Biology subjects

Jhingan, N.

Publications and source records attributed to Jhingan, N..

3 recordsLinked to original sources

bioRxiv2024

The language network ages well: Preserved selectivity, lateralization, and within-network functional synchronization in older brains

Healthy aging is associated with structural and functional brain changes. However, cognitive abilities vary in how they change with age: whereas executive functions, like working memory, show age-related decline, aspects of linguistic processing remain relatively preserved. The heterogeneity of the cognitive-behavioral landscape in aging predicts differences among brain networks in whether and how they should change with age. To evaluate this prediction, we used individual-subject fMRI analyses (precision fMRI) to examine the language-selective network and--for control purposes--the Multiple Demand (MD) network, which supports executive functions, in older adults (n=64) relative to young controls (n=483). In line with past claims, relative to young adults, the MD network of older adults shows weaker, less spatially extensive, and more topographically variable activations during an executive function task and reduced within-network functional connectivity. However, in stark contrast to the MD network, we find remarkable preservation of the language network in older adults. Their language network responds during language comprehension as strongly and selectively as in younger adults, and shows a similar degree of left-hemispheric lateralization and within-network functional connectivity. Our findings suggest that the language network remains young-like--at least on standard measures of function and connectivity--and align with behavioral preservation of language comprehension in healthy aging.

neuroscience↗

bioRxiv2024

Linguistic inputs must be syntactically parsable to fully engage the language network

Human language comprehension is remarkably robust to ill-formed inputs (e.g., word transpositions). This robustness has led some to argue that syntactic parsing is largely an illusion, and that incremental comprehension is more heuristic, shallow, and semantics-based than is often assumed. However, the available data are also consistent with the possibility that humans always perform rule-like symbolic parsing and simply deploy error correction mechanisms to reconstruct ill-formed inputs when needed. We put these hypotheses to a new stringent test by examining brain responses to a) stimuli that should pose a challenge for syntactic reconstruction but allow for complex meanings to be built within local contexts through associative/shallow processing (sentences presented in a backward word order), and b) grammatically well-formed but semantically implausible sentences that should impede semantics-based heuristic processing. Using a novel behavioral syntactic reconstruction paradigm, we demonstrate that backward- presented sentences indeed impede the recovery of grammatical structure during incremental comprehension. Critically, these backward-presented stimuli elicit a relatively low response in the language areas, as measured with fMRI. In contrast, semantically implausible but grammatically well-formed sentences elicit a response in the language areas similar in magnitude to naturalistic (plausible) sentences. In other words, the ability to build syntactic structures during incremental language processing is both necessary and sufficient to fully engage the language network. Taken together, these results provide strongest to date support for a generalized reliance of human language comprehension on syntactic parsing. Significance statementWhether language comprehension relies predominantly on structural (syntactic) cues or meaning- related (semantic) cues remains debated. We shed new light on this question by examining the language brain areas responses to stimuli where syntactic and semantic cues are pitted against each other, using fMRI. We find that the language areas respond weakly to stimuli that allow for local semantic composition but cannot be parsed syntactically--as confirmed in a novel behavioral paradigm--and they respond strongly to grammatical but semantically implausible sentences, like the famous Colorless green ideas sleep furiously sentence. These findings challenge accounts of language processing that suggest that syntactic parsing can be foregone in favor of shallow semantic processing.

neuroscience↗

bioRxiv2023

Constructed languages are processed by the same brain mechanisms as natural languages

What constitutes a language? Natural languages share features with other domains: from math, to music, to gesture. However, the brain mechanisms that process linguistic input are highly specialized, showing little response to diverse non-linguistic tasks. Here, we examine constructed languages (conlangs) to ask whether they draw on the same neural mechanisms as natural languages, or whether they instead pattern with domains like math and programming languages. Using individual-subject fMRI analyses, we show that understanding conlangs recruits the same brain areas as natural language comprehension. This result holds for Esperanto (n=19 speakers) and four fictional conlangs (Klingon (n=10), Navi (n=9), High Valyrian (n=3), and Dothraki (n=3)). These findings suggest that conlangs and natural languages share critical features that allow them to draw on the same representations and computations, implemented in the left-lateralized network of brain areas. The features of conlangs that differentiate them from natural languages--including recent creation by a single individual, often for an esoteric purpose, the small number of speakers, and the fact that these languages are typically learned in adulthood-- appear to not be consequential for the reliance on the same cognitive and neural mechanisms. We argue that the critical shared feature of conlangs and natural languages is that they are symbolic systems capable of expressing an open-ended range of meanings about our outer and inner worlds. Significance StatementWhat constitutes a language has been of interest to diverse disciplines - from philosophy and linguistics to psychology, anthropology, and sociology. An empirical approach is to test whether the system in question recruits the brain system that processes natural languages. In spite of their similarity to natural languages, math and programming languages recruit a distinct brain system. Using fMRI, we test brain responses to stimuli not previously investigated--constructed languages (conlangs)--and find that they are processed by the same brain network as natural languages. Thus, an ability for a symbolic system to express diverse meanings about the world-- but not the recency, manner, and purpose of its creation, or a large user base--is a defining characteristic of a language.

neuroscience↗