Dopaminergic and prefrontal dynamics co-determine mouse decisions in a spatial gambling task.

The neural mechanisms by which animals initiate goal-directed actions, choose between options, or explore opportunities remain unknown. Here, we develop a spatial gambling task in which mice, to obtain intracranial self-stimulation rewards, self-determine the initiation, direction, vigor, and pace of their actions based on their knowledge of the outcomes. Using electrophysiological recordings, pharmacology, and optogenetics, we identify a sequence of oscillations and firings in the ventral tegmental area (VTA), orbitofrontal cortex (OFC), and prefrontal cortex (PFC) that co-encodes and co-determines self-initiation and choices.

Chronic nicotine increases midbrain dopamine neuron activity and biases individual strategies towards reduced exploration in mice.

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.

Author Correction: Mice adaptively generate choice variability in a deterministic task.

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

Mice adaptively generate choice variability in a deterministic task.

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.