Neural and Behavioral Psychology Studies

This study examines how sulcal morphology in the lateral prefrontal cortex changes with age and relates to reasoning performance in children and adolescents.

This study uses simultaneous EEG-fMRI to examine thalamic activity during working memory delay periods under different memory loads, finding reduced thalamic activation with higher load.

This study investigates how mice and monkeys adjust to different types of uncertainty in reward environments using choice data and reinforcement learning models.

The paper investigates how brain structure, specifically grey matter density in the parietal cortex, mediates age-related increases in model-based control and learning from positive feedback during adolescence to adulthood.

This study shows that losartan improves reward learning by enhancing ventral striatal prediction errors and frontostriatal communication in healthy humans.

This study shows that noradrenergic projections to the rodent orbitofrontal cortex are necessary for updating action-outcome relationships during reversal learning.

This study investigates how social network structures influence human learning through a real-time distributed learning task, showing that learning is weighted toward well-connected neighbors based on informativeness.

This study examines age differences in frontoparietal connectivity during task switching in children and adults, finding that children's performance is linked to less adult-like activation patterns and increased IFJ-lPFC connectivity.

The paper proposes a cognitive space theory to reconcile domain-general and domain-specific cognitive control by showing conflicts are organized in a low-dimensional space based on similarity.

The paper challenges the interpretation of larger error negativity peak amplitudes under accuracy instructions, suggesting they reflect better neuro-cognitive alignment rather than more intense error processing.

The paper assesses the reliability of reinforcement learning and prospect theory parameters from two tasks and finds they predict future behavior reliably.

This study shows that children's multiple demand network is selective to cognitive demand, distinct from language regions, and increases in selectivity with improved executive function, independent of age.

This paper challenges claims about BOLD variability as a unique marker of cognitive performance by testing hypotheses related to its relationship with mean BOLD and trial design.

This study explores how cognitive control affects the geometry of cognitive maps in the brain and in neural networks, showing stable and context-dependent representations that relate to task demands.

This study investigates how single pulse TMS over prefrontal and premotor areas affects cognitive control processes during a Simon task, finding enhanced P300 amplitude but no behavioral changes.

The paper investigates how latent cause inference can lead to instrumental insensitivity to reinforcer devaluation without requiring model-free mechanisms.

This study investigates strategic exploration in macaques, showing distinct neural circuits in ACC/MCC-dlPFC and OFC that support strategic decision-making and counterfactual learning.

The paper shows that people can transfer learned expectations about cognitive flexibility demands across different task environments and stimuli.

The paper shows that prosocial behavior and interoceptive awareness are linked to neural similarity and dissimilarity in the ACCg and AI during vicarious reward processing.

This study investigates how task-irrelevant visual salience influences attention and valuation systems to drive incidental learning through behavioral and neuroimaging experiments.

This study shows that monkeys' food choices and learning are influenced by the nutrient content of rewards, with a nutrient-sensitive reinforcement learning model capturing these behaviors.

The paper uses computational modeling and EEG to study how contextual and individual differences in altruism arise from distinct neural processes during decision-making.

This paper introduces task-DyVA, a deep learning framework that models human response time sequences, capturing subject-specific behavioral differences and providing insights into cognitive theories.

This study investigates how stop-signal salience affects inhibitory control by analyzing electro-cortical responses during stop-signal tasks.

This paper investigates the link between impulsivity and preSMA activity during sequential gambling using rTMS, finding that preSMA modulation alters the relationship between trait and state impulsivity.

The paper identifies a neural substrate in mice where female-specific modulation of motivation by value is mediated by ACC-DMS neurons.

The paper shows that a drift diffusion model with collapsing decision thresholds better captures time-dependent decision policies than fixed threshold models, supported by EEG beta power lateralization analysis.

The paper investigates how value biases influence fast-paced perceptual decisions through distinct phases of anticipation, detection, and discrimination.