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Distinct neural networks for detecting violations of adjacent versus nonadjacent sequential dependencies: An fMRI study
[Display omitted] The ability to learn and process sequential dependencies is essential for language acquisition and other cognitive domains. Recent studies suggest that the learning of adjacent (e.g., “A-B”) versus nonadjacent (e.g., “A-X-B”) dependencies have different cognitive demands, but the n...
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Published in: | Neurobiology of learning and memory 2020-03, Vol.169, p.107175-107175, Article 107175 |
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container_title | Neurobiology of learning and memory |
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The ability to learn and process sequential dependencies is essential for language acquisition and other cognitive domains. Recent studies suggest that the learning of adjacent (e.g., “A-B”) versus nonadjacent (e.g., “A-X-B”) dependencies have different cognitive demands, but the neural correlates accompanying such processing are currently underspecified. We developed a sequential learning task in which sequences of printed nonsense syllables containing both adjacent and nonadjacent dependencies were presented. After incidentally learning these grammatical sequences, twenty-one healthy adults (age M = 22.1, 12 females) made familiarity judgments about novel grammatical sequences and ungrammatical sequences containing violations of the adjacent or nonadjacent structure while in a 3T MRI scanner. Violations of adjacent dependencies were associated with increased BOLD activation in both posterior (lateral occipital and angular gyrus) as well as frontal regions (e.g., medial frontal gyrus, inferior frontal gyrus). Initial results indicated no regions showing significant BOLD activations for the violations of nonadjacent dependencies. However, when using a less stringent cluster threshold, exploratory analyses revealed that violations of nonadjacent dependencies were associated with increased activation in subcallosal cortex, paracingulate cortex, and anterior cingulate cortex (ACC). Finally, when directly comparing the adjacent condition to the nonadjacent condition, we found significantly greater levels of activation for the right superior lateral occipital cortex (BA 19) for the adjacent relative to nonadjacent condition. In sum, the detection of violations of adjacent and nonadjacent dependencies appear to involve distinct neural networks, with perceptual brain regions mediating the processing of adjacent but not nonadjacent dependencies. These results are consistent with recent proposals that statistical-sequential learning is not a unified construct but depends on the interaction of multiple neurocognitive mechanisms acting together. |
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The ability to learn and process sequential dependencies is essential for language acquisition and other cognitive domains. Recent studies suggest that the learning of adjacent (e.g., “A-B”) versus nonadjacent (e.g., “A-X-B”) dependencies have different cognitive demands, but the neural correlates accompanying such processing are currently underspecified. We developed a sequential learning task in which sequences of printed nonsense syllables containing both adjacent and nonadjacent dependencies were presented. After incidentally learning these grammatical sequences, twenty-one healthy adults (age M = 22.1, 12 females) made familiarity judgments about novel grammatical sequences and ungrammatical sequences containing violations of the adjacent or nonadjacent structure while in a 3T MRI scanner. Violations of adjacent dependencies were associated with increased BOLD activation in both posterior (lateral occipital and angular gyrus) as well as frontal regions (e.g., medial frontal gyrus, inferior frontal gyrus). Initial results indicated no regions showing significant BOLD activations for the violations of nonadjacent dependencies. However, when using a less stringent cluster threshold, exploratory analyses revealed that violations of nonadjacent dependencies were associated with increased activation in subcallosal cortex, paracingulate cortex, and anterior cingulate cortex (ACC). Finally, when directly comparing the adjacent condition to the nonadjacent condition, we found significantly greater levels of activation for the right superior lateral occipital cortex (BA 19) for the adjacent relative to nonadjacent condition. In sum, the detection of violations of adjacent and nonadjacent dependencies appear to involve distinct neural networks, with perceptual brain regions mediating the processing of adjacent but not nonadjacent dependencies. These results are consistent with recent proposals that statistical-sequential learning is not a unified construct but depends on the interaction of multiple neurocognitive mechanisms acting together.