Evidence for a procedural-learning-based system in perceptual category learning

Maddox, W. Todd; Bohil, Corey J.; Ing, A. David

doi:10.3758/bf03196726

Cited by 106 publications

(125 citation statements)

References 24 publications

(24 reference statements)

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“…Thus, it appears that response locations are learned in information-integration categorization, but specific motor programs are not. The importance of response locations in information-integration category learning but not in rule-based category learning was confirmed in a recent study by Maddox et al (2004b). These information-integration results essentially replicate results found with traditional procedural-learning tasks (Willingham et al 2000).…”

supporting

confidence: 78%

Two types of thought: Evidence from aphasia

Davidoff

2005

Behav Brain Sci

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Abstract:The distinction between rules and similarity is central to our understanding of much of cognitive psychology. Two aspects of existing research have motivated the present work. First, in different cognitive psychology areas we typically see different conceptions of rules and similarity; for example, rules in language appear to be of a different kind compared to rules in categorization. Second, rules processes are typically modeled as separate from similarity ones; for example, in a learning experiment, rules and similarity influences would be described on the basis of separate models. In the present article, I assume that the rules versus similarity distinction can be understood in the same way in learning, reasoning, categorization, and language, and that a unified model for rules and similarity is appropriate. A rules process is considered to be a similarity one where only a single or a small subset of an object's properties are involved. Hence, rules and overall similarity operations are extremes in a single continuum of similarity operations. It is argued that this viewpoint allows adequate coverage of theory and empirical findings in learning, reasoning, categorization, and language, and also a reassessment of the objectives in research on rules versus similarity.Keywords: categorization; cognitive explanation; language; learning; reasoning; rules; similarity Emmanuel Pothos received his D.Phil. in Experimental Psychology from the University of Oxford in 1998. Since then he has been a lecturer of psychology at the University of Wales at Bangor and at the University of Edinburgh; he is currently at the University of Crete. His research activity includes computational models in unsupervised categorization and statistical methods for disambiguating rules and similarity in learning. Additionally, he is looking at applications of learning models in the study of addictive behavior. A focal point of his work is the a priori comparability of theoretical accounts and explanatory concepts in cognitive psychology.partly because they uniquely have in common a particular set of properties or features, as research in basic level categorization and spontaneous classification shows (Pothos & Chater 2002;Rosch & Mervis 1975). I postulate that the object properties relevant in deciding how to categorize it amongst a number of candidate categories are the properties uniquely common to the instances of each category (cf. Aha & Goldstone 1992). For example, in order to decide whether my car keys are a member of the category 'things to take out of my house when it is on fire," I will have to consider only whether car keys match the uniquely common properties of the other members of that category ("credit cards," "my cat," "my university diploma") (Barsalou 1991). Note that the present proposal does not involve any commitment about the form of features or properties (cf. Marr 1982). We simply require that at some level it is possible to represent objects in terms of discrete entities (perceptual features, abstract proper...

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supporting

confidence: 78%

Two types of thought: Evidence from aphasia

Davidoff

2005

Behav Brain Sci

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“…Most studies construe this negative feedback as trial-by-trial feedback (Ashby et. al, 1998;Maddox, Ashby & Waldron, 2002;Maddox, Bohil, & Ing, 2004). However, our experiments showed approximately equally poor supervised and unsupervised learning of multidimensional categories.…”

Section: Discussionmentioning

confidence: 92%

Supervised and unsupervised learning of multidimensionally varying non-native speech categories

Goudbeek

Cutler

Smits

2008

Speech Communication

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Learning to recognize the contrasts of a language-specific phonemic repertoire can be viewed as forming categories in a multidimensional psychophysical space. Research on the learning of distributionally defined visual categories has shown that categories defined over 1 dimension are easy to learn and that learning multidimensional categories is more difficult but tractable under specific task conditions. In 2 experiments, adult participants learned either a unidimensional or a multidimensional category distinction with or without supervision (feedback) during learning. The unidimensional distinctions were readily learned and supervision proved beneficial, especially in maintaining category learning beyond the learning phase. Learning the multidimensional category distinction proved to be much more difficult and supervision was not nearly as beneficial as with unidimensionally defined categories. Maintaining a learned multidimensional category distinction was only possible when the distributional information that identified the categories remained present throughout the testing phase. We conclude that listeners are sensitive to both trial-by-trial feedback and the distributional information in the stimuli. Even given limited exposure, listeners learned to use 2 relevant dimensions, albeit with considerable difficulty.

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“…In these information-integration tasks, the optimal strategy is difficult to verbalize and learning requires many repetitions (Ashby & Maddox, 2005). In line with the idea that procedural learning underlies information integration, it has been found that disrupting motor processing harms performance in information-integration tasks more strongly than performance with rule-based categorizations (Ashby, Ell, & Waldron, 2003;Maddox, Bohil, & Ing, 2004; but see Zaki & Kleinschmidt, 2014).…”

Section: Future Directionsmentioning

confidence: 88%

Pillars of judgment: How memory abilities affect performance in rule-based and exemplar-based judgments.

Hoffmann

Helversen

Rieskamp

2014

Journal of Experimental Psychology: General

108

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Making accurate judgments is an essential skill in everyday life. Although how different memory abilities relate to categorization and judgment processes has been hotly debated, the question is far from resolved. We contribute to the solution by investigating how individual differences in memory abilities affect judgment performance in 2 tasks that induced rule-based or exemplar-based judgment strategies. In a study with 279 participants, we investigated how working memory and episodic memory affect judgment accuracy and strategy use. As predicted, participants switched strategies between tasks. Furthermore, structural equation modeling showed that the ability to solve rule-based tasks was predicted by working memory, whereas episodic memory predicted judgment accuracy in the exemplar-based task. Last, the probability of choosing an exemplar-based strategy was related to better episodic memory, but strategy selection was unrelated to working memory capacity. In sum, our results suggest that different memory abilities are essential for successfully adopting different judgment strategies.

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Evidence for a procedural-learning-based system in perceptual category learning

Cited by 106 publications

References 24 publications

Two types of thought: Evidence from aphasia

Two types of thought: Evidence from aphasia

Supervised and unsupervised learning of multidimensionally varying non-native speech categories

Pillars of judgment: How memory abilities affect performance in rule-based and exemplar-based judgments.

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