Learning the Exception to the Rule: Model-Based fMRI Reveals Specialized Representations for Surprising Category Members. Davis, T., Love, B. C., & Preston, A. R. Cerebral Cortex, 22(2):260--273, February, 2012. Paper doi abstract bibtex Category knowledge can be explicit, yet not conform to a perfect rule. For example, a child may acquire the rule “If it has wings, then it is a bird,” but then must account for exceptions to this rule, such as bats. The current study explored the neurobiological basis of rule-plus-exception learning by using quantitative predictions from a category learning model, SUSTAIN, to analyze behavioral and functional magnetic resonance imaging (fMRI) data. SUSTAIN predicts that exceptions require formation of specialized representations to distinguish exceptions from rule-following items in memory. By incorporating quantitative trial-by-trial predictions from SUSTAIN directly into fMRI analyses, we observed medial temporal lobe (MTL) activation consistent with 2 predicted psychological processes that enable exception learning: item recognition and error correction. SUSTAIN explains how these processes vary in the MTL across learning trials as category knowledge is acquired. Importantly, MTL engagement during exception learning was not captured by an alternate exemplar-based model of category learning or by standard contrasts comparing exception and rule-following items. The current findings thus provide a well-specified theory for the role of the MTL in category learning, where the MTL plays an important role in forming specialized category representations appropriate for the learning context.
@article{ davis_learning_2012,
title = {Learning the Exception to the Rule: Model-Based {fMRI} Reveals Specialized Representations for Surprising Category Members},
volume = {22},
issn = {1047-3211, 1460-2199},
shorttitle = {Learning the Exception to the Rule},
url = {http://cercor.oxfordjournals.org/content/22/2/260},
doi = {10.1093/cercor/bhr036},
abstract = {Category knowledge can be explicit, yet not conform to a perfect rule. For example, a child may acquire the rule {“If} it has wings, then it is a bird,” but then must account for exceptions to this rule, such as bats. The current study explored the neurobiological basis of rule-plus-exception learning by using quantitative predictions from a category learning model, {SUSTAIN}, to analyze behavioral and functional magnetic resonance imaging ({fMRI)} data. {SUSTAIN} predicts that exceptions require formation of specialized representations to distinguish exceptions from rule-following items in memory. By incorporating quantitative trial-by-trial predictions from {SUSTAIN} directly into {fMRI} analyses, we observed medial temporal lobe ({MTL)} activation consistent with 2 predicted psychological processes that enable exception learning: item recognition and error correction. {SUSTAIN} explains how these processes vary in the {MTL} across learning trials as category knowledge is acquired. Importantly, {MTL} engagement during exception learning was not captured by an alternate exemplar-based model of category learning or by standard contrasts comparing exception and rule-following items. The current findings thus provide a well-specified theory for the role of the {MTL} in category learning, where the {MTL} plays an important role in forming specialized category representations appropriate for the learning context.},
language = {en},
number = {2},
urldate = {2013-01-18},
journal = {Cerebral Cortex},
author = {Davis, Tyler and Love, Bradley C. and Preston, Alison R.},
month = {February},
year = {2012},
keywords = {{SUSTAIN}, category learning, category representation, exception learning, hippocampus, medial temporal lobe},
pages = {260--273}
}
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R.</span>\n\t<!-- <span class=\"bibbase_paper_year\">2012</span>. -->\n</span>\n\n\n\n<i>Cerebral Cortex</i>,\n\n22(2):260--273.\n\nFebruary 2012.\n\n\n\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content\">\n \n \n <!-- <i -->\n <!-- onclick=\"javascript:log_download('davis-love-preston-learningtheexceptiontotherulemodelbasedfmrirevealsspecializedrepresentationsforsurprisingcategorymembers-2012', 'http://cercor.oxfordjournals.org/content/22/2/260')\">DEBUG -->\n <!-- </i> -->\n\n <a href=\"http://cercor.oxfordjournals.org/content/22/2/260\"\n onclick=\"javascript:log_download('davis-love-preston-learningtheexceptiontotherulemodelbasedfmrirevealsspecializedrepresentationsforsurprisingcategorymembers-2012', 'http://cercor.oxfordjournals.org/content/22/2/260')\">\n <img src=\"http://bibbase.org/img/filetypes/blank.png\"\n\t alt=\"Learning the Exception to the Rule: Model-Based fMRI Reveals Specialized Representations for Surprising Category Members [.org/content/22/2/260]\" \n\t class=\"bibbase_icon\"\n\t style=\"width: 24px; height: 24px; border: 0px; vertical-align: text-top\" ><span class=\"bibbase_icon_text\">Paper</span></a> \n \n \n \n <a href=\"javascript:showBib('davis_learning_2012')\"\n class=\"bibbase link\">\n <!