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ComputersCybernetics

Cognitive Models for Learning to Control Dynamic Systems

Authors: Zhong-Ping Jiang; APPLIED SR TECHNOLOGIES INC BROOKLYN NY
Abstract:
Wald's sequential probability ratio test (SPRT) model and the equivalent discrete drift diffusion model have been widely used to explain human and animal decision making in psychophysical tasks. These models assume that observers gradually accumulate evidence from noisy inputs and make a decision when the evidence reaches a threshold. It is discovered that stochastic-resonance (SR) like behavior arises in the SPRT model when the actual input signal is significantly weaker than anticipated by the model. Analytical expressions and conditions for the SR-like behavior are found. Therefore appropriate amount of noise can improve the decision making process when the input signal is significantly weaker than anticipated. Research in adaptive SPRT demonstrates that there is an optimal nonzero and finite weight to achieve the best accuracy when the real distributions are wider than the prior distribution.

Limitations: APPROVED FOR PUBLIC RELEASE
Description: Final rept. 15 Dec 2007-14 Sep 2008
Pages: 33
Report Date: 26-Sep-2008
Report Number: A720884
Keywords relating to this report:
*ALGORITHMS
*CONTROL THEORY
*DECISION MAKING
*MODELS
ADAPTIVE SYSTEMS
COGNITION
NOISE
STOCHASTIC PROCESSES
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