Loading…

Parameter learning but not structure learning: a Bayesian network model of constraints on early perceptual learning

Visual scientists have shown that people are capable of perceptual learning in a large variety of circumstances. Are there constraints on such learning? We propose a new constraint on early perceptual learning, namely, that people are capable of parameter learning-they can modify their knowledge of...

Full description

Saved in:
Bibliographic Details
Published in:Journal of vision (Charlottesville, Va.) Va.), 2007-01, Vol.7 (1), p.4-4
Main Authors: Michel, Melchi M, Jacobs, Robert A
Format: Article
Language:English
Subjects:
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c306t-4888608f693be878cbd66f3148f8ab789d27125edc88b96e9113e06ceef70a223
cites
container_end_page 4
container_issue 1
container_start_page 4
container_title Journal of vision (Charlottesville, Va.)
container_volume 7
creator Michel, Melchi M
Jacobs, Robert A
description Visual scientists have shown that people are capable of perceptual learning in a large variety of circumstances. Are there constraints on such learning? We propose a new constraint on early perceptual learning, namely, that people are capable of parameter learning-they can modify their knowledge of the prior probabilities of scene variables or of the statistical relationships among scene and perceptual variables that are already considered to be potentially dependent-but they are not capable of structure learning-they cannot learn new relationships among variables that are not considered to be potentially dependent, even when placed in novel environments in which these variables are strongly related. These ideas are formalized using the notation of Bayesian networks. We report the results of five experiments that evaluate whether subjects can demonstrate cue acquisition, which means that they can learn that a sensory signal is a cue to a perceptual judgment. In Experiment 1, subjects were placed in a novel environment that resembled natural environments in the sense that it contained systematic relationships among scene and perceptual variables that which are normally dependent. In this case, cue acquisition requires parameter learning and, as predicted, subjects succeeded in learning a new cue. In Experiments 2-5, subjects were placed in novel environments that did not resemble natural environments-they contained systematic relationships among scene and perceptual variables that are not normally dependent. Cue acquisition requires structure learning in these cases. Consistent with our hypothesis, subjects failed to learn new cues in Experiments 2-5. Overall, the results suggest that the mechanisms of early perceptual learning are biased such that people can only learn new contingencies between scene and sensory variables that are considered to be potentially dependent.
doi_str_mv 10.1167/7.1.4
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_70433446</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>70433446</sourcerecordid><originalsourceid>FETCH-LOGICAL-c306t-4888608f693be878cbd66f3148f8ab789d27125edc88b96e9113e06ceef70a223</originalsourceid><addsrcrecordid>eNpNkD1PwzAURS0EoqX0LyAvsKXYsWs7bFDxJVWCAWbLcV5QILGD7ajqvyeoVWF6V3rn3OEiNKdkQamQ13JBF_wITemS8UwykR__yxN0FuMnITlZEnqKJlRyMUr5FMVXE0wHCQJuwQTXuA9cDgk7n3BMYbBpCHB43WCD78wWYmMcdpA2PnzhzlfQYl9j692omMaliL3Do9NucQ_BQp8G0x5aztFJbdoI8_2dofeH-7fVU7Z-eXxe3a4zy4hIGVdKCaJqUbASlFS2rISoGeWqVqaUqqhySfMlVFapshBQUMqACAtQS2LynM3Q1a63D_57gJh010QLbWsc-CFqSThjnIsRvNyBNvgYA9S6D01nwlZTon_X1VJTzUfuYl84lB1Uf9R-TvYDxbB1zw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>70433446</pqid></control><display><type>article</type><title>Parameter learning but not structure learning: a Bayesian network model of constraints on early perceptual learning</title><source>DOAJ Directory of Open Access Journals</source><creator>Michel, Melchi M ; Jacobs, Robert A</creator><creatorcontrib>Michel, Melchi M ; Jacobs, Robert A</creatorcontrib><description>Visual scientists have shown that people are capable of perceptual learning in a large variety of circumstances. Are there constraints on such learning? We propose a new constraint on early perceptual learning, namely, that people are capable of parameter learning-they can modify their knowledge of the prior probabilities of scene variables or of the statistical relationships among scene and perceptual variables that are already considered to be potentially dependent-but they are not capable of structure learning-they cannot learn new relationships among variables that are not considered to be potentially dependent, even when placed in novel environments in which these variables are strongly related. These ideas are formalized using the notation of Bayesian networks. We report the results of five experiments that evaluate whether subjects