR Supplementary Classify as Supplementary if the data were obtained from the supplementary SUP Primary Test Performance Moderator Variables This section includes the variables related to moderator variables. If data for any variable is not provided, it should be listed as Not Reported (NR). Encoding Activity PMdr_Enc_Activity Record the type of processing the pictures during encoding phase. Type of processing Instruction Code Semantic Classify encoding task as a semantic if participants were asked to assess the semantic properties of stimulus during encoding (e.g., how do you like this picture?) SEM Perceptual Classify encoding task as a perceptual if participants were asked to assess the perceptual features of stimulus (e.g., colors) PERC Non_Constrained by specific instructions Classify encoding task as a Non-Constrained if participants viewed the picture during encoding phase without specific instruction (e.g., Look at the picture the whole time) NON-CONST Length of Retention Interval PMdr_LengRetInterval Record the time between the end of study phase and the start of test phase (i.e., report whatever the study reports: minutes, hours, days etc.). Retention Interval PMdr_RetInterval 217 Record the length of period (short vs. long) between the end of study phase and the start of test phase. Length Instruction Code Short Classify retention interval as short if the cognitive context between the encoding and testing phases was the same, and the memory test was administered immediately or after a filler task (i.e., participants had an intervention activity). SHRT Long Classify retention interval as long if the cognitive and environmental contexts between the encoding and testing phase changed (e.g., participants go back to life then came back again for the testing phase) LNG Valence Type PMdr_Valence_dataset Report the valence of the dataset (negative OR positive). Used to distinguish between negative vs positive effect sizes. Valence Instruction Code Negative Classify a valence type as negative if the effect size for the dataset estimate was assessed negative emotion on memory Neg Positive Classify a valence type as positive if the effect size for the dataset estimate was assessed positive emotion on memory Pos Sex of Participants PMdr_Sex Record whether the memory performance results came from female, male, or mixed sample. Sex Instruction Code Males Classify a sex as Males if the memory performance results came from male participants M Females Classify a sex as Females if the memory performance results came from female participants F Mixed Classify a sex as Mixed if the memory performance results came from mixed participants (both males and females) MIX Exploratory Test Performance Moderator Variables This section includes the variables related to exploratory variables. If data for any variable is not provided, it should be listed as Not Reported (NR). Test Awareness at Study EMdr_TestAwar Awareness of the test Instruction Code 218 Aware Classify test awareness as aware if participants were informed study to-be-remembered pictures with awareness of that memory for these pictures will be tested (i.e., participants were informed about the subsequent memory test when encoding pictures) AWR Not Aware Classify test awareness as not aware if participants study to-be-remembered pictures without awareness of that memory for these pictures will be tested (i.e., participants were not informed about the subsequent memory test when encoding pictures- surprised memory test) Non-AWR Picture-related variables Source of Pictures EMdr_PicChar_Source Record whether pictures were selected from a standardized data base or not. Source Instruction Code Standardized Classify a picture source as standardized if the pictures were selected from standardized database (e.g., IAAPS, NAPS) STD Unstandardized Classify a picture source as non-standardized if the pictures were selected from non-standardized database (e.g., clip art, google, pictures are taken by the researcher etc.) Non-STD Name of Source of Pictures EMdr_PicChar_NameSource Record the name of pictures source (i.e., IAAPS, NAPS, clip art, google, pictures are taken by the researcher) Mean Valence Rating of Neutral Pictures PMdr_PicChar_MeanValNeuPic Record the mean of normative ratings of neutral picturesâ valence included in the experiment. If only rating range is reported, report the midrange of the rating range. Report NR if normative rating is not reported. Mean Valence Rating of Valenced Pictures PMdr_PicChar_MeanValValencedPic Record the mean of normative ratings of valenced picturesâ valence (either negative or positive) included