Canadian Journal of Experimental Psychology / Revue canadienne de psychologie expérimentale 2015, Vol. 69, No. 1, 17–27

© 2015 Canadian Psychological Association 1196-1961/15/$12.00 http://dx.doi.org/10.1037/cep0000029

Individual Differences in Working Memory Capacity and Attentional Control Nina Hiebel and Hubert D. Zimmer Saarland University Visual working memory (WM) has a very limited online capacity making it considerably important to control the gating of encoding into WM. Recent studies have suggested that attention control is of importance in doing so, especially the time needed to disengage. However, the disengagement mechanism operates on a later stage of processing after the initial selection of information has already been initiated. We assume that individual differences in WM capacity are also driven by individual variations in the voluntary engagement of attention. In 2 experiments we investigated whether individuals with high- and low-WM capacity differ in the efficiency and speed of this attention control process. We realised different versions of the task in which different amounts of attention control were necessary, a more automatically triggered allocation of attention and a voluntary initiation of attention engagement, respectively. We further manipulated the time course to look for differences in the latency of attention control. The results revealed that participants with low-WM capacity were less effective to exhibit voluntary attention control processes and they were also slower in doing so compared with high-WM capacity individuals. However, this effect seems to be partly moderated by the ability to update the current task set. If the trial structure did not require task set updating smaller individual differences involving WM capacity could be found. Keywords: working memory capacity, attention control, individual differences

initially presented sample. As one easily notes, this trial structure is a variant of the sequential comparison procedure developed by Phillips (1974) that corresponds to a standard short term memory paradigm. Therefore, we assume that we are basically speaking of the same mechanisms when making reference to working, immediate and short-term memory (STM). It seems mainly to be a matter of vogue which term is used. This article is, therefore, not only about reasons of the limited capacity of WM but also about the limits of immediate memory. However, we continue speaking of WM because this term was nearly exclusively used in the articles we refer to. WM plays a pivotal role in numerous cognitive tasks and its variations in capacity correlate with individual differences on many cognitive tasks such as fluid intelligence (Conway, Tuholski, Shisler, & Engle, 1999; Daneman & Carpenter, 1980; Engle, Tuholski, Laughlin, & Conway, 1999; Fukuda, Vogel, Mayr, & Awh, 2010). Consequently, considerable amount of research has been devoted to identify and better characterise factors that determine variations in WM capacity. Individual variations in WM capacity are often explained by differences in cognitive or the underlying neural storage space, metaphorically called the number of available slots. It is argued that each individual has his or her personal upper limit in the number of units that can be held in WM (Fukuda, Awh, & Vogel, 2010; Vogel & Machizawa, 2004). Another important aspect of WM capacity is how effectively the apparent limited store is used. From this perspective, individual differences in WM capacity do not exclusively reflect differences in the number of slots, rather they reveal how efficiently this storage capacity is used, which mainly depends on how well the items gaining access to WM can be voluntarily controlled (Awh & Vogel, 2008). Because of this

Human visual working memory (WM) is a form of online work space that keeps about three to four objects in an accessible state. The first notion of the term WM goes back to Baddeley and Hitch in the 1970s (Baddeley & Hitch, 1974). They highlighted that WM is more than a passive store for the temporary maintenance of information (see immediate memory, Miller, 1956) but a component that actively maintains, manipulates, and updates attended items. Hence, WM is a cognitive system that allows more than retaining information over a period of time. WM can be measured by a variety of tasks. One commonly used measure is the change detection task that is generally seen as the purest task to investigate the maintenance process of WM (e.g., Luck & Vogel, 1997). In a classic example of this task, a sample of items is briefly shown and (re-) presented after a short delay either identically or changed in some aspects. Participants have to decide whether the probe item does match or does not match the

Nina Hiebel, The International Research Training Group “Adaptive Minds”: Neural and Environmental Constraints on Learning and Memory, Department of Psychology, Saarland University; Hubert D. Zimmer, Department of Psychology, Brain and Cognition Unit, Saarland University. This research was conducted within the International Research Training Group “Adaptive Minds” and funded by the German Research Foundation (DFG; Grant GRK 1457). We thank Ruby Coulson and Lisa Rau for assistance with data collection. Correspondence concerning this article should be addressed to Nina Hiebel, The International Research Training Group “Adaptive Minds,” Department of Psychology, Saarland University, Campus A1 7, D-66041 Saarbruecken, Germany. E-mail: [email protected] 17

