{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T04:06:19Z","timestamp":1777521979450,"version":"3.51.4"},"reference-count":85,"publisher":"SAGE Publications","issue":"3-4","license":[{"start":{"date-parts":[[2010,6,1]],"date-time":"2010-06-01T00:00:00Z","timestamp":1275350400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Adaptive Behavior"],"published-print":{"date-parts":[[2010,6]]},"abstract":"<jats:p>This article investigates how a motivational module can drive an animat to learn a sensorimotor cognitive map and use it to generate flexible goal-directed behavior. Inspired by the rat\u2019s hippocampus and neighboring areas, the time growing neural gas (TGNG) algorithm is used, which iteratively builds such a map by means of temporal Hebbian learning. The algorithm is combined with a motivation module, which activates goals, priorities, and consequent activity gradients in the developing cognitive map for the self-motivated control of behavior. The resulting motivated TGNG thus combines a neural cognitive map learning process with top-down, self-motivated, anticipatory behavior control mechanisms. While the algorithms involved are kept rather simple, motivated TGNG displays several emergent behavioral patterns, self-sustainment, and reliable latent learning. We conclude that motivated TGNG constitutes a solid basis for future studies on self-motivated cognitive map learning, on the design of further enhanced systems with additional cognitive modules, and on the realization of highly adaptive, interactive, goal-directed, cognitive systems.<\/jats:p>","DOI":"10.1177\/1059712310376842","type":"journal-article","created":{"date-parts":[[2010,7,23]],"date-time":"2010-07-23T20:52:32Z","timestamp":1279918352000},"page":"315-337","source":"Crossref","is-referenced-by-count":22,"title":["Self-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior"],"prefix":"10.1177","volume":"18","author":[{"given":"Martin V.","family":"Butz","sequence":"first","affiliation":[{"name":"Department of Psychology, University of W\u00fcrzburg,\rGermany, butz@psychologie.uni-wuerzburg.de"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elshad","family":"Shirinov","sequence":"additional","affiliation":[{"name":"Department of Psychology, University of W\u00fcrzburg,\rGermany, thereddog@gmx.net"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin L.","family":"Reif","sequence":"additional","affiliation":[{"name":"Department of Psychology, University of W\u00fcrzburg,\rGermany, kevin.reif@stud-mail.uni-wuerzburg.de"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2017,2,6]]},"reference":[{"key":"atypb1","doi-asserted-by":"publisher","DOI":"10.1007\/s00426-009-0242-2"},{"key":"atypb2","doi-asserted-by":"publisher","DOI":"10.1007\/s004220000171"},{"key":"atypb3","first-page":"329","volume":"63","year":"2008","journal-title":"The European Physical Journal B - Condensed Matter"},{"key":"atypb4","volume-title":"Parallel and distributed computation: Numerical methods","year":"1989"},{"key":"atypb5","doi-asserted-by":"crossref","unstructured":"Bishop, C.M., Hinton, G.E. & Strachan, I.G.D. 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