The ability to form long-term memories begins in early infancy. However, little is known about the specific mechanisms that guide memory formation during this developmental stage. We demonstrate the emergence of a long-term memory for a novel voice in three-month-old infants using the EEG mismatch response (MMR) to the word “baby”. In an oddball-paradigm, a frequent standard, and two rare deviant voices (novel and mother) were presented before (baseline), and after (test) familiarizing the infants with the novel voice and a subsequent nap. Only the mother deviant but not the novel deviant elicited a late frontal MMR (∼850 ms) at baseline, possibly reflecting a long-term memory representation for the mother’s voice. Yet, MMRs to the novel and mother deviant significantly increased in similarity after voice familiarization and sleep. Moreover, both MMRs showed an additional early (∼250 ms) frontal negative component that is potentially related to deviance processing in short-term memory. Enhanced spindle activity during the nap predicted an increase in late MMR amplitude to the novel deviant and increased MMR similarity between novel and mother deviant. Our findings indicate that the late positive MMR in infants might reflect emergent long-term memory that benefits from sleep spindles.
Reinforcement learning, crucial for behavior in dynamic environments, is driven by rewards and punishments, modulated by dopamine (DA) changes. This study explores the dopaminergic system’s influence on learning, particularly in Parkinson’s disease (PD), where medication leads to impaired adaptability. Highlighting the role of tonic DA in signaling the valence of actions, this research investigates how DA affects response vigor and decision-making in PD. DA not only influences reward and punishment learning but also indicates the cognitive effort level and risk propensity in actions, which are essential for understanding and managing PD symptoms.
In this work, we adapt our existing neurocomputational model of basal ganglia (BG) to simulate two reversal learning tasks proposed by Cools et al. We first optimized a Hebb rule for both probabilistic and deterministic reversal learning, conducted a sensitivity analysis (SA) on parameters related to DA effect, and compared performances between three groups: PD-ON, PD-OFF, and control subjects.
In our deterministic task simulation, we explored switch error rates after unexpected task switches and found a U-shaped relationship between tonic DA levels and switch error frequency. Through SA, we classify these three groups. Then, assuming that the valence of the stimulus affects the tonic levels of DA, we were able to reproduce the results by Cools et al.
As for the probabilistic task simulation, our results are in line with clinical data, showing similar trends with PD-ON, characterized by higher tonic DA levels that are correlated with increased difficulty in both acquisition and reversal tasks.
Our study proposes a new hypothesis: valence, signaled by tonic DA levels, influences learning in PD, confirming the uncorrelation between phasic and tonic DA changes. This hypothesis challenges existing paradigms and opens new avenues for understanding cognitive processes in PD, particularly in reversal learning tasks.

