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Bayesian approximations to the theory of visual attention (TVA) in a foraging task

Sofia Tkhan Tin Le Orcid Logo, Árni Kristjánsson Orcid Logo, Joe MacInnes Orcid Logo

Quarterly Journal of Experimental Psychology, Volume: 76, Issue: 3, Pages: 497 - 510

Swansea University Author: Joe MacInnes Orcid Logo

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DOI (Published version): 10.1177/17470218221094572

Abstract

Foraging as a natural visual search for multiple targets has increasingly been studied in humans in recent years. Here, we aimed to model the differences in foraging strategies between feature and conjunction foraging tasks found by Á. Kristjánsson et al. Bundesen proposed the theory of visual atten...

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Published in: Quarterly Journal of Experimental Psychology
ISSN: 1747-0218 1747-0226
Published: SAGE Publications 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa63429
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spelling v2 63429 2023-05-11 Bayesian approximations to the theory of visual attention (TVA) in a foraging task 06dcb003ec50192bafde2c77bef4fd5c 0000-0002-5134-1601 Joe MacInnes Joe MacInnes true false 2023-05-11 SCS Foraging as a natural visual search for multiple targets has increasingly been studied in humans in recent years. Here, we aimed to model the differences in foraging strategies between feature and conjunction foraging tasks found by Á. Kristjánsson et al. Bundesen proposed the theory of visual attention (TVA) as a computational model of attentional function that divides the selection process into filtering and pigeonholing. The theory describes a mechanism by which the strength of sensory evidence serves to categorise elements. We combined these ideas to train augmented Naïve Bayesian classifiers using data from Á. Kristjánsson et al. as input. Specifically, we attempted to answer whether it is possible to predict how frequently observers switch between different target types during consecutive selections (switches) during feature and conjunction foraging using Bayesian classifiers. We formulated 11 new parameters that represent key sensory and bias information that could be used for each selection during the foraging task and tested them with multiple Bayesian models. Separate Bayesian networks were trained on feature and conjunction foraging data, and parameters that had no impact on the model’s predictability were pruned away. We report high accuracy for switch prediction in both tasks from the classifiers, although the model for conjunction foraging was more accurate. We also report our Bayesian parameters in terms of their theoretical associations with TVA parameters, πj (denoting the pertinence value), and βi (denoting the decision-making bias). Journal Article Quarterly Journal of Experimental Psychology 76 3 497 510 SAGE Publications 1747-0218 1747-0226 1 3 2023 2023-03-01 10.1177/17470218221094572 http://dx.doi.org/10.1177/17470218221094572 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University Not Required 2023-06-08T14:44:05.2897173 2023-05-11T11:43:37.2609291 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Sofia Tkhan Tin Le 0000-0003-1489-4263 1 Árni Kristjánsson 0000-0003-4168-4886 2 Joe MacInnes 0000-0002-5134-1601 3
title Bayesian approximations to the theory of visual attention (TVA) in a foraging task
spellingShingle Bayesian approximations to the theory of visual attention (TVA) in a foraging task
Joe MacInnes
title_short Bayesian approximations to the theory of visual attention (TVA) in a foraging task
title_full Bayesian approximations to the theory of visual attention (TVA) in a foraging task
title_fullStr Bayesian approximations to the theory of visual attention (TVA) in a foraging task
title_full_unstemmed Bayesian approximations to the theory of visual attention (TVA) in a foraging task
title_sort Bayesian approximations to the theory of visual attention (TVA) in a foraging task
author_id_str_mv 06dcb003ec50192bafde2c77bef4fd5c
author_id_fullname_str_mv 06dcb003ec50192bafde2c77bef4fd5c_***_Joe MacInnes
author Joe MacInnes
author2 Sofia Tkhan Tin Le
Árni Kristjánsson
Joe MacInnes
format Journal article
container_title Quarterly Journal of Experimental Psychology
container_volume 76
container_issue 3
container_start_page 497
publishDate 2023
institution Swansea University
issn 1747-0218
1747-0226
doi_str_mv 10.1177/17470218221094572
publisher SAGE Publications
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://dx.doi.org/10.1177/17470218221094572
document_store_str 0
active_str 0
description Foraging as a natural visual search for multiple targets has increasingly been studied in humans in recent years. Here, we aimed to model the differences in foraging strategies between feature and conjunction foraging tasks found by Á. Kristjánsson et al. Bundesen proposed the theory of visual attention (TVA) as a computational model of attentional function that divides the selection process into filtering and pigeonholing. The theory describes a mechanism by which the strength of sensory evidence serves to categorise elements. We combined these ideas to train augmented Naïve Bayesian classifiers using data from Á. Kristjánsson et al. as input. Specifically, we attempted to answer whether it is possible to predict how frequently observers switch between different target types during consecutive selections (switches) during feature and conjunction foraging using Bayesian classifiers. We formulated 11 new parameters that represent key sensory and bias information that could be used for each selection during the foraging task and tested them with multiple Bayesian models. Separate Bayesian networks were trained on feature and conjunction foraging data, and parameters that had no impact on the model’s predictability were pruned away. We report high accuracy for switch prediction in both tasks from the classifiers, although the model for conjunction foraging was more accurate. We also report our Bayesian parameters in terms of their theoretical associations with TVA parameters, πj (denoting the pertinence value), and βi (denoting the decision-making bias).
published_date 2023-03-01T14:44:03Z
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score 11.013731

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