Tactile sensory system: encoding from the periphery to the cortex
Bibliographic record
Abstract
Specialized mechanoreceptors in the skin respond to mechanical deformation and provide the primary input to the tactile sensory system. Although the morphology of these receptors has been documented, there is still considerable uncertainty as to the relation between cutaneous receptor morphology and the associated physiological responses to stimulation. Labelled-line models of somatosensory processes in which specific mechanoreceptors are associated with particular sensory qualities fail to account for the evidence showing that all types of tactile afferent units respond to a varying extent to most types of natural stimuli. Neurophysiological and psychophysical experiments have provided the framework for determining the relation between peripheral afferent or cortical activity and tactile perception. Neural codes derived from these afferent signals are evaluated in terms of their capacity to predict human perceptual performance. One particular challenge in developing models of the tactile sensory system is the dual use of sensory signals from the skin. In addition to their perceptual function they serve as inputs to the sensorimotor control system involved in manipulation. Perceptions generated through active touch differ from those resulting from passive stimulation of the skin because they are the product of self-generated exploratory processes. Recent research in this area has highlighted the importance of shear forces in these exploratory movements and has shown that