Noémi Éltető

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

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.

This study investigates how initial experience influences risky decisions after a change point, finding that recent experiences adjust behavior while initial experience still has an impact.

This study finds adolescents do not take more risks than children or adults in a novel uncertain situation, challenging the idea of increased risk-taking during adolescence.

The paper investigates how sequence and statistical learning change during training and off-line periods, finding that sequence learning improves more with practice and shows different consolidation patterns than statistical learning.