DocumentCode :
1754612
Title :
Prospective Optimization
Author :
Sejnowski, Terrence J. ; Poizner, Howard ; Lynch, Gary ; Gepshtein, Sergei ; Greenspan, Ralph J.
Author_Institution :
Salk Inst. for Biol. Sci., Howard Hughes Med. Inst., Howard, WI, USA
Volume :
102
Issue :
5
fYear :
2014
fDate :
41760
Firstpage :
799
Lastpage :
811
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 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.
Keywords :
brain models; cognition; neurophysiology; action sequences; basal ganglia; cerebral cortex; dynamic programming approximation; goal directed behavior; human performance; motor tasks; optimal behavior; past experience; perceptual tasks; prospective optimization; reinforcement learning; vertebrate brain; Basal ganglia; Brain modeling; Educational institutions; Learning (artificial intelligence); Observers; Optimization; Uncertainty; Basal ganglia; cerebral cortex; classical conditioning; dynamic programming; hippocampus; ideal observer; limbic system; optimization; reinforcement learning; temporal-difference learning;
fLanguage :
English
Journal_Title :
Proceedings of the IEEE
Publisher :
ieee
ISSN :
0018-9219
Type :
jour
DOI :
10.1109/JPROC.2014.2314297
Filename :
6803897
Link To Document :
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