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Lifespan changes in covert attention alignment Brodeur, Darlene Adel 1993

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LIFESPAN CHANGES IN COVERT ATTENTION ALIGNMENTbyDARLENE ADEL BRODEURB.A., Dalhousie University,1987M.A., The University of Western Ontario, 1989A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinTHE FACULTY OF GRADUATE STUDIES(Department of Psychology)We accept this thesis as conformingired standardTHE UNIVERSITY OF BRITISH COLUMBIA1993© Darlene Adel Brodeur, 1993In presenting this thesis in partial fulfilment of the requirements for an advanceddegree at the University of British Columbia, I agree that the Library shall make itfreely available for reference and study. I further agree that permission for extensivecopying of this thesis for scholarly purposes may be granted by the head of mydepartment or by his or her representatives. It is understood that copying orpublication of this thesis for financial gain shall not be allowed without my writtenpermission.(SignatureDepartment of^Psychol ogyThe University of British ColumbiaVancouver, CanadaDate  August 13. 1993DE-6 (2/88)ABSTRACTThere are two ways that attentional resources can be aligned in visualspace. They can be "pulled" automatically by stimulus cues, or "pushed" in amore strategic manner by the observer in response to information cues. Thepresent study was designed to determine if the ability to align attention in thesetwo ways changes throughout the course of the lifespan. Subjects (6,8,10,23,and 73 years) were tested in two conditions. In the first, subjects werepresented with a stimulus cue (a dot that appears briefly at a target location)prior to the presentation of a target. Attention was automatically aligned to apossible target location in response to the stimulus cue. In the second, anarrow was presented prior to the target, allowing the subject to align attentionstrategically in response to the cue. Cues were either valid or invalid predictorsof target location, cue-target SOA was varied so the time course for the effectiveuse of both types of cues was measured and compared. Eye movements weremonitored to control for confounding developmental differences in vision. In asecond experiment, lifespan changes in the interaction between the two formsof orienting were assessed by presenting subjects with both cues on each trial.The location information provided by each cue could be either compatible orincompatible with each other. The results of both experiments suggest that theability to align attention automatically changes very little from early childhoodthrough old age. Strategic attention alignment becomes more efficient in earlyadulthood. Children have difficulty sustaining attention at locations specified byinformation cues and seniors have difficulty using the information rapidly. Bothchildren and seniors are less able to use information cues when stimulus cuesare also available where as young adults can use both. These findings suggestthat separate mechanisms are required to implement attention alignments toi iautomdtic and strategic cues.TABLE OF CONTENTSTitle page^AbstractTable of Contents^ iiiList of Tables viiList of Figures^ viiiList of Appendices xiAcknowledgements^ xiiDedication^ xiiiVisual attention^ 1Overview 2Covert alignments of attention^ 2The study of visual attention across the lifespan^ 6Lifespan changes in covert attention alignment 71. Are there lifespan age changes in attention shifts mediated bystimulus cues?^ 7Automatic attention alignments in young children^7Automatic attention alignments in the aged 14Automatic attention alignments in special populations^ 16Lifespan age changes in automatic attention alignment^ 182. Are there lifespan age changes in attention shifts mediated byinformation cues?^ 19Strategic attention alignments in early life^ 19Strategic attention alignments in the aged 20ivLifespan age changes in strategic attention alignment^223. Are there lifespan age differences in the nature of inhibition-of-return?^ 23Lifespan changes in the nature of inhibition-of-return^244. Are there lifespan age differences in cross versus withinhemifield cueing effects?^ 25Lifespan changes in cross versus within hemifield cueing^265. What is the nature of age differences throughout the lifespan forattention shifts mediated by stimulus and information cues?^27Lifespan changes in stimulus and information drivenattention alignments^ 30Experiment 1^ 31Method 32Subjects^ 32Stimuli and apparatus^ 33Procedure^ 36Eye movement scoring procedure^ 37Results^ 38Stimulus Cue Experiment^ 40Information Cue Experiment 46Hemifield Analyses^ 52Stimulus Cue Experiment^ 53Information Cue Experiment 55Location Analyses^ 56Stimulus cue experiment^ 57Information Cue Experiment 60Accuracy Analyses^ 61VEye Movement Data^ 62Discussion^ 65Experiment 2 66Method^ 67Subjects 67Stimuli and apparatus^ 68Procedure^ 69Eye movement scoring procedure^ 69Results and Discussion^ 69Control Comparisons 70Experimental Conditions^ 71Accuracy Analyses 76Eye Movement Data^ 77General Discussion^ 781. Are there lifespan age differences in attention shifts mediatedby stimulus cues?^ 782. Are there lifespan age changes in attention shifts mediated byinformation cues?^ 823. Are there lifespan age differences in the nature of inhibition-of-return ?^ 834. Are there lifespan age differences in cross versus withinhemifield cueing effects?^ 855. What is the nature of age differences throughout the lifespan forattention shifts mediated by stimulus and information cues?^88Theoretical Issues^ 89Strategies versus Capacities^ 891 heories of aging^ 91viSpotlight versus Zoomlens?^ 92Common mechanisms? 93Neuropsychological Development^ 94Conclusions^ 95References 98LIST OF TABLESPage1. Mean RTs (standard deviations) for all age groups by Cue type,^106Validity and SOA conditions in Experiment 1.2. Mean percent correct scores (standard deviations) for all age^108groups by Cue type, Validity and SOA conditions in Experiment 1.3. Mean number of eye movements (standard deviations) over^110three minutes of sampling for subjects of all ages in the stimulusand information cue conditions of Experiment 1.4. Trial type labels for the nine possible combinations of Stimulus^111and Information cue validity in Experiment 2.5. Mean percent correct scores (standard deviations) for all ages by^112Validity and SOA conditions in Experiment 2.6. Mean number of eye movements (standard deviations) for all^113age groups in Experiment 2.viiLIST OF FIGURESPage1. Example stimulus displays for valid, invalid and neutral trials^114in the information cue condition of Experiment 1.2. Example stimulus displays for valid, invalid and neutral trials^115in the stimulus cue condition of Experiment 1.3. Mean RT difference scores (Invalid RT - Valid RT) as a function^116of SOA for all ages in the stimulus cue condition.4. Mean RT difference scores (Invalid RT - Valid RT) as a function^117of SOA for all ages in the stimulus cue condition.5. Mean RTs of valid, within hemifield invalid, and across^118hemifield invalid trials as a function of SOA for young adults inthe stimulus cue condition.6. Mean RTs of valid, within hemifield invalid, and across^119hemifield invalid trials as a function of SOA for children in thestimulus cue condition.7. Mean RTs of valid, within hemifield invalid, and across hemifield^120invalid trials as a function of SOA for seniors in the stimulus cuecondition.8. Mean RTs of valid, within hemifield invalid, and across^121hemifield invalid trials as a function of SOA for young adultsin the information cue condition.9.^Mean RTs of valid, within hemifield invalid, and across^122hemifield invalid trials as a function of SOA for children in theinformation cue condition. viiiix10. Mean RTs of valid, within hemifield invalid, and across hemifield^123invalid trials as a function of SOA for seniors in the informationcue condition.11. Mean RT difference scores (Invalid RTs-Valid RTs) as a function^124of target location for young adults in the stimulus cue condition.12. Mean RT difference scores (Invalid RTs-Valid RTs) as a function^125of target location for children in the stimulus cue condition.13. Mean RT difference scores (Invalid RTs-Valid RTs) as a function^126of target location for seniors in the stimulus cue condition.14. Mean RT difference scores (Invalid RTs-Valid RTs) as a function^127of target location for young adults in the information cue condition.15. Mean RT difference scores (Invalid RTs-Valid RTs) as a function^128of target location for children in the information cue condition.16. Mean RT difference scores (Invalid RTs-Valid RTs) as a function^129of target location for seniors in the information cue condition.17. Example trial sequence for Experiment 2. This particular example 130represents a valid compliment trial in which both information andstimulus cues are valid.18. Mean RTs for all age groups as a function of cue validity and^131SOA in the information cue control condition of Experiment 2.Standard errors are represented by vertical bars.19. Mean RTs for all age groups as a function of cue validity and^132SOA in the stimulus cue control condition of Experiment 2.Standard errors are represented by vertical bars.^20.^Mean RTs for young adults as a function of cue validity and SOA^133in the experimental conditions of Experiment 2. Standard errorsare represented by vertical bars.x21. Mean RTs for six year olds as a function of cue validity and SOA^134in the experimental conditions of Experiment 2. Standard errorsare represented by vertical bars.22. Mean RTs for ten year olds as a function of cue validity and SOA^135in the experimental conditions of Experiment 2. Standard errorsare represented by vertical bars.23. Mean RTs for seniors as a function of cue validity and SOA in^136the experimental conditions of Experiment 2. Standard errorsare represented by vertical bars.24. Mean RT difference scores as a function of SOA for young^142adults in the stimulus cue condition.25. Mean RT difference scores as a function of SOA for children^143in the stimulus cue condition.26. Mean RT difference scores as a function of SOA for seniors^144in the stimulus cue condition.27. Mean RT difference scores as a function of SOA for young^145adults in the information cue condition.28. Mean RT difference scores as a function of SOA for children^146in the information cue condition.29.^Mean RT difference scores as a function of SOA for seniors^147in the information cue condition.LIST OF APPENDICESPageAppendix A. Instructions for information cues (Experiment 1).^137Appendix B. Instructions for the stimulus cues (Experiment 1).^138Appendix C. Experiment 1: Cost/Benefit Analyses^ 139Appendix D. Instructions for Experiment 2.^ 148x iACKNOWLEDGEMENTSI wish to thank my advisor, Dr. James Enns, for his guidance andencouragement through the ups and downs of this work. I am also grateful forthe advise of my dissertation committee, Dr. Dare Baldwin, Dr. Lawrence Wardand with special thanks to Dr. Janet Werker for picking up where Dr. Baldwin leftoff. I extend further thanks to Tracey Wood and Carmen Stossel for helping withdata collection, and Marc Romancyia for program development. Support andhelpful suggestions from the entire visual search lab, Deborah Aks, DavidShore and Dr. Lana Trick, are also appreciated. Funding for this project fromNatural Sciences and Engineering Research Council, U.B.C. GraduateFellowships, and British Columbia Health Research Foundation is gratefullyacknowledged. Last, but not least, I extend my deepest gratitude to myhusband, John, for his continued devotion and encouragement.xiiDEDICATIONTo Jacob and Samuel, for putting my life into perspectiveVisual attentionFew would argue with William James' (1890/1950) assessment that"everyone knows what attention is". Despite this fact, when one is faced with thetask of defining attention, the difficulty in pinning down the concept becomesevident. Recently Enns (1990) has tried to isolate some of the components ofattention that would comprise at least in part a picture of what researchers meanwhen they study visual attention. According to Enns the main function thatattention serves is to allow us to identify objects in a visual field filled with otheritems. This function can be broken down into several aspects of attention (i.e.,integration of object features, filtering of other objects, search for target objects,priming of object representations over time) that together serve to accomplish thegoal of selectivity. A similar taxonomy of attention can be found in Coren, Wardand Enns (1993). According to these authors, attention can be categorizedaccording to task (orienting, filtering, searching, expecting), modality (visual;auditory), and condition (focused, divided).The component of visual attention that is most relevant to the presentpaper is visual search as defined by Enns (1990), or orienting according toCoren, et al. (1993). This component allows individuals to look for objects thatmay or may not be present in the visual field, or for an object that they know ispresent but whose specific location is not known. There are two ways in whichsearch can be accomplished. Shifts in attention may be accompanied by shifts ingaze (overt), or they may occur independently of shifts in gaze (covert).1OverviewBecause overt attention alignments are accompanied by shifts in gaze it isdifficult to study the development of such shifts independently of the associateddevelopment of the eye movement system. As a result, the present research willfocus on the development of covert attention alignment in order to minimize theseeffects and thereby strengthen the conclusions that can be made about thedevelopment of attention itself.First, research conducted on covert orienting abilities in young adults willbe covered, followed by a presentation of data addressing the early and latedevelopment of orienting. This discussion will include a look at questions thathave been answered and those that remain to be answered. Following this, twoexperiments designed to fill the gaps in our knowledge of lifespan changes incovert attention alignments will be presented.Covert alignments of attentionThe study of covert shifts of attention has been advanced mainly throughthe use of the Cost-Benefit paradigm developed by Posner and colleagues(Posner, 1980; Posner, Nissen & Ogden, 1978; Posner & Snyder, 1975; Posner,Snyder & Davidson, 1980). Within this paradigm, the subject's task is typically todetect a luminance change that may occur at different locations in the visual field.Preceding the luminance change is a warning cue that can be either neutral (innone or all target locations), valid (coincident with target location), or invalid (in anon-target location). There are two ways in which eye movements are controlledin such an experiment. Subjects' eye movements may be monitored so that they2can be factored out of other measures, or the stimulus onset asynchrony (SOA)from cue to target may be kept shorter than the time required to initiate an eyemovement (200-250 ms). It is generally found that subjects are faster to respondon valid trials than on neutral trials, and slower to respond on invalid trials thanon neutral trials. These results are interpreted as benefits and costs,respectively, of aligning attentional resources to a location in space prior to thetarget presentation.According to Posner (1980), covert orienting facilitates processing throughthe prior activation of neural pathways that will subsequently be used to processthe target stimulus. Furthermore, Posner distinguishes between two types ofpathway activation: exogenous (or automatic) pathway activation, andendogenous (or strategic) pathway activation. Exogenous orienting refers to theinvoluntary orienting of attention to locations signaled by a salient stimulusdifference, such as the brief flash of light, that functions as a cue within the cost-benefit paradigm. Endogenous orienting, on the other hand, refers to thestrategic choice of an observer to attend to a particular region of visual space,ideally in order to maximize benefits and minimize costs.Unfortunately, there seems to be little agreement in the literature on theterms that should be used to refer to the two types of covert orienting. The termsreflexive, involuntary, automatic and exogenous orienting are often usedinterchangeably, whereas voluntary, strategic and endogenous orienting areoften used interchangeably. A similar situation is also true for the two types ofcues that are used to elicit these different modes of attentional alignment. Ingeneral, exogenous orienting has been studied with what are often referred to as"peripheral cues". These are cues that appear at possible target locations. Onthe other hand, endogenous orienting has typically been studied using "centralcues". Central cues are cues that usually appear in the center of a visual display3and provide subjects with some predictive information as to target locations. Twokinds of central cues often used are arrows that actually point to probable targetlocations and numbers that are each assigned to a probable target location.Central and peripheral cues will be referred to in this paper as informationand stimulus cues respectively. The decision to do so is based on the advice ofCoren, et al. (1993) that the terms central and peripheral are misleading. That is,"central" cues need not be presented centrally; moreover "peripheral" cues canalso be presented at central locations. Given this, it seems more suitable to usethe terms information cues and stimulus cues.A considerable amount of research using variations of the cost-benefitparadigm has been conducted to address several issues concerning visualattention. In doing so, several functional differences between the twomechanisms of attentional alignment have been identified. First, as mentionedabove, stimulus-driven alignments of attention are reflexive, whereas information-based alignments of attention are voluntary (Jonides, 1981; Jonides & Yantis,1988; Muller & Rabbitt, 1989; Nakayama & Mackeben, 1989; Yantis & Jonides,1984; Yantis & Jonides, 1990). The magnitude of cueing effects also differs.Stimulus-driven alignments of attention generally lead to larger cueing effectsthan do information-based alignments of attention (Jonides, 1981; Muller &Rabbitt, 1989; Nakayama & Mackeben, 1989). As well, the time course forprocessing information and stimulus cues is different. Information cues lead toslower rising but longer lasting responses, whereas stimulus cues lead to quickbut brief responses (Jonides, 1981; Muller & Rabbitt, 1989; Nakayama &Mackeben, 1989; Posner & Cohen, 1984). This difference is related to anotherphenomenon of covert attention alignment that is referred to as inhibition-of-return. Inhibition-of-return is the reversal of a cueing effect that occurs undercertain circumstances. That is, inhibition-of-return occurs when cueing a target4Vocation actually slows performance for detecting a target. This reluctance toprocess a location that has just been attended to seems only to occur withstimulus cues and not with information cues (Maylor, 1985; Posner & Cohen,1984; Rafal, Calabresi, Brennan, & Sciolto, 1989).Physiological differences between endogenous and exogenousmechanisms have also been identified. Rafal et al., (1989) have suggested thatthe exogenous alignment mechanism is linked to the eye movement systemwhereas the endogenous system is not. This argument was based on severalfindings: 1) Exogenous orienting produced inhibition-of-return when attentionwas drawn to a location, a saccade was executed to a location or when asaccade was merely prepared for a location; 2) Endogenous orienting did notproduce inhibition-of-return. The first result led the authors to conclude thatprogramming or executing a saccade was necessary for inhibition-of-return.Because only exogenous orienting produced this effect it, and not endogenousorienting was concluded to be linked to the saccade system. It has also beensuggested on the basis of orienting research with brain lesioned patients that theneurophysiological substrates of these mechanisms differ. Exogenous orientinginvolves midbrain structures, specifically the superior colliculi while endogenousorienting involves geniculostriate pathways, specifically the parietal cortex(Posner, Cohen, & Rafal, 1982; Rafal et al., 1989; Robinson & Peterson, 1986;Wurtz, 1985).The purpose of this dissertation is to apply what is known about covertorienting to the study of lifespan development of visual attention. By borrowingparadigms, ideas, theoretical concepts, and results from the mainstreamcognitive literature it is hoped that important developmental questions can beaddressed.5The study of visual attention across the lifespanThe forgoing discussion of endogenous and exogenous orienting is only aglimpse at the work that has been carried out. However, it does serve as anintroduction to several concepts, including a paradigm that can be usefullyapplied to the study of age changes in visual attention across the lifespan. Aswith most tasks, performance on attention demanding tasks would be expectedto improve with age during the early years of life, plateau through the middleyears and decline during the later years of life. This inverted U-shaped functionof performance is one that is often found and generally expected when looking atthe cognitive abilities of subjects whose ages vary across the lifespan (Salthouse,1982).Although the inverted U-shaped function is descriptive of lifespan researchit does not provide enough information about the development of specificcognitive abilities. That is, it is unlikely that it provides the complete story of agechanges in attentional alignment abilities. In order to get this information it isnecessary to conduct experiments that involve testing subjects at various agesusing the same specific tasks, or alternatively (and perhaps less practically),testing the same subjects repeatedly as they progress through the lifespan. Thecost-benefit paradigm discussed above is particularly useful for the study of thedevelopment of endogenous and exogenous orienting because it allows for thestudy of attentional mechanisms relatively free from the confound ofdevelopmental differences in the motoric components and optical factors of visionin early life (Kowler & Martins, 1982; Miller, 1969; 1973; Taylor, 1982), and inlater life (Kline & Schieber, 1985; Scialfa, 1990). For example, both children(Miller, 1969) and elderly people (Kline & Schieber, 1985) are slower to initiate6eye movements. By studying covert attentional alignment the interpretation ofage differences does not need to be clouded by the possibility that the results aredue to age differences in eye movements and not attention movements.Despite the availability of workable paradigms and the need for moredevelopmental work that looks simultaneously at each end of the lifespan (Baltes,Reese, & Nesselroade, 1977; Baltes & Reese, & Lipsitt, 1980) there have beenno previous attempts to address the lifespan development of visual attention.The present work is an attempt to look at changes throughout the lifespan in oneaspect of visual attention, that is, covert attentional alignment abilities. Fivequestions will be addressed in turn.Lifespan changes in covert attention alignment1. Are there lifespan age changes in attention shifts mediated by stimuluscues?Automatic attention alignments in young childrenUsing the cost-benefit paradigm developed by Posner (1980; & Snyder,1975), researchers have been able to address at least four questions concerningchildren's covert orienting abilities: (1) Can children automatically orient attentionin response to a salient spatial cue independent of eye movements? (2) Are theredevelopmental differences in the ability to shift attention automatically? (3) Canchildren supplement automatic orienting with strategic orienting when cuepredictability increases? and finally, (4) Does covert orienting in young childrencompete for resources with other attention demanding task components such asresponse selection priming and visual filtering? Four studies have providedanswers to these questions.7In search for an answer to the first question, Enns & Brodeur (1989) beganinvestigating covert orienting in children (aged 6 & 8 years) and adults using aspeeded classification task . The target stimuli were arrowheads pointing in oneof two directions (<,>). On any given trial, an arrow would appear in one of threepossible locations (left, centre or right of fixation) and the subjects' task wassimply to respond to the pointing direction of the arrow. Preceding thepresentation of the target, subjects saw a cue that consisted of a brief flash oflight that appeared in one of the three target locations. In keeping with the cost-benefit paradigm, the cue could appear in the same location as the precedingtarget (valid), in one of the two locations other than the one the target wouldsubsequently appear in (invalid), or in all three locations simultaneously (neutral).Eye movements were controlled by maintaining a cue-target SOA of 250 ms.Valid and invalid reaction times were compared in a condition of completecue-target unpredictability to determine if children could automatically orientattention. It was found that 6 years olds' reaction times on valid trials wereapproximately 100 ms faster than on invalid trials, suggesting that they could.This conclusion was based on three considerations. First, because valid RT'swere faster than invalid RTs, the cue was able to attract attentional resourcesthat would subsequently lead to faster target responding. Second, because theSOA between cue and target was not longer than the time required to initiate eyemovements, it was assumed that attention was allocated independently fromfoveal fixation. Finally, because there was no predictive relationship betweencue and target, attention was concluded to have been drawn automatically.The above result was subsequently supported in several other studies(Akhtar & Enns, 1989; Brodeur, Enns & Ellis, 1991, Pearson & Lane, 1990).Akhtar and Enns (1989) and Pearson and Lane (1990) replicated it in four-year-8olds and eight-year-olds respectively. Both of these studies employed similarmethodologies to Enns & Brodeur and will be discussed in more detail below.Brodeur, Enns & Ellis (1991) recently completed a longitudinal study of severalcomponents of visual attention. The project began in 1988 with the subject poolconsisting of daycare children aged 3 to 5 years old that were tested once a yearfor a total of three years. Although several components of visual selection werestudied (e.g., overt search, filtering, spatial and temporal integration), only thefindings concerning covert orienting in children will be discussed here.The covert orienting task employed in the longitudinal study wasessentially the same as that used in Enns & Brodeur (1989). The main differencebetween the studies concerned the stimuli, and the age of the subjects tested.Rather