The parallel rule activation and rule synthesis (PRAS) model is a computational model for generalisation in category learning, proposed by Vandierendonck (1995). An important concept underlying the PRAS model is the distinction between primary and secondary generalisation. In Vandierendonck (1995), an empirical study is reported that provides support for the concept of secondary generalisation. In this paper, we re-analyse the data reported by Vandierendonck (1995) by fitting three different variants of the Generalised Context Model (GCM) which do not rely on secondary generalisation. Although some of the GCM variants outperformed the PRAS model in terms of global fit, they all have difficulty in providing a qualitatively good fit of a spec...
We explore humans' rule-based category learning using analytic approaches that highlight their psych...
<div><p>We explore humans’ rule-based category learning using analytic approaches that highlight the...
Stimulus generalization is often regarded as a fundamental component of category learning, yet it ha...
The parallel rule activation and rule synthesis (PRAS) model is a computational model for generalisa...
Exemplar theories of categorization depend on similarity for explaining subjects’ ability to general...
We develop a model of the interaction between representation building and category learning. Our mod...
Exemplar and prototype accounts of categorization phenomena differ primarily in terms of the manner ...
Early theories of categorization assumed that either rules, or prototypes, or exemplars were exclusi...
Category learning is often modeled as either an exemplar-based or a rule-based process. This paper s...
In recent evidence, classification training can elicit two qualitative patterns of generalization: o...
Over the last three years, in our Early Algebra Thinking Project, we have been studying Years 3 to 5...
. A new way of measuring generalization in unsupervised learning is presented. The measure is based ...
Category representations can be broadly classified as containing within-category information or betw...
A class of dual-system theories of categorization assumes a categorization system based on actively ...
Naive observers typically perceive some groupings for a set of stimuli as more intuitive than others...
We explore humans' rule-based category learning using analytic approaches that highlight their psych...
<div><p>We explore humans’ rule-based category learning using analytic approaches that highlight the...
Stimulus generalization is often regarded as a fundamental component of category learning, yet it ha...
The parallel rule activation and rule synthesis (PRAS) model is a computational model for generalisa...
Exemplar theories of categorization depend on similarity for explaining subjects’ ability to general...
We develop a model of the interaction between representation building and category learning. Our mod...
Exemplar and prototype accounts of categorization phenomena differ primarily in terms of the manner ...
Early theories of categorization assumed that either rules, or prototypes, or exemplars were exclusi...
Category learning is often modeled as either an exemplar-based or a rule-based process. This paper s...
In recent evidence, classification training can elicit two qualitative patterns of generalization: o...
Over the last three years, in our Early Algebra Thinking Project, we have been studying Years 3 to 5...
. A new way of measuring generalization in unsupervised learning is presented. The measure is based ...
Category representations can be broadly classified as containing within-category information or betw...
A class of dual-system theories of categorization assumes a categorization system based on actively ...
Naive observers typically perceive some groupings for a set of stimuli as more intuitive than others...
We explore humans' rule-based category learning using analytic approaches that highlight their psych...
<div><p>We explore humans’ rule-based category learning using analytic approaches that highlight the...
Stimulus generalization is often regarded as a fundamental component of category learning, yet it ha...