Perceived speed at low luminance: Lights out for the Bayesian observer?

To account for perceptual bias, Bayesian models use the precision of early sensory measurements to weight the influence of prior expectations. As precision decreases, prior expectations start to dominate. Important examples come from motion perception, where the slow-motion prior has been used to ex...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Vision research (Oxford) 2022-12, Vol.201, p.108124-108124, Article 108124
Hauptverfasser: Freeman, Tom C.A., Powell, Georgie
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 108124
container_issue
container_start_page 108124
container_title Vision research (Oxford)
container_volume 201
creator Freeman, Tom C.A.
Powell, Georgie
description To account for perceptual bias, Bayesian models use the precision of early sensory measurements to weight the influence of prior expectations. As precision decreases, prior expectations start to dominate. Important examples come from motion perception, where the slow-motion prior has been used to explain a variety of motion illusions in vision, hearing, and touch, many of which correlate appropriately with threshold measures of underlying precision. However, the Bayesian account seems defeated by the finding that moving objects appear faster in the dark, because most motion thresholds are worse at low luminance. Here we show this is not the case for speed discrimination. Our results show that performance improves at low light levels by virtue of a perceived contrast cue that is more salient in the dark. With this cue removed, discrimination becomes independent of luminance. However, we found perceived speed still increased in the dark for the same observers, and by the same amount. A possible interpretation is that motion processing is therefore not Bayesian, because our findings challenge a key assumption these models make, namely that the accuracy of early sensory measurements is independent of basic stimulus properties like luminance. However, a final experiment restored Bayesian behaviour by adding external noise, making discrimination worse and slowing perceived speed down. Our findings therefore suggest that motion is processed in a Bayesian fashion but based on noisy sensory measurements that also vary in accuracy.
doi_str_mv 10.1016/j.visres.2022.108124
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2721263382</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0042698922001304</els_id><sourcerecordid>2721263382</sourcerecordid><originalsourceid>FETCH-LOGICAL-c357t-fddac213d421bfd8fe2bf3b6cb09ab497ca1f8e539cb42c2d1d9efc166cc4d613</originalsourceid><addsrcrecordid>eNp9kE1LAzEQhoMotn78A5EcvWzNV7O7HhQtWoWCHvQcssnEpuxHTXYr_fdu2erRywwMzzvDPAhdUDKhhMrr1WTjY4A4YYSxfpRRJg7QmGZplkylkIdoTIhgicyzfIROYlwRQtIpy4_RiEuac0nEGM3fIBjwG7A4rqGvusVl843LrvK1rg3c4IX_XLYRN12LXRNwuwT8oLcQva5xU0QIGwh3Z-jI6TLC-b6foo-nx_fZc7J4nb_M7heJ4dO0TZy12jDKrWC0cDZzwArHC2kKkutC5KnR1GUw5bkpBDPMUpuDM1RKY4SVlJ-iq2HvOjRfHcRWVT4aKEtdQ9NFxVJGmeQ8Yz0qBtSEJvamnFoHX-mwVZSonUK1UoNCtVOoBoV97HJ_oSsqsH-hX2c9cDsA0P-58RBUNB56VdYHMK2yjf__wg_pxYUI</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2721263382</pqid></control><display><type>article</type><title>Perceived speed at low luminance: Lights out for the Bayesian observer?</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Freeman, Tom C.A. ; Powell, Georgie</creator><creatorcontrib>Freeman, Tom C.A. ; Powell, Georgie</creatorcontrib><description>To account for perceptual bias, Bayesian models use the precision of early sensory measurements to weight the influence of prior expectations. As precision decreases, prior expectations start to dominate. Important examples come from motion perception, where the slow-motion prior has been used to explain a variety of motion illusions in vision, hearing, and touch, many of which correlate appropriately with threshold measures of underlying precision. However, the Bayesian account seems defeated by the finding that moving objects appear faster in the dark, because most motion thresholds are worse at low luminance. Here we show this is not the case for speed discrimination. Our results show that performance improves at low light levels by virtue of a perceived contrast