than using arrowheads pointing in opposite directions (Enns & Brodeur,1989), targets consisted of a small open square (0), or a plus sign (+). As well,children younger than had been tested previously were tested (3 years old).One of the most important findings of this study is that children as youngas three years old automatically allocate attention in response to visual stimuluscues. In other words, RTs on valid trials were significantly shorter than RTs oninvalid trials. Furthermore, it was found that as children got older the size of thecueing effect was reduced. Practice in the task over years also served to reducethe size of the cueing effect.As suggested above there are also significant age differences in the abilityto shift attention. All studies that have investigated covert orienting in childrenand adults report larger cueing effects for children than for adults (Akhtar & Enns,1989; Brodeur, Enns & Ellis, 1991; Enns & Brodeur, 1989; Pearson and Lane,1990). What is particularly interesting, however, is the finding reported byBrodeur, Enns and Ellis that practice and age are both important contributors toage differences. This result is important for understanding the underlying9mechanisms that are mediating age differences. Clearly, if attention deficits inyoung children are due solely to the limited capacity of their cognitive resources(Pascual-Leone, 1978), then one would not necessarily expect practice tocontribute significantly to reduction in size of the cueing effect. In thislongitudinal study however, practice contributed at least as much as age tomaking cueing in young children look more adultlike.Results relevant to the third question have come from several sources.Although stimulus cues are generally thought to elicit exogenous orienting,Jonides (1980; 1981) found that strategies could influence orienting to stimuluscues under some conditions. Specifically, costs and benefits were both found toincrease as the predictability of a valid stimulus cue increased. Thus, in Enns &Brodeur (1989), each subject participated in three conditions that differed in howpredictive the cue location was of the target location. In the unpredictablecondition the cue was "valid" on a random basis, i.e., one third of the trials. In thepredictable condition the cue was valid on 80% of the trials. Finally, in the neutralcondition the cue consisted of a flash of light at all three locations, therebyeliminating the possibility of predicting (or priming) a specific target location.Only adults showed any difference in the size of the orienting effect whencomparing the unpredictable and predictable conditions. As predictabilityincreased so did the size of the orienting effect. It should be cautioned here,however, that because children did not use endogenous orienting in this taskdoes not mean that they are unable to do so in other situations.The final question concerning competition for attentional resources wasexamined in two studies, Enns & Brodeur (1989) and Akhtar & Enns (1989). Forall age groups in the Enns & Brodeur study, response priming was found in boththe neutral and unpredictable conditions, but not in the predictable condition.Response priming was measured by comparing the differences between RTs on10trials that required the same response as the preceding trial (repeat trials), andtrials that required the opposite response than was required by the preceding trial(alternate trials). Repeat trials were found to be faster than alternate trials in theneutral and unpredictable conditions, but not in the predictable condition.Because the predictable condition was the only condition that permittedendogenous orienting, it seems reasonable to conclude that some aspect ofendogenous orienting competes for the same processes as response priming inthis task. Because the shared resources were being used for endogenousorienting, none remained to produce a response priming effect. It was also foundthat this effect became less pronounced with age.The above finding is interesting both for what it replicates and what it doesnot replicate. First, the finding that response priming effects interact with otherattention demanding task components, and that this effect diminishes with age,supports a similar finding of Enns & Cameron (1987). The difference in thesetwo experiments lies in the task component that was found to interact withresponse priming effects. In Enns & Brodeur (1989), response priming interactedwith endogenous orienting, while in Enns & Cameron (1987) the interactingcomponent was the filtering of task-irrelevant distracters. Second, in contrast toEnns & Cameron (1987), who found that response priming did not compete forresources with overt search, Enns & Brodeur (1989) found that response primingshared resources with another type of search-- covert orienting. It would seemas a result, that the independence of search from other components of attentionreported in Enns & Cameron (1987) may not reflect the independence of searchin general, but rather the independence of eye movement control. This clearlyillustrates the value of studying covert orienting when looking for developmentaldifferences in visual attention.To get a better understanding of the relationship between covert orienting,11overt orienting, response priming and the filtering out of irrelevant information,Akhtar & Enns (1989) looked for interactions among covert orienting, responsepriming, and filtering in children and adults. As with the studies described above,covert orienting was measured by having subjects perform a speededclassification task with two possible targets (^ and +) and two possible targetlocations (left and right of fixation). Also, a valid, invalid, or neutral cue precededthe presentation of the target and the cue/target interval was limited to 250 ms tocontrol for eye movements. The filtering component of the task was added byflanking targets on two-thirds of the trials with distracters that could be compatibleor incompatible with the required response. Compatible distracters consisted offlanking items that were identical to the target (i.e., + + + , ^^^) andincompatible distracters consisted of flanking items that were targets thatrequired an opposite response (i.e., +^+ , ^ +^). On these trials it wasnecessary for subjects to filter out the irrelevant distracters and attend only to thetarget. For the remaining one-third of the trials, targets appeared alone.Performance was measured under two conditions in this experiment: A neutralcondition in which both possible target locations were cued, and an unpredictablecondition in which the single cue was not predictive of target location (valid on50% of the trials). Unlike previously discussed designs, however, neutral cuetrials and locational cue trials were randomly mixed within each block.The result of particular interest here is that covert orienting and filteringinteracted in a capacity sharing fashion, and also, that the size of this interactiondecreased with age. The reduction in size of the interaction suggests thatattentional resources are allocated to multiple task components more efficientlywith age.To further support the findings of this experiment, Akhtar & Enns (1989)conducted a second, control experiment that was identical to Experiment 1 with12the exception of the neutral cue. Because of the warning issued by Jonides &Mack (1984) that neutral and locational cues must be identical, they replaced theneutral cue consisting of two flashes of light at each of the possible locations, toone flash of light at fixation. As a result, both the locational and neutral cueswere physically identical. The specific concern addressed here was the generalalerting function of cues. Because the neutral cue in Experiment 1 consisted oftwo simultaneous flashes of light, and the locational cues consisted of only oneflash of light, it was thought that the two types of cues may have had differentgeneral alerting functions. This being the case, it would be inappropriate tocompare costs and benefits without the addition of the proper control condition.In general the findings of Experiment 2 matched those of Experiment 1,providing further support for the capacity sharing nature of covert orienting andfiltering. It should be noted once again that finding covert orienting sharesprocessing resources with filtering is inconsistent with results reported by Enns &Cameron (1987) who found that overt search developed independently offiltering. This result further supports the conclusion that these two methods ofvisual search may depend on separate mechanisms.One surprising result reported by Akhtar & Enns (1989) is that children asyoung as five years old were able to filter incompatible distracters as well asadults. This result is at odds with most previous developmental studies offiltering (Day, 1978; Enns & Akhtar, 1988; Enns & Cameron, 1987; Enns &Girgus, 1985; Gibson & Yonas, 1966; Well et al., 1980). The explanationprovided for this finding was that the neutral cue, as well as the locational cue, inExperiment 2 led to an automatic focusing of attentional resources over a narrowrange of visual space, thereby eliminating distracters. On the other hand, thepresentation of two separate flashes of light in Experiment 1 would havebroadened the focus of attention leaving young children, in particular, more13susceptible to distracters.Automatic attention alignments in the agedThe study of visual attention in the elderly often yields several consistentresults, and the studies specifically designed to investigate attentional alignmentsare no exception. First, all studies show that older adults are slower to respondoverall than are younger adults. The general slowing of operations in old age isone of the most prevalent findings in aging research. In fact, it is such aubiquitous result that it has led authors such as Birren, Woods, & Williams (1980)and Salthouse (1982; 1985) to build theories on the basis of a "general slowinghypothesis". The slowing hypothesis attributes response slowing with age to theslowing of processing rate that is rooted in the slowing of neural operations. As aresult, all operations are slowed to a certain degree, although there are at leastthree factors that can significantly reduce slowing according to Salthouse: 1)Healthy, active older adults show less slowing than do unhealthy, less activeadults; 2) vocal reaction times show less slowing than do manual reaction times,and 3) practice with a specific task can reduce age related slowing for thatparticular task. Interestingly, in terms of a lifespan perspective, slower reactiontimes in older adults resemble slower reaction times in young children. What isnot known however, is if slower reaction times in both groups can be attributed tosimilar underlying mechanisms.Second, with one exception (Nissen & Corkin, 1985), all attentionalalignment studies conducted with seniors presented subjects with targets flankedby distracters on at least some trials. In studies that measured the disruptiveeffects of adding distracters (i.e., those studies that had trials both with andwithout distracters), older adults were disrupted to a greater extent than wereyounger adults (e.g., Madden, 1990). Similar results are commonly reported inthe literature on visual attention and aging (e.g., Rabbitt, 1965), and seem to14indicate that older adults have more difficulty separating relevant information fromirrelevant information. In fact, such a result would be expected on the basis ofthe general slowing hypothesis. Salthouse asserts that because slowing occursat the neural level, it will be compounded over several levels when older subjectsare required to perform higher level operations. This compounding leads tolarger age deficits for tasks that require higher levels of operations than for tasksthat are more low level. Cerella (1985) has referred to this phenomenon as the"complexity effect". In other words, increased task complexity leads to anincrease in age decrements. Although this explanation is intuitively appealing,Salthouse (1985) correctly points out that the utility of the complexity effect islimited because it requires an analysis of tasks into components in order toassess its power in terms of explaining age differences on any given tasks. Onceagain, however, it is interesting to note that there is a tendency for similaritybetween older adults ability to filter distracters and young children's ability to filterdistracters.Although the results discussed above are interesting and important, theeffect of specific interest to the present paper is the cueing effect. That is, arethere age related differences in the abilities of adults to use spatial cues in orderto aid attentional orienting?To date there have only been three studies conducted to investigate theability of seniors to automatically align attention in response to stimulus cues(Hartley, Keiley, & Slabach, 1990; Madden, 1986; 1990). Interestingly, despiteseveral differences in the details of the experiments, all three studies showed noage differences in the size of the cueing effects. This was true under severalcue-target SOAs: 1500 ms ( Madden, 1986); 100, 200, 300 ms (Hartley et al.,1990) , 50, 80, 116, 150, 183 ms (Madden, 1990), as well as with different typesof stimulus cues and variations in the validity of the cue: 100% valid (Madden,151986;1990), 75% valid (Hartley et al., 1990).The one possible exception to the lack of age by cueing effects is amarginal age by SOA interaction found when Hartley et al., 1990 analyzed costsplus benefits. This interaction suggests that older adults have a larger cueingeffect at smaller SOAs, but as SOA increases the size of the cueing effect forolder adults levels off at a lower asymptote than for young adults. As mentioned,this result is only marginally significant so strong conclusions cannot be made. Itis suggestive, however, that there may be some age differences that will beapparent under certain circumstances. This aside, it must be concluded on thebasis of available data that the ability to orient attention automatically in responseto stimulus cues may not deteriorate in old age. It should be mentioned here thatold age here is defined by the ages of the samples tested in the studies and noconclusions can be made concerning attentional alignments in older adults ingeneral. The mean age of the older adult groups were: 71.6 years(Madden,1986); 71.3 years (Hartley et al., 1990), and 64.7 years (Madden,1990).Automatic attention alignments in special populationsRecently investigators have become intrigued by the notion thatperformance measures of attention may actually inform clinical assessment ofrelated disorders. Specifically, attempts have been made to isolate differencesbetween children suffering from Attention Deficit Hyperactivity Disorder (ADHD)and "normal" children on performance measures of covert orienting.One such attempt was made in the longitudinal study reported above(Brodeur, Enns, & Ellis, 1991). Along with RT measures of several componentsof visual attention, a clinical measure of attention deficit was acquired for eachchild during the first year of testing. The measure used was the Connors'Teacher Checklist (Connors, 1987). Teachers were requested to rate each16child's attention/inattention based on their classroom activity using the twenty-eight item checklist. It was hoped initially that a relationship would be foundbetween this clinical measure of attention deficit and the psychophysicalmeasures of attention. Unfortunately no significant relationships were found. Ofcourse it would be premature to rule out the existence of a relationship on thebasis of these data. In fact, there is at least one other study that suggests theremay actually be some differences between covert orienting abilities in ADHDchildren and nonclinical children.Swanson, et al. (1991) used Posner's cost-benefit paradigm to comparevisual-spatial attention in clinically diagnosed ADHD children, an age matchedcontrol group and adults. Interestingly, ADHD children performed like theircontrols, and like adults in some ways, but differed in others. All subjectsshowed similar stimulus cueing effects at the shorter SOA (100 ms), but at thelonger SOA (800 ms), although all subjects showed a decrease in reactiontimes, ADHD children only showed a decrease on trials where the cue was validand in the right visual field. Also, normal subjects exhibited a RT advantagewhen stimuli appeared in the right visual field on invalid trials, where as ADHDsubjects exhibited a right visual field disadvantage on invalid trials. Takentogether, the authors of this report interpret these results as being suggestive thatADHD may be partially localized as an anterior attention system deficit. That is,the difficulty for these children lies in sustaining attention rather than engaging,disengaging or moving attention.Clearly the above research is a good beginning at using performancemeasures to inform us about related clinical disorders, although much more workis still needed in this area. The Swanson, et al. (1991) study also provides uswith useful information concerning the longitudinal study previously mentioned.Although Swanson and colleagues were successful in finding differences17between ADHD children and normals, they also found that these differences only18^.appear between "true" cases of ADHD and normals, and not between childrenthat are suffering attention deficits as a result of external life stressors andnormals. This obviously points to the need for careful diagnosis, and as well forthe need for better diagnostic techniques. There is some promise that perhapseventually performance measures of attention may fulfill the second need. Atany rate, if "true" ADHD children alone show differences in covert orienting, thanit is unlikely that one would expect to find relationships between RT measuresand clinical measures if the sample consists only of non-ADHD children, as wasthe case in the longitudinal study. Although there was a significant range in theConnors' Checklist scores for the children tested, none of them would bediagnosed as ADHD.Lifespan age changes in automatic attention alignmentBy reviewing the research conducted independently on subjects of varyingages it appears as though the automatic alignment system develops fairly early inlife, and is not particularly sensitive to deterioration in old age. It is difficult,however, to make direct comparisons between the research conducted at theearly end of the lifespan and the later end of the lifespan because of the manydifferences in experimental methodologies. In order to get a clearer picture ofhow automatic attention alignments change throughout the course ofdevelopment the experiments to be described in this dissertation employed thesame stimulus cue experiment to test subjects varying in age from earlychildhood to old age. Several valuable pieces of information can be gained fromsuch experiments. For example, subjects were presented with targets that werenot flanked by distracters (unlike all of the studies previously conducted withseniors) making it possible to get measures of orienting at different agesunconfounded by the effects of filtering, that may or may not change in the sameway with age. Also, by varying cue-target SOA, it was possible to directlycompare the time course of automatic alignments of attention in subjects varyingin age.2. Are there lifespan age changes in attention shifts mediated byinformation cues?Strategic attention alignments in early lifeAlthough a considerable amount of work has been conducted on children'sability to automatically orient their attention in response to stimulus cues, thesame is not true for children's ability to align attention strategically in response toinformation cues. To date there has been only one study published that testsyoung children using the information cue paradigm.Pearson and Lane (1990) have recently demonstrated that children asyoung as eight years old are able to orient their attentional resourcesendogenously in response to information cues. In this study, orienting effects forchildren and adults were plotted as a function of SOA for both stimulus andinformation cues. Subjects were required to identify a target letter (W or 0), thatcould appear at one of two distances from fixation (near or far), and either to theleft or right of fixation. In the information cueing condition the cue consisted of acentrally-located arrow that pointed to the side where the target would appear on66.7% of the trials (valid), the opposite side from the target on 16.7% of the trials(invalid), or to both sides on 16.7% of the trials (neutral). Five cue to target SOAswere used (83, 150, 200, 267, and 300 ms).Interestingly, the effects of stimulus and information cues were remarkablysimilar. First, orienting effects decreased with age, in keeping with findingsreported above. Second, orienting effects were larger for all age groups when19the distance between cue and target was larger. This finding suggests that agedifferences in orienting effects are a not straightforward result of age differencesin peripheral acuity (for a discussion of age differences in peripheral acuity asthey pertain to visual attention research see Akhtar, 1990). Third, stimuluscueing resulted in greater costs for all age groups than did information cueing.Finally, there was an increase in benefits with longer SOAs for all ages on validtrials, whereas costs increased on valid trials only for eight year olds. Evidentlyonly older children are able to increase benefits without increasing costs. Theseresults taken together provide two important pieces of information concerningchildren's abilities to strategically orient attention. At least by the age of eight,children are able to use some information cues with equal efficiency to adults,and with equal efficiency to stimulus cues.Strategic attention alignments in the agedUnlike research with young children, the majority of orienting researchconducted with seniors has provided data on their ability to strategically alignattention in response to information cues. Unfortunately, it would appear that theresults are too divergent to draw any strong conclusions. Some studies showlarger cueing effects for older adults (Hartley, Kieley, & Slabach,1990[Experiment 2]; Madden, 1983; Nissen & Corkin, 1985), while others showsmaller cueing effects (Hoyer & Familant, 1987 [Experiments 1 and 2]). Thereare many differences among these studies (e.g., type of target, locations oftarget, presence/absence of distracters, number of distracters, validity of cues)but, for the most part, these differences are too unsystematic to explain differentresults. However, there are two factors that seem to vary systematically with thedifferences in results: 1) SOA, and 2) the type of information cue. The studieswith the shorter SOAs all report smaller cueing effects for older adults, andstudies with longer SOAs report larger cueing effects for older adults. Hoyer &20Familant (1987) conducted two experiments using information cues. The firstexperiment used an SOA of 250 ms and no cueing effects whatsoever werefound for older subjects, although they were found with younger subjects. Theauthors interpreted this finding as indicating that older adults required more timeto use the information provided by information cues. To address this, theyconducted a second experiment using SOAs of 300, 500, 750, 1000, 1250 ms.Interestingly, the effects of the cue were again less substantial for older subjectsthan for younger subjects. In fact, the cue only seemed to have an effect forolder subjects at SOAs of 750 ms or greater on the second block of trials.Clearly these results suggest that not only do the elderly require more time to useinformation cues than do younger adults, they also appear to require morepractice.Experiments showing a larger cueing effect for older subjects have tendedto involve the use of longer SOAs than those used in the two studies mentionedabove. Madden (1983) used an SOA of one second and Nissen & Corkin (1985)used SOAs of two and three seconds. Although Hartley, et al., (1990) usedshorter SOAs than the two studies just mentioned (100, 300, 500 ms), they onlyfound a larger cueing effect for older adults at the 300 and 500 ms SOAs. On asmaller scale, this study conforms to the assessment that older adults requiremore time to process information cues than do younger adults. Taken togetherhowever, these studies do suggest that older adults are able to use informationcues when given enough time.Although SOA works reasonably well as an explanatory tool for thedivergent results found in studies that used information cues, there is one aspectof this explanation that is not completely satisfying. That is, Hoyer & Familant(1987) found that older subjects required a SOA of 750 ms and practice to useinformation cues whereas Hartley, et al., (1990) found that older subjects only21required 300 ms to use information cues. Although both of these studies22reported age differences in the length of the SOA required to get a cueing effect,the difference in the minimum length required across studies seems quite large.The answer to this discrepancy may lie in the type of information cue used in theindividual studies. There are two common types of information cues used,numbers and arrows. Number cues involve the assignment of numbers to givenlocations, and the subject is required to learn what number represents whatlocation in order to get any value from locational cueing. Arrow cues point to theappropriate location and the subject's task is to direct their attentional resourcesto the location that the arrow is indicating. Intuitively, arrows seem to be themore direct, or natural of the two types of information cues. Hartley, et al. (1990)were able to get cueing effects with older adults at a shorter SOA using arrowsas cues whereas Hoyer & Familant (1987) used a longer SOA in order to getcueing effects using numbers. Madden (1983) and Nissen & Corkin (1985) alsoused arrows as cues, but they only tested SOAs that were very long (1 to 3seconds) making it impossible to assess whether the cues could have been usedat shorter SOAs.Although there has been considerable research conducted on theattentional alignment abilities of the elderly, a clear picture has yet to emergeconcerning which, if any, age related deficits exist and which specific situationslead to deficits. In any case, it would appear that SOA and type of informationcue are important factors in determining age differences.Lifespan age changes in strategic attention alignmentClearly there is still much to be learned about the lifespan development ofstrategic attention alignments. At the early end of the lifespan we have no datafor children younger than eight years, and at the later end of the lifespan theexperiments are too varied to allow strong conclusions about the deterioration ofabilities. In order to address these issues, as well as to make direct comparisons23between young children and older adults, the present study tested