Résultats de recherche pour « Delayed Feedback »

Abstract Forward models in the brain issue predictions of sensory movement consequences that can establish self-other distinction through comparison with actual feedback. But forward models can also be updated by sensory prediction errors, as in sensorimotor adaptation. Disentangling the neuronal correlates of processes related to sensorimotor incongruence detection from those related to adaptation can be challenging. Here, we approached this challenge with a novel, virtual reality based hand-target tracking task that allowed us to manipulate the behavioral relevance of delayed visual movement feedback, while recording hemodynamic brain activity with functional magnetic resonance imaging (fMRI). Participants performed continuous right hand grasping movements to track a target oscillation with either their real, unseen hand or with a glove-controlled virtual hand. The movements of the virtual hand were delayed in a roving oddball fashion. Therefore, tracking with the virtual hand (VH task) required visuomotor adaptation, whereas tracking with the real hand (RH task) required ignoring visuomotor delays. VH > RH task execution produced stronger activity in the left posterior parietal cortex (PPC) and the bilateral extrastriate visual cortex. Delays correlated with activity in several regions, prominently including right temporoparietal regions, in both tasks. Crucially, the cerebellum showed a stronger delay dependent activation, and increased functional connectivity with the PPC, in the VH > RH task. Delay changes and errors correlated with activity in the anterior insulae (AI), and more strongly so in the VH>RH task. Thus, the instructed behavioral relevance of delayed visual movement feedback enhanced responses of the cerebellum and PPC, and their communication, likely for visuomotor adaptation. In contrast, temporoparietal regions compared predicted and actual visual movement feedback irrespective of its behavioral relevance, while the AI signaled visual mismatches particularly when these were behaviorally relevant.

Abstract Adaptive feedback systems that correct errors from delayed sensory signals face an inherent vulnerability: corrective updates may no longer reflect the current state of the system. While classical stability analyses characterize asymptotic behavior near equilibrium, the conditions under which such systems can recover from finite perturbations remain poorly understood. Here we investigate the recoverability boundary in a minimal delayed adaptive feedback model, extending from a one-dimensional phase system to a two-dimensional phase–velocity system with inertia and stochastic drift. We demonstrate that recovery success does not degrade gradually with increasing delay; instead, it collapses abruptly at a critical threshold τ_c, within a transition interval of less than 0.25 s across all tested conditions. Notably, the linear stability bound τ_c^{lin} = π/(2K*) systematically overestimates the empirical τ_c, with the discrepancy reaching more than threefold at higher adaptation rates — establishing that linear analysis is not a reliable design criterion for recoverability in adaptive systems. A two-dimensional parameter sweep over feedback delay and damping (γ ∈ [0.3, 3.0]) reveals that τ_c varies by only 0.035 s across a tenfold range of damping, indicating that the boundary is governed by the loss of temporal correlation between delayed error signals and the instantaneous state — a mechanism we term information staleness — rather than by the physical timescale of the plant. Faster adaptation shifts the boundary but cannot eliminate it. As τ approaches τ_c, mean recovery time increases up to tenfold and its variance diverges, providing a prediction-free early-warning signal of imminent failure. These results delineate a structural constraint on prediction-free adaptive control and establish a quantitative baseline for evaluating when predictive architectures become necessary in delayed biological and engineered systems.