Taking the energy out of spatio-temporal energy models of human motion processing: The Component Level Feature Model
► An extended explicit simulation of the Component Level Feature Model. ► CLFM accurately computes the IOC direction for 200 plaids without computing energy. ► Output is compared with critical psychophysical studies of perceived direction. ► Includes novel explanations for plaid motion not perceived...
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description | ► An extended explicit simulation of the Component Level Feature Model. ► CLFM accurately computes the IOC direction for 200 plaids without computing energy. ► Output is compared with critical psychophysical studies of perceived direction. ► Includes novel explanations for plaid motion not perceived in the IOC direction.
Standard biologically inspired spatio-temporal energy models of how humans perceive moving two-dimensional patterns often have two critical stages. In the first stage, suitable filters are convolved with the pattern over time to extract information at the “component” level. Motion energy is then computed for each component. The second stage typically computes pattern velocity using the intersection of constraints rule (IOC). This paper describes a new implementation of the Component Level Feature Model (
Bowns, 2002) that computes motion direction that is similar to these two stages except that it does not compute motion energy. Here the model computes direction for 200 randomly generated plaids. The output linearly matched that predicted by the IOC. The model was also able to predict the perceived direction even when it deviated from the IOC due to the following variables – speed ratio (
Bowns, 1996); duration (
Yo & Wilson, 1992); adaptation (
Bowns & Alais, 2006). The model provides a novel explanation for each of the above and for why multiple directions can be represented for the same stimuli (
Bowns & Alais, 2006); and why some second-order information attributed to non-linearities (
Derrington, Badcock, & Holroyd, 1992) reverses perceived motion direction. Finally, CLFM is invariant to contrast and phase. |
doi_str_mv | 10.1016/j.visres.2011.09.014 |
format | Article |
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Standard biologically inspired spatio-temporal energy models of how humans perceive moving two-dimensional patterns often have two critical stages. In the first stage, suitable filters are convolved with the pattern over time to extract information at the “component” level. Motion energy is then computed for each component. The second stage typically computes pattern velocity using the intersection of constraints rule (IOC). This paper describes a new implementation of the Component Level Feature Model (
Bowns, 2002) that computes motion direction that is similar to these two stages except that it does not compute motion energy. Here the model computes direction for 200 randomly generated plaids. The output linearly matched that predicted by the IOC. The model was also able to predict the perceived direction even when it deviated from the IOC due to the following variables – speed ratio (
Bowns, 1996); duration (
Yo & Wilson, 1992); adaptation (
Bowns & Alais, 2006). The model provides a novel explanation for each of the above and for why multiple directions can be represented for the same stimuli (
Bowns & Alais, 2006); and why some second-order information attributed to non-linearities (
Derrington, Badcock, & Holroyd, 1992) reverses perceived motion direction. Finally, CLFM is invariant to contrast and phase.</description><identifier>ISSN: 0042-6989</identifier><identifier>EISSN: 1878-5646</identifier><identifier>DOI: 10.1016/j.visres.2011.09.014</identifier><identifier>PMID: 22005388</identifier><identifier>CODEN: VISRAM</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Biological and medical sciences ; CLFM ; Contrast Sensitivity - physiology ; Eye and associated structures. Visual pathways and centers. Vision ; Fundamental and applied biological sciences. Psychology ; Humans ; Intersection of constraints ; IOC ; Models, Psychological ; Motion ; Motion Perception - physiology ; Time Factors ; Vector average ; Vertebrates: nervous system and sense organs</subject><ispartof>Vision research (Oxford), 2011-12, Vol.51 (23), p.2425-2430</ispartof><rights>2011 Elsevier Ltd</rights><rights>2015 INIST-CNRS</rights><rights>Copyright © 2011 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c469t-2cc09f280848d308866e86932d3cf93a066d1ee3016cfeb9b838bf2d4254511f3</citedby><cites>FETCH-LOGICAL-c469t-2cc09f280848d308866e86932d3cf93a066d1ee3016cfeb9b838bf2d4254511f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0042698911003531$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,65309</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=25293667$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22005388$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bowns, Linda</creatorcontrib><title>Taking the energy out of spatio-temporal energy models of human motion processing: The Component Level Feature Model</title><title>Vision research (Oxford)</title><addtitle>Vision Res</addtitle><description>► An extended explicit simulation of the Component Level Feature Model. ► CLFM accurately computes the IOC direction for 200 plaids without computing energy. ► Output is compared with critical psychophysical studies of perceived direction. ► Includes novel explanations for plaid motion not perceived in the IOC direction.