in the experiment. Mean Arousal Rating of Neutral Pictures PMdr_PicChar_MeanArouNeuPic Record the mean of normative ratings of neutral picturesâ arousal included in the experiment. Arousal Rating of Valenced Pictures PMdr_PicChar_MeanArouValencedPic Record the mean of normative ratings of valenced picturesâ arousal (either negative or positive) included in the experiment. Report NR if normative rating is not reported. Controlling of Arousal 219 PMdr_PicChar_ControlArou Record whether normative arousal ratings were controlled between negative and positive picture sets. Report not applicable (NA) if there a single valence condition (either negative or positive) Controlling of Arousal Instruction Code Controlled 1 Classify as Controlled if the negative and positive picture sets have the same range of arousal with statistical test indicated that no-sig differences in the arousal between the valenced sets CNT1 Controlled 2 Classify as Controlled if the negative and positive picture sets have the same range of arousal, but no statistical test was reported. CNT2 Uncontrolled Classify as Uncontrolled if the negative and positive picture sets do not have the same range of arousal UCNT Encoding Phase variables Picture Presentation EMdr_Enc_PicPresnt Record whether the duration time for presenting each picture on the screen is timed or self-paced. Presentation Instruction Code Timed Classify picture presentation as Timed if duration time for presenting a picture on the screen is fixed for each picture TM Self-paced Classify picture presentation as Self-paced if duration time for presenting a picture on the screen is decided by the participant (no time limit) SP Encoding Pictures Order PMdr_Enc_PicOrder Record the order in which the valenced and neutral stimuli were presented to participants during encoding phase. Picture Order Instruction Code Mixed Classify the order as a mixed all pictures (negative, neutral and positive) are mixed and presented to the same group of participants MIX Blocked Classify the order as a blocked if each picture valence (negative, neutral or positive) was presented in a separate block to the same participants or to separate groups of participants BLCK Amount of Display Time EMdr_Enc_DispTime Record the display time of the picture on the screen in MILLISECONDS. Record not applicable (NA) if presentation time was self-paced. 220 Total N of pictures in Encoding EMdr_Enc_TotalNPic Record the total number of pictures presented during encoding phase including valenced and neutral pictures. This number is not including puffers and practice pictures. Encoding Activity Emotion Focus EMdr_Enc_EmoActivity If the encoding activity is semantic, record whether the activity focused on the emotionality of the pictures or not. Record not applicable (NA) if the encoding activity is perceptual or Non_Constrained by specific instructions. Activity Instruction Code Emotional Focus Classify encoding task as an emotional if the encoding activity emotional oriented (e.g., how does this picture make you feel?) EM Non-Emotional Focus Classify encoding task as a non-emotional if the encoding activity non-emotional oriented (e.g., is this picture outdoor?) Non-EM Testing Phase variables These variables are relevant only to recognition memory tests. Report not applicable (NA) for recall memory test. Total N of pictures testing PMdr_Test_ TotalNPic Record the total number of pictures presented during recognition testing phase including valenced and neutral pictures. Proportion of old pictures PMdr_Test_FracOld Record the proportion of studied pictures (pictures presented during encoding task) in the recognition memory test. 221 Appendix 3. Summary of the experiments included in the meta-analysis. Author Country Exp. P (N) Mage Rage Population Female ratio Test type Val Encoding activity Interval Length of retention interval Awareness Study items (N) Pres- time Msec Emo Of encoding Source pictures ES (N) Abercrombie et al. (2008) USA 1st 53 NR >18 UNS 0 RECAL +/- Non-CON LNG 2 days Non-AWR 63 6000 NA IAPS 2 Ackermann et al. (2012) Switzerland 1st 234 21.88 18-32 NR 0.59 RECAL +/- SEM SHRT 10 min Non-AWR 72 2500 EM IAPS 4 Ackermann et al. (2013) Switzerland 1st 1225 22.49 18-35 UNS/CLI 0.66 RECAL +/- SEM SHRT 10 min Non-AWR 72 2500 EM IAPS, In-house PicSet 4 Ackermann et al. (2015) Switzerland 1st 55 24.73 NR UNS/CLI NR RECAL +/- SEM SHRT 10 min Non-AWR 72 2500 EM IAPS, In-house PicSet 8 Ahn et al. (2015) Korea 1st 34 22.76 NR UNS 0.5 RECAL +/- SEM SHRT Immediate Non-AWR 198 4000 EM In-house normed affect-Pic 12 Arnone et al. (2011) Italy 1st 64 25 NR UNS 1 RECAL +/- Non-CON