Are visual texture-selective areas recruited during haptic texture discrimination?
Bibliographic record
Abstract
Shape and surface texture provide salient cues to object identity, both when objects are explored using vision and via touch (haptics). In vision, shape information is processed within the lateral occipital complex (LOC), while surface texture is selectively processed in medial ventro-temporal cortex, in the collateral sulcus (CoS) and parahippocampul gyrus (pHG). Accumulating evidence indicates that the LOC, despite its location within the ventral visual system, is also consistently recruited during haptic shape processing. Here we used fMRI to examine whether ventral ‘visual’ texture-selective areas are similarly recruited when observers discriminate surface texture via touch. We used a blocked design in which participants attended to, and made same/different judgments about, sequential pairs of 3-dimensional plastic objects. During each block, the stimuli varied either in their shape (but not surface texture), or their surface texture (but not shape). In half of the scans, participants explored the stimuli using vision alone, and in the remaining scans the stimuli were explored via touch alone. In vision, the strongest fMRI responses to surface texture (versus shape) perception were observed within medial ventral temporal cortex – a result consistent with recent fMRI findings in vision. When stimuli were explored via touch, however, the strongest fMRI responses to surface texture (versus shape) perception were observed dorsally, within the parietal operculum (in the vicinity of secondary somatosensory cortex) and inferior frontal cortex. Importantly, however, we also observed significant texture-selective fMRI responses in medial ventral temporal cortex within areas analogous to those recruited during visual texture discrimination. Our imaging data demonstrate for the first time that ventro-medial temporal areas known to process visual textures are also recruited during the perception of surface texture via touch. Meeting abstract presented at VSS 2013
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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 | 0.000 | 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".