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mechanism, variance in WM capacity is partially caused by individual differences in executive attention processes (Engle, 2002; Kane, Bleckley, Conway, & Engle, 2001). This viewpoint is supported by multiple studies that suggest a tight link between memory capacity and control of attention. More important, many of these studies underwent attentional tasks with minimal memory requirements but high needs of attention control capabilities, specifically in the face of competition between habitual response schemas and the actual required task demands (Conway, Cowan, & Bunting, 2001; Heitz & Engle, 2007; Kane et al., 2001; Kane & Engle, 2003; Machizawa & Driver, 2011; Redick & Engle, 2006). Across all these tasks, WM capacity correlated with task performance. That is, low-WM capacity individuals were less proficient inhibiting irrelevant information relative to highcapacity individuals. These differences in the efficiency to inhibit irrelevant information might be one reason for individual differences in WM capacity. If low-WM capacity individuals do not exclusively focus attention on task relevant but also select irrelevant items or they do not efficiently exclude initially attended irrelevant items from further processing, they are more likely to overload limited WM resources with irrelevant information. Recently, Vogel, McCollough, and Machizawa (2005) (see also Jost, Bryck, Vogel, & Mayr, 2011; McNab & Klingberg, 2008) provided evidence that individuals with low-WM capacity are indeed less proficient in hindering irrelevant information from accessing WM. In their study, participants were required to voluntarily store a subset of items from a memory array (e.g., blue oriented bars out of an array of red and blue bars). As online measure of the number of stored items the amplitude of contralateral delay activity (Vogel & Machizawa, 2004) was used—a negative slow wave observed at posterior sites contralateral to the visual field in which the to-be-memorised items are presented (also called sustained posterior contralateral negativity, Robitaille & Jolicoeur, 2006). The results of the study indicated that individuals with high-WM capacity were predominantly storing relevant items and efficiently filtering out irrelevant stimuli. In contrast, low-WM capacity participants were unnecessarily also storing distractors. One mechanism of considerable importance to explain these individual variations in filter efficiency is speed of disengagement. Individuals differ in the ability to reallocate their focus of attention once it has been captured by a distractor and those individuals with low-WM capacity need more time to do so (Cashdollar et al., 2013; Fukuda & Vogel, 2011). At the onset of the study array, each item is initially encoded and identified as target or distractor. Once a distractor has been detected, further encoding has to be inhibited. The gate has to be closed (Olivers & Meeter, 2008). If individuals with low-WM capacity are slow in identifying an item as distractor to stop its further processing, distractor items are very often unnecessarily represented in WM and they compete with the relevant ones for storage space. Therefore, the individual’s speed of disengagement may be a critical trait that determines WM capacity. Participants with low-WM capacity are delayed in disengagement. However, disengagement operates at a relatively late stage of processing after the object has been selected for encoding and it acts after the item has been classified as distractor. Furthermore, both studies that have shown this effect deviated in a critical aspect from the original study of Vogel, McCollough

and Machizawa (2005) demonstrating the relationship between unnecessary storage and WM capacity. In the studies of Fukuda and Vogel (2011) as well as Cashdollar and colleagues (2013), distractors were presented before the study display and they exposed the selection feature making them a possible target. Delayed disengagement was, therefore, shown upon contingent capture. Here, the selection criterion determines the attribute for which attention is set. Any stimulus containing this attribute will trigger the initiation of a target response. Thus, initially the distractor is seemingly relevant and selected as target, and then the initiation of the target response has to be inhibited, after the processed stimulus has been classified as distractor. According to this, disengagement is a late process part of the removal of intruded distractors from WM. In the original study, distractors and targets were presented simultaneously and distractors did not display the selection criterion. Hence, in these studies, distractors could principally be excluded from further processing by early operations being effective before or at the moment of selection. For example, participants being prepared that distractors may appear can boost processing of task relevant items so that distractors never win the race for representation. By doing so, they shield WM against distraction. Slow disengagement would be unimportant in this case because distractors are never erroneously selected as targets. In a recent electrophysiological study from our lab we found evidence for such early effects (Liesefeld, Liesefeld, & Zimmer, 2014). In the same time window in which distractor present and distractor absent items were differentiated, we observed a prefrontal bias signal. The latency of this signal was positively and its amplitude negatively correlated with the latency of distractor detection and with unnecessary storage. Furthermore, its amplitude correlated positively with WM capacity. In other words, participants with high-WM capacity were faster and more efficient in the control of early selection processes. Hence, high-WM participants may generally be more prepared to execute cognitive control with the consequence that all control processes of attention have short delays and high impact. In this article we report two experiments in which we tested the possibility that individual differences in WM capacity are related to early processing operations that control the initial selection of information. We investigated individual differences in WM capacity assessed by a classical colour change detection task (see above, e.g., Luck & Vogel, 1997). We assumed that, if participants with high-WM capacity exhibit faster and more efficient executive control processes than low-WM participants, we should see time depending effects in voluntary engagement of attention to targets. The clearest way testing this aim was to use an experimental design containing a 100% spatially valid cue. The location specified the target and participants were instructed to use the information of the cue to voluntarily allocate attention on the target location. To make cue processing and usage of its information necessary, the probe displayed a competitor at another location than the target. The purpose of the competitor was to ensure that individuals must make usage of the cue’s information to solve the task. Thus, the presence of the competitor is a crucial manipulation to test WM-capacity related effects in the control of early selection processes. However, compared with studies testing the effects of attentional disengagement, the competitor should never be used to

