The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks

被引:1176
作者
Bogacz, Rafal
Brown, Eric
Moehlis, Jeff
Holmes, Philip
Cohen, Jonathan D.
机构
[1] Princeton Univ, Ctr Study Brain Mind & Behav, Princeton, NJ 08544 USA
[2] Princeton Univ, Program Appl & Computat Math, Princeton, NJ 08544 USA
[3] Princeton Univ, Dept Mech & Aerosp Engn, Princeton, NJ 08544 USA
[4] Princeton Univ, Dept Psychol, Princeton, NJ 08544 USA
基金
英国工程与自然科学研究理事会;
关键词
drift diffusion model; reward rate; optimal performance; speed-accuracy trade-off; perceptual choice;
D O I
10.1037/0033-295X.113.4.700
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
In this article, the authors consider optimal decision making in two-alternative forced-choice (TAFC) tasks. They begin by analyzing 6 models of TAFC decision making and show that all but one can be reduced to the drift diffusion model, implementing the statistically optimal algorithm (most accurate for a given speed or fastest for a given accuracy). They prove further that there is always an optimal trade-off between speed and accuracy that maximizes various reward functions, including reward rate (percentage of correct responses per unit time), as well as several other objective functions, including ones weighted for accuracy. They use these findings to address empirical data and make novel predictions about performance under optimality.
引用
收藏
页码:700 / 765
页数:66
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