Abstract
Pupils can signify various internal processes and states, such as attention, arousal, and working memory. Changes in pupil size are reportedly associated with learning speed, prediction of future events, and deviation from prediction in human studies. However, the detailed relationship between pupil size change and prediction is unclear. We explored the dynamics of the pupil size in mice performing a Pavlovian delay conditioning task. 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 pupil size of behaving mice. By manipulating the predictability of the reward in the Pavlovian delay conditioning task, we demonstrated that the pupil size of mice is modulated by reward prediction and consumption, as well as body movements, but not by the unpredicted reward delivery. Furthermore, we clarified that the 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 the changes in the pupil size reflect the reward prediction signals and do not reflect reward prediction error signals, thus we provide important evidence to reconsider the neuronal circuit computing the 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 field Predicting upcoming events is essential for the survival of many animals, including humans. Accumulating evidence suggests that pupillary responses reflect autonomic activities modulated by noradrenergic, cholinergic, and serotonergic neurotransmission. However, the relationship between pupillary responses and reward prediction and reward prediction error remains 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 behavioral states modulation induced by reward prediction. Pharmacological manipulation allowed us to differentiate the reward prediction signal itself and the signal modulated by body movements. This study integrated behavioral analysis techniques, image analysis, pupillometry, and pharmacological manipulation. We revealed that the changes of the pupil size (1) reflect reward prediction signals and (2) do not reflect reward prediction error signals. These results provide important evidence to reconsider the neuronal circuit computing reward prediction errors. The approach used in this study will pave the way for understanding the psychological and neurobiological mechanisms of the prediction and the prediction error that are essential in learning and behavior.
- dopamine
- reward prediction error
- pupil
- licking
- Pavlovian conditioning
- mice
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
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Figure 4E revised