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guide attention nor shared the selection feature of the target because the target was in advance defined by its position. We used a modified variant of the antisaccade task. The antisaccade task is an attentional task with minimal memory requirements, nevertheless performance correlates with WM capacity (Kane et al., 2001; Unsworth, Schrock, & Engle, 2004). Moreover, the role of saccade execution could be recently linked to attention control (Edlin & Lyle, 2013). In our variant of the task— called cued categorisation task—the task demands should be comparable to those in an antisaccade task but not require the measurement of eye movements. Participants had to identify the colour of a target item that was defined by its location. We realised three different versions of the task. In two of them cue-dependent allocation of attention was necessary with different amounts of executive attention control. In these trials, a competitor accompanied the target and an endogenous peripheral location cue indicated validly the target’s location. The cue either informed participants that the target would appear at the cued (same) location (stay trials) or at the opposite side of the cued location (shift trials). When the target appeared at the same location as the cue, the attention allocation is thought to be automatically triggered and no higher executive control mechanism should be involved (like on prosaccade trials). Conversely, when the target will appear at the opposite side, a voluntary initiation of an attention shift is required. Like on antisaccade trials attention control processes should operate and a voluntary attention shift must be programmed to the new target location. Thus, our task realised two different conditions of directing attention to targets. In stay trials, nothing had additionally to be done and the coming target at the actual attended location had to be processed. In shift trials, an additional voluntary attention shift was necessary that made executive control necessary. If the implementation of this shift is too slow or too error prone the false target is selected and the colour of the competitor is classified. Because we assumed that low-WM participants are less efficient in this voluntary control we expected better performance for high-WM than low-WM participants in shift trials. In stay trials, we expected no or only small differences because it was not necessary to change attention. For comparison we realised a third condition in which only one target item was presented. This condition served as baseline to validate that the groups do not per se show performance differences. Because in the baseline condition the target is a single stimulus that is displayed it should be automatically selected and we, therefore, expected similar performance between the two groups. Additionally, we had hypothesised that participants with highor low-WM capacity differ in the speed of their control processes. To see effects of this, we realised two different cue to target Stimulus-Onset-Asynchronies (SOAs). The time courses of the SOAs were chosen to guarantee optimal effectiveness of the cues at any time interval manipulation. Wright and Ward (1994) emphasise that the effectiveness of information cues increases gradually as the SOA raises from 0 to 300 ms. Hence, the shortest SOA we realised in our study was set to 300 ms and we contrasted this with a longer SOA of 450 ms. High-WM but not low-WM participants should be able to shift attention even within the short SOA, so that SOA should influence performance only for low-WM participants.

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Experiment 1 In this experiment, participants were required to categorise the colour of a target that was presented at a position left or right on a computer monitor. In the cued trials, a competitor accompanied the target. The cue indicated whether the item at the cued location (stay trials) had to be selected as target or at the opposite side (shift trials). SOA between cue and target was varied. On baseline trials, only one item was presented as target and no competitor. Baseline trials were included to elaborate if any differences found in stay or shift trials are because of general differences between the two groups. We further manipulated the delay between cue and target array presentation. We hypothesised that if high-WM and low-WM individuals differ in speed and efficiency of executive control than they should differ in the time needed to voluntarily orient attention. Low-WM but not high-WM individuals should show costs in shift trials and additional costs when the SOA between cue and target is short. No individual variations involving WM capacity were expected on baseline and stay trials because they evoke little voluntary demands on controlled attention.

Method Apparatus. All experiments were run under Windows 7 controlled by E-Prime Version 2.0 (www.pstnet.com). Participants viewed the presented stimuli on 17” LCD monitor with a screen resolution of 1,240 ⫻ 1,024 pixels at an approximate distance of 65 cm. Responses were collected via Cedrus RB-834 response pads. Participants. Sixty-seven volunteers were recruited for participation in exchange for 8€ per hour or course credit. Three participants were excluded because their performance on the cued categorisation task was below chance level, three participants because of experimental errors and one participant showed a PR-score (difference between hits minus false alarms) below chance level for array size four. All analyses were based on the remaining 60 participants (age range ⫽ 15–35 years, M ⫽ 23.73, 41 female). This and all subsequent experiments have been conducted in accordance with ethical guidelines and received ethical clearance. Participants gave informed written consent after the nature of the study has been explained to them. Stimuli and procedure. Cued categorisation task. The procedure is illustrated in Figure 1a. Participants were required to categorise the colour of a target item (a small square presented at one of two locations in the periphery of the computer monitor). On two thirds of the trials the target was accompanied by a colour competitor an identical square of a different colour on the opposite side. At the beginning of each trial, the word ready? was presented for 1,500 ms to warn participants that a trial was about to start. A black fixation cross (green in baseline trials) appeared for a time period that was randomly chosen from 500 –1,500 ms. In the cued condition, a peripheral cue was flashing for 250 ms. The cue subtended 0.3° of visual angle and provided valid information of the target’s location. On stay trials (circle), the item appearing at the same location as the cue was target. In contrast, on shift trials (diamond) the stimulus appearing at the opposite side was target. On the remaining trials, a blank display was presented for the same duration as the cue and an isolated item was shown as target either left or right. Target, cue, and competitor were shown at locations 11.5° of visual angle

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Figure 1. Schematics for experimental procedures. Basic procedure remained the same in all experiments. All contained the presentation of a cue display. Participants were required to categorise the object indicated by the cue. On stay trials, the object appearing at the same location was target, and on shift trials the item appearing at the opposite side was target. The remaining trials contained no cue and these trials served as baseline. (a) Experiment 1: The cue was presented at the peripheral display location and flashing for 250 ms. The target array contained of two coloured squares that remained for 1,700 ms on the screen or until response. (b) Experiment 2: The stimuli material was changed into Landolt C, the duration of the target array shortened (100 ms) and four stimuli presented. See the online article for the color version of this figure.

