<p>The vertical line separating Category A and Category B represents the strategy that maximizes categorization accuracy (Ashby & Gott, 1988). Points on the left are members of Category A and points on the right are members of Category B. The learner must base responding on the frequency dimension while ignoring irrelevant variation on the orientation dimension. The optimal rule could be phrased as: “Crystal balls with few lines go in Category A, crystal balls with many lines go in Category B”.</p
Although exemplar models of category learning have been successfully applied to a wide range of clas...
<p>Rows correspond to values of “nucleus roundness”; columns correspond to values of “membrane thick...
In innate Categorical Perception (CP) (e.g., colour perception), similarity space is "warped," with ...
Current theories of rules in category learning define rules as one-dimensional boundaries. However, ...
efficiently and effectively. Categories that are overlapping when represented in 1 dimensionality ma...
<p>In the rule-based task (A), only one feature is relevant for the sorting rule (inside size). In t...
Abstract This study simultaneously manipulates within-category (rule-based vs. similarity-based), be...
We report a supervised category learning experiment in which the training phase contains both classi...
We investigated the mechanisms by which concepts are learned from examples by manipulating the prese...
Classification based on multiple dimensions of stimuli is usually associated with similarity-based r...
<div><p>We explore humans’ rule-based category learning using analytic approaches that highlight the...
We present an account of human concept learning-that is, learning of categories from examples-based ...
We explore humans' rule-based category learning using analytic approaches that highlight their psych...
International audienceThis study of supervised categorization shows how different kinds of category ...
had criterial features and that category membership could be determined by logical rules for the com...
Although exemplar models of category learning have been successfully applied to a wide range of clas...
<p>Rows correspond to values of “nucleus roundness”; columns correspond to values of “membrane thick...
In innate Categorical Perception (CP) (e.g., colour perception), similarity space is "warped," with ...
Current theories of rules in category learning define rules as one-dimensional boundaries. However, ...
efficiently and effectively. Categories that are overlapping when represented in 1 dimensionality ma...
<p>In the rule-based task (A), only one feature is relevant for the sorting rule (inside size). In t...
Abstract This study simultaneously manipulates within-category (rule-based vs. similarity-based), be...
We report a supervised category learning experiment in which the training phase contains both classi...
We investigated the mechanisms by which concepts are learned from examples by manipulating the prese...
Classification based on multiple dimensions of stimuli is usually associated with similarity-based r...
<div><p>We explore humans’ rule-based category learning using analytic approaches that highlight the...
We present an account of human concept learning-that is, learning of categories from examples-based ...
We explore humans' rule-based category learning using analytic approaches that highlight their psych...
International audienceThis study of supervised categorization shows how different kinds of category ...
had criterial features and that category membership could be determined by logical rules for the com...
Although exemplar models of category learning have been successfully applied to a wide range of clas...
<p>Rows correspond to values of “nucleus roundness”; columns correspond to values of “membrane thick...
In innate Categorical Perception (CP) (e.g., colour perception), similarity space is "warped," with ...