MEG evidence for left temporal and orbitofrontal involvement in breaking down inflected words and putting the pieces back together
Bibliographic record
Abstract
A major puzzle in the visual word recognition literature is how the human brain deals with complex words (e.g., presuppose). Prior work has shown that a multi-stage process is involved, starting with the early, form-based decomposition stage where a word is broken down into smaller pieces called morphemes {pre-}+{suppose} and ending with the recombination stages where the pieces are put back together to access the word's full meaning. However, most neurolinguistic studies have focused on the first stage, and/or on derivational morphology, which inherently carries both syntactic and semantic information, and this research has overwhelmingly investigated Indo-European languages. Here, we investigate visual word recognition of Tagalog complex words, focusing on inflectional prefixes which allows us to zero in on the contribution of syntactic information during the recombination stage, where both syntactic and semantic information are expected to be analyzed. Using MEG, we replicate previous findings implicating the left fusiform gyrus in segmenting complex words into pieces. We also show that the recombination stages, where the morphological pieces are put back together, activate the left posterior temporal lobe and left orbitofrontal cortex. Although our results support a multi-stage comprehension model of complex words and confirm that these distinct stages are associated with distinct spatiotemporal profiles, we also observed some spatiotemporal differences compared to previous studies on derivational morphology. For the first time, we show that inflected words activate the same core processing profile as derived words in the early (decomposition) stage, while later (recombination) stages of morphological processing point to an earlier and faster recombination of inflected words.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".