Statistical learning of temporal community structure in the hippocampus
ABSTRACT The hippocampus is involved in the learning and representation of temporal statistics, but little is understood about the kinds of statistics it can uncover. Prior studies have tested various forms of structure that can be learned by tracking the strength of transition probabilities between...
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| Veröffentlicht in: | Hippocampus 2016-01, Vol.26 (1), p.3-8 |
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| creator | Schapiro, Anna C. Turk-Browne, Nicholas B. Norman, Kenneth A. Botvinick, Matthew M. |
| description | ABSTRACT
The hippocampus is involved in the learning and representation of temporal statistics, but little is understood about the kinds of statistics it can uncover. Prior studies have tested various forms of structure that can be learned by tracking the strength of transition probabilities between adjacent items in a sequence. We test whether the hippocampus can learn higher‐order structure using sequences that have no variance in transition probability and instead exhibit temporal community structure. We find that the hippocampus is indeed sensitive to this form of structure, as revealed by its representations, activity dynamics, and connectivity with other regions. These findings suggest that the hippocampus is a sophisticated learner of environmental regularities, able to uncover higher‐order structure that requires sensitivity to overlapping associations. © 2015 Wiley Periodicals, Inc. |
| doi_str_mv | 10.1002/hipo.22523 |
| format | Article |
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The hippocampus is involved in the learning and representation of temporal statistics, but little is understood about the kinds of statistics it can uncover. Prior studies have tested various forms of structure that can be learned by tracking the strength of transition probabilities between adjacent items in a sequence. We test whether the hippocampus can learn higher‐order structure using sequences that have no variance in transition probability and instead exhibit temporal community structure. We find that the hippocampus is indeed sensitive to this form of structure, as revealed by its representations, activity dynamics, and connectivity with other regions. These findings suggest that the hippocampus is a sophisticated learner of environmental regularities, able to uncover higher‐order structure that requires sensitivity to overlapping associations. © 2015 Wiley Periodicals, Inc.</description><identifier>ISSN: 1050-9631</identifier><identifier>EISSN: 1098-1063</identifier><identifier>DOI: 10.1002/hipo.22523</identifier><identifier>PMID: 26332666</identifier><identifier>CODEN: HIPPEL</identifier><language>eng</language><publisher>United States: Blackwell Publishing Ltd</publisher><subject>background connectivity ; Brain Mapping ; Cerebrovascular Circulation - physiology ; event representation ; fMRI ; Functional Laterality ; Hippocampus - physiology ; Humans ; Magnetic Resonance Imaging ; Neural Pathways - physiology ; Neuropsychological Tests ; Oxygen - blood ; pattern analysis ; Prefrontal Cortex - physiology ; Probability Learning ; Time Perception - physiology ; transition probability</subject><ispartof>Hippocampus, 2016-01, Vol.26 (1), p.3-8</ispartof><rights>2015 Wiley Periodicals, Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c6203-73090c14e5d5913108ccfdce0cdd6dd4a4db611b13fb976e4fbeab7ed07932613</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fhipo.22523$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fhipo.22523$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,776,780,881,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26332666$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Schapiro, Anna C.</creatorcontrib><creatorcontrib>Turk-Browne, Nicholas B.</creatorcontrib><creatorcontrib>Norman, Kenneth A.</creatorcontrib><creatorcontrib>Botvinick, Matthew M.</creatorcontrib><title>Statistical learning of temporal community structure in the hippocampus</title><title>Hippocampus</title><addtitle>Hippocampus</addtitle><description>ABSTRACT
The hippocampus is involved in the learning and representation of temporal statistics, but little is understood about the kinds of statistics it can uncover. Prior studies have tested various forms of structure that can be learned by tracking the strength of transition probabilities between adjacent items in a sequence. We test whether the hippocampus can learn higher‐order structure using sequences that have no variance in transition probability and instead exhibit temporal community structure. We find that the hippocampus is indeed sensitive to this form of structure, as revealed by its representations, activity dynamics, and connectivity with other regions. These findings suggest that the hippocampus is a sophisticated learner of environmental regularities, able to uncover higher‐order structure that requires sensitivity to overlapping associations. © 2015 Wiley Periodicals, Inc.