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The effect of implied social presence on non-social attention tasks Kendall, William 2015

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  THE EFFECT OF IMPLIED SOCIAL PRESENCE ON NON-SOCIAL ATTENTION TASKS by  William Kendall  B.Sc. H., The University of Toronto, 2013  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF ARTS in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Psychology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)   August 2015  © William Kendall, 2015 ii  Abstract  The presence of another person, even if implied, has been shown to affect various social behaviours. Recently, the effect of implied presence has been extended to the field of social attention, specifically its impact on where people look at social stimuli. The present study investigated if implied presence, triggered by a recording camera switched on in the testing room, would have an effect on non-social spatial attention tasks: visual search, attentional capture, spatial cuing, and the SART. Implied social presence had a significant performance effect on all tasks by facilitating response time without any cost to accuracy. Critically, this facilitatory social presence effect was additive with the effect of spatial orienting. These data indicate that the significant effect of implied social presence is not limited to social domains, and even very simple lab-based nonsocial attention tasks are affected. Social presence operates independent of the spatial attention system and does not require real social observation -- simply the idea of observation through a camera is sufficient to enhance task performance.   iii  Preface The following research was conducted in the Brain and Attention Research Laboratory at the University of British Columbia. I programmed the experiments, collected or supervised all the data used in this thesis, and analyzed the results in collaboration with Dr. Alan Kingstone, my thesis supervisor. Ethical approval for this research was provided by the UBC Behavioural Research Ethics Board (BREB) using the approval number H10-00527. iv  Table of Contents  Abstract .......................................................................................................................................... ii Preface ........................................................................................................................................... iii Table of Contents ......................................................................................................................... iv List of Tables ................................................................................................................................ vi List of Figures .............................................................................................................................. vii Acknowledgements .................................................................................................................... viii Chapter 1: Introduction ................................................................................................................1 1.1 Social facilitation ............................................................................................................ 1 1.1.1 Three models of social facilitation .............................................................................. 2 1.1.2 Evidence for three models, and parsimony ................................................................. 4 1.1.2.1 Drive theory ........................................................................................................ 4 1.1.2.2 Evaluation apprehension theory .......................................................................... 5 1.1.2.3 Comparison theory .............................................................................................. 7 1.2 Social presence as a social norm ..................................................................................... 8 1.3 Social presence and social attention ............................................................................... 9 1.4 The present investigation .............................................................................................. 11 Chapter 2: Experiment ................................................................................................................14 2.1 Methods......................................................................................................................... 14 2.1.1 Design and participants ............................................................................................. 14 2.1.2 Tasks and procedures ................................................................................................ 15 2.2 Results ........................................................................................................................... 17 v  2.2.1 Visual search ............................................................................................................. 20 2.2.1.1 Popout search .................................................................................................... 20 2.2.1.2 Conjunction search............................................................................................ 21 2.2.2 Attentional cueing ..................................................................................................... 22 2.2.3 Attentional capture .................................................................................................... 23 2.2.4 S.A.R.T ..................................................................................................................... 24 Chapter 3: Conclusion .................................................................................................................26 3.1 General discussion ........................................................................................................ 26 3.1.1 Attention tasks .......................................................................................................... 27 3.1.2 Social facilitation revisited ....................................................................................... 27 3.2 Impact of this research .................................................................................................. 30 3.3 Future directions ........................................................................................................... 32 References .....................................................................................................................................34 Appendices ....................................................................................................................................39 Appendix A : Scripts ................................................................................................................. 39 Appendix B : Task means ......................................................................................................... 40 Appendix C : RT distributions .................................................................................................. 42  vi  List of Tables  Table 1 RT and accuracy means for the visual search task in Appendix B .................................. 40 Table 2 RT and accuracy means for the attentional cueing tasks in Appendix B ........................ 40 Table 3 RT and accuracy means for the attentional capture task in Appendix B ......................... 41 Table 4 RT and accuracy means for the SART in Appendix B .................................................... 41  vii  List of Figures  Figure 1 Illustration of the camera present condition ................................................................... 15 Figure 2 Reaction time (RT) performance for all tasks performed by participants ...................... 19 Figure 3 RT distributions .............................................................................................................. 42     viii  Acknowledgements First and foremost, this work would not have been possible without Alan Kingstone, whose support and advice continues to help me grow as a researcher and as a person. Thank you to my colleagues in the BAR lab for your guidance, but especially to Grayden, who always has a good answer, no matter what the question is, and Trish, who continually sets an example to live up to. Finally, thank you to the folks from Ontario who are always there to support me: my mother, without whom I would never have gotten here, my father, my brother John, my loving sister Anne, and my best friend Daniel.    This work was supported by the Natural Science and Engineering Research Council of Canada, and the University of British Columbia. 