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LAG-1: A dynamic, integrative model of learning, attention, and gaze

It is clear that learning and attention interact, but it is an ongoing challenge to integrate their psychological and neurophysiological descriptions. Here we introduce LAG-1, a dynamic neural field model of learning, attention and gaze, that we fit to human learning and eye-movement data from two c...

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Autores principales: Barnes, Jordan, Blair, Mark R., Walshe, R. Calen, Tupper, Paul F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8929614/
https://www.ncbi.nlm.nih.gov/pubmed/35298465
http://dx.doi.org/10.1371/journal.pone.0259511
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author Barnes, Jordan
Blair, Mark R.
Walshe, R. Calen
Tupper, Paul F.
author_facet Barnes, Jordan
Blair, Mark R.
Walshe, R. Calen
Tupper, Paul F.
author_sort Barnes, Jordan
collection PubMed
description It is clear that learning and attention interact, but it is an ongoing challenge to integrate their psychological and neurophysiological descriptions. Here we introduce LAG-1, a dynamic neural field model of learning, attention and gaze, that we fit to human learning and eye-movement data from two category learning experiments. LAG-1 comprises three control systems: one for visuospatial attention, one for saccadic timing and control, and one for category learning. The model is able to extract a kind of information gain from pairwise differences in simple associations between visual features and categories. Providing this gain as a reentrant signal with bottom-up visual information, and in top-down spatial priority, appropriately influences the initiation of saccades. LAG-1 provides a moment-by-moment simulation of the interactions of learning and gaze, and thus simultaneously produces phenomena on many timescales, from the duration of saccades and gaze fixations, to the response times for trials, to the slow optimization of attention toward task relevant information across a whole experiment. With only three free parameters (learning rate, trial impatience, and fixation impatience) LAG-1 produces qualitatively correct fits for learning, behavioural timing and eye movement measures, and also for previously unmodelled empirical phenomena (e.g., fixation orders showing stimulus-specific attention, and decreasing fixation counts during feedback). Because LAG-1 is built to capture attention and gaze generally, we demonstrate how it can be applied to other phenomena of visual cognition such as the free viewing of visual stimuli, visual search, and covert attention.
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spelling pubmed-89296142022-03-18 LAG-1: A dynamic, integrative model of learning, attention, and gaze Barnes, Jordan Blair, Mark R. Walshe, R. Calen Tupper, Paul F. PLoS One Research Article It is clear that learning and attention interact, but it is an ongoing challenge to integrate their psychological and neurophysiological descriptions. Here we introduce LAG-1, a dynamic neural field model of learning, attention and gaze, that we fit to human learning and eye-movement data from two category learning experiments. LAG-1 comprises three control systems: one for visuospatial attention, one for saccadic timing and control, and one for category learning. The model is able to extract a kind of information gain from pairwise differences in simple associations between visual features and categories. Providing this gain as a reentrant signal with bottom-up visual information, and in top-down spatial priority, appropriately influences the initiation of saccades. LAG-1 provides a moment-by-moment simulation of the interactions of learning and gaze, and thus simultaneously produces phenomena on many timescales, from the duration of saccades and gaze fixations, to the response times for trials, to the slow optimization of attention toward task relevant information across a whole experiment. With only three free parameters (learning rate, trial impatience, and fixation impatience) LAG-1 produces qualitatively correct fits for learning, behavioural timing and eye movement measures, and also for previously unmodelled empirical phenomena (e.g., fixation orders showing stimulus-specific attention, and decreasing fixation counts during feedback). Because LAG-1 is built to capture attention and gaze generally, we demonstrate how it can be applied to other phenomena of visual cognition such as the free viewing of visual stimuli, visual search, and covert attention. Public Library of Science 2022-03-17 /pmc/articles/PMC8929614/ /pubmed/35298465 http://dx.doi.org/10.1371/journal.pone.0259511 Text en © 2022 Barnes et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Barnes, Jordan
Blair, Mark R.
Walshe, R. Calen
Tupper, Paul F.
LAG-1: A dynamic, integrative model of learning, attention, and gaze
title LAG-1: A dynamic, integrative model of learning, attention, and gaze
title_full LAG-1: A dynamic, integrative model of learning, attention, and gaze
title_fullStr LAG-1: A dynamic, integrative model of learning, attention, and gaze
title_full_unstemmed LAG-1: A dynamic, integrative model of learning, attention, and gaze
title_short LAG-1: A dynamic, integrative model of learning, attention, and gaze
title_sort lag-1: a dynamic, integrative model of learning, attention, and gaze
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8929614/
https://www.ncbi.nlm.nih.gov/pubmed/35298465
http://dx.doi.org/10.1371/journal.pone.0259511
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