Article

Extending the ALCOVE model of category learning to featural stimulus domains

2019-08-19

Abstract excerpt

<p>The ALCOVE model of category learning, despite its considerable success in accounting for human performance across a wide range of empirical tasks, is limited by its reliance on spatial stimulus representations. Some stimulus domains are better suited to featural representation, characterizing stimuli in terms of the presence or absence of discrete features, rather than as points in a multidimensional space. We...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 80976944-de21-57b7-9ccc-b0d8ebe00315
DOI: 10.31234/osf.io/uvtbf

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Extending the ALCOVE model of category learning to featural stimulus domainsDOI 10.31234/osf.io/uvtbfRead

Unifying principles of generalization: past, present, and futureSemantic proximity Read

Comparing perceptual category learning across modalities in the same individualsSemantic proximity Read

Creating Something Different: Similarity, Contrast, and Representativeness in CategorizationSemantic proximity Read

Frequency Effects in Human Category LearningSemantic proximity Read

Categorical Biases in Human Occipitoparietal CortexSemantic proximity Read

Disentangling Perceptual and Process-Related Sources of Behavioral Variability in CategorizationSemantic proximity Read

Auditory category learning is robust across training regimesSemantic proximity Read

How Can We Characterize Human Generalization and Distinguish it from Generalization in Machines?Semantic proximity Read