Representational dynamics of the main dimensions of object space: Face/body selectivity aligns temporally with animal taxonomy but not with animacy
Bibliographic record
Abstract
Object representations are organized according to multiple dimensions, with an important role for the distinction between animate and inanimate objects and for selectivity for faces versus bodies. For other dimensions, questions remain how they stand relative to these two primary dimensions. One such dimension is a graded selectivity for the taxonomic level that an animal belongs to. Earlier research suggested that animacy can be understood as a graded selectivity for animal taxonomy, although a recent functional magnetic resonance imaging study suggested that taxonomic effects are instead due to face/body selectivity. Here we investigated the temporal profile at which these distinctions emerge with multivariate electroencephalography (N = 25), using a stimulus set that dissociates taxonomy from face/body selectivity and from animacy as a binary distinction. Our findings reveal a very similar temporal profile for taxonomy and face/body selectivity with a peak around 150 ms. The binary animacy distinction has a more continuous and delayed temporal profile. These findings strengthen the conclusion that effects of animal taxonomy are in large part due to face/body selectivity, whereas selectivity for animate versus inanimate objects is delayed when it is dissociated from these other dimensions.
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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 | 0.002 |
| 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 | 0.001 |
| 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".