Prospective Optimization
Identifieur interne : 001B50 ( Pmc/Corpus ); précédent : 001B49; suivant : 001B51Prospective Optimization
Auteurs : Terrence J. Sejnowski ; Howard Poizner ; Gary Lynch ; Sergei Gepshtein ; Ralph J. GreenspanSource :
- Proceedings of the IEEE. Institute of Electrical and Electronics Engineers [ 0018-9219 ] ; 2014.
Abstract
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
Url:
DOI: 10.1109/JPROC.2014.2314297
PubMed: 25328167
PubMed Central: 4201124
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