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Summary

We modeled human gaze dynamics during social attention using foraging principles. This computational approach explains how we choose and process information from different social cues.

Keywords:
audio-visual attentiondecision theorydrift-diffusion modelgaze modelsmultimodal perceptionperceptual decisionssocial interaction

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Area of Science:

  • Computational Neuroscience
  • Cognitive Science
  • Human-Computer Interaction

Background:

  • Understanding social attention is crucial for designing interactions between humans and artificial agents.
  • Gaze dynamics in humans provide insights into how we process socially relevant multimodal information.

Purpose of the Study:

  • To computationally model the spatio-temporal dynamics of human gaze shifts during social attention.
  • To frame social interaction as a foraging problem, balancing exploitation of local information with exploration of the environment.

Main Methods:

  • Distilled social interactions into multimodal patches representing varying social value.
  • Applied Langevin-type stochastic differential equations to describe gaze shift dynamics.
  • Modeled value-based patch selection as a multi-alternative perceptual decision-making process using a race-to-threshold model.

Main Results:

  • Demonstrated that gaze shift dynamics can be parsimoniously described by stochastic differential equations.
  • Reduced complex value-based patch choice to a race-to-threshold decision-making mechanism.
  • Showcased the effectiveness of perceptual evidence integrators in modeling patch selection.

Conclusions:

  • The study provides a computational framework for understanding human social attention and gaze behavior.
  • The foraging analogy offers a novel perspective on decision-making in dynamic social environments.
  • Findings can inform the development of more natural and intuitive human-AI interactions.