Pupillary Dynamics of Mice Performing a Pavlovian Delay Conditioning Task Reflect Reward-Predictive Signals.

Abstract

Pupils can signify various internal processes and states, such as attention, arousal, and working memory. Changes in pupil size have been associated with learning speed, prediction of future events, and deviations from the prediction in human studies. However, the detailed relationships between pupil size changes and prediction are unclear. We explored pupil size dynamics in mice performing a Pavlovian delay conditioning task. A head-fixed experimental setup combined with deep-learning-based image analysis enabled us to reduce spontaneous locomotor activity and to track the precise dynamics of pupil size of behaving mice. By setting up two experimental groups, one for which mice were able to predict reward in the Pavlovian delay conditioning task and the other for which mice were not, we demonstrated that the pupil size of mice is modulated by reward prediction and consumption, as well as body movements, but not by unpredicted reward delivery. Furthermore, we clarified that pupil size is still modulated by reward prediction even after the disruption of body movements by intraperitoneal injection of haloperidol, a dopamine D2 receptor antagonist. These results suggest that changes in pupil size reflect reward prediction signals. Thus, we provide important evidence to reconsider the neuronal circuit involved in computing reward prediction error. This integrative approach of behavioral analysis, image analysis, pupillometry, and pharmacological manipulation will pave the way for understanding the psychological and neurobiological mechanisms of reward prediction and the prediction errors essential to learning and behavior. Manuscript contributions to the fieldPredicting upcoming events is essential for the survival of many animals, including humans. Accumulating evidence suggests that pupillary responses reflect autonomic activity and are modulated by noradrenergic, cholinergic, and serotonergic neurotransmission. However, the relationships between pupillary responses, reward prediction, and reward prediction errors remain unclear. This study examined changes in pupil size while water-deprived mice performed a Pavlovian delay conditioning task using a head-fixed setup. The head-fixed experimental setup, combined with deep-learning-based image analysis, enabled us to reduce spontaneous locomotor activity and to track the precise dynamics of the licking response and the pupil size of behaving mice. A well-controlled, rigid behavioral experimental design allowed us to investigate the modulation of behavioral states induced by reward prediction. While pharmacological manipulation might affect pupil size, the combined approach of pupillometry and pharmacological manipulation allowed us to differentiate reward prediction signals and signals modulated by body movements. We revealed that the changes in pupil size (1) reflect reward prediction signals and (2) do not reflect signals of reward prediction error. These results provide novel insights into the neuronal circuitry potentially involved in computing reward prediction errors. The integrative approach of behavioral analysis, image analysis, pupillometry, and pharmacological manipulation used in this study will pave the way for understanding the psychological and neurobiological mechanisms of prediction and the prediction errors essential in learning and behavior.