can demonstrate cue acquisition, which means that they can learn that a sensory signal is a cue to a perceptual judgment. In Experiment 1, subjects were placed in a novel environment that resembled natural environments in the sense that it contained systematic relationships among scene and perceptual variables that which are normally dependent. In this case, cue acquisition requires parameter learning and, as predicted, subjects succeeded in learning a new cue. In Experiments 2-5, subjects were placed in novel environments that did not resemble natural environments-they contained systematic relationships among scene and perceptual variables that are not normally dependent. Cue acquisition requires structure learning in these cases. Consistent with our hypothesis, subjects failed to learn new cues in Experiments 2-5. Overall, the results suggest that the mechanisms of early perceptual learning are biased such that people can only learn new contingencies between scene and sensory variables that are considered to be potentially dependent.</description><identifier>ISSN: 1534-7362</identifier><identifier>EISSN: 1534-7362</identifier><identifier>DOI: 10.1167/7.1.4</identifier><identifier>PMID: 17461672</identifier><language>eng</language><publisher>United States</publisher><subject>Bayes Theorem ; Cues ; Humans ; Learning - physiology ; Light ; Motion Perception - physiology ; Neural Networks (Computer) ; Sound ; Vision Disparity</subject><ispartof>Journal of vision (Charlottesville, Va.), 2007-01, Vol.7 (1), p.4-4</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c306t-4888608f693be878cbd66f3148f8ab789d27125edc88b96e9113e06ceef70a223</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,864,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/17461672$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Michel, Melchi M</creatorcontrib><creatorcontrib>Jacobs, Robert A</creatorcontrib><title>Parameter learning but not structure learning: a Bayesian network model of constraints on early perceptual learning</title><title>Journal of vision (Charlottesville, Va.)</title><addtitle>J Vis</addtitle><description>Visual scientists have shown that people are capable of perceptual learning in a large variety of circumstances. Are there constraints on such learning? We propose a new constraint on early perceptual learning, namely, that people are capable of parameter learning-they can modify their knowledge of the prior probabilities of scene variables or of the statistical relationships among scene and perceptual variables that are already considered to be potentially dependent-but they are not capable of structure learning-they cannot learn new relationships among variables that are not considered to be potentially dependent, even when placed in novel environments in which these variables are strongly related. These ideas are formalized using the notation of Bayesian networks. We report the results of five experiments that evaluate whether subjects can demonstrate cue acquisition, which means that they can learn that a sensory signal is a cue to a perceptual judgment. In Experiment 1, subjects were placed in a novel environment that resembled natural environments in the sense that it contained systematic relationships among scene and perceptual variables that which are normally dependent. In this case, cue acquisition requires parameter learning and, as predicted, subjects succeeded in learning a new cue. In Experiments 2-5, subjects were placed in novel environments that did not resemble natural environments-they contained systematic relationships among scene and perceptual variables that are not normally dependent. Cue acquisition requires structure learning in these cases. Consistent with our hypothesis, subjects failed to learn new cues in Experiments 2-5. Overall, the results suggest that the mechanisms of early perceptual learning are biased such that people can only learn new contingencies between scene and sensory variables that are considered to be potentially dependent.</description><subject>Bayes Theorem</subject><subject>Cues</subject><subject>Humans</subject><subject>Learning - physiology</subject><subject>Light</subject><subject>Motion Perception - physiology</subject><subject>Neural Networks (Computer)</subject><subject>Sound</subject><subject>Vision Disparity</subject><issn>1534-7362</issn><issn>1534-7362</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><recordid>eNpNkD1PwzAURS0EoqX0LyAvsKXYsWs7bFDxJVWCAWbLcV5QILGD7ajqvyeoVWF6V3rn3OEiNKdkQamQ13JBF_wITemS8UwykR__yxN0FuMnITlZEnqKJlRyMUr5FMVXE0wHCQJuwQTXuA9cDgk7n3BMYbBpCHB43WCD78wWYmMcdpA2PnzhzlfQYl9j692omMaliL3Do9NucQ_BQp8G0x5aztFJbdoI8_2dofeH-7fVU7Z-eXxe3a4zy4hIGVdKCaJqUbASlFS2rISoGeWqVqaUqqhySfMlVFapshBQUMqACAtQS2LynM3Q1a63D_57gJh010QLbWsc-CFqSThjnIsRvNyBNvgYA9S6D01nwlZTon_X1VJTzUfuYl84lB1Uf9R-TvYDxbB1zw</recordid><startdate>20070116</startdate><enddate>20070116</enddate><creator>Michel, Melchi