cue that is more salient in the dark. With this cue removed, discrimination becomes independent of luminance. However, we found perceived speed still increased in the dark for the same observers, and by the same amount. A possible interpretation is that motion processing is therefore not Bayesian, because our findings challenge a key assumption these models make, namely that the accuracy of early sensory measurements is independent of basic stimulus properties like luminance. However, a final experiment restored Bayesian behaviour by adding external noise, making discrimination worse and slowing perceived speed down. Our findings therefore suggest that motion is processed in a Bayesian fashion but based on noisy sensory measurements that also vary in accuracy.</description><identifier>ISSN: 0042-6989</identifier><identifier>EISSN: 1878-5646</identifier><identifier>DOI: 10.1016/j.visres.2022.108124</identifier><identifier>PMID: 36193604</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Bayesian models ; Bias ; Contrast Sensitivity ; Humans ; Illusions ; Luminance ; Motion Perception ; Perceived speed</subject><ispartof>Vision research (Oxford), 2022-12, Vol.201, p.108124-108124, Article 108124</ispartof><rights>2022 The Authors</rights><rights>Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c357t-fddac213d421bfd8fe2bf3b6cb09ab497ca1f8e539cb42c2d1d9efc166cc4d613</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0042698922001304$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36193604$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Freeman, Tom C.A.</creatorcontrib><creatorcontrib>Powell, Georgie</creatorcontrib><title>Perceived speed at low luminance: Lights out for the Bayesian observer?</title><title>Vision research (Oxford)</title><addtitle>Vision Res</addtitle><description>To account for perceptual bias, Bayesian models use the precision of early sensory measurements to weight the influence of prior expectations. As precision decreases, prior expectations start to dominate. Important examples come from motion perception, where the slow-motion prior has been used to explain a variety of motion illusions in vision, hearing, and touch, many of which correlate appropriately with threshold measures of underlying precision. However, the Bayesian account seems defeated by the finding that moving objects appear faster in the dark, because most motion thresholds are worse at low luminance. Here we show this is not the case for speed discrimination. Our results show that performance improves at low light levels by virtue of a perceived contrast cue that is more salient in the dark. With this cue removed, discrimination becomes independent of luminance. However, we found perceived speed still increased in the dark for the same observers, and by the same amount. A possible interpretation is that motion processing is therefore not Bayesian, because our findings challenge a key assumption these models make, namely that the accuracy of early sensory measurements is independent of basic stimulus properties like luminance. However, a final experiment restored Bayesian behaviour by adding external noise, making discrimination worse and slowing perceived speed down. Our findings therefore suggest that motion is processed in a Bayesian fashion but based on noisy sensory measurements that also vary in accuracy.</description><subject>Bayesian models</subject><subject>Bias</subject><subject>Contrast Sensitivity</subject><subject>Humans</subject><subject>Illusions</subject><subject>Luminance</subject><subject>Motion Perception</subject><subject>Perceived speed</subject><issn>0042-6989</issn><issn>1878-5646</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kE1LAzEQhoMotn78A5EcvWzNV7O7HhQtWoWCHvQcssnEpuxHTXYr_fdu2erRywwMzzvDPAhdUDKhhMrr1WTjY4A4YYSxfpRRJg7QmGZplkylkIdoTIhgicyzfIROYlwRQtIpy4_RiEuac0nEGM3fIBjwG7A4rqGvusVl843LrvK1rg3c4IX_XLYRN12LXRNwuwT8oLcQva5xU0QIGwh3Z-jI6TLC-b6foo-nx_fZc7J4nb_M7heJ4dO0TZy12jDKrWC0cDZzwArHC2kKkutC5KnR1GUw5bkpBDPMUpuDM1RKY4SVlJ-iq2HvOjRfHcRWVT4aKEtdQ9NFxVJGmeQ8Yz0qBtSEJvamnFoHX-mwVZSonUK1UoNCtVOoBoV97HJ_oSsqsH-hX2c9cDsA0P-58RBUNB56VdYHMK2yjf__wg_pxYUI</recordid><startdate>202212</startdate><enddate>202212</enddate><creator>Freeman, Tom C.A.