Differential Involvement of the Anterior Temporal Lobes in Famous People Semantics
Bibliographic record
Abstract
The ability to recognize a famous person occurs through semantic memory. Previous neuroimaging studies have shown that the anterior temporal lobes (ATLs) are involved in the recognition of famous people. However, it is still a matter of debate whether the semantic processing of names or pictures of famous people has an impact on the activation of ATLs. The aim of this study was to explore the pattern of activation associated with a semantic processing of famous people based on face and written name stimuli. Fifteen healthy young individuals participated in our fMRI study, in which they were asked to perform a semantic categorization judgment task, based on profession, of visually presented pictures, and names of famous people. Neuroimaging findings showed a common pattern of activation for faces and names mainly involving the inferior frontal regions, the posterior temporal lobe, the visual cortex, and the ATLs. We found that the comparison names vs. pictures lead to significant activation in the anterior superior temporal gyrus. On the other hand, faces vs. names seemed associated with increased activation in the medial ATL. Moreover, our results demonstrated that the functional connectivity network anchored to the medial ATL, compared to the anterior STG, is more connected to the bilateral occipital lobe and fusiform gyrus that are regions implicated in the visual system and visual processing of faces. This study provides critical evidence of the differential involvement of ATL regions in semantics of famous people.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".