Neural Basis of the Implicit Learning of Complex Artificial Grammar with Nonadjacent Dependencies

Abstract

The capacity for the implicit learning/processing of complex grammar with nonadjacent dependencies is one of important features of human language learning. In this fMRI study, using an implicit AGL paradigm, we explored the neural basis of the implicit learning of the nonadjacent dependency rule, disentangling from sequence-based chunk knowledge (i.e., local sequential regularities or substring) by focusing on the low chunk strength items (which were naturally less similar to training strings), based on tracking neural responses during training and test phases. After listening to and memorizing a series of strings of 10 syllables generated from nonadjacent artificial grammar in the training phase, participants implicitly acquired the knowledge of grammar and chunks. Regarding grammaticality, Broca's area was specifically related to low chunk strength grammatical strings relative to nongrammatical strings in the test phase. This region showed decreased activity with time in the training phase, and a lesser decrease in activity was associated with higher performance in grammar learning. Furthermore, Broca's area showed significantly higher strength of functional connectivity with the left superior temporal gyrus in the low chunk strength grammatical string compared with nongrammatical strings, and this functional connectivity increased with the training time. For the chunks, the performance of accurate discrimination of high chunk strength from low chunk strength nongrammatical strings was predicted by hippocampal activity in the training phase. Converging evidence from the training and test phases showed that Broca's area and its functional connectivity with the left superior temporal gyrus were engaged in the implicit learning/processing of the nonadjacent dependency rule, separating the effects of chunks.

Facilitating Implicit Learning of Multiple Non-adjacent Dependencies Regardless of Language Experience: The Role of a Brief Pre-exposure

“…In the current work, the implicit nature of the knowledge was further estimated using the structural knowledge attributions of Dienes and Scott (2005), which can sensitively establish the conscious status of the acquired knowledge. (e.g., Guo et al, 2013;Jurchiș & Dienes, 2023;Kemény & Lukács, 2013;Ling et al, 2022;Ling et al, 2018;Neil & Higham, 2012;Rebuschat et al, 2013;Waroquier et al, 2020).…”

“What” and “when” predictions jointly modulate speech processing

“…When unexpected auditory contents are encountered even in artificial speech, those prediction errors are reflected in neural activity (Ylinen et al, 2016). Studies have shown that these prediction errors typically increase activity in the inferior frontal gyrus (IFG) (Petersson et al, 2012; Wilson et al, 2015), as well as in the superior temporal cortex (Ling et al, 2022), among other regions. Importantly, “what” predictions can span multiple levels, ranging from phonemes to syllables, words, and longer phrases (Heilbron et al, 2022; Su et al, 2023).…”