LNG 10 days Non-AWR 60 2000 NA IAPS 4 Bai et al. (2017) China 1st 20 36.5 NR NR 0.85 RECAL +/- SEM SHRT Immediate Non-AWR 45 3000 EM CAPS 2 Baraly et al. (2019) Canada 1st 47 19.62 < 35 UNS 0.83 RECAL +/- Non-CON SHRT 1 min AWR 64 2000 NA IAPS, Internet 4 France 2nd 61 20.31 < 35 UNS 0.77 RECAL +/- Non-CON SHRT 1 min AWR 46 2000 NA IAPS, Internet 4 Baran et al. (2012) USA 1st 24 20.4 18-30 UNS 0.75 RECOG _ SEM SHRT 45 min Non-AWR 60 1000 EM IAPS, In-house PicSet 2 Barber et al. (2017) USA 1st 128 19.86 18-31 UNS 0.74 RECAL +/- Non-CON SHRT 15 min AWR 70 5000 NA IAPS 8 Barnacle et al. (2016) Germany 1st 23 26 NR UNS/CLI 1 RECAL _ Non-CON SHRT 1 min AWR 224 2000 NA previous research 2 Barnacle et al. (2018) UK 1st 25 NR 18-35 UNS/CLI NR RECOG _ Non-CON SHRT 1 min AWR 192 2000 NA IAPS 2 Bellace et al. (2013) USA 1st 12 NR 26-37 UNS .58 RECAL _ Non-CON SHRT 10 min AWR 40 2000 NA IAPS 1 Bennion et al. (2015) USA 1st 84 22.4 18-34 NR 0.535 RECOG _ SEM SHRT 20 min Non-AWR 124 3000 EM NR 2 Bergt et al. (2018) Germany 1st 51 25.35 18-35 NR 0.5 RECAL _ SEM SHRT Immediate AWR 150 3000 EM IAPS 1 Bessette-Symons (2018) USA 1st 60 NR NR UNS NR RECOG +/- SEM SHRT 10-15 min AWR 135 2500 EM IAPS 4 2nd 60 NR NR UNS NR RECOG +/- SEM SHRT 10-15 min AWR 135 2500 EM IAPS 4 3rd 94 NR NR UNS NR RECOG +/- SEM LNG 1 week AWR 135 2500 EM IAPS 4 4th 60 NR NR UNS NR RECOG +/- SEM LNG 1 week AWR 135 2500 EM IAPS 4 Binder et al. (2012) Switzerland 1st 59 23.59 19-33 UNS 1 RECAL _ SEM SHRT 30 min Non-AWR 48 7000 EM IAPS 2 Bourque et al. (2013) Canada 1st 21 30.3 18-55 NR 0 RECOG +/- Non-CON SHRT 15 min AWR 30 3000 NA IAPS 2 Bowen & Spaniol (2011) Canada 1st 122 20.8 17-38 UNS 0.787 RECOG +/- PERC SHRT 1 hour Non-AWR 150 2000 NA IAPS 3 Bradly et al. (2017) USA 1st 595 19 NR UNS 0.62 RECAL +/- SEM SHRT 5 min Non-AWR 60 6000 EM IAPS 24 Bywaters et al. (2004) UK 1st 37 30 18-42 UNS 0.64 RECAL +/- SEM LNG 1 week NR 25 6000 EM IAPS 4 Chainay et al. (2012) France 1st 40 20.1 NR UNS 0.8 RECOG +/- SEM SHRT Immediate AWR 30 2000 Non-EM Previous research 2 40 20.1 NR UNS 0.8 RECOG +/- SEM SHRT Immediate Non-AWR 30 2000 Non-EM Previous research 2 Chapman et al. (2013) Canada 1st 28 18 NR UNS 0.64 RECAL _ PERC SHRT 10 min Non-AWR 48 2000 NA IAPS, other sources 2 23 18 NR UNS 0.64 RECAL _ PERC SHRT 45 min Non-AWR 48 2000 NA IAPS, other sources 2 222 Author Country Exp. P (N) Mage Rage Population Female ratio Test type Val Encoding activity Interval Length of retention interval Awareness Study items (N) Pres- time Msec Emo Of encoding Source pictures ES (N) 2nd 23 22.8 NR UNS 0.565 RECAL _ PERC SHRT 45 min Non-AWR 48 2000 NA IAPS, other sources 2 Charles et al. (2003) USA 1st 48 24.56 18-29 CLI 0.5 RECAL +/- Non-CON SHRT 15 min Non-AWR 32 2000 NA IAPS 2 Choi et al. (2017) USA 1st 72 NR NR UNS NR RECAL +/- SEM SHRT 20 min AWR 135 5000 EM Hemera photo-objects 2 Chung (2010) USA 1st 47 21.23 18-30 UNS NR RECAL +/- Non-CON SHRT 30 min Non-AWR 30 4000 NA Internet 2 Corbett et al. (2017) USA 1st 39 20.31 18-29 UNS/CLI 0 RECOG _ SEM LNG 48 hours Non-AWR 225 3000 Non-EM NAPS 2 Corbett et al. (2020) USA 1st 22 22.68 18-38 UNS/CLI 0.59 RECOG _ SEM SHRT Immediate NR 320 3000 EM IAPS, NAPS 2 Cordon et al. (2013) USA 1st 30 19.67 NR UNS 0.53 RECOG _ SEM LNG 1 week Non-AWR 100 4000 EM IAPS, Books, Magazines, Internet 1 Cox et al. (2018) USA 1st 71 21.3 18-33 NR NR RECOG _ SEM SHRT Immediate Non-AWR 128 1000 EM NAPS 2 Cudeiro et al. (2023) Spain 1st 34 NR 19-29 UNS NR RECOG _ Non-CON LNG 3 days NR 50 2000 NA IAPS 1 Diaz Abrahan et al. (2020) Argentina 1st 31 24.5 18-46 UNS .59 RECAL +/- SEM SHRT 3 min NR 37 7000 EM IAPS 2 Dieleman et al. (2015) Netherlands 1st 37 23.1 NR CLI 0 RECAL +/- Non-CON SHRT Immediate AWR 60 4000 NA IAPS 2 Dolcos & Cabeza (2002) USA 1st 15 NR NR UNS 1 RECAL +/- SEM IMD Immediate AWR 180 2000 EM IAPS 2 Emery & Hess (2008) USA 1st 29 18.48 NR UNS 0.46 RECAL +/- SEM SHRT 26.6 min Non-AWR 48 5000 EM IAPS 2 29 18.48 NR UNS 0.46 RECAL +/- Non-CON SHRT 26.6 min Non-AWR 48 5000 NA IAPS 2 2nd 23 20.73 NR UNS 0.52 RECAL +/- PERC SHRT 26.6 min Non-AWR 48 5000 EM IAPS 2 23 20.73 NR UNS 0.52 RECAL +/- Non-CON SHRT 26.6 min Non-AWR 48 5000 NA IAPS 2 Ertman et al. (2011) USA 1st 60 19.79 18-28 UNS 1 RECAL _ SEM LNG 7 days NR 120 3000 EM IAPS 2 Farris & Toglia (2019) USA 1st 24 20.62 NR CLI NR RECOG +/- Non-CON LNG 48 hours AWR 120 1000 NA IAPS, www.flickr.com 4 Fastenrath et al. (2014) Switzerland 1st 560 22.37 18-35 NR 0.62 RECAL +/- SEM SHRT 10 min Non-AWR 72 2500 EM IAPS, In-house PicSet 2 Fishman & Ashbaugh (2022) Canada 1st 116 19.8 17-29 UNS .63 RECAL +/- Non-CON SHRT 20 min NR 34 NR NA IAPS 2 Fung et