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