Dynamics of EEG functional connectivity during statistical learning

Abstract

Statistical learning is a fundamental mechanism of the brain, which extracts and represents regularities of our environment. Statistical learning is crucial in predictive processing, and in the acquisition of perceptual, motor, cognitive, and social skills. Although previous studies have revealed competitive neurocognitive processes underlying statistical learning, the neural communication of the related brain regions (functional connectivity, FC) has not yet been investigated. The present study aimed to fill this gap by investigating FC networks that promote statistical learning in humans. Young adults (N=28) performed a statistical learning task while 128-channels EEG was acquired. The task involved probabilistic sequences, which enabled to measure incidental/implicit learning of conditional probabilities. Phase synchronization in seven frequency bands was used to quantify FC between cortical regions during the first, second, and third periods of the learning task, respectively. Here we show that statistical learning is negatively correlated with FC of the anterior brain regions in slow (theta) and fast (beta) oscillations. These negative correlations increased as the learning progressed. Our findings provide evidence that dynamic antagonist brain networks serve a hallmark of statistical learning.

Retrieval of a well-established skill is resistant to distraction: evidence from an implicit probabilistic sequence learning task

“…This possibility would fit in well with the hypothesis of competition between the frontal-lobe mediated control functions and the model-free processes. Several studies have detected negative correlations between frontal functions and probabilistic statistical learning by means of behavioral and electrophysiological measurements in healthy subjects (Filoteo, Lauritzen, & Maddox, 2010;Tóth et al, 2017), by studying the developmental aspects of learning (Janacsek, Fiser, & Nemeth, 2012) or populations with hypofrontality (Virag et al, 2015) by using hypnosis to reduce frontal functions (Nemeth, Janacsek, Polner, & Kovacs, 2013), or by disrupting it with brain stimulation techniques (Ambrus et al, 2019). Here we did not find improved performance in the dual-task condition as would be predicted by the competition theory.…”

When less is more: Enhanced statistical learning of non-adjacent dependencies after disruption of bilateral DLPFC

“…Supporting the competition model, the degree of learning drops with the onset of adolescence, coinciding with the maturation of the frontal lobe. Besides the above-mentioned behavioral studies, Tóth et al (2017) have found evidence for this inverse relationship at the level of neural oscillations. Namely, they have found increased statistical learning to be associated with weaker fronto-parietal connectivity in theta frequency which band has a crucial role in memory access (Düzel, Penny, & Burgess, 2010).…”

“…This statistical structure can be learnt by relying on model-free processes (where the complete knowledge is gained directly by actions of trials and error), thus without frontal lobe-mediated hypothesis testing and model-based processes (where an initial top-down model of the environment is acquired prior to action) (Daw et al, 2005;Fiser et al, 2010;Janacsek, Fiser, & Nemeth, 2012;Nemeth, Janacsek, & Fiser, 2013). Evidence for better statistical learning associated with lower frontal functions comes mostly from developmental observations (Janacsek et al, 2012), neuropsychological studies (Virag et al, 2015), electrophysiological studies (Tóth et al, 2017) and from studies that experimentally manipulate the engagement of the two systems (Filoteo, Lauritzen, & Maddox, 2010;Nemeth, Janacsek, Polner, & Kovacs, 2013). These findings match with the well-known theory of language acquisition namely less-is-more by Newport (Goldowsky & Newport, 1993;Newport, 1990).…”

Intention to learn differentially affects subprocesses of procedural learning and consolidation: Evidence from a probabilistic sequence learning task

“…The timing of an experimental trial was the following: The duration of stimulus presentation was 500 ms (when participants were required to respond to the stimulus), then the four empty circles were presented for 120 m before the next stimulus appeared, thus, the total ISI was 700 ms. These values are defined based on previous studies investigating healthy young adults, where participants had an average response time under 450 ms at the beginning of the task and 430 ms by the end of the Learning Phase Nemeth, Janacsek, Polner, & Kovacs, 2013;Tóth et al, 2017;Unoka et al, 2017).…”