scholarly journals A cognitive category-learning model of rule abstraction, attention learning, and contextual modulation.

2021 ◽  
Author(s):  
René Schlegelmilch ◽  
Andy J. Wills ◽  
Bettina von Helversen
Keyword(s):  
Category Learning ◽  
Learning Model ◽  
Contextual Modulation ◽  
Rule Abstraction

scholarly journals Category learning strategies in younger and older adults: Rule abstraction and memorization.

2016 ◽  
Vol 31 (4) ◽  
pp. 346-357 ◽  
Author(s):  
Christopher N. Wahlheim ◽  
Mark A. McDaniel ◽  
Jeri L. Little
Keyword(s):  
Older Adults ◽  
Learning Strategies ◽  
Category Learning ◽  
Rule Abstraction

scholarly journals Contextual modulation of attention in human category learning

2012 ◽  
Vol 40 (4) ◽  
pp. 530-541 ◽  
Author(s):  
David N. George ◽  
John K. Kruschke
Keyword(s):  
Category Learning ◽  
Contextual Modulation

scholarly journals A Cognitive Category-Learning Model of Rule Abstraction, Attention Learning, and Contextual Modulation

2020 ◽  
Author(s):  
René Schlegelmilch ◽  
Andy Wills ◽  
Bettina von Helversen
Keyword(s):  
Working Memory ◽  
Category Learning ◽  
Peak Shift ◽  
Attention Control ◽  
Stimulus Generalization ◽  
Data Sets ◽  
Learning Mechanisms ◽  
Contextual Modulation ◽  
Cognitive Framework ◽  
Knowledge Structuring

We introduce the CAL model (Category Abstraction Learning), a cognitive framework formally describing category learning built on similarity-based generalization, dissimilarity-based abstraction, two attention learning mechanisms, error-driven knowledge structuring and stimulus memorization. Our hypotheses draw on an array of empirical and theoretical insights connecting reinforcement and category learning, and working memory. The key novelty of the model is its explanation of how rules are learned from scratch based on three central assumptions. (1) Category rules emerge from two processes of stimulus generalization (similarity) and its direct inverse (category contrast) on independent dimensions. (2) Two attention mechanisms guide learning by focusing on rules, or on the contexts in which they produce errors. (3) Knowing about these contexts inhibits executing the rule, without correcting it, and consequently leads to applying partial rules in different situations. We show that the model decisively outperforms the established category-learning models ALCOVE (Kruschke, 1992), SUSTAIN (Love, Medin, & Gureckis, 2004) and ATRIUM (Erickson & Kruschke, 1998) on data sets from benchmark studies, including cross-validations based on trial-wise eye-movements. Additionally, CAL's three free parameters, which measure abstraction, memorization and attention control, are related to abilities measured in working memory tasks in a theoretically meaningful way. We illustrate the model's explanatory scope by simulating several phenomena (peak shift, sample size, instruction effects, extrapolation), which were so far unexplained (or unexplained within a single model). We discuss CAL's relation to existing accounts, and its promise in understanding the role of attention control and working memory in category learning and related domains.

scholarly journals Effects of phonological label differences on category learning

2020 ◽  
Author(s):  
Bojan Lalic ◽  
Suzy J Styles ◽  
Vanja Kovic
Keyword(s):  
Category Learning ◽  
Learning Model ◽  
Perceptual Features ◽  
Model Based ◽  
Category Labels ◽  
Phonetic Features

The present study tested whether the degree of phonological difference between the labels of categories influence category learning and generalization. For this purpose, two experiments were conducted. In the first experiment, participants learned two categories labelled with pseudo-words that were controlled for the degree of difference between the phonetic features of their labels, such that they each pair was maximally different, minimally different, or somewhere in between. These labelled conditions were compared against a no-label control condition. Results showed that participants learned faster and generalized better for categories when category labels differed more.In the second experiment we tested whether these effects could be attributed to the sound symbolic congruence of the labels to the category members. Results showed that phonological differences influence learning independently from the sound symbolic match. Further we specify a category learning model based on generalized perceptual discriminability of the to-be categorized information, including perceptual features of both items and their labels: Categories are easier to learn when their features (including labels) are easier to discriminate.

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