Biology subjects

Davidow, J. Y.

Publications and source records attributed to Davidow, J. Y..

3 recordsLinked to original sources

bioRxiv2025

Twice as nice: Boosts in adolescent reinforcement learning from Pavlovian bias and age-related prioritization of reward-motivated incidental memory.

Adolescence is a period marked by profound changes in both capacities for learning and the motivational drives that guide behavior. Motivated learning, including the ability to associate cues with actions that lead to positive or negative outcomes, is a fundamental component of adaptive behavior and is essential for survival. Equally important is the encoding of events during learning, which may be influenced by the valence of outcomes. Given the substantial neurocognitive changes in motivated learning and memory that occur from childhood to adulthood, adolescence provides a unique window to investigate mechanisms of these adaptive behaviors. Yet, we know surprisingly little about the development of these behaviors, with sparse extant research fraught with inconsistent findings. In this study, we examined motivated learning and incidental memory using a validated affective learning task in a sample of 174 participants aged 8 to 25 years. The task orthogonalized action and outcome valence and included incidental encoding of trial-unique images presented during feedback, followed by a delayed memory test. We show that adolescents outperform both children and adults in learning by leveraging Pavlovian response biases. In contrast, children exhibit enhanced memory for stimuli associated with positive outcomes compared to adolescents and adults. These findings point to distinct developmental advantages: enhanced learning performance in adolescence and enhanced memory for rewarding events in childhood, each potentially adaptive at their respective developmental stages. Together, these findings suggest opportunities to leverage learning and memory in youth for practical applications, such as education and policy setting.

neuroscience↗

bioRxiv2025

Disentangling Prediction and Feedback in Social Brain Networks: A Predictive Processing Approach

While much research in social cognitive neuroscience has focused on which brain regions are engaged when processing social content, it remains unclear what these areas are doing in terms of underlying mechanisms. Here, we approached this question using predictive processing theory, which suggests that the brain instantiates a generative model of its sensory environment. Using a novel animated shapes fMRI task, we observed a functional double dissociation between brain regions that were engaged when forming a prediction for agentic movement - which involved the premotor cortex and the lateral parietal cortex, previously implicated in action observation and mirroring - from those associated with processing feedback for updating abstract priors to guide predictions - which involved the dorsomedial and ventrolateral prefrontal cortex, the temporoparietal area, and the lateral temporal cortex, previously associated with mentalizing/theory of mind. We observed parallel functional dissociations in the cerebellar areas affiliated with these networks. These findings suggest new insights into how brain regions associated with action observation/mirroring and mentalizing/theory of mind play complementary roles in supporting facets of a predictive processing architecture underlying social cognition.

neuroscience↗

bioRxiv2016

Dynamic flexibility in striatal-cortical circuits supports reinforcement learning

Complex learned behaviors must involve the integrated action of distributed brain circuits. While the contributions of individual regions to learning have been extensively investigated, understanding how distributed brain networks orchestrate their activity over the course of learning remains elusive. To address this gap, we used fMRI combined with tools from dynamic network neuroscience to obtain time-resolved descriptions of network coordination during reinforcement learning. We found that learning to associate visual cues with reward involves dynamic changes in network coupling between the striatum and distributed brain regions, including visual, orbitofrontal, and ventromedial prefrontal cortex. Moreover, we found that flexibility in striatal network dynamics correlates with participants learning rate and inverse temperature, two parameters derived from reinforcement learning models. Finally, we found that not all forms of learning relate to this circuit: episodic memory, measured in the same participants at the same time, was related to dynamic connectivity in distinct brain networks. These results suggest that dynamic changes in striatal-centered networks provide a mechanism for information integration during reinforcement learning.\n\nSignificance StatementLearning from the outcomes of actions-referred to as reinforcement learning-is an essential part of life. The roles of individual brain regions in reinforcement learning have been well characterized in terms of the updating of values for actions or sensory stimuli. Missing from this account, however, is a description of the manner in which different brain areas interact during learning to integrate sensory and value information. Here we characterize flexible striatal-cortical network dynamics that relate to reinforcement learning behavior.

neuroscience↗