Predictive Assistance and the Temporal Dynamics of Exploratory Compression

预测性辅助与探索性压缩的时间动力学

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.

摘要： 经典的认知理论将问题解决描述为在结构化问题空间中的探索性搜索，其中重复的交互作用会逐渐将搜索过程压缩为高效的表征结构。

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.

预测性人工智能系统引入了一种独特的机制，在这种机制下，稳定化可能在探索性多样化展开之前就已发生，从而在内部生成的搜索之前就提供了解决方案和决策轨迹。

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.

本文开发了一个几何动力学框架，其中注意力在策略景观上演化，该景观由稳定漂移、内源性探索扰动和响应门控学习所塑造。

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.

预测性辅助被建模为一种外源性探索压缩过程，它在自我生成的探索拓宽策略空间的可访问区域之前，先稳定了轨迹。

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.

该框架得出了三个主要结果。首先，持续的预测性稳定通过减弱内在扰动的有效影响，降低了探索响应性，即使在探索变异性依然存在的情况下也是如此。

Second, curvature accumulates and relaxes asymmetrically, producing hysteresis and delayed recovery of exploratory mobility after assistance withdrawal.

其次，曲率呈现非对称的积累与松弛，导致在撤销辅助后出现滞后现象，以及探索性移动能力的恢复延迟。

Third, developmental outcomes depend critically on the timing of stabilization, with early intervention narrowing future exploratory traversal before broad representational diversification has occurred.

第三，发展结果在很大程度上取决于稳定化的时机；早期干预会在广泛的表征多样化发生之前，限制未来的探索遍历范围。

The framework generates empirically testable predictions concerning exploratory entropy, premature convergence, and delayed recovery following predictive stabilization.

该框架针对预测性稳定后的探索熵、过早收敛和恢复延迟，生成了可进行实证检验的预测。

More broadly, the results suggest that predictive systems may reshape the geometry of exploratory cognition itself.

更广泛地说，这些结果表明，预测性系统可能会重塑探索性认知本身的几何结构。