1  Chapter 1: Introduction Consider the following scenario: you are alone on a bus, passing the time by using your phone to chat with friends and family members. One of your friends keeps sending you humorous images that catch you unawares, including some photos which are creative enough that you occasionally even laugh out loud. Suddenly, at the next stop, the bus fills completely, and the seats around you are all now occupied. Your friend sends you another photo, but after looking at it for a moment, you quickly put your phone back in your pocket. It now seems uncomfortable to hold it out and stare at it – after all, who knows if someone on the bus is looking at your phone? Even worse, you might laugh out loud again, making you look crazy. It seems much safer to spend the next half hour staring out the window, even if that does leave you bored.   Though this example is modern (and to some perhaps seems a little bit neurotic), it is simply a contemporary example of a general phenomenon that all people experience regularly: that the presence of others changes how you behave. This fact is obvious, and its existence needs no explanation; you simply know to try and avoid audibly passing gas in public, loudly humming a Taylor Swift song that is caught in your head on the bus, or staring at attractive people around you. What is harder to answer is why your behaviour changes, or if it’s always for the same reasons, and what those reasons might be.  1.1 Social facilitation There is a long history of studying such social presence effects in psychology. The most famous example of this is the social facilitation effect expounded by Zajonc (1965), where, in various experiments, he found that a subject will perform better at easy tasks (or tasks they are skilled at) and worse at difficult tasks (or tasks they are unfamiliar with) when in the presence of 2  another person. This finding started the exploration into social presence effects in modern psychology, which still continues to this day.  The following pages will discuss social presence effects in more detail, but first, an important distinction needs to be made between the terms social facilitation and social presence. For the purposes of this thesis, a social presence effect is any effect caused by the introduction of another person (or something representing a person) into an experiment, so that the participant's perception may be that they are not alone. Social facilitation, in contrast, specifically refers to the finding that participants will improve on simple/easy tasks and get worse at complex/difficult tasks in the presence of another person as opposed to when acting alone. That is to say, social facilitation is the most well-known example of a social presence effect, but is not an exhaustive description of all possible social presence effects.  1.1.1 Three models of social facilitation Zajonc posited that social facilitation occurred due to physiological arousal (i.e., the mere presence of another person increases arousal, altering performance) rather than anything specific to social interaction. This idea of an arousal mechanism behind social facilitation was influential, and encompasses several lines of later research, including many studies that introduced variations on the original theory: e.g., the arousal change is caused by unfamiliar and unpredictable audiences (Guerin and Innes, 1982), or is caused by a cardio-vascular threat response in the participant, which is beneficial in simple tasks but impedes performance on complex tasks (Blascovich, Mendes, Hunter, and Salomon, 1999). Because of the scope of these theories, and the fact that they all agree on a physiological change causing social presence effects, it is reasonable to consider them all together; as such, from here on they will be referred to as drive models. 3   Before long, not only were there variations on the original arousal position, there were also theories which centered around mechanisms other than arousal as well. However, of these, there were only two kinds of competing theories about social facilitation that were strongly different conceptually and also had solid empirical support: first, evaluation apprehension models, and then later, comparison models.  An evaluation apprehension account of social facilitation states that, rather than arousal being the cause of the phenomenon, it is the threat of being evaluated on your performance that leads to the social facilitation effect (Henchy and Glass, 1968; or for applications of this idea, Martens and Landers, 1972; Sanna and Shotland, 1990). This idea was based on findings that a social facilitation effect was only recorded when the person who was watching a participant was not only watching that participant but also actively evaluating them. Specifically, in Henchy and Glass (1968), they had participants perform a simple word-recognition task either alone, in the presence of a non-evaluative audience, or an evaluating audience. They found that people would recall and recognize words they had spent more time learning more easily, but recall words they had spent less time learning with more difficulty, in the presence of an evaluative presence. They found no change in skin conductance or heart rate across conditions; i.e., they found a clear social facilitation effect without evidence for arousal, and only in the presence of an audience who could be evaluating performance. The use of the phrase ‘comparison model’ here is actually used to describe several theories, each sharing a conceptual link in their explanations: that, in social presence, participants change their behaviour because they are comparing their own behaviour against some social standard. For example, Carver and Scheier (1981) conjectured that, in the presence of others, participants become more self-aware of their own behaviour, and try and match their current 4  performance to be closer to what they think it should be. Another example of a comparison model was used by Bond (1982), when he put forward the idea that social facilitation was occurring because participants simply wanted to look good in front of others – i.e., they adjust their behaviour towards what they believe others’ expectations would be. Yet another way to look at this idea is that participants behave differently around others because they compare their behaviour against a perceived social norm that is enforced by social presence (e.g., Baxter, Manstead, Stradling, Campbell, Reason and Parker, 2011). 1.1.2 Evidence for three models, and parsimony 1.1.2.1 Drive theory As research into social facilitation has progressed it has become clear that the influence of social presence is not always easy to predict. Each theory of social facilitation has evidence to support it, but given the decades of accumulated data in the field, do they all explain these various studies equally well? The biggest deficits in explanatory power are clearly at the feet of the drive theories. For a drive theory of social facilitation to be correct, a social presence effect must fit two criteria: (i) there must be some kind of physiological change in the participant as a result of the social presence, and (ii) the mere presence must be sufficient, regardless of any aspects tied to who that person is (with the single caveat of it possibly only occurring in response to strangers; e.g., Guerin and Innes, 1982). Supporting this idea, in 1983 a meta-analysis of 240 studies found that this arousal-based social presence effect was real and pervasive, although the effect was relatively weak (Bond and Titus, 1983). However, over time, social presence has been shown to affect many different kinds of behaviours, some of which would be very difficult to ascribe to a physiological change. For 5  example, Worringham and Messsick (1983) found that in a naturalistic study, runners would accelerate in the presence of a female confederate half way through a race track – but only if she was facing the runners as they ran by. Clearly, mere presence was not enough, and there was something special about her watching the runners in addition to her being there. Or, in another such example, consider a study by Geen (1983), where participants performed a learning task in one of three conditions: alone, with an evaluating experimenter, or with an evaluating experimenter who promised to help them perform a similar task later. The author found that having a helpful experimenter in the room was no different than the participant being alone; the social facilitation effect only appeared in the presence of a non-helpful evaluative experimenter. That is, it was more to do with how the experimenter was perceived than their mere presence.  