Linguistic and visual similarity judgements predict EEG representational dynamics in visual perception and sentence reading
Bibliographic record
Abstract
Emerging evidence in cognitive and computational neuroscience suggests that multi-modal computational models converge on representations that improve the performance in each of the modalities used. This latent representational space also enables the prediction of brain response profiles across modalities but it remains unclear how vision and linguistics share meaningful representations in the human brain. Here, we collected ~7 hours of electroencephalography (EEG) data from each of six participants passively viewing 100 natural scene images or actively reading 100 sentence captions describing the images. The activity pattern similarity was estimated using a cross-validated Mahalanobis distance computed on a spatiotemporal transformation of the modality-specific EEG data across all pairs of conditions. To establish the presence of shared representations in both modalities and to assess their behavioural relevance, we collected behavioural similarity judgements through multiple arrangement (MA) tasks on the set of images and sentences from two independent groups of participants (n = [24, 22]). This was used to construct the visual and linguistic fixed model RDMs each characterising the unique similarity structure of the two modalities. We then quantified the extent to which the behavioural model RDMs generalise to the visual and linguistic EEG RDMs using cosine similarity. We observed a significant relationship between the visually evoked EEG RDMs and both MA models (visual MA: 0.138 ± 0.024; linguistic MA: 0.136 ± 0.024). Interestingly, both MA models also revealed significant overlap with the linguistic EEG RDMs (visual MA: 0.042 ± 0.007; linguistic MA: 0.043 ± 0.007, all p < 0.001). These results remain when controlling for the potential influence of prior exposure to the cross-modal stimuli. We demonstrate that a similar representation emerges regardless of whether participants viewed an image or read its sentence caption and provide further evidence for behaviourally relevant shared representations in vision and language.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".