-- <img src=\"http://bibbase.org/img/filetypes/bib.png\" -->\n\t<!-- alt=\"Learning the Exception to the Rule: Model-Based fMRI Reveals Specialized Representations for Surprising Category Members [bib]\" -->\n\t<!-- class=\"bibbase_icon\" -->\n\t<!-- style=\"width: 24px; height: 24px; border: 0px; vertical-align: text-top\"><span class=\"bibbase_icon_text\">Bibtex</span> -->\n BibTeX\n <i class=\"fa fa-caret-down\"></i></a>\n \n \n \n <a class=\"bibbase_abstract_link bibbase link\"\n href=\"javascript:showAbstract('davis_learning_2012')\">\n Abstract\n <i class=\"fa fa-caret-down\"></i></a>\n \n \n \n\n \n \n \n</span>\n\n<div class=\"well well-small bibbase\" id=\"bib_davis_learning_2012\"\n style=\"display:none\">\n <pre>@article{ davis_learning_2012,\n title = {Learning the Exception to the Rule: Model-Based {fMRI} Reveals Specialized Representations for Surprising Category Members},\n volume = {22},\n issn = {1047-3211, 1460-2199},\n shorttitle = {Learning the Exception to the Rule},\n url = {http://cercor.oxfordjournals.org/content/22/2/260},\n doi = {10.1093/cercor/bhr036},\n abstract = {Category knowledge can be explicit, yet not conform to a perfect rule. For example, a child may acquire the rule {“If} it has wings, then it is a bird,” but then must account for exceptions to this rule, such as bats. The current study explored the neurobiological basis of rule-plus-exception learning by using quantitative predictions from a category learning model, {SUSTAIN}, to analyze behavioral and functional magnetic resonance imaging ({fMRI)} data. {SUSTAIN} predicts that exceptions require formation of specialized representations to distinguish exceptions from rule-following items in memory. By incorporating quantitative trial-by-trial predictions from {SUSTAIN} directly into {fMRI} analyses, we observed medial temporal lobe ({MTL)} activation consistent with 2 predicted psychological processes that enable exception learning: item recognition and error correction. {SUSTAIN} explains how these processes vary in the {MTL} across learning trials as category knowledge is acquired. Importantly, {MTL} engagement during exception learning was not captured by an alternate exemplar-based model of category learning or by standard contrasts comparing exception and rule-following items. The current findings thus provide a well-specified theory for the role of the {MTL} in category learning, where the {MTL} plays an important role in forming specialized category representations appropriate for the learning context.},\n language = {en},\n number = {2},\n urldate = {2013-01-18},\n journal = {Cerebral Cortex},\n author = {Davis, Tyler and Love, Bradley C. and Preston, Alison R.},\n month = {February},\n year = {2012},\n keywords = {{SUSTAIN}, category learning, category representation, exception learning, hippocampus, medial temporal lobe},\n pages = {260--273}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" id=\"abstract_davis_learning_2012\"\n style=\"display:none\">\n Category knowledge can be explicit, yet not conform to a perfect rule. For example, a child may acquire the rule “If it has wings, then it is a bird,” but then must account for exceptions to this rule, such as bats. The current study explored the neurobiological basis of rule-plus-exception learning by using quantitative predictions from a category learning model, SUSTAIN, to analyze behavioral and functional magnetic resonance imaging (fMRI) data. SUSTAIN predicts that exceptions require formation of specialized representations to distinguish exceptions from rule-following items in memory. By incorporating quantitative trial-by-trial predictions from SUSTAIN directly into fMRI analyses, we observed medial temporal lobe (MTL) activation consistent with 2 predicted psychological processes that enable exception learning: item recognition and error correction. SUSTAIN explains how these processes vary in the MTL across learning trials as category knowledge is acquired. Importantly, MTL engagement during exception learning was not captured by an alternate exemplar-based model of category learning or by standard contrasts comparing exception and rule-following items. The current findings thus provide a well-specified theory for the role of the MTL in category learning, where the MTL plays an important role in forming specialized category representations appropriate for the learning context.