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A reinforcement-learning model of active avoidance behavior: Differences between Sprague Dawley and Wistar-Kyoto rats
یک مدل یادگیری تقویتی رفتار اجتناب فعال: تفاوت بین موشهای Sprague Dawley و Wistar-Kyoto-2020 Avoidance behavior is a typically adaptive response performed by an organism to avert harmful situations.
Individuals differ remarkably in their tendency to acquire and perform new avoidance behaviors, as seen in
anxiety disorders where avoidance becomes pervasive and inappropriate. In rodent models of avoidance, the
inbred Wistar-Kyoto (WKY) rat demonstrates increased learning and expression of avoidance compared to the
outbred Sprague Dawley (SD) rat. However, underlying mechanisms that contribute to these differences are
unclear. Computational modeling techniques can help identify factors that may not be easily decipherable from
behavioral data alone. Here, we utilize a reinforcement learning (RL) model approach to better understand strain
differences in avoidance behavior. An actor-critic model, with separate learning rates for action selection (in the
actor) and state evaluation (in the critic), was applied to individual data of avoidance acquisition from a large
cohort of WKY and SD rats. Latent parameters were extracted, such as learning rate and subjective reinforcement
value of foot shock, that were then compared across groups. The RL model was able to accurately represent WKY
and SD avoidance behavior, demonstrating that the model could simulate individual performance. The model
determined that the perceived negative value of foot shock was significantly higher in WKY than SD rats,
whereas learning rate in the actor was lower in WKY than SD rats. These findings demonstrate the utility of
computational modeling in identifying underlying processes that could promote strain differences in behavioral
performance. Keywords: Strain differences | Avoidance | Wistar Kyoto rat | Reinforcement learning | Computational modeling |
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