Cory Shain

All Publications

• A language network in the individualized functional connectomes of over 1,000 human brains doing arbitrary tasks. bioRxiv : the preprint server for biology Shain, C., Fedorenko, E. 2025

  Abstract

  A century and a half of neuroscience has yielded many divergent theories of the neurobiology of language. Two factors that likely contribute to this situation include (a) conceptual disagreement about language and its component processes, and (b) intrinsic inter-individual variability in the topography of language areas. Recent functional magnetic imaging (fMRI) studies of small numbers of intensively scanned individuals have argued that a language-selective brain network emerges from correlations (individualized functional connectomics, iFC) in task-free (e.g., rest) or task-regressed activation timecourses. Here we test this hypothesis at scale and evaluate its practical utility for task-agnostic language localization: we apply iFC separately to each of 1,971 (fMRI) scanning sessions (1,199 unique brains), each consisting of diverse tasks. We find that iFC indeed reveals a left-lateralized frontotemporal network that is more stable within individuals than between them, robust to the granularity of the parcellation, and selective for language. These results support the hypothesis that this network is a key structure in the functional organization of the adult brain and show that it can be recovered retrospectively from arbitrary imaging data, with implications for neuroscience, neurosurgery, and neural engineering.

  View details for DOI 10.1101/2025.03.29.646067

  View details for PubMedID 40236089

• Functional Identification of Language-Responsive Channels in Individual Participants in MEG Investigations. bioRxiv : the preprint server for biology Huybrechts, M., Bruffaerts, R., Pongos, A., Shain, C., Lipkin, B., Siegelman, M., Wens, V., Sjøgård, M., Blank, I., Goldman, S., De Tiège, X., Fedorenko, E. 2025

  Abstract

  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, is 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 visual 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). In both the time and frequency domains, we demonstrated that the 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.

  View details for DOI 10.1101/2023.03.23.533424

  View details for PubMedID 36993378

  View details for PubMedCentralID PMC10055362

• What we mean when we say semantic: Toward a multidisciplinary semantic glossary. Psychonomic bulletin & review Reilly, J., Shain, C., Borghesani, V., Kuhnke, P., Vigliocco, G., Peelle, J. E., Mahon, B. Z., Buxbaum, L. J., Majid, A., Brysbaert, M., Borghi, A. M., De Deyne, S., Dove, G., Papeo, L., Pexman, P. M., Poeppel, D., Lupyan, G., Boggio, P., Hickok, G., Gwilliams, L., Fernandino, L., Mirman, D., Chrysikou, E. G., Sandberg, C. W., Crutch, S. J., Pylkkänen, L., Yee, E., Jackson, R. L., Rodd, J. M., Bedny, M., Connell, L., Kiefer, M., Kemmerer, D., de Zubicaray, G., Jefferies, E., Lynott, D., Siew, C. S., Desai, R. H., McRae, K., Diaz, M. T., Bolognesi, M., Fedorenko, E., Kiran, S., Montefinese, M., Binder, J. R., Yap, M. J., Hartwigsen, G., Cantlon, J., Bi, Y., Hoffman, P., Garcea, F. E., Vinson, D. 2024

  Abstract

  Tulving characterized semantic memory as a vast repository of meaning that underlies language and many other cognitive processes. This perspective on lexical and conceptual knowledge galvanized a new era of research undertaken by numerous fields, each with their own idiosyncratic methods and terminology. For example, "concept" has different meanings in philosophy, linguistics, and psychology. As such, many fundamental constructs used to delineate semantic theories remain underspecified and/or opaque. Weak construct specificity is among the leading causes of the replication crisis now facing psychology and related fields. Term ambiguity hinders cross-disciplinary communication, falsifiability, and incremental theory-building. Numerous cognitive subdisciplines (e.g., vision, affective neuroscience) have recently addressed these limitations via the development of consensus-based guidelines and definitions. The project to follow represents our effort to produce a multidisciplinary semantic glossary consisting of succinct definitions, background, principled dissenting views, ratings of agreement, and subjective confidence for 17 target constructs (e.g., abstractness, abstraction, concreteness, concept, embodied cognition, event semantics, lexical-semantic, modality, representation, semantic control, semantic feature, simulation, semantic distance, semantic dimension). We discuss potential benefits and pitfalls (e.g., implicit bias, prescriptiveness) of these efforts to specify a common nomenclature that other researchers might index in specifying their own theoretical perspectives (e.g., They said X, but I mean Y).

  View details for DOI 10.3758/s13423-024-02556-7

  View details for PubMedID 39231896

  View details for PubMedCentralID 4215955

• Word Frequency and Predictability Dissociate in Naturalistic Reading OPEN MIND-DISCOVERIES IN COGNITIVE SCIENCE Shain, C. 2024; 8: 177-201

  Abstract

  Many studies of human language processing have shown that readers slow down at less frequent or less predictable words, but there is debate about whether frequency and predictability effects reflect separable cognitive phenomena: are cognitive operations that retrieve words from the mental lexicon based on sensory cues distinct from those that predict upcoming words based on context? Previous evidence for a frequency-predictability dissociation is mostly based on small samples (both for estimating predictability and frequency and for testing their effects on human behavior), artificial materials (e.g., isolated constructed sentences), and implausible modeling assumptions (discrete-time dynamics, linearity, additivity, constant variance, and invariance over time), which raises the question: do frequency and predictability dissociate in ordinary language comprehension, such as story reading? This study leverages recent progress in open data and computational modeling to address this question at scale. A large collection of naturalistic reading data (six datasets, >2.2 M datapoints) is analyzed using nonlinear continuous-time regression, and frequency and predictability are estimated using statistical language models trained on more data than is currently typical in psycholinguistics. Despite the use of naturalistic data, strong predictability estimates, and flexible regression models, results converge with earlier experimental studies in supporting dissociable and additive frequency and predictability effects.

  View details for DOI 10.1162/opmi_a_00119

  View details for Web of Science ID 001567971600003

  View details for PubMedID 38476662

  View details for PubMedCentralID PMC10932590