subjectsranging in age from early childhood to old age, using an identical information cuefor all age groups. By varying cue-target SOA, it was possible to look fordevelopmental differences in the time course of strategic attention alignment, andto help sort out some of the inconsistencies in the previous research.3. Are there lifespan age differences in the nature of inhibition-of-return?The presentation of a stimulus cue not only leads to facilitation, it can alsolead to inhibition. In general, it is found that if attention is aligned in response toa stimulus cue, and is given enough time to disengage from the cued location(i.e., a long enough cue target SOA), then an inhibitory mechanism will reducethe probability that attention will realign itself with the previously attendedlocation. This effect has been labeled inhibition-of-return (Posner, Rafal, Choate,& Vaughan, 1985). Interestingly, inhibition-of-return only seems to happen whenstimulus cues are used, and not when information cues are used (Maylor, 1985;Posner & Cohen, 1984; Rafal, Calabresi, Brennan, & Sciolto, 1989). Thissuggests that the mechanisms responsible for producing exogenous andendogenous cueing may be different. More specifically it suggests that theportion of the brain responsible for automatic attention alignments is also theportion responsible for inhibition-of-return.Posner, Petersen, Fox and Raichle (1988) on the bases of data from manysources have localized three parts of attention alignment in three areas of thebrain. The thalamus is thought to underlie engagement of attention, the superiorcolliculus is thought to be responsible for the movement of attention and theparietal lobe is implicated in the disengagement of attention. If inhibition-of-returnis a failure to move attention to a previously attended location than according to24Posner, et al., the superior colliculus would be the brain area responsible for thephenomenon. Rafal, et al., (1989) have also localized inhibition-of-return in themidbrain on the basis of their finding that inhibition-of-return (under monocularviewing conditions) was greater in the temporal hemifield than in the nasalhemifield.Lifespan changes in the nature of inhibition-of-returnUnfortunately, very little is known about the development of the inhibitorymechanism that mediates inhibition-of-return. There has been no work withyoung children or older adults, although there has been work conducted with veryyoung infants. Although the study being proposed here does not include aninfant sample, the infant results are not only interesting, they are relevant.Rothbart, Posner, and Boylan (1990) have recently summarized their researchwith infants using a paradigm similar to Posner's original paradigm with directionof gaze as the dependent measure. To summarize their results, infants as youngas six months exhibited inhibition-of-return using both a covert and overt orientingmeasure. Three month olds were only tested using the overt method andshowed no inhibition-of-return. More recently Johnson and Tucker (1993)demonstrated with direction and latency of eye movements that infants as youngas four months of age exhibit inhibition-of-return. These results are clearlyenticing enough to suggest that there is valuable information to be gained bystudying attentional alignment abilities in infants. As well, these results arerelevant to present work because they suggest that inhibition-of-return should bepresent in young children. What is left to be determined, however, is whether ornot the nature of the effect is similar in subjects of varying ages and with amanual detection task.In the present study inhibition-of-return was measured using a stimuluscue paradigm in subjects ranging in age from early childhood to old age. Therange of SOAs was long enough to capture the switch from facilitation toinhibition in all subjects. Because stimulus cues generally mediate quickresponses, it was predicted that the range of attention alignment effects shouldbe apparent by varying SOA from 50 ms to 800 ms. This of course has yet to beconfirmed for older adults. It is possible that general slowing may set the entiremechanism back to such an extent that inhibition will not be tapped by 800 ms.However, on the basis of previously discussed data showing no differences incueing effects between young and old adults this would seem unlikely. At anyrate, it was possible to look for age differences in the onset of inhibition-of-return,and in the relative magnitude of inhibition. Because inhibition has not been foundpreviously in information cue experiments, it was not expected that inhibitionwould be found in that condition.4. Are there lifespan age differences in cross versus within hemifieldcueing effects?A favoured metaphor by some researchers is that attention is like thebeam of a moving spotlight (e.g., Posner, 1980; Treisman & Gelade, 1980; Tsal,1983) and this metaphor has been elaborated on by others such as Eriksen (&St. James, 1986; & Yeh, 1985) who claim that attentional focus is more like azoom lens. Within the zoom lens model, the spatial area that the attentionalresources cover can vary from very little to very broad. Still others havesuggested that there may be more than one spotlight of attentional resources(Egley, & Homa, 1984; Pylyshyn & Storm, 1987).Although the spotlight metaphor is appealing and intuitive, its validity hasbeen questioned on several grounds. For example, Hughes and Zimba (1985;25261987) have made the claim that attention is not directed towards a specific cuedlocation, but rather widely dispersed within the cued visual hemifield. Thisconclusion was based on their finding that when targets could appear in thesame or in the opposite hemifield as an invalid cue, costs were found only whenthe target and cue were in opposite hemifields. Furthermore, these costs werethe same regardless of the eccentricity of the target, suggesting that attentionalresources are evenly distributed throughout the hemifield. They also foundsimilar results when they separated the visual field into upper and lowerhemifields.On the other hand, Rizzolatti et al. (1987), Downing and Pinker (1985) andothers have found cueing effects that vary as a function of distance throughout ahemifield. The discrepancies in results are most likely due to methodologicaldifferences in these studies, and it is important to note that Hughes and Zimbahave been criticized on several grounds pertaining to their specific methods.Nonetheless, it is an interesting question as to whether or not there aredevelopmental differences in the nature of allocation of attentional resourcesthroughout visual space.Lifespan changes in cross versus within hemifield cueingAlthough the nature of how attention is aligned in visual space is unclearfor any age group, it is an interesting question as to whether there are agechanges throughout the lifespan in how attention is aligned. Unfortunatelyhowever, there is no developmental data at either end of the lifespan that directlyaddresses this issue. One way that it is possible to begin looking fordevelopmental differences in the nature of how attention is aligned is to employ alogic similar to Hughes & Zimba. By designing an experiment that allows forcues to be invalid within a visual hemifield, as well as across hemifields, and byusing this experiment to test subjects of different ages, it was possible todetermine if attention is aligned within a hemifield, or with a specific cuedlocation. That is, if costs are found for across hemifield invalid cues but not forwithin hemifield cues it would be suggestive that attention has been dispersedthroughout an entire hemifield. It was also possible to look for variation in resultsdue to age and/or cue type (stimulus versus information).5. What is the nature of age differences throughout the lifespan forattention shifts mediated by stimulus and information cues?What do we know about the interaction between development and the twoattention alignment processes? Although no study has compared attention shiftsmediated by stimulus and information cues across the full range of the lifespan,there has been one study that has compared the two processes in children eightyears of age (Pearson & Lane, 1990). Pearson and Lane found that childrenwere able to shift attention in response to information cues and stimulus cueswith equal efficiency. This result is somewhat surprising in light of the fact that itis generally accepted that children have more difficulty than adults employingstrategies. It is possible, however, that by the age of eight, children are able toemploy the simple strategies necessary to use information cues but at youngerages the same will not be true. Of course strategies differ in their complexity,and there are no doubt other strategies that eight year olds would have difficultywith. Clearly what needs to be established here is the age at which children arefirst able to use stimulus and information cues. The answer for each cue typemay be different.There are two studies that compare attention alignments made toinformation and stimulus cues in elderly subjects (Hartley, Kieley, & Slabach,271990; Folk & Hoyer, 1992). In general, the results reported by Hartley, et al.mimic the results that can be extracted by comparing the performance of elderlysubjects in independent stimulus and information cue experiments. Like Hartleyet al., the other studies that used information cues all found significantdifferences in the size of the cueing effect for older and younger adults (Hoyer &Familant, 1987; Madden, 1983; Nissen & Corkin, 1985). Also like Hartley et al.,the other cueing studies that used stimulus cues found no significant differencesin the size of the cueing effect for older and younger adults. Folk & Hoyer foundlittle age difference in the stimulus cue experiment, and interestingly, only foundage differences in the information cue experiment cue difficulty was increased.In one experiment the cues were arrows presented at fixation. In this experimentage differences were found. In another experiment the cues were arrowheadspresented off fixation in the direction indicated by the arrow. As a result botharrow point direction and arrow location indicated the probable target location. Inthis experiment age differences were diminished. This finding supports theabove interpretation of divergent aging results being related to cue difficulty.Overall these results are at least suggestive that aging may influence theability to use information cues but not stimulus cues. In general it is found thateveryone finds it more difficult to use information cues, from childhood throughadulthood. At the very least it is the case that information cues require more timeto be used effectively, most likely because of the necessity to employ consciousstrategies to make use of them. The finding that endogenous cueing deteriorateswith age is consistent with the complexity effect discussed above (Cerella, 1985;Salthouse, 1985). The use of strategies for information cueing requires higherlevel operations than would automatic cueing, and as a result one would expectage differences to be considerably larger for information cues.One final avenue that can be used to explore the nature of automatic andstrategic attention alignments is to assess the nature of the interaction between28the two types of orienting. This has been attempted with a young adultpopulation relatively recently (Berger, Henik, & Rafal, 1991). Rafal, et al. placedsubjects in a situation where they were given strategic information and automaticinformation about the possible location of a subsequent target. By varying thecompatibility of these two sources of information within each trial (e.g., both valid,one valid and one invalid, etc.), it was possible for them to determine if the twoalignment mechanisms functioned independently, or whether they interacted in amanner that suggested one mechanism could override another. Their resultssuggested that subjects are able to use both cues in an additive fashion. Thatis, subjects performed best when both cues were valid, and worst when bothwere invalid.Recently, Rafal and Henik (in press) employed another innovativeparadigm to look at the relationship between automatic and strategic cueing. Inthis study subjects were presented with two possible target locations to the leftand right of fixation. On 80% of the trials a stimulus cue at one location would befollowed by a target at the opposite location (predictive trials). On 20% of thetrials the stimulus cue would be followed by a target at the same location (matchtrials). Cue to target SOA was varied and young adult and older adult subjectswere tested. The logic behind this manipulation was that to maximizeperformance subjects would have to move their attention strategically from thelocation that it had been automatically drawn in order to respond to the target. Atthe earliest SOAs subjects from both age groups were not able to employ thestrategies necessary to shift attention on the basis of probable target location(i.e., subjects were faster on match trials than predictive trials). When a longerperiod of time intervened between cue and target subjects were faster onpredictive trials than on match trials. Older adults took a considerably longer time29than young adults to effectively employ the necessary strategies to takeadvantage of the predictive nature of the stimulus cue (approximately 150 ms foryoung adults, 400 ms for seniors). Clearly these results suggest that it ispossible to use stimulus cues in a strategic manner, and once again there isevidence that seniors are significantly slower at strategic orienting than areyounger adults. It is of course an open and very interesting question as towhether or not the interaction of these two systems changes throughout thecourse of development.Lifespan changes in stimulus and information driven attention alignmentsIn order to make more clear cut comparisons between the use ofinformation and stimulus cues in subjects that vary in age the first experiment ofthe present study tested the same subjects in both a stimulus cue condition, andan information cue condition. There are several levels on which agecomparisons were made. First, the magnitudes of cueing effects were comparedby comparing costs and benefits relative to a neutral condition, or by comparingInvalid-Valid differences. The latter measure gives an estimate of the cueingeffect independent of the problems associated with neutral conditions (Jonides &Mack, 1984). On the other hand, it is useful to look at costs and benefitsbecause it is possible that developmental differences in costs do not mimicdevelopmental differences in benefits. Second, by varying cue-target SOA it waspossible to compare the time course of processing each cue type for every agegroup tested. Specifically, it was expected that stimulus cues would be employedearlier than information cues. Does the magnitude of this difference change withage? The level of asymptotic performance may also differ for each cue type, andagain this difference may vary with age. Third, as discussed previously inQuestion 3, there may be a decay of cueing effects. At the very least this wouldbe expected for stimulus cues, although whether or not age differences should be30expected was not known. As discussed in Question 4 comparisons can be made31between cross versus within hemifield cueing for each cue type. Once again, thedevelopmental implications of this manipulation were not known.Finally, a second experiment was conducted to assess the nature ofdevelopmental change in the interaction between the two alignment mechanisms.One would expect some differences solely on the basis that stimulus drivenalignments tend to be operable at an earlier point in development than dostrategic alignments. Likewise they also seem to face less age relateddeterioration later in life. What role does this play in changing the role eachmechanism plays in a situation where both forms of cue are presented? It wouldseem likely that because the strategic form of orienting is more difficult toimplement, it would be more likely to be overridden by the automatic form thanthe other way around. This effect should be accentuated in children and theelderly.Experiment 1This study was designed to examine developmental differences in theability to have attention pulled by an event (exogenous orienting) and to pushattention voluntarily (endogenous orienting). To do so, subjects of varying ageswere sampled from across the lifespan and asked to complete attention tasksbased on Posner's (1980) cost-benefit paradigm. One task involved making atwo-choice forced recognition decision following the presentation of a stimuluscue that was meant to pull attention to a probable target location. The other taskinvolved making the same forced choice following an information cue that wasintended to signal the subject to push attention to a probable target location. Anattempt was made to look at the time course of pushing and pulling attention, andhow these change with age by varying cue-target SOA. As well, eye movementswere monitored in order to ensure that what we got were measures of covertalignments of attentional resources.MethodSubjects . Subjects from five age groups were tested. Three age groupsof 20 subjects each were tested in a Vancouver School Board public school: 1)Kindergarten/ Grade 1 with a mean age of 6.3 years, s.d.=0.55 years (9 females),and 2) Grades 2/ 3 with a mean age of 7.9 years, s.d.=0.52 years (13 females)and 3) Grades 4/5 with a mean age of 10.5 years, s.d.=0.61 years (10 females).The children's ages were selected for two reasons. First in an attempt to testchildren in the information cue condition at a younger age than had been testedprebviously (8 years), and to have several groups that differed enough in age tomaximize the chance of discovering developmental differences. As a result, thegroups that were available from the school board that met these criteria wereselected. One group of 20 undergraduates selected from the undergraduatesubject pool at UBC comprised a young adult group with a mean age of 22.8years, s.d.=3.21 years (6 females), and an older adult group consisting of 20seniors with a mean age of 72.8 years, s.d.=5.47 years (12 females) solicited vianewspaper advertisements. Older subjects were paid a five dollar honorarium fortheir participation and undergraduates received course credit. Childrenvolunteered to participate with the permission of the school and each subject'sparents.All subjects were screened for serious visual impairment (e.g., cataractsand glaucoma in the elderly). A total of 25 seniors participated in the study, fiveof which were eliminated on the bases of potential visual impairment such asprevious cataract surgery or the beginnings of glaucoma. The remaining32subjects all had normal or corrected to normal vision although many of the olderadults required bifocals for correction.Information on the number of years of education was also obtained foreach subject. Children had an average of 0.75, 2.4 and 4.5 years of educationfor each group. Young adults had an average of 15.4 years of education andseniors had an average of 13.4 years of education.Stimuli and Apparatus. 1) Data collection: Stimulus presentation,feedback and data collection were controlled by a Macintosh Plus for children, aMacintosh Plus with hard drive for undergraduates and a Macintosh SE forseniors. Responses were made by pushing one of two keyboard buttons on anygiven trial (M or N). Each button was covered with a sticker that illustrated one ofthe two possible target items. Subjects were seated approximately 45 cm in frontof the computer with their fingers resting on the keyboard buttons. The testingroom was illuminated with normal fluorescent lighting with the exception of thoseelderly subjects tested in their own homes where a variety of lighting was used2) Eye movement monitoring: Subjects' eyes were videotaped using aSony CD F210 handy cam placed on a tripod. The camera was positioned sothat it was possible to get a close up zoom of only the subjects' eyes. To ensurethat subjects did not move their heads to a degree that would remove their eyesfrom the view finder, a chin rest was used. To score video tapes for eyemovements, a Quasar VCR and a JVC television monitor were used.3) Stimulus presentation for the information cue condition: Examples ofthe three possible trial types for the information cue condition are illustrated inFigure 1. Each trial type began with the presentation of a fixation point for 500ms. The fixation point was an eighteen point, bold faced exclamation point. Fourlocation markers that indicated the four possible target locations appeared with33the fixation as well. The location markers were four dashes that appeared underthe actual possible target locations, and these markers then remained on thescreen throughout the duration of a block of trials. The four possible targetlocations included two to the left and two to the right of fixation. The two possibletarget locations that were near fixation corresponded to 2 degrees of visual arc tothe left and right of fixation. The two far locations corresponded to 6 degrees ofvisual arc to the left and right of fixation.Next, a cue item presented at fixation appeared on the screen.There were five possible information cues that could appear at fixation, a neutralcue (.) and four information cues, each of which corresponded to one of the fourpossible target locations. Single arrowheads pointing left or right (<,>) served ascues that corresponded to the left and right near locations respectively. Twoarrowheads pointing left or right («,») served as cues that corresponded to thetwo far locations. All cues were bold faced, of a 36 point font, and appeared atfixation for 50 ms.There were two possible targets (0 or X) that were equally likely to followa cue on any given trial at varying SOAs. The targets were also bold faced, of a36 point font (.95° visual angle) and remained on the screen for a 50 ms duration.Although the experiment was initially set up to test the following five SOAs; 50,100, 200, 400 or 800 ms, a programming error was discovered after datacollection that interacted differently with different computers. This createddifferent SOAs for the different age groups. As a result, children were actuallytested under three SOAs (133, 250 and 450 ms). 1 Young adults were testedunder four SOA conditions (133, 200, 400, 800 ms), and seniors were testedunder four SOA conditions (150, 200, 400, 800 ms). Although unfortunate, theSOAs that were tested still provide a considerable range of data for each group.1 Children's RTs were also recorded incorrectly by the cueing program. After extensive testing itwas determined that children's RTs were inflated by a multiplicative constant of 1.6. In order tocorrect for this, children's RTs were subsequently divided by a factor of 1.6 prior to analyses.3435Following the offset of a target, subjects were given three seconds torespond before the computer timed out and continued with the following trial.Time out trials were treated as errors. Feedback consisted of a 5 ms, 2000 Hztone that was presented to subjects only when they made an error or failed torespond in the three seconds provided. There was a one second intertrialinterval.The relationship between cue and target could be broken down into threebasic trial types (see Figure 1). Neutral trials consisted of the presentation of theneutral cue followed by a target in any one of the four possible target location.This trial type is referred to as neutral because it did not provide the subject withany locational information about the subsequent target. Valid trials consisted ofany one of the four information cues followed by a target that appeared in thelocation indicated by the cue (e.g., > followed by a target in the near, rightlocation). There were four possible types of valid trials, one for each location.Invalid trials consisted of any one of the information cues followed by a target thatappeared at any one of the three locations not indicated by the cue. As a result,there were three possible ways any given cue could be invalid (e.g., > followedby a target in the far right or the near or far left locations).In order to ensure that subjects actively tried to use information cues, itwas necessary to make the cues predictive of target location. As such, 66.667%of the total 240 trials were valid, 16.667% were invalid, and 16.667% wereneutral. Taken aside, the information cues (those other than the neutral cue)consisted of 80% valid trials and 20% invalid trials.All trial types, SOAs and target types were presented randomly mixedwithin each of five blocks of 48 trials.4) Stimulus presentation for the stimulus cue condition: Examples of thethree possible trial types in the stimulus cue condition are illustrated in Figure 2.36In the stimulus cue condition, everything was identical to the information cuecondition with the exception of the cues, their predictability, and the number oftrials. Rather than arrowheads, the cues consisted of a small filled in circle thatappeared at one of the four possible target locations on any given valid or invalidtrial. On neutral trials the cue consisted of the same small filled in circle thatappeared at fixation. The fixation point, location markers, targets and all timingparameters were identical to the information cue condition.Subjects were asked to complete two blocks of 45 trials each, andbecause it was preferred that subjects did not easily employ strategies that wouldinfluence their performance, stimulus cues were predictive of target location onlyon a random number of trials. That is, the cue and target appeared at the samelocation on 25 % of the non-neutral trials. The remaining 75% of non-neutraltrials consisted of a target appearing at one of the three locations other than theone cued.Procedure. Children were tested individually in a small room located inthe school that they were attending. Undergraduates were tested in a laboratoryin the psychology department at UBC, and seniors were tested in their homes.Completion of the entire experiment took twenty to twenty-five minutes. Adultscompleted the entire experiment in one session, children participated in twosessions.For the information cue condition, subjects were instructed to keep theireyes at fixation throughout the trial sequence. They were also instructed on howto use the different cues (e.g., the relation between arrowheads and locations)and what button to push for each target. In order to insure that the informationprovided by a cue was clear, experimenters asked each subject to indicate whichcue corresponded to which location prior to beginning the computer task. If asubject failed to remember a cue/location correspondence then instructions wererepeated. It was stressed to subjects that using the cues could improve theirperformance. Finally, subjects were be asked to respond as quickly as possiblewithout making too many errors. (See Appendix A