Early warning signals in motion inference

Yuval Hart, Maryam Vaziri-Pashkam and L Mahadevan

PLOS Computational Biology, 2020, vol. 16, issue 5, 1-16

Abstract: The ability to infer intention lies at the basis of many social interactions played out via motor actions. We consider a simple paradigm of this ability in humans using data from experiments simulating an antagonistic game between an Attacker and a Blocker. Evidence shows early inference of an Attacker move by as much as 100ms but the nature of the informational cues signaling the impending move remains unknown. We show that the transition to action has the hallmark of a critical transition that is accompanied by early warning signals. These early warning signals occur as much as 130 ms before motion ensues—showing a sharp rise in motion autocorrelation at lag-1 and a sharp rise in the autocorrelation decay time. The early warning signals further correlate strongly with Blocker response times. We analyze the variance of the motion near the point of transition and find that it diverges in a manner consistent with the dynamics of a fold-transition. To test if humans can recognize and act upon these early warning signals, we simulate the dynamics of fold-transition events and ask people to recognize the onset of directional motion: participants react faster to fold-transition dynamics than to its uncorrelated counterpart. Together, our findings suggest that people can recognize the intent and onset of motion by inferring its early warning signals.Author summary: Intention inference is one of the fundamental skills that social organisms need to master. Recent studies indicate that people can predict motion onset before it occurs, raising the question of what informational cues underlie this ability. We use data from an antagonistic game between two participants, an Attacker and a Blocker wherein the Blocker is asked to parry the movements of the Attacker. We find that well before the Attacker starts to move, the fluctuations of the Attacker’s body about a static pose are accompanied by early warning signals akin to critical transitions in dynamical systems. These early warning signals allow for the anticipation of the Attacker’s impending motion before it occurs. Analysis of the early warning signals allows us to characterize the nature of the critical transition, and to simulate it in a simple dot motion experiment. When the dot motion simulations are shown to human participants, we find that they react faster if there is a coming critical transition relative to a transition devoid of the early warning signals. This suggests that people recognize and act upon early warning signals when inferring the onset of motion, and more generally in motor decision making processes.

Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007821 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 07821&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1007821

DOI: 10.1371/journal.pcbi.1007821

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().