Karolina Janacsek

This study examines how children and adults update beliefs during statistical learning, finding children show more frequent prediction shifts and greater sensitivity to new evidence.

This study investigates how executive functions relate to updating probabilistic predictive representations during habit rewiring, finding positive links with inhibition and negative links with semantic fluency.

This study inhibits the right and bilateral DLPFC in adults during a statistical learning task and finds enhanced learning compared to sham, suggesting a hemisphere-dependent role of DLPFC in modulating unsupervised learning.

The paper uses EEG data to analyze similarity in brain activity during learning and rest periods to predict memory consolidation, finding distinct patterns for serial-order and probability information.

The paper investigates the relationship between implicit statistical learning and executive functions, finding a consistent negative correlation driven by verbal fluency and working memory.

The paper distinguishes between statistical learning during task performance and high-order rule learning during rest periods using visuomotor sequence tasks.

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.

The paper measures ERP markers of anticipation in a reaction time task to show larger CNV for unpredictable events, suggesting enhanced attentional resources for implicit anticipation of probabilistic regularities.

The study shows that retrieving well-established probabilistic sequence knowledge is not affected by a concurrent secondary task, despite increased reaction times in the primary task.

The paper investigates how statistical and rule-based learning mechanisms are represented in EEG data and identifies distinct neural correlates for each.

This study shows that implicit prior knowledge of predictable visual stimuli influences processing of unpredictable stimuli, with prior knowledge persisting longer than it takes to update.

The paper infers individualized internal models from response times in a visuomotor learning task, revealing structured inductive bias varying across individuals.

This study shows that disrupting the DLPFC with TMS improves statistical learning of non-adjacent dependencies, suggesting a shift from model-based to model-free learning.

This study investigates how stress affects the acquisition of statistical and sequential regularities, finding stress enhances statistical learning but not sequential learning.

The study examines whether short rest periods improve implicit probabilistic sequence learning and general skill, finding that general skill benefits from longer rest periods while probabilistic knowledge decreases regardless of rest duration.

This paper tests the relationship between subjective sleep quality and cognitive performance in healthy young adults across three studies and finds no significant association.

This study investigates how speed and accuracy instructions affect statistical learning in a probabilistic sequence task, finding similar underlying representations despite performance differences.

The paper examines procedural learning trajectories across the lifespan, finding children outperform older adults in general skill learning when controlling for response times, while statistical learning declines gradually.

This study investigates how intentional versus incidental learning affects procedural memory acquisition and consolidation using a probabilistic sequence learning task.

This study shows that slower stimulus presentation rates reduce performance in procedural learning tasks but do not affect the underlying knowledge acquired.

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.

This study investigates the relationship between implicit probabilistic sequence learning and risky decision making, identifying clusters of participants with distinct risk-taking behaviors and sensitivity to statistical regularities.