🤖 AI Summary
This study investigates how predictive artificial intelligence prematurely stabilizes decision trajectories before humans complete autonomous exploration, thereby suppressing cognitive exploration and the development of representational structures. By constructing a geometric dynamical framework, the work characterizes the evolution of attention in policy space as jointly driven by stable drift, endogenous exploratory perturbations, and response-gated learning, modeling predictive assistance as an exogenous mechanism that compresses exploration. The research uncovers three key mechanisms: predictive assistance reduces exploratory responsiveness, asymmetric accumulation of policy-space curvature induces hysteresis in recovery, and early intervention severely constrains subsequent exploration breadth. It further proposes a testable exploration entropy metric and predictions for premature convergence. Simulations demonstrate that sustained prediction attenuates endogenous perturbation effects, delays the restoration of exploratory capacity upon withdrawal, and that intervention timing critically shapes cognitive developmental trajectories.
📝 Abstract
Classical theories of cognition describe problem solving as exploratory search through structured problem spaces in which repeated interaction gradually compresses search into efficient representational structures. Predictive artificial intelligence systems introduce a distinct regime in which stabilization may occur before exploratory diversification unfolds, supplying solutions and decision trajectories prior to internally generated search. This paper develops a geometric dynamical framework in which attention evolves over a landscape of strategies shaped by stabilizing drift, endogenous exploratory perturbation, and responsiveness-gated learning. Predictive assistance is modeled as a process of exogenous exploratory compression that stabilizes trajectories before self-generated exploration broadens the accessible regions of strategy space. The framework yields three main results. First, sustained predictive stabilization reduces exploratory responsiveness by attenuating the effective influence of intrinsic perturbations even when exploratory variability remains present. Second, curvature accumulates and relaxes asymmetrically, producing hysteresis and delayed recovery of exploratory mobility after assistance withdrawal. Third, developmental outcomes depend critically on the timing of stabilization, with early intervention narrowing future exploratory traversal before broad representational diversification has occurred. The framework generates empirically testable predictions concerning exploratory entropy, premature convergence, and delayed recovery following predictive stabilization. More broadly, the results suggest that predictive systems may reshape the geometry of exploratory cognition itself.