to the left and right of the centre. After the offset of the cue display, at both locations a square was presented as mask (1.16° ⫻ 1.16°) for 50 or 200 ms. Each square was filled with random lines. The masks were replaced by two coloured squares (0.7° ⫻ 0.7°) as probe items. Square colours were randomly chosen from a set of four highly discriminable colours (red, blue, green, or yellow) with the restriction that no colour could appear twice. The response was collected by pressing the button on the response pad that matched the colour of the target. At the end of 1,700 ms or after the participant made a response, the display remained blank for 1,700 ms. On the experimental procedure, participants completed 180 trials in total, 60 trials for every cue. All trials were randomly intermixed. Before the test procedure started, participants performed a practice block consisting of 12 trials with feedback about the correctness of the measured response. To check whether cues could be identified, all participants underwent a short block wherein they needed to classify the cue’s shape at the beginning of the experiment. Participants performed this block completing a minimum of 10 trials and continuing until reaching at least 80% correct of performance or maximum of 20 trials.

WM task. To measure an individual’s working memory capacity, we used a centralized colour change detection task (Luck & Vogel, 1997). Subjects viewed (100 ms) four or six coloured squares on a gray background. After a brief blank delay period of 900 ms, memory was tested with the presentation of a test array. Participants had to detect a colour change that occurred in 50% of the time. In change trials, only one square changed colour to a different colour in the colour set with the restriction that no colour could appear twice at the present display. Square colours were randomly chosen from a set of seven clearly discriminable colours (blue, green, red, yellow, white, black, and purple). All stimuli were presented within 9.8° ⫻ 7.3° region on the monitor and the stimuli were separated by at least 2° (centre to centre). The location of the stimuli always remained the same. Subjects pressed one button to indicate whether the array was identical and another to state a difference. The accuracy of this discrimination was assessed as the number of stimuli (K) that could be accurately stored in working memory. We used a standard formula to estimate K score as a measure for working memory capacity (Cowan, 2001). The formula is K ⫽ S(H-F), with S being the size of the array, H the hit rate and F the false alarm rate. We computed K at

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each set size and used the maximum K across set sizes as the subject’s capacity estimation. Forty trials were presented for each set size. Before this, participants completed 10 practice trials with feedback indicating a correct or false response.1 Design. The design was 3 ⫻ 2 ⫻ 2, with Cue (baseline, stay, shift) and SOA (300, 450) as within subject variables and WM Capacity (high, low) as between subject variable. Greenhouse Geisser adjustment for nonsphericity was applied when appropriate. In this case, the original degrees of freedom, GreenhouseGeisser coefficient (ε) and corrected probability levels are reported.

Results WM task. The mean WM capacity estimate was 3.74 (SD ⫽ 1.00) ranging from 1.40 to 5.70. Participants were divided into two groups by a median split, high-capacity (M ⫽ 4.56, SD ⫽ 0.54) and low-capacity (M ⫽ 2.92, SD ⫽ 0.60) individuals, respectively. Cued categorisation task. Figure 2 shows the mean accuracy achieved by high- and low-WM capacity individuals in all experimental conditions. On baseline trials, all participants performed equally irrespective of WM capacity or SOA. However, the cue conditions in which a competitor was presented together with the target seemed to make the task requirements harder for low-WM capacity individuals. This is most pronounced on shift trials and a short SOA. No such effects were found for high-WM capacity individuals. A three way 3 (Cue: baseline, stay, shift) ⫻ 2 (SOA: 300 vs. 450) ⫻ 2 (WM Capacity: high vs. low) repeated measure analysis (analysis of variance [ANOVA]) with WM capacity as between subject factor was conducted to examine the effect of cue and SOA on accuracy for the two capacity groups. We do not report main effects because they are qualified by the two significant two-way interactions Cue ⫻ WM capacity and SOA ⫻ WM capacity. The two-way interaction of Cue ⫻ WM Capacity, F(2, 116) ⫽ 3.83, p ⬍ .05, ␩p2 ⫽ .06, suggests a differential relationship of accuracy for cues between high- and low-WM participants. No differences

Figure 2. Experiment 1: Mean categorisation performance as a function of cue type, cue to target interval (SOA) and WM capacity. Error bars represent 1 SEM.

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in accuracy between cue conditions were found for high-WM individuals (largest F(1, 58) ⫽ 1.44, p ⫽ .24). In contrast, for low-WM participants, accuracy in shift trials were lower compared with baseline, F(1, 58) ⫽ 21.93, p ⬍ .01; even performance on stay trials was significantly impaired relative to baseline, F(1, 58) ⫽ 13.88, p ⬍ .01. The difference between stay and shift trials was not significant, F(1, 58) ⫽ 1.93, p ⬎ .05). Direct group comparisons between low and high, revealed that accuracy in shift trials was significantly greater for high-WM individuals, F(1, 58) ⫽ 8.78, p ⬍ .01. As expected, no differences in baseline were found (F ⬍ 1.00). The two-way interaction of SOA ⫻ WM Capacity was marginally significant, F(1, 58) ⫽ 2.87, p ⬍ .10. Low-WM individuals showed poorer performance on trials with short SOA than on trials with long SOA, F(1, 58) ⫽ 10.28, p ⬍ .01. High-WM participants were not influenced by variation in the length of the SOA (F ⬍ 1.00). For low-WM participants, a short SOA impaired performance strongly in shift trials, F(1, 58) ⫽ 6.91, p ⫽ .01, the small difference in stay trials was not significant, F(1, 58) ⫽ 1.57, p ⬎ .10.

