Learning exceptions to the rule in human and model via hippocampal encoding

Abstract Category learning helps us process the influx of information we experience daily. A common category structure is “rule-plus-exceptions,” in which most items follow a general rule, but exceptions violate this rule. People are worse at learning to categorize exceptions than rule-following ite...

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Autores principales:	Emily M. Heffernan, Margaret L. Schlichting, Michael L. Mack
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3201a78b7f604cfdb6513aa68d8856d4
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id	oai:doaj.org-article:3201a78b7f604cfdb6513aa68d8856d4
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spelling	oai:doaj.org-article:3201a78b7f604cfdb6513aa68d8856d42021-11-08T10:54:28ZLearning exceptions to the rule in human and model via hippocampal encoding10.1038/s41598-021-00864-92045-2322https://doaj.org/article/3201a78b7f604cfdb6513aa68d8856d42021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-00864-9https://doaj.org/toc/2045-2322Abstract Category learning helps us process the influx of information we experience daily. A common category structure is “rule-plus-exceptions,” in which most items follow a general rule, but exceptions violate this rule. People are worse at learning to categorize exceptions than rule-following items, but improved exception categorization has been positively associated with hippocampal function. In light of model-based predictions that the nature of existing memories of related experiences impacts memory formation, here we use behavioural and computational modelling data to explore how learning sequence impacts performance in rule-plus-exception categorization. Our behavioural results indicate that exception categorization accuracy improves when exceptions are introduced later in learning, after exposure to rule-followers. To explore whether hippocampal learning systems also benefit from this manipulation, we simulate our task using a computational model of hippocampus. The model successful replicates our behavioural findings related to exception learning, and representational similarity analysis of the model’s hidden layers suggests that model representations are impacted by trial sequence: delaying the introduction of an exception shifts its representation closer to its own category members. Our results provide novel computational evidence of how hippocampal learning systems can be targeted by learning sequence and bolster extant evidence of hippocampus’s role in category learning.Emily M. HeffernanMargaret L. SchlichtingMichael L. MackNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Emily M. Heffernan Margaret L. Schlichting Michael L. Mack Learning exceptions to the rule in human and model via hippocampal encoding
description	Abstract Category learning helps us process the influx of information we experience daily. A common category structure is “rule-plus-exceptions,” in which most items follow a general rule, but exceptions violate this rule. People are worse at learning to categorize exceptions than rule-following items, but improved exception categorization has been positively associated with hippocampal function. In light of model-based predictions that the nature of existing memories of related experiences impacts memory formation, here we use behavioural and computational modelling data to explore how learning sequence impacts performance in rule-plus-exception categorization. Our behavioural results indicate that exception categorization accuracy improves when exceptions are introduced later in learning, after exposure to rule-followers. To explore whether hippocampal learning systems also benefit from this manipulation, we simulate our task using a computational model of hippocampus. The model successful replicates our behavioural findings related to exception learning, and representational similarity analysis of the model’s hidden layers suggests that model representations are impacted by trial sequence: delaying the introduction of an exception shifts its representation closer to its own category members. Our results provide novel computational evidence of how hippocampal learning systems can be targeted by learning sequence and bolster extant evidence of hippocampus’s role in category learning.
format	article
author	Emily M. Heffernan Margaret L. Schlichting Michael L. Mack
author_facet	Emily M. Heffernan Margaret L. Schlichting Michael L. Mack
author_sort	Emily M. Heffernan
title	Learning exceptions to the rule in human and model via hippocampal encoding
title_short	Learning exceptions to the rule in human and model via hippocampal encoding
title_full	Learning exceptions to the rule in human and model via hippocampal encoding
title_fullStr	Learning exceptions to the rule in human and model via hippocampal encoding
title_full_unstemmed	Learning exceptions to the rule in human and model via hippocampal encoding
title_sort	learning exceptions to the rule in human and model via hippocampal encoding
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3201a78b7f604cfdb6513aa68d8856d4
work_keys_str_mv	AT emilymheffernan learningexceptionstotheruleinhumanandmodelviahippocampalencoding AT margaretlschlichting learningexceptionstotheruleinhumanandmodelviahippocampalencoding AT michaellmack learningexceptionstotheruleinhumanandmodelviahippocampalencoding
_version_	1718442509439336448

Learning exceptions to the rule in human and model via hippocampal encoding

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