</description><identifier>ISSN: 1074-7427</identifier><identifier>EISSN: 1095-9564</identifier><identifier>DOI: 10.1016/j.nlm.2020.107175</identifier><identifier>PMID: 32018026</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Adult ; Angular gyrus ; Anterior cingulate cortex ; Artificial grammar learning ; Brain - physiology ; Brain Mapping ; Female ; Humans ; Learning - physiology ; Magnetic Resonance Imaging ; Male ; Neural Pathways - physiology ; Nonadjacent dependencies ; Pattern Recognition, Visual - physiology ; Recognition, Psychology - physiology ; Sequential processing ; Statistical learning ; Young Adult</subject><ispartof>Neurobiology of learning and memory, 2020-03, Vol.169, p.107175-107175, Article 107175</ispartof><rights>2020 Elsevier Inc.</rights><rights>Copyright © 2020 Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c451t-6b4699fc1f7be230576e21f2c5c83ee5fa6e6094605304646f77e27af87d50c83</citedby><cites>FETCH-LOGICAL-c451t-6b4699fc1f7be230576e21f2c5c83ee5fa6e6094605304646f77e27af87d50c83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32018026$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Conway, Christopher M.</creatorcontrib><creatorcontrib>Eghbalzad, Leyla</creatorcontrib><creatorcontrib>Deocampo, Joanne A.</creatorcontrib><creatorcontrib>Smith, Gretchen N.L.</creatorcontrib><creatorcontrib>Na, Sabrina</creatorcontrib><creatorcontrib>King, Tricia Z.</creatorcontrib><title>Distinct neural networks for detecting violations of adjacent versus nonadjacent sequential dependencies: An fMRI study</title><title>Neurobiology of learning and memory</title><addtitle>Neurobiol Learn Mem</addtitle><description>[Display omitted]
The ability to learn and process sequential dependencies is essential for language acquisition and other cognitive domains. Recent studies suggest that the learning of adjacent (e.g., “A-B”) versus nonadjacent (e.g., “A-X-B”) dependencies have different cognitive demands, but the neural correlates accompanying such processing are currently underspecified. We developed a sequential learning task in which sequences of printed nonsense syllables containing both adjacent and nonadjacent dependencies were presented. After incidentally learning these grammatical sequences, twenty-one healthy adults (age M = 22.1, 12 females) made familiarity judgments about novel grammatical sequences and ungrammatical sequences containing violations of the adjacent or nonadjacent structure while in a 3T MRI scanner. Violations of adjacent dependencies were associated with increased BOLD activation in both posterior (lateral occipital and angular gyrus) as well as frontal regions (e.g., medial frontal gyrus, inferior frontal gyrus). Initial results indicated no regions showing significant BOLD activations for the violations of nonadjacent dependencies. However, when using a less stringent cluster threshold, exploratory analyses revealed that violations of nonadjacent dependencies were associated with increased activation in subcallosal cortex, paracingulate cortex, and anterior cingulate cortex (ACC). Finally, when directly comparing the adjacent condition to the nonadjacent condition, we found significantly greater levels of activation for the right superior lateral occipital cortex (BA 19) for the adjacent relative to nonadjacent condition. In sum, the detection of violations of adjacent and nonadjacent dependencies appear to involve distinct neural networks, with perceptual brain regions mediating the processing of adjacent but not nonadjacent dependencies. These results are consistent with recent proposals that statistical-sequential learning is not a unified construct but depends on the interaction of multiple neurocognitive mechanisms acting together.</description><subject>Adult</subject><subject>Angular gyrus</subject><subject>Anterior cingulate cortex</subject><subject>Artificial grammar learning</subject><subject>Brain - physiology</subject><subject>Brain Mapping</subject><subject>Female</subject><subject>Humans</subject><subject>Learning - physiology</subject><subject>Magnetic Resonance Imaging</subject><subject>Male</subject><subject>Neural Pathways - physiology</subject><subject>Nonadjacent dependencies</subject><subject>Pattern Recognition, Visual - physiology</subject><subject>Recognition, Psychology - physiology</subject><subject>Sequential processing</subject><subject>Statistical learning</subject><subject>Young Adult</subject><issn>1074-7427</issn><issn>1095-9564</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kU2PFCEQhonRuOvqD_BiOHrpWaD56NbEZLN-7CZrTIyeCQPFytgDI9Cz2X8vnVknevFUQL31UlUPQi8pWVFC5flmFaftihG23BVV4hE6pWQU3Sgkf7ycFe8UZ-oEPStlQwilYhyeopOeEToQJk_R3ftQaoi24ghzNlML9S7lnwX7lLGDCralb_E-pMnUkGLByWPjNsZCrHgPucwFxxSPTwV-zS2G5uVgB9FBtAHKG3wRsf_89RqXOrv75-iJN1OBFw_xDH3_-OHb5VV38-XT9eXFTWe5oLWTay7H0Vvq1RpYT4SSwKhnVtihBxDeSJBk5JKInnDJpVcKmDJ-UE6QpjlD7w6-u3m9Bbd02KbUuxy2Jt_rZIL-NxPDD32b9loRyZlYDF4_GOTUBitVb0OxME0mQpqLZr2ggrBBLVJ6kNqcSsngj99QohdgeqMbML0A0wdgrebV3_0dK_4QaoK3BwG0Le0DZF3aOqMFF3Jjo10K_7H_DX0lqUw</recordid><startdate>20200301</startdate><enddate>20200301</enddate><creator>Conway, Christopher M.</creator><creator>Eghbalzad, Leyla</creator><creator>Deocampo, Joanne A.</creator><creator>Smith, Gretchen N.L.</creator><creator>Na, Sabrina</creator><creator>King, Tricia Z.