Discussion In Experiment 1, participants with high- and low-WM capacity were tested in a modified antisaccade task, the cued categorisation task, respectively. We hypothesised that performance on baseline trials would be equal irrespective of WM capacity, but that especially on shift trials performance of low-WM participants is worse than of high-WM participants. Indeed, we found low-WM capacity individuals to perform less accurate on shift trials compared with baseline trials. By contrast, no such effects were found for those participants with high-WM capacity. These were the predicted findings. Low-WM participants seem to be poorer in executive control of attention and they seem to need more time to set up this control than the high-WM group. Unexpectedly, also in stay trials low WM participants showed poorer performance than in the baseline and the effect was as large as in shift trials with long SOA. A possible reason for this impairment in stay trials is that low-WM participants are generally poorer in specifying the task set when higher executive control is necessary. Stay as well as shift conditions are trials in which voluntary control is necessary and the target has to be selected endogenously; in baseline trials the target can be selected automatically. A cue has first to be evaluated specifying the actual task set, that is, “do nothing and stay at the actual position” or “shift attention to the opposite side.” If this is correct, low-WM participants would not be specifically slow in voluntary initiating attention shifts but generally in specifying task sets for executive control. This is similar to continuous performance tasks (Braver & Barch, 2002; Cohen & Servan-Schreiber, 1992; Rosvold, Mirsky, Sarason, Bransome, & Beck, 1956) or context-updating tasks (Lenartowicz, Escobedo-Quiroz, & Cohen, 2010) a specific version of the continuous performance task in 1 Participants further underwent the Operation Span Task (Unsworth, Heitz, Schrock, & Engle, 2005), the Attentional Network Task (Fan, McCandliss, Sommer, Raz, & Posner, 2002) and a lateralized orientation change detection with filtering requirements (Vogel et al., 2005). However, these tasks are not relevant for the current issue of the article and are not reported here.

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which the context specifies the next to-be-performed action. In the standard version of the continuous performance task, participants view a continuous stream of letters and respond to a specific target. For example, in the AX variant of the task, participants should detect the target letter “X” with a certain button press only when it follows the letter “A.” On all other letters, including “X” is not preceded by an “A,” participants should react by pressing another button. The AX trial usually is the most frequent one. Thus, the correct reaction on the target letter X is contingent of the context provided by a cue (A letter or no A letter). This is similar to our cued categorisation task in which the location of the to-becategorised target is contingent on the cue. Recently, Redick (2014) (see also Redick & Engle, 2011) showed that variations in WM capacity are indeed related to different preparation processes for response selection in the continuous performance task. In his study, participants underwent the AX version of the task wherein he further manipulated the proportions of AX trials. The results of the study indicated that performance of low-WM capacity individuals depended on the overall response frequencies but less on the cue whereas their high-WM capacity counterparts used the cue information to prepare a response in advance when the proportion of AX trials was high and therefore advantageous. In other words, high-WM participants behaved adaptively and they used the task context. Moreover, differences in cognitive tasks involving WM capacity seem to be stronger in contexts in which the actual task requirements compete with predominant responses. Low-WM capacity individuals seem more likely to disregard task demands when a habitual or automatic response is predominated (Kane & Engle, 2003; Morey et al., 2012). For instance, Kane and Engle (2003) found evidence for this using the Stroop task wherein participants are instructed to name the ink of the colour the word is written in. They revealed WMcapacity related differences to be associated with the proportion of trials being congruent Stroop trials. The largest differences in errors related to WM capacity have been found when most of the trials were congruent of ink colours and words. The proportion of congruent Stroop trials has not affected performance of high-WM capacity individuals. In contrast, low-WM capacity individuals made more errors on incongruent trials when the amount of congruent trials was high. Given these differences between high- and low-WM participants in updating the task set upon task context, it is possible that the results of Experiment 1 go back to the necessity of updating the task set (stay or shift) upon task context (the cue) on a trial by trial basis. To test this, we ran a second experiment in which we used our cued categorisation task of Experiment 1 in different randomizations.

