PREDICTIVE COGNITION PRIORITIZES FUTURE INTERACTIONS IN DYNAMIC ENVIRONMENTS

Prediction in dynamic situations, in which relevant elements evolve over time, is a fundamental cognitive function. The brain relies on specialized predictive mechanisms, including time compaction, a process that supports dynamic processing by embedding temporal information into space and transforming future interactions into salient spatial representations. Here we investigated how future interactions are prioritized during dynamic events and how this prioritization shapes behavior. Participants performed a visuomotor prediction task in which they estimated the future trajectory of a moving object after observing only the initial portion of its motion, while another object was simultaneously present and could generate either interactive or non-interactive dynamics. Although accurate performance required extrapolating motion solely from kinematic information, participants’ predictions were systematically biased toward locations associated with future interactions. Prediction accuracy was reduced in situations involving potential future interactions compared to non-interactive dynamics. Importantly, participants consistently responded closer to predicted interaction points, even when this strategy did not improve accuracy or trajectory extrapolation. Substantial inter-individual variability was observed, revealing conservative and risk-taking predictive strategies with systematic group differences. When participants were explicitly instructed to improve performance, overall accuracy did not increase; instead, predictive behavior shifted toward greater reliance on interaction-related locations. We propose that this interaction-driven bias reflects a core property of time compaction, supporting the idea that predictive cognition relies on future interactions as stable reference points under dynamic uncertainty.