NÉMETH LAB

“In an ever-changing environment, synchronizing automatic and controlled behaviors is essential for successful adaptation. Yet, the relationship between these behaviors is often debated, partly because of the diverse approaches used to investigate them. The present study examined how statistical learning of environmental regularities, a process that supports automatic behaviors, and interference suppression, which contributes to controlled responding in the presence of distraction, interact when both operate concurrently. Participants completed a visual four-choice reaction time paradigm that combined a statistical learning task with the Eriksen flanker task. In this novel paradigm, a central target stimulus, surrounded by flanker stimuli, was either predictable or unpredictable based on the statistical regularities induced by the sequence of target stimuli. (…)”

“Statistical learning enables us to identify patterns in our environment and predict future events, thereby supporting a range of motor, cognitive, and linguistic skills. Despite its significance, the developmental trajectory of statistical learning has remained unclear due to a lack of comprehensive longitudinal studies. Cross-sectional research has hinted at enhanced statistical learning in children up to 11-13 years, followed by a decline into adulthood, yet definitive insights require a longitudinal approach. (…)”

“Statistical learning, defined as the implicit extraction of environmental regularities, is considered a fundamental cognitive mechanism that supports predictive processing across individuals, domains, and species. However, whether it is modulated by gender remains unclear. This study investigated potential gender-related differences in statistical learning using an age-matched sample of 129 women and 129 men (N = 258) who completed a well-established visuomotor probabilistic learning task. Statistical learning was revealed in both reaction time and accuracy measures, with participants responding faster and more accurately to high-probability than to low-probability trials. Critically, neither the magnitude nor the trajectory of statistical learning differed between women and men. (…)”

Implicit learning and implicit memory both refer to the nonconscious effects that prior information processing may exert on subsequent behavior. Memory for a past event is implicit when it influences ongoing behavior in the absence of conscious recollection of that event. Learning is implicit when people are found to have become sensitive to the regularities shared by a stimulus domain in the absence of a correlated ability to report on the acquired knowledge. Both domains are characterized by continuing definitional, methodological, and theoretical debates about the nature of the differences between information processing with and without awareness. (…)

Obsessive-compulsive disorder (OCD) has been theorized to reflect an imbalance between automatic and goal-directed processes. However, evidence for this imbalance has relied almost exclusively on reward- and feedback-based paradigms, leaving unsupervised learning mechanisms largely unexplored. We investigated this imbalance by examining the relationship between statistical learning (SL) -a feedback-free, implicit mechanism for detecting environmental regularities – and cognitive flexibility, the ability to adapt behavior to changing demands. Adopting a spectrum approach to obsessive-compulsive (OC) tendencies, a total of 404 university students completed an online probabilistic sequence learning task and a card-sorting task

“The ability to extract structured sensory patterns from a noisy environment is fundamental to cognition, yet how the brain learns complex regularities remains unclear. Using magnetoencephalography during a visuomotor task, we tracked the neural dynamics as humans learned non-adjacent temporal dependencies embedded in noise. We reveal that learning is supported by two temporally dissociable mechanisms. Neural predictive activity emerged rapidly, with stimulus-specific patterns appearing before stimulus onset and preceding measurable behavioral improvements. (…)”

“Research at the intersection of neuroscience and education has generated substantial empirical findings but has often lacked a unifying theoretical framework capable of explaining why those findings belong together. Predictive processing, a family of accounts characterizing the brain as a hierarchical system that continuously generates expectations and updates internal models in response to prediction errors, has been proposed as a candidate umbrella framework for the cognitive sciences, though it remains actively debated. We examine whether its core concepts transfer meaningfully to educational contexts. (…)”

“Human adults can extract regularities through implicit learning, resulting in non-conscious knowledge, or through explicit learning, leading to conscious and reportable knowledge. Experiments aiming to disentangle implicit from explicit learning are limited by their heavy reliance on verbal instructions. This prevents the creation of a fully implicit situation and restricts the populations studied, effectively excluding non-verbal individuals. To address these limitations, the current study validates a non-verbal version of a standard test to assess implicit and explicit sequence knowledge: the Process Dissociation Procedure (PDP).  (…)”

” The ability to build predictive models of the environment fundamentally drives adaptive behavior. Yet, the real-time dynamics of how these internal models are formed and updated remain poorly understood. Conventional methods often rely on indirect, offline measures or noisy motor responses, limiting insight into the fine-grained computational processes underlying learning. Here, we introduce a generalizable, gaze-based analytical framework that directly tracks the trial-by-trial dynamics of expectation formation and updating. Applying this framework to an unsupervised probabilistic learning task, we categorized anticipatory saccades to dissociate prediction errors arising from environmental stochasticity from those reflecting an inaccurate internal model, and quantified how these predictions were iteratively revised. (…)”

“Compared to other developmental processes, such as cognitive control, there is relatively little consensus on the developmental trajectory of statistical learning (the ability to implicitly extract environmental regularities). The literature is further complicated as statistical learning may comprise distinct subtypes, differing in the regularities being tracked. Three statistical learning abilities and cognitive control were assessed in one hundred eighty-seven 5–12-year-olds (42.8% female, 84.6% White) at a university laboratory between March 2023 and August 2024. The ability to extract cue-based and nonadjacent statistics was age invariant, whereas adjacent dependency extraction yielded a greater benefit in younger than in older children. (…) “