</description><subject>background connectivity</subject><subject>Brain Mapping</subject><subject>Cerebrovascular Circulation - physiology</subject><subject>event representation</subject><subject>fMRI</subject><subject>Functional Laterality</subject><subject>Hippocampus - physiology</subject><subject>Humans</subject><subject>Magnetic Resonance Imaging</subject><subject>Neural Pathways - physiology</subject><subject>Neuropsychological Tests</subject><subject>Oxygen - blood</subject><subject>pattern analysis</subject><subject>Prefrontal Cortex - physiology</subject><subject>Probability Learning</subject><subject>Time Perception - physiology</subject><subject>transition probability</subject><issn>1050-9631</issn><issn>1098-1063</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkc1u1DAUhS0EoqWw4QFQJDZsUq7_4w0SKjCtVFEqimBnOY7TcUniYDvAvD2eThkBK1a-8v3O8ZEPQk8xHGMA8nLt53BMCCf0HjrEoJoag6D3tzOHWgmKD9CjlG4AMOYAD9EBEZQSIcQhWn3MJvuUvTVDNTgTJz9dV6GvshvnEMulDeO4TD5vqpTjYvMSXeWnKq9dVR6egzXjvKTH6EFvhuSe3J1H6NO7t1cnp_X5xers5PV5bQUBWksKCixmjndcYYqhsbbvrAPbdaLrmGFdKzBuMe1bJYVjfetMK10HUpXEmB6hVzvfeWlHV5RTLiH1HP1o4kYH4_Xfm8mv9XX4rpnEnClaDF7cGcTwbXEp69En64bBTC4sSWMphWAEmPwPVEDTNJLygj7_B70JS5zKTxSKS6ZIo1Shnv0Zfp_6dx0FwDvghx_cZr_HoLdF623R-rZofXr24eJ2Kpp6pyktup97jYlftZBUcv35_UpfiqtL_oZ-0Yr-Aorgq3o</recordid><startdate>201601</startdate><enddate>201601</enddate><creator>Schapiro, Anna C.</creator><creator>Turk-Browne, Nicholas B.</creator><creator>Norman, Kenneth A.</creator><creator>Botvinick, Matthew M.</creator><general>Blackwell Publishing Ltd</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>7QG</scope><scope>7TK</scope><scope>K9.</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>201601</creationdate><title>Statistical learning of temporal community structure in the hippocampus</title><author>Schapiro, Anna C. ; Turk-Browne, Nicholas B. ; Norman, Kenneth A. ; Botvinick, Matthew M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c6203-73090c14e5d5913108ccfdce0cdd6dd4a4db611b13fb976e4fbeab7ed07932613</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>background connectivity</topic><topic>Brain Mapping</topic><topic>Cerebrovascular Circulation - physiology</topic><topic>event representation</topic><topic>fMRI</topic><topic>Functional Laterality</topic><topic>Hippocampus - physiology</topic><topic>Humans</topic><topic>Magnetic Resonance Imaging</topic><topic>Neural Pathways - physiology</topic><topic>Neuropsychological Tests</topic><topic>Oxygen - blood</topic><topic>pattern analysis</topic><topic>Prefrontal Cortex - physiology</topic><topic>Probability Learning</topic><topic>Time Perception - physiology</topic><topic>transition probability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Schapiro, Anna C.</creatorcontrib><creatorcontrib>Turk-Browne, Nicholas B.</creatorcontrib><creatorcontrib>Norman, Kenneth A.</creatorcontrib><creatorcontrib>Botvinick, Matthew M.</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>Animal Behavior Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Hippocampus</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Schapiro, Anna C.</au><au>Turk-Browne, Nicholas B.</au><au>Norman, Kenneth A.</au><au>Botvinick, Matthew M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Statistical learning of temporal community structure in the hippocampus</atitle><jtitle>Hippocampus</jtitle><addtitle>Hippocampus</addtitle><date>2016-01</date><risdate>2016</risdate><volume>26</volume><issue>1</issue><spage>3</spage><epage>8</epage><pages>3-8</pages><issn>1050-9631</issn><eissn>1098-1063</eissn><coden>HIPPEL</coden><abstract>ABSTRACT
The hippocampus is involved in the learning and representation of temporal statistics, but little is understood about the kinds of statistics it can uncover. Prior studies have tested various forms of structure that can be learned by tracking the strength of transition probabilities between adjacent items in a sequence. We test whether the hippocampus can learn higher‐order structure using sequences that have no variance in transition probability and instead exhibit temporal community structure. We find that the hippocampus is indeed sensitive to this form of structure, as revealed by its representations, activity dynamics, and connectivity with other regions. These findings suggest that the hippocampus is a sophisticated learner of environmental regularities, able to uncover higher‐order structure that requires sensitivity to overlapping associations. © 2015 Wiley Periodicals, Inc.</abstract><cop>United States</cop><pub>Blackwell Publishing Ltd</pub><pmid>26332666</pmid><doi>10.1002/hipo.22523</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | Hippocampus, 2016-01, Vol.26 (1), p.3-8 |
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| source | MEDLINE; Wiley Online Library Journals Frontfile Complete |
| subjects | background connectivity Brain Mapping Cerebrovascular Circulation - physiology event representation fMRI Functional Laterality Hippocampus - physiology Humans Magnetic Resonance Imaging Neural Pathways - physiology Neuropsychological Tests Oxygen - blood pattern analysis Prefrontal Cortex - physiology Probability Learning Time Perception - physiology transition probability |
| title | Statistical learning of temporal community structure in the hippocampus |
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