Decoding representations of discriminatory and hedonic information during appetitive and aversive touch
Bibliographic record
Abstract
Abstract Emotion is typically understood to be an internal subjective experience originating in the brain. Yet in the somatosensory system hedonic information is coded by mechanoreceptors at the point of sensory contact before it reaches the central nervous system. It remains unknown, however, how these distinct peripheral channels for tactile hedonic information contribute to representations of interoceptive states relative to exteroceptive experience. In this fMRI study we applied representational similarity analyses with pattern component modeling, a technique that deconstructs representational states into a weighted set of distinct predefined constructs, to dissociate how discriminatory vs. hedonic tactile information, carried by A- and C-/CT-fibers respectively, contributes to population code representations in the human brain. Results demonstrated that information about appetitive and aversive tactile sensation is represented separately from non-hedonic tactile information across cortical structures. Specifically, although hedonic touch originates as a peripheral signal, labeled at the point of contact, representations in somatosensory cortices are guided by experiences of non-hedonic touch, By contrast, representations in regions associated with interoception and affect encode signals of hedonic touch. This provides evidence of complex tactile encoding that involves both external-exteroceptive and internal-interoceptive dimensions. Importantly, hedonic touch contributes to representations of internal state as well as those of externally generated stimulation.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".