Active Sensing Shapes Real-World Decision-Making through Dynamic Evidence Accumulation

TL;DR

This paper extends evidence accumulation models to real-world driving, showing how active sensing through eye movements influences decision-making by adapting evidence collection based on environmental affordance.

Contribution

It generalizes evidence accumulation modeling to real-world scenarios and introduces a cognitive scheme linking active sensing, evidence affordance, and decision-making in driving.

Findings

Negative correlation between evidence affordance and attention recruitment.

Evidence affordance and attention positively influence decision propensity.

The scheme plausibly models drivers' mental belief accumulation.

Abstract

Human decision-making heavily relies on active sensing, a well-documented cognitive behaviour for evidence gathering to accommodate ever-changing environments. However, its operational mechanism in the real world remains non-trivial. Currently, an in-laboratory paradigm, called evidence accumulation modelling (EAM), points out that human decision-making involves transforming external evidence into internal mental beliefs. However, the gap in evidence affordance between real-world contexts and laboratory settings hinders the effective application of EAM. Here we generalize EAM to the real world and conduct analysis in real-world driving scenarios. A cognitive scheme is proposed to formalize real-world evidence affordance and capture active sensing through eye movements. Empirically, our scheme can plausibly portray the accumulation of drivers' mental beliefs, explaining how active sensing transforms evidence into mental beliefs from the perspective of information utility. Also, our results demonstrate a negative correlation between evidence affordance and attention recruited by individuals, revealing how human drivers adapt their evidence-collection patterns across various contexts. Moreover, we reveal the positive influence of evidence affordance and attention distribution on decision-making propensity. In a nutshell, our computational scheme generalizes EAM to real-world contexts and provides a comprehensive account of how active sensing underlies real-world decision-making, unveiling multifactorial, integrated characteristics in real-world decision-making.