# Intentional binding reflects pair dynamics and sense of agency in embodied joint action in human-human dyads but not in human-computer dyads

## TL;DR

The study shows that intentional binding, a measure of shared agency, is linked to movement dynamics and sense of control in human-human interactions but not in human-computer ones.

## Contribution

The novel finding is that intentional binding reflects co-regulation dynamics and agency in human-human dyads but not in human-computer dyads.

## Key findings

- Intentional binding magnitude was not affected by partner type in intra-subjective comparisons.
- Stronger sense of agency in human-human dyads correlated with stronger binding effects and leader-follower movement dynamics.
- Human-computer dyads showed no relationship between agency and binding effects.

## Abstract

Intentional binding has been proposed as an implicit measure of shared sense of agency in joint action, yet it remains unclear whether it distinguishes between individual and different forms of joint control. We compared intentional binding across individual action, human-computer joint control, and human-human joint control in a physically coupled button-pressing task using haptic feedback devices. Binding magnitude was analyzed both intra-subjectively across conditions and inter-subjectively in relation to reported sense of agency and movement dynamics. Contrary to predictions from the we agency literature, we found no intra-subjective main effect of partner type on binding magnitude. However, within human-human dyads, participants who reported a stronger sense of agency exhibited stronger binding effects, corresponding to emergent leader-follower dynamics in their movement trajectories. No comparable relationship was observed in human-computer interactions. These findings suggest that temporal binding effects primarily reflect sensorimotor predictability rather than intentionality or social context per se. While binding alone does not provide a sufficient marker of “human-like” agency in artificial systems, it may reflect the distribution of predictive control within a dyad and thus serve as a quantifiable signature of how effectively partners co-regulate their actions.