Informational entropy thresholds as a physical mechanism to explain power-law time distributions in sequential decision-making

TL;DR

This paper proposes an entropy-threshold mechanism based on Shannon's entropy to explain power-law distributions in decision times during complex, sequential decision-making tasks, supported by human navigation experiments.

Contribution

It introduces a novel entropy-based model for sequential decision-making that accounts for power-law decision time distributions, unlike traditional models.

Findings

Humans use prospection during maze navigation.

Decision times follow power-law distributions.

Entropy thresholds explain decision timing patterns.

Abstract

While frameworks based on physical grounds (like the Drift-Diffusion Model) have been exhaustively used in psychology and neuroscience to describe perceptual decision-making in humans, analogous approaches for more complex situations like sequential (tree-like) decision making are still absent. For such scenarios, which involve a reflective prospection of future options to reach a decision, we offer a plausible mechanism based on the internal computation of the Shannon's entropy for the different options available to the subjects. When a threshold in the entropy is reached this will trigger the decision, which means that the amount of information that has been gathered through sensory evidence is enough to assess the options accurately. Experimental evidence in favour of this mechanism is provided by exploring human performances during navigation through a maze on the computer screen monitored with the help of eye-trackers. In particular, our analysis allows us to prove that: (i) prospection is effectively being used by humans during such navigation tasks, and a quantification of the level of prospection used is attainable, (ii) the distribution of decision times during the task exhibits power-law tails, a feature that our entropy-based mechanism is able to explain, in contrast to classical decision-making frameworks.