Humans and other animals require flexibility to make decisions appropriate for an ever-changing environment. The way mammalian brains achieve flexible decision making is not fully understood. The probabilistic reversal learning (PRL) task is a powerful tool used to assess behavioral flexibility and how positive and negative feedbacks influence decision making, both in healthy subjects and in patients with a wide range of neurological conditions like Autistic Spectrum Disorder and Schizophrenia, as well as animal models. We trained C57BL/6 mice on PRL and conducted high-density single unit recordings using Neuropixels probes from the medial frontal cortex in well-performing animals. The animals had binary choices (e.g., left vs right) associated with different reward probabilities (e.g., 80% vs 20%), which they discovered via trial and error. Once a consistent choice was established, the contingencies were reversed, so the previously highly rewarded side became unfavorable and vice versa. After one month of training, mice were able to flexibly switch contingencies multiple times in one training session. So, as contingency undergoes multiple changes, animals were able to maximize the accumulated reward, via the integration of positive and negative feedback. We aim to identify the neural correlates for the choice value (i.e., the probability of reward) and the reward prediction error (RPE, i.e., when the obtained reward differs from the value). We will then examine how the choice value and RPE propagate in the mammalian brain.