Commonalities and Asymmetries in the Neurobiological Infrastructure for Language Production and Comprehension

Abstract

The neurobiology of sentence production has been largely understudied compared to the neurobiology of sentence comprehension, due to difficulties with experimental control and motion-related artifacts in neuroimaging. We studied the neural response to constituents of increasing size and specifically focused on the similarities and differences in the production and comprehension of the same stimuli. Participants had to either produce or listen to stimuli in a gradient of constituent size based on a visual prompt. Larger constituent sizes engaged the left inferior frontal gyrus (LIFG) and middle temporal gyrus (LMTG) extending to inferior parietal areas in both production and comprehension, confirming that the neural resources for syntactic encoding and decoding are largely overlapping. An ROI analysis in LIFG and LMTG also showed that production elicited larger responses to constituent size than comprehension and that the LMTG was more engaged in comprehension than production, while the LIFG was more engaged in production than comprehension. Finally, increasing constituent size was characterized by later BOLD peaks in comprehension but earlier peaks in production. These results show that syntactic encoding and parsing engage overlapping areas, but there are asymmetries in the engagement of the language network due to the specific requirements of production and comprehension.

Information flow across the cortical timescales hierarchy during narrative construction

“…The simulated responses were generated using a linearly increasing temporal integration function (Figure 5a), based on prior work showing that information accumulation is accompanied by gradually increasing activation within phrases/sentences (Chang et al, 2020;Fedorenko et al, 2016;Giglio et al, 2021;Matchin et al, 2017;Nelson et al, 2017;Pallier et al, 2011) and paragraphs (Ezzyat & Davachi, 2011;Yarkoni et al, 2008) (a similar sentence/paragraph length effect was also observed in our data; see Figure S10). The linearly increasing temporal integration function accumulates activity within the interval between linguistic boundaries at given levels and flushes out the accumulated activity at linguistic boundaries.…”

“…The simulated response amplitudes were generated using a linearly increasing temporal integration function (Fig. 4B), based on prior work showing that information accumulation is accompanied by gradually increasing activation within phrases/sentences (36)(37)(38)(39)(40)(41) and paragraphs (29, 32) (a similar sentence/paragraph length effect was also observed in our data; see SI Appendix, Fig. S12).…”

“…The simulation provides a simple model which bridges the discovery of TRWs using natural stimuli (Hasson et al, 2008;Lerner et al, 2014) and the accumulation of activity within linguistic units found using simple, well-controlled stimuli (e.g. sentences with similar structures) (Chang et al, 2020;Fedorenko et al, 2016;Giglio et al, 2021;Matchin et al, 2017;Nelson et al, 2017;Pallier et al, 2011Pallier et al, )al., 2017Pallier et al, 2011). Importantly, we note that our model is not the only one that could generate the lag gradient.…”

“…We replicated the sentence length (Chang et al, 2020;Fedorenko et al, 2016;Giglio et al, 2021;Matchin et al, 2017;Nelson et al, 2017;Pallier et al, 2011) and paragraph length (Ezzyat & Davachi, 2011;Yarkoni et al, 2008) effect with the "Sherlock" and "Merlin" datasets, which were collected from the same group of participants. The onsets and offsets of each word, sentence, and paragraph (event) were manually time-stamped.…”

When inefficient speech-motor control affects speech comprehension: atypical electrophysiological correlates of language prediction in stuttering

“…However, these asymmetries may not necessarily reflect the separation between two distinct systems (Keenan, 1968;Tremblay & Dick, 2016;Chater, McCauley, & Christiansen, 2016). Furthermore, a growing body of research shows that the neural substrates underlying comprehension and production largely overlap (AbdulSabur et al, 2014;Giglio et al, 2021;Lukic et al, 2020;Silbert et al, 2014;Walenski et al, 2019), supporting the view that comprehension and production draw from processes and representations that are at least partially shared (Chater et al, 2016;Dell & Chang, 2014;Fairs et al, 2021;Gambi & Pickering, 2017;McQueen & Meyer, 2019;Pickering & Garrod, 2013).…”