Attentional control in categorisation: towards a computational synthesis
He, Liusha (2020) Attentional control in categorisation: towards a computational synthesis. Doctoral thesis, Birkbeck, University of London.
|
PDF
reviewed_thesis.pdf - Full Version Download (16MB) | Preview |
Abstract
This thesis develops an integrated computational model of task switching in heterogeneous categorisation by combining theories of cognitive control and category learning. The thesis considers the strengths and shortcomings of a range of existing computational accounts of categorisation (ALCOVE, SUSTAIN, ATRIUM and COVIS) by reimplementing each and applying each to human data from the categorisation literature. It is argued that most of these models cannot account for heterogeneous categorisation, i.e., situations where the category structure includes subsets with incompatible boundaries. Moreover, the only one of the four computational models that can account for heterogeneous categorisation, ATRIUM, does not completely account for the influence of top-down control during categorisation tasks. The models are also limited because they are based purely on feedforward principles, and while they are able to learn to categorise stimuli adequately, they do not account for categorisation response times, or for task-switching effects observed in recent research on heterogeneous categorisation. In order to address these limitations, the thesis presents a model that combines an interactive activation account of task-switching with a modular architecture of categorisation. The model is shown to successfully simulate reaction time costs and effects of preparation time on task switching.
Metadata
| Item Type: | Thesis |
|---|---|
| Copyright Holders: | The copyright of this thesis rests with the author, who asserts his/her right to be known as such according to the Copyright Designs and Patents Act 1988. No dealing with the thesis contrary to the copyright or moral rights of the author is permitted. |
| Depositing User: | Acquisitions And Metadata |
| Date Deposited: | 14 Jul 2020 10:19 |
| Last Modified: | 01 Nov 2023 14:24 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/40486 |
| DOI: | https://doi.org/10.18743/PUB.00040486 |
Statistics
Additional statistics are available via IRStats2.
Archive Staff Only (login required)
 |
Edit/View Item |