A note on DeCaro, Thomas, and Beilock (2008): Further data demonstrate complexities in the assessment of information–integration category learning

Tharp, Ian J.; Pickering, Alan D.

doi:10.1016/j.cognition.2008.10.003

Cited by 35 publications

(46 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The present study adds to the growing body of work exploring the relationship between WMC and categorization (Blair et al, 2009;DeCaro et al, 2009;DeCaro et al, 2008;Erickson, 2008;Lewandowsky, 2011;Tharp & Pickering, 2009). Consistent with previous work (e.g., DeCaro et al, 2009;Lewandowsky, 2011), WMC was related to participants' category-learning performance-participants with higher WMC had higher categorization accuracy.…”

Section: Discussionsupporting

confidence: 87%

Whichever way you Choose to Categorize, Working Memory Helps you Learn

Craig

Lewandowsky

2012

Quarterly Journal of Experimental Psychology

View full text Add to dashboard Cite

There has been growing interest in the relationship between the capacity of a person's working memory and their ability to learn to categorize stimuli. While there is evidence that working memory capacity (WMC) is related to the speed of category learning, it is unknown whether WMC predicts which strategies people use when there are multiple possible solutions to a categorization problem. To explore the relationship between WMC, category learning, and categorization strategy use, 173 participants completed two categorization tasks and a battery of WMC tasks. WMC predicted the speed of category learning, but it did not predict which strategies participants chose to perform categorization. Thus, WMC does not predict which categorization strategies people use but it predicts how well they will use the strategy they select.

show abstract

Section: Discussionsupporting

confidence: 87%

Whichever way you Choose to Categorize, Working Memory Helps you Learn

Craig

Lewandowsky

2012

Quarterly Journal of Experimental Psychology

View full text Add to dashboard Cite

show abstract

“…First, a number of researchers (Waldron and Ashby, 2001; Maddox et al, 2004; Zeithamova and Maddox, 2006, 2007; Filoteo et al, 2010) have shown that reflective-optimal visual category learning was impaired when participants were asked to perform a demanding working-memory dual task, whereas reflexive-optimal visual category learning was not affected. Second, and more directly (DeCaro et al, 2008; Tharp and Pickering, 2009; however, see Lewandowsky et al, 2012) showed that increases in working memory capacity were associated with enhanced reflective-optimal visual category learning but did not lead to advantages in reflexive-optimal visual category learning.…”

Section: Toward An Auditory Version Of Covismentioning

confidence: 96%

Toward a dual-learning systems model of speech category learning

Chandrasekaran¹,

Koslov²,

Maddox³

2014

Front. Psychol.

View full text Add to dashboard Cite

More than two decades of work in vision posits the existence of dual-learning systems of category learning. The reflective system uses working memory to develop and test rules for classifying in an explicit fashion, while the reflexive system operates by implicitly associating perception with actions that lead to reinforcement. Dual-learning systems models hypothesize that in learning natural categories, learners initially use the reflective system and, with practice, transfer control to the reflexive system. The role of reflective and reflexive systems in auditory category learning and more specifically in speech category learning has not been systematically examined. In this article, we describe a neurobiologically constrained dual-learning systems theoretical framework that is currently being developed in speech category learning and review recent applications of this framework. Using behavioral and computational modeling approaches, we provide evidence that speech category learning is predominantly mediated by the reflexive learning system. In one application, we explore the effects of normal aging on non-speech and speech category learning. Prominently, we find a large age-related deficit in speech learning. The computational modeling suggests that older adults are less likely to transition from simple, reflective, unidimensional rules to more complex, reflexive, multi-dimensional rules. In a second application, we summarize a recent study examining auditory category learning in individuals with elevated depressive symptoms. We find a deficit in reflective-optimal and an enhancement in reflexive-optimal auditory category learning. Interestingly, individuals with elevated depressive symptoms also show an advantage in learning speech categories. We end with a brief summary and description of a number of future directions.

show abstract

“…The list is not exhaustiveother important IV sets, not covered here for brevity, include cultural differences (Medin & Atran, 2004), personality variables (Ward, 1983;Wills, Longmore & Milton, 2011), and other individual differences (DeCaro, Milton, Longmore & Wills, 2008;Tharp & Pickering, 2009). …”

Section: Defining Domains Through Ivsmentioning

confidence: 99%

On the adequacy of current empirical evaluations of formal models of categorization.

Wills

Pothos

2012

Psychological Bulletin

124

View full text Add to dashboard Cite

This is the unspecified version of the paper.This version of the publication may differ from the final published version. Permanent repository link: AbstractCategorization is one of the fundamental building blocks of cognition, and the study of categorization is notable for the extent to which formal modeling has been a central and influential component of research. However, the field has seen a proliferation of divergent, non-complementary models with little consensus on the relative adequacy of these accounts.Progress on assessing relative adequacy of formal categorization models against these criteria has, to date, been limited because (a) formal model comparisons are narrow in the number of models and phenomena considered, and (b) models do not often clearly define their explanatory scope. Progress is further hampered by the practice of fitting models with arbitrarily variable parameters to each data set independently. Reviewing examples of good practice in the literature, we conclude that model comparisons are most fruitful when relative adequacy is assessed by comparing well-defined models on the basis of the number and proportion of irreversible, ordinal, penetrable successes (principles of minimal flexibility, breadth, good-enough precision, maximal simplicity, and psychological focus).Keywords: categorization; cluster; exemplar; model selection; modeling; prototype. EMPIRICAL EVALUATION OF CATEGORIZATION MODELS 2The study of categorization is a fascinating endeavor. The process of constructing and using categories underpins our capacity to encode and apply information in the world in an efficient and competent manner but also, ultimately, our ability to think in terms of abstract kind of categorization models should we aim to develop? The lack of consensus regarding such key issues has resulted in categorization research being carried out in increasingly independent strands and this has been inhibiting overall progress in the field. Nosofsky, Gluck, Palmeri, McKinley and Glauthier (1994) wrote, "Recent years have seen an avalanche of newly proposed models of category learning and representation. As such models grow increasingly more sophisticated, there is a need to develop increasingly more rigorous testing grounds so that one may choose among them" (p. 352). Almost 20 years later, progress towards this goal remains limited.In the current article, we first provide a definition of the term formal model, consider the principal advantages of formal modeling over other forms of theorizing, briefly summarize some of the leading formal models of categorization, and assess progress to date on the empirical evaluation and comparison of these models. We then set out the approaches EMPIRICAL EVALUATION OF CATEGORIZATION MODELS 3 we believe are most likely to lead to progress in the future. We organize our conclusions in terms of a set of criteria for assessing the relative adequacy of models, and a list of dependent and independent variables that any adequate formal model of categorization should be expected to...

show abstract

A note on DeCaro, Thomas, and Beilock (2008): Further data demonstrate complexities in the assessment of information–integration category learning

Cited by 35 publications

References 13 publications

Whichever way you Choose to Categorize, Working Memory Helps you Learn

Whichever way you Choose to Categorize, Working Memory Helps you Learn

Toward a dual-learning systems model of speech category learning

On the adequacy of current empirical evaluations of formal models of categorization.

Contact Info

Product

Resources

About