Proactive interference and practice effects in visuospatial working memory span task performance

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1 MEMORY, 2011, 19 (1), 8391 Proactive interference and practice effects in visuospatial working memory span task performance Lisa Durrance Blalock 1 and David P. McCabe 2 1 School of Psychological and Behavioral Sciences, University of West Florida, Pensacola, FL, USA 2 Department of Psychology, Colorado State University, Fort Collins, CO, USA Downloaded By: [Blalock, Lisa Durrance] At: 17:00 13 January 2011 In the current study the influence of proactive interference (PI) and practice on recall from a visuospatial working memory (WM) task was examined. Participants completed a visuospatial WM span task under either high-pi conditions (a traditional span task) or low-pi conditions (a span task with breaks between trials). Trials of each length (i.e., two to five to-be-remembered items) were equally distributed across three blocks in order to examine practice effects. Recall increased across blocks to a greater extent in the low-pi condition than in the high-pi condition, indicating that reducing PI increased recall from WM. Additionally, in the final block the correlation between fluid intelligence and WM recall was stronger for the high-pi condition than the low-pi condition, indicating that practice reduced the strength of the correlation between span task recall and fluid intelligence, but only in the low-pi condition. These results support current theories that propose that one source of variability in recall from WM span task is the build-up of PI, and that PI build-up is an important contributing factor to the relation between visuospatial WM span task recall and higher-level cognition. Keywords: Visuospatial working memory; Proactive interference; Fluid intelligence; Practice. Working memory (WM; Baddeley, 1986; Baddeley & Hitch, 1974; Baddeley & Logie, 1999) has been conceptualised as a memory system consisting of a set of subcomponents that work together to facilitate online cognition. From moment to moment, WM allows humans to understand and represent the current environment, retain and manipulate recent information, create and maintain current goals, and acquire new knowledge (Baddeley & Logie, 1999; Logie, 2003). Although there are many different theoretical explanations of WM (see Conway, Jarrold, Kane, Miyake, & Towse, 2007, for a recent review), many of the most influential theories of working memory suggest that WM is made up of a domain-general, central attentional component, and domain-specific maintenance components (e.g., Baddeley, 1986; Engle & Kane, 2004). WM is often measured using complex span tasks that involve concurrently processing and maintaining information. The majority of the complex span tasks that have been used in WM research are verbal tasks. For example, the reading span task (Daneman & Carpenter, 1980) involves verifying whether presented sentences are truthful (the processing component) and encoding the final word of each sentence (the maintenance component). Trials typically range in length from two to six sentences, and after all the sentences on a given trial are presented the encoded words are recalled in serial order. Thus, by requiring concurrent processing and maintenance, complex span tasks tax the central executive Address correspondence to: Lisa Durrance Blalock, School of Psychological and Behavioral Sciences, University of West Florida, University Parkway, Pensacola, FL 32514, USA. lblalock@uwf.edu # 2011 Psychology Press, an imprint of the Taylor & Francis Group, an Informa business DOI: /

2 84 BLALOCK AND MCCABE component of WM (a domain-general resource) in addition to domain-specific maintenance abilities. These complex span tasks are strongly related to higher-level cognitive performance, including fluid intelligence (gf), reading comprehension, writing, and vocabulary learning, to name but a few (see Engle & Kane, 2004, for a review). Moreover, complex span tasks are more strongly related to higher-level cognition than are simple span tasks that only require domainspecific maintenance and retrieval (Kane, Hambrick, & Conway, 2005). PROACTIVE INTERFERENCE AND WM SPAN Several explanations of the underlying relationship between complex span tasks and higher-level cognitive tasks have been proposed, and one of the most influential is the two-factor theory of executive attention proposed by Engle, Kane, and colleagues (e.g., Engle & Kane, 2004). The essence of this theory is that the central executive is responsible for (1) controlling attention in order to maintain goal-relevant information in an active state, and (2) maintaining and retrieving information in the face of interference or distraction. Thus working memory capacity (as measured by complex span tasks) is a mental workspace, and individual differences in working memory capacity reflect the ability to control attention to maintain information in an active, quickly retrievable state (Engle, 2002, p. 20). More specifically, a key function of the central executive is to resist proactive interference (PI), which occurs when previously learned information interferes with learning new information (Engle, 2002; Kane & Engle, 2000; Lustig, May, & Hasher, 2001). For example, when learning lists of words the first list will be relatively easy to recall but subsequent lists become increasingly more difficult to recall because words from previous lists will interfere with the retrieval of current list items (Kane & Engle, 2000; Keppel & Underwood, 1962). PI also influences performance on WM span tasks. For example, May, Hasher, and Kane (1999) compared recall for high- and low-pi versions of reading span tasks. They found that when PI was reduced relative to a typical span task (e.g., by implementing breaks between trials) span task recall improved. Lustig et al. (2001) later replicated