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Updated: Jul 19, 2026

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
Published on: August 2, 2018
The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice
Rafal Bogacz1, Eric Brown, Jeff Moehlis
1Center for the Study of Brain, Mind and Behavior, Princeton University, Princeton, NJ, USA. r.bogacz@bristol.ac.uk
Abstract:
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.
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