A humanness dimension to visual object coding in the brain
Bibliographic record
Abstract
Abstract Neuroimaging studies investigating human object recognition have largely focused on a relatively small number of object categories, in particular, faces, bodies, scenes, and vehicles. More recent studies have taken a broader focus, investigating hypothesised dichotomies, for example animate versus inanimate, and continuous feature dimensions, such as biologically similarity. These studies typically have used stimuli that are clearly identified as animate or inanimate, neglecting objects that may not fit into this dichotomy. We generated a novel stimulus set including standard objects and objects that blur the animate-inanimate dichotomy, for example robots and toy animals. We used MEG time-series decoding to study the brain’s emerging representation of these objects. Our analysis examined contemporary models of object coding such as dichotomous animacy, as well as several new higher order models that take into account an object’s capacity for agency (i.e. its ability to move voluntarily) and capacity to experience the world. We show that early brain responses are best accounted for by low-level visual similarity of the objects; and shortly thereafter, higher order models of agency/experience best explained the brain’s representation of the stimuli. Strikingly, a model of human-similarity provided the best account for the brain’s representation after an initial perceptual processing phase. Our findings provide evidence for a new dimension of object coding in the human brain – one that has a “human-centric” focus.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".