this effect and showed that when tested under low-pi conditions span task recall no longer predicted prose recall. This suggests that reducing PI increases WM span recall but it reduces or eliminates the relationship between span task recall and higher-level cognition. Bunting (2006) provided additional evidence for the impact of PI on WM span by examining the influence of item similarity on WM span recall and its prediction of higher-level cognition. In this study the to-be-remembered items (digits or words) on the WM span task trials were either changed or not changed from one trial to the next, with PI building up more when to-be-remembered items of the same item type were presented across successive trials. Bunting (2006) showed that span task recall varied significantly with PI: recall was lower when PI was greater and recall increased when PI was reduced. Additionally, in the high-pi conditions correlations with a measure of gf were significant, but in the low-pi conditions they were not. These results converge with Lustig et al. (2001) in showing that reducing PI increases recall from WM span tasks, but reduces the strength of the relationship between WM capacity and higher-level cognition. The research reviewed thus far indicates that PI directly influences verbal WM span task recall and its predictive power but, to a large extent, has not addressed whether PI operates similarly for visuospatial WM span tasks. One notable exception is a recent study by Rowe, Hasher, and Turcotte (2008), although the visuospatial task was a simple span task (i.e., it included domainspecific maintenance and retrieval, but did not include a processing component). Rowe et al. either presented to-be-remembered items in ascending order, beginning with the shortest trials and proceeding to the longest trials, or descending order, beginning with the longest trials and proceeding to the shortest trials. In the ascending condition the influence of PI on recall is believed to be greater than in the descending condition. According to Rowe et al. this is because in the ascending condition there are more items from previous trials that need to be deleted or suppressed from gaining access to WM in order to constrain recall to the current trial (thus, increasing response competition). Interestingly, Rowe et al. reported that older adults recall was greater in the descending condition than in the ascending condition, which is consistent with the idea that PI built up across trials, but for younger adults the opposite pattern was found. Rowe et al. suggested that for younger adults the influence of practice outweighed the influence of PI (see May et al.,

3 1999, and Lustig et al., 2001, for similar results with verbal tasks), but for older adults the opposite was true due to age-related declines in inhibitory control (see also Rowe, Hasher, & Turcotte, 2009, for a similar result). Rowe et al. (2008, 2009) suggested that practice effects (i.e., learning the task) might mask PI effects on WM recall. However, they did not directly investigate the impact of practice effects on visuospatial span task performance, and this issue has not been addressed using verbal span tasks either. The primary purposes of the current study were to decouple the influences of PI and practice on recall from visuospatial WM span tasks, and to examine the influence of PI and practice on the relationship between visuospatial WM span tasks and gf. Towards this end, we compared high- and low-pi versions of a complex visuospatial WM task across successive, equivalent blocks of visuospatial WM task trials. Although there are fewer trials in a typical complex span task than in traditional research examining practice effects (e.g., Ackerman, 1988), the results from Rowe et al. (2008) suggest that practice effects may influence span task recall even with small numbers of trials. Thus practice effects should increase WM span task recall even when a relatively small number of trials are included in the task. Indeed, most span tasks include few trials (i.e., typically three trials at each trial length), and thus, examining practice effects early in task performance will be most generalisable to previous research. Comparing span task recall on successive blocks of trials allowed us to directly examine whether practice increased complex span task recall, and whether recall differed depending on the level of PI involved in the task. WORKING MEMORY CAPACITY AND FLUID INTELLIGENCE We also examined the influence of PI and practice on the relationship between a visuospatial WM span task and gf. Previous research indicates that there is a strong relationship between WM capacity (i.e., the ability to maintain and manipulate information) and gf (i.e., the ability to solve novel problems and note relations among events; Carpenter, Just, & Shell, 1990), but practice effects have not received much attention in the WM literature. By contrast, research in differential psychology has shown that performance on VISUOSPATIAL WORKING MEMORY SPAN 85 cognitive tasks will typically increase with practice, but increased practice will decrease the correlations between the practiced task and gf (e.g., Ackerman, 1987). The theoretical explanation for these reduced correlations is that, as task performance becomes more automated with practice, individuals will become more similar in their performance, attenuating the variability in performance between participants. Although the finding that practice reduces correlations between cognitive tasks and gf is intuitive, considering practice leads to greater automation in performance, it may initially seem to contrast with the above-mentioned research on PI and WM. That is, the PI data suggest that with more practice on WM tasks (i.e., more trials), WM recall should decrease because there will be more PI build-up, making it harder to discriminate current to-be-remembered items from items on previous trials. By contrast, the finding that task practice typically decreases correlations between cognitive tasks and gf makes a prediction opposite from the notion that PI increases correlations between WM span tasks and gf. Of course, it is possible that the amount of practice that typically occurs on a complex WM span task is not sufficient to automate performance enough to reduce the correlation with higher-level cognition. However, we should note that the idea that practice and PI can influence recall differently, and affect the correlation between recall and a third variable (e.g., gf) are not incompatible. That is, both of these influences, PI and practice, can affect span task recall and/or correlations independently of one another. The design of the current study allowed us (1) to simultaneously examine the influences of practice and PI on visuospatial WM performance, (2) to determine how practice and PI influence the correlation between visuospatial WM span task recall and fluid intelligence, and (3) to determine whether the influence of practice varies depending on the level of PI involved in the task. THE CURRENT EXPERIMENT In order to examine the above mentioned issues, in the current study we used a complex visuospatial WM span task (rotation-matrix span). This task required maintenance and recall of the location of a filled square within 44 matrices, and a processing component (mental rotation of letters) interspersed prior to the presentation of each

4 86 BLALOCK AND MCCABE matrix. The task was either completed under low- PI conditions (a 15-second break between trials; cf. Lustig et al., 2001; May et al., 1999) or high-pi conditions (a traditional span task without breaks between trials). Span task trials were presented across three consecutive blocks that were equated for trial length within each block. This allowed us to examine the interaction between practice and PI. We expected that, as a result of practice, recall would increase across blocks for both PI conditions, consistent with speculations from previous researchers (e.g., Rowe et al., 2008), but recall should be higher overall in the low-pi condition. As for the relationship with gf, there were two potential outcomes. Based on findings showing PI increases the relation of WM span task recall and gf, the correlation between span task recall and gf should be highest in the high-pi condition, particularly in later blocks, when PI is strongest. Conversely, based on findings from the differential psychology literature (see Ackerman, 1987, for an early review) task practice could reduce the correlation between span task recall and gf, leading to weaker correlations across blocks. Participants METHOD A total of 117 introductory psychology students participated for partial course credit (mean age in years18.8; age range1727; 72 females, 43 males). Participants were randomly assigned to one of the two PI conditions (high-pi; n60; low-pi; n57). Materials To measure WM capacity a rotation-matrix span task was used, which was modelled after similar tasks used by Kane et al. (2004) and Miyake, Friedman, Rettinger, Shah, and Hegarty (2001). In this task participants made judgements about whether a rotated letter would be in its normal orientation, or mirror-reversed, if it were upright. Following each of these letter rotation judgements, a 44 grid is presented for 1 second, with 1 of the 16 locations filled in. Participants were asked to remember the location of the filled square for each grid, while maintaining nearly perfect accuracy on the letter rotation task (see Figure 1). Trial lengths of two to five were distributed in a fixed random order within each of three blocks in the experiment. Thus, each block contained 1 trial of each length for a total of 12 trials (4 per block; note there was no break between blocks). At the end of the trial, participants marked the to-be-remembered locations in serial order on an answer sheet with a set of empty matrices. The span task was the same for both the highand low-pi conditions, with the exception that in the low-pi condition an approximately 15-second break occurred between each trial (cf. Lustig et al., 2001). During this break participants completed a symbol comparison task. In the symbol comparison task two strings of alphanumeric symbols were shown on either side of a line presented at the centre of the screen (e.g.,!@*%# _!@*%#), and participants had to compare the two strings to determine if they were the same or if they were different. There were 10 symbol comparison trials following each trial, half of which were the same and half of which differed (i.e., two symbols switched positions). These comparison trials were presented one at a time in the centre of the screen until the participant responded. The length of the breaks were shorter compared to previous research (e.g., May et al., 1999; Lustig et al., 2001) that used 30- or 45- second breaks. The breaks were shorter for practical reasons, specifically to fit the entire experiment into a 1-hour time block. Level of PI in the span task (high versus low) was manipulated between participants. The Raven s Advanced Progressive Matrices test was administered to measure gf. This version included the 18 odd-numbered questions from the original set of problems (Raven, Raven, & Court, 2003). Each problem included eight objects (i.e., configural stimuli) presented in three rows and three columns, with the bottom right item missing. Eight possible answer choices were presented below the problem, and the participant had to determine the relation among the items in the problem in order to select the answer (i.e., the item that best completed the pattern). Two practice problems were completed prior to completing the 18 scored test problems. Participants had 10-minutes to complete as many problems as they could, and their score was the total number of correct responses.

5 VISUOSPATIAL WORKING MEMORY SPAN 87 Downloaded By: [Blalock, Lisa Durrance] At: 17:00 13 January 2011 Figure 1. Trial procedures for high- and low-pi span tasks. In each trial participants mentally rotated letters and had to remember a location on a 44 grid. In the low-pi condition a 15-second pause occurred between each trial in which participants completed an alphanumeric symbol comparison task. Procedure For the span task, an experimenter first read through instructions on how to perform the task that included practice on the matrix task only, the letter rotation task only, and practice on both tasks together. Once it was clear that participants understood the task, the experimenter began the trials. For each trial, a rotated letter (F, L, or R were used) was shown, and participants reported whether it was normal or reversed. After the participant responded aloud, the experimenter advanced the screen to show a 44 matrix with one filled square shown for 1 second, which the participant was asked to commit to memory. After the last item in each trial was presented, a blank grid appeared on the screen to prompt participants to recall the locations in the order in which they were presented. Once participants were finished recalling on the answer sheet, the experimenter advanced to either the next trial (high-pi condition) or to the symbol comparison task (low-pi condition). For the symbol comparison task, participants compared the two symbol strings and responded same or different aloud while the experimenter recorded their responses. For the Raven s task the experimenter provided instructions for the task and the participants completed two practice problems with feedback. The order of the span task and Raven s task was counterbalanced across participants such that half the participants did the span task first and then Raven s, and half did Raven s first then the span task. RESULTS An alpha level of.05 was set for all statistical tests. Mean percent recall was calculated for each block for both PI conditions and correlated with the mean score on the matrix reasoning task. We used the partial unit scoring method (see Conway et al., 2005) to score the complex span task. In partial unit scoring, participants are given credit for each item within a trial that was recalled in the correct serial position. For example, in a trial with four to-be-remembered locations, a participant would receive a score of.75 if they correctly marked the first, second, and fourth positions in the correct serial positions. Neither Raven s score

6 88 BLALOCK AND MCCABE nor span task accuracy significantly differed based on counterbalancing order (i.e., completed first or second), Raven s: M first 8.68, M second 8.86, t(115)0.33; Span Accuracy: M first 0.60, M second 0.60, t(115)0.01. The recall data, which are shown in Figure 2, were submitted to a 2 (PI condition: high or low)3 (block: 1, 2, 3) mixed analysis of variance (ANOVA). This analysis yielded a significant main effect for block, F(2, 230)70.82, MS e.017, h 2.38, no main effect for PI condition, F(1, 115)1.629, p.20, h 2.01, and a significant blockpi interaction, F(2, 230)3.16, MS e.02, h To further examine the interaction, accuracy was compared between high- and low-pi conditions for each block. Recall did not differ between PI conditions in block 1, t(115) 0.02, but in block 2 recall was greater in the low-pi than high-pi condition, t(115) Although there was a nominal difference in recall in block 3 this difference did not reach significance, t(115)1.15. Overall these results indicate that while recall increased across blocks, this increase was greater in the low-pi condition than in the high-pi condition (see Figure 2). Mean reaction time (in milliseconds) for the mental rotation task was also analysed to examine practice effects on the processing component of the visuospatial WM span task. Due to a computer malfunction, the reaction time data for seven participants were lost, thus this analysis includes 110 participants. The analysis yielded a main effect for block, F(2, 216)27.94, MS e , h 2.21, with reaction times being significantly longer in block 1 (M2,156.6; SD722.16) than in blocks 2 (M2,049.1; SD563.39) or 3 (M2,045.2; SD709.25). Response times did not differ between blocks 2 and 3 (FB1). The main effect for PI condition and the blockpi interaction were not significantly different from one another (FsB1 for both tests). Thus practice, but not PI, influenced the speed with which the processing component (i.e., letter rotation) of the WM task was completed. Correlations between Raven s score and span score were calculated for each block in both PI conditions, and are presented in Figure 3. Raven s score did not significantly differ between the high-pi (M8.43, SD2.90) and low-pi (M 9.12, SD2.77) conditions, t(115)1.31. The correlation between visuospatial WM and gf did not significantly differ between high- and low PI-conditions in blocks 1 or 2 but they did differ in block 3, such that the correlation was higher in the high-pi condition (r.55) than in the low-pi condition (r.20; Z2.13). All of the correlations were significantly greater than zero except for the low-pi condition in block 3. These results indicate that practice effects only reduced the relationship between WM capacity and gf under low-pi conditions, whereas under high-pi conditions, practice had little influence and correlations remained moderate and significant across blocks. In order to confirm that the abovementioned difference in the correlations for block 3 was not Mean Percent Recall Correlation Block Figure 2. Percent correct recall on WM span task as a function of block and PI condition (error bars represent one standard error of the mean). Across the three blocks, performance increased for both PI conditions (demonstrating practice effects), but this increase was greater in the low-pi condition as evidenced by a significant blockpi interaction. Block Figure 3. Correlation between WM span recall and Raven s score as a function of block and PI condition. There is a significant difference between high- and low-pi conditions in Block 3 only.

7 VISUOSPATIAL WORKING MEMORY SPAN 89 Downloaded By: [Blalock, Lisa Durrance] At: 17:00 13 January 2011 an artefact of differences in the reliability of the span task between the high- and low-pi conditions, we computed the reliability of the matrixrotation span task for each block across each PI condition. Overall, the matrix-rotation span task showed reasonable reliability within blocks. For the low-pi version, coefficient a for blocks 1, 2, and 3 was.67,.73, and.67, respectively. For the high-pi version, coefficient a for blocks 1, 2, and 3 was.62,.60, and.71, respectively. Thus the average reliabilities between conditions were similar, and were actually slightly higher for the low-pi version compared to the high-pi version. Therefore the difference in the correlations for block 3 was not an artefact of differences in reliability for the highand low-pi versions of the span task. DISCUSSION The influence of proactive interference (PI) on verbal working memory (WM) span recall has been well established in the literature; however the way in which PI operates in a complex visuospatial span task, as well as how PI interacts with practice effects, had not previously been directly addressed. Two main conclusions can be drawn from the current study. First, results indicate that WM span recall does increase with practice, but there is a smaller increase under high-pi (i.e., typical) conditions. Therefore we conclude that both practice and PI influence complex span task recall, with practice leading to increases in performance but PI leading to decreases in performance. This result is consistent with the proposed influence of practice suggested in Rowe et al. (2008, 2009), but the current study builds on that previous work by directly manipulating practice in the task, using a complex span task rather than a simple span task, and by examining how practice and PI influence the relationship between gf and span task recall. Second, the correlational data indicate that the relation between gf and WM capacity is reduced by practice, but only in the low-pi condition, and only later in the task. This suggests that the impact of PI will, to some degree, outweigh practice effects on span recall, at least with regard to the relationship between span task recall and gf. Additionally, this study shows how PI and practice influence the predictive power of complex span tasks in the visuospatial domain, which had not yet been addressed in the literature. Indeed, our results nicely complement Rowe et al. (2008, 2009) by demonstrating that the effects of PI on visuospatial simple span tasks found in their studies generalised to complex visuospatial span tasks. This is an important finding because it informs the debate regarding the role of controlled attention in simple and complex span tasks in the visuospatial domain (e.g., Kane et al., 2004; Miyake et al., 2001). Demonstrating that PI plays a role in both simple visuospatial span tasks (Rowe et al., 2009) and complex visuospatial span tasks (the current study) suggests that more controlled attention might be required for simple visuospatial span tasks as compared to verbal tasks, although future work will need to more directly examine this relationship. Working memory capacity, proactive interference, and practice From a theoretical standpoint, these data support the idea that reducing PI build-up is a key function of WM capacity. More specifically, these data are consistent with Kane and Engle s view that individual differences in WM capacity are driven by a domain-general executive that maintains task goals and resists build up of PI (Engle & Kane, 2004; Kane & Engle, 2000, 2003). These data are also consistent with an inhibition view of WM, which suggests that WM span tasks measure the ability to inhibit irrelevant information from memory, thus reducing interference from previous trials (see Friedman & Miyake, 2004; Hasher, Lustig, & Zacks, 2007). Thus these data are consistent with theoretical approaches that suggest that PI contributes to the correlation between WM capacity and gf. The current work also has relevance to the influence of practice effects in WM span tasks, which has not hitherto been directly addressed. As mentioned previously, research from differential psychology has established that practice automates task performance, thereby attenuating individual differences in performance, and the relation between skilled performance and gf (see Ackerman, 1987). The data we report were not consistent with these previous findings, although we note some potential reasons for this difference. Specifically, it appears that under typical span task conditions (i.e., the high-pi condition), the amount of practice involved in the task improves performance as the task progresses, but not enough to allow the task to become sufficiently automated to reduce the relation

8 90 BLALOCK AND MCCABE between task recall and higher-level cognition. Of course, presumably if many more blocks were completed, eventually the task would become more automated, thereby reducing correlations between WM capacity and gf. Future research will be required to investigate this issue more thoroughly. Because practice improves recall across span task trials but PI reduces recall across span task trials they can, and should, have opposite effects on WM recall. While increased practice on a task can improve performance and lead to more automated performance, this process is likely hindered under high-pi conditions. Stated differently, it is possible that higher levels of PI in a task will delay or hinder the development of automaticity that usually results from practice. Future research will need to be conducted to investigate whether the influence of practice will impact the correlation between working memory capacity and gf if more trials are included. However, the current study provides evidence that even with the small number of trials used in a typical span task, which was the primary theoretical target of our study, practice can lead to improvements in span task recall. Under typical span task conditions, in which few trials are included, it appears that performance will not become automated to the point that it would reduce the predictive power of WM span tasks. Additionally, although practice sped up performance on the processing component of the span task (i.e., mental rotation), PI had little effect on processing times. The lack of an effect of PI on processing times, coupled with effects of processing times on span task recall, suggests that PI has its effect primarily on retrieval of to-beremembered items. This supports the two-factor theory (Engle & Kane, 2004) which suggests that PI reduces access to to-be-remembered items because it disrupts retrieval due to interference. Specifically, PI reduces the ability to resist interference in the face of distracting or goal irrelevant information, and this ability is a key predictor of individual differences in WM capacity (Bunting, 2006; Engle, 2002; Engle & Kane, 2004). Group versus individual differences Interestingly, the difference in PI conditions was strongest in the second of the three blocks of trials, but the reduction in the span task-gf correlation did not occur until the third block. Although at first glance it might seem unusual that influences on group differences and individual differences would differ across blocks, this is not an atypical result (e.g., Fleishman & Hempel, 1955). Indeed, the effects of a variable on the average difference between groups, and the rank ordering of individuals within those groups (i.e., correlations), can be very different (Ackerman, 1987; Cohen, Cohen, West, & Aiken, 2003). In the current study it appears that in the high- PI condition the rank ordering of participants on Raven s and span task recall were similar across all blocks, but in the low-pi condition the rank ordering of participants on Raven s and span task recall became dissimilar on block 3. However, there is no reason that the rank ordering of participants in a group and the overall mean level of performance of a group should be directly related to one another, and in the current study they were not. Thus the current study provides a demonstration of an ability-treatment interaction that can be discovered by combining experimental and individual differences approaches, but cannot be discovered by either approach alone (Cronbach, 1957). Summary The current study examined how PI and practice influence performance on a visuospatial WM span task and how they impact the relationship between WM span recall and gf. The results suggest that both practice and PI influence span recall and the relation between WM capacity and gf, but to some extent PI will outweigh any practice effects, at least over the relatively small number of trials used in typical WM span tasks. These data provide an important theoretical contribution to current theories of WM, and suggest that the effects of practice for WM span tasks should be considered with respect to overall recall performance, as well as with respect to the relation between span tasks and other ability measures (cf., Rowe et al., 2008, 2009). Manuscript received 3 August 2009 Manuscript accepted 9 June 2010 REFERENCES Ackerman, P. (1987). Individual differences in skill learning: An integration of psychometric and

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