{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T10:02:30Z","timestamp":1764842550983},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:00:00Z","timestamp":1635465600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,29]]},"abstract":"<jats:p>Within their mental and social processes, humans often learn, adapt and apply specific mental models of processes in the world or other persons, as a kind of blueprints. In this paper, it is discussed how analysis of this provides useful inspiration for the development of new computational approaches from a Machine Learning and Network-Oriented Modeling perspective. Three main elements are: applying the mental model by internal simulation, developing and revising a mental model by some form of adaptation, and exerting control over this adaptation in a context-sensitive manner. This concept of controlled adaptation relates to the Plasticity Versus Stability Conundrum from neuroscience. The presented analysis has led to a three-level computational architecture for controlled adaptation. It is discussed and illustrated by examples of applications how this three-level computational architecture can be specified based on a self-modeling network and used to model controlled learning and adaptation processes based on mental models in a context-sensitive manner.<\/jats:p>","DOI":"10.3233\/faia210288","type":"book-chapter","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T19:33:01Z","timestamp":1635881581000},"source":"Crossref","is-referenced-by-count":3,"title":["Context-Sensitive Control of Adaptation: Self-Modeling Networks for Human Mental Processes Using Mental Models"],"prefix":"10.3233","author":[{"given":"Raj","family":"Bhalwankar","sequence":"first","affiliation":[{"name":"Social AI Group, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laila","family":"van Ments","sequence":"additional","affiliation":[{"name":"AutoLeadStar, Jerusalem, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Treur","sequence":"additional","affiliation":[{"name":"Social AI Group, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Modern Management based on Big Data II and Machine Learning and Intelligent Systems III"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA210288","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T19:33:06Z","timestamp":1635881586000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA210288"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,29]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia210288","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,29]]}}}