Anne Gabrielle Eva Collins

The paper shows humans can learn counterintuitive goals but with initial costs, supported by fast revaluation and slow value updating mechanisms.

This study identifies dual process impairments in reinforcement learning and working memory in individuals with physiological anxiety, showing reduced learning rates and increased WM decay.

This study shows dopamine enhances both reinforcement learning and working memory, reduces effort costs, and highlights the need to account for its effects on working memory when studying reinforcement learning.

The paper shows that dopamine release in the nucleus accumbens during instrumental learning reflects Bayesian inference of probabilistic beliefs about hidden environmental states.

The paper investigates reinforcement learning in male and female mice with ASD-linked mutations, finding enhanced learning in males but not females, with effects modulated by reward uncertainty.

This study shows adolescent and adult mice use both reinforcement learning and short-term memory strategies to learn a conditional odor association task, with no developmental difference in learning rates but increased reliance on perseverative strategies in males.

This paper uses artificial neural networks to estimate parameters and identify cognitive models without likelihood computation, demonstrating effectiveness for models with inter-trial dependencies.

This paper introduces a method to dynamically estimate noise levels in decision-making tasks, improving model fit and parameter estimation compared to static noise assumptions.

The paper investigates assumptions about parameter generalization and interpretability in reinforcement learning models across different tasks and participants.

This study investigates the role of the indirect pathway in choice suppression using chemogenetic manipulation in mice, finding that activation, not inhibition, of iSPNs disrupts suppression of nonrewarded choices.

This study finds adolescents outperform younger and older participants in a stochastic reversal-learning task, with optimal RL and BI parameters and unique processing of positive feedback.

The paper investigates how working memory capacity and forgetting rate relate to event segmentation and memory performance in different tasks.

This study investigates how probabilistic reinforcement learning changes with age during adolescence and adulthood, finding increased learning rates and decreased exploration with age, and a link between testosterone and learning rates in mid-adolescence.

This study investigates how humans use hierarchical temporal and state abstractions to transfer knowledge across tasks, showing transfer effects not explained by simpler models.

This study shows human behavior in complex environments can be explained by hierarchical reinforcement learning, outperforming flat RL and hierarchical Bayesian models in capturing observed patterns.

The paper investigates how the brain learns from transient goals using behavior, modeling, and neuroimaging, finding that executive function and reward circuits interact to support flexible learning.

This study investigates how reinforcement learning and working memory contribute to learning changes during adolescence, finding greater developmental changes in reinforcement learning than working memory.

The paper investigates how fronto-basal ganglia circuitry contributes to response inhibition and conflict-induced slowing using fMRI and reinforcement-learning models.

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

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