1.1.2.2 Evaluation apprehension theory With drive models failing to properly account for much of the evidence gathered in the 1980s and beyond, what of evaluative apprehension models? The threat of evaluation could alter behaviour as well as (depending on the situation) increase arousal, and it would also account for the fact that who the social presence is can influence performance. For example, one study found that stress levels increase only when giving speeches to people who could evaluate the speaker, and not to a confederate (Dickerson, Mycek, and Zaldivar, 2008). This is something obviously difficult to explain with arousal, but it seems intuitive when you picture speaking in front of a crowd; people do not seem to feel nearly as nervous rehearsing in front of their friends as they do in front of their professors.  However, there is an inherent flaw in the evaluation apprehension hypothesis: it assumes that the situation is one where the participant's performance is being directly evaluated. For example, a study in 2004 found that the expression and tolerance of pain can be modulated by 6  the type of experimenter administering the painful treatment – specifically, that it is easier to deal with pain in the presence of someone who seems more professional, and who is of the opposite sex (Kállai, Barke, and Voss, 2004). It is unclear here why a participant would feel ‘evaluated’ by the experimenter, or if feeling pain is something subjected to evaluation in the first place, and certainly there is no obvious reason why a fear of evaluation would happen in the presence of only some experimenters.  Another recent study showed children increased how many ‘private utterances’ they produced when there was someone in the room to listen to them. That is, when they were alone, they would be silent, but in the presence of an adult listener, they would say things under their breath (i.e., both related to what they were doing, and randomly) (McGonigle-Chalmers, Slater and Smith, 2014). Here we see a social presence effect, and one that occurs due to an authority figure being present, but there is nothing truly to be evaluated – besides which, the children presumably did not know that their random utterances were important to the adults. So this social presence effect appears to occur because of someone watching the children, and not because of an apprehension due to evaluation.  The use of social presence instead of social facilitation in the previous paragraph is not accidental, and it serves to illustrate where an evaluation apprehension account (and indeed, an arousal account as well) falters: it only fits well in situations where there is a clear performance variable in evidence, and therefore where social facilitation could apply. It does not easily explain situations where there is no measure of performance that is obvious to the participant. In this way, it fails to be a theory which could generalize from a social facilitation effect to any other kind of social presence effect.  7  1.1.2.3 Comparison theory There are any number of good examples of comparison theories in the field. Consider a study by Buck, Losow, Murphy and Costanzo (1992). The authors of this study had participants view slides with emotionally loaded imagery on them. While they were looking at these slides, a second set of participants were tasked with guessing what emotion was being expressed by the first group. They found that people would express less emotion in response to the imagery in the presence of strangers, and that in contrast, in the presence of friends, the content of the imagery determined how much emotion they expressed on their faces.  This is clear support for a comparison theory because the authors found a differential result with whether the social presence was a stranger or a friend, and this is easiest to explain by the participants comparing their behaviour against what they believe is most appropriate, especially against the expectations of the people watching. This is intuitive as well: friends expect you to express emotions to them, but it is often more normal to not signal emotion to strangers at all.  We need not look far for studies that are best explained with participants comparing their potential behaviour against some standard; indeed, this seems to be the best explanation for many of the studies that were incongruent with the other theories above.  For example, in Geen (1983), an experimenter who tells participants they are using data from the task so that they can help the participant later is also telling the participant implicitly that it is acceptable to find the task difficult. An experimenter who says nothing about helping is, in contrast, enforcing the idea that participants should be trying as hard as they can. The experimenter is not only present, but providing a social norm for the participant to compare their behaviour against.  8  Or consider the results of McGonigle-Chalmers et al. (2004): a clear explanation of why children produce more utterances in the presence of an adult listener is not because they are being evaluated, but because it is socially expected to communicate, or invite communication, with other people. The presence of the adult listener activates a social norm where the children feel that they should talk more.  1.2 Social presence as a social norm Thus, the idea of social comparisons, and adhering to a perceived social norm, is one which can often provide a more reasonable explanation for various social presence effects, including ones which are hard to explain using traditional social facilitation models. This is the great advantage of using a social norm account: it can incorporate all the results instead of only some. In essence, it is not that a social presence may never cause arousal, or that social facilitation is never caused by evaluation apprehension, but instead it is that a comparison model is more generalizable and is often a more elegant explanation. For example, comparing your behaviour against what is expected of you in a testing environment could be exacerbated by a teacher watching you, which is evaluation apprehension; but outside of a testing environment, the presence of that teacher may cause you to act in a different way even though the threat of your performance being evaluated has disappeared.  Another good example of comparing one’s behaviour to social norms was demonstrated by Herman, Roth, and Polivy (2003). They were faced with a familiar problem: different psychological literatures disagreed about how people changed their eating habits in response to a social presence, with social facilitation studies suggesting people eat more around others, while various impression management studies suggested just the opposite. To reconcile these results, the authors put forward the idea that both sets of experiments were actually finding one result: 9  that people eat to conform to a perceived social norm. This norm could be affected by how many people were in the room, by the status of those individuals, and how much the other people were eating. People would eat less in front of people they wanted to impress, and more if others were eating as well (i.e., who were creating a social norm that it was okay to eat).  1.3 Social presence and social attention This idea of social norms affecting behaviour has recently been taken from social psychology and extended into the area of social attention. Laidlaw, Foulsham, Kuhn and Kingstone (2011) found that participants would attend to a video-taped confederate, but avoid attending to a real confederate. A real person was different, in their explanation, because it afforded the chance for social interactions, which a video did not; or to put this in another way, there is a social norm not to stare at others, but this does not extend to images of others on video screens. In another study, Gallup, Chong, and Couzin (2012) found that people walking down a hall would look to something another pedestrian had looked at, but that this was only true if the first gazing person was facing away from them (i.e., walking in the same direction); if the person was facing them, it actually inhibited the second person from looking. Like the Laidlaw et al. study, this suggested that there is a social norm towards avoiding contact with others that does not affect participants when they believe themselves to be free of observation.  In 2011, Risko and Kingstone created a paradigm where participants were situated in a room with a sexually provocative calendar. The social norm here is clear: do not behave in a way that sexually objectifies women in the presence of others who could see you do it. The twist of their study was that participants were fitted with an eye tracker, and were manipulated into believing it was either actively recording their eye movements, or was inoperational. In truth, there was a hidden camera in the calendar, and so the measure was how many times the 10  participants would look at the calendar when they believed their eye-movements were recorded versus when they felt alone. Risko and Kingstone found that people were far less likely to look at the calendar when they believed that their eyes were being tracked.  