How are visual words represented? Insights from EEG‐based visual word decoding, feature derivation and image reconstruction
Bibliographic record
Abstract
Investigations into the neural basis of reading have shed light on the cortical locus and the functional role of visual-orthographic processing. Yet, the fine-grained structure of neural representations subserving reading remains to be clarified. Here, we capitalize on the spatiotemporal structure of electroencephalography (EEG) data to examine if and how EEG patterns can serve to decode and reconstruct the internal representation of visually presented words in healthy adults. Our results show that word classification and image reconstruction were accurate well above chance, that their temporal profile exhibited an early onset, soon after 100 ms, and peaked around 170 ms. Further, reconstruction results were well explained by a combination of visual-orthographic word properties. Last, systematic individual differences were detected in orthographic representations across participants. Collectively, our results establish the feasibility of EEG-based word decoding and image reconstruction. More generally, they help to elucidate the specific features, dynamics, and neurocomputational principles underlying word recognition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".