Neural and Behavioral Psychology Studies

The paper shows humans use forward thinking to guide social choices, with neural activity reflecting control beliefs in specific brain regions.

This study shows acute stress reduces prediction error signals in the dorsal striatum during reinforcement learning, particularly for positive outcomes.

This study uses fMRI meta-analysis to identify brain regions involved in effort valuation, finding distinct roles for pre-SMA and vmPFC in effort cost and net value integration.

The paper identifies subpopulations of lOFC neurons that encode reward history and choice during decision-making in rats.

This study shows that hunger enhances reinforcement-driven decision-making but not planned actions, suggesting a bias toward primitive learning when hungry.

This study investigates how noradrenergic system perturbations affect reversal learning by using a block-based reversal design, finding that choice accuracy is independent of win-stay behavior and noradrenergic control influences sensitivity to positive feedback.

This study investigates neuronal activity in the dorsal anterior cingulate cortex during economic choices with variable action costs, finding that ACCd encodes post-decision variables but not pre-decision variables.

The paper shows that vmOFC neurons signal relative reward salience, influencing learning rate through thalamic inputs in mice.

The paper examines how striatal BOLD and midfrontal theta power relate to motivation for action during a Go/NoGo task, finding distinct roles for early and late theta components in reflecting motivational biases and behavioral responses.

This study examines prosocial reinforcement learning in adolescents, finding increased ventromedial prefrontal cortex activity with age during learning for others.

This study decodes health and taste representations from brain activity during food decision-making, showing health information is processed earlier than taste in implicit decisions.

This study identifies brain regions involved in tracking effort expenditure during sustained goal pursuit using fMRI and multivariate pattern analysis.

The paper introduces a computational model to measure the affective value of intrinsic and extrinsic rewards using neural data from a reinforcement learning task.

This study shows that prefrontal cortex recruitment improves with age, supporting better memory for valuable information in children, adolescents, and adults.

The paper shows humans use socially adjusted value strategies in foraging, with fMRI revealing mid-cingulate and ventromedial prefrontal cortex encoding this value.

This study compares massed and spaced training in reward-based learning, finding that spaced training reduces working memory contributions and isolates gradual learning mechanisms.

The paper shows that reversible fronto-occipitotemporal signaling supports task encoding and behavioral flexibility under ambiguous cues by alternating top-down and bottom-up signals.

The paper investigates how bodily arousal affects decision-making in macaques by analyzing neural activity in the orbitofrontal cortex and dorsal anterior cingulate cortex, showing changes in HR encoding after amygdala lesions.

This study investigates the roles of prefrontal and parietal cortices in sequential categorical decisions using neural recordings and computational models.

This study identifies a visual-to-motor inhibitory circuit in the ACC that drives goal-directed actions in mice through gated feedforward inhibition.

The paper shows that medial frontal cortex activity predicts information sampling in economic decisions and explains how choice difficulty is encoded in this region.

The paper investigates the neural origin of dual-task cost in humans using intracranial EEG, finding gamma-band interferences in task-relevant brain regions and showing adaptive strategies to minimize interference.

The paper investigates brain circuits involved in metacognitive awareness of difficulty during action selection, finding increased activity in the rostral cingulate zone and anterior insula during subjective difficulty ratings.

This paper introduces a linear reinforcement learning model that explains flexible planning, cognitive control, and biases in decision-making by reusing temporally abstracted future event maps.

The DMCC project investigates two cognitive control modes, proactive and reactive, using neuroimaging and behavioral tasks, finding a domain-general neural signature for reactive control.

This paper presents a rational analysis of cognitive fatigue and boredom as opportunity cost measures in controlled behavior, offering a unified framework for understanding these phenomena.

This paper introduces a TD model of human decision-making with two critics to explain how serotonin and dopamine influence risk and uncertainty processing.

The paper investigates how the brain represents and updates the value of information (VOI) during decision-making using fMRI and a modified beads task.

This study identifies brain regions consistently activated during intentional decisions and categorizes decision types using meta-analyses of fMRI/PET studies.

This study examines how metacognitive accuracy changes with age using ERPs, finding a decline in accuracy beyond task performance decline, while behavioral adaptation remains intact.