</creator><general>Elsevier Inc</general><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>5PM</scope></search><sort><creationdate>20200301</creationdate><title>Distinct neural networks for detecting violations of adjacent versus nonadjacent sequential dependencies: An fMRI study</title><author>Conway, Christopher M. ; Eghbalzad, Leyla ; Deocampo, Joanne A. ; Smith, Gretchen N.L. ; Na, Sabrina ; King, Tricia Z.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c451t-6b4699fc1f7be230576e21f2c5c83ee5fa6e6094605304646f77e27af87d50c83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adult</topic><topic>Angular gyrus</topic><topic>Anterior cingulate cortex</topic><topic>Artificial grammar learning</topic><topic>Brain - physiology</topic><topic>Brain Mapping</topic><topic>Female</topic><topic>Humans</topic><topic>Learning - physiology</topic><topic>Magnetic Resonance Imaging</topic><topic>Male</topic><topic>Neural Pathways - physiology</topic><topic>Nonadjacent dependencies</topic><topic>Pattern Recognition, Visual - physiology</topic><topic>Recognition, Psychology - physiology</topic><topic>Sequential processing</topic><topic>Statistical learning</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Conway, Christopher M.</creatorcontrib><creatorcontrib>Eghbalzad, Leyla</creatorcontrib><creatorcontrib>Deocampo, Joanne A.</creatorcontrib><creatorcontrib>Smith, Gretchen N.L.</creatorcontrib><creatorcontrib>Na, Sabrina</creatorcontrib><creatorcontrib>King, Tricia Z.</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><collection>PubMed Central (Full Participant titles)</collection><jtitle>Neurobiology of learning and memory</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Conway, Christopher M.</au><au>Eghbalzad, Leyla</au><au>Deocampo, Joanne A.</au><au>Smith, Gretchen N.L.</au><au>Na, Sabrina</au><au>King, Tricia Z.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Distinct neural networks for detecting violations of adjacent versus nonadjacent sequential dependencies: An fMRI study</atitle><jtitle>Neurobiology of learning and memory</jtitle><addtitle>Neurobiol Learn Mem</addtitle><date>2020-03-01</date><risdate>2020</risdate><volume>169</volume><spage>107175</spage><epage>107175</epage><pages>107175-107175</pages><artnum>107175</artnum><issn>1074-7427</issn><eissn>1095-9564</eissn><abstract>[Display omitted]
The ability to learn and process sequential dependencies is essential for language acquisition and other cognitive domains. Recent studies suggest that the learning of adjacent (e.g., “A-B”) versus nonadjacent (e.g., “A-X-B”) dependencies have different cognitive demands, but the neural correlates accompanying such processing are currently underspecified. We developed a sequential learning task in which sequences of printed nonsense syllables containing both adjacent and nonadjacent dependencies were presented. After incidentally learning these grammatical sequences, twenty-one healthy adults (age M = 22.1, 12 females) made familiarity judgments about novel grammatical sequences and ungrammatical sequences containing violations of the adjacent or nonadjacent structure while in a 3T MRI scanner. Violations of adjacent dependencies were associated with increased BOLD activation in both posterior (lateral occipital and angular gyrus) as well as frontal regions (e.g., medial frontal gyrus, inferior frontal gyrus). Initial results indicated no regions showing significant BOLD activations for the violations of nonadjacent dependencies. However, when using a less stringent cluster threshold, exploratory analyses revealed that violations of nonadjacent dependencies were associated with increased activation in subcallosal cortex, paracingulate cortex, and anterior cingulate cortex (ACC). Finally, when directly comparing the adjacent condition to the nonadjacent condition, we found significantly greater levels of activation for the right superior lateral occipital cortex (BA 19) for the adjacent relative to nonadjacent condition. In sum, the detection of violations of adjacent and nonadjacent dependencies appear to involve distinct neural networks, with perceptual brain regions mediating the processing of adjacent but not nonadjacent dependencies. These results are consistent with recent proposals that statistical-sequential learning is not a unified construct but depends on the interaction of multiple neurocognitive mechanisms acting together.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>32018026</pmid><doi>10.1016/j.nlm.2020.107175</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Adult Angular gyrus Anterior cingulate cortex Artificial grammar learning Brain - physiology Brain Mapping Female Humans Learning - physiology Magnetic Resonance Imaging Male Neural Pathways - physiology Nonadjacent dependencies Pattern Recognition, Visual - physiology Recognition, Psychology - physiology Sequential processing Statistical learning Young Adult |
title | Distinct neural networks for detecting violations of adjacent versus nonadjacent sequential dependencies: An fMRI study |
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