Early warning signals in motion inference.

Area of Science:

• Cognitive neuroscience
• Human-computer interaction
• Social robotics

Background:

• Social interactions rely on inferring intentions from motor actions.
• Anticipating actions is crucial for effective human-human and human-robot collaboration.
• Previous research indicated early intention inference but lacked clarity on signaling mechanisms.

Purpose of the Study:

• To identify the informational cues signaling impending motor actions.
• To investigate if action transitions exhibit critical transition dynamics.
• To determine if humans can detect and utilize these early warning signals.

Main Methods:

• Analysis of motion data from an antagonistic game (Attacker-Blocker).
• Identification of critical transition indicators: increased autocorrelation and decay time.
• Simulation of fold-transition dynamics to test human recognition of early warning signals.

Main Results:

• Early warning signals detected up to 130 ms before motion onset.
• Signals characterized by rising motion autocorrelation and decay time, consistent with fold-transition dynamics.
• Participants reacted faster to simulated early warning signals, indicating effective detection.

Conclusions:

• Action onsets exhibit critical transition dynamics with detectable early warning signals.
• Humans can perceive and act upon these early warning signals to infer intent and predict motion.
• Findings advance understanding of intention inference in social motor interactions.