Maria Katharina Eckstein

Reuse of learned subtask representations during human planning

The paper demonstrates that humans reuse learned subtask representations during planning, showing behavioral, computational, and neural evidence of subtask reactivation and reuse.

AI-Discovered Cognitive Models Reveal Novel Insights into Human and Animal Learning

This paper introduces DataDIVER, an automated method for discovering computational models from data, which reveals novel insights into human and animal learning.

Functional reorganization of motor cortex connectivity during learning

This study shows how rapid learning in mice involves structured changes in motor cortex connectivity, rerouting preparatory activity to control a conditioned neuron.

Reasoning with programs in replay

The paper investigates how the brain executes programs by analyzing neural activity during mental program execution, finding that fast neural sequences represent candidate solutions.

Low dimensional latent structure underlying the choices of mice

The paper applies a hybrid model to mouse decision-making data to uncover cognitive processes by combining theory-driven and data-driven approaches.

Hybrid Neural-Cognitive Models Reveal Flexible Context-Dependent Information Processing in Reversal Learning

This study introduces HybridRNNs, neural-cognitive models that combine reinforcement learning with recurrent networks to better explain human reversal learning behavior.

Discovering Symbolic Cognitive Models from Human and Animal Behavior

LLM-driven evolutionary program synthesis (CogFunSearch) discovers interpretable Python cognitive models that predict bandit-task choices of humans, rats and fruit flies better than leading hand-designed models.

Predictive and Interpretable: Combining Artificial Neural Networks and Classic Cognitive Models to Understand Human Learning and Decision Making

The paper combines artificial neural networks with classic cognitive models to create interpretable hybrid models that reveal human learning mechanisms similar to reinforcement learning.

Cognitive Model Discovery via Disentangled RNNs

This paper learns cognitive models from behavioral data using disentangled RNNs, recovering interpretable models from synthetic and rat behavioral data.

The Interpretation of Computational Model Parameters Depends on the Context

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

Reinforcement Learning and Bayesian Inference Provide Complementary Models for the Unique Advantage of Adolescents in Stochastic Reversal

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.

Modeling Changes in Probabilistic Reinforcement Learning during Adolescence

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.

Computational evidence for hierarchically-structured reinforcement learning in humans

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.

Distentangling the systems contributing to changes in learning during adolescence

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.