用演化模型模拟心理适应与非适应行为的形成机制
Evolutionary Recurrent Decision Model in Developing Adaptive and Maladaptive Behaviors
- 构建演化循环决策模型，模拟威胁、捕猎和同盟等生存情境
- 在不同童年逆境下自然涌现出习得性无助、回避等行为策略
- 适合研究心理障碍的演化根源，为认知计算提供新工具
本研究提出演化递归决策模型（ERDM），一种用于探究演化错配、有限理性与满意原则如何导致适应与非适应行为的计算强化学习框架。该模型在包含威胁、猎物/目标追逐及联盟关系的演化递归环境中模拟代理，通过抽象自生存指标的竞争性奖励进行学习。在不同童年逆境条件下进行有效性验证，结果显示无需预设，自然涌现出习得性无助、回避、健康关系与攻击等行为策略，结果与实证文献一致，展现出生态效度。研究表明，许多与心理病理相关的表现可被理解为现代-祖先环境错配下有限认知系统的产物，表明ERDM是可扩展至其他研究的关键计算认知工具。
原文摘要 · Abstract (English)
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.
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