Symbolic categorization of novel multisensory stimuli in the human brain
Bibliographic record
Abstract
When primates (both human and non-human) learn to categorize simple visual or acoustic stimuli by means of non-verbal matching tasks, two types of changes occur in their brain: early sensory cortices increase the precision with which they encode sensory information, and parietal and lateral prefrontal cortices develop a categorical response to the stimuli. Contrary to non-human animals, however, our species mostly constructs categories using linguistic labels. Moreover, we naturally tend to define categories by means of multiple sensory features of the stimuli. Here we trained adult subjects to parse a novel audiovisual stimulus space into 4 orthogonal categories, by associating each category to a specific symbol. We then used multi-voxel pattern analysis (MVPA) to show that during a cross-format category repetition detection task three neural representational changes were detectable. First, visual and acoustic cortices increased both precision and selectivity to their preferred sensory feature, displaying increased sensory segregation. Second, a frontoparietal network developed a multisensory object-specific response. Third, the right hippocampus and, at least to some extent, the left angular gyrus, developed a shared representational code common to symbols and objects. In particular, the right hippocampus displayed the highest level of abstraction and generalization from a format to the other, and also predicted symbolic categorization performance outside the scanner. Taken together, these results indicate that when humans categorize multisensory objects by means of language the set of changes occurring in the brain only partially overlaps with that described by classical models of non-verbal unisensory categorization in primates.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".