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Related Experiment Video

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Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Published on: April 16, 2014

Decision-theoretic models of visual perception and action.

Laurence T Maloney¹, Hang Zhang

¹Department of Psychology, Center for Neural Science, New York University, New York, NY 10003, United States. ltm1@nyu.edu

Vision Research

|October 12, 2010

Summary

Statistical decision theory (SDT) and Bayesian decision theory (BDT) offer mathematical frameworks for modeling ideal performance in perception and action. This review explores their application to biological systems, comparing ideal models with actual observer capabilities.

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Area of Science:

Cognitive Science
Computational Neuroscience
Mathematical Psychology

Background:

Statistical decision theory (SDT) and Bayesian decision theory (BDT) are related mathematical frameworks.
These theories model ideal performance in visual and motor tasks.
Key elements include gain functions, likelihoods, and priors, interpretable via observer information.

Purpose of the Study:

To describe SDT and BDT.
To review recent work applying SDT and BDT as models of biological perception and action.
To emphasize the use of gain functions and priors as variables.

Main Methods:

Review of existing literature on SDT and BDT in biological perception and action.
Analysis of studies employing gain functions and priors.
Discussion of comparing ideal performance with actual human performance.

Main Results:

BDT enables computation of ideal task performance for comparison with human observers.
SDT and BDT are explored as process models for perception as Bayesian inference.
The relationship between ideal models and imperfect observers is discussed.

Conclusions:

SDT and BDT provide valuable frameworks for understanding ideal performance in perception and action.
The "Bayesian hypothesis" of perception can be experimentally tested.
Further research can explore the nuances of ideal models versus actual observer limitations.