Dezső Németh

The paper examines the relationship between mind wandering and seven symptom dimensions in a non-clinical sample, finding that ADHD symptoms uniquely predict mind wandering after accounting for shared variance.

This study finds no gender differences in statistical learning, showing similar reaction times and accuracy in men and women during a visuomotor task.

This study examines how obsessive-compulsive tendencies affect the balance between statistical learning and cognitive flexibility in a university student sample.

This study shows that learning regularities in noise involves both rapid neural predictive activity and slower representational changes, with the former preceding behavioral improvements.

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.

The paper examines the relationship between cognitive flexibility and disordered eating attitudes in a non-clinical sample using performance-based measures of executive function.

This study shows that early reaction time variability predicts implicit statistical learning, with certain metrics indicating a functional role rather than a deficit.

The paper investigates how childhood adversity affects implicit statistical learning speed and finds that greater adversity exposure correlates with faster learning, while lower socio-economic status is linked to poorer performance.

This study investigates how ADHD-like traits affect the interaction between response inhibition and statistical learning in a non-clinical sample.

This paper introduces a gaze-based method to track real-time prediction errors and prior updates during learning, finding that updating depends more on prior consistency than stimulus match.

This study shows that frontal theta synchronization via tACS improves the updating of statistical regularities by reducing erroneous anticipations and enabling flexible predictions.

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 shows that mind wandering is linked to reduced inhibitory control but improved implicit statistical learning, with the learning benefit depending on inhibition efficacy.

The paper investigates individual differences in implicit probabilistic learning and predictive representation updating in people with OC tendencies using two online experiments.

This study investigates how dorsolateral prefrontal cortex (DLPFC) intervention affects the retrieval of statistical knowledge using rTMS, finding enhanced access to statistical regularities with bilateral DLPFC suppression.

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.

This study investigates whether tDCS affects frontal alpha asymmetry and inhibitory control, finding no effect on FAA or inhibitory control metrics but identifying a correlation between reward-related inhibitory activity and trait FAA.

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

This study investigates the effect of tDCS on frontal alpha asymmetry and attentional bias in reward contexts, finding no impact on FAA or behavior but suggesting noradrenergic mechanisms.

The paper investigates how rest period duration affects statistical learning and general skill learning during a task, finding that shorter rest periods improve learning dynamics while longer periods enhance between-block learning.

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.