al. (2010) Hong Kong 1st 114 21.21 NR UNS 0.59 RECAL +/- Non-CON SHRT 15 min NR 32 2000 NA IAPS 2 Galli et al. (2014) UK 1st 30 23.3 NR NR 1 RECOG _ SEM LNG 1 day Non-AWR 320 1000 EM IAPS 2 Gavazzeni et al. (2012) Sweden 1st 34 25.84 NR NR .55 RECOG _ SEM LNG 1 year Non-AWR 110 6000 EM IAPS 2 Gomez et al. (2020) Switzerland 1st 65 26.6 20-34 CLI 0.54 RECOG +/- SEM SHRT 10 min Non-AWR 84 2000 EM IAPS 2 Gruhn et al. (2007) USA 1st 48 25.34 18-31 CLI NR RECOG +/- Non-CON SHRT Immediate NR 42 800 NR IAPS 4 Hall et al. (2007) UK 1st 20 35.5 NR CLI 0.35 RECAL +/- SEM SHRT 10 min Non-AWR 120 6000 EM IAPS 2 Hamann et al. (1999) USA 1st 10 22.8 20-32 UNS 0 RECAL +/- SEM SHRT 10 min Non-AWR 156 7500 EM IAPS 2 Ha Ìmmerera et al. (2017) UK 1st 17 24 18-30 UNS 0.68 RECOG _ SEM SHRT Immediate Non-AWR 120 3000 EM IAPS, Internet 1 Harris & Pashler (2005) USA 1st 55 NR NR UNS 0.65 RECAL _ Non-CON SHRT Immediate AWR 60 250 NA IAPS 1 Heinbockel et al. (2021) Germany 1st 25 24.6 19-35 UNS .49 RECOG _ SEM LNG 24 hours Non-AWR 300 2000 EM IAPS 1 Herbener et al. (2007) USA 1st 28 40.82 NR CLI 0.54 RECOG +/- SEM LNG 24 hours Non-AWR 90 3000 EM IAPS 2 223 Author Country Exp. P (N) Mage Rage Population Female ratio Test type Val Encoding activity Interval Length of retention interval Awareness Study items (N) Pres- time Msec Emo Of encoding Source pictures ES (N) Hess et al. (2013) USA 1st 64 NR 20-44 CLI 0.5 RECAL +/- Non-CON SHRT 15-20 min AWR/ Non-AWR 36 5000 NA IAPS 2 Hess et al. (2017) USA 1st 50 33.7 20-43 CLI 0.5 RECAL +/- Non-CON SHRT 5 min Non-AWR 35 5000 NA IAPS 4 2nd 32 30 20-40 CLI NR RECAL +/- SEM SHRT 5 min Non-AWR 35 5000 EM IAPS 8 Hourihan (2020) Canada 1st 43 NR NR UNS NR RECOG +/- SEM SHRT 3 min AWR 147 500 Non-EM IAPS 2 2nd 38 NR NR UNS NR RECOG +/- SEM SHRT 3 min AWR 147 500 Non-EM IAPS 2 Hourihan & Bursey (2017) Canada 1st 25 NR NR UNS NR RECOG + SEM SHRT 3 min AWR 146 500 Non-EM IAPS 1 2nd a 27 NR NR UNS NR RECOG + SEM SHRT 3 min AWR 146 300 Non-EM freedigitalphotos.net 1 2nd b 35 NR NR UNS NR RECOG + SEM SHRT 3 min AWR 146 750 Non-EM freedigitalphotos.net 1 Hsu et al. (2018) Australia 1st 51 22.99 NR UNS/CLI .62 RECAL +/- Non-CON LNG 48 hours Non-AWR 60 6000 NA IAPS 4 Huijbers et al. (2011) Netherlands 1st 23 22.5 19-30 UNS 0.57 RECOG +/- PERC SHRT 10 min Non-AWR 36 1500 NA IAPS 2 Hurleman et al. (2007) UK 1st 16 25.6 20-34 NR 1 RECAL +/- Non-CON SHRT 30 seconds NR 36 2000 NA IAPS 2 Jaworek et al. (2014) Germany 1st 19 24 NR UNS/CLI 0.58 RECOG +/- Non-CON LNG 1 week Non-AWR 84 3000 NA IAPS, EmoPicS 2 Jones & Spencer (2019) USA 1st 25 19.96 18-28 NR 0.74 RECOG _ SEM SHRT 3 hours NR 90 2000 EM IAPS 1 Jouberta et al. (2018) Canada 1st 37 19.51 18-25 UNS 0.76 RECAL +/- SEM SHRT Immediate Non-AWR 32 5000 Non-EM IAPS, Previous research 2 Kaida et al. (2015) Japan 1st 32 21.1 NR NR 0 RECOG +/- Non-CON SHRT 10 min AWR 180 500 NA IAPS 4 Kalpouzos et al. (2012) Sweden 1st 19 25 20-30 NR 1 RECOG _ SEM SHRT Immediate AWR 300 3500 EM IAPS 1 Kensinger et al. (2002) USA 1st 20 20.5 NR CLI 0.55 RECAL +/- SEM SHRT Immediate NR 45 5000 EM IAPS 4 Kern et al. (2005) USA 1st 40 NR >18 UNS 0.75 RECAL _ Non-CON SHRT 2 min NR 23 5000 NA IAPS 1 2nd 84 NR >18 UNS 0.75 RECAL +/- Non-CON SHRT 2 min NR 23 5000 NA IAPS 2 Kim & Hamann (2012) USA 1st 32 20.19 NR UNS 0.47 RECAL _ SEM SHRT 10 min Non-AWR 144 4000 EM IAPS 6 Kissler & Hauswald (2008) Germany 1st 12 27.6 NR UNS 0.4 RECOG +/- Non-CON SHRT 2 min AWR 288 900 NA IAPS 2 Knight & Ponzio (2013) USA 1st 55 18.91 18-25 UNS 0.85 RECAL +/- SEM SHRT 15 min Non-AWR 72 6000 EM IAPS 6 2nd 52 19.57 18-25 UNS 0.83 RECAL +/- SEM SHRT 15 min Non-AWR 72 6000 EM IAPS 6 Kugler & Kuhbandner (2015) Germany 1st 63 24.92 NR UNS 0.71 RECAL _ SEM SHRT 1 min Non-AWR 48 10000 EM IAPS, GAPED 1 Lakis et al. (2011) Canada 1st 37 31.81 NR NR 0.49 RECOG +/- SEM SHRT 15 min AWR 50 3000 Non-EM IAPS 4 Lavoie & Connor (2013) Canada 1st 20 25 19-48 CLI 1 RECOG +/- Non-CON SHRT 10 min NR 75 4000 NA IAPS 2 Leveton et al. (2018) USA 1st 29 23.9 NR UNS 0.9 RECOG +/- SEM LNG 9.38 days NR 60 2000 EM IAPS 4 2nd 25 20.39 NR UNS 0.76 RECOG +/- SEM Non-CON LNG 7.64 days NR 90 2000 EM NA IAPS 6 3rd 16 20.45 NR UNS 0.9 RECOG +/- Non-CON LNG 7.75 days NR 60 4000 NA IAPS 2 Maksimovskiy et al. (2022) USA 1st 46 27.17 NR UNS .78 RECOG +/- SEM LNG 24 hours Non-AWR 120 1800 Non-EM licensed websites 2 224 Author Country Exp. P (N) Mage Rage Population Female ratio Test type Val Encoding activity Interval Length of retention interval Awareness Study items (N) Pres- time Msec Emo Of encoding Source pictures ES (N) Massol et al. (2021) France 1st 26 21.75 18-28 UNS .5 RECAL +/- SEM SHRT I min AWR 180 700 