Experiment 2 In Experiment 1, stay and shift trials were presented in an intermixed order, so that for each trial task sets have to be checked and updated. The deficit of low-WM participants could therefore also reflect task set updating costs. To test this we additionally realised the stay and shift conditions as single task blocks. In this part of the experiment, all trials of a block were either shift or stay trials. Additionally, we increased the proportion of stay trials compared with shift and baseline trials. As stated above (Kane & Engle, 2003; Redick, 2014) low-WM capacity individuals are

more likely to disregard task requirements when the habitual response is predominant in the task set. In our experiment, stay trials, that is, keeping the attention at the attended location, can be considered the more habitual reaction. Hence, increasing the amount of these trials should make the task more demanding and magnify differences in the abilities of attention control. Furthermore, in Experiment 2 we increased the motivation of an early control of attention to specify the task set in advance to be prepared before the target is presented. The presentation duration of the target array in Experiment 1 was very long, which provided individuals with sufficient time to solve the task by a reactive strategy. From this account, individuals might not have exclusively used the cue information in advance to initiate early target selection processes but might have used memory of the cue at a late point in time when the target was already presented. When participants remember the cue and its location, they can retrospectively infer which of the two squares was indicated as target. That this possibility exists may reduce the motivation to proactively control attention. We took a number of actions to increase the motivation of proactive control: we decreased the time duration of the target array, we masked the target, and we made the perceptual task more demanding so that proactive control of attention is most efficient. The procedure is illustrated in Figure 1b. We changed the stimulus material into Landolt Cs. Unlike colours, Landolt Cs are more complex stimuli and require focal attention at the time of presentation to process them correctly. We further added two additional competitors, so that the target display now consisted of four stimuli. Only one of these was the target. We shortened the presentation time of the target array and implemented a backward mask. All these changes should make the use of reactive strategies less effective. Furthermore, we tested whether task set updating costs contribute to the overall impairment of low-WM individuals. In mixed blocks, we expected the same results as in Experiment 1. If low-WM capacity individuals are not generally impaired to voluntarily exhibit attention control, but limited in their ability or willingness to update the task set currently, we should find no or smaller individual differences on single task blocks. Low- and high-WM capacity individuals should perform equally well.

Method Participants. From those participants that completed all assigned tasks in Experiment 1, we recruited 41 participants. Six participants were excluded owing to accuracies below chance in the cued categorisation task of Experiment 2. All analyses were conducted on the remaining 35 participants (age range ⫽ 18 –35 years, M ⫽ 23.63, 23 female). All participants were compensated for their time with 8€ per hour for their participation. Stimuli and procedure. Cued categorisation task. The basic procedure remained the same but the targets were changed. In this task version targets were Landolt Cs (0.8° ⫻ 0.8°) and each target was accompanied by three competitive items. All stimuli were presented at the corners of an imaginary polygon within 2.8° ⫻ 2.4° region on the monitor. Subjects were required to identify the orientation of the cued item at a single spatial location (at the same position as the cue or at the horizontal opposite side of it). Shortly before the onset and after

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the offset of the target display, four placeholders (0.8° ⫻ 0.8°) were displayed. The presentation duration of all displays remained the same except the duration of the target array. This was set to 100 ms. On baseline trials a green fixation cross was flashing for the same duration time as the cue. The response was collected by pressing the button on the response pad that matched the Landolt C that was currently target. Participants performed three blocks. One stay, one shift, and one mixed block. At the beginning of each block, participants were given practice trials. The task order of the three experimental blocks was counterbalanced in an ABC-CABBCA design between subjects, respectively. Within each pure block, participants completed 20 trials. For the mixed block, 30 stay trials, 20 shift trials and 20 baseline trials were presented randomly intermixed.2

Results WM capacity. We used the K scores as in Experiment 1 to estimate individual variations in WM capacity. Based on a median split, participants were divided into high- and low-WM subgroups with mean K scores of 4.69 (SD ⫽ .53, range 3.90 –5.70) and 3.17 (SD ⫽ .53, range 2.00 –3.80) for high- and low-WM individuals, respectively. Cued categorisation task. The same analysis as Experiment 1 was conducted for the mixed block condition. The pattern of results is summarised in Figure 3a. The two-way interaction of SOA ⫻ WM capacity was marginally significant, F(1, 33) ⫽ 3.14, p ⬍ .09, ␩p2 ⫽ .09. Again, whereas, high-WM individuals performed equally well independent of SOA (F ⬍ 1.00), low-WM individuals, improved their performance with a prolonged time course, F(1, 33) ⫽ 10.44, p ⬍ .01. Again, this is mainly because of shift trials. For low-WM capacity participants, a short SOA impaired performance strongly in shift trials, t(33) ⫽ 4.16, p ⬍ .01. This time also high-WM capacity individuals performed less accurate on shift trials with a short SOA, t(33) ⫽ 1.72, p ⬍ .05, but the impairment was still smaller compared with low-WM capacity individuals, t(33) ⫽ 1.90, p ⬍ .05. The differences between stay and shift trials contrasted with the differences between the long and short SOA manipulation was greater for those participants with low-WM capacity relative to those with high-WM capacity, t(33) ⫽ 1.88, p ⬍ .05. The second analysis was conducted to examine the individual differences when it was not necessary to update the different task sets. If the effects found in the mixed trials are caused by task set updating costs, we should find no or smaller capacity-related individual differences in the two single task blocks. The pattern of accuracy is shown in Figure 3b. First, we entered accuracy on stay blocks in an ANOVA with SOA (300, 450) as within subject factor and WM capacity (low, high) as between subject factor. There were no significant effects (largest value F(1, 33) ⫽ 1.47, p ⫽ .23). The same analysis as before was done for single shift blocks. It revealed a significant main effect of SOA, F(1, 33) ⫽ 8.34, p ⬍ .01, indicating greater accuracy on trials with long than short SOA. Even the interaction was not significant this effect goes mainly back to low-WM participants. Low-WM participants performed less accurate on trials with short than long SOA, t(33) ⫽ 2.51, p ⬍ .01. This time also high-WM participants performed less well on short SOA, t(33) ⫽ 1.54, p ⬍ .07, but the impairment was smaller relative to low-WM participants, t(33) ⫽ 1.46, p ⬍ .08. Compared

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with mixed block, performance on single shift blocks is clearly higher, F(1, 33) ⫽ 19.65, p ⬍ .01.