\n</div>\n\n\n</div>\n","downloads":0,"bibbaseid":"davis-love-preston-learningtheexceptiontotherulemodelbasedfmrirevealsspecializedrepresentationsforsurprisingcategorymembers-2012","urls":{"Paper":"http://cercor.oxfordjournals.org/content/22/2/260"},"role":"author","year":"2012","volume":"22","urldate":"2013-01-18","url":"http://cercor.oxfordjournals.org/content/22/2/260","type":"article","title":"Learning the Exception to the Rule: Model-Based fMRI Reveals Specialized Representations for Surprising Category Members","shorttitle":"Learning the Exception to the Rule","pages":"260--273","number":"2","month":"February","language":"en","keywords":"SUSTAIN, category learning, category representation, exception learning, hippocampus, medial temporal lobe","key":"davis_learning_2012","journal":"Cerebral Cortex","issn":"1047-3211, 1460-2199","id":"davis_learning_2012","doi":"10.1093/cercor/bhr036","bibtype":"article","bibtex":"@article{ davis_learning_2012,\n title = {Learning the Exception to the Rule: Model-Based {fMRI} Reveals Specialized Representations for Surprising Category Members},\n volume = {22},\n issn = {1047-3211, 1460-2199},\n shorttitle = {Learning the Exception to the Rule},\n url = {http://cercor.oxfordjournals.org/content/22/2/260},\n doi = {10.1093/cercor/bhr036},\n abstract = {Category knowledge can be explicit, yet not conform to a perfect rule. For example, a child may acquire the rule {“If} it has wings, then it is a bird,” but then must account for exceptions to this rule, such as bats. The current study explored the neurobiological basis of rule-plus-exception learning by using quantitative predictions from a category learning model, {SUSTAIN}, to analyze behavioral and functional magnetic resonance imaging ({fMRI)} data. {SUSTAIN} predicts that exceptions require formation of specialized representations to distinguish exceptions from rule-following items in memory. By incorporating quantitative trial-by-trial predictions from {SUSTAIN} directly into {fMRI} analyses, we observed medial temporal lobe ({MTL)} activation consistent with 2 predicted psychological processes that enable exception learning: item recognition and error correction. {SUSTAIN} explains how these processes vary in the {MTL} across learning trials as category knowledge is acquired. Importantly, {MTL} engagement during exception learning was not captured by an alternate exemplar-based model of category learning or by standard contrasts comparing exception and rule-following items. The current findings thus provide a well-specified theory for the role of the {MTL} in category learning, where the {MTL} plays an important role in forming specialized category representations appropriate for the learning context.},\n language = {en},\n number = {2},\n urldate = {2013-01-18},\n journal = {Cerebral Cortex},\n author = {Davis, Tyler and Love, Bradley C. and Preston, Alison R.},\n month = {February},\n year = {2012},\n keywords = {{SUSTAIN}, category learning, category representation, exception learning, hippocampus, medial temporal lobe},\n pages = {260--273}\n}","author_short":["Davis, T.","Love, B.<nbsp>C.","Preston, A.<nbsp>R."],"author":["Davis, Tyler","Love, Bradley C.","Preston, Alison R."],"abstract":"Category knowledge can be explicit, yet not conform to a perfect rule. For example, a child may acquire the rule “If it has wings, then it is a bird,” but then must account for exceptions to this rule, such as bats. The current study explored the neurobiological basis of rule-plus-exception learning by using quantitative predictions from a category learning model, SUSTAIN, to analyze behavioral and functional magnetic resonance imaging (fMRI) data. SUSTAIN predicts that exceptions require formation of specialized representations to distinguish exceptions from rule-following items in memory. By incorporating quantitative trial-by-trial predictions from SUSTAIN directly into fMRI analyses, we observed medial temporal lobe (MTL) activation consistent with 2 predicted psychological processes that enable exception learning: item recognition and error correction. SUSTAIN explains how these processes vary in the MTL across learning trials as category knowledge is acquired. Importantly, MTL engagement during exception learning was not captured by an alternate exemplar-based model of category learning or by standard contrasts comparing exception and rule-following items. The current findings thus provide a well-specified theory for the role of the MTL in category learning, where the MTL plays an important role in forming specialized category representations appropriate for the learning context."},"bibtype":"article","biburl":"http://bibbase.org/zotero/nbusch","downloads":0,"search_terms":["learning","exception","rule","model","based","fmri","reveals","specialized","representations","surprising","category","members","davis","love","preston"],"title":"Learning the Exception to the Rule: Model-Based fMRI Reveals Specialized Representations for Surprising Category Members","year":2012,"dataSources":["9Wz8i3YBFkeJte2aR"]}