Non-EM DAPS 2 Mather & Knight (2005) USA 1st 24 19.73 18-29 UNS/CLI 0.75 RECAL +/- Non-CON SHRT 20 min Non-AWR 48 2000 NA IAPS 2 24 19.73 18-29 UNS/CLI 0.75 RECAL +/- Non-CON LNG 2 days Non-AWR 48 2000 NA IAPS 2 2nd 19 21.7 18-28 UNS/CLI 0.52 RECAL +/- Non-CON SHRT 20 min Non-AWR 80 2000 NA IAPS 4 3rd 16 22.8 UNS/CLI NR RECAL +/- Non-CON SHRT 20 min Non-AWR 80 2000 NA IAPS 2 Michalowski et al. (2014) Poland 1st 15 NR NR UNS 1 RECOG _ SEM LNG 1 week Non-AWR 160 1500 EM IAPS 1 Mitchell et al. (2006) USA 1st 16 20.6 18-27 UNS 0.53 RECOG _ Non-CON LNG 51.4 days Non-AWR 200 990 NA IAPS, other sources 1 Moeck et al. (2021) Australia 1st 48 20.25 18-36 NR .73 RECAL _ PERC SHRT 10 min NR 42 2000 NA IAPS 2 50 20.25 18-36 UNS .73 RECAL _ PERC SHRT 45 min NR 42 2000 NA IAPS 2 Nicholson et al. (2014) Australia 1st 20 21.8 NR UNS/CLI 0.6 RECAL +/- Non-CON LNG 2 days Non-AWR 60 6000 NA IAPS 2 Olsen et al. (2015) USA 1st 33 38.39 18-58 CLI 1 RECOG +/- SEM LNG 1 day Non-AWR 90 3000 EM IAPS 2 Ozcelik (2020) Turkey 1st 15 23.07 21-29 UNS .53 RECOG _ Non-CON LNG 1 week NR 48 4000 NA Internet 1 Pais-Vieira et al. (2016) USA 1st 17 22.7 NR NR 0.53 RECOG +/- SEM PERC LNG 3 days Non-AWR 450 3000 EM NA IAPS 4 Palomba et al. (1997) Italy 1st 20 25.3 NR UNS 0.85 RECAL +/- Non-CON SHRT Immediate AWR 60 6000 NA IAPS 2 Pollatos & Schandry (2008) Germany 1st 30 28.5 NR UNS 0.6 RECOG +/- Non-CON SHRT 1 hour Non-AWR 90 5000 NA IAPS 2 Pompili et al. (2016) Italy 1st 46 23.4 21-27 UNS 1 RECAL +/- Non-CON LNG 7 days Non-AWR 150 2000 NA IAPS 2 Pottage & Schaefer (2012) UK 1st 27 22.67 18-54 UNS/CLI 0.56 RECAL _ Non-CON SHRT 2 min NR 192 2000 NA IAPS, Google 3 Ritchey et al. (2011) USA 1st 20 23 NR NR 0.48 RECOG +/- SEM PERC LNG 2 days 420 2000 EM NA IAPS, In-house PicSet 4 Ritchey et al. (2013) USA 1st 19 23.3 18-29 NR 0.47 RECOG +/- SEM LNG 2 days NR 420 2000 EM IAPS 2 Sakaki et al. (2019) UK 44 19.71 18-23 UNS .90 RECAL +/- PERC SHRT 3 min NR 45 2000 NA IAPS 2 Sava et al. (2016) France 1st 26 21.77 NR UNS 0.58 RECOG +/- SEM IMD 2 min Non-AWR 24 4000 Non-EM IAPS 2 Schienle et al. (2021) Austria 1st 50 23.4 20-23 UNS/CLI .5 RECOG _ Non-CON SHRT 20 min AWR 24 3000 NA IAPS, Internet 2 Schmidt et al. (2011) USA 1st 24 19.7 18-24 UNS 0.58 RECOG +/- SEM SHRT Immediate Non-AWR 270 3000 Non-EM IAPS 4 Schönfeld et al., (2014) Germany 1st 72 23.2 NR UNS 0.5 RECAL _ Non-CON SHRT Immediate AWR 50 3000 NA IAPS 1 Schu Ìmann & Sommer (2018) Germany 1st 70 28 22-38 NR 1 RECOG _ SEM SHRT 3 min NR 160 2000 Non-EM NR 2 Schu Ìmanna et al. (2018) Germany 1st 646 24.4 18-36 NR 0.72 RECOG +/- SEM SHRT 10 min Non-AWR 120 1000 Non-EM IAPS, Internet 4 Schwabe et al. (2013) Canada 1st 48 20.98 NR UNS 0.5 RECAL _ Non-CON SHRT Immediate NR 50 2000 NA IAPS 1 Shermohammed et al. (2019) USA 1st 23 NR NR UNS/CLI NR RECOG _ SEM LNG 2 weeks Non-AWR 72 8000 EM IAPS 1 Spalek et al. (2015) Switzerland 1st 510 21.2 18-28 UNS/CLI 0.73 RECAL +/- SEM SHRT 10 min Non-AWR 30 2500 EM IAPS 4 2nd 1481 22.4 18-35 UNS/CLI 0.67 RECAL +/- SEM SHRT 10 min Non-AWR 72 2500 EM IAPS 4 225 Author Country Exp. P (N) Mage Rage Population Female ratio Test type Val Encoding activity Interval Length of retention interval Awareness Study items (N) Pres- time Msec Emo Of encoding Source pictures ES (N) 3rd 104 24.1 18-38 UNS/CLI 0.64 RECAL +/- SEM SHRT 10 min Non-AWR 72 2500 EM IAPS 4 4th 1137 22.4 18-35 UNS/CLI 0.60 RECAL +/- SEM SHRT 10 min Non-AWR 72 2500 EM IAPS 4 Spaniol et al. (2008) Canada 1st 24 22.54 19-28 CLI 0.5 RECOG +/- SEM SHRT 20 min Non-AWR 144 2000 NA IAPS 2 2nd 25 22.32 18-32 CLI 0.48 RECOG +/- Non-CON SHRT 20 min Non-AWR 72 2000 NA IAPS 2 Talmi et al. (2007) Canada 1st 30 19.67 NR UNS 0.67 RECAL _ PERC SHRT 1 min AWR 45 2000 NA IAPS 2 2nd a 24 22 NR UNS 0.58 RECAL _ PERC SHRT 1 min AWR 45 2000 NA IAPS 1 2nd b 24 21.54 NR UNS 0.68 RECAL _ PERC SHRT 1 min AWR 45 2000 NA IAPS 1 3rd 36 21.19 NR UNS 0.81 RECAL _ PERC SHRT 5 min Non-AWR 45 2000 NA IAPS 1 4th 24 18.73 NR UNS 0.75 RECAL _ Non-CON SHRT 1 min AWR 45 2000 NA IAPS 1 Talmi et al. (2007) B. Canada 1st 24 19.02 NR UNS 0.67 RECAL +/- Non-CON SHRT 20 sec AWR 30 2000 NA IAPS, Internet 2 2nd 24 21.19 NR UNS 0.58 RECAL _ Non-CON SHRT 20 sec AWR 30 2000 NA IAPS, Internet 1 Talmi et al. (2008) Canada 1st 11 24.43 NR NR 0.45 RECOG _ SEM Non-CON SHRT 10 min Non-AWR 192 1000 Non-EM NA IAPS 2 Tomaszcyk et al. (2008) Canada 1st 36 19.68 18-25 UNS 0.69 RECAL +/- Non-CON SHRT 5 min Non-AWR 48 4000 NA IAPS 2 36 19.68 18-25 UNS 0.69 RECAL +/- SEM SHRT 5 min Non-AWR 48 4000 EM IAPS 2 Tomaszczyk et al. (2013) Canada 1st 30 20.8 18-25 UNS 0.53 RECAL +/- SEM SHRT 5-10 min AWR 30 4000 Non-EM IAPS 4 Wang & Yang (2017) China 1st 26 21 NR UNS 0.48 RECOG +/- SEM LNG 24 hours NR 110 3000 EM IAPS, Internet 8 Wassell et al. (2015) Australia 1st 64 20.16 NR UNS 0.625 RECAL _ SEM LNG 2 days Non-AWR 40 5000 Non-EM IAPS 4 Watts et al. (2014) UK 1st 27 23.18 NR UNS/CLI 0.74 RECAL _ SEM SHRT 1.5 min Non-AWR 240 1500 EM IAPS, google 2 West & Mulligan (2021) USA 1st 25 NR 18-22 UNS NR RECAL _ SEM SHRT 45 min AWR 24 2000 Non-EM Internet 2 2nd 25 NR 18-22 UNS NR RECAL _ Non-CON SHRT 45 min AWR 24 2000 NA Internet 2 3rd 31 NR 18-22 UNS NR RECAL _ PERC SHRT 45 min AWR 24 2000 Non-EM Internet 2 3rd 31 NR 18-22 UNS NR RECAL _ PERC SHRT 45 min Anon-WR 24 2000 Non-EM Internet 2 Weymar et al. (2009) Germany 1st 30 21.9 19-29 UNS NR RECOG +/- Non-CON LNG 1 week Non-AWR 90 3000 NA IAPS 4 Weymar et al. (2011) Germany 1st 19 24 19-31 UNS 0 RECOG +/- Non-CON LNG 1 week Non-AWR 90 3000 NA IAPS 2 Weymar et al. (2013) USA 1st 25 19.9 NR UNS 0.4 RECOG +/- Non-CON SHRT 5 min Non-AWR 36 3000 NA IAPS 2 Whalley et al. (2009) UK 1st 14 35.4 NR CLI 0.29 RECOG + SEM SHRT Immediate NR 72 4000 EM IAPS 1 Wichert et al. (2013) Germany 1st 72 24.8 NR UNS 0.5 RECAL _ Non-CON SHRT Immediate AWR 60 2000 NA IAPS 1 Wirkner et al. (2018) Germany 1st 32 22.6 19-32 UNS 1 RECOG _ Non-CON SHRT Immediate Non-AWR 144 3000 NA IAPS, EmoPicS 1 25 22.6 19-32 UNS 1 RECOG _ Non-CON LNG 1 week Non-AWR 144 3000 NA IAPS, EmoPicS 1 Wirkner et al. (2019) Germany 1st 20 21.1 NR NR 1 RECOG +/- Non-CON LNG 1 week Non-AWR 90 3000 NA IAPS, EmoPicS 2 Wirkner et al. (2022) Germany 1st 21 21 18-26 CLI 1 RECOG +/- Non-CON LNG 1 week Anon-WR 90 3000 NA IAPS, EmoPicS 2 226 Author Country Exp. P (N) Mage Rage Population Female ratio Test type Val Encoding activity Interval Length of retention interval Awareness Study items (N) Pres- time Msec Emo Of encoding Source pictures ES (N) Witherby et al. (2022) USA 1st 39 20.33 NR UNS .77 RECAL +/- SEM SHRT Immediate AWR 30 3000 Non-EM IAPS 2 2nd 40 19.15 NR UNS .85 RECAL +/- SEM SHRT Immediate AWR 30 3000 Non-EM IAPS 2 Xing & Isaacowitz (2006) USA 1st 28 19.13 18-22 UNS 0.64 RECAL +/- Non-CON SHRT 3 min Non-AWR 30 10000 NA IAPS 2 1st 27 19.13 18-22 UNS 0.64 RECAL +/- SEM SHRT 3 min Non-AWR 30 10000 Non-EM IAPS 2 1st 29 19.13 18-22 UNS 0.64 RECAL +/- SEM SHRT 3 min Non-AWR 30 10000 EM IAPS 2 Xu et al. (2011) 1 China 1st 17 22 NR UNS 0.47 RECOG +/- PERC SHRT 3 min Non-AWR 27 2000 NA IAPS, Internet 2 16 22 NR UNS 0.47 RECOG +/- SEM SHRT 3 min Non-AWR 27 2000 EM IAPS, Internet 2 Yang & Ornstein (2011) Canada 1st 43 21.09 17-33 UNS NR RECAL +/- Non-CON SHRT 5 min AWR 48 2000 NA IAPS 4 Yonelinas et al. (2011) USA 1st 30 24 NR UNS/CLI 1 RECAL _ PERC LNG 1.5 hour Non-AWR 120 2000 NA IAPS 2 Zarubin et al. (2020) USA 1st 84 19.98 18-22 UNS .69 RECAL _ Non-CON SHRT 1 min AWR 22 2000 NA IAPS, GAPED, Google 1 Zheng et al. (2019) China 1st 27 22.48 19-25 UNS 0.48 RECOG +/- SEM SHRT 2 min AWR 216 1000 EM IAPS 2 Note. P(N): total number of participants included in analysis, Mage: The mean age of participants in years, Rage: the range of participantsâ age, UNS: University students, CLI: Community living Individuals, RECAL: Free recall, RECOG: Recognition, Val: Valence of picture set to be compared with neutral set, +/- : Experiment focused on memory for negatively and positively valenced pictures , + : Experiment focused on memory for positively valenced pictures, - : Experiment focused on memory for negatively and positively valenced pictures, SEM: Semantic encoding activity, PERC: Perceptual encoding activity, Non-CONST: No specific instruction for encoding activity, SHRT: Memory test was administered after short delay LNG: Memory test was administered after long delay, AWR: Participants were informed about the subsequent memory test when encoding pictures, Non-AWR: participants were not informed about the subsequent memory test when encoding pictures- surprised memory test, Presentation time Msec: The display time of the picture on the screen in milliseconds, EM: Encoding activity is emotional oriented (e.g., how does this picture make you feel?), Non-EM: Encoding activity is non-emotional oriented (e.g., is this picture outdoor?), IAPS: International Affective Picture System, NAPS: Nencki Affective Picture, CAPS: Chinese Affective Pictures System, DAPS: Developmental Affective Photo System, GAPED: Geneva affective picture database, EmoPicS: Emotional Picture Set, NR: Not reported, NA: Not applicable, ES: number of effect sizes calculated from the experiment. 