استدلال خلاف به عنوان یک کلید برای توضیح رفتار سازشی در یک محیط در حال تغییر

It is crucial for animals to detect changes in their surrounding environment, and reinforcement learning is one of the well-known processes to explain the change detection behavior. However, reinforcement learning itself cannot fully explain rapid, relatively immediate changes in strategy in response to abrupt environment changes. A previous model employed reinforcement learning and counterfactual reasoning to explain adaptive behavior observed in a changing market simulation environment. In this paper, we used the same model mechanisms to simulate data from two additional tasks that require participants, who played the role of intelligence analysts, to detect the changes of a computer-controlled adversary’s tactics based on intelligence evidence and feedback. The results show that our model captures participants’ adaptive behavior accurately, which further supports our previous conclusion that counterfactual reasoning is a missing piece for explaining adaptive behavior in a changing environment.