for exact instructions)The instructions for the stimulus cue condition were similar except thatsubjects were merely told that they would see a filled circle prior to each targetpresentation. They were not instructed to use these cues in any way. (SeeAppendix B for exact instructions).Before each condition subjects were given practice in that particularcondition. Subjects who failed to obtain an accuracy level of 70% in the firstblock of practice were not used in the experiment. This happened only in the twoyoungest age groups, and even then it was very rare (3 subjects in group 1 and 2in group 2). The order of condition presentation was counterbalanced amongsubjects within each age group, as were the buttons required to respond to eachtarget. The computer recorded response times and errors. Response timeswere measured from the onset of the target and if subjects failed to respondwithin three seconds that trial was counted as an error.Eye movement scoring procedure.Frequency counts of subjects' eye movements were recorded using a timesampling procedure. For each subject in each condition, three one minutesessions were counted. The first minute was started after the first couple of trialshad passed, the second minute was recorded near the middle of a session, andthe final minute was counted just prior to end of a session, not including the endof the session. Only horizontal eye movements were counted and eyemovements between blocks were not included. Because the beep presented tosubjects following an error illicited a series of eye movements, these were notincluded in the counts. These movements would have occurred during the37intertrial interval and are therefore not relevant to the analysis.ResultsThe following section will first briefly outline the results of the overallANOVAs for each age group and condition in Experiment 1, followed by a moredetailed look at RT effects for each group in the stimulus cue and information cueconditions. Hemifield cuing effects and location effects will be presented as will adiscussion of accuracies. Finally an analysis of eye movement data will concludethe results for this Experiment.Reaction times are presented in Table 1 and percent correct scores arepresented in Table 2. These were submitted to individual analyses for each ofthe five age groups (children (mean ages=6, 8, 10); young adults; seniors). Agegroups were analyzed separately because each group's results were collectedunder different SOA conditions, as discussed in the methods section. However,children from all ages were tested under identical SOA conditions so the resultsof an age analysis for this group will be reported. As well, adults and seniorsreceived almost identical SOA conditions (the only difference was at the smallestSOA--133 ms vs. 150 ms). As a result an age analysis comparing these twogroups will also be reported below. Overall analyses consisted of repeatedmeasures ANOVAs with three factors: 1) Cue type (stimulus versus information),2) Stimulus onset asynchrony (children: 133, 250, 450 ms; young adults: 150,200, 400, 800 ms; seniors: 133, 200, 400, 800 ms), and 3) Validity (valid, invalid,neutral trials). Huynh-Feldt corrected probabilities will be reported in an effort tobe conservative in the event that the assumption of sphericity was violated in therepeated measures analyses.38Young AdultsThe overall RT ANOVA for the young adult age group yielded a significantmain effect of SOA, F(3,57)=12.537, MSE=2678.993, p<0.001 indicating that RTdecreased as SOA increased. There was a significant validity effect,F(2,38)=10.786, MS E=21639.947, p<0.001 reflecting the faster overall RTs onvalid trials. There were also two significant interactions: 1) Cue type by Validity,F(2,38)=4.796, MSE=12273.563, p<0.022 and 2) SOA by Validity,F(6,114)=2.521, MSE=4202.768, p<0.049. The first of these interactions reflectsa larger validity effect for information cues than for stimulus cues, and the secondinteraction is a result of a larger validity effect at shorter SOAs than at longerSOAs.Children Six year olds: The overall RT ANOVA for the youngest group of childrenyielded only a significant effect of validity, F(2,38)=14.952, MSE=61279.756,p<0.001 reflecting the faster responses on valid trails overall.Eight year olds: For this group the overall RT ANOVA yielded asignificant validity effect F(2,38)=15.411, MSE=83121.485, p<0.001 resultingfrom a similar pattern of results obtained with the younger children. There wasalso a significant Cue type by Validity interaction, F(2,38)=4.221,MSE=16432.598, p<0.038. These effects will be more carefully examined below.Ten year olds: The overall RT ANOVA yielded a significant effect ofvalidity as with the previous two groups of children, F(2,38)=14.549,MSE=31593.139, p<0.001. There were also two significant interactions: 1) Cuetype by Validity, F(2,38)=6.136, MSE=11679.170, p<0.010 and, 2) SOA byValidity, F(4,76)=3.998, MSE=9038.216, p<0.011.39SeniorsThe overall RT ANOVA for seniors yielded two significant main effects: 1)RTs were slower overall in the stimulus cue condition, F(1,19)=10.576,MSE=188853.002, p<0.004, and 2) an overall marginally significant validity effectreflecting the faster responses made overall on valid trials, F(2,38)=3.603,MSE=28710.152, p<0.061.Because it is more important to compare and contrast the effects of thetwo cue types individually rather than by collapsing across them, the results fromeach cue type experiment will be examined separately and in detail below. Allresults reported below are based on the overall ANOVA tables discussed aboveso MSEs for each planned comparison reported were obtained from theappropriate table. Significant results from the ANOVAs and plannedcomparisons will be discussed when relevant. Unless otherwise statedsignificance is defined as p<0.05.Stimulus Cue ExperimentYoung adults Reaction times: RT results for the young adult comparisongroup are presented in Table 1. As well, Figure 3 illustrates RT differences foryoung adults. Planned comparisons revealed that RT decreased as a function ofSOA. Specifically, there was no difference between SOAs of 150 and 200 ms, asignificant decrease between SOAs of 200 and 400 ms, 419)=2.582, p<0.012,but no difference between 400 and 800 ms. There are no overall differencesbetween the three trial types, apparently due to the switch in position of valid andinvalid RTs at approximately 325 ms. Although valid RTs are significantly faster40than invalid RTs at tne 150 ms SOA, t(19)=-2.095, p<0.038, and the 200 msSOA, 019)=-2.076, p<0.040, there are no significant differences between validand invalid RTs at the longer SOAs. By looking at Table 1 it is clear thatalthough not significant, invalid RTs were faster than valid RTs at both the 400and 800 ms SOAs. This finding is consistent with previous work demonstratinginhibition-of-return, although there is too much variability in the effect for it to besignificant in the present study. Finally, at 150 and 200 ms SOAs valid RTs aresignificantly faster than neutral RTs (019)=-2.064, p<0.041; t(19)=-3.270,p<0.001, respectively). However, at the 800 ms SOA valid RTs are significantlyslower than neutral RTs, t(19)=2.186, p<0.031. All other effects fail to reachsignificance at /-).0.05.Attention effectsAttention effects may be measured with costs and benefits, using theneutral condition as a partitioning variable. However, because neutral conditionsare inherently untrustworthy (Jonides, 1980), measuring orienting effects is oftendone more safely by subtracting valid RTs where subjects are expected to haveoriented to the correct location before responding, from invalid RTs wheresubjects are expected to have oriented to a incorrect location. This attentionmeasure thus frees conclusions from any of the possible problems associatedwith the neutral condition. Cost/Benefit analyses can be found in Appendix C.The magnitude of invalid - valid RT difference scores are illustrated inFigure 3. Invalid - valid RTs are above zero for SOAs of 150 and 200 ms anddrop below zero for the last two SOAs. This is again expected because of theinhibition-of-return result apparent in overall RTs. T-tests conducted to determineif difference scores are significantly different from zero suggest that at all SOAsthere is a significant invalid - valid RT difference (019)=3.577; 4.117; 2.76; 3,201respectively, all p<0.05). Planned comparisons on RT difference scores revealed41that the drop in magnitude of this attention effect between 200 and 400 ms ismarginally significant t(19)=1.943, p<0.057, as is the drop between 200 and 800ms, t(19)=1.973, p<0.053.Children (six years). Reaction times. Reaction times for six year olds arepresented in Table 1. Planned comparisons revealed that valid RTs are fasterthan invalid RTs overall, t(19)=-2.638, p<0.012. Exploring this effect further, itwas found that only at the 250 ms SOA is there a significant difference betweenvalid and invalid RTs, t(19)=-3.105, p<0.003. Also, valid RTs are significantlyfaster than neutral RTs at the 250 ms SOA, t(19)=-2.236, p<0.028. Although thevalidity effect appears to be present earlier than 250 ms, the lack of significanceis surprising given previous results suggesting young children show validityeffects as fast as 50 ms (Akhtar & Enns, 1989; Enns & Brodeur, 1989)Children (eight years). Reaction times . RTs for children with a mean ageof eight years are presented in Table 1. Overall planned comparisons revealed asignificant decrease in RT between SOAs of 250 and 450 ms, 419)=2.209,p<0.033. As well, there is a significant difference between all three stimulus cuetrial types. Valid RTs are faster than neutral RTs, 419)=-2.642, p<0.012, andinvalid RTs, t(19)=-5.397, p<0.001. Neutral RTs are faster overall, than invalidRTs, 419)=2.755, p<0.009. Further planned comparisons to pull apart theeffects of SOA and validity on RT show a significant difference between valid andinvalid RTs at the 133 ms SOA, t(19)=-3.516, p<0.001; the 250 ms SOA, t(19)=-2.711, p<0.008; and the 450 ms SOA, 419)=-2.008, p<0.042. In all casessubjects are faster when the stimulus cue is valid with respect to target locationthan when the cue is invalid. Neutral RTs are faster than invalid RTs at the 13342ms SOA, 419)=2.831, p<0.006, and at the 250 ms SOA they are slower thanvalid RTs, t(19)=-2.914, p<0.005. All other effects fail to reach significance(p>.05).Children (ten years). Reaction times. RTs for children with a mean age often years are presented in Table 1. Planned comparisons revealed no overalleffect of SOA. There are, however, overall validity effects. Subjects in this agegroup were faster overall at responding on valid trials than on invalid trials,t(19)=-2.745, p<0.009. As well, they were slower responding on invalid trialsthan on neutral trials, 09)=2.038, p<0.049. Further analysis revealed however,that the locus of the overall validity effect is a significant difference between validand invalid RTs at the 133 ms SOA, 09)=-2.140, p<0.036. These resultssuggest that the stimulus cue is effective by 133 ms(and probably earlier), and isbeginning to have a diminishing effect there after.Attention Effects for childrenInvalid - valid RTs for children at each SOA are presented in Figure3. Six year olds show smaller effects at the 133 ms SOA than at the two longerSOAs although t-tests suggest that no orienting effects are different from zero forthis group. Eight year olds show orienting effects that differ from zero at the 133ms SOA (t(19) = 3.371, p<0.05) and 250 ms SOA (t(19) = 2.640, p<0.05). Tenyear olds only show effects that differ from zero at the 250 ms SOA (t(19) =3.084, p<0.05).Seniors. Reaction times. Reaction times for seniors are presented inTable 1. As well, RT differences are presented in Figure 3. Planned comparisonsreveal that there is no overall SOA effect, although there is an overall validityeffect. Seniors were significantly faster to respond on valid trials than on invalidtrials 419)=-3.274, p<0.002, and neutral trials, 09)=-2.359, p<0.024. By43breaking down this effect further it is revealed that valid trials are faster thaninvalid trials at the 133 and 200 ms SOAs (419)=-2.352; -2.329, p<0.020; 0.022respectively). As well, valid trials are significantly faster than neutral trials at the200 ms SOA, t(19)=-2.179, p<0.031. It appears that seniors are able to takeadvantage of valid stimulus cue information as early as 133 ms afterpresentation, and although this effect does not reverse itself in a manner thatsuggests inhibition of return, it does appear to diminish somewhat.Attention EffectsAs with the above age groups, invalid - valid RT difference scores werecalculated for seniors in attempt to get a cleaner measure of the effect oforienting attention in response to a stimulus cue. For seniors, the differencescores are largest at the 133 and 200 ms SOAs. They drop, but are still quitelarge at the 400 and 800 ms SOAs. Only the difference score for the 133 msSOA is significantly different from zero, t(19)=3.129, p<0.05.Age Comparisons. Although the different SOAs for individual age groupsmakes it impossible to do overall statistical age comparisons, it is possible tosimply look at patterns across age groups. First, it is important to note that allages show evidence of attention alignment in response to a stimulus cue,although the magnitude and generality of this result varies with age. For youngadults the effect is present early (150 ms) and then disappears by 200 ms andappears to reverse at some point before 400 ms. The young adult group is theonly group to show inhibition-of-return. The youngest age group does not show avalidity effect until 250 ms whereas the other two groups of children show theeffect at 133 ms. As mentioned above the lack of a stimulus cue effect by 133ms for young children is somewhat of a surprise. In fact eight year olds show thevalidity effect at all three SOAs. The oldest group of children more closely4445resembles the young adults, in that the effect disappears statistically after 133 msalthough it does not reverse itself. The seniors also show a validity effect by 133ms, but unlike the young adults the effect is still present at 200 ms. Like theoldest group of children however, the validity effect disappears statistically butdoes not appear to reverse itself. I will return to the age dependence ofinhibition-of-return in the discussion.Because all three groups of children were tested under the same SOAconditions, it was possible to compute an overall ANOVA on reaction times withage as a between subjects factor and SOA and stimulus cue validity as repeatedmeasures factors. There was a main effect of age, F(2,57)=15.984, in thisanalysis, MSE=196200.682, p<0.001 suggesting that overall reaction timesdecreased with the increasing age of subjects. There was also a main effect ofvalidity, F(2,114)=17.599, MSE=4705.403, p<0.001 suggesting that valid RTs arefaster than invalid RTs overall. Age did not interact with SOA or validity in anyfashion, suggesting that SOA and validity behaved similarly with respect to RTsfor all age groups.Although the SOAs for young adults and seniors differ slightly, it is unlikelythat a difference in the earliest SOA of 17 ms is practically important. All otherSOAs are identical. Therefore it seemed reasonable to conduct an overallANOVA with age as a between subjects factor with two levels (young adults;seniors) and SOA and validity as repeated measures factors as was done withthe children. The analysis revealed an overall age effect suggesting that seniorswere slower to respond overall than were young adults, F(1,38)=51.17,MSE=236482.503, p<0.001. Planned comparisons reveal that this effect is dueto a significant difference between valid RTs and invalid RTs, t(39)=-2.382,p<0.020. Age did not interact significantly with either SOA or validity suggestingthat the effect of the stimulus cue was similar for both age groups.Invalid - valid difference scores also show interesting age trends. For thefour oldest age groups (children ages 8 & 10 years; young adults; seniors)difference scores are large at the early SOAs and then decline at the longerSOAs. For young adults they become negative scores at the longer SOAsindicating inhibition-of-return. This pattern of results for the older groups can becontrasted with the pattern of results in the youngest age group (6 years old).For this group difference scores start out relatively small and then peak at the250 ms SOA. Thereafter they again drop. Clearly these results are at leastsuggestive of developmental differences in the time course of orienting attentionin response to stimulus cues. Stimulus cues produce maximum orienting effectsearlier for older children and adults than for young children.Information Cue ExperimentYoung Adults. Reaction times . RTs for the young adult comparisongroup are presented in Table 1. RT differences for this group are presented inFigure 4. Planned comparisons revealed that RTs decreased as a function ofSOA. Specifically, there was a significant drop between 200 and 400 ms SOAs,t(19)=3.185, p<0.002, and no significant difference between 150 and 200 msSOAs or between 400 and 800 ms SOAs.There is also a significant overall validity effect. Valid RTs are significantlyfaster than invalid RTs 419)=-4.971, p<0.001, and faster than neutral RTs t(19)=-3.464, p<0.001. Further investigation of the relationship between SOA andvalidity shows a significant difference between valid and invalid RTs at all fourSOAs (419)=-3.108; -4.355; -3.085; -2.82, p<0.002; 0.001; 0.003; 0.006respectively). As well, valid RTs are faster than neutral RTs at SOAs 150, 20046and 400 ms (419)=-3.125; -2.526; -2.379, p<0.002; 0.013; 0.019). Clearly youngadults have no problem using information cues to align attentional resources tolocations in visual space.Attention EffectsTo obtain a measure of orienting that is not subject to possible problemswith the neutral condition used, valid RTs were subtracted from invalid RTs toobtain a measure of the magnitude of the orienting effect. Orienting effects arepresented in Figure 4. Invalid - valid difference scores are large for young adultsat the first three SOAs and then decrease at the 800 ms SOA. T-tests revealedno significant effects suggesting that the difference scores in this group do notdiffer significantly from zero. This is somewhat surprising given the magnitude ofthe scores. As well planned comparisons revealed no differences in invalid -valid RTs related to SOA. This should be expected given that the scores arevery similar across SOAs.Children (six years). Reaction times. RTs for six year olds are presentedin Table 1. Orienting effects for children from each age group are presented inFigure 4. Planned comparisons suggest that there is no effect of SOA on overallRT. However, there is an overall difference between valid and invalid RTs forchildren in this age group, 419)=-2.698, p<0.010. The locus of this effect is at the133 ms SOA. Children are significantly faster to respond on valid trials than oninvalid trials t(19)=-2.924, p<0.004, or neutral trials, t(19)=-2.735, p<0.008.Clearly young children are able to use information cues to align their attentionalresources to a particular location in visual space. This ability, however, appearsas early as 133 ms following the presentation of the cue (and perhaps earlier),but decays at longer SOAs.47Children (eight years). Reaction times. RTs for children with a mean ageof eight years are presented in Table 1. As with the previous group of children,SOA does not significantly effect RT. Validity, on the other hand, does. Thisgroup of children were significantly faster on valid trials than on invalid trials,09)=-3.215, p<0.003, or neutral trials 09)=-4.567, p<0.001. As is apparent inthe Figure 4, the main locus of this validity effect is the difference between validand invalid RTs at the 133 ms SOA, 09)=-4.046, p<0.001. Valid RTs are faster.Valid RTs are also faster than neutral RTs at the 133 and the 450 ms SOAs(09)=-4.445; -2.086, p<0.001; 0.040 respectively). Once again, it appears asthough young children can use information cues to align attention as early as 133ms. This effect diminished with longer SOAs however.Children (ten years). Reaction times. RTs for children with a mean age often years are presented in Table 1. As with the previous two groups of children,SOA does not effect overall RT. Once again, however, valid RTs are significantlyfaster than invalid RTs, 09)=-4.851, p<0.001, and neutral RTs 09)=-5.644,p<0.001. Again in line with the previous two groups of children, the locus of thevalidity effect is at the 133 ms SOA, where valid RTs are significantly faster thaninvalid RTs and neutral RTs (09)=-6.051, p<0.001 for both). As discussedabove, this pattern of results suggests that children in this group are able to useinformation cues as early as 133 ms (and perhaps earlier), and this cueing effectbegins to diminish at some point thereafter.Attention effects for childrenThe pattern of invalid - valid difference scores for children is presented inFigure 4. An analysis of the effects in all children combined revealed largeeffects at the 133 ms SOA and then a drop for the longer SOAs. Only the48difference score at the 133 ms SOA is significantly greater than zero for 6, 8 andten year olds (t(19) = 5.417; 8.101; 11.090, p<0.05).Seniors. Reaction times. The reaction time results for seniors arepresented in Table 1. RT differences for seniors are presented in Figure 4. Asis apparent from the table seniors' RTs decreased with SOA. There is asignificant drop in RT between the 133 ms SOA and the 400 and 800 ms SOAs(t(19)=2.430; 2.042, p<0.018; 0.046 respectively). There is no effect ofinformation cue validity on overall RT. However, when the relationship betweenSOA and validity is scrutinized, valid RTs appear to be faster than invalid RTsand neutral RTs at the longest, 800 ms SOA (t(19)=-1.967; -3.298, p<0.052;0.001). Clearly seniors in this experiment were unable to use information cues toalign their attentional resources to a location in visual space before 800 ms. It isunknown whether the effect becomes larger and/or diminishes in a fashionsimilar to that of the children discussed above.Attention effectsInvalid - valid difference scores for seniors are presented in Figure 4. Indoing so it is apparent that the pattern of results is not particularly consistent. At133 ms the difference score is a reasonable size, whereas at the next two SOAsthe difference scores drop below zero only to appear above zero again at 800ms. According to this pattern, subjects were faster to respond on invalid trialsthan on valid trials at the 200 and 400 ms SOAs. Difference scores for these twoSOAs are not however, significantly different from zero. In fact, only at the 800ms SOA is the difference score greater than zero, t(19)=3.319, p<0.05,suggesting that only at this SOA is the information cue leading to consistenteffects on responding.49Age Comparisons. As with the stimulus cue experiment, the differentSOAs for different age groups makes it impossible to make direct statistical agecomparisons. Again, it is possible to make comparisons among age groups onthe bases of their RT patterns. Of primary importance is the ability of subjectsvarying in age to use the location information provided by information cues toalign their attentional resources. Clearly the young adult comparison group isable to do so as early as 150 ms, and furthermore, they are able to maintain theinformation provided long enough to produce a significant validity effect throughto 800 ms. The children in all three groups also show evidence of being able touse information cues early (133 ms), but unlike the young adults, they seemunable to maintain the information provided much longer than that. Although thepicture presented by the graphs suggest that the effect may still be theresomewhat, it fails to reach significance at the longer SOAs for any of the childrengroups. The seniors, on the other hand, appear to have a different source ofdifficulty in using information cues. It appears that they require a substantialamount of time to use the information provided by the cues to aid targetidentification performance. In fact there is no sign of a Validity effect for seniorsuntil 800 ms, and there is a possibility that this effect may be tainted with speedaccuracy trade-offs.As with the stimulus cue experiment it was possible to run an overallANOVA on the children's data with age as a between subjects factor and SOAand validity as repeated measures factors. The analysis revealed a significantmain effect of age, F(2,57)=13.495, MSE=200439.718, p<0.001, suggesting thatoverall RT decreases as age increases. There is a significant main effect ofvalidity, F(2,114)=24.246, MSE=3957.491, p<0.001, suggesting that valid RTsare faster than Invalid RTs overall. Finally there is a significant SOA by validity50interaction F(4,228)=11.741, MSE=3168.334, p<0.001, reflecting a larger validityeffect at the 133 ms SOA for all age groups. Not surprisingly, given all agegroups showed identical validity patterns over the three SOAs, age did notinteract statistically with SOA or validity.Although seniors and young adults did not receive identical SOAconditions, they are similar enough to warrant an overall ANOVA as discussedwith the stimulus cue condition. In fact, the only SOA difference is that the lowestSOA for adults was 150 ms and the lowest SOA for seniors was 133 ms. It isunlikely that 17 ms makes any practical difference. The analysis revealed anoverall age effect suggesting that seniors are slower to respond overall than areyoung adults, F(1,38)=42.229, MSE=220852.615, p<0.001. There is also anoverall SOA effect reflecting faster reaction times at longer SOAs,F(3,114)=7.669, MSE=5572.508, p<0.001 and an overall validity effect,F(2,76)=13.512, MSE=2148.233, p<0.001 reflecting faster responses on validthan on invalid and neutral trials. These overall effects are compromised bysignificant interaction however. There is a significant validity by age interaction,F(2,76)=5.746, MSE=2148.233, p<0.005 that can be accounted for by thepresence of a validity effect in young adults and not in seniors. As well there is asignificant SOA by validity by age interaction, F(6,228)=3.221, MSE=1899.429,p<0.011 reflecting the presence of a validity effect for young adults at all SOAsbut only at the longest (800 ms) SOA for seniors.In terms of attention effects, it appears that young adults and children areboth using the information cues presented to align attentional resources.Seniors on the other hand do not appear to use the cues in a manner similar tothe other groups. In fact it appears as though they do not use them at all until the800 ms SOA. This trend in the results is supported by the invalid - validdifference scores. They remain larger for young adults throughout the time5152course of processing, but drop off early for children of all ages. Again, seniors donot show a difference score of any magnitude until 800 ms.As with overall RTs and accuracies invalid -valid difference scores werealso