MEG sensor patterns reflect perceptual but not categorical similarity of animate and inanimate objects
Bibliographic record
Abstract
Abstract Human high-level visual cortex shows a distinction between animate and inanimate objects, as revealed by fMRI. Recent studies have shown that object animacy can similarly be decoded from MEG sensor patterns. Which object properties drive this decoding? Here, we disentangled the influence of perceptual and categorical object properties by presenting perceptually matched objects (e.g., snake and rope) that were nonetheless easily recognizable as being animate or inanimate. In a series of behavioral experiments, three aspects of perceptual dissimilarity of these objects were quantified: overall dissimilarity, outline dissimilarity, and texture dissimilarity. Neural dissimilarity of MEG sensor patterns was modeled using regression analysis, in which perceptual dissimilarity (from the behavioral experiments) and categorical dissimilarity served as predictors of neural dissimilarity. We found that perceptual dissimilarity was strongly reflected in MEG sensor patterns from 80ms after stimulus onset, with separable contributions of outline and texture dissimilarity. Surprisingly, when controlling for perceptual dissimilarity, MEG patterns did not carry information about object category (animate vs inanimate) at any time point. Nearly identical results were found in a second MEG experiment that required basic-level object recognition. These results suggest that MEG sensor patterns do not capture object animacy independently of perceptual differences between animate and inanimate objects. This is in contrast to results observed in fMRI using the same stimuli, task, and analysis approach: fMRI showed a highly reliable categorical distinction in visual cortex even when controlling for perceptual dissimilarity. Results thus point to a discrepancy in the information contained in multivariate fMRI and MEG patterns.
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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) | 0.001 | 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".