Perceiving material properties of objects through sight or sound activates ventral occipitotemporal cortex
Bibliographic record
Abstract
Knowledge of an object's material composition (i.e., what it is made of) alters how we interact with that object. Seeing the bright glint or hearing the metallic crinkle of a foil plate for example, confers information about that plate before we have even touched it. In a previous study (Cant & Goodale, 2007) it was shown that visually attending to an object's material properties as opposed to its shape or color, elicited greater hemodynamic activity in ventral occipitotemporal regions. In the present fMRI study, we investigated whether such areas are also sensitive to material properties of objects derived from sound only. Participants first completed a passive adaptation visual paradigm in which the material composition, shape or color of objects was manipulated in different blocks. In a separate series of blocks, participants were randomly presented with two-second normalized sound clips and asked to categorize them as either Material (crumpling styrofoam, plastic, tinfoil or paper), Noise (scrambled versions of each of the material sounds), or Human sounds (coughing, yawning, snoring or throat clearing). Replicating results from Cant & Goodale (2007), the visual task revealed a prominent area in the right parahippocampal region that was most selective for material as compared to shape or color properties. Although functional analyses of the auditory data within this visual material region did not reveal any discernable auditory modulation, expanding the region to include all of parahippocampal cortex revealed an area immediately adjacent to the visual material region that was highly selective for Material sounds as compared to Noise or Human sounds. Our findings point to an important multimodal role for the parahippocampal region in the analysis of material properties, and are in keeping with the notion that the medial aspect of the ventral pathway is specialized for processing the surface properties of objects.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".