Collectively, these results suggest two important things: (i) that a social norm does not need to be a person in the room - the effect can be created even through an implied social presence, where the participant is alone but believes they are being monitored (e.g., with a camera1); (ii), that even a measure of attention (looking behaviour) can be affected by a social presence. Both of these ideas deserve further discussion.  First, the idea that a social presence need only be implied is important because, as has been shown across the studies in the social psychology literature, a real physical social presence can have very different effects depending on how a participant perceives that social presence. In contrast, being recorded by a device represents an ideal control: for example, there are no worries as to whether a camera will look more or less professional, and it does not intrinsically have a gender (two factors which influenced social presence effects in the Kállai et al. study, that found differences in pain perception). The idea of an implied social presence was explored further by Nasiopoulos, Risko, Foulsham and Kingstone in 2014. They found that when participants believe that they are being monitored through an eye-tracker, they will act more pro-socially – but that the effect must be ‘refreshed’ periodically by returning participants’ attention                                                   1 There are other ways of introducing a social presence as well, e.g. thoughts of a supernatural or imagined being watching you can enhance prosocial behavour  (Piazza, Bering and Ingram, 2010; Sharif & Norenzayan, 2007) 11  to the eye-tracker, e.g., in the absence of a true social presence, people will begin to forget that they are being monitored.  Each of these effects shows changes in looking behaviour due to a social presence. But can we assume that an influence on looking behaviour is the same as attention itself being affected, or could a change in attentional behaviour simply be the result of a participant responding to a social norm?  Social presence effects have typically been relegated to the social sphere of human behaviour.  However, these social attention studies (e.g., Risko and Kingstone, 2011; Gallup et al., 2012) have found a social presence effect using attentional paradigms, and so the idea that social presence is a purely social phenomenon is now called into question. It is possible that the effects of a social presence are more wide-spread, and change how people direct their attention as well. Unfortunately, this is hard to discern because to date the attention studies have also involved social stimuli (i.e., stimuli representing another person, or something which suggests the same; for example, a picture of eyes, or the word “observed” are both references to others). If someone looks at a social stimulus differently in the presence of another person, it is difficult to know if we should ascribe this change to attention or the social aspects of the stimulus.  1.4 The present investigation The present study sought to bridge this gap by investigating if an implied social presence can have an effect on a participant's spatial attentional allocation even when the testing situation is not explicitly social in nature, that is, when the testing situation does not involve other people (e.g., as used in Foulsham et al., 2011; Freeth et al., 2013; Gallup et al, 2012; Laidlaw et al., 2011) or images of people (e.g., Risko and Kingstone, 2011; Nasiopoulos et al., 2014, both used such images as stimuli). Where past studies generally looked at the influence of a social presence 12  on tasks that had social information in them, the present study used tasks with stimuli that were socially neutral, such as coloured shapes and numbers. Thus, if any social presence effects were found, they could not be due to the presence of social stimuli, but instead would be representative only of the effect of the implied social presence.    Only one study has tackled this issue before, and unfortunately, its findings failed to shed light on the effect of a camera on attentional allocation.  Miyazaki (2013) investigated how the presence or absence of a recording camera affected performance on a difficult target detection visual search task. Participants were presented with a visual display (set size 4 or 16) composed of distractor 'L's that may or may not also contain a target 'T'. Of special note is that the stimulus array was heavily overlaid with visual static noise which rendered the task extremely difficult (Giesbrecht, Bischof & Kingstone, 2003), e.g., the mean response latencies approached 6 seconds and the mean target miss rates nudged 40%. Miyazaki found that in this difficult search task the camera induced participants to trade off response speed for response accuracy, that is, when the camera was present participants went significantly slower to improve response accuracy. Unfortunately, in light of such a speed accuracy trade-off, any effect of the camera on the allocation of attention remains unknown.  To determine what effect, if any, implied social presence has on attention in a nonsocial testing situation, in the present study, participants were given a battery of chronometric attention tasks. To give the implied social presence effect the maximum opportunity to affect attentional performance, I chose a broad range of attention tasks (visual search, attentional capture, attentional cueing, SART) that tapped into exogenous (reflexive) and/or endogenous (volitional) attention. For example, one paradigm was a basic visual search task adapted from Horowitz and Wolfe (1998), with popout search measuring exogenous orienting and conjunction search 13  tapping into volitional control. These tasks were also nonsocial in that, with one exception, they did not reference or contain images of other people.  All participants were verbally instructed to respond as fast and as accurately as possible, establishing the social norm the participant should adhere to. With this in place, critically, all of the tasks were low in difficulty, so that any changes in response latency would not be traded against changes in response accuracy (or vice versa), thereby allowing for my chronometric analysis to shed light on attention. Finally, for the implied social presence manipulation, half of the participants completed the battery of tasks in the presence of an active recording camera; for the remaining participants the camera was clearly turned off. The question being asked was if the implied social presence of a camera would have a systematic effect on behaviour when the testing situation and the stimuli were nonsocial in nature.  There were two clear possibilities. One is that implied social presence will have a selective and systematic effect on the allocation of attention, demonstrated through the implied social presence interacting with each task's measurement of attention, e.g., the magnitude of the cue validity effect. The other possibility is that implied social presence will drive people to conform to the norm of the testing situation, improving response time and/or accuracy overall without interacting with the attention effects returned by each task. The null hypothesis was that there would be no effect of the camera in a nonsocial setting, that is, it would have no measureable influence on performance.  14  Chapter 2: Experiment 2.1  Methods 2.1.1 Design and participants To investigate if implied social presence influences reflexive and volitional attention, alone, as well as in combination, participants were presented with four basic attention tasks. The tasks were: (1) Visual search, which measures reflexive orienting to a pop-out target and volitional orienting to a conjunction target; (2) Attentional capture, which measures reflexive attention to a singleton target and volitional control of reflexive attention when a distractor onset co-occurs with the singleton target; (3) Spatial cuing, with central non-predictive social (face) and nonsocial (arrow) cues to measure reflexive orienting to social and nonsocial stimuli (this allowed us to explore if presence effects are modulated by the social meaning of the stimuli),  and (4) Sustained attention response task (SART) to measure the maintenance, and failure, of attention.  Unlike the other tasks which rely on spatial attention, the SART measures a general ability to sustain attention. As such, the SART is often used as a behavioural correlate of mind-wandering, as measured through various indices, but primarily through response errors (Mooneyham and Schooler, 2013).  