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Long-term exposure to nicotine alters brain circuits and induces profound changes in decision-making strategies, affecting behaviors both related and unrelated to drug seeking and consumption. Using an intracranial self-stimulation reward-based foraging task, we investigated in mice the impact of chronic nicotine on midbrain dopamine neuron activity and its consequence on the trade-off between exploitation and exploration. Model-based and archetypal analysis revealed substantial inter-individual variability in decision-making strategies, with mice passively exposed to nicotine shifting toward a more exploitative profile compared to non-exposed animals. We then mimicked the effect of chronic nicotine on the tonic activity of dopamine neurons using optogenetics, and found that photo-stimulated mice adopted a behavioral phenotype similar to that of mice exposed to chronic nicotine. Our results reveal a key role of tonic midbrain dopamine in the exploration/exploitation trade-off and highlight a potential mechanism by which nicotine affects the exploration/exploitation balance and decision-making. Chronic nicotine exposure impacts various components of decision-making processes, such as exploratory behaviors. Here, the authors identify the cellular mechanism and show that chronic nicotine exposure increases the tonic activity of VTA dopaminergic neurons and reduces exploration in mice.

Can decisions be made solely by chance? Can variability be intrinsic to the decision-maker or is it inherited from environmental conditions? To investigate these questions, we designed a deterministic setting in which mice are rewarded for non-repetitive choice sequences, and modeled the experiment using reinforcement learning. We found that mice progressively increased their choice variability. Although an optimal strategy based on sequences learning was theoretically possible and would be more rewarding, animals used a pseudo-random selection which ensures high success rate. This was not the case if the animal is exposed to a uniform probabilistic reward delivery. We also show that mice were blind to changes in the temporal structure of reward delivery once they learned to choose at random. Overall, our results demonstrate that a decision-making process can self-generate variability and randomness, even when the rules governing reward delivery are neither stochastic nor volatile. Marwen Belkaid et al. develop a deterministic task to study variability in decision-making processes. They show that mice embrace behavioral variability as an effective decision-making strategy, highlighting the brain’s ability to generate random decisions independently from the environment statistics.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Flexible decision-making requires not only updating values, but redefining which features constitute an action in a given context. We recorded nucleus accumbens (NAc) dopamine release while mice navigated a three-target intracranial self-stimulation foraging task in which outcomes were evaluated under three distinct reward delivery rules. Despite a constant motor repertoire, dopamine transients reorganized across contingencies and generalized linear models revealed context-dependent dopamine signal reflecting action direction, recent outcome-history, or target identity. Reinforcement-learning model comparison showed that these signatures are best explained by distinct reward prediction errors (RPEs) defined over different state-action representations, rather than a single fixed model-free scheme. A single deep reinforcement-learning agent trained by temporal-difference learning, recapitulated both the rule-specific policies and the corresponding dopamine signature. These results identify NAc dopamine as a dynamic readout of representation learning, remapping prediction errors onto the task features that define successful action as contingencies change.Competing Interest StatementThe authors have declared no competing interest.Footnotes* This revised version adds a neural-network reinforcement-learning (deep RL) agent to complement the interpretable RL/RPE analyses. A single deep RL agent trained by temporal-difference learning reproduces the rule-specific behavioral policies and recapitulates the rule-dependent structure of dopamine-like prediction-error signals, resulting in new/updated main figures (Figs. 6-7) and associated Methods and Supplementary analyses. We also updated the title, text and author list/affiliations accordingly.Funder Information DeclaredFondation pour la Recherche Médicale, Equipe FRM DEQ2013326488, PhD fellowhsip ECO201806006688, Fourth-year PhD fellowship FDT201904008060Institut National du Cancer, TABAC-19-020, SPA-21-002Agence Nationale de la Recherche, ANR-19-CE16-0028 Bavar, ANR-23-CE37-0017 VarSeek

Summary Long-term exposure to nicotine alters brain circuits and induces profound changes in decision-making strategies, affecting behaviors both related and unrelated to drug seeking and consumption. Using an intracranial self-stimulation reward-based foraging task, we investigated the impact of chronic nicotine on the trade-off between exploitation and exploration, and the role of ventral tegmental area (VTA) dopamine (DA) neuron activity in decision-making unrelated to nicotine-seeking. Model-based and archetypal analysis revealed a substantial inter-individual variability in decision-making strategies, with mice passively exposed to chronic nicotine visiting more frequently options associated with higher reward probability and therefore shifting toward a more exploitative profile compared to non-exposed animals. We then mimicked the effect of chronic nicotine on the tonic activity of VTA DA neurons using optogenetics, and found that photo-stimulated mice had a behavioral phenotype very close to that of mice exposed to nicotine, suggesting that the dopaminergic control of the exploration/exploitation balance is altered under nicotine exposure. Our results thus reveal a key role of tonic midbrain DA in the exploration/exploitation trade-off and highlight a potential mechanism by which nicotine affects decision-making. Competing Interest Statement The authors have declared no competing interest.

Can decisions be made solely by chance? To investigate this question, we designed a deterministic setting in which mice are rewarded for non-repetitive choice sequences, and modeled the experiment using reinforcement learning. We found that mice progressively increased their choice variability using a memory-free, pseudo-random selection, rather than by learning complex sequences. Our results demonstrate that a decision-making process can self-generate variability and randomness even when the rules governing reward delivery are neither stochastic nor volatile. Footnotes * Introduction, task description and discussion updated to clarify; Figure 3 updated and Figure 4 added to describe additional results.