Neural and Behavioral Psychology Studies

This study shows that covert attention shifts modulate orbitofrontal cortex activity to encode reward value during decision making.

The paper investigates metacognition by analyzing fMRI data from perceptual and memory tasks, finding both domain-specific and domain-general neural representations.

The paper demonstrates that working memory gating policies can be transferred across task contexts, highlighting their role in flexible behavior and task generalization.

The paper decodes dynamical neural signals in humans to reveal cooperation between cognitive and habit learning systems.

The paper examines the association between cortical thickness and impulsive choice in adolescents, finding reduced cortical thickness in specific brain regions linked to impulsive decision-making.

This study examines subcortical brain volume changes across adolescence in males and females using three longitudinal samples, finding sex differences but inconsistent trajectories across samples.

The paper investigates neural mechanisms of stopping information search in self-paced learning, finding increased rule-relevant activation and vmPFC engagement correlates with accurate stopping decisions.

This study shows that using working memory to learn quickly impairs long-term retention of associations, revealing interference between working memory and reinforcement learning.

This study shows prefrontal cortex suppresses salient distractors before their effect is seen in scalp-recorded ERP signals, linking neural activity to distraction control.

The paper proposes a theory that the orbitofrontal cortex provides state representations for decision-making and reinforcement learning, integrating diverse functions through its connectivity.

The paper examines neural responses to negative feedback in stimulant-dependent individuals and finds reduced dACC activity linked to dependence and stimulant use severity.

The paper proposes a neuronal theory explaining sequential economic choice through dynamic attention and value coding mechanisms.

The paper shows advisers strategically modulate their confidence based on their influence level and relative merit, with distinct brain regions tracking these factors and influencing behavior.

The paper identifies pure neural correlates of exploration and exploitation in the human brain using an observe-or-bet task.

This study identifies multimodal neural patterns related to cognitive control using two HCP cohorts and mCCA+jICA, finding consistent components across imaging modalities.

The paper shows that four brain regions represent value estimates on different time-scales, interacting to support flexible decision-making.

The paper shows humans use feature-based learning in dynamic environments and switch to option-based learning when features predict object values accurately.

This study models subjective conflict during intertemporal choice and finds that midfrontal theta and pupil dilation track both conflict and surprising ease of decisions.

This study investigates the role of rostrolateral prefrontal cortex in rule-based category learning by comparing matching and classification tasks, finding that it is involved in evaluating evidence for a rule.

This study shows that neuronal coding in the orbitofrontal cortex uses linear tuning functions with range adaptation to maximize expected payoff in economic decisions.

The paper examines how individual differences in resisting temptation relate to salience network dynamics using fMRI and gaze data.

This study uses MEG and MVPA to show that valence and arousal dimensions are quickly represented in the brain, while some discrete emotions are also rapidly encoded, and different emotions have unique temporal dynamics.

The paper shows that affective biases in processing rewards and punishments reflect estimates of their information content, with implications for depression treatment.

This study shows that right frontopolar cortex inhibition selectively reduces directed exploration, indicating distinct neural systems for directed and random exploration strategies.

The paper shows that striatum responses reflect a surprise signal from a Bayesian rule-learning model, not reward prediction error, during explicit rule learning.

The paper tests a model of lateral prefrontal cortex dynamics using transcranial magnetic stimulation and finds that causal manipulations refine the original dynamic causal model.

The paper introduces hBayesDM, an R package for computational modeling of reinforcement learning and decision-making tasks, enabling user-friendly Bayesian analysis and visualization.

This study investigates the successor representation (SR) in human reinforcement learning, showing it balances flexibility and efficiency by being insensitive to task sequence changes but responsive to reward changes.

The paper uses diffusion and functional MRI data to identify a tripartite parcellation of the human substantia nigra based on connectivity and functional coding of value and salience, linking these to impulsivity traits.

This study investigates ERP responses to appetitive and aversive tastes, finding distinct patterns for salience and prediction error, challenging existing interpretations of the feedback-related negativity.