All 64 undergraduate participants (mean age 20 years; 50 were female) took part for course credit. Participants were randomly assigned to one of three groups: (i) camera on - professor viewing, (ii) camera on - student viewing, or (iii) camera off (baseline condition) (see Figure 1). The sole difference between the implied social presence groups (i) and (ii) concerned who participants were told was viewing the camera recordings, i.e., the professor who ran the lab or a student research assistant. This distinction was made in case knowledge of who was watching would make an impact on an implied presence effect. As will be reported in the results, 15  this manipulation had a single minor effect on performance, and it will not be considered in detail.    Figure 1 illustrates a camera present condition. The camera and laptop (in grey) were immediately visible to a participant when entering the testing room, with the camera feed displayed on the laptop screen. They were positioned behind and to the left of where the participant sat.  The camera and laptop were turned off and turned away, but visible, in the no-camera condition.   2.1.2 Tasks and procedures  The visual search task was composed of pop-out and conjunction search, based on the design of Horowitz and Wolfe (1998). For pop-out search participants looked for a target defined by a salient stimulus feature, specifically, a green T target amongst grey L distractors; in conjunction search the target was defined by a conjunction of stimulus features, specifically a T hidden among Ls. For both types of search the target and distractors could adopt any of 4 orientations (right side up, or rotated 90˚, 180˚, or 270˚). Displays were composed of 8 or 16 16  items. A target was present 50% of the time. Participants were instructed to press '/' to indicate that the target was present and 'z' to indicate it was absent. Target location, presence and set size varied randomly from trial to trial.  Pop-out and conjunction search were presented in separate runs of 4 blocks composed of 24 trials. Search order was counterbalanced across participants.  The cueing task presented gaze and arrow cues in a separate run of trials, with the order of these blocks counterbalanced across participants.  The gaze cueing task was modeled on Friesen, Moore and Kingstone (2005), whereby a schematic face could look left or right.  Independent of gaze direction a target (H or F) appeared 10˚ to the left or right of fixation, with one target letter mapped to the 'z' key and the other target letter mapped to the '/' key.  The non-target letter X appeared mirror opposite the target location to ensure that both locations experienced simultaneous and equivalent onsets. Each trial began with a schematic face appearing at central fixation. After 1000 ms the pupils appeared looking left or right. The stimulus onset asynchrony (SOA) between this directional cue and target onset was either 100 ms or 600 ms.  A key press terminated the display. Cue direction, SOA, target location, and target identity varied randomly between trials.  Participants were informed that these variables were randomized and unrelated, e.g., cue direction did not reliably predict the target location or identity. Participants performed 5 blocks of 32 trials with gaze cues, and again for arrow cues. Stimulus timing and onsets were the same for arrow cues as gaze cues, with the key difference being that arrow cues (modeled on Ristic, Friesen & Kingstone, 2002) replaced gaze cues.  The attentional capture task was a precise replication of the paradigm used by Chisholm et al. (2009).  Items were displayed on a black background, around a gray fixation point at an even distance, with a radius of 11˚. Each item was a circle or a diamond, coloured either green or red, and within each of these shapes was a line segment. Non-target items had line segments 17  which were rotated 22.5˚ away from the vertical or horizontal plane. Target items contained either a perfectly vertical or perfectly horizontal line segment. Participants were instructed to report whether the target contained a perfectly horizontal or vertical line by pressing either ‘z’ or ‘/’ respectively. On half of the trials, there was a single distractor item which had a different colour than the rest – i.e., if the items on the display were red, the distractor item would be green, or vice versa. The distractor item was randomly assigned a location with the exception that it was never directly adjacent to the target. Participants performed 4 blocks of 40 trials.  The SART was modeled on Seli, Cheyne, Barton, and Smilek (2012). Participants were presented with the individual numbers 1-9 at central fixation, with the instruction to press ‘/’ when a number appeared, unless the number was '3', wherein they were tasked with withholding a response. Each number was randomly selected and displayed for 250 ms and masked for 900 ms. The font size of each number varied randomly between 48, 72, 94, 100, and 120. There were a total of 270 trials, the first 55 of which were practice.  2.2 Results The reaction time (RT) and accuracy means for all conditions in all tasks can be found in Appendix B. RT analyses were confined to correct responses, save for the SART. Also, for the data analyses reported below, I collapsed the two 'camera viewer conditions' (professor or student) into one 'camera condition' because viewer status had no effect on any of the data, save for one main effect in the cueing study, [F(1,29)= 4.33, p=.046], where the overall RT mean for the student-watching condition (M=607.83) was higher than the professor-watching condition (M=549.35).   In addition, while all of my participants in the camera-on conditions believed themselves to be recorded by the camera only roughly half believed they were being actively watched (i.e., 18  56.3%, as found by post-test questionnaires, with 43.8% reporting that they believed their actions were ‘actively monitored’). This suggests that although all participants believed their actions in the experiment were being recorded, some also believed they were being watched by someone through the camera, though it is unclear what participants believed the distinction between being watched and being monitored was. 19  Figure 2. Reaction time (RT) performance for all tasks performed by participants. Error bars represent standard error of the mean. 20  2.2.1 Visual search The data from one participant was lost due to a software malfunction. For both popout and conjunction search ANOVAs were conducted with target (present, absent), set size (8, 16), and camera (on, off) as factors.  Interpretation is focused on the target present trials as the decision to terminate search when a target is found is easy compared to the decision to abandon search when a target has not been discovered - the latter measuring a number of additional factors other than attentional orienting (Chun & Wolfe, 1996; Horowitz & Wolfe, 1998).  2.2.1.1 Popout search Response errors were rare (less than 2.3% overall), though there was a main effect of target condition, with an error typically involving a target miss (i.e., incorrectly responding 'target absent'), [F(1,60)=8.75, p=.004, η2p =.13]. The camera had no significant effect on response accuracy (p > .05) and there was no effect of set size or interactions (all ps > .1). For RTs (see Figure 2, top left) there was a significant main effect for target [F(1,60)=16.01, p<.001, η2p =.21], with participants responding more quickly to target present displays, and a main effect for set-size [F(1,60)=8.68, p=.005, η2p =.13] with RTs increasing slightly with set size (5 ms/item). There was also a significant interaction between these two variables [F(1,60)=7.17, p=.01, η2p =.11] reflecting that though search efficiency for both present and absent trials was excellent (slope < 10 ms/item), the measurement of spatial orienting on target present trials was clearly reflexive (slope of 0 ms/item for target present vs. 9.5 ms/item for target absent). Most critically, there was no main effect of camera (p>.05) or interactions between camera and any other variable (all ps> .1). 21  2.2.1.2 Conjunction search Response accuracy was again very high overall, with errors occurring on less than 5% of the trials.  And again, when an error did occur, it tended to be a target misses [F(1,60)=21.22, p<.001, η2p =.34].  There was also an effect of set size [F(1,60)=11.77, p=.001, η2p =.16], with response accuracy declining at the larger set size, and the two interacted [F(1,60)=11.64, p=.001, η2p =.16], with the tendency to miss a target increasing as the number of items in the display increased. Camera had no significant effect on the accuracy data (p >.1), and no interactions with between the camera and other variables were significant as well (all ps > .1).  