Rabuffetti, M. | Search bioRxiv

bioRxiv2025

Rational Expectations and Kinematic Information in Coordination Games

Successful coordination often requires integrating strategic reasoning with real-time observations of others actions, yet how humans resolve conflicts between these information sources remains unclear. This study aimed to fill this gap by examining how people coordinate in a strategic game when observing partial kinematic information from their partners actions. Participants played a HI-LO game with a virtual partner, choosing payoffs based on grasping movements toward invisible large and small targets. Hand movements were presented as schematic animations, with partners grasping targets linked to higher or lower payoffs across two configurations. Participants relied exclusively on kinematic cues from hand shape changes in maximum grip aperture to infer their partners choices. There were two main findings. While participants preferred higher payoffs consistent with rational game-theoretic expectations, reliable kinematic cues overrode these expectations. When early grip aperture changes indicated the partner was reaching for a large target associated with a lower payoff, participants abandoned their default preference for higher payoffs. They chose the lower option instead, achieving a high coordination success rate. These findings demonstrate that people prioritize kinematic cues about others choices over theoretical assumptions about rational behavior when coordinating. This suggests that movement-based inferences about others actions in natural social interactions may be weighted more heavily than strategic reasoning when the two sources of information conflict.

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bioRxiv2025

EARLY TARGET PREDICTION IN ACTION OBSERVATION

Previous research has established that observers can predict action targets through hand preshaping. However, two critical questions remain unexplored: how predictions adapt to the available kinematic information and evolve throughout the movement timeline. We address these fundamental gaps by combining kinematic analysis with machine-learning approaches that differentiate between motor and visual cues. Using motion capture technology, we recorded reach-to-grasp actions toward large and small objects and had participants predict target size from hand kinematics at varying time points. Our analysis revealed that prediction performance not only evolved with increasing kinematic information but, crucially, differed significantly between target size choices. To provide insight into the underlying processes, we developed a comparative framework using two distinct machine learning approaches: Support Vector Machines (SVM) modeling kinematic information and CNN-RNN networks extracting visual patterns. The stronger alignment between human performance and SVM predictions offers empirical evidence that kinematic cues, rather than visual patterns, mostly guide target prediction. These findings advance our understanding of action prediction and have significant implications for social cognition and human-machine interaction. PUBLIC SIGNIFICANCE STATEMENTUnderstanding others intentions by observing their hand movements is crucial for social interaction, from passing objects to coordinating complex tasks. This study reveals that people use different cues to predict whether someone is reaching for a large versus a small object from the earliest stages of hand movement. By comparing human performance with Artificial Intelligence models, we found that people primarily rely on motor cues to make these predictions. These insights could improve rehabilitation techniques for individuals with social interaction difficulties and enhance the design of intuitive robotic assistants.

neuroscience↗