227 Appendix 4. Protocol of Transcription, Scoring of Audio Recordings of Memory for Emotional Pictures Workspace in the Shared Folder: - All data related to the experiment is stored in a shared folder on OneDrive. - Please note that all materials and data in the shared OneDrive are confidential. Do not share them with anyone outside the project team without permission. - The experiment folder consists of various subfolders, including forms, questionnaires, meeting minutes, and the main study. - The main study folder contains individual folders for each RA, where you will find your workspace for scoring, questionnaires, debriefing questions, Excel sheets, and word transcription documents. - Ensure that your work is saved on OneDrive for easy access and backup. Uploading Audio Recordings to OneDrive: - After testing each participant, upload the audio recording to the "Participant Recordings" folder in the main study folder in OneDrive. To upload the recording, connect the recorder to your laptop using the USB adapter. Then transfer the recording from the recorder to the "Participant Recordings" folder - Label the uploaded audio file using the following format: Sub#.Pro#.Ver#.RAInitials.DD/MM/YYYY. - If any issues arise during the experiment (deviations from protocol, skipped photos, program errors, recording issues, participant discontinuation, etc.), please document the details in the "Participant Issues" folder. In this folder, include the participant #, RA, location, Program #, Instruction version #, and a description of the issue. Upload and Create Transcription for Audio Recordings in Microsoft Word: - For each participant's transcription Word document, include the following information at the top: Participant ID/# Date of the experiment Location of the interview (Lab #) Program # Version of recall instruction # Experimenter name Total recall time: [hour: minutes: seconds]. This is calculated by subtracting the start recall time from the end recall time, representing the overall duration of the recall performance. Start recall time: [hour: minutes: seconds]. This is the time stamp indicating when the participant begins recalling the first picture. End recall time: [hour: minutes: seconds]. This is the time stamp indicating when the participant finishes recalling the last picture. This protocol is designed to facilitate systematic and accurate scoring of pictures recalled, ensuring data consistency and reliability. 228 - While listening to the audio recording, transcribed the audio recording verbatim write word by word Scoring memory performance: Memory performance will be assessed for overall accuracy in scoring Excel sheets. Preparation for scoring in Excel sheet: - Use the provided Excel sheet template for scoring, available on OneDrive. - The Excel sheet program contains 60 pictures (20 Negative, 20 Neutral, and 20 Positive) displayed at the top of the sheet. - For each participant, begin by filling in the information in the Excel sheet (Initial Experimenter, Participantâs Number, Version of Description, etc.). - There are several rows in the sheet correspond to each participant and include the following cells: o Description cell: This cell contains the participant's description of the picture, which is used for word count. To filing out this, copy the description of each picture from the transcription record and paste it into the description cell that corresponds to the displayed picture. o Overall score: This cell is used to record the score of overall accuracy of recalled pictures (0/1) o Order cell: This cell indicates the order of the picture recalled by the participant. For example, if the participant recalled a picture as the first one in the sequence during the recalling session, input "1" as the order. Note that his applies to critical pictures, not practice ones. Overall accuracy and order The scoring will be based on the extent to which the description of the picture recalled allows the experimenter to identify and distinguish it from other pictures viewed. Steps for scoring overall accuracy: - Read the descriptions of recalled pictures provided by the participant in the transcription Word document. - If the recalled picture matches one of the to-be remembered pictures (i.e., critical pictures presented during the encoding phase), enabling you to identify it from other pictures, highlight the description in bright yellow and indicate "critical" along with the order of the remembered pictures at the end of the description (e.g., critical 1). - In the Overall Scoring Excel sheet, select appropriate program (1,2 or 3), and locate the corresponding picture at the top. The pictures are grouped by valence conditions. - Copy the description of the recalled picture from the transcription Word document and paste it into the appropriate description cell underneath the corresponding picture. - Assign/code a score of "1" in the score cell if the recalled picture is accurate. - Record the order of the recalled picture in the order cell. For instance, if the participant recalls a picture as the third one in the sequence during the recalling session, you should input the order as "3". This applies only to critical pictures, not practice pictures or intrusions. - Assign a score of "0" in the score cell for the rest of the pictures that were not recalled. 