A novel multidimensional reinforcement task in mice elucidates sex-specific behavioral strategies.
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ABSTRACT: A large body of work has focused on understanding stimulus-driven behavior, sex differences in these processes, and the neural circuits underlying them. Many preclinical mouse models present rewarding or aversive stimuli in isolation, ignoring that ethologically, reward seeking requires the consideration of potential aversive outcomes. In addition, the context (or reinforcement schedule under) in which stimuli are encountered can engender different behavioral responses to the same stimulus. Thus, delineating neural control of behavior requires a dissociation between stimulus valence and stimulus-driven behavior. We developed the Multidimensional Cue Outcome Action Task (MCOAT) to dissociate motivated action from cue learning and valence in mice. First, mice acquire positive and negative reinforcement in the presence of discrete discriminative stimuli. Next, discriminative stimuli are presented concurrently allowing for parsing innate behavioral strategies based on reward seeking and avoidance. Lastly, responding in the face of punishment is assessed, thus examining how positive and negative outcomes are relatively valued. First, we identified sex-specific behavioral strategies, showing that females prioritize avoidance of negative outcomes over seeking positive, while males have the opposite strategy. Next, we show that chemogenetically inhibiting D1 medium spiny neurons (MSNs) in the nucleus accumbens-a population that has been linked to reward-driven behavior-reduces positive and increases negative reinforcement learning rates. Thus, D1 MSNs modulate stimulus processing, rather than motivated responses or the reinforcement process itself. Together, the MCOAT has broad utility for understanding complex behaviors as well as the definition of the discrete information encoded within cellular populations.
SUBMITTER: Kutlu MG
PROVIDER: S-EPMC7360782 | biostudies-literature |
REPOSITORIES: biostudies-literature
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