AI 中文总结
本研究提出进化循环决策模型（ERDM），通过模拟智能体在进化环境中的学习，揭示适应性与非适应性行为的涌现机制，为解读精神病理学相关方面提供计算认知工具。
AI 中文摘要
本研究提出进化循环决策模型（ERDM），这是一种计算强化学习框架，旨在探究进化错配、有限理性和满意原则如何促成适应性与非适应性行为。ERDM在包含威胁、猎物/目标追逐及联盟的进化循环环境中模拟智能体，智能体通过从生存指标抽象出的竞争奖励进行学习。在不同童年不良经历下开展的有效性研究表明，习得性无助、回避、健康关系、攻击等不同的适应性与非适应性策略会自然涌现，而非预先固化。这些结果与实证文献一致，体现了生态效度。研究结果提示，许多与精神病理学相关的方面可被解读为在现代-祖先环境错配下运作的有限认知系统，使ERDM成为可拓展至其他研究的关键计算认知工具。
英文摘要
This study introduces the evolutionarily recurrent decision model (ERDM), a computational reinforcement learning framework designed to examine how evolutionary mismatch, bounded rationality, and satisficing contribute to adaptive and maladaptive behavior. ERDM simulates agents across evolutionary recurrent environments, including threat, prey/goal-pursuits, and alliances. Agents learn through competing rewards abstracted from survival metrics. A validity study under varying adverse childhood experiences demonstrates that distinct adaptive and maladaptive strategies, such as learned helplessness, avoidance, healthy relationships, and aggression, emerge naturally without being hardwired. These results align with empirical literature, showcasing ecological validity. The results suggest that many psychopathology-relevant aspects may be interpreted as bounded cognitive systems operating under modern-ancestral environmental mismatch, positioning ERDM as a key computational cognitive tool that can be extended to other studies.
