Computational Modeling Differentiates Learning Rate From Reward Sensitivity Deficits Produced by Early-Life Adversity...

Exposure to adversity, including unpredictable environments, during early life is associated with neuropsychiatric illness in adulthood. One common factor in this sequela is anhedonia, the loss of responsivity to previously reinforcing stimuli. To accelerate the development of new treatment strategies for anhedonic disorders induced by early-life adversity, animal models have been developed to capture critical features of early-life stress and the behavioral deficits that such stressors induce. We have previously shown that rats exposed to the limited bedding and nesting protocol exhibited blunted reward responsivity in the probabilistic reward task, a touchscreen-based task reverse translated from human studies.

To test the quantitative limits of this translational platform, we examined the ability of Bayesian computational modeling and probability analyses identical to those optimized in previous human studies to quantify the putative mechanisms that underlie these deficits with precision. Specifically, 2 parameters that have been shown to independently contribute to probabilistic reward task outcomes in patient populations, reward sensitivity and learning rate, were extracted, as were trial-by-trial probability analyses of choices as a function of the preceding trial.

Significant deficits in reward sensitivity, but not learning rate, contributed to the anhedonic phenotypes in rats exposed to early-life adversity.

The current findings confirm and extend the translational value of these rodent models by verifying the effectiveness of computational modeling in distinguishing independent features of reward sensitivity and learning rate that complement the probabilistic reward task's signal detection end points. Together, these metrics serve to objectively quantify reinforcement learning deficits associated with anhedonic phenotypes.