Bibliographic record
Abstract
In this paper we review recent computational approaches to the study of language with neuroimaging data. Recordings of brain activity have long played a central role in furthering our understanding of how human language works, with researchers usually choosing to focus tightly on one aspect of the language system. This choice is driven both by the complexity of that system, and by the noise and complexity in neuroimaging data itself. State-of-the-art computational methods can help in two respects: in teasing more information from recordings of brain activity and by allowing us to test broader and more articulated theories and detailed representations of language tasks. In this chapter, we first set the scene with a succinct review of neuroimaging techniques and what they have taught us about language processing in the brain. We then describe how recent work has used machine learning methods with brain data and computational models of language to investigate how words and phrases are processed. We finish by introducing emerging naturalistic paradigms that combine authentic language tasks (e.g., reading or listening to a story) with rich models of lexical, sentential, and suprasentential representations to enable an allround view of language processing. Introduction The study of language, like other cognitive sciences, requires of us to indulge in a kind of mind reading. We use a variety of methods in an attempt to access the hidden representations and processes that allow humans to converse. In formal linguistics intuitive judgments by the theorist are used as primary evidence – an approach that brings well-understood dangers of bias (Gibson and Fedorenko, 2010), but in practice can work well (Sprouse et al., 2013). Aggregating judgments over groups of informants is widely used in cognitive and computational linguistics, through both experts in controlled environments and crowdsourcing of naive annotators (Snow et al., 2008). Experimental psycholinguists have used a range of methods that do not rely on intuition, judgments, or subjective reflection, such as the speed of self-paced reading, or the order and timing of gaze events as recorded with eye-tracking technologies (Rayner, 1998). Brain-recording technologies offer a different kind of evidence, as they are the closest we can get empirically to the object of interest: human cognition. Despite the technical challenges involved, especially the complexity of the recorded signals and the extraneous noise that they contain, brain imaging has a decades-long history in psycholinguistics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".