Neurophysiological and functional neuroanatomical coding of statistical and deterministic rule information during sequence learning

Abstract

Humans are capable of acquiring multiple types of information presented in the same information stream. It has been suggested that at least two parallel learning processes are important during learning of sequential patterns—statistical learning and rule‐based learning. Yet, the neurophysiological underpinnings of these parallel learning processes are not fully understood. To differentiate between the simultaneous mechanisms at the single trial level, we apply a temporal EEG signal decomposition approach together with sLORETA source localization method to delineate whether distinct statistical and rule‐based learning codes can be distinguished in EEG data and can be related to distinct functional neuroanatomical structures. We demonstrate that concomitant but distinct aspects of information coded in the N2 time window play a role in these mechanisms: mismatch detection and response control underlie statistical learning and rule‐based learning, respectively, albeit with different levels of time‐sensitivity. Moreover, the effects of the two learning mechanisms in the different temporally decomposed clusters of neural activity also differed from each other in neural sources. Importantly, the right inferior frontal cortex (BA44) was specifically implicated in visuomotor statistical learning, confirming its role in the acquisition of transitional probabilities. In contrast, visuomotor rule‐based learning was associated with the prefrontal gyrus (BA6). The results show how simultaneous learning mechanisms operate at the neurophysiological level and are orchestrated by distinct prefrontal cortical areas. The current findings deepen our understanding on the mechanisms of how humans are capable of learning multiple types of information from the same stimulus stream in a parallel fashion.

A Process-Oriented View of Procedural Memory Can Help Better Understand Tourette’s Syndrome

“…In subsequent studies, statistical and sequence learning were found to be dissociable based on the learning trajectories they show during task execution (Simor et al, 2019;Quentin et al, 2021) and electrophysiological correlates (Kóbor et al, 2018;Zavecz et al, 2020;Takács et al, 2021) as well. On the behavioral level, sequence learning seems to increase gradually during training, while statistical learning seems to plateau early (Kóbor et al, 2018;Simor et al, 2019).…”

Regularity detection under stress: Faster extraction of probability-based regularities

“…In contrast, acquiring serialorder information seems to require more time, suggested by the gradual learning trajectory [19,21]. Considering the extant evidence showing that the learning of these regularities is distinguishable [19][20][21][22], we will refer to them as learning of serial-order information (where second-order transitional probabilities are equal to 1.0) and learning of probability-based information (where second-order transitional probabilities are less than 1.0) in the rest of the paper. In the present study, we aimed to investigate how stress might affect two aspects of regularity extraction, namely, the learning of serial-order regularities and probability-based regularities.…”

“…As described above, from a theoretical perspective, both aspects of learning investigated in the current study can be viewed as a form of statistical learning, i.e., as learning of transitional probabilities. Nevertheless, a growing body of research shows that the learning of serial-order and probability-based regularities are distinguishable on both neural and behavioral levels: they develop differently at the level of event-related potentials [ 19 , 20 ], and they show distinct neural oscillations during consolidation [ 21 ]. Crucially, on the behavioral level, participants acquire probability-based regularities rapidly and show consistent, stable performance thereafter [ 19 , 21 ].…”

Access to Procedural Memories After One Year: Evidence for Robust Memory Consolidation in Tourette Syndrome

“…Hence, extracting probability-based information enables the differentiation between more and less probable outcomes to learn stochastic relations between events: for instance, when event A is followed by event B in 75% of the cases and followed by event C in 25% of the cases. Although both regularities can be considered as learning of transitional probabilities (also often referred to as statistical learning), prior studies have shown considerable differences between them in healthy young adults ( Nemeth et al, 2013 ; Kóbor et al, 2018 ; Simor et al, 2019 ; Takács et al, 2021 ). They revealed that the learning of serial-order regularities develops rather gradually, whereas the learning of probability-based regularities reaches its plateau in a quick manner ( Nemeth et al, 2013 ; Kóbor et al, 2018 ; Simor et al, 2019 ), which is also reflected in the neurophysiological correlates ( Kóbor et al, 2018 ; Takács et al, 2021 ).…”

“…Although both regularities can be considered as learning of transitional probabilities (also often referred to as statistical learning), prior studies have shown considerable differences between them in healthy young adults ( Nemeth et al, 2013 ; Kóbor et al, 2018 ; Simor et al, 2019 ; Takács et al, 2021 ). They revealed that the learning of serial-order regularities develops rather gradually, whereas the learning of probability-based regularities reaches its plateau in a quick manner ( Nemeth et al, 2013 ; Kóbor et al, 2018 ; Simor et al, 2019 ), which is also reflected in the neurophysiological correlates ( Kóbor et al, 2018 ; Takács et al, 2021 ). In other words, the learning of serial-order regularities occurs relatively slowly, whereas participants acquire probability-based regularities rapidly and then show consistent, stable performance.…”