analyzed for the three groups of children in an overall ANOVA with age as abetween groups factor. Given the consistency of results exhibited by children inall three groups it is not surprising that age did not produce a main effect, nor didit interact with any of the remaining repeated measures factors.Hemifield AnalysesBecause there were four possible locations for cues and targets, it waspossible for cues to be invalid but still be presented in the same visual hemifield(left or right) as the target (within hemifield invalid trials). Cues could also beinvalid and appear in the opposite visual hemifield as the target (across hemifieldinvalid trials).In order to look for differences in the validity effects of across versus withinhemifield cueing, an overall analysis was calculated for each age group withthree repeated measures factors: 1) Cue type (stimulus , information); 2) SOA(133, 250, 450 ms for children; 150, 200, 400, 800 for young adults; 133, 200,400, 800 for seniors), and 3) Validity (valid, within hemifield invalid, acrosshemifield invalid). All three age groups of children were collapsed because of thelack of age related interactions found in overall RT analyses.In order to avoid losing complete subjects in the ANOVA, missing cells inthe above analysis were replaced with the mean value of the appropriatecondition. Missing cells are the result of two possible factors: 1) the smallnumber of trials that fall into each condition make it possible to make an error ons an^nals are randomly assignedto condition by the program and it is possible that for any one subject a specificcondition was not represented. Although it is recognized that replacing cells witha mean value may potentially create artifact effects by reducing variability, ananalysis before replacement was conducted to insure that no new significanteffects resulted from replacement. Because of the relatively small number ofreplacements necessary (less than 5% of cells were replaced in all age groups ineach condition) it was found that no new effects were created by adding themeans. The level of significance for significant effects did increase somewhathowever.Stimulus Cue ExperimentYoung Adults. Mean RTs for valid, invalid-within hemifield and invalid-across hemifield trials for the young adult age group are presented in Figure 5.Planned comparisons revealed no significant overall SOA or validity effects. Infact there are actually no significant differences between validity conditions whencompared individually at each SOA. This is unfortunate because the graphillustrates a rather intriguing pattern. That is, both invalid-within and invalid-across hemifield trials have long mean RTs (within trials being longer) at the earlySOAs that drop below valid RTs by 400 ms (inhibition-of-return), after which onlythe invalid-across hemifield trials return to the level of valid RTs.Although it would be expected that invalid cues presented in the samehemifield as the target would lead to smaller cueing effects than if the invalid cueappeared in the opposite hemifield, the reverse of that seems apparent for thisyoung adult group. The larger cueing effect is found for invalid-within trials at the150 ms SOA, and the longer lasting inhibition-of-return is found for invalid-withinhemifield trials. These results suggest that although cueing does not appear tobe hemifield defined, inhibition-of-return may be. However, because these53differences fail to reach significance, the only conclusion that we can safely takeaway is that there are no differences in hemifield cueing effects.Children. Mean RTs for valid, invalid-within and invalid-across hemifieldtrials for the children are presented in Figure 6. Planned comparisons based onthe overall ANOVA revealed no significant SOA effect, but valid RTs were foundto be significantly faster than invalid-within hemifield invalid RTs, t(59)=5.063,p<0.001, and faster than invalid-across hemifield RTs, t(59)=3.536, p<0.001.Furthermore, these differences hold if you look at the 133 and 250 ms SOAsindividually (all p<0.05). At the 450 ms SOA however, valid RTs are found to besignificantly faster than invalid-within hemifield RTs only, 459)=3.150, p<0.002.To further confirm this effect, invalid-within hemifield RTs are significantly longerthan invalid-across hemifield RTs at the 450 ms SOA, 459)=1.961, p<0.051.For this group of children there is a suggestion that hemifield differencesmay play a role at the longer SOAs. Specifically, across hemifield cueing effectsappear to diminish for children around 450 ms whereas the same is not true forwithin hemifield cueing effects. Although children show no evidence of inhibition-of-return, it is possible that with longer SOAs at least the invalid-across hemifieldtrials may produce such an effect.Seniors  . Mean RTs for valid, invalid-within and invalid-across hemifieldtrials for seniors are presented in Figure 7. Planned comparisons based on theoverall ANOVA reveal no significant overall effect of SOA, but like the children,seniors were faster to respond on valid trials than on invalid-within hemifield trials(t(19).2.745, p<0.009), and on invalid-across hemifield trials (419)=2.511,p<0.016). When broken down by SOA, it appears as though for the 150 msS_OA,valid Ric are faster than invalithwithiaRTs, t(19)-2.1-79, p<0.031. Invalid 54across RTs do not differ from valid or invalid-within RTs. This is true at the 400ms SOA as well, 419)=1.949, p<0.053. At the 800 ms SOA, however, the trendreverses and invalid-across RTs are significantly slower than valid RTs,t(19)=2.119, p<0.036. This pattern suggests that at early SOAs both invalid-within and invalid-across hemifield cueing are factors for seniors, but it is possiblethat with longer SOAs the effectiveness of invalid-within hemifield cueingdiminishes.Information Cue ExperimentYoung Adults. Figure 8 illustrates hemifield cueing effects in theinformation cue experiment for young adults. Planned comparisons of thesemeans based on the overall ANOVA indicate that there is an overall decrease inRT between 200 and 400 ms SOAs, 419)=4.638, p<0.001, and a significantoverall increase in RT between 400 and 800 ms SOAs, t(19)=2.175, p<0.034.As well, valid RTs are faster than invalid-across hemifield RTs overall,t(19)=2.781, p<0.008, but no overall difference between valid RTs and invalid-within hemifield RTs. Broken down by SOA, it appears as though only at the 200ms SOA are invalid-within RTs slower than valid RTs (419)=3.454, p<0.001)whereas valid RTs are faster than invalid-across RTs at 200, 400 and 800 msSOAs (t(19)=3.765; 1.988; 2.239, p<0.001; 0.049; 0.027 respectively).For the information cue condition it appears as though there are hemifieldinfluences on the nature of cueing effects. Specifically, invalid-across hemifieldcueing appears to be stronger than invalid-within hemifield cueing. Possibleexplanations for this will be addressed in the discussion.55Children. Figure 9 depicts invalid-within and invalid-across hemifieldma ion cue experiment. Clearly the only- .. ''^ - 5 ' -cueing effect is apparent at the 133 ms SOA. This is of course to be expectedfollowing the analysis of overall RTs presented earlier. Planned comparisonsbased on the overall ANOVA confirm the picture provided in Figure 9. Valid trialsare significantly faster than invalid-within hemifield trials, 419)=2.907, p<0.004,and faster than invalid-across hemifield trials, 419)=3.008, p<0.003, at the 133ms SOA only.For children there appears to be no difference in cueing effects as afunction of the hemifield location of targets and cues. This is true for both themagnitude and the time course of cueing effects.Seniors. In the overall analysis of seniors RTs in the information cueexperiment, it was found that there was no cueing effect to speak of until the 800ms SOA. Figure 10 illustrates the valid, invalid-within and invalid-acrosshemifield trials for seniors in the information cue experiment. As is apparent,dividing invalid trials does very little to change the picture outlined above. In thisanalysis there are no significant effects and the data appear to be rather messy.It is interesting to note however, that at the 800 ms SOA, only invalid-acrosshemifield RTs are slower than valid RTs (although not significantly so) suggestingthat the overall effect found in the main analysis at this SOA is chiefly due toinvalid-across hemifield cueing effects.Location AnalysesIn this experiment subjects were presented cues and targets at fourdifferent locations. Two of these locations were relatively close and to the leftand right of fixation, and the remaining two were farther away but also to the leftand^ toilixatiom-144-e-relef-tertleterrnfrreitoriffects are different in the56• • • .... a A I - 41^ii - •periphery than they are in near fixation, location analyses were conducted. Inthese analyses RTs were divided according to target location. In doing so aconsiderable number of missing cells were created because not all subjects sawall possible combinations of SOA, validity and location conditions. To minimizethis problem, data were collapsed across certain SOAs. The SOAs collapsed forgroups differed depending on the pattern of orienting effect demonstrated in theInvalid - Valid RT difference score analyses. In other words, SOAs thatcontained an orienting effect were collapsed, and those that did not werecollapsed. In most cases this resulted in two SOA groups for each age, althoughin some instances all SOAs were collapsed. The specific nature of thiscollapsing will be presented with the results for each group. The remainingmissing cells (less than 6% for all groups in each condition) were filled with themean RT for the relevant condition as was done above in the hemifield analyses.Because collapsing was not always the same for the two cueing conditionsin a given group, separate ANOVAs were conducted on each age group for eachcueing experiment. Because attention effects are of main concern for thisanalyses, invalid-valid RT difference scores were used as the independentmeasure. The overall analysis for each group included a repeated measureslocation factor and in most cases an SOA factor. With the exception of childrenin the stimulus cue experiment, all groups' difference scores were divided intotwo SOA conditions (long; short). Difference scores for children in the stimuluscue condition were collapsed across all SOA conditions.Stimulus cue experimentYoung Adults. The invalid-valid difference scores broken down by targetlocation and SOA are presented for the young adult group in Figure 11. Thegroup was Obtained by 6)1-lapsing the 150 and 20057ms SOA conditions, and the long SOA condition was obtained by collapsing the400 and 800 ms SOAs. Although it appears as though the largest validity effectis in location four (that is, the far right hand location), there is actually nosignificant location effect. In fact the only significant planned comparison is adifference between the difference scores in the fourth location for the long andshort SOAs. This result simply replicates the inhibition-of-return resultspresented earlier.Children. The invalid-valid difference scores broken down by targetlocation for children are presented in Figure 12. For this group all SOAs werecollapsed because all SOAs showed evidence of a cueing effect in the overallanalysis of invalid-valid difference scores. The ANOVA conducted on children'sdifference scores revealed a significant location effect, F(3,174)=2.82,MSE=50629.444, p<0.046. Looking at this overall effect more closely, plannedcomparisons show that the validity effects for locations one and three aresignificantly smaller than for location four (t(59)=2.562; 2.469, p<0.011; 0.014respectively)Seniors. The invalid-valid difference scores broken down by location forseniors are presented in Figure 13. For this group the short SOA is acombination of the 133 ms and 200 ms SOA conditions. The long SOA conditionis a combination of the 400 ms and 800 ms SOA conditions. The overall ANOVAfor this group revealed no significant effects although planned comparisonsrevealed that the validity effect at location four in the long SOA condition wassignificantly different from all other validity effects. There were no significantdifferences in the size of effect across the four locations at the shorter SOA. At- --the -validity effect --at TOCatibn one was larger than at location four,58t(19)=2.998, p<0.004; the effect at location two was larger than at location four,t(19)=2.078, p<0.042, and the effect at location three was close to beingsignificantly larger than at location four, t(19)=1.872, p<0.066. This pattern ofresults represents the fact that at location four in the long SOA condition there isa negative validity effect. That is, for this condition, unlike any of the others,invalid trials were actually faster on average than were valid trials. In the overallRT analyses, seniors did not show evidence of inhibition-of-return, although thelocation analysis suggests that although overall there was no inhibition-of-returnat longer SOAs, there may have been at location four alone.Age Comparisons. Given the above discussion it would appear thatlocation is having very little effect on the magnitude of attentional effects. Thisanalysis was initially conducted to examine a possible age confound of peripheralacuity. Because all age groups show the same pattern of location effects it isunlikely that poor peripheral acuity is producing the effects or lack of effects inchildren and/or seniors. It is curious to note however, that for all age groups thefourth location, that is, the far right hand location seems to be special in that itproduces the largest effects, whether they be facilitation or inhibition effects.Explanations for two parts of the above result are needed: 1) Why a rightlocation? and 2) Why a far location? The answer to the first question may befound in neurophysiology and the role that the left hemisphere plays in orienting.The second question may be answered by postulating that because the nearlocations are close to fixation the attention movements required are too small toproduce any measurable benefits or costs.59Information Cue ExperimentYoung Adults. The invalid-valid difference scores broken down by locationfor the young adult group are presented in Figure 14. For this group andcondition, the short SOA condition is a combination of the 150 ms and the 200ms SOA conditions. The long SOA condition is a combination of the 400 ms and800 ms SOA conditions. The overall ANOVA revealed no significant effects andplanned comparisons based on the SOA by location interaction also revealed nosignificant effects. There, in fact, was only one effect that even came close, andthat is the rather large difference between the validity effect for the short and longSOAs at location four t(19)=1.765, p<0.083.Children. The invalid-valid difference scores broken down by location forthe children are presented in Figure 15. For this group, the short SOA conditionis 133 ms SOA condition, and the long SOA condition is a combination of the 250ms and 450 ms conditions. The overall analysis for this group revealed nosignificant effects, and there was only one significant planned comparison basedon the SOA by location interaction. That is, the validity effect at location one inthe long SOA condition is significantly smaller than the validity effect at locationfour in the long SOA condition, t(19)=2.188, p<0.030. Otherwise, location doesnot seem to play an important role for this group.Seniors. Invalid-valid difference scores broken down by location forseniors are presented in Figure 16. For this group the short SOA condition is acombination of the 133 ms, 200 ms and 400 ms SOA conditions. The long SOAcondition is the 800 ms SOA condition. The overall ANOVA for this group onceagain failed to produce significant overall effects. Planned comparisons similarly60did is is not surprising considering the noisyI•^•• — -pattern of results exhibited for seniors in the information cue experiment basedon overall RTs. The large difference between location one and two for the longSOA condition does however approach significance, 419)=-1.797, p<0.077.Age comparisons. For the information cue experiment, as for the stimuluscue experiment, location does not seem to play a critical role in defining cueingeffects. Furthermore this is true across ages providing evidence that peripheralacuity differences are not producing artifactual effects. It is once againinteresting to note that for the information cue experiment location four againappears to produce larger effects, at least at longer SOAs. As in the stimuluscue condition it is assumed that this is due to the role of the left hemisphere instrategic orienting and the closeness of the near locations to fixation.Accuracy AnalysesAccuracies for each age group presented in Table 2 were subjected to thesame overall ANOVAs as RTs. In all but one case the pattern of results foraccuracies mimics the pattern of results for RTs suggesting that speed-accuracytrade offs were not a problem in this experiment. The results however, rarelyreach significance, most likely due to the high level of accurate responding by allages. Young adults' mean percent correct scores ranged from 92% to 99% in thestimulus cue condition and 93% to 98% in the information cue condition. Six yearolds' mean percent correct scores in the stimulus cue ranged from 79% to 86%and 82% to 89% in the information cue condition. Eight year olds' mean percentcorrect scores ranged from 87% to 93% in the stimulus cue condition and 89% to94% in the information cue condition. Ten year olds' level of accuracy ranged61from ion and 91% to 95% in the• • •^• • on o'information cue condition. Finally, seniors' accuraccies ranged from 88% to 95%in the stimulus cue condition and 88% to 94% in the information cue condition.The exception to the trend of RTs and accuracies being similar is found forseniors in the information cue condition. In this condition seniors only showorienting effects at the 800 ms SOA according to RT data, but according toaccuracy data they fail to show orienting everywhere. In fact, they are lessaccurate on valid trials than on invalid trials at the 800 ms SOA suggesting thatthe orienting effect may be a product of trading speed for accuracy. This effect isnot significant so it must beinterpreted with caution. If it is indeed the case thatthe orienting effect is a product of trade-offs than it would strengthen theargument further that seniors have difficulty using information cues. Thus, in thiscase as in all the others, accuracy results to not change any conclusions basedon RTs alone.Eye Movement DataDescriptive Data . Eye movement counts for all ages in both cueingconditions are presented in Table 3. Clearly all subjects made some eyemovements but even for groups that made the largest number of eyemovements, the total count for three minutes of scoring is lower than would beexpected in three minutes of a free eye movement condition given the speed atwhich eye movements are made. This suggests that at some level all groupsattenuated eye movements in response to instructions.An overall analysis of variance revealed a significant effect of age,F(4,88)=27.095, MSE=709.524, p<0.001, suggesting that children moved theireyes more than adults. This is further confirmed by planned comparisons thatsuggestthatatthough-cmade more eye movements than adults62F(1,88)=78.763, MSE=709.524, p<0.001, seniors did not make more eyemovements than did young adults, F(1,88)=.765, MSE=709.524, p<0.384. Therewas a significant effect of cue, F(1,88)=4.696, MSE=94.096, p<0.033 resultingfrom a slightly higher number of eye movements in the information cue condition.There was also a significant age by cue interaction F(4,88)=4.874, MSE=94.096,p<0.001 that can be localized to a decrease in the number of eye movementsmade between the ages of eight and ten years for the stimulus cue condition butnot for the information cue condition F(1,88)=10.046, MSE=94.096, p<0.002.Eye movements and attention effects. Most important for the presentanalysis is the role that eye movements play in mediating attention effects, andspecifically, age differences in attention effects. In order to determine if agedifferences in attention effects were due to age difference in eye movements,subjects' eye movement scores were entered as a covariate in the analysis ofinvalid-valid reaction time scores. Four separate ANCOVAs were conducted withage and SOA as variables and eye movements as the covariate. Two for thechildren, one for each cueing condition and two for the adults (young adults andseniors combined), one for each cueing condition.For all analyses, eye movements did not appear to contribute significantlyto age and attention effects. The eye movement covariate was only significant inthe adult ANCOVA in the information cue condition, F(1,36)=5.191,MSE=3689.216, p<0.029. Despite this, there was no change in the SOA or ageeffects, and least square means from the ANCOVA exhibit a similar pattern to themeans from the ANOVA. This is not surprising given that seniors did not movetheir eyes more frequently than did young adults. For the children, least squaremeans for the six year olds varied from the original means because of the loss offive subjects in this group. Unfortunately, video recordings of these subjects' eye----mevement-s-were63Nonetheless, the eye movement covariates for children in both cueing conditionswere not significant contributors to the variance.The above separate analyses of children and adults was necessary giventhe discrepant SOAs for these two groups. As a result the only thing we candetermine about the role of eye movements in age differences in attention effectsis that within each group, those that made more eye movements did not producedifferent orienting effects than those that made fewer movements. In order toimprove on the age related information somewhat, two ANCOVAs (one for eachcue type) for children and adults together were conducted. In this analysis thethree SOAs used with children (133, 250, 450 ms) were equated with three of thefour SOAs used with adults (150, 200, 400 ms). This treatment of SOA wasbased on an assumption that the differences were too small to be relevant, andthe 800 ms SOA for adults was dropped because there was no long SOAcounterpart for children. For the ANCOVAs age and SOA were factor and eyemovements served as the covariate. As in the above analyses, the eye covariatewas not significant for either the stimulus cue or information cue experiment. AnSOA effect in the stimulus cue condition that was significant without the eyecovariate failed to reach significance when eye movements were covaried out.Again this is most likely due to a reduction of power resulting from lost subjects.There were no effect changes in the information cue experiment and an analysisof the least square means indicated that only for the six year olds is there anynotable difference from standard means. As above, this is due to the loss ofsubjects.Although the above analysis must be treated with caution because of theunderlying assumptions made on the part of the researcher, it does support theconclusion that eye movements are not creating age effects. It is believed that64the information provided by this less than adequate analysis is better than noinformation addressing the issue.DiscussionClearly the most important finding of Experiment 1 is that attentionalignments mediated by stimulus cues show remarkably little change with agewhereas alignments mediated by information cues show considerable change. Itappears as though children and seniors are able to use stimulus cues in amanner similar to young adults. Orienting effects appear relatively early for allages although the magnitude of cueing effects is larger for children than adults.Also, there is no reversal of cueing effects around 400 ms for children or seniorsas there is for young adults. Children, like young adults, are also able to processand use information cues relatively quickly. Young children, however, do seemto have some difficulty sustaining their attention at cued locations for longerdurations. Seniors on the other hand do not seem to be able to use informationcues as efficiently as do young adults. That is, they require a longer period oftime to employ the strategies necessary to use information cues. Finally, becauseseniors only exhibited endogenous orienting effects at 800 ms it was not possibleto determine if they could sustain attention at cued locations.In terms of hemifield cueing differences, it appears as though it is possibleto get cueing at all ages both within and across a hemifield. There aredifferences in these effects depending on the type of cue used however. Acrosshemifield cueing differences seem to be more pronounced in the information cuecondition. For the stimulus cue experiment it appears as though inhibition-of-return is affected more by hemifield than are basic cueing effects.65Finally, it is interesting to note that age differences in attention alignmenteffects cannot be explained by either peripheral acuity differences, or eyemovement differences. These issues, and those mentioned above will bediscussed further in the general discussion following a look at the interactionbetween information and stimulus cueing effects in Experiment 2.Experiment 2In Experiment 1 developmental differences in two forms of orienting wereexamined separately. Although age changes were noted for each type oforienting, it was not possible to access how and if these two forms of orientinginteract when placed in situations where they either compliment or compete withone another. In other words, how do these two systems function when both arecalled upon in the same situation? This is often the case when we are trying tofunction in everyday situations. For example, when a person is driving theyshould be consciously directing their attention to what lies ahead, but it ispossible that at any given time something will occur in their visual periphery thatwill automatically attract their attention such as a pedestrian running on to thestreet. In situations like this which attention alignment system takes precedence?How does timing effect this? Are there age related differences in how theattention alignment systems interact? These are all questions that are addressedin Experiment 2.In order to experimentally mimic the example given above it wasnecessary to present subjects with both a stimulus and an information cue on66each  IriaL_By_presenting- - •^• same situation it was possible tohave the automatic and strategic cues placed in competition in somecircumstances, or provide the same information in others and thereby provideanswers to the questions listed.MethodSubjects. Twenty-four six year olds (mean age=6.8 years, s.d.=0.24; 12females), 16 ten year olds (mean age=9.94, s.d.=0.37; 8 females),16 youngadults (mean age=25.4 years, s.d.=3.5; 5 females), and 20 seniors (meanage=73.8, s.d.