Decoding visual objects in somatosensory cortex: the effect of prior visuo-haptic experience
Bibliographic record
Abstract
Neurons, even in the earliest sensory areas of cortex, are subject to a large number of contextual influences from both within and across modality connections. In previous work (Smith & Goodale, 2012, ECVP) we have shown that early regions of somatosensory cortex (S1) surprisingly contain content-specific information about visually presented object categories (i.e. in the absence of any tactile stimulation). In the present experiment, we investigate whether prior visuo-haptic experience with the object categories is necessary in order to observe this effect. In an fMRI experiment, we presented participants with visual images of either familiar visuo-haptic categories (wine glasses, mobile phones or apples; replicating our initial experiment) or artificially created visual shapes (cubies, spikies or smoothies; see Op De Beeck et al 2008). Participants fixated and performed an orthogonal task (counting the numbers of fixation cross color changes occurring in each run). We predicted that MVPA decoding of object category should be above chance in S1 and S2 only for the familiar visuo-haptic shape categories, whereas in visual cortex, decoding should be above chance for both familiar and unfamiliar categories. MVPA revealed reliable decoding in both S1 and S2 for familiar but not unfamiliar object categories. In contrast, in early vision, both unfamiliar and familiar object categories could be decoded with very high accuracy. Our findings suggest that visual presentation alone of familiar but not unfamiliar objects trigger content specific activity patterns even in the earliest regions of somatosensory cortex. Thus the present data support the idea that cross-modal activation of content-specific representations in early sensory cortices is based on prior experience of concurrent multi-modal stimulation (e.g. Meyer & Damasio 2009). 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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".