A continuous attractor neural network model of divided visual attention
The biologically realistic model of selective visual attention by Deco et al. uses a continuous attractor neural network to simulate a saliency map in posterior parietal cortex. We test the ability of the model to explain experimental evidence on the distribution of spatial attention. The majority o...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 2902 vol. 5 |
|---|---|
| container_issue | |
| container_start_page | 2897 |
| container_title | |
| container_volume | 5 |
| creator | Standage, D.I. Trappenberg, T.P. Klein, R.M. |
| description | The biologically realistic model of selective visual attention by Deco et al. uses a continuous attractor neural network to simulate a saliency map in posterior parietal cortex. We test the ability of the model to explain experimental evidence on the distribution of spatial attention. The majority of evidence supports the view that attention is a unitary construct, but recent experiments provide evidence for split attention foci. We simulate two such experiments. Our results suggest that the ability to divide attention depends on sustained endogenous signals from short term memory to the saliency map, stressing the interplay between working memory mechanisms and attention. Our results also point to a mechanism whereby inhibitory endogenous signals may play a role in dividing attention, suggesting a possible mechanism for inhibition of return. |
| doi_str_mv | 10.1109/IJCNN.2005.1556385 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_1556385</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>1556385</ieee_id><sourcerecordid>1556385</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-1e888c13f1477cd5686278bcfc801d08c10d0d48fb305036197667338c9e1e053</originalsourceid><addsrcrecordid>eNo1kMtOwzAQRS0eEm3hB2DjH0iYycSvZRVBKarKBtZVajuSIY1R4hTx90RqWZ3F0bmLy9g9Qo4I5nH9Wm23eQEgchRCkhYXbFagxKwsQV2yOSgNZKDUxdW_IEM3bD4MnwAFGUMztlpyG7sUujGOA69T6mubYs87P_Z1OyH9xP6LH6LzLY8Nd-EYnHf8GIZx8lPgpzp2t-y6qdvB3525YB_PT-_VS7Z5W62r5SYLqETK0GutLVKDpVLWCallofTeNlYDOpgUOHClbvYEAkiiUVIqIm2NRw-CFuzhtBu897vvPhzq_nd3PoD-ACpaTSg</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>A continuous attractor neural network model of divided visual attention</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Standage, D.I. ; Trappenberg, T.P. ; Klein, R.M.</creator><creatorcontrib>Standage, D.I. ; Trappenberg, T.P. ; Klein, R.M.</creatorcontrib><description>The biologically realistic model of selective visual attention by Deco et al. uses a continuous attractor neural network to simulate a saliency map in posterior parietal cortex. We test the ability of the model to explain experimental evidence on the distribution of spatial attention. The majority of evidence supports the view that attention is a unitary construct, but recent experiments provide evidence for split attention foci. We simulate two such experiments. Our results suggest that the ability to divide attention depends on sustained endogenous signals from short term memory to the saliency map, stressing the interplay between working memory mechanisms and attention. Our results also point to a mechanism whereby inhibitory endogenous signals may play a role in dividing attention, suggesting a possible mechanism for inhibition of return.</description><identifier>ISSN: 2161-4393</identifier><identifier>ISBN: 0780390482</identifier><identifier>ISBN: 9780780390485</identifier><identifier>EISSN: 2161-4407</identifier><identifier>DOI: 10.1109/IJCNN.2005.1556385</identifier><language>eng</language><publisher>IEEE</publisher><subject>Biological system modeling ; Biology ; Brain modeling ; Computational modeling ; Computer science ; Computer simulation ; Electronic mail ; Neural networks ; Predictive models ; Psychology</subject><ispartof>Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005, 2005, Vol.5, p.2897-2902 vol. 5</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1556385$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,4036,4037,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1556385$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Standage, D.I.</creatorcontrib><creatorcontrib>Trappenberg, T.P.</creatorcontrib><creatorcontrib>Klein, R.M.</creatorcontrib><title>A continuous attractor neural network model of divided visual attention</title><title>Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005</title><addtitle>IJCNN</addtitle><description>The biologically realistic model of selective visual attention by Deco et al. uses a continuous attractor neural network to simulate a saliency map in posterior parietal cortex. We test the ability of the model to explain experimental evidence on the distribution of spatial attention. The majority of evidence supports the view that attention is a unitary construct, but recent experiments provide evidence for split attention foci. We simulate two such experiments. Our results suggest that the ability to divide attention depends on sustained endogenous signals from short term memory to the saliency map, stressing the interplay between working memory mechanisms and attention. Our results also point to a mechanism whereby inhibitory endogenous signals may play a role in dividing attention, suggesting a possible mechanism for inhibition of return.