=6.2; 12 females) participated in Experiment 2. As before, childrenwere recruited from the Vancouver School Board. Because the differencesbetween 6 and 10 year olds in Experiment 1 were sufficient to illustratedevelopmental differences in early school years, no 8 year old sample wasrecruited for Experiment 2. Young adults were selected from the UBCPsychology Department undergraduate subject pool and seniors were volunteersthat responded to an advertisement in a community newspaper. Twelve of theseniors in Experiment 2 also participated in Experiment 1. Undergraduates weregiven course credit points for participating and seniors were given a five dollarhonorarium.All subjects were screened for serious visual impairment (e.g., cataractsand glaucoma in the elderly). A total of 26 seniors participated in the study, six ofwhich were eliminated on the bases of potential visual impairment such asprevious cataract surgery or the beginnings of glaucoma. The remainingsubjects all had normal or corrected to normal vision although many of the olderadults required bifocals for correction.Information on the number of years of education was also obtained foreach subject. Children had an average of 1 and 4 years of education for each67group respectively. Young adults had an average of 16.75 years of educationand seniors had an average of 13.6 years of education.Stimuli and Apparatus. 1) Data collection: Stimulus presentation,feedback and data collection were controlled by a Macintosh Plus with hard drivefor undergraduates and a Macintosh SE for children and seniors. Responseswere made by pushing one of two keyboard buttons on any given trial (M or N).Each button was covered with a sticker that illustrated one of the two possibletarget items. Subjects were seated approximately 45 cm in front of the computerwith their fingers left resting on the keyboard buttons. The testing room wasilluminated with normal fluorescent lighting with the exception of those elderlysubjects tested in their own homes.2) Eye movement monitoring: Subjects' eyes were monitored in the samemanner as in Experiment 1.3) Stimulus presentation: Stimulus presentation was the same as inExperiment 1 with the following exceptions (see also Figure 17).An information cue was presented at fixation as in Experiment 1.Following the information cue there was a cue to cue SOA that intervenedbetween the information cue and the presentation of the stimulus cue. Theexperiment was initially set up to test the following five cue to cue SOAs; 50, 100,200, 400 or 800 ms, but due to the same error discussed in Experiment 1 data onSOAs of 133, 150, 400 and 800 ms were actually collected. Althoughunfortunate, the SOAs that were tested still provided a considerable range ofinformation. The stimulus cue portion of Experiment 2 was the same as thestimulus cue condition in Experiment 1. The cue-target SOA that intervenedbetween the stimulus cue and the target was 133 ms long.The relationship between cues and target in this experiment can bebroken do I I...Te_^, • -^a• e . Both types of cues were68valid, neutral or invalid with respect to identifying target location on any giventrial. All possible combinations of cue types and validities were tested providingbaseline trials, information cue control trials where only the information cue wasinformative, and stimulus cue control trials where only the stimulus cue wasinformative. There were also trials where the information provided by the twocues was the same (whether valid or invalid), and trials where the two cuesprovided contradictory information. All subjects were presented all trial typesmixed randomly among five blocks containing 48 trials each.Procedure. The data collection procedure for Experiment 2 wasessentially the same as in Experiment 1. Subjects were instructed to use theinformation cues and told only that they would see the stimulus cue. (seeAppendix D for complete instructions).Eye movement scoring procedure.Eye movements were handled in a manner similar to that in Experiment 1.Three minutes of scoring were sampled from each session in Experiment 2.Results and DiscussionReaction times and accuracies for each age group were each subjected toseparate 2 factor ANOVAs with two within subject factors (SOA: 140, 400, 800ms, and Validity: valid-valid(VV), valid-invalid(VI), valid-neutral(VN), invalid-valid(IV), invalid-invalid(II), invalid-neutral(IN), neutral-valid(NV), neutral-invalid(N1), neutral-neutral(NN)). For each level of the validity factor there is alabel that indicates the validity of the information cue (presented first) and thevalidity of the stimulus cue (presented second). Data at four SOAs were actuallyobtained but because the first two SOAs were 133 ms and 150 ms and produced_______no_different-results-rthey were -cat i-a-Ige-d^SOA that is referred to as the69140 ms SOA. First the results from control conditions for all ages groups will beoutlined followed by an analysis of validity effects for each age group.Control ComparisonsRT data from information cue control conditions are presented in Figure18. These graphs represent the role of the information cue, when there is nosubsequent location information provided by the stimulus cue. That is, thestimulus cue is neutral. This data should replicate the results presented for theinformation cue condition in Experiment 1, but if the graphs are examined it isclear that there are no cueing effects for any age group, at any SOA. This isconfirmed by the lack of any significant planned comparisons.There are two possibilities why the information cue failed to producesignificant effects when the stimulus cue was neutral. First, it is possible that thecue manipulation used was not strong enough to produce effects. Thisalternative is unlikely considering that the same cue manipulation producedeffects in Experiment 1, and in Experiment 2 when the stimulus cue was notneutral (see results discussed below). Second, it is more likely that theinformation cue was rendered useless by the neutral stimulus cue. In a typicalinformation control trial, subjects were cued to allocate their attention to alocation represented by an arrow. Following this cue they were presented with aflash at fixation (neutral stimulus cue). It is likely that subjects began these trialswith attention at fixation, shifted attention (or started to) in response to the arrow,and then had their attention automatically drawn back to fixation. Havingattention at fixation should produce the cueing results presented in Figure 18.This interpretation is supported by the experimental conditions presented belowwhere evidence of stimulus cues overriding information cues is also apparent.70Stimulus cue control conditions are presented in Figure 19. Unlike theinformation control conditions, there is evidence of the stimulus cue producingorienting effects when the information cue provides no location information. Thisis true for all ages at all SOAs. Because there was no significant SOA by validityinteraction for any age group, the significant planned comparisons between NVand NI trials for each group (6 years: t(8)= -4.471, p<0.001; 10 years: t(8)=-3.583,p<0.001; young adults: t(8)=-3.560, p<0.001; seniors: 48)=-3.201, p<0.002)sufficiently addresses the presence of a cueing effect for all ages and SOAs.This result supports the strength of the stimulus cue manipulation in thisexperiment and replicates stimulus cueing results presented in Experiment 1.Experimental ConditionsExperimental results are presented in Figures 20 through 23. Informationcue effects can be obtained by judging the slope of the lines in each graph, andstimulus cue effects can be measured by the separation of the two lines in eachgraph. The presence of an interaction (i.e., difference in slopes) between the twolines in each graph is indicative of a dependence between the effects of the twocue types.Young Adults Reaction Times. Experimental results for young adults are presented inFigure 20. The overall ANOVA on RTs revealed only one significant effect.There was a significant effect of validity, F(8,120)=8.157, MSE=3603.499,p<0.001 suggesting overall differences among the nine possible validity trialtypes. The validity effects will be discussed in detail below.To further examine validity effects, specific planned comparisons werelooked at. Trials where both cues were valid or invalid and trials where one cuecal — c1110...1— -- was valid d^1  er invalid were compared at each SOA. For young adults,71trials when both cues were valid were faster than trials when both cues wereinvalid at all three SOAs (140 ms: t(8)=-3.966, p<0.001; 400 ms: t(8)=-2.228,p<0.027; 800 ms: t(8)=-5.588, p<0.001) indicating an overall cueing effect. Moreinterestingly however, is that valid complement (VV) trials were not found to beany faster than stimulus cue bias trials (IV) or faster than information cue biastrials (VI). This result suggests that young adults are able to use either cue typewhen it is valid, well enough to overcome the competing information provided byan invalid cue. Further support for this claim was provided by the lack of anydifference between information cue bias and stimulus cue bias trials. Finally, aswould be expected, IV trials were faster than II trials at all three SOAs (140 ms:t(8)=-2.718, p<0.007; 400 ms: t(8)=-2.278, p<0.024; 800 ms: t(8)=-3.883,p<0.001), and VI trials were faster than II trials at two of the three SOAs (140 ms:t(8)=-2.367, p<0.019; 800 ms: t(8)=-3.894, p<0.001). Overall these resultssuggest there is a main effect of both information and stimulus cue, and thesetwo cue types interact in a manner that produces larger information cueingeffects when the stimulus cue is valid than when it is invalid. This is perhaps dueto a process utilized by subjects that involves switching attention (or beginning to)to the location specified by the arrow, followed by an automatic grabbing ofattention by the stimulus cue. On trials when the stimulus cue is valid the gameis over and the subject responds. On trials when the stimulus cue is invalidsubjects attention then goes to the location indicated by the arrow. Incomparison to stimulus cue valid trials, the information cue has more opportunityto improve responding on stimulus cue invalid trials.Six Year Olds Reaction times. Experimental results for six year olds are presented inican mäiffeffects. There was72a significant effect of SOA, F(2,46)=4.051, MSE=18889.318, p<0.024 resultingfrom an increase in overall RT at the 400 ms SOA. Follow up comparisonsrevealed that RTs at 400 ms were slower than those at 140 ms (42)=-2.211,p<0.032) and those at 800 ms (02)=2.658, p<0.011). There was also asignificant validity effect, F(8,184)=7.068, MSE=30216.823, p<0.001. The natureof the validity effect will be explored below.As with the young adult comparison group, six year olds were significantlyfaster when both cues were valid than when they were both invalid at 140, 400and 800 ms, (t(8)=-2.966;-4.601;-3.577, p<0.001; 0.001; 0.001, respectively).Unlike the young adults however, valid complement trials (VV) were found to besignificantly faster than information cue bias trials (VI) at the two shorter SOAs(140 ms: t(8)=-2.68, p<0.008; 400 ms: t(8)=-2.439, p<0.015) suggesting that, until800 ms, valid information cues cannot override invalid stimulus cues to produce acueing effect. On the other hand, stimulus cue bias trials (IV) were not found todiffer from valid complement trials suggesting that at all SOAs valid stimulus cuesalone can produce cueing effects that override invalid information cues. Thisinterpretation also receives support in the finding that at all SOAs IV trials arefaster than invalid complement trials (II) (140 ms: t(8)=-2.953, p<0.003; 400 ms:t(8)=-3.639, p<0.001; 800 ms: t(8)=-3.442, p<0.001). However, only at the 400ms SOA (08)=-2.161, p<0.313) and marginally at the 800 ms SOA areinformation cue bias trials faster than invalid complement trials (t(8)=-1.819,p<0.070). Taken together these results support the picture provided in Figure 21.There is a main effect of stimulus cue at all SOAs, a main effect of informationcues at 400 and 800 ms SOAs, and the two interact in a manner similar to adultsat the 800 ms SOA. That is, information cues show larger cueing effects whenthe stimulus cue is invalid than when it is valid.73Although it appears as though six year olds use information cues, they areless likely to do so in the presence of competing information from stimulus cues.In fact, it appears as though six year olds can extract information frominformation cues if they are given a long enough time. However, the timerequired to get this information is much greater (over 400 ms in Experiment 2)than the time required to get similar information when only an information cue ispresented (133 ms in Experiment 1).Ten Year Olds Reaction Times. Reaction times for ten year olds are presented in Figure22. The overall ANOVA revealed only a significant effect of validity,F(8,120)=3.509, MSE=21643.029, p<0.006. Planned comparisons calculated tofurther explore this validity effect revealed one oddity in the ten year olds data.That is, at the 400 ms SOA there were no significant comparisons. It is unclearwhat would lead to the wash out of effects at 400 ms but it should be noted thatfor young adults there appears to be a similar dampening of magnitude in effects.Keeping this in mind, valid complement trials were found to be faster than invalidcomplement trials at 140 ms, t(8)=-2.785, p<0.006, and at 800 ms, t(8)=-3.301,p<0.001. Information cue bias trials were found to be slower than validcomplement trials at two SOAs (140 ms: t(8)=-2.36, p<0.019, 800 ms: t(8)=-2.147, p<0.033) suggesting that information cues are not used by ten year oldsin the presence of conflicting stimulus cue information. Stimulus cue bias trialswere found to be slower than VV trials only at the 800 ms SOA, t(8)=-2.363,p<0.019. These findings suggest that stimulus cues can be used by ten yearolds even in the presence of conflicting information cues at shorter SOAs.Furthermore, the effectiveness of stimulus cues may fall off by 800 ms as would-be--expec-ted-give-n-past-research and e tesulls of Experiment 1. These results74are further supported by the lack of any difference between information cue biastrials and invalid complement trials. Furthermore, stimulus cue bias trials areonly faster than invalid complement trials at the 140 ms SOA, t(8)=-2.355,p<0.019.In sum, ten year olds seem to not use information cues when presentedwith stimulus cues, and the effectiveness of stimulus cues appears to diminishwith increasing SOA. For this group there is a main effect of information cue andstimulus cue at the 140 and 800 ms SOAs and the two interact at the 800 msSOA. This interaction is due to the larger information cue effect when stimuluscues are valid than when they are invalid. This result is in conflict with the resultsof other age groups and appears to be related to the drop off of stimulus cueeffects for ten year olds.Seniors Reaction Times. Reaction times for seniors are presented in Figure 23.The overall ANOVA revealed two significant effects. There was a significanteffect of SOA, F(2,38)=6.379, MSE=7938.386, p<0.004, that can be attributed toa higher overall RT at the 140 ms SOA, t(2)=3.386, p<0.002. There was also asignificant effect of validity, F(8,152)=4.952, MSE=10351.665, p<0.001, that willbe discussed further below.As with all the other ages, seniors were faster to respond on validcomplement trials than on invalid complement trials, (140 ms: t(8)=-2.42,p<0.016; 400 ms: t(8)=-2.558, p<0.011; 800 ms: t(8)=-4.064, p<0.001). Stimuluscue bias trials appear to produce similar results to valid complement trialssuggesting that valid stimulus cue information can override invalid informationcues. Information cue bias trials were not different from valid complement trials at 140 I 411 II^a , :11 MS,a — V V752.898, p<0.004. Information cue bias trials do not differ significantly from invalidcomplement trials suggesting that valid information cues cannot overcome invalidstimulus cues to reduce costs. On the other hand, valid stimulus cues only (IV)produce significantly faster RTs than invalid complement trials(II), (140 ms: t(8)=-2.405, p<0.017; 400 ms: t(8)=-2.317, p<0.021; 800 ms: t(8)=-3.281, p<0.001)This finding further suggests that stimulus cues override information cues inseniors except at the longest SOA.Overall the results suggest a main effect of stimulus cues, and aninteraction of the two at the shortest SOAs with information cues producing largereffects when the stimulus cue is invalid.Age Comparisons Clearly the most robust finding of this study is that children and seniors areunable to use information cues in the presence of stimulus cues. Young adultson the other hand do seem to be able to use information from both sources. Thisresult is consistent with the findings of Experiment 1 suggesting that informationcues are more difficult for children and seniors to use.Accuracy AnalysesAccuracies for Experiment 2 are presented in Table 5. An ANOVA usingthe same factors used with RTs was performed on the accuracy data for eachage group. As in Experiment 1 the majority of effects failed to reach significance.This lack of effects is most likely due to the high level of performance by subjects(range=85-96% for 6 year olds; 86-95% for 10 year olds; 94-100% for youngadults, and 88-98% for seniors). The pattern of results for accuracies is similar to76.^•^- - IP " . 7^0 III II"_the  RT results e presence of speed-accuracy trade-offs. As a result, conclusions based on RT results are not compromised bysubjects accuracy of responding.Eye Movement DataThe mean number of eye movements made over six minutes of samplingfor each age group are presented in Table 6. An ANOVA on the eye movementsrevealed a significant age effect, F(3,72)=42.874, MSE=526.772, p<0.001.Children made more eye movements than adults, F(1,72)=121.851,MSE=526.772, p<0.001. The two groups of children did not differ and youngadults did not differ from seniors.As in Experiment 1 eye movements were covaried with attention effects inan analysis of covariance in order to determine if differences in eye movementscould be held accountable for differences in validity effects. The eye covariatewas not significant for any age group although for both children and seniors thesignificant validity effects were eliminated in the overall analyses. Anexamination of least square means demonstrated that the actual pattern ofeffects did not change to any great degree with eye movements covaried outsuggesting that the loss of significance is most likely due to weaker power.Given this, and the consistency of results for Experiments 1 and 2, it is believedthat eye movements did not play a significant role in determining differences invalidity effects for each group.To address the issue of age differences in validity effects more directly anoverall analysis was conducted with all age groups included. The factors testedwere age, SOA and validity, and eye movements served as the covariate. In thisanalysis nothing was changed by the addition of the eye movement covariate,except slightly lower p-values that did not change enough to change conclusions_^u ermore, an analysis of the least square means77revealed no differences in the pattern exhibited by standard means. This directexamination of age effects with eye movements covaried out provides evidencethat age related differences in the frequency of eye movements did not contributesignificantly to age differences in orienting effects.General DiscussionBoth of the experiments reported above provide evidence fordevelopmental changes in attention alignments throughout the course of thelifespan. The following discussion will examine these findings more closely asthey relate to the five questions outlined in the introduction, and finally, ananalysis of several relevant theoretical issues from child development,mainstream cognitive and aging literatures will be presented.1. Are there lifespan age differences in attention shifts mediated bystimulus cues?The short answer to this question is yes. Although all subjects, regardlessof age, show evidence of automatic attention alignment, the generality of thiseffect varies according to age. Perhaps most surprisingly is the lack of asignificant cueing effect for six year olds at the 133 ms SOA. This result isinconsistent with previous research that shows young children show cueingeffects at shorter SOAs (Akhtar & Enns, 1989; Enns & Brodeur, 1989; Brodeur,Enns & Ellis, 1991). One possible explanation for this inconsistency may simplybe a lack of power in the present experiment. The invalid-valid difference scorefor the 6 year olds is 31 ms. Such a difference has been sufficient to produce asignificant result in other experiments, and in other groups in this experiment78 ow a 33 ms effect). It is possible that the increased6 7-..^I) e SSvariability associated with young age may be responsible for the lack ofsignificance for six year olds. Unfortunately, the stimulus cue experimentconsisted of relatively few trials (perhaps too few trials) and it is possible thatincreasing the number of trials would have given the design enough power toshow a cueing effect for six year olds at the lowest SOA. Regardless, it seemslikely that this effect would still be smaller than the effect at longer SOAs,suggesting that for younger children the peak effectiveness of stimulus cueshappens later than it does for older children and adults.As well, cueing effects are larger overall for children than they are foryoung adults. There are at least a two possible explanations for the pervasivefinding that children show larger effects than adults. It could be that children'scueing effects are largely due to costs. If children are slower to move attention,than shifting attention from an invalid cue to a target would slow RTs on invalidtrials to a greater extent for children than adults. The results of Experiment 1suggest that costs are relatively large, especially at 133 ms and 450 ms SOAs.However, using the costs measured in this study as support for this explanationnecessitates the assumption that the neutral condition allows for adequatemeasure of costs and benefits. A second explanation is that young adults havereached a ceiling of performance, thereby limiting the amount of facilitation thatvalid cues can produce.Results for seniors are similar to younger adults in that cueing effects arepresent at the shortest SOA. At this SOA however, seniors show a larger effectthan do younger adults and at longer SOAs seniors effects do not reversethemselves (although they diminish) whereas younger adults do. This latercomparison is also true when comparing children and young adults, and will bediscussed later when age differences in inhibition-of-return are discussed. Thee consistent in part, with previous findings that seniors79do not differ from younger adults in their exogenous orienting abilities (Hartley, etal., 1990; Hoyer & Familant, 1992; Madden, 1990), although there are certainlyareas of difference that are important to note.According to Posner, et al. (1987), orienting attention involves threedifferent processes. Attention is engaged at fixation, the presentation of a cueresults in attention being disengaged from fixation and then subsequently moved to the cue location. It is conceivable that developmental differences in the abilityto initiate and/or follow through with any of these operations could result in agedifferences in cueing effects. For example, if younger children have difficultydisengaging attention it could result in an increase in the time required to respondto a cue. This could explain the lack of a cueing effect for six year olds at 133 ms.It could also explain larger cueing effects for children and seniors if it is assumedthat following a successful movement of attention to a cued location, subjects areslower to disengage their attention from the cued location on invalid trials leadingto larger costs. In the present data this explanation is supported for the childrenthat show larger costs than do young adults, but seniors do not. On the otherhand, given that the neutral condition may not be an adequate control by which tomeasure costs and benefits, it may actually be the case that increased costs arethe culprit in larger cueing effects.Although it is equally likely that moving attention, and/or engagingattention may be the processes involved in developmental differences (or for thatmatter perhaps all three may be involved), the present experiment cannotdefinitively isolate one process from the other, and perhaps more research isneeded on this front. In the past however, researchers have reported data thatsuggests the disengage process may be the important determining factor at bothends of the lifespan (Akhtar & Enns, 1989 for children; D'Aloisio & Klein, 1990 for80 a seniors were slower to search an array ofII 5^•items for a specific target than were younger adults. Because the task requiredsubjects to complete the engage, disengage and move operation several times itwas assumed that deficiency in at least one of these operations was responsiblefor aging effects. On the basis of previous work suggesting larger costs in anorienting paradigm with brain damaged patients (Posner, et al., 1984) D'Aloisioand Klein speculated that it was the disengage operation that was slower inseniors. Similarly, children exhibited larger costs in a study conducted by Akhtarand Enns, (1989) suggesting that the disengage argument may apply to earlydevelopment.The results of Experiment 2 suggest further that subjects of all ages areable to use stimulus cues. Furthermore, there seems to be very little differencein their ability to do so that can be attributed to age. For all age groups theattention alignments made to stimulus cues are more reliable than those made toinformation cues, thereby attesting to the automatic nature of this form oforienting. This point is accentuated by the fact that stimulus cues were notpredictive of target location. That is, they appeared at a location other than thetarget location far more often than at the same location. As a result it would be toa subject's advantage not to attend to these cues. By not ignoring the stimuluscues in this experiment subjects have demonstrated the insensitivity of theexogenous orienting system to strategy manipulation.Taken together, the results of these experiments suggest that exogenousorienting is an early developing ability that is maintained late into life. Thesignificance of age related change in this ability is perhaps less significant thanchange for other abilities to be discussed later. It may simply be a matter of theefficiency of systems to engage, disengage and move attention. Thisinterpretation is given some support by evidence that developmental differences81begin to diminish with practice in younger children (Akhtar & Enns, 1989; Enns &Brodeur, 1989).2. Are there lifespan age changes in attention shifts mediated byinformation cues?Once again the simple answer to this question is yes. In