Shared perception is different from individual perception: a new look on context dependency

TL;DR

This study investigates how social interactions influence human perception, showing that social robot behavior affects the use of prior information and can improve perceptual accuracy through a Bayesian model of shared perception.

Contribution

It introduces a novel experimental framework examining social versus mechanical robot behavior's impact on perception and models shared perception using Bayesian approaches.

Findings

Social robot behavior alters the use of priors in perception.

Social interaction improves perceptual accuracy and reduces errors.

Bayesian modeling provides insights into shared perception mechanisms.

Abstract

Human perception is based on unconscious inference, where sensory input integrates with prior information. This phenomenon, known as context dependency, helps in facing the uncertainty of the external world with predictions built upon previous experience. On the other hand, human perceptual processes are inherently shaped by social interactions. However, how the mechanisms of context dependency are affected is to date unknown. If using previous experience - priors - is beneficial in individual settings, it could represent a problem in social scenarios where other agents might not have the same priors, causing a perceptual misalignment on the shared environment. The present study addresses this question. We studied context dependency in an interactive setting with a humanoid robot iCub that acted as a stimuli demonstrator. Participants reproduced the lengths shown by the robot in two conditions: one with iCub behaving socially and another with iCub acting as a mechanical arm. The different behavior of the robot significantly affected the use of prior in perception. Moreover, the social robot positively impacted perceptual performances by enhancing accuracy and reducing participants overall perceptual errors. Finally, the observed phenomenon has been modelled following a Bayesian approach to deepen and explore a new concept of shared perception.