Analysis of the RT data (Figure 2, top right) returned main effects for all variables: target condition [F(1,60) =227.85, p<.001, η2p =.79], where latencies were shortest for target present displays; set-size [F(1,60)=274.58, p<.001, η2p =.82], with RT increasing with set size; and camera [F(1,60)=5.14, p=.027, η2p =.08] with shorter latencies when the camera was turned on.  In addition, a significant target by set-size interaction [F(1,60)=123.34, p<.001, η2p =.67] reflects the standard finding that target absent RT increases as a function of set size more than target present RT. These effects were qualified by a significant two way interaction between camera and target-presence [F(1,60)=4.60, p=.036, η2p =.07] as well as a significant three way interaction between camera, target-presence, and set-size  [F(1,60)=6.19, p=.016, η2p =.09], which, as suggested by Figure 2, appears to be driven by the target absent RTs being affected by the camera and this effect growing as search becomes longer (i.e., as the slopes are different for target-absent and target-present trials already, as shown through the interaction between target-presence and set size, this could drive the interactions including the camera as well).  This was confirmed with a followup analysis.  For target absent trials, there was a significant main effect for set size [F(1,60)=233.21, p<.001, η2p =.79] as well as for camera 22  [F(1,60)=5.25, p=.025, η2p =.08], with no interaction (p>.05). But for the straightforward target present trials, only set size returned a significant effect [F(1,60)=163.04, p<.001, η2p =.73].  All other p's, including those involving the camera, were not significant (ps>.05).  In sum, the data indicate that a camera turned on in a testing room does not have a significant effect on the orienting of attention in visual search.  This is true both when attention is shifted exogenously in popout search, and endogenously in conjunction search. When the target is absent and search is prolonged, as in conjunction search, participants are more willing to abandon their search if the camera is turned on. But as demonstrated by the target present conjunction condition, turning the camera on does not alter the way that attention itself is being allocated when searching for a conjunction target.  2.2.2 Attentional cueing  Performance was analyzed with cue (gaze, arrow), SOA (100, 600 ms) and cue-validity (valid, invalid) as repeated measures variables, and camera (on, off) as a between-subjects variable. The overall error rate was less than 6%, though it did increase significantly with SOA, 5.5% (100 ms) versus 6.3% (600 ms). There were no other main effects or interactions (all ps>.05) For RT (Figure 2, bottom) all main effects were significant: cue [F(1,61)= 14.52, p<.001, η2p =.19], with shorter latencies for arrow than gaze cues (590 ms vs. 620 ms); validity, [F(1,61)=7.00, p=.01, η2p =.10], with shorter latencies for targets appearing at the valid than invalid location (600 ms vs. 610 ms); SOA [F(1,61)= 60.99, p<.001, η2p =.50] reflecting the standard cue-target warning signal effect (Fernandez-Duque & Posner, 1997), with latencies falling as the SOA increased (621 ms vs. 589 ms); and camera [F(1,61)= 4.21, p=.044, η2p =.07], 23  with shorter latencies when the camera was on rather than off (578 ms vs. 632 ms).  These findings were qualified only by one interaction, between validity and SOA [F(1,61)= 10.99, p=.002, η2p =.15], which derived from the fact that the cueing effect (valid vs. invalid) grew with SOA. All other effects returned p's > .05. In summary, a significant effect of attention orienting was observed for spatially non-predictive gaze cues and arrow cues, with RT faster when the cue was valid than invalid.  For both types of cues the validity effect was enhanced at the longer SOA. There was also a very large 50 ms effect of the camera on RT, with participants’ response latencies being profoundly shorter when the camera was turned on.  As in the other tasks, however, this significant effect of the camera did not interact with the effect of attentional orienting to a central spatial cue. This held both when the cue depicted a biologically meaningful stimulus (a person looking left or right), or merely an arrow pointing left or right.  2.2.3 Attentional capture  A 2x2 mixed-design ANOVA was performed with distractor (present, absent) as a repeated measures variable, and camera (on, off) as a between-subjects variable.  Overall, response accuracy was high, with the error rate less than 5%.  Nevertheless, response errors were 6.4% when the camera was turned on and 3.4% when it was off, yielding a main effect of camera [F(1,61)=4.81, p= .032, η2p = .07].  There was no effect of distractor, and no interaction between camera and distractor (all ps >.1). For RT (Figure 2, middle left) there was a main effect of distractor, where the presence of a distractor increased RTs [F(1,61)=150.18, p<.001, η2p = .71]. This is the expected attentional capture effect found in the literature. There was also a main effect of camera [F(1,61)=3.99, p=.05, η2p = .06], with lower response latencies when the camera was turned on.  However, the 24  interaction between the attentional capture effect, and the camera, was not significant, [F(1,61)=3.32, p>.05].   Collectively these data indicate that a camera, when turned on, can induce people in the attentional capture task to respond faster at a nominal cost in accuracy.  But this significant effect of camera has no impact on the magnitude of the significant attentional capture effect itself. In other words, the significant effect of the camera and the significant effect of the distractor combine in an additive manner.   2.2.4 S.A.R.T  As expected for the SART many errors were made when participants were supposed to withhold their response (42.77% for 'no go' trials vs. 0.03% for 'go' trials), yielding a significant performance difference, t(62)= 17.84, p<.001, d = 3.18.   However, for reaction times for correct responses, specifically, responses on 'go' trials (Figure 2, middle right) these were significantly shorter when a camera was on (306 ms on vs. 353 ms off), t(62)= 2.19, p=.032, d = 0.58. Similarly, ‘no go’ RT, how fast a participant pressed a key in error on a ‘no go’ trial, was also significantly shorter when the camera was on (248 ms on vs. 297 ms off), t(61)= 2.30, p=.025, d = 0.55.  There are several indices in the SART that can be used for measuring attentional disengagement: (i) errors on ‘no go’ trials (i.e., failure to withhold a response), (ii) errors on ‘go’ trials (i.e., failure to respond), (iii) the speeding of response times in ‘go’ trials immediately preceding ‘no go’ trials, especially preceding ‘no go’ errors, (iv) the number of responses to ‘go’ trials that are too fast (less than 100ms, and so assumed to be anticipations rather than responses), (v) the coefficient of variability (CV), which is measured as SD/Mean (Cheyne et al., 2009). For each of these indices, there were no significant differences between the camera on 25  and camera off conditions (all ps> .05).  In summary, turning a camera on speeded RT performance overall, both for correct response execution and for responses made in error, without any cost in accuracy.  There was no evidence for the camera having an attentional effect, as measured by the various indices for attentional disengagement.     26  Chapter 3: Conclusion 3.1 General discussion The present study explored whether an implied social presence, manipulated by turning a camera on and off in the testing room, would affect performance on simple chronometric attention tasks that lacked anything overtly social within them. It also tested whether implied social presence would create an attentional change, or simply a behavioural change without implicating attentional processes.  The data established that non-social lab-based attention tasks are sensitive to the effects of implied social presence. Across four simple attention tasks performed in social isolation, I found that the introduction of a recording camera had a significant enhancing effect on performance, generally speeding RT without any significant impact on response accuracy. In visual search, target present conjunction search was speeded by turning the camera on, with no cost to response accuracy.  In attentional capture, the significant speeding effect caused by the camera did not interact with the significant interfering effect of the distractor. In the SART, when the camera was turned on participants responded faster overall, including trials where they were not supposed to respond; nevertheless, there was no change in accuracy, nor in any of the other measures of attentional disengagement that are used with the SART. And, in the attentional cueing tasks, it was found once more that responses were speeded when the camera was on without interacting with the attentional effects of the central gaze and arrow cues.   