Text-related functionality and dynamics of visual human pre-frontal activations revealed through neural network convergence
Bibliographic record
Abstract
Human prefrontal areas show enhanced activations when individuals are presented with images, under diverse task conditions. However, the functional role of these increased activations remains a deeply debated question. Here we addressed this question by comparing, dynamically, the relational structure of prefrontal activations and both visual and textual-trained deep neural networks (DNNs) during a visual memorization task. We analyzed intra-cranial recordings, conducted for clinical purposes, while patients viewed and memorized images of familiar faces and places. Our results reveal that relational structures in the frontal cortex elicited during visual memorization were predicted by text and not visual DNNs. Importantly, the temporal dynamics of these correlations showed striking differences, with a rapid decline over time for the visual component, but persistent dynamics including a significant image offset response for the text component. The results point to a dynamic text-related function of prefrontal cortex during visual memorization in the human brain. This study used intracranial recordings from epilepsy patients performing a visual memorization task to compare prefrontal cortex activity with representations from visual- and text-trained deep neural networks. Results showed that fronto-parietal responses to visual stimuli aligned more closely with text-based model, revealing a non-visual, temporally dynamic role for the prefrontal cortex in processing visual stimuli.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".