Discussion Experiment 2 provided further evidence for the assumption that individuals with low-WM capacity are limited in their ability to exhibit attention control, whereas those individuals with high-WM capacity have a fast and efficient executive control. For the intermixed task set, we could replicate our previous finding that low-WM capacity individuals take longer to voluntarily control the engagement of attention when higher executive control is demanded (shift trials). With prolonging the time course they could overcome these costs. Unexpectedly, we now also found costs for high-WM participants on shift trials. Compared with Experiment 1, the task requirements in this variant of the cued categorisation task prohibited alternative task solving strategies. If the task structure is more demanding, also high-WM capacity individuals come at cost. Nevertheless, relative to participants with low-WM capacity these costs have been much smaller and importantly the influence of SOA was stronger in low- than high-WM capacity individuals. The second aim of Experiment 2 was to specify whether general updating costs of the task contributed to poorer performance of low-WM capacity individuals. Therefore, participants underwent trials with single and intermixed trial order. Overall, on single blocks performance was much better compared with blocks with an intermixed trial order. Here, the whole block could be processed with the same task set. This supports our assumption that if the task demands did not require updating of the task set, individual differences involving WM capacity were reduced. This could be because of limited abilities of low-WM participants in updating the required executive attention control set on a trial-by-trial basis. However, individual differences in task set updating cannot account for all WM-capacity related effects we found. On single task shift blocks, wherein there was no need to update the task set, we still found decreases in performance although much smaller compared with trials with an intermixed order. Before executive attention control can operate, the need for control has first to be detected (Botvinick, Braver, Barch, Carter, & Cohen, 2001). Recently, we could show such a causal chain between the implementation of higher executive control and the detection of the demand of control in our own lab (Liesefeld et al., 2014). We assume that task set updating is one central mechanism linking this causal chain. Indeed, updating is central whenever a goal or task rule has to be flexibly adapted to other task demands or when attention was distracted by irrelevant competitors. Such situations require the detection that the task set needs to be adapted in the first place. Based on our findings, we believe that individuals with low-WM capacity might be delayed in the detection and activation process of task sets. In our experiments the change in task set was explicitly 2 We further measured performance on a lateralized colored change detection (Vogel & Machizawa, 2004), as well as performance on two further centralized color change detection tasks with distractors, one with shapes as distractors (Arend & Zimmer, 2012) and another where distractors where indicated by placeholder, respectively. Because these tasks are not relevant for the current issue of the article, we do not report these data.

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Figure 3. Experiment 2 (a) Mean proportion correct and SE for baseline, stay, and shift trials separated by the manipulated time interval between cue and target (SOA) and WM capacity. (b) Mean proportion correct and SE for performance on single stay and shift blocks separated by SOA and WM capacity.

signalled by a cue. If the task set still needed to be adapted before a proper task performance could be initiated we would expect more inaccurate performance on trials with short SOA. This was exactly what we found for participants with low-WM capacity on shift trials. Note that, on stay trials updating the task set was not required for accurate performance. Behaviour guided by the proper task rule or prepotent response would have led to the same response. Thus, we assume that one central mechanism associated with the implementation of executive control, is the detection and activation of task set reconfiguration and that variations in WM capacity could be explained in terms of differences in latency of this updating process. From this perspective, task rules are setting up the frames for cognitive control processes that perceptual and categorical filter

are later operating on (Bundesen, 1990). If participants are prepared that competitors for limited processing resources might appear, they boost the task relevant items so that they win the race for representation. In contrast, the gate should be closed when a competitor has been selected (Olivers & Meeter, 2008). Thus, setting up a proper task set should facilitate item selection in the face of interference.

General Discussion The primary goal of our study was to explore whether individual differences in WM capacity are related to a more efficient and faster initiation of higher executive control processes. We assumed that individuals with high-WM capacity would