</description><subject>Biological system modeling</subject><subject>Biology</subject><subject>Brain modeling</subject><subject>Computational modeling</subject><subject>Computer science</subject><subject>Computer simulation</subject><subject>Electronic mail</subject><subject>Neural networks</subject><subject>Predictive models</subject><subject>Psychology</subject><issn>2161-4393</issn><issn>2161-4407</issn><isbn>0780390482</isbn><isbn>9780780390485</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1kMtOwzAQRS0eEm3hB2DjH0iYycSvZRVBKarKBtZVajuSIY1R4hTx90RqWZ3F0bmLy9g9Qo4I5nH9Wm23eQEgchRCkhYXbFagxKwsQV2yOSgNZKDUxdW_IEM3bD4MnwAFGUMztlpyG7sUujGOA69T6mubYs87P_Z1OyH9xP6LH6LzLY8Nd-EYnHf8GIZx8lPgpzp2t-y6qdvB3525YB_PT-_VS7Z5W62r5SYLqETK0GutLVKDpVLWCallofTeNlYDOpgUOHClbvYEAkiiUVIqIm2NRw-CFuzhtBu897vvPhzq_nd3PoD-ACpaTSg</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Standage, D.I.</creator><creator>Trappenberg, T.P.</creator><creator>Klein, R.M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2005</creationdate><title>A continuous attractor neural network model of divided visual attention</title><author>Standage, D.I. ; Trappenberg, T.P. ; Klein, R.M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-1e888c13f1477cd5686278bcfc801d08c10d0d48fb305036197667338c9e1e053</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Biological system modeling</topic><topic>Biology</topic><topic>Brain modeling</topic><topic>Computational modeling</topic><topic>Computer science</topic><topic>Computer simulation</topic><topic>Electronic mail</topic><topic>Neural networks</topic><topic>Predictive models</topic><topic>Psychology</topic><toplevel>online_resources</toplevel><creatorcontrib>Standage, D.I.</creatorcontrib><creatorcontrib>Trappenberg, T.P.</creatorcontrib><creatorcontrib>Klein, R.M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Standage, D.I.</au><au>Trappenberg, T.P.</au><au>Klein, R.M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A continuous attractor neural network model of divided visual attention</atitle><btitle>Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005</btitle><stitle>IJCNN</stitle><date>2005</date><risdate>2005</risdate><volume>5</volume><spage>2897</spage><epage>2902 vol. 5</epage><pages>2897-2902 vol. 5</pages><issn>2161-4393</issn><eissn>2161-4407</eissn><isbn>0780390482</isbn><isbn>9780780390485</isbn><abstract>The biologically realistic model of selective visual attention by Deco et al. uses a continuous attractor neural network to simulate a saliency map in posterior parietal cortex. We test the ability of the model to explain experimental evidence on the distribution of spatial attention. The majority of evidence supports the view that attention is a unitary construct, but recent experiments provide evidence for split attention foci. We simulate two such experiments. Our results suggest that the ability to divide attention depends on sustained endogenous signals from short term memory to the saliency map, stressing the interplay between working memory mechanisms and attention. Our results also point to a mechanism whereby inhibitory endogenous signals may play a role in dividing attention, suggesting a possible mechanism for inhibition of return.</abstract><pub>IEEE</pub><doi>10.1109/IJCNN.2005.1556385</doi></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 2161-4393 |
| ispartof | Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005, 2005, Vol.5, p.2897-2902 vol. 5 |
| issn | 2161-4393 2161-4407 |
| language | eng |
| recordid | cdi_ieee_primary_1556385 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Biological system modeling Biology Brain modeling Computational modeling Computer science Computer simulation Electronic mail Neural networks Predictive models Psychology |
| title | A continuous attractor neural network model of divided visual attention |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-20T23%3A30%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=A%20continuous%20attractor%20neural%20network%20model%20of%20divided%20visual%20attention&rft.btitle=Proceedings.%202005%20IEEE%20International%20Joint%20Conference%20on%20Neural%20Networks,%202005&rft.au=Standage,%20D.I.&rft.date=2005&rft.volume=5&rft.spage=2897&rft.epage=2902%20vol.%205&rft.pages=2897-2902%20vol.%205&rft.issn=2161-4393&rft.eissn=2161-4407&rft.isbn=0780390482&rft.isbn_list=9780780390485&rft_id=info:doi/10.1109/IJCNN.2005.1556385&rft_dat=%3Cieee_6IE%3E1556385%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=1556385&rfr_iscdi=true |