In sum, applying additive factors logic, the implication is that the mechanisms of attention engaged by a battery of chronometric tasks are separate from the effect of implied social presence that is engaged by introducing a camera into a nonsocial testing situation. That is, the task effects on attention are separate from the effect of implied social presence. This 27  conclusion holds even for the situation involving attention to a face cue that gazed to the left or right -- my only condition that involved any explicit experimental reference to a social stimulus.  3.1.1 Attention tasks The results of the present study are novel, and so it is worth delving more into my choice of tasks. For the purposes of exploring how an implied social presence extends from a social domain into a non-social domain, there are many paradigms to choose from. Basic attention tasks were used for two reasons: given that implied social presence has been found to affect attention (looking behaviour) in explicitly social situations, (e.g., Foulsham et al., 2011; Gallup et al., 2012; Risko & Kingstone, 2011) it was a logical next step to ask if the effect of implied social presence extends to attention in basic nonsocial experimental situations.  However, another reason to use attention tasks is that attention is known to have different forms (e.g., reflexive versus volitional), and by utilizing tasks that measure attention in different ways, it was possible to investigate if there are specific situations that an implied social presence affects attention. In short, although each of the tasks involve simple button-responses, each provides a different attentional measurement, and collectively they provide a broad sampling of attentional orienting. For example, where one task type measures primarily reflexive attention (e.g., gaze- and arrow-cueing) another looks at reflexive orienting and distractor inhibition (attentional capture), a third measures volitional attention (conjunction search), and yet a fourth examines failures to maintain sustained attention (the SART). The results showed that implied social presence has a consistent effect on all of the tasks -- speeding of responses --  regardless of the kind of attention that was being engaged. 3.1.2 Social facilitation revisited  Two questions deserve to be posed here: first, could the camera results of the present 28  study reflect a social facilitation effect (characterized by improvement in easy/simple tasks and a detriment to hard/difficult tasks), or is it a social presence effect of a different sort? Second, can any of the theories behind social facilitation (e.g., drive theories and evaluation apprehension theories), be extended to social presence effects in general, to fit the data? Simple attention tasks are ideally suited to looking for a social facilitation effect, in that social facilitation is primarily something that describes how people perform on tasks that have very clear measures of performance. At first blush, it might appear that the present results could be filed under social facilitation. While no detriment was found to having a camera recording performance in any of the tasks, the tasks had been explicitly selected to be as easy as possible (save for the SART) to maximize shifts in response time rather than accuracies. As such, a social facilitation effect might be expressed by the performance improvements observed here. However, a more thorough consideration shows some limitations to this interpretation.  First, although all the tasks were easy, not all of the conditions were uniformly easy; and so, under social facilitation, a less easy condition (e.g., increasing the number of distractors in a search task) should have also resulted in less facilitation. Second, in the SART, participants clearly found the task to be difficult (i.e., as measured by high error rates), and so a social facilitation account would have predicted that a shift in behaviour opposite to what was found in the other tasks (i.e., social interference rather than facilitation). Thus, it is most reasonable and parsimonious to conclude that the effect observed in the present study reflects the more general social presence effect rather than specific instances of social facilitation.  And what of the drive theories of social facilitation? Could the data be accounted for by assuming that the camera is increasing the overall level of arousal of the participant? While I did not include any physiological measures in my investigation, such changes in phasic alertness 29  would be expected to yield a RT facilitation improvement at the cost in response accuracy (e.g., McMorris, Sproule, Turner & Hale, 2011; Yagi, Coburn, Estes & Arruda, 1999). In the present study, RT was typically facilitated at no cost in accuracy.  Moreover, arousal has been tested, and excluded, as a viable explanation for social presence effects in other performance domains (e.g., Hugeut et al., 1999), and therefore without any compelling support in its favour it need not be invoked here.  Alternatively, the present results could reflect the effects of perceived social evaluation, as it is easy to imagine that participants might see the camera as an evaluative device. However, the fact that no overall differences were found between the two camera-on conditions (Professor vs. Student observing) would argue against this alternative; as would the finding that only half of the camera condition participants believed that they were being actively watched (56.3%) or that their actions were being actively monitored (43.8%). As such it would seem more reasonable to view the camera as having been perceived not as an active evaluator of performance but as a social presence that was monitoring and recording performance (i.e., participants did not generally believe a live person was evaluating their actions, though it is unknown if they believed that someone would review the recorded video feed later).  Finally, given that the social facilitation explanation fails to encompass the present data in general, it is unparsimonous to invoke the evaluation account as an explanation of some of the data, when the social norm explanation can explain all the data.  Collectively, these data converge with the general notion that implied social presence drives people to behave in a manner consistent with the behavioural norm of the situation. In the present case, like most chronometric tasks in attention, that norm was established when participants were instructed to respond as fast and as accurately as possible. Introducing a 'live' 30  camera into the testing situation resulted in participants striving to conform to that norm. In essence, the implied presence triggered by the camera appears to drive participants to behave as 'good participants' who respond quickly without a cost in accuracy. Or in other words, the camera kept participants responding nearer to the edge of their speed-accuracy threshold, with fewer slow but accurate responses. To test this proposal, I went back and examined the RT distributions. Consistent with my interpretation, this analysis revealed that participants executed more of their fast-accurate responses and fewer of their slow-accurate responses, rather than shifting their entire RT distribution toward faster responding (See Figure 3 in appendix C for the distributions of each task).    3.2 Impact of this research The fact that an effect of implied social presence was observed in multiple non-social attention tasks drives home the idea that implied social presence is a powerful factor for manipulating human performance, which could prove to be a very powerful tool for researchers in the future. For example, in many of the repetitive cognitive tasks where there is a tendency for participants to become bored and their responses to become slow, a camera may prove an effective way of keeping subjects "on task". Similarly, cameras may prove an effective way to mitigate the "end of term testing effect" where participants may be less motivated to do their best on tasks.  As such, this could prove to be a research tool for investigators to use in the future in order to improve the quality of the data that they collect.  The fact that implied social presence can enhance response time without interacting with attention, by effectively driving participants "to be better participants", sheds light on previous social presence experiments that found an effect of presence on attention. In these previous studies a change in the allocation of attention was the way for participants to comply with the 31  social norm. In essence, attention change and norm compliance were confounded.   