Modality-Independent Coding of Scene Categories in Prefrontal Cortex
Bibliographic record
Abstract
Natural environments convey information through multiple sensory modalities, all of which contribute to people9s percepts. Although it has been shown that visual or auditory content of scene categories can be decoded from brain activity, it remains unclear how humans represent scene information beyond a specific sensory modality domain. To address this question, we investigated how categories of scene images and sounds are represented in several brain regions. A group of healthy human subjects (both sexes) participated in the present study, where their brain activity was measured with fMRI while viewing images or listening to sounds of different real-world environments. We found that both visual and auditory scene categories can be decoded not only from modality-specific areas, but also from several brain regions in the temporal, parietal, and prefrontal cortex (PFC). Intriguingly, only in the PFC, but not in any other regions, categories of scene images and sounds appear to be represented in similar activation patterns, suggesting that scene representations in PFC are modality-independent. Furthermore, the error patterns of neural decoders indicate that category-specific neural activity patterns in the middle and superior frontal gyri are tightly linked to categorization behavior. Our findings demonstrate that complex scene information is represented at an abstract level in the PFC, regardless of the sensory modality of the stimulus. <b>SIGNIFICANCE STATEMENT</b> Our experience in daily life includes multiple sensory inputs, such as images, sounds, or scents from the surroundings, which all contribute to our understanding of the environment. Here, for the first time, we investigated where and how in the brain information about the natural environment from multiple senses is merged to form modality-independent representations of scene categories. We show direct decoding of scene categories across sensory modalities from patterns of neural activity in the prefrontal cortex (PFC). We also conclusively tie these neural representations to human categorization behavior by comparing patterns of errors between a neural decoder and behavior. Our findings suggest that PFC is a central hub for integrating sensory information and computing modality-independent representations of scene categories.
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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 | 0.000 |
| 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) | 0.000 | 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".