{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T10:11:21Z","timestamp":1767175881913,"version":"build-2238731810"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1011445","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T00:00:00Z","timestamp":1697414400000}}],"reference-count":100,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T00:00:00Z","timestamp":1696377600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["NRT-1449828"],"award-info":[{"award-number":["NRT-1449828"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000088","name":"Directorate for Social, Behavioral and Economic Sciences","doi-asserted-by":"publisher","award":["BCS-1824737"],"award-info":[{"award-number":["BCS-1824737"]}],"id":[{"id":"10.13039\/100000088","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000083","name":"Directorate for Computer and Information Science and Engineering","doi-asserted-by":"publisher","award":["IIS- 1813709"],"award-info":[{"award-number":["IIS- 1813709"]}],"id":[{"id":"10.13039\/100000083","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>We propose the \u201cruntime learning\u201d hypothesis which states that people quickly learn to perform unfamiliar tasks as the tasks arise by using task-relevant instances of concepts stored in memory during mental training. To make learning rapid, the hypothesis claims that only a few class instances are used, but these instances are especially valuable for training. The paper motivates the hypothesis by describing related ideas from the cognitive science and machine learning literatures. Using computer simulation, we show that deep neural networks (DNNs) can learn effectively from small, curated training sets, and that valuable training items tend to lie toward the centers of data item clusters in an abstract feature space. In a series of three behavioral experiments, we show that people can also learn effectively from small, curated training sets. Critically, we find that participant reaction times and fitted drift rates are best accounted for by the confidences of DNNs trained on small datasets of highly valuable items. We conclude that the runtime learning hypothesis is a novel conjecture about the relationship between learning and memory with the potential for explaining a wide variety of cognitive phenomena.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1011445","type":"journal-article","created":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T13:46:10Z","timestamp":1696427170000},"page":"e1011445","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Rapid runtime learning by curating small datasets of high-quality items obtained from memory"],"prefix":"10.1371","volume":"19","author":[{"given":"Joseph Scott","family":"German","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7994-915X","authenticated-orcid":true,"given":"Guofeng","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2183-822X","authenticated-orcid":true,"given":"Chenliang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6607-908X","authenticated-orcid":true,"given":"Robert A.","family":"Jacobs","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2023,10,4]]},"reference":[{"issue":"3","key":"pcbi.1011445.ref001","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1037\/0033-295X.85.3.207","article-title":"Context theory of classification learning","volume":"85","author":"DL Medin","year":"1978","journal-title":"Psychological Review"},{"issue":"4","key":"pcbi.1011445.ref002","doi-asserted-by":"crossref","first-page":"378","DOI":"10.3758\/BF03198278","article-title":"Differential forgetting of prototypes and old instances: Simulation by an exemplar-based classification model","volume":"8","author":"DL Hintzman","year":"1980","journal-title":"Memory & Cognition"},{"issue":"1","key":"pcbi.1011445.ref003","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1037\/0096-3445.115.1.39","article-title":"Attention, similarity, and the identification-categorization relationship","volume":"115","author":"RM Nosofsky","year":"1986","journal-title":"Journal of Experimental Psychology: General"},{"issue":"6","key":"pcbi.1011445.ref004","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1037\/0033-295X.82.6.407","article-title":"A spreading-activation theory of semantic processing","volume":"82","author":"AM Collins","year":"1975","journal-title":"Psychological Review"},{"issue":"2","key":"pcbi.1011445.ref005","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/S0022-5371(69)80069-1","article-title":"Retrieval time from semantic memory","volume":"8","author":"AM Collins","year":"1969","journal-title":"Journal of Verbal Learning and Verbal Behavior"},{"key":"pcbi.1011445.ref006","unstructured":"Kipf TN, Welling M. 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