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Combining the Projective Consciousness Model and Virtual Humans for Immersive Psychological Research: A Proof-of-concept Simulating a ToM Assessment

David Rudrauf, G. Sergeant-Perhtuis, Yvain Tisserand, Teerawat Monnor, Valentin Durand de Gevigney, O. Belli

ACM Transactions on Interactive Intelligent Systems February 21, 2023 DOI: 10.1145/3583886 (opens in new tab)

Study at a glance

AI-extracted from the abstract
Characteristics Proof-of-concept simulation study Peer reviewed
Population Artificial agents embodied as virtual humans
Citations 5
Key points Agents implementing the Projective Consciousness Model showed expected approach and avoidance behaviours with consistent Theory of Mind parameters, correctly estimated each other's order of ToM up to second order, and in a supplementary simulation simultaneously estimated others' ToM order and preferences to optimise behavioural outcomes. The authors present these simulation results as a proof of concept for a future framework to assess hidden psychological parameters in human virtual reality experiments.

Abstract

Relating explicit psychological mechanisms and observable behaviours is a central aim of psychological and behavioural science. One of the challenges is to understand and model the role of consciousness and, in particular, its subjective perspective as an internal level of representation (including for social cognition) in the governance of behaviour. Toward this aim, we implemented the principles of the Projective Consciousness Model (PCM) into artificial agents embodied as virtual humans, extending a previous implementation of the model. Our goal was to offer a proof-of-concept, based purely on simulations, as a basis for a future methodological framework. Its overarching aim is to be able to assess hidden psychological parameters in human participants, based on a model relevant to consciousness research, in the context of experiments in virtual reality. As an illustration of the approach, we focused on simulating the role of Theory of Mind (ToM) in the choice of strategic behaviours of approach and avoidance to optimise the satisfaction of agents’ preferences. We designed a main experiment in a virtual environment that could be used with real humans, allowing us to classify behaviours as a function of order of ToM, up to the second order. We show that agents using the PCM demonstrated expected behaviours with consistent parameters of ToM in this experiment. We also show that the agents could be used to estimate correctly each other’s order of ToM. Furthermore, in a supplementary experiment, we demonstrated how the agents could simultaneously estimate order of ToM and preferences attributed to others to optimize behavioural outcomes. Future studies will empirically assess and fine tune the framework with real humans in virtual reality experiments.

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