</creator><creator>Powell, Georgie</creator><general>Elsevier Ltd</general><scope>6I.</scope><scope>AAFTH</scope><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>202212</creationdate><title>Perceived speed at low luminance: Lights out for the Bayesian observer?</title><author>Freeman, Tom C.A. ; Powell, Georgie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c357t-fddac213d421bfd8fe2bf3b6cb09ab497ca1f8e539cb42c2d1d9efc166cc4d613</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Bayesian models</topic><topic>Bias</topic><topic>Contrast Sensitivity</topic><topic>Humans</topic><topic>Illusions</topic><topic>Luminance</topic><topic>Motion Perception</topic><topic>Perceived speed</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Freeman, Tom C.A.</creatorcontrib><creatorcontrib>Powell, Georgie</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><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>Vision research (Oxford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Freeman, Tom C.A.</au><au>Powell, Georgie</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Perceived speed at low luminance: Lights out for the Bayesian observer?</atitle><jtitle>Vision research (Oxford)</jtitle><addtitle>Vision Res</addtitle><date>2022-12</date><risdate>2022</risdate><volume>201</volume><spage>108124</spage><epage>108124</epage><pages>108124-108124</pages><artnum>108124</artnum><issn>0042-6989</issn><eissn>1878-5646</eissn><abstract>To account for perceptual bias, Bayesian models use the precision of early sensory measurements to weight the influence of prior expectations. As precision decreases, prior expectations start to dominate. Important examples come from motion perception, where the slow-motion prior has been used to explain a variety of motion illusions in vision, hearing, and touch, many of which correlate appropriately with threshold measures of underlying precision. However, the Bayesian account seems defeated by the finding that moving objects appear faster in the dark, because most motion thresholds are worse at low luminance. Here we show this is not the case for speed discrimination. Our results show that performance improves at low light levels by virtue of a perceived contrast cue that is more salient in the dark. With this cue removed, discrimination becomes independent of luminance. However, we found perceived speed still increased in the dark for the same observers, and by the same amount. A possible interpretation is that motion processing is therefore not Bayesian, because our findings challenge a key assumption these models make, namely that the accuracy of early sensory measurements is independent of basic stimulus properties like luminance. However, a final experiment restored Bayesian behaviour by adding external noise, making discrimination worse and slowing perceived speed down. Our findings therefore suggest that motion is processed in a Bayesian fashion but based on noisy sensory measurements that also vary in accuracy.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>36193604</pmid><doi>10.1016/j.visres.2022.108124</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0042-6989
ispartof Vision research (Oxford), 2022-12, Vol.201, p.108124-108124, Article 108124
issn 0042-6989
1878-5646
language eng
recordid cdi_proquest_miscellaneous_2721263382
source MEDLINE; Elsevier ScienceDirect Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
subjects Bayesian models
Bias
Contrast Sensitivity
Humans
Illusions
Luminance
Motion Perception
Perceived speed
title Perceived speed at low luminance: Lights out for the Bayesian observer?
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-06T05%3A36%3A51IST&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=Perceived%20speed%20at%20low%20luminance:%20Lights%20out%20for%20the%20Bayesian%20observer?&rft.jtitle=Vision%20research%20(Oxford)&rft.au=Freeman,%20Tom%20C.A.&rft.date=2022-12&rft.volume=201&rft.spage=108124&rft.epage=108124&rft.pages=108124-108124&rft.artnum=108124&rft.issn=0042-6989&rft.eissn=1878-5646&rft_id=info:doi/10.1016/j.visres.2022.108124&rft_dat=%3Cproquest_cross%3E2721263382%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2721263382&rft_id=info:pmid/36193604&rft_els_id=S0042698922001304&rfr_iscdi=true