Making meaningful inferences about the functional architecture of the language system requires the ability to refer to the same neural units across individuals and studies. Traditional brain imaging approaches align and average brains together in a common space. However, lateral frontal and temporal cortices, where the language system resides, are characterized by high structural and functional inter-individual variability, which reduces the sensitivity and functional resolution of group-averaging analyses. This issue is compounded by the fact that language areas lay in close proximity to regions of other large-scale networks with different functional profiles. A solution inspired by vision neuroscience is to identify language areas functionally in each individual brain using a 'localizer' task (e.g., a language comprehension task). This approach has proven productive in fMRI, yielding a number of robust and replicable findings about the language system. Here, we extend this approach to MEG. Across two experiments (one in Dutch speakers, n=19; one in English speakers, n=23), we examined neural responses to the processing of sentences and a control condition (nonword sequences). We demonstrate that the sensor and source topography of neural responses to language is spatially stable within individuals but varies across individuals. Consequently, analyses that take this inter-individual variability into account are characterized by greater sensitivity, compared to the group-level analyses. In summary, similar to fMRI, functional identification within individuals yields benefits in MEG, thus opening the door to future investigations of language processing including questions where whole-brain coverage and temporal resolution are both critical.
Mathias Huybrechts, R. Bruffaerts, Alvince L. Pongos et al.· Journal of Neurophysiology· 1 citation
The question of whether the relationship between a word's predictability and its processing time is linear or logarithmic is of substantial theoretical importance, as it arbitrates between theories of the predictability effect (preactivation vs. surprisal) and has implications for sentence processing generally. While several previous corpus studies have obtained evidence for a logarithmic relationship, Brothers and Kuperberg (2021b) obtained evidence for a linear relationship in a large self-paced reading study with well-controlled experimental materials. Here, the authors use Brothers and Kuperberg's materials in an eye tracking during reading experiment. The authors find clear evidence for a logarithmic relationship between predictability and the eye-movement measures of first fixation duration and gaze duration; this relationship is clearest when using predictability estimates from the large language model Generative Pre-Trained Transformer-2, which can distinguish small differences in predictability at the low end of the scale. The authors find that this conclusion is robust to log transformation of the reading time measures, and the authors find that the relationship between predictability and the log odds of word skipping may also be logarithmic. These results support surprisal as an account of predictability effects in natural reading. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Ryan Buggy, Stephanie Cho, Cory Shain et al.· Journal of Experimental Psyc...· 1 citation
Healthy aging is associated with structural and functional brain changes. However, cognitive abilities vary in how they change with age: executive functions decline, while aspects of linguistic processing remain relatively preserved. This heterogeneity predicts differences among brain networks in whether and how they change with age. To evaluate this prediction, we used precision fMRI to examine the language-selective network and the Multiple Demand (MD) network, which supports executive functions, in older adults (N = 64) relative to young controls (N = 483). 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. In contrast, we find remarkable preservation of the language network in older adults: it responds during language comprehension as strongly and selectively as in younger adults, with similar left-hemispheric lateralization and within-network functional connectivity. These findings align with behavioral preservation of language comprehension in healthy aging. Here, the authors show that aging affects brain networks differently, with neural changes mirroring the preservation or decline of the cognitive functions they support. The multiple demand network exhibits age-related decline while the language network remains stable with age.
A. Billot, Niharika Jhingan, M. Varkanitsa et al.· Nature Communications· 0 citations
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