DIFFERENCES IN WMC AND ATTENTIONAL CONTROL

perform better on trials when voluntary engagement of attention was necessary (shift trials) compared with their low-WM capacity counterparts and that they would do so faster. In two experiments we found the same pattern of results. Figure 4 summarizes the time depending costs for all realised shift trials in both experiments (difference between trials with long minus short SOA). In Experiment 1 as well as in the intermixed block of Experiment 2 low-WM capacity individuals showed larger costs in performing shift trials (Experiment 1, t(33) ⫽ 1.69, p ⬍ .05; mixed block Experiment 2, t(33) ⫽ ⫺1.90, p ⬍ .05). In the single task shift block of Experiment 2 the same tendency was seen but it was reduced and no longer significant, t(33) ⫽ ⫺0.82, p ⬎ .10. Overall, participants with low-WM capacity were less effective to exhibit voluntary attention control processes and in doing so they were also slower compared with those participants with high-WM capacity. Alternatively, our results could also be considered in the context of attentional disengagement. According to this perspective, the pattern of findings might not exclusively depend on differences in voluntary engagement of attention but in the time individuals need to disengage attention from a capturing object (in this case the cue). In our cued categorisation task, attention must be clearly oriented and engaged in processing the cue’s information. This is true for stay and shift trials. In both cases participants have to use the information of the cue to voluntarily allocate attention on the target’s location. Only in shift trials, however, individuals would be required to disengage their focus of attention from the cue first to move their attention onto the target. Thus, the necessity to disengage attention from its current position goes hand in hand with voluntarily engaging attention. Previous studies have demonstrated that especially low-WM capacity individuals are limited in rapidly disengaging their attention from processing information (Cashdollar et al., 2013; Fukuda & Vogel, 2011). However, individuals with high- or low-WM capacity do not always differ in the magnitude of attention capture.

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In both studies the disengagement effect was not shown on just any information but only when the distractor shared the target defining feature. This feature is mostly set by the task. According to this rationale, congruent cues on the target’s selection property should produce disengagement effects only. However, in the shift condition the target never shared the target defining property. Target and shift cue were never displayed at the same location. In other words, though participants needed to engage in the processing of the cue information, and as a consequence in shift trials disengage their attention first from the cue’s position, the magnitude of this effect should not differ between high- and low-WM capacity individuals.

Résumé La mémoire de travail (MT) visuelle dispose de capacités très limitées en ligne. Il devient alors très important de contrôler l’encodage sélectif dans la MT. Les récentes études ont montré que le contrôle de l’attention joue un rôle important, particulièrement au niveau du temps relié au désengagement. Or, le mécanisme de désengagement se produit a` un stade ultérieur du traitement, c’est-a`-dire une fois que la première sélection de l’information a été initiée. Nous émettons l’hypothèse que les différences individuelles au niveau de la capacité de la MT sont également le résultat de variations individuelles au niveau de l’engagement volontaire de l’attention. À l’aide de deux expériences, nous avons cherché a` savoir si les individus dotés d’une faible capacité de MT et d’une forte capacité de MT divergeaient en ce qui a trait a` l’efficacité et a` la vitesse de ce procédé de contrôle de l’attention. Nous avons réalisé différentes versions de la tâche selon lesquelles différents niveaux de contrôle d’attention étaient requis, une attribution de l’attention davantage déclenchée de manière automatique et une initiation volontaire de l’engagement de l’attention, respectivement. Nous avons ensuite manipulé la durée afin de détecter des écarts au niveau du temps de latence du contrôle d’attention. Les résultats ont montré que les participants dotés d’une faible capacité de MT étaient moins efficaces a` afficher les procédés de contrôle d’attention volontaire et le faisaient plus lentement comparativement aux individus dotés de forte capacité de MT. Cet effet semble toutefois partiellement modéré par la capacité de mise a` jour de l’ensemble de tâches actuel. Si la structure de l’essai ne nécessitait pas la mise a` jour de l’ensemble de tâches, des écarts individuels plus faibles impliquant la capacité de MT pourraient être détectés. Mots-clés : capacité de la mémoire de travail, contrôle de l’attention, écarts individuels.

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Received June 30, 2014 Accepted September 7, 2014 䡲

Special Issue of the Canadian Journal of Experimental Psychology on “The Production Effect in Memory” Guest Editors Glen E. Bodner, University of Calgary Colin M. MacLeod, University of Waterloo Editor-in-Chief: Penny Pexman Theme of the Special Issue The production effect occurs when producing an item (e.g., saying it aloud, typing it, or even singing it) results in better memory than does silent encoding. First reported by Hopkins and Edwards (1972), the effect was largely overlooked until MacLeod, Gopie, Hourihan, Neary, and Ozubko delineated the effect in 2010, and also provided it a name. Since then over 20 papers on the effect have been published. Studying via production is easy to do, relative to most other encoding tasks that benefit memory (e.g., generation, levels-of-processing), and its effects typically are robust. Recent research has begun to examine when and why production works. These studies have raised important questions asked of other encoding tasks, and have led to important new questions and methodological advances as well. This special issue will bring together original papers that advance our current understanding of the myriad ways that the simple act of production can influence memory. Call for Papers Researchers with an interest in issues relevant to the production effect are invited to submit a full paper to the CJEP Manuscript Central site, indicating in the covering letter that the manuscript should be considered for this special issue. Submitted papers should contain original, unpublished work and can be written in either English or French. Manuscripts should be submitted electronically in accordance with APA guidelines. All submitted papers will go through rigorous reviewas usual at CJEP, with consideration given to their originality, their theoretical contribution, their methodological soundness, the clarity of the presented results and conclusions, and the relevance of the submission for the special issue. Please send expressions of interest by February 1, 2015, via email to the Guest Editors ([email protected], [email protected]). The deadline for submission of papers is June 30, 2015. Note that, unlike edited books, journal production deadlines make this a firm closing date.

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