The lateral‐occipital and the inferior‐frontal cortex play different roles during the naming of visually presented objects
Bibliographic record
Abstract
We reasoned that if an area is devoted to processing only the visual features of objects, then transcranial magnetic stimulation (TMS) applied to this area in either hemisphere would affect the naming of objects presented in contralateral but not ipsilateral space. In contrast, if an area is involved in language, then one might expect to see effects of TMS when applied over the left but not the right hemisphere, regardless whether objects are in contralateral or ipsilateral space. Our experiments reveal two important findings. First, TMS delivered to the lateral-occipital complex (LOC), a visual-form area, affected the naming of objects presented in contralateral but not ipsilateral space, independent of which hemisphere was stimulated. In two additional experiments, when participants named the color of objects or made judgments about the size of stimuli as shown physically on a computer screen, TMS over the contralateral LOC did not affect color naming but did affect the participants' ability to make size judgments. Second, TMS delivered to the left but not the right posterior inferior-frontal gyrus (pIFG) affected the naming of objects irrespective of whether objects were presented in contralateral or ipsilateral space. In a separate experiment, when participants were asked to either read or categorize words, TMS over the left but not the right pIFG affected word categorization but not word reading. On the basis of these findings, we propose that when people name visually-presented objects, LOC processes the visual form of objects while the left pIFG processes the semantics of objects.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".