Neural populations in the language network differ in the size of their temporal receptive windows

Abstract

A left-lateralized network of frontal and temporal brain regions is specialized for language processing-spoken, written, or signed. Different regions of this 'language network' have all been shown to be sensitive to various forms of linguistic information, from combinatorial sentence structure to word meanings, to sub-lexical regularities. However, whether neural computations are the same across and within these different brain regions remains debated. Here, we examine responses during language processing recorded intracranially in patients with intractable epilepsy. Across two datasets (Dataset 1: n=6 participants, m=177 language-responsive electrodes; Dataset 2: n=16 participants, m=362 language-responsive electrodes), we clustered language-responsive electrodes and found three distinct response profiles, with differences in response magnitude between linguistic conditions (e.g., sentences vs. lists of words), different temporal dynamics over the course of the stimulus, and different degrees of stimulus locking. We argue that these profiles correspond to different temporal receptive windows that vary in size between sub-lexical units and multi-word sequences. These results demonstrate the functional heterogeneity of neural responses in the language network and highlight the diversity of neural computations that may be needed in order to extract meaning from linguistic input. Importantly, electrodes that exhibit these distinct profiles do not cluster spatially and are instead interleaved across frontal and temporal language areas, which likely made It difficult to uncover functional differences in past fMRI studies. This mosaic of neural responses across the language network suggests that all language regions have direct access to distinct response types-a property that may be crucial for the efficiency and robustness of language processing mechanisms.

Driving and suppressing the human language network using large language models

“…As discussed earlier, there are good reasons to adopt this approach: the different regions of this network i) have similar functional response profiles, both with respect to their selectivity for language (e.g., 13–15,17,18 ) and their responses to linguistic manipulations (e.g., 21,58 ), and ii) exhibit highly correlated time courses during naturalistic cognition paradigms (e.g., 59,60,83,11 ). However, some functional heterogeneity has been argued to exist within the language network (e.g., 162–164,160,165,166 ). Future efforts using an approach like the one adopted here may discover functional differences within the language network (by searching for stimuli that would selectively drive particular regions within the network) as well as between the core LH language network and the RH homotopic areas and other language-responsive cortical, subcortical, and cerebellar areas.…”

The Language Network Reliably “Tracks” Naturalistic Meaningful Nonverbal Stimuli

“…One constraint on this space of hypotheses has to do with the size of the language network’s temporal integration (or receptive) window (e.g., Lerner et al, 2011 ). In particular, previous work has shown that the temporal integration window of the language network is relatively short, on the order of a clause or sentence (e.g., Blank & Fedorenko, 2020 ; Lerner et al, 2011 ; Regev et al, 2022 ; Shain, Kean, et al, 2023 ). It is therefore likely that nonverbal meanings that the language system is concerned with have to do with individually described states or agent-patient interactions, but not longer-timescale representations like situation models whose construction can span long sequences of events ( Johnson-Laird, 1983 ; Loschky et al, 2020 ; Zwaan & Radvansky, 1998 ).…”

Domain-general and language-specific contributions to speech production in a second language: an fMRI study using functional localizers

“…It is engaged in language comprehension [32,33] and production [34] across different modalities and tasks. It supports language processing at the sub-lexical [35], lexical, and syntactic levels [36][37][38], it is also sensitive to the difficulty of language processing [39][40] and responds to differences between comprehension in native and non-native languages in polyglots [41].…”

“…It is engaged in language comprehension [32,33] and production [34] across different modalities and tasks. It supports language processing at the sub-lexical [35], lexical, and syntactic levels [36–38], it is also sensitive to the difficulty of language processing [39–40] and responds to differences between comprehension in native and non-native languages in polyglots [41]. What is more, the language network was shown to be sensitive to lexical access demands [34], and lexical access difficulty related to speaking in the L2 compared to the native language [42].…”