Mashiah, O. | Search bioRxiv

bioRxiv2026

Domain-general computational integration in the Sense of Agency

The Sense of Agency (SoA), the experience of being in control of ones own actions, is thought to emerge from the comparison of internal sensorimotor predictions with afferent feedback. Classical comparator models treat SoA as arising from a prediction-feedback comparison. Volitional action likely engages multiple forward models that predict distinct features of an outcome, such as its timing and spatial trajectory. Whether prediction errors arising from these distinct forward models are integrated into a domain-general representation of agency, and if so by what computational logic, remains unresolved. We addressed this question using a Virtual Reality reaching task. Participants observed a virtual hand replicating their movements while we independently manipulated two sensorimotor domains: temporal delay and spatial angle deviation, in isolation and in factorial combinations. After each trial, participants made an SoA judgment. We examined whether SoA responses show computational hallmarks of integration between different features of sensorimotor prediction. Specifically, we pre-registered three computational models (Multiplicative, Minimum, and Mean) and compared their fit to per-trial responses. Across an exploratory sample (N = 16) and a pre-registered replication (N = 38), SoA declined monotonically with conflict magnitude in both domains. Critically, a Multiplicative integration rule consistently outperformed the Minimum and Mean rules. These results provide direct evidence for domain-general integration between prediction errors in SoA, governed by a multiplicative computational logic.

neuroscience↗

bioRxiv2024

The Body Knows Better: Sensorimotor signals reveal "Suboptimal" inference of the Sense of Agency in the human mind

Sense of Agency (SoA) is the feeling of control over our actions. SoA has been suggested to arise from both implicit sensorimotor integration as well as higher-level decision processes. SoA is typically measured by collecting participants subjective judgments, conflating both implicit and explicit processing. Consequently, the interplay between implicit sensorimotor processing and explicit agency judgments is not well understood. Here, we evaluated in one exploratory and one preregistered experiment (N=60), using a machine learning approach, the relation between a well-known mechanism of implicit sensorimotor adaptation and explicit SoA judgments. Specifically, we examined whether subjective judgments of SoA and sensorimotor conflicts could be inferred from hand kinematics in a sensorimotor task using a virtual hand (VH). In both experiments participants performed a hand movement and viewed a virtual hand making a movement that could either be synchronous with their action or include a parametric temporal delay. After each movement, participants judged whether their actual movement was congruent with the movement they observed. Our results demonstrated that sensorimotor conflicts could be inferred from implicit motor kinematics on a trial by trial basis. Moreover, detection of sensorimotor conflicts from machine learning models of kinematic data provided more accurate classification of sensorimotor congruence than participants explicit judgments. These results were replicated in a second, preregistered, experiment. These findings show evidence of diverging implicit and explicit processing for SoA and suggest that the brain holds high-quality information on sensorimotor conflicts that is not fully utilized in the inference of conscious agency.

neuroscience↗