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Grounding Bayesian accounts of numerosity and variability effects in a similarity-based framework: the case of self-organising maps

Abstract:

In this paper, we propose self-organising maps as possible candidates to explain the psychological mechanisms underlying category generalisation. Self-organising maps are psychologically and biologically plausible neural network models that can learn after limited exposure to positive category examples, without any need of contrastive information. They reproduce human behaviour in category generalisation, in particular, the Numerosity and Variability effects, which are usually explained with ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1080/20445911.2019.1637880

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Institution:
University of Oxford
Division:
MSD
Department:
Experimental Psychology
Oxford college:
St Hugh's College
Role:
Author
ORCID:
0000-0003-0216-7480
Publisher:
Taylor and Francis
Journal:
Journal of Cognitive Psychology More from this journal
Volume:
31
Issue:
5-6
Pages:
605-618
Publication date:
2019-07-07
Acceptance date:
2019-06-18
DOI:
EISSN:
2044-592X
ISSN:
2044-5911
Language:
English
Keywords:
Pubs id:
pubs:1031976
UUID:
uuid:8eeeeb90-17ab-4a7d-9d3a-64106390761f
Local pid:
pubs:1031976
Source identifiers:
1031976
Deposit date:
2019-07-15

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