California Institute of Technology, Humanities and Social Sciences, Pasadena, California, United States of America.
University of Santiago, CESS-Santiago, Faculty of Business and Economics, Santiago, Chile.
PLoS Comput Biol. 2022 Sep 9;18(9):e1010410. doi: 10.1371/journal.pcbi.1010410. eCollection 2022 Sep.
In the natural world, stimulus-outcome associations are often ambiguous, and most associations are highly complex and situation-dependent. Learning to disambiguate these complex associations to identify which specific outcomes will occur in which situations is critical for survival. Pavlovian occasion setters are stimuli that determine whether other stimuli will result in a specific outcome. Occasion setting is a well-established phenomenon, but very little investigation has been conducted on how occasion setters are disambiguated when they themselves are ambiguous (i.e., when they do not consistently signal whether another stimulus will be reinforced). In two preregistered studies, we investigated the role of higher-order Pavlovian occasion setting in humans. We developed and tested the first computational model predicting direct associative learning, traditional occasion setting (i.e., 1st-order occasion setting), and 2nd-order occasion setting. This model operationalizes stimulus ambiguity as a mechanism to engage in higher-order Pavlovian learning. Both behavioral and computational modeling results suggest that 2nd-order occasion setting was learned, as evidenced by lack and presence of transfer of occasion setting properties when expected and the superior fit of our 2nd-order occasion setting model compared to the 1st-order occasion setting or direct associations models. These results provide a controlled investigation into highly complex associative learning and may ultimately lead to improvements in the treatment of Pavlovian-based mental health disorders (e.g., anxiety disorders, substance use).
在自然界中，刺激-结果的关联往往是模糊的，大多数关联都是高度复杂和情境依赖的。学习如何消除这些复杂的关联，以确定在哪些情况下会出现哪些特定的结果，对于生存至关重要。条件刺激是指决定其他刺激是否会产生特定结果的刺激。条件设定是一种既定的现象，但对于条件刺激本身存在模糊性（即，它们不能始终表明另一个刺激是否会得到强化）时，如何消除这些模糊性，几乎没有进行过研究。在两项预先注册的研究中，我们调查了高阶条件刺激在人类中的作用。我们开发并测试了第一个预测直接联想学习、传统条件设定（即一阶条件设定）和二阶条件设定的计算模型。该模型将刺激模糊性作为一种参与高阶条件学习的机制。行为和计算建模的结果都表明，二阶条件设定是被学习到的，这表现在当预期的和实际的结果出现时，条件设定属性的转移缺失和存在，以及我们的二阶条件设定模型比一阶条件设定或直接关联模型更好地拟合。这些结果为高度复杂的联想学习提供了受控的研究，可能最终会导致基于条件反射的心理健康障碍（例如，焦虑障碍、药物使用障碍）的治疗方法得到改进。
