Neural substrate underlying the learning of a passage with unfamiliar vocabulary and syntax

Abstract

Speech comprehension is a complex process involving multiple stages, such as decoding of phonetic units, recognizing words, and understanding sentences and passages. In this study, we identify cortical networks beyond basic phonetic processing using a novel passage learning paradigm. Participants learn to comprehend a story composed of syllables of their native language, but containing unfamiliar vocabulary and syntax. Three learning methods are employed, each resulting in some degree of learning within a 12-min learning session. Functional magnetic resonance imaging results reveal that, when listening to the same story, the classic temporal-frontal language network is significantly enhanced by learning. Critically, activation of the left anterior and posterior temporal lobe correlates with the learning outcome that is assessed behaviorally through, e.g. word recognition and passage comprehension tests. This study demonstrates that a brief learning session is sufficient to induce neural plasticity in the left temporal lobe, which underlies the transformation from phonetic units to the units of meaning, such as words and sentences.

Functional characterization of the language network of polyglots and hyperpolyglots with precision fMRI

“…This "language network" supports the processing of typologically diverse languages (Malik-Moraleda, Ayyash et al, 2022; see e.g., Illes et al, 1999;Chee et al, 1999;Hernandez et al, 2001;Briellmann et al, 2004 for earlier evidence from smaller sets of languages), including even constructed languages, like Esperanto and Klingon (Malik-Moraleda et al, 2023). On the other hand, diverse nonlinguistic inputs and tasks elicit little or no response in these brain areas (e.g., Monti et al, 2012;Fedorenko et al, 2012;Deen et al, 2015;Pritchett et al, 2018;Jouravlev et al, 2019;Amalric & Dehaene, 2019;Ivanova et al, 2020;Liu et al, 2020;Chen et al, 2023; for reviews, see Fedorenko et al, in press). Cross-linguistic universality and selectivity for language jointly suggest that some properties of the language network make it well-suited to support the broadly common features of languages that distinguish language processing from other cognitive processes.…”

“…This choice is motivated by two sets of findings. First, the five language regions exhibit highly similar functional response profiles: they all show strong selectivity for language processing relative to diverse non-linguistic inputs and tasks (e.g., Fedorenko et al, 2011;Monti et al, 2012;Deen et al, 2015;Pritchett et al, 2018;Jouravlev et al, 2019;Amalric & Dehaene, 2019;Ivanova et al, 2020;Liu et al, 2020, Chen et al, 2023 for reviews, see Fedorenko & Blank, 2020 and Fedorenko et al, subm. ), and they are all similarly modulated by linguistic manipulations, showing sensitivity to regularities at different levels of linguistic structure and engagement in computations related to lexical access, syntactic-structure building, and semantic composition (e.g., Fedorenko et al, 2010Fedorenko et al, , 2016Blank et al, 2016;Bautista & Wilson, 2016;Shain, Blank et al, 2020;Hu, Small et al, 2022;Shain, Kean et al, 2023;Tuckute et al, 2024).…”

“…So, a stimulus whose processing requires these computations should elicit a high response, and a stimulus whose processing does not engage these computations should elicit a low response. In line with this prediction, past studies have found that, within a given language, a) sentences, which engage all of these computations, elicit a high response, b) linguistically degraded stimuli (like lists of unconnected words, 'Jabberwocky' sentences, or lists of pseudowords), which only engage some of these computations, elicit a lower response (e.g., Humphries et al, 2006;Fedorenko et al, 2010;Pallier et al, 2011;Bedny et al, 2011;Fedorenko et al, 2016;Shain, Kean et al, 2023), and c) non-linguistic inputs fail to engage the language network (e.g., Fedorenko et al, 2011;Monti et al, 2012;Deen et al, 2015;Pritchett et al, 2018;Jouravlev et al, 2019;Amalric & Dehaene, 2019;Ivanova et al, 2020;Liu et al, 2020, Chen et al, 2023 for reviews, see Fedorenko & Blank, 2020 and Fedorenko et al, in press).…”

“…shown to affect responses in the language areas (e.g., Wartenburger et al, 2003;Perani et al, 2003;De Bleser et al, 2003;Rodriguez Fronells et al, 2005;Wang et al, 2023).…”