Abstract
We present a theory of decision-making in the presence of multiple choices that departs from traditional approaches by explicitly incorporating entropic barriers in a stochastic search process. We analyze response time data from an on-line repository of 15 million blitz chess games, and show that our model fits not just the mean and variance, but the entire response time distribution (over several response-time orders of magnitude) at every stage of the game. We apply the model to show that (a) higher cognitive expertise corresponds to the exploration of more complex solution spaces, and (b) reaction times of users at an on-line buying website can be similarly explained. Our model can be seen as a synergy between diffusion models used to model simple two-choice decision-making and planning agents in complex problem solving.
Highlights
Decision-making has been studied in great detail relying on binary choices, modeled as the noisy accumulation of a decision variable to a threshold
We show that it breaks down when used to describe real-life human decision involving multiple options
We show instead that including obstacles in the diffusion model can describe the data with great degree of accuracy
Summary
Decision-making has been studied in great detail relying on binary choices by the Two-Alternative Forced-Choice paradigm (2AFC). In 2AFC tasks, choices are made between two alternatives with limited information while speed and accuracy are registered. For simplicity, in the vast majority of the experiments, the decision variable is a single scalar The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
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