Neurophysiological and functional neuroanatomical coding of statistical and deterministic rule information during sequence learning. Issue 10 (2nd April 2021) - Takács, Ádám; Kóbor, Andrea; Kardos, Zsófia; Janacsek, Karolina; Horváth, Kata; Beste, Christian; Nemeth, Dezso
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. Abstract : We investigated parallel processes in visual sequence learning, for that, EEG signal decomposition and source localization were used. Statistical and rule‐based learning were distinguishable in the decomposed signal, moreover, decomposed EEG and source analyses suggest distinct neural learning mechanisms. We suggest that parallel learning mechanisms are controlled by different prefrontal areas.