Take, for example, the Risko and Kingstone (2011) study that measured the effect of an implied social presence on looking behaviour to a sexually provocative calendar. A social norm was clearly in effect here: that you should not stare at sexual imagery if people can see you do it. Risko and Kingstone found that an implied social presence decreased the likelihood of participants looking at the calendar (i.e., it enforced the social norm). Critically, because this was a change in looking behaviour, their results implicated that there could be an implied social presence effect on attention.  If given only a cursory analysis, this may seem to be incongruent with the present results. However, in the Risko and Kingstone task, the social norm explicitly involved looking behaviour, whereas in the present study the social norm was simply to perform better (i.e., faster and more accurate). Thus, to conform to the social norm in the Risko and Kingstone task meant avoiding looking at the calendar, and that is exactly what was found.  In my task, to perform to the social norm meant increasing speed and/or accuracy, and that is what I found. As such, the most reasonable explanation is that while implied social presence must not necessarily influence attention, if the social norm demands it, it can and will affect attention.  The conclusion that the social presence invoked by a camera can enhance norm compliance dovetails with the myriad of situations where cameras are now being used to increase prosocial behaviour. The obvious examples of this are in the public (e.g., airports) and private work places (e.g., taxis) where cameras are used to increase safety and behavioural etiquette.  Perhaps the most relevant example today however is in how cameras relate to crime and law enforcement. While this is, of course, a tangential extension of this kind of research, it still is related in that the present study has  shown that a camera enforces a social norm without a 32  negative impact on cognition, and in doing so, validates the use of cameras in every-day scenarios.  The camera and its potential to prevent certain kinds of behaviour by putting people under a watchful eye can be seen as threatening, but it has become clear that this is sometimes not only unobjectionable, but also desirable. A clear example of this is, in the wake of several instances of police brutality, the decision by many local governments (e.g., Potter, 2015) to institute mandatory body cameras for active police officers. The degree to which a camera recording will induce pro-social behaviour and alter performance has never been more relevant.  3.3 Future directions In this study, the data support the idea that people are behaving to a social norm given through instructions – but whether this was actually what formed the social norm was not controlled for. A future study could study whether participants try to behave to a social norm given explicitly by the experimenter, or whether they have internal values they try to adhere to that are resistant to explicit change via instructions.  Another of the questions still unanswered in the social presence literature is how the different kinds of social presence may differ; for example, is a camera really the same thing as a person in the room, or is it qualitatively different? There is certainly an element to an implied social presence (i.e., that you may be permanently recorded) which is not present with a real person. While there is evidence that some social presences are different from others, this has generally been explored in the form of the status of the individual creating the presence, rather than looking at an implied social presence versus a real one.  Having found that attention does not seem to be directly affected by an implied social presence, it would be interesting to see if implied social presence is additive with other forms of 33  cognition – for example those invoked by memory or reasoning tasks. In addressing this issue one can determine the extent that the present results are specific to attention or cognition more generally.  Finally, recall that Miyazaki (2013) found that a social presence created a shift in the speed accuracy tradeoff function – i.e., slower responses to try to improve accuracy. While shifts in speed accuracy tradeoffs can render data interpretation fundamentally ambiguous -- especially when of the large magnitude reported by Miyazaki -- it is nevertheless interesting to speculate that by changing a single aspect of a study (e.g., difficulty) one may be able to change what is perceived to be the social norm, and in turn, create a profoundly different effect of social presence on performance. That is, in the Miyazaki study, because it was an extremely difficult task participants seemed to assume that acting as a better participant meant slowing down and trying to improve accuracy; whereas in the present series of experiments, participants sped up. Social presence effects may prove an interesting way to expose and isolate the key factors that underlie a social norm in a situation.  34  References Baxter, J. S., Manstead, A. S., Stradling, S. G., Campbell, K. A., Reason, J. T., & Parker, D. (1990). Social facilitation and driver behaviour. British Journal of Psychology, 81(3), 351-360. Birmingham, E., Bischof, W.F. & Kingstone, A. (2008). Gaze selection in complex social scenes. Visual Cognition, 15, 341-355.  Blascovich, J., Mendes, W. B., Hunter, S. B., & Salomon, K. (1999). Social" facilitation" as challenge and threat. Journal of personality and social psychology, 77(1), 68. Bond, C. F., & Titus, L. J. (1983). Social facilitation: a meta-analysis of 241 studies. 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Effects of aerobic exercise and gender on visual and auditory P300, reaction time, and accuracy. European journal of applied physiology and occupational physiology, 80(5), 402-408. Yu, J., Tseng, P., Muggleton, N. G., & Juan, C. H. (2015). Being watched by others eliminates the effect of emotional arousal on inhibitory control. Frontiers in Psychology, 6, 4. 39  Appendices Appendix A  : Scripts 1. Camera present – professor viewing: So before I get you started on the first task, I would just like you to know that for this session you will be recorded using this camera behind you. My professor needs to see how people perform in this experiment, and so they'll be watching you from another room down the hall. 2. Camera present – student viewing:  So before I get you started on the first task, I would just like you to know that for this session you will be recorded using this camera behind you. A new undergraduate student in my lab needs to see how people perform in this experiment, and so they'll be watching you from another room down the hall.            40  Appendix B  : Task means TABLE 1. RT and accuracy means for the conjunction and popout search tasks. Conjunction Search Absent trial (no target) Present trial (target in display)  Set Size 8 Set size 16 Set size 8 Set size 16 No camera present     RTs (ms) 1123.99 1739.75 835.29 1022.95 ACCURACY (%) 97.95 98.29 95.80 92.98 Camera present     RTs (ms) 1001.58 1479.74 758.50 996.38 ACCURACY (%) 97.58 97.58 93.95 90.05 Popout search Absent trial (no target) Present trial (target in display) Set Size 8 Set size 16 Set size 8 Set size 16 No camera present     RTs (ms) 648.56 737.98 505.92 501.82 ACCURACY (%) 99.03 98.82 98.02 97.39 Camera present     RTs (ms) 535.49 598.36 444.55 455.42 ACCURACY (%) 97.18 97.98 96.91 96.51   TABLE 2. RT and accuracy means for the attentional cueing tasks. Gaze Cueing Invalid Cues Valid Cues SOA 100ms SOA 600ms SOA 100ms SOA 600ms No camera present    RTs (ms) 656.53 649.86 656.48 625.18 ACCURACY (%) 94.39 93.68 93.20 94.00 Camera present     RTs (ms) 606.22 585.31 610.42 567.65 ACCURACY (%) 93.54 93.65 95.36 93.28 Arrow Cueing Invalid Cues Valid Cues SOA 100ms SOA 600ms SOA 100ms SOA 600ms No camera present    RTs (ms) 628.36 613.62 636.21 588.40 ACCURACY (%) 95.11 94.17 95.77 94.34 Camera present     41  RTs (ms) 589.09 548.11 581.85 532.54 ACCURACY (%) 94.02 92.20 95.03 94.55   TABLE 3. RT and accuracy means for the attentional capture task.   No Distractor Distractor  No camera present   RTs (ms) 1056.77 1258.58 ACCURACY (%) 96.38 96.81 Camera present   RTs (ms) 966.02 1115.61 ACCURACY (%) 93.87 93.41      TABLE 4. RT and accuracy means for the SART.    No camera present Camera present Go RT (ms) 352.64 305.81 NoGo RT (ms) 297.16 247.79 NoGo ACCURACY (%) 60.29 54.06               42  Appendix C  : RT distributions Figure 3. RT distributions, showing each task which had a significant main effect of camera, collapsed across internal factors. Red bars represent the frequency of trials at each RT for the ‘camera-on’ condition, blue bars represent the ‘camera-off’ condition, and purple represents the overlap of the two distributions.  

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