Spatiotemporal analysis of neuromagnetic activation associated with mirror reading.
Bibliographic record
Abstract
Our previous report has confirmed that visually presented words can elicit neuromagnetic activities from the visual cortex, angular gyrus and Broca's area. It is not clear how the words are visuospatially transformed and recognized by the human brain. Mirror reading is characterized by reading which runs in the opposite direction to normal reading, with reversals of letters. It would be very interesting to find out if there are any neuromagnetic differences between the mirror reading and normal reading. Four right-handed healthy adults have been studied with a whole cortex Magnetoencephalography (MEG) system. The stimuli consisted of eight normal oriented words and eight inverted exclamation mark section "reversed words" (mirror-image of the words). All stimuli were randomly presented on the screen in front of the subjects using DirectX. MEG data were analyzed using both single dipole modeling and synthetic aperture magnetometry (SAM). Four responses to the reversed words were identified in all four subjects. In comparison to the normally oriented words, the reversed words elicited a stronger response at a latency of 248+/-6 ms. SAM results indicated that the reversed words invoked strong activations in the left and right parietal cortices but the normally oriented words did not. The mirror reading elicited one magnetic response which is different from that of the normal reading. The difference between the mirror reading and the normal reading in terms of neuromagnetic activation may reflect the development of novel representations for reversed 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 | 0.001 |
| 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".