Prospective Optimization

Human performance approaches that of an ideal observer and optimal actor in some perceptual and motor tasks. These optimal abilities depend on the capacity of the cerebral cortex to store an immense amount of information and to flexibly make rapid decisions. However, behavior only approaches these l...

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Bibliographische Detailangaben
Veröffentlicht in:Proceedings of the IEEE. Institute of Electrical and Electronics Engineers. - 1998. - 102(2014), 5 vom: 05. Mai
1. Verfasser: Sejnowski, Terrence J (VerfasserIn)
Weitere Verfasser: Poizner, Howard, Lynch, Gary, Gepshtein, Sergei, Greenspan, Ralph J
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2014
Zugriff auf das übergeordnete Werk:Proceedings of the IEEE. Institute of Electrical and Electronics Engineers
Schlagworte:Journal Article Basal ganglia cerebral cortex classical conditioning dynamic programming hippocampus ideal observer limbic system optimization reinforcement learning temporal-difference learning
Beschreibung
Zusammenfassung:Human performance approaches that of an ideal observer and optimal actor in some perceptual and motor tasks. These optimal abilities depend on the capacity of the cerebral cortex to store an immense amount of information and to flexibly make rapid decisions. However, behavior only approaches these limits after a long period of learning while the cerebral cortex interacts with the basal ganglia, an ancient part of the vertebrate brain that is responsible for learning sequences of actions directed toward achieving goals. Progress has been made in understanding the algorithms used by the brain during reinforcement learning, which is an online approximation of dynamic programming. Humans also make plans that depend on past experience by simulating different scenarios, which is called prospective optimization. The same brain structures in the cortex and basal ganglia that are active online during optimal behavior are also active offline during prospective optimization. The emergence of general principles and algorithms for goal-directed behavior has consequences for the development of autonomous devices in engineering applications
Beschreibung:Date Revised 12.11.2023
published: Print
Citation Status PubMed-not-MEDLINE
ISSN:0018-9219
DOI:10.1109/JPROC.2014.2314297