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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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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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0259511</identifier><identifier>PMID: 35298465</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Attention ; Attention - physiology ; Attention task ; Biology and Life Sciences ; Cognition ; Control systems ; Evaluation ; Experiments ; Eye Movements ; Feedback ; Fixation ; Fixation, Ocular ; Human motion ; Humans ; Information processing ; Information sources ; Learning ; Learning strategies ; Neurophysiology ; Optimization ; Research and Analysis Methods ; Saccades ; Saccadic eye movements ; Social Sciences ; Visual perception ; Visual signals ; Visual stimuli</subject><ispartof>PloS one, 2022-03, Vol.17 (3), p.e0259511-e0259511</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Barnes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 Barnes et al 2022 Barnes et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c571t-1e80f84138f0664fc535fc3b01d24db859aab0f63d6f7b63fc10231532455e563</cites><orcidid>0000-0002-6623-1747</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8929614/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8929614/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35298465$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Barnes, Jordan</creatorcontrib><creatorcontrib>Blair, Mark R</creatorcontrib><creatorcontrib>Walshe, R Calen</creatorcontrib><creatorcontrib>Tupper, Paul F</creatorcontrib><title>LAG-1: A dynamic, integrative model of learning, attention, and gaze</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>It is clear that learning and attention interact, but it is an ongoing challenge to integrate their psychological and neurophysiological descriptions. 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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.</description><subject>Analysis</subject><subject>Attention</subject><subject>Attention - physiology</subject><subject>Attention task</subject><subject>Biology and Life Sciences</subject><subject>Cognition</subject><subject>Control systems</subject><subject>Evaluation</subject><subject>Experiments</subject><subject>Eye Movements</subject><subject>Feedback</subject><subject>Fixation</subject><subject>Fixation, Ocular</subject><subject>Human motion</subject><subject>Humans</subject><subject>Information processing</subject><subject>Information sources</subject><subject>Learning</subject><subject>Learning strategies</subject><subject>Neurophysiology</subject><subject>Optimization</subject><subject>Research and Analysis Methods</subject><subject>Saccades</subject><subject>Saccadic eye movements</subject><subject>Social Sciences</subject><subject>Visual perception</subject><subject>Visual signals</subject><subject>Visual stimuli</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNkluLEzEYhgdR3IP-A9EBQRS2NeeZeCGUVddCYcHTbUgzyTQlTbqTzLLrrze1s0tH9kJykZA83_sd8hbFCwimEFfw_Tr0nZduug1eTwGinEL4qDiGHKMJQwA_PjgfFScxrgGguGbsaXGEKeI1YfS4-LSYXUzgh3JWNrdebqw6K61Puu1kste63IRGuzKY0mnZeevbs1KmpH2yweejb8pW_tbPiidGuqifD_tp8fPL5x_nXyeLy4v5-WwxUbSCaQJ1DUxNIK4NYIwYRTE1Ci8BbBBpljXlUi6BYbhhploybBQECEOKEaFUU4ZPi1d73a0LUQwDiAIxAlDNCCOZmO-JJsi12HZ2I7tbEaQVfy9C1wrZJaucFgQBoyopiWkAAaTmuSIpK66J4hXCVdb6OGTrlxvdqNx1J91IdPzi7Uq04VrUHHEGd8W8HQS6cNXrmMTGRqWdk16Hfl835xDUIKOv_0Ef7m6gWpkbsN6EnFftRMWM8QpSWHOYqekDVF6Nzv-b3WJsvh8FvBsFZCbpm9TKPkYx__7t_9nLX2P2zQG70tKlVQyu35knjkGyB1UXYuy0uR8yBGJn9rtpiJ3ZxWD2HPby8IPug-7cjf8AIUv2FA</recordid><startdate>20220317</startdate><enddate>20220317</enddate><creator>Barnes, Jordan</creator><creator>Blair, Mark R</creator><creator>Walshe, R Calen</creator><creator>Tupper, Paul F</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-6623-1747</orcidid></search><sort><creationdate>20220317</creationdate><title>LAG-1: A dynamic, integrative model of learning, attention, and gaze</title><author>Barnes, Jordan ; Blair, Mark R ; Walshe, R Calen ; Tupper, Paul F</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c571t-1e80f84138f0664fc535fc3b01d24db859aab0f63d6f7b63fc10231532455e563</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Analysis</topic><topic>Attention</topic><topic>Attention - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Barnes, Jordan</au><au>Blair, Mark R</au><au>Walshe, R Calen</au><au>Tupper, Paul F</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>LAG-1: A dynamic, integrative model of learning, attention, and gaze</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2022-03-17</date><risdate>2022</risdate><volume>17</volume><issue>3</issue><spage>e0259511</spage><epage>e0259511</epage><pages>e0259511-e0259511</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>35298465</pmid><doi>10.1371/journal.pone.0259511</doi><tpages>e0259511</tpages><orcidid>https://orcid.org/0000-0002-6623-1747</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Analysis Attention Attention - physiology Attention task Biology and Life Sciences Cognition Control systems Evaluation Experiments Eye Movements Feedback Fixation Fixation, Ocular Human motion Humans Information processing Information sources Learning Learning strategies Neurophysiology Optimization Research and Analysis Methods Saccades Saccadic eye movements Social Sciences Visual perception Visual signals Visual stimuli |
title | LAG-1: A dynamic, integrative model of learning, attention, and gaze |
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