Experiment 1there is evidence that children are able to use information cues to align theirattention early on, but as SOA increases their ability to sustain this alignmentdiminishes. Young adults on the other hand are able to retrieve location frominformation cues and use this information over longer durations. In contrast toboth of these, seniors do not seem to be able to use information cues quickly, butrather require as long as 800 ms to align attention in response to a cue.Clearly using information cues is a more conscious and difficult task thanis using stimulus cues. Interestingly however, the results of Experiment 1suggest that children and seniors do not necessarily exhibit the same difficultieswhen it comes to implementing the strategies necessary to information cues.Strategy deficits are commonly associated with poorer performance on the part ofchildren (Chi, 1976; Guttentag & Ornstein, 1990) and seniors (Meyer, Young, &Bartlett, 1989; Stine & Wingfield, 1987), and clearly in this study it is reasonableto suspect that it is the strategic nature of the endogenous cueing task that isleading to developmental differences.Children appear to be able to initiate the strategies necessary to useinformation cues, but they have difficulty in sustaining their attention at a givenlocation. Sustaining attention no doubt requires some level of conscious effort,and difficulties with this component of attention have been documented in--previe-a-s-vvark wittrcff[twr(Kupietz, & Richardson, 1978).82Seniors on the other hand, seem to have difficulty implementing thestrategies necessary to use information cues. At the very least they are slower touse information cues, which is consistent with the view of aging beingaccompanied by the general slowing of cognitive operations (Salthouse, 1982).Furthermore, these results mimic the results reported in previous work (Hartley,et al., 1990; Hoyer & Familant, 1987). Unfortunately, it is impossible todetermine whether seniors are slower at using strategies similar to youngeradults, or whether they are in fact using different, less efficient strategies. Itwould seem, however, that an attention alignment task such as the one used inthe present experiments is simple enough to preclude the possibility of manydifferent strategies.The use of information cues in Experiment 2 provides a more complicatedlook at developmental differences. Clearly the use of information cues in thisexperiment is compromised by the presence of a stimulus cue that tends toproduce overshadowing cueing effects for children and seniors, but less so foryoung adults. This is consistent with what would be expected given the results ofExperiment 1.3. Are there lifespan age differences in the nature of inhibition-of-return?At the risk of being redundant, the answer to this question is again "yes".In the stimulus condition of Experiment 1 only the young adults exhibitedinhibition-of-return around the 400 ms SOA. Although the cueing effects forchildren and seniors disappeared at the longer SOAs, they did not reversecompletely with invalid RTs becoming faster than valid RTs. In the mainstreamcognitive literature the transient nature of stimulus cue effects is well documented{danides-, 1981, Muller & Rabbitt, 1989; Nakayama & Mackeben, 1989; Posner &83Cohen, 1984). As such, it is not surprising that the cueing effect for all subjects isgone by 400 ms. Why, however, does the effect only reverse itself for youngadults?Evidence for the existence of inhibition-of-return was first documented inyoung adult subjects (Maylor, 1985; Posner & Cohen, 1984; Rafal, Calabresi,Brennan, & Sciolto, 1989) and has subsequently been found in infants (using adifferent measure) as young as 6 months (Rothbart & Posner, 1990) and 4months (Johnson & Tucker, 1993). Obviously if the mechanisms required toproduce inhibition are in place this young, it is unlikely that they are no longerpresent in older children. This is perhaps suggestive that the paradigm used inthese experiments is not appropriate for demonstrating inhibition. There areseveral differences between the present paradigm and the paradigm used byothers. For example, detection has been the most commonly used task in adultresearch, and the present experiments used a forced choice recognition taskinstead. Also, in the infant studies direction and speed of eye movements werethe dependent measures as opposed to the manual RTs measured in most of theadult work. Although these differences may be important, it is unlikely that thepresent experimental procedure can be used as a complete explanation becauseit was sufficient to produce the effect in the young adult group.Inhibition-of-return has not been found with seniors in the past, and thiswas again so in the present experiment. It is possible for this group that themechanism responsible for producing inhibition has either faced selectivedeterioration with age, or perhaps has just become significantly slow so as not toproduce the effect by 800 ms. This later explanation may also be applied tochildren who only were exposed to SOAs of 450 ms or less in Experiment 1. It isinteresting to note, however, that the selective deterioration hypothesis-neeessitatesitrattre-mechal-iismTs) - responsible for producing the cueing effects84are different from the mechanism(s) responsible for producing inhibition.Although there is evidence that this may be the case (e.g., cueing withoutinhibition), it is difficult to argue that inhibition-of-return is not a phenomenon thataccompanies exogenous orienting.Finally, it is also interesting that the drop in cueing effects at longer SOAsfor children and seniors is only evident on across hemifield cueing trials. It maybe possible that inhibition is applied to hemifields whereas attention is allocatedto smaller regions (at least for children and seniors). As a result, the largenumber of within hemifield cueing trials may have served to water down thepresence of any inhibition. This is of course only speculation as there is no directevidence to support such a claim.In Experiment 2 there is no evidence of inhibition-of-return in any agegroup, although it is interesting that for the 400 ms SOA effects seem to bereduced, and then reappear at the 800 ms SOA. It is possible that thisdepression of effects may be related to inhibition-of-return in some way, but thepresence of information cues prevents the exogenous cueing effect fromcompletely reversing. It is unclear exactly how these mechanisms would interactto produce such effects but it is interesting to speculate on the fate of inhibition-of-return when there is extra information provided as in Experiment 2.4. Are there lifespan age differences in cross versus within hemifieldcueing effects?This question was initially motivated by a debate in the mainstreamcognitive literature concerning the nature of attention allocation in visual space. Ithas been argued that attention is allocated to visual hemifields (Hughes & Zimba,1985; 1987) db opposed to specific locations, as would be suggested by85traditional spotlight models of attention (e.g., Posner, 1980; Treisman & Gelade,1980; Tsal, 1983).Recently, Reuter-Lorenz and Fendrich (1992) looked at both manual andeye movement responses to stimulus and information cues (in separateexperiments). Within these studies it was possible to have within and acrosshemifield cueing as in the present study. The authors reported that with stimuluscues there was evidence of both within and across hemifield cueing for bothresponse types. In the information cue experiment there was also both types ofcueing found, although the across hemifield cueing effects were larger than thewithin hemifield effects, and again this was true for both response types. Thisstudy, contrary to Hughes and Zimba's results reported above support the notionthat attention is allocated to specific locations in visual space perhaps in amanner akin to a spotlight.In the present study (Experiment 1) young adults exhibited both within andacross hemifield cueing effects in the stimulus cue experiment, and larger acrosshemifield cueing effects in the information cue experiment. These resultsreplicate the findings reported above (Reuter-Lorenz & Fendrich, 1992). Oneinterpretation of these results, and perhaps the divergent results in the literatureis that stimulus cues produce allocation to specific locations whereas informationcues tend to produce allocation to broader areas of visual space, potentiallydefined by visual meridians (Reuter-Lorenz & Fendrich, 1992; Shepherd &Muller, 1989). Furthermore, this explanation is consistent with the interpretationof attention as a zoom lens (Eriksen & Yeh, 1985) that can be narrowed orexpanded according to task demands. For example, when the attention task isone that is driven automatically and quickly, attention moves to a specific,narrowly defined location. When the task is more strategic, subjects may choose86• • • ... e : •^• ''^G - u - •^.^"' in or•er to maximize their efficiency inresponding (i.e., most speed and accuracy for smallest effort). This interpretationis supported by Muller and Humphreys (1991) who have recently reportedevidence suggesting that attention placed in response to a stimulus cue isnarrowly focused whereas attention placed in response to an information cue ismore like a gradient.If the above discussion is indeed descriptive of how orienting works inyoung adults, how can it be used to interpret the results found with children andseniors? For children, there was evidence of within and across hemifield cueingin both the information cue and stimulus cue experiment. This result isinconsistent with the explanation provided above, although it is easy to reconcilethe discrepancy with a developmental interpretation. It is possible that childrenare less able to narrow or expand the focus of their attention resources (Enns, &Girgus, 1986), so they fail to broaden their attention in the information cuecondition as do the adults. As a reminder, children only show evidence of cueingat the 133 ms SOA in the information cue condition. It was perhaps impossiblefor them to utilize a broadening strategy within that small amount of time. As aresult the spotlight of attention remained narrow producing both within hemifieldand across hemifield cueing.Seniors' results are similar to children's, with the exception being thatthere is no significant cueing effect for within or across trials in the informationcue experiment. This null effect can be attributed to the lack of any robust effectin the overall RT analysis. The cueing effect at 800 ms obviously does notremain powerful enough to detect when trials are reduced into two groups.Nonetheless, the pattern appears to be similar to that of children, and perhapsthe same explanation can be used. It is also interesting to note that seniorsshow only across hemifield effects at the longest SOA in the stimulus cuemoped-meat.^- • posse • e t at the narrow focus of attention created by a87stimulus cue begins to pass with time, leading to a gradual broadening of focusthat diminishes the within hemifield effects.In summary, the results presented here support the idea that attention isfocused primarily on locations, but functions somewhat like a zoom lens that canbe narrowed or broadened over time to cover variable amounts of visual space.Furthermore, the efficiency with which this zoom lens can be manipulated varieswith age.5. What is the nature of age differences throughout the lifespan forattention shifts mediated by stimulus and information cues?This final question was intended to be an overarching look at thedevelopment of both forms of orienting as they function separately and together.What has emerged quite clearly from the results of both Experiment 1 and 2 isthat attention alignments made in response to stimulus cues show relatively littlechange over the course of the lifespan whereas alignments made in response toinformation cues seem to reach a peak efficiency at some point during adulthood.Perhaps the most telling finding was the ability of young adults to useinformation cues even when competing stimulus cue information was alsopresent. Children and seniors on the other hand did not seem to possess suchan ability. In some respects Experiment 2 is similar to the more traditional dualtask paradigm where subjects' performance on both a primary and secondarytask are measured simultaneously. Children and seniors are noted to havedifficulty in such situations (e.g., Guttentag, 1989; McDowd & Craik, 1988,respectively) so it is perhaps not surprising that they have difficulty with this task.However, in Experiment 2 subjects were only instructed to perform one task and88ma ica y. The fact that the automated taska.^i .II..th_e_otherso easily produced effects greater than the instructed task suggests that forwhatever reason, information cueing is difficult for children and seniors. Somepotential reasons will be discussed below in the section on theoretical issues.Theoretical IssuesThere are several theoretical notions that can be addressed with the dataprovided in Experiment 1 and 2. These notions however, do not predict findingsthat are in opposition to one another. Rather, the level of detail in prediction, andthe descriptive mechanisms offered differ for these ideas. The purpose of initiallypresenting them separately here is not to indicate that the data provided by thepresent research supports one and not others, but is simply a reflection of thefact that these ideas seem to have been investigated separately in previousresearch. In fact, it is my view that the ideas are not only compatible, but alsoneed to be integrated in an informative matter. Taken together, these ideas canprovide a reasonable, although not detailed, picture of what one might expect tofind over the course of lifespan development of attention alignment.Strategies versus CapacitiesIt is generally believed that people that fail at either end of the lifespan arenot as readily able to employ mental strategies as efficiently as younger adults.As mentioned above, children's poor performance in attention demanding ormemory tasks has often been attributed to their lack of ability to employstrategies such as using a predictive cue to direct attention (Enns & Brodeur,1989) or to spontaneously use memory strategies such as rehearsal (Chi, 1976;Guttentag & Ornstein, 1990; Miller, Seier, Probert, & Aloise, 1991). Similarly, at•^ n suggested that decreased functioning that89"^• 1! "6 ; e"accompanies old age is magnified when subjects are asked to perform tasks thatare more complex (e.g., require the use of more complex strategies) (Cerella,1985; Salthouse, 1982). Given this, it is not surprising that larger developmentaldifferences were found for the information cue components of the previous twostudies. In general the typical inverted U-shaped performance function wasfound for tasks that required the conscious employment of strategies such as inthe information cue experiment. On the other hand, in the stimulus cueexperiment where conscious strategies were not necessarily called into playthere was relative stability across the lifespan.Unfortunately, the results of the present experiments can be explainedequally as well with a limited capacity view of attention development (Pascual-Leone, 1978). It may be the case that the difference between stimulus andinformation cue tasks is the amount of attentional resources required to completeeach task. Shiffrin and Schnieder, (1977) have made a distinction betweencontrolled and automated behaviour. Controlled behaviour in general is believedto require more resources than is automated behaviour. Information cueing ismost likely to be a controlled process and may suffer in children and seniorsbecause of insufficient resources. Stimulus cueing on the other hand isautomated and would therefore require minimal resources.Although the present data does not distinguish these alternatives, it is mycontention that it is perhaps not possible, nor necessary to do so. As with theage-old nature-nurture debate, it may very well be the case that both factors areimportant determinants of development. Perhaps a more fruitful approach wouldbe to determine what and how strategies differ over the course of lifespandevelopment. In fact, it is quite possible that deficiencies in early and later lifemay be the result of different strategic factors as well as capacity limitations.90Theories of agingThere are at least two general theories of aging that can be used asexplanatory tools for the results of Experiments 1 and 2. First is Salthouse's(1982) general cognitive slowing theory. According to this theory there is an agerelated slowing of all cognitive operations. This slowing is the result of neuronaldegeneration and permeates every aspect of functioning. As discussed in theintroduction, tasks that require more complex operations such as informationcueing would show greater levels of age decrement than would simpler taskssuch as stimulus cueing. Not surprisingly, the results of the present experimentssupport this rather general view of aging. It is important to note as well, thatSaithouse views aging deficits as the result of central (cognitive) deficits ratherthan peripheral deficits. Others, however, have attributed age differences invisual attention to more peripheral factors such as vision (Scailfa, 1990) or theextent of the functional field of view ( Ball, et al., 1990).Another theory of cognitive aging that is related to Salthouse's is theNeural Noise Theory (Welford, 1981). This theory asserts that as humans age,the signal to noise ratio of neuronal firing increases, making it more difficult forolder adults to detect signals. As a result more time is needed to acquire theinformation necessary to pass a threshold. Three possible reasons for thisincreased signal to noise ratio, that may also be viewed as possible explanationsfor Salthouse's general slowing account are: 1) loss of neurons; 2) increasedrandom firing of neurons, and 3) decreased cerebral blood flow. In terms of thistheory, larger age decrements for information cueing can be attributed to thenecessity of more, complex information passing a threshold than would benecessary in the stimulus cue experiment.The present results seem to be explained better by a more central theory91erip era views could account for the11^111^11_!reasonable stability of stimulus cueing across the lifespan. The general slowingand neural noise accounts both provide an avenue for the difference between thetwo attention alignment tasks, but they require as well that seniors be slower inthe stimulus cue experiment (which they are). These theories do however, havedifficulty explaining studies where no differences are found and offer no insightinto development early in life. As a result, they can only be used in conjunctionwith other views in terms of developing a lifespan view of visual attention.Spotlight versus Zoomlens? As alluded to above in the discussion of hemifield cueing effects, therehad been considerable debate in the mainstream cognitive literature concerningthe nature of attention allocation. The results of Experiment 1 seem to imply thatattention may function as a zoom lens, at least for young adults. In theinformation cue experiment young adults only exhibited within hemifield cueingeffects at the 200 ms SOA despite an overall large cueing effect. Clearly it wasnot necessary in this experiment to distinguish between single and doubleheaded arrows to perform well. Young adults may have adopted the moreefficient strategy of widening their attentional resources to cover an entirehemifield in response to the directionality of arrows and presumably this requireda certain length of time to accomplish (i.e., somewhat more than 200 ms) therebyaccounting for the presence of within hemifield cuing effects early on but not forthe longer SOAs. Children and seniors demonstrated both within and acrosshemifield cueing suggesting that they were unable to adopt a similar strategy. Inthe stimulus cue experiment a narrower beam of attention would be moresuitable.92Common mechanisms? There is sufficient evidence to suggest that exogenous and endogenousorienting function quite differently. First, as mentioned above, exogenousalignments of attention are reflexive, whereas endogenous alignments ofattention are voluntary (Jonides, 1981; Jonides & Yantis, 1988; Muller & Rabbitt,1989; Nakayama & Mackeben, 1989; Yantis & Jonides, 1984; Yantis & Jonides,1990). Furthermore the magnitude (Jonides, 1981; Muller & Rabbitt, 1989;Nakayama & Mackeben, 1989), time course and duration(Jonides, 1981; Muller& Rabbitt, 1989; Nakayama & Mackeben, 1989; Posner & Cohen, 1984) ofcueing effects differ for these two forms of orienting. There are alsophysiological differences between endogenous and exogenous mechanisms(Posner, Cohen, & Rafal, 1982; Rafal et al., 1989; Robinson & Peterson, 1986;Wurtz, 1985).The present experiments provide one further piece of evidence thatexogenous and endogenous orienting are different forms of attention alignment.Namely, they follow different developmental trajectories. The ability to usestimulus cue develops early and shows only very limited deterioration with age.Information cues on the other hand, are not used well until early adulthood andshow signs of selective deterioration in old age.Do the above mentioned differences necessitate the postulation thatendogenous and exogenous orienting are independent mechanisms? This iscertainly one interpretation but it is also reasonable to assume that both forms oforienting tap a common resource of attention that mediates orienting, that can becontrolled by either automatic or strategic factors. Such an interpretationsuggests that orienting is a common function that can be driven automatically bystimulus cues, or strategically by information cues. This interpretation can be-- mappcd on to physiological hypot eses discussed below.93Neuropsychological DevelopmentThere is neuroanatomical evidence that localizes certain functions ofattention in certain structures of the brain, as well as neuroanatomical evidencethat provides information concerning developmental changes in brain structures.In general, evidence suggests that development at the beginning of the lifespanoccurs fastest for the brainstem and midbrain, slower for the primary corticalareas and parietal lobes, and slowest for the prefrontal cortex (Lecours, 1975).At the opposite end of the lifespan however, there is some evidence that issuggestive that peripheral brain function deteriorates more rapidly than centralbrain function (Scailfa, 1990; Sekular, et al., 1982).How does the above developmental information map on toneuroanatomical data that is specific to attentional alignment? Posner andcolleagues (1988; Posner & Peterson, 1990; Rothbart, Posner, & Boylan, 1990)have used brain damaged patients to localize covert orienting functions (inresponse to stimulus cues) in the superior colliculus (move operation) and thethalamus (disengage operation), and the posterior parietal cortex (engageoperation). As well, saccadic eye movements and inhibition-of-return are alsocontrolled by these midbrain regions (Posner et al., 1985). In terms ofdevelopment, this would suggest that covert orienting would be a relatively earlydeveloping function that would be unlikely to deteriorate rapidly in old age unlessthrough the result of deterioration of the visual system itself. The presentexperiments support this claim. Further developmental predictions can beaddressed by comparing the results of the stimulus and information cueexperiments. Because information cues can only be used voluntarily, it ispossible that changes with age in the connections between the midbrainstructures (orienting) and the prefrontal structures (strategy use) are importantg. it may be that as these connections become94stronger during early development the use of information cues becomes moreefficient. The present results would support the claim that this strengthening ofconnections proceeds significantly between the ages of six years and ten years,reaching peak strength at some point in adulthood. At the opposite end itappears as though these connection may be weakening perhaps as a result ofcell death or myelin degeneration.Although neuroanatomical data were not collected in the presentexperiments, the data collected are consistent with the developmentalneuroanatomical data already obtained.ConclusionsIn broad terms, the results of the present experiments portray the lifespandevelopment of attention alignments as resembling the traditional inverted U-shaped function. However, the U-shaped function is clearly not complete enoughto provide an adequate description of the results presented in this paper.First, it is necessary to postulate two inverted U-shaped functions. Onerelatively shallow inverted U for exogenous alignments, and a deeper inverted Ufor endogenous alignments. Second, it is necessary to provide different accountsfor the age-related differences found in children and in seniors. This point isperhaps made most strongly by the information cue condition of Experiment 1.Here the deficits displayed by children are in the ability to sustain attention,whereas seniors appear to have difficulty employing strategies needed to alignattention in the first place.Although the data provided are more normative than definitive in terms oftheory development, it is possible to suggest a picture of lifespan development ofattention that borrows concepts from several literatures and is consistent with ther f.;^of is study.  As an example, attention alignment may be viewed as a95mechanism that functions as a zoom lens. It can be moved around in the visualfield, and it can be broadened or narrowed in accordance with task demands.Furthermore, alignment of this zoom lens can occur automatically via a reflexivemechanism, or it can be driven consciously and strategically by higher cognitiveprocesses. In terms of lifespan development, the zoom lens is present early inlife, and the reflexive movement of the lens throughout the visual field is an abilitythat appears early. There is however, continuing development in the accuracyand efficiency of attention alignments. The conscious manipulation of the zoomlens throughout visual space develops as early as 6 years but the ability tosustain the lens at a spot is a later developing skill as is the ability to broaden andnarrow the focus of the lens strategically. At the later end of life, the automaticcomponent remains relatively intact and the strategic alignments facedeterioration. This view of attention points to actual strategies that may beresponsible for age differences, as well as mechanisms that account fordifferences in the two forms of orienting.What then accounts for differences between children and seniors? Theanswer to this no doubt lies in the neurophysiology underlying the deficitsexhibited in each age group. Clearly early development is characterized by arelatively well defined course in brain maturation. Myelinization continues andnew pathways become functional. In old age however, the extent and thelocalization of brain deterioration may vary greatly from one individual to another,and it is unlikely that even if there are commonalties among seniors in the courseof myelin degeneration etc., that this degeneration returns the brain to a statethat resembles the brain of a young child. As a result different deficits should beexpected, along with those that are similar.In closing, there is a need for continued lifespan research that addresses96en ion7Wcst only is there much to be learned about• "^" • " S -^•attention and brain development, there is also a considerable amount to begained in the understanding of both attention disorders such as ADHD and agerelated disorders such as Alzheimer's disease. 