Rutgers New Jersey Medical School, Rutgers Biomedical Health Sciences, 185 South Orange Avenue, Newark, NJ, 07103, USA; Rutgers School of Graduate Studies, Rutgers Biomedical Health Sciences, 185 South Orange Avenue, Newark, NJ, 07103, USA.
Rutgers New Jersey Medical School, Rutgers Biomedical Health Sciences, 185 South Orange Avenue, Newark, NJ, 07103, USA; Rutgers School of Graduate Studies, Rutgers Biomedical Health Sciences, 185 South Orange Avenue, Newark, NJ, 07103, USA.
Behav Brain Res. 2020 Sep 1;393:112784. doi: 10.1016/j.bbr.2020.112784. Epub 2020 Jun 22.
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.
回避行为是生物体为避免有害情况而表现出的一种典型的适应性反应。个体在获得和表现新的回避行为方面存在显著差异，这种差异在焦虑障碍中尤为明显，回避行为变得普遍且不适当。在回避的啮齿动物模型中，与杂交的 Sprague Dawley（SD）大鼠相比，近交 Wistar-Kyoto（WKY）大鼠表现出更强的回避学习和表达能力。然而，导致这些差异的潜在机制尚不清楚。计算建模技术可以帮助确定仅从行为数据不易识别的因素。在这里，我们利用强化学习（RL）模型方法来更好地理解回避行为中的品系差异。我们应用了一种带有独立学习率的行动者-批评者模型，用于对来自大量 WKY 和 SD 大鼠回避获得的个体数据进行分析。提取了潜在参数，例如行动选择（行动者）和状态评估（批评者）的学习率以及脚电击的主观强化值，然后在组间进行比较。RL 模型能够准确地表示 WKY 和 SD 的回避行为，表明该模型可以模拟个体表现。该模型确定 WKY 大鼠对脚电击的感知负面价值明显高于 SD 大鼠，而行动者中的学习率在 WKY 大鼠中低于 SD 大鼠。这些发现表明计算建模在确定潜在过程方面具有实用性，这些潜在过程可能会促进行为表现中的品系差异。