Standard biologically inspired spatio-temporal energy models of how humans perceive moving two-dimensional patterns often have two critical stages. In the first stage, suitable filters are convolved with the pattern over time to extract information at the “component” level. Motion energy is then computed for each component. The second stage typically computes pattern velocity using the intersection of constraints rule (IOC). This paper describes a new implementation of the Component Level Feature Model (
Bowns, 2002) that computes motion direction that is similar to these two stages except that it does not compute motion energy. Here the model computes direction for 200 randomly generated plaids. The output linearly matched that predicted by the IOC. The model was also able to predict the perceived direction even when it deviated from the IOC due to the following variables – speed ratio (
Bowns, 1996); duration (
Yo & Wilson, 1992); adaptation (
Bowns & Alais, 2006). The model provides a novel explanation for each of the above and for why multiple directions can be represented for the same stimuli (
Bowns & Alais, 2006); and why some second-order information attributed to non-linearities (
Derrington, Badcock, & Holroyd, 1992) reverses perceived motion direction. Finally, CLFM is invariant to contrast and phase.</description><subject>Biological and medical sciences</subject><subject>CLFM</subject><subject>Contrast Sensitivity - physiology</subject><subject>Eye and associated structures. Visual pathways and centers. Vision</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Humans</subject><subject>Intersection of constraints</subject><subject>IOC</subject><subject>Models, Psychological</subject><subject>Motion</subject><subject>Motion Perception - physiology</subject><subject>Time Factors</subject><subject>Vector average</subject><subject>Vertebrates: nervous system and sense organs</subject><issn>0042-6989</issn><issn>1878-5646</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkUFv0zAYhi0EYqXwDxDyBXFK9tlJXJsD0lSxMalol-5suc6XzSWJi-1U2r_HUTt2g5Nl-Xk_f3ofQj4yKBkwcbkvjy4GjCUHxkpQJbD6FVkwuZJFI2rxmiwAal4IJdUFeRfjHgBWDVdvyQXnAE0l5YKkrfnlxgeaHpHiiOHhifopUd_ReDDJ-SLhcPDB9M-vg2-xjzPwOA1mzPdMjfQQvMUY86ivdJtnrX2OjTgmusEj9vQaTZoC0p9z_D1505k-4ofzuST319-36x_F5u7mdn21KWwtVCq4taA6LkHWsq1ASiFQClXxtrKdqgwI0TLEKrdhO9ypnazkruNtzZu6YayrluTLaW7e7veEMenBRYt9b0b0U9SKccVzJ6v_kyCaRohc2pLUJ9IGH3P_nT4EN5jwpBnoWYze65MYPYvRoHQWk2Ofzh9MuwHbv6FnExn4fAZMtKbvghmtiy9cFlcJMW_67cRlC3h0GHS0DkeLrQtok269-_cmfwBCWq5h</recordid><startdate>20111208</startdate><enddate>20111208</enddate><creator>Bowns, Linda</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>IQODW</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><scope>7TK</scope></search><sort><creationdate>20111208</creationdate><title>Taking the energy out of spatio-temporal energy models of human motion processing: The Component Level Feature Model</title><author>Bowns, Linda</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c469t-2cc09f280848d308866e86932d3cf93a066d1ee3016cfeb9b838bf2d4254511f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Biological and medical sciences</topic><topic>CLFM</topic><topic>Contrast Sensitivity - physiology</topic><topic>Eye and associated structures. Visual pathways and centers. Vision</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Humans</topic><topic>Intersection of constraints</topic><topic>IOC</topic><topic>Models, Psychological</topic><topic>Motion</topic><topic>Motion Perception - physiology</topic><topic>Time Factors</topic><topic>Vector average</topic><topic>Vertebrates: nervous system and sense organs</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bowns, Linda</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Pascal-Francis</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><collection>Neurosciences Abstracts</collection><jtitle>Vision research (Oxford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bowns, Linda</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Taking the energy out of spatio-temporal energy models of human motion processing: The Component Level Feature Model</atitle><jtitle>Vision research (Oxford)</jtitle><addtitle>Vision Res</addtitle><date>2011-12-08</date><risdate>2011</risdate><volume>51</volume><issue>23</issue><spage>2425</spage><epage>2430</epage><pages>2425-2430</pages><issn>0042-6989</issn><eissn>1878-5646</eissn><coden>VISRAM</coden><abstract>► An extended explicit simulation of the Component Level Feature Model. ► CLFM accurately computes the IOC direction for 200 plaids without computing energy. ► Output is compared with critical psychophysical studies of perceived direction. ► Includes novel explanations for plaid motion not perceived in the IOC direction.
Standard biologically inspired spatio-temporal energy models of how humans perceive moving two-dimensional patterns often have two critical stages. In the first stage, suitable filters are convolved with the pattern over time to extract information at the “component” level. Motion energy is then computed for each component. The second stage typically computes pattern velocity using the intersection of constraints rule (IOC). This paper describes a new implementation of the Component Level Feature Model (
Bowns, 2002) that computes motion direction that is similar to these two stages except that it does not compute motion energy. Here the model computes direction for 200 randomly generated plaids. The output linearly matched that predicted by the IOC. The model was also able to predict the perceived direction even when it deviated from the IOC due to the following variables – speed ratio (
Bowns, 1996); duration (
Yo & Wilson, 1992); adaptation (
Bowns & Alais, 2006). The model provides a novel explanation for each of the above and for why multiple directions can be represented for the same stimuli (
Bowns & Alais, 2006); and why some second-order information attributed to non-linearities (
Derrington, Badcock, & Holroyd, 1992) reverses perceived motion direction. Finally, CLFM is invariant to contrast and phase.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><pmid>22005388</pmid><doi>10.1016/j.visres.2011.09.014</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Biological and medical sciences CLFM Contrast Sensitivity - physiology Eye and associated structures. Visual pathways and centers. Vision Fundamental and applied biological sciences. Psychology Humans Intersection of constraints IOC Models, Psychological Motion Motion Perception - physiology Time Factors Vector average Vertebrates: nervous system and sense organs |
title | Taking the energy out of spatio-temporal energy models of human motion processing: The Component Level Feature Model |
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