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Journal of Experimental Psychology: Human Perception and Performance, 16, 121-134.105Table 1Mean RTs (standard deviations) for all age groups by Cue type, Validity and SOA conditions inExperiment 1.Stimulus InformationAge SOA(ms) Valid Invalid Neutral Valid Invalid Neutral6 years 133 885(166) 916(186) 881(206) 813(181) 898(180) 892(194)250 821(179) 911(149) 886(176) 858(138) 900(178) 870(188)450 822(208) 895(192) 867(220) 862(116) 872(180) 838(180)8 years 133 689(121) 768(172) 704(158) 694(140) 784(155) 793(162)250 694(148) 755(165) 759(218) 768(168) 774(198) 778(214)450 681(113) 726(139) 691(120) 740(188) 754(186) 787(244)10 years 133 615(145) 648(126) 624(129) 555(90) 647(106) 647(117)250 607(147) 624(155) 615(142) 622(120) 629(113) 647(150)450 615(180) 631(173) 614(132) 608(134) 624(131) 625(281)^23 years 150^578(111)^603(105)^602(118)^578(105)^615(129)^615(137)200^575(99)^600(129)^614(132)^564(111)^616(132)^594(132)400^581(135)^568(106)^561(107)^538(102)^575(114)^566(99)800^583(130)^569(120)^557(113)^556(105)^589(128)^571(109)73 years 133^866(173)^918(150)^907(142)^873(135)^883(153)^908(175)200^857(133)^908(191)^905(183)^869(153)^852(140)^853(170)400^893(178)^925(235)^920(240)^852(149)^841(172)^839(156)800^894(201)^916(222)^892(224)^812(166)^856(189)^885(240)Table 2Mean percent correct scores (standard deviations) for all age groups by Cue type, Validity and SOAconditions in Experiment 1.Stimulus^ InformationAge^SOA(ms)^Valid^Invalid^Neutral^Valid^Invalid^Neutral6 years^133^85 (14)^81 (11)^83 (13)^85 (7)^84 (10)^87 (11)250^86 (15)^83 (10)^85 (11)^85 (11)^82 (14)^82 (18)450^82 (21)^84 (11)^79 (17)^88 (9)^87 (9)^85 (14) 3 years^133^87 (11)^90 (7)^91 (7)^91 (7)^94 (7)^93 (8)250^92 (13)^88 (15)^88 (17)^93 (7)^92 (9)^91 (12)450^92 (15)^92 (16)^93 (14)^93 (8)^89 (10)^87 (10)10 years 133^94 (11)^91 (11)^92 (10)^95 (5)^94 (8)^95 (9)250^95 (11)^92 (9)^90 (14)^94 (6)^91 (10)^93 (12)450^94 (10)^88 (14)^89 (12)^92 (9)^92 (9)^94 (9) ^23 years 150^97 (5)^98 (4)^99 (4)^95 (4)^93 (8)^93 (10)200^97 (7)^97 (7)^98 (5)^95 (5)^96 (5)^98 (5)400^98 (5)^97 (6)^95 (10)^96 (6)^96 (7)^97 (6)800^92 (14)^94 (8)^95 (10)^97 (4)^95 (7)^98 (10)73 years 133^91 (12)^92 (8)^89 (14)^92 (8)^92 (8)^94 (7)200^93 (14)^88 (14)^88 (13)^91 (10)^93 (6)^92 (11)400^90 (15)^91 (7)^93 (8)^89 (10)^91 (9)^91 (11)800^90 (10)^95 (17)^92 (17)^88 (10)^92 (12)^93 (14)Table 3Mean number of eye movements (standard deviations) over threeminutes of sampling for subjects of all ages in the stimulus andinformation cue conditions of Experiment 1.Age Group Stimulus Cue Information Cue6 year olds 64.3 (15.4) 62.87 (8.6)8 year olds 64.8 (31.4) 58.2 (19.0)10 year olds 52.7 (25.9) 59.8 (20.5)23 year olds 13.8 (13.2) 19.6 (13.4)73 year olds 16.7 (15.5) 28.1 (23.0)110Table 4Trial type labels for the nine possible combinations of Stimulus andInformation cue validity in Experiment 2.Cue TypeInformation^Stimulus^Trial Type111Validity Neutral^Neutral BaselineValid Neutral^Information ControlInvalid^Neutral^Information ControlNeutral Valid Stimulus ControlNeutral^Invalid^Stimulus ControlValid Valid^Valid ComplementInvalid^Invalid^Invalid ComplementValid Invalid^Information Cue BiasInvalid^Valid^Stimulus Cue BiasTable 5Mean percent correct scores (standard deviations) for all ages by Validity andSOA conditions in Experiment 2.Cue ValidityMeanage^SOA(years)^(ms) VV VI VN IV^II^IN NV NI NN6 140 92 90 91 91 94 91 92 89 92(6) (6) (6) (11) (8) (9) (9) (11) (7)400 91 92 91 91 92 96 88 95 94(9) (8) (9) (18) (16) (10) (18) (190) (13)800 92 90 90 92 85 96 91 90 92(8) (10) (10) (16) (18) (10) (16) (16) (14)10 140 92 93 92 94 91 90 95 93 92(10) (9) (11) (9) (11) (11) (7) (11) (12)400 92 93 94 92 86 88 94 88 95(8) (8) (9) (15) (26) (18) (11) (29) (10)800 93 91 91 91 89 92 94 91 86(7) (10) (10) (18) (16) (15) (11) (15) (22)25 140 97 97 97 97 95 97 97 94 96(3) (4) (4) (7) (6) (5) (8) (11) (7)400 97 97 96 98 97 98 100 98 95(4) (5) (5) (6) (9) (6) (0) (6) (10)800 99 96 97 97 95 98 96 98 97(3) (7) (5) (9) (10) (6) (11) (6) (9)74 140 93 94 95 94 88 96 96 94 96(7) (7) (5) (8) (13) (5 (8) (9) (6)400 94 96 95 95 91 95 96 90 94(7) (5) (7) (10) (15) (10) (12) (17) (11)800 91 92 94 89 89 98 94 96 91(10 (7) (7) (17) (21) (8) (11) (9) (15)112Table 6Mean number of eye movements (standard deviations) for all age groupsin Experiment 2.Age Eye Movements6 years 95.9 (14.3)10 years 85.6 (22.7)25 years 27.8 (22.0)74 years 31.3 (30.1)1130TargetValidCue-TargetSOACueFixationTargetInvalidCueCue-TargetSOAFixation0TargetNeutral Cue-TargetSOACueTimeFixationFigure 1. Example stimulus displays for valid, invalid and neutral trials in the114information cue condition of Experiment 1.•- _0Target- -ValidCueCue-TargetSOAFixation_ - TargetInvalid •- _CueCue-TargetSOA0- - _FixationTarget_•_ Cue-TargetSOANeutralCueTimeFixationFigure 2. Example stimulus displays for valid, invalid and neutral trials in the115stimulus cue condition of Experiment 1.100 -116 •80 - Ageo^ 5 years7 years^ati.^9 years^it^23 years—o— 73 years60 -40 -20 -• . % ;■^aI^I^I 1^0^200^400^600^800^1000SOAFigure 3. Mean RT difference scores (Invalid RT-Valid RT) as a function of SOAfor all ages in the stimulus cue condition.0-200^200Age^ 6 years8 years• ..................^iti ^10 years a-^23 years--Ai-- 73 years117SOAFigure 4. Mean RT difference scores (Invalid RT-Valid RT) as a function of SOAfor all ages in the information cue condition.680 -660 -640 -620 -Valid600 -—0-- Withincc580 - --s— Across560 -540 -520 i i •0^200^400^600^800^1000SOAFigure 5. Mean RTs of valid, within hemifield invalid, and across hemifield invalidtrials as a function of SOA for young adults in the stimulus cue condition.118800 -780 -(7')g- 760 -i-x740 -—.— Valid•-0— Within—11-- Across720 -700 -840820 -119680 I^^I100^200^300^400^500SOAFigure 6. Mean RTs of valid, within hemifield invalid, and across hemifield invalidtrials as a function of SOA for children in the stimulus cue condition.Valid—0— WithinAcross1200^200^400^600^800^1000SOAFigure 7. Mean RTs of valid, within hemifield invalid, and across hemifield invalidtrials as a function of SOA for seniors in the stimulus cue condition.660 -640 -•620 -r)E- 600 -i-^-cc580 -—o-- Valid--0-- Within---11-- Across560 -1540 -I520680 -1210^200^400^600SOA800^1000Figure 8. Mean RTs of valid, within hemifield invalid, and across hemifield invalidtrials as a function of SOA for young adults in the information cue condition.400200 300.^1500840 -820 -800 -780 -760 -740 -720 -700680^100--4-- Valid--0-- Within---II-- Across122SOAFigure 9. Mean RTs of valid, within hemifield invalid, and across hemifield invalidtrials as a function of SOA for children in the information cue condition.940 -920 -900 -880f.^•860 -•cc840820800780—4-- ValidWithinAcross•1230^200^400^600^800 1000SOAFigure 10. Mean RTs of valid, within hemifield invalid, and across hemifieldinvalid trials as a function of SOA for seniors in the information cue condition.100 -75 -co 50 -2o 25 -0u)0 ^010c -25 -222.-. -50 -.a1._ -75 -cc-100 --125 - 124—0— Short SOALong SOA-150^-^1 .^10 1^2^3 4^5LocationFigure 11. Mean RT difference scores (Invalid RTs-Valid RTs) as a function oftarget location for young adults in the stimulus cue condition.100 -75 -125-25 -Ua)" -50 -ccI— -75 --100-125 -•-150^•^i^I^i^i^10 1 2 3 4 5LocationFigure 12. Mean RT difference scores (Invalid RTs-Valid RTs) as a function oftarget location for children in the stimulus cue condition.—0-- Long SOAShort SOA12^3^4^51100 -75 -50 -25 -0 '-25-50-75-100 --125 --1500LocationFigure 13. Mean RT difference scores (Invalid RTs-Valid RTs) as a function oftarget location for seniors in the stimulus cue condition.1261 .^i100 -80 - 60 -40 -—0— Short SOALong SOA20 -0-20 -127-400^1^2^3^4^5LocationFigure 14. Mean RT difference scores (Invalid RTs-Valid RTs) as a function oftarget location for young adults in the information cue condition.10080 -60 -40 -•20 -0 ^-20 -—0— Short SOALong SOA128 -400^1^2^3^4^5LocationFigure 15. Mean RT difference scores (Invalid RTs-Valid RTs) as a function oftarget location for children in the information cue condition.-40-20 -01100 -80 -60 -40 -20 -129—0--- Short SOALong SOA0^1^2^3^4^5LocationFigure 16. Mean RT difference scores (Invalid RTs-Valid RTs) as a function oftarget location for seniors in the information cue condition.TargetCue-TargetSOAStimulusCueCue-CueSOAInformationCue•FixationFigure 17. Example trial sequence for Experiment 2. This particular examplerepresents a valid compliment trial in which both information and stimulus cuesare valid.130140 ms SOA1 50 -1 50-1050-50-50-1250 -1150 -1050 -950 -850 -750-650  -550 -450 T^T1T50-50-50-0 -----------------Age (years)61025--a-- 73400 ms SOA^ 800 ms SOA1250 -^1150 -^ T^ 050 -^.1950 -850 -^timmiray.s.11.m....••■•••■■■•■••■ TT•••750 -650 -550 -450Valid^Invalid^Valid^Invalid^Valid^InvalidValidity Validity ValidityFigure 18. Mean RTs for all age groups as a function of cue validity and SOA in the information cuecontrol condition of Experiment 2. Standard errors are represented by vertical bars.800 ms SOA140 ms SOA^400 ms SOAAge (years)—0— 6—0-- 102573.......o ...........ValidityValidity^ Validity1'50-,1 50 -1 50 -50 -50 -I-1250 -1150 -1050 -950 -850 -750 -650 -550 -4501250 -1150-1050 -950 -850 -750 -650 --r--------------^550-Valid^InvalidFigure 19. Mean RTs for all age groups as a function of cue validity and SOA in the stimulus cue controlcondition of Experiment 2. Standard errors are represented by vertical bars.T................50-50-50-50 Valid^Invalid450Valid^Invalid700-700- 700 -650-650- 650 -Stimulus600-600- —o-- Valid—4)-- Invalid600-550- 550-550 -500- 500-500 -450 ^ 4501^ 450 11 1^ 1TI.1140 ms SOA 400 ms SOA^800 ms SOAValid^Invalid Valid^Invalid Valid InvalidInformation Information^InformationFigure 20. Mean RTs for young adults as a function of cue validity and SOA in the experimental conditionsof Experiment 2. Standard errors are represented by vertical bars.1TIStimulus—0— Valido^ Invalid140 ms SOA^ 400 ms SOA^800 ms SOA1250 -1200 -1150 -1100 -1050 -1000 -950 ^1250 -1200 -I.......................... ...if^1150 -1 ^1100 -T^1-1050 -o oI 1000-I 1 950 1250 -1200 -..,^115  -^/..,.".S•^1100-^o..,1050 -^I1000 - 1^1950 1^1Valid^Invalid^Valid^Invalid^Valid InvalidInformation Information InformationFigure 21. Mean RTs for six year olds as a function of cue validity and SOA in the experimental conditionsof Experiment 2. Standard errors are represented by vertical bars.Stimulus—o— Valid----co. ......^Invalid140 ms SOA^400 ms SOA900 -^ 900 -800 ms SOA900 -H850 -00 -50-00 -850 -800 -750 -700 -850 -800-750 -700-50 1^ 1^650 ,^i^650Valid Invalid Valid InvalidInformation^ InformationI^IValid InvalidInformationFigure 22. Mean RTs for ten year olds as a function of cue validity and SOA in the experimental conditionsof Experiment 2. Standard errors are represented by vertical bars.1000 -^ 1000 -750 1^ 750Valid^Invalid InvalidII1000 -950 -900 -850 -950 -900 -850 -800 -Stimulus950 -Ii 800 -850 -900 -800 -I^IValid Invalid750—o— Valido^ Invalid140 ms SOA 400 ms SOA^800 ms SOAInformation Information^InformationFigure 23. Mean RTs for seniors as a function of cue validity and SOA in the experimental conditions ofExperiment 2. Standard errors are represented by vertical bars.Appendix AExperiment 1 Information Cue Condition InstructionsIn this part of the experiment you will see several things appear briefly onthe screen, one after another. The first thing you will see is an exclamationmark in the center of the screen. In this experiment we want you to keep youreyes focused on the center of the screen. We do not want you to move youreyes at all if it can be avoided. This exclamation mark will appear at the start ofeach trial to help you keep your eyes at the center of the screen. Following theexclamation mark, a cue will appear in the center of the screen. There are fivedifferent types of cues, although only one will appear at a time. The first fourtypes of cues represent different locations on the screen and can actually beused to improve your performance on this task. The first cue is a single arrowpointing to your left - this cue represents a location that is on the left and close tothe center of the screen. The second cue is a double arrow pointing left - - thiscue represents a location that is to the left but farther away from the center of thescreen. The third cue is a single arrow pointing right - this cue represents alocation to the right and close to the center of the screen. The fourth cue is adouble arrow pointing right and represents a location to the right and fartheraway from the center of the screen. The final cue is an equal sign. This cuedoes not represent any location.The four cues represent four different locations on the screen that will beindicated by underline dashes when the cue is present. These four locationsare four possible locations that a target letter may appear in following the cue.Because the cue will more often than not correctly indicate the location of thetarget letter to follow, it is important that you try to use these cues to help performthe task.Following the cue a target letter will be presented in one of the fourlocations indicated by the underline dashes on the screen. The target will beeither an X or an 0. If it is an X push this button here (indicate appropriatebutton on the keyboard). If it is an 0 push this button (indicate appropriatebutton on keyboard).If you push the wrong button you will hear a beep, if you hit the rightbutton the next trial will be presented without a beep.Remember to try to keep your eyes on the center of the screen, andrespond as fast as you can without making too many errors.137Appendix BExperiment 1 Stimulus Cue Condition InstructionsIn this part of the experiment you will see several things appear briefly onthe screen, one after another. The first thing you will see is an exclamationmark in the center of the screen. In this experiment we want you to keep youreyes focused on the center of the screen. We do not want you to move youreyes at all if it can be avoided. This exclamation mark will appear at the start ofeach trial to help you keep your eyes at the center of the screen. Following theexclamation mark, a small dark filled in circle will appear. You do not need todo anything with this circle so just wait for it to pass and it will be followed by atarget letter. The target letter will either be an X or an 0. If it is an X push thisbutton here (indicate appropriate button on the keyboard). If it is an 0 push thisbutton (indicate appropriate button on keyboard).You will notice that the target will appear in one of four possiblelocations. These locations are indicated by the underline dashes that you willsee on the screen.If you push the wrong button you will hear a beep, if you hit the rightbutton the next trial will be presented without a beepRemember to try to keep your eyes on the center of the screen, andrespond as fast as you can without making too many errors.138Appendix CExperiment 1: Cost/Benefit AnalysesStimulus Cue Experiment:Young Adults. The cost of orienting to an invalid cue was measured bysubtracting neutral RTs from invalid RTs. Similarly, the benefit of orienting to avalid cue was measured by subtracting valid RTs from neutral RTs. Costs andbenefit associated with the stimulus cue for young adults are illustrated in Figure24. The graph appears to show only benefits and no costs at the shorter SOAs.This trend reverses itself at longer SOAs where, costs but no benefits are found.T-tests revealed that only at the 200 ms SOA are costs significantly different fromzero, t(19)=2.26, p<0.05. In this instance costs are significantly lower than zeromaking them difficult to interpret. Subjects at this SOA were slower to respondon neutral trials than on invalid trials. Planned comparisons based on the overallANOVA revealed that there is a significant difference in the magnitude of costsversus benefits at the 200 and 800 ms SOAs only, 419)=3.114; -2.223,p=0.0029; 0.0302 respectively. At the 200 ms SOA benefits are significantlygreater than costs, and at the 800 ms SOA costs are significantly greater thanbenefits. These results are consistent with the evidence suggesting inhibition-of-return in the overall RT analysis. That is, because the advantage of a valid cuediminishes as a function of SOA while the disadvantage of an invalid cuedecreases it necessarily follows that benefits will decrease as costs increase.Furthermore, benefits were found to decrease significantly between 200 and 400ms SOAs, 419)=3.572, p=0.0007 but not between 150 and 200 ms or between400 and 800 ms. Costs on the other hand do not reveal any significantdifferences across SOAs.Children. Costs and benefits for children were calculated in the samemanner as for young adults. Because there was no main effect of age, or anyinteractions involving age in the overall ANOVA, all three age groups werecombined to improve power. Costs and benefits are presented for this group inFigure 25. Benefits start out small and then increase dramatically by 250 ms, onlyto drop again by 450 ms. Costs on the other hand, start out large, drop at 250ms and increase again by 450 ms. T-tests calculated to determine what costsand benefits differ from zero indicate that costs at both the 133 and 450 msSOAs are significantly greater than zero, 419)=3.808; 2.866 respectively, p<0.05.As well, benefits at the 250 ms SOA differ significantly from zero, t(19)=2.579,p<0.05. Planned comparisons based on the overall ANOVA reveal that thedecrease in benefits between 250 and 450 ms SOAs is significant, t(19)=2.131,p=0.0352. All other effects failed to reach significance.Seniors. Costs and benefits were calculated for seniors in the samefashion as for young adults. The resulting scores are illustrated in Figure 26.Costs appear to be quite small until the 800 ms SOA. On the other hand,Benefits start out large at the 133 ms SOA and drop until they are gone by the800 ms SOA. None of the benefits or costs depicted in Figure 26 are significantlydifferent than zero however. Planned comparisons based on the overall ANOVAreveal similar information. The magnitude of costs and benefits do not differsignificantly, and costs and benefits both fail to show significant change over SOA. Again this lack of significance is surprising considering the graph, but is139again most likely due to low power related to the small number of trials in thestimulus cue experiment.Age Comparisons:Children from all age groups behaved similarly in the stimulus cueexperiment. In fact, age did not interact significantly with any other variable in anoverall ANOVA conducted on costs and benefits, with age as a between subjectsfactor. Essentially, children are showing larger benefits at the 250 ms SOA andlarger costs at 133 and 450 ms SOA. Young adults also show large benefitsearly that disappear at the longer SOAs and small costs throughout the timecourse. The seniors pattern of costs and benefits closely resembles the youngadults. Benefits are large at the early SOAs and virtually disappear by 800 ms.Costs remain small until 800 ms however. The main difference between agegroups is the presence of large costs for children at the 133 and 450 ms SOAs.Information Cue Experiment:Young Adults. Benefits and costs associated with orienting attention inresponse to an information cue were calculated in the same manner as they werein the stimulus cue condition. Costs and benefits for the young adult age groupare presented in Figure 27. There appears to be no costs at the 150 ms SOA butby 200 ms costs have grown and remain constant throughout. Benefits on theother hand appear to be large early, and show gradual decline with SOA. T-testscalculated to determine if costs and benefits differed from zero at each SOA werecalculated. There are significant benefits associated with both the 150 and 400ms SOAs, 419). 3.205; 2.727, p<0.05. Significant costs were only found at the200 ms SOA, t(19)=2.176, p<0.05. Planned comparisons based on the overallANOVA however reveal no significant cost by SOA interaction or benefit by SOAinteraction. Benefits were found to be significantly larger than costs at the 133ms SOA however, t(19)=2.192, p=0.0325.Children , . Costs and benefits for the combined children's group arepresented in Figure 28. For this group, benefits start out large at 133 ms andthen drop off for the remaining two SOAs. Costs however, are virtuallynonexistent for this group at all SOAs. In line with this description, only benefitsat the 133 ms SOA were found to be significantly greater than zero, 419)=9.54,p<0.05. This is further supported by the presence of a significant plannedcomparison based on the overall ANOVA. Benefits decrease significantlybetween the 133 and 250 ms SOAs, t(19)=3.293, /0.0013. As well, benefits aresignificantly larger than costs at the 133 ms SOA, 419)=4.041, p=0.0001.Seniors. Costs and benefits associated with orienting attention inresponse to the information cue were calculated as above and are illustrated inFigure 29. Clearly costs and benefits follow no coherent pattern for this groupuntil the longest (800 ms) SOA. At this SOA benefits are large and costs aresmall. Although to a smaller degree, this pattern is also present at the 133 msSOA. T-tests reveal that costs at the 133 ms SOA are significantly lower thanzero 419)=2.355, p<0.05. Essentially this evidence of negative costs translatesinto additional benefits. That is, subjects are slower to respond when the cue isvalid than when it is neutral. As well, the benefits at the 800 ms SOA aresignificantly greater than zero, 419)=2.667, p<0.05. Planned comparisons basedon the overall ANOVA also support the trends seen in Figure 29. There is a140significant increase in the magnitude of benefits between 400 and 800 ms,019)=-2.096 p=0.0405. As well benefits are significantly greater than costs at the800 ms SOA, t(19)=2.5, ,c0.0153.Age Comparisons: Young adults appear to be producing costs and benefits at most SOAs.Children on the other hand seem to be producing large benefits that decreasewith SOA and consistently small costs. Although somewhat different in theirpatterns, it appears that young adults and children are both using the informationcues presented to align attentional resources. Seniors on the other hand do notappear to use the cues in a manner similar to the other groups. In fact it appearsas though they do not use them at all until the 800 ms SOA, and then the result islarge benefits on valid trials, and no costs associated with invalid trials.141—0— Benefits(N-V)--ii-- Costs(I-N)-40100 -80 -60 -40 -20 -0-20 -1420^200^400^600SOA800^1000Figure 24. Mean RT difference scores as a function of SOA for young adults inthe stimulus cue condition.100 -80 -caa)145 60 -1u)o 40 -0Ca) 20 -zzci^0 ^cc-20 -143—0— BenefitCost -40^i s^I^I100^200^300^400^500SOAFigure 25. Mean RT difference scores as a function of SOA for children in thestimulus cue condition.—0— Benefits(N-V)Costs(I-N)1000800200100 -80 -60 -40 -20 -0-20 --400 400^600SOAFigure 26. Mean RT difference scores as a function of SOA for seniors in thestimulus cue condition.144100 -80 -60 -40 -20  -0—0-- Benefits(N-V)Costs(I-N)-20 --400 200I^1400^600SOA800•^I1 000Figure 27. Mean RT difference scores as a function of SOA for young adults inthe information cue condition.145100 -80 -60 -40 ---a-- Benefits(N-V)20 - Costs(I-N)0-20 --40 .^1 i i 1100^200^300^400^500SOAFigure 28. Mean RT difference scores as a function of SOA for children in theinformation cue condition.146147100 -80U)o 604020*I•■•ca 0 -I-CC-20 --40—o— Benefits(N-V)Costs(I-N)0^200^400^600^800 1000SOAFigure 29. Mean RT difference scores as a function of SOA for seniors in theinformation cue condition.Appendix DExperiment 2 InstructionsIn this part of the experiment you will see several things appear briefly onthe screen, one after another. The first thing you will see is an exclamationmark in the center of the screen. In this experiment we want you to keep youreyes focused on the center of the screen. We do not want you to move youreyes at all if it can be avoided. This exclamation mark will appear at the start ofeach trial to help you keep your eyes at the center of the screen. Following theexclamation mark, a cue will appear in the center of the screen. There are fivedifferent types of cues, although only one will appear at a time. The first fourtypes of cues represent different locations on the screen and can actually beused to improve your performance on this task. The first cue is a single arrowpointing to your left - this cue represents a location that is on the left and close tothe center of the screen. The second cue is a double arrow pointing left - - thiscue represents a location that is to the left but farther away from the center of thescreen. The third cue is a single arrow pointing right - this cue represents alocation to the right and close to the center of the screen. The fourth cue is adouble arrow pointing right and represents a location to the right and fartheraway from the center of the screen. The final cue is an equal sign. This cuedoes not represent any location.The four cues represent four different locations on the screen that will beindicated by underline dashes when the cue is present. These four locationsare four possible locations that a target letter may appear in following the cue.Because the cue will more often than not correctly indicate the location of thetarget letter to follow, it is important that you try to use these cues to help performthe task.Following the presentation of this cue you will see a small dark filled incircle. You do not need to do anything with this circle so just wait for it to passand it will be followed by a target letter.Finally, a target letter will be presented in one of the four locationsindicated by the underline dashes on the screen. The target will be either an Xor an 0. If it is an X push this button here (indicate appropriate button on thekeyboard). If it is an 0 push this button (indicate appropriate button onkeyboard).If you push the wrong button you will hear a beep, if you hit the rightbutton the next trial will be presented without a beep.Remember to try to keep your eyes on the center of the screen, andrespond as fast as you can without making too many errors.148

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