229 - Count the total number of accurately recalled pictures for each participant across each valence conditions (Negative, neutral, and positive). - If the recalled picture is one of the practice pictures, highlight the description in a different color and indicate "practice." - If the recalled picture does not match any of the to-be remembered pictures (i.e., not applicable to any of the pictures), consider it random error. Highlight the description in bright blue and indicate "Random intrusion." - Copy the intrusion from the transcription Word document and paste it into the intrusion cell in the scoring Excel sheet. - Copy the Practice description from the transcription Word document and paste it into the practice cell underneath the corresponding picture. - If the description is vague and general; applicable to more than one to-be remembered pictures (e.g., pictures that have animals, happy persons, land area), consider it an error. Highlight the description in bright blue and indicate "General intrusion." - Copy this description from the transcription Word document and paste it into the general description cell in the scoring Excel sheet. - Follow these steps for each participant, recording scores, descriptions, and orders in the corresponding cells. - Record the total time of recalling for each participant in the appropriate cell in Excel sheet. 230 Appendix 5. Transitions in Recall In a further effort to reveal strategic influences on free recall, I examined transitions in recall, that is, the likelihood of recalling a picture from the same or a different valence condition immediately after recalling a picture from one of the valence conditions. For this purpose, I used the method described by Siddiqui and Unswort (2011). For each participant, I counted how often recalling a picture of one valence was immediately followed by another picture of the same or different valence. For example, if a participant recalled a negative picture, I recorded how frequently it followed by another negative, positive, or neutral picture. I counted transitions separately for each valence condition, with each valence include three possible transitions (e.g., positive to positive, positive to neutral, and positive to negative). I then calculated the proportions of each particular transitions (e.g., positive to positive) by dividing the total number of that transitions by the total number of transitions for that valence condition (e.g., total number of all positive transitions). The transitions in recall within and between valence conditions for both sexes and the total sample are shown in Table 5.2. The same pattern occurs for both female and male participants, and for this reason, I focus on the total sample. The results suggest that when participants recalled a valenced picture, either negative or positive, they were more likely to subsequently recall another picture of the same valence condition. In contrast, when recalling a neutral picture, they were equally likely to subsequently recall a picture from any valence condition. A Chi-square values of total sample confirmed that transitions for negative pictures, ÏÂ² (2) = 103, p < .01, and positive pictures ÏÂ² (2) = 73.65, p < .0, are significantly differed from what would be expected by chance (i.e., .333). In contrast, transitions for neutral pictures didnât differ from chance, p > .05. Table 5.2. Transitions in Recalling Negative, Neutral, and Positive Pictures Females Males Total Sample Nth Valence of Picture N + 1 Negative Neutral Positive Negative Neutral Positive Negative Neutral Positive Negative 0.476 0.255 0.269 0.459 0.253 0.288 0.468 0.254 0.278 Neutral 0.309 0.361 0.330 0.351 0.338 0.311 0.328 0.351 0.321 Positive 0.312 0.248 0.440 0.320 0.231 0.449 0.316 0.240 0.444 Note. Nth = first picture recalled, N+1 = the subsequent picture recalled
Thesis/Dissertation
eng
Psychology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Graf, Peter (Professor of psychology)
Valence influences on episodic memory : a cognitive processing account
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