M</creator><creator>Jacobs, Robert A</creator><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>7X8</scope></search><sort><creationdate>20070116</creationdate><title>Parameter learning but not structure learning: a Bayesian network model of constraints on early perceptual learning</title><author>Michel, Melchi M ; Jacobs, Robert A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c306t-4888608f693be878cbd66f3148f8ab789d27125edc88b96e9113e06ceef70a223</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Bayes Theorem</topic><topic>Cues</topic><topic>Humans</topic><topic>Learning - physiology</topic><topic>Light</topic><topic>Motion Perception - physiology</topic><topic>Neural Networks (Computer)</topic><topic>Sound</topic><topic>Vision Disparity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Michel, Melchi M</creatorcontrib><creatorcontrib>Jacobs, Robert A</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of vision (Charlottesville, Va.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Michel, Melchi M</au><au>Jacobs, Robert A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Parameter learning but not structure learning: a Bayesian network model of constraints on early perceptual learning</atitle><jtitle>Journal of vision (Charlottesville, Va.)</jtitle><addtitle>J Vis</addtitle><date>2007-01-16</date><risdate>2007</risdate><volume>7</volume><issue>1</issue><spage>4</spage><epage>4</epage><pages>4-4</pages><issn>1534-7362</issn><eissn>1534-7362</eissn><abstract>Visual scientists have shown that people are capable of perceptual learning in a large variety of circumstances. Are there constraints on such learning? We propose a new constraint on early perceptual learning, namely, that people are capable of parameter learning-they can modify their knowledge of the prior probabilities of scene variables or of the statistical relationships among scene and perceptual variables that are already considered to be potentially dependent-but they are not capable of structure learning-they cannot learn new relationships among variables that are not considered to be potentially dependent, even when placed in novel environments in which these variables are strongly related. These ideas are formalized using the notation of Bayesian networks. We report the results of five experiments that evaluate whether subjects can demonstrate cue acquisition, which means that they can learn that a sensory signal is a cue to a perceptual judgment. In Experiment 1, subjects were placed in a novel environment that resembled natural environments in the sense that it contained systematic relationships among scene and perceptual variables that which are normally dependent. In this case, cue acquisition requires parameter learning and, as predicted, subjects succeeded in learning a new cue. In Experiments 2-5, subjects were placed in novel environments that did not resemble natural environments-they contained systematic relationships among scene and perceptual variables that are not normally dependent. Cue acquisition requires structure learning in these cases. Consistent with our hypothesis, subjects failed to learn new cues in Experiments 2-5. Overall, the results suggest that the mechanisms of early perceptual learning are biased such that people can only learn new contingencies between scene and sensory variables that are considered to be potentially dependent.</abstract><cop>United States</cop><pmid>17461672</pmid><doi>10.1167/7.1.4</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1534-7362
ispartof Journal of vision (Charlottesville, Va.), 2007-01, Vol.7 (1), p.4-4
issn 1534-7362
1534-7362
language eng
recordid cdi_proquest_miscellaneous_70433446
source DOAJ Directory of Open Access Journals
subjects Bayes Theorem
Cues
Humans
Learning - physiology
Light
Motion Perception - physiology
Neural Networks (Computer)
Sound
Vision Disparity
title Parameter learning but not structure learning: a Bayesian network model of constraints on early perceptual learning
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T23%3A58%3A57IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Parameter%20learning%20but%20not%20structure%20learning:%20a%20Bayesian%20network%20model%20of%20constraints%20on%20early%20perceptual%20learning&rft.jtitle=Journal%20of%20vision%20(Charlottesville,%20Va.)&rft.au=Michel,%20Melchi%20M&rft.date=2007-01-16&rft.volume=7&rft.issue=1&rft.spage=4&rft.epage=4&rft.pages=4-4&rft.issn=1534-7362&rft.eissn=1534-7362&rft_id=info:doi/10.1167/7.1.4&rft_dat=%3Cproquest_cross%3E70433446%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c306t-4888608f693be878cbd66f3148f8ab789d27125edc88b96e9113e06ceef70a223%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=70433446&rft_id=info:pmid/17461672&rfr_iscdi=true