2012
DOI: 10.1037/a0027298
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Working memory does not dissociate between different perceptual categorization tasks.

Abstract: Working memory is crucial for many higher level cognitive functions, ranging from mental arithmetic to reasoning and problem solving. Likewise, the ability to learn and categorize novel concepts forms an indispensable part of human cognition. However, very little is known about the relationship between working memory and categorization. This article reports 2 studies that related people's working memory capacity (WMC) to their learning performance on multiple rule-based and information-integration perceptual c… Show more

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Cited by 57 publications
(102 citation statements)
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“…The results obtained go a further step by explicitly demonstrating that temporary storage and the shifting process are important for the sub-components of category learning. However, it should be noted that the magnitudes of the correlations between working memory processes and category learning were relatively small compared with those reported by several previous studies (e.g., Craig & Lewandowsky, 2012;Lewandowsky, 2011;Lewandowsky et al, 2012). The smaller magnitude probably resulted from the sample of this research, which were all university undergraduates who are homogeneous in abilities.…”
Section: Discussioncontrasting
confidence: 47%
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“…The results obtained go a further step by explicitly demonstrating that temporary storage and the shifting process are important for the sub-components of category learning. However, it should be noted that the magnitudes of the correlations between working memory processes and category learning were relatively small compared with those reported by several previous studies (e.g., Craig & Lewandowsky, 2012;Lewandowsky, 2011;Lewandowsky et al, 2012). The smaller magnitude probably resulted from the sample of this research, which were all university undergraduates who are homogeneous in abilities.…”
Section: Discussioncontrasting
confidence: 47%
“…A number of theoretical accounts of category learning suggest that the acquisition of new categories is a complex activity that may involve a series of specific cognitive abilities or processes (e.g., Ashby & Maddox, 2011;Ashby & O'Brien, 2005). Several recent studies have indicated that working memory plays an important part in category learning (e.g., Craig & Lewandowsky, 2012DeCaro, Carlson, Thomas, & Beilock, 2009;DeCaro, Thomas, & Beilock, 2008;Erickson, 2008;Lewandowsky, 2011;Lewandowsky, Yang, Newell, & Kalish, 2012;Sewell & Lewandowsky, 2012). However, those studies were complicated by the complexity of working memory which is not a unitary construct but consists of multiple components or processes (Baddeley, 1986;Miyake et al, 2000), and it remains unclear which specific processes are involved in category learning.…”
Section: Introductionmentioning
confidence: 99%
“…This may also explain the seemingly contradictory finding that higher cognitive capacity sometimes leads to more adaptive strategy use (Bröder, 2003;Lewandowsky et al, 2012), but at other times to the use of overly complex, nonadaptive strategies (Beilock & DeCaro, 2007). In the experiment by Beilock and DeCaro (2007) and in the asymptotic processes in this study, complex strategies were successful during earlier trials, but a simpler solution became available later on.…”
Section: Discussionmentioning
confidence: 69%
“…Intuitively, higher memory capacity should improve prediction accuracy because people need to consider information about the past of a process in order to forecast its future. And, indeed, research has shown that higher WMC is related to higher prediction accuracy for continuous processes (function learning) and categorization tasks (Bröder, Newell, & Platzer, 2010;Lewandowsky, Yang, Newell, & Kalish, 2012;McDaniel, Cahill, Robbins, & Wiener, 2014). One explanation for this observation is that higher WMC is associated with an improved ability to actively maintain and manipulate information, which is needed to calibrate cognitive prediction algorithms to the learning data and to abstract systematic regularities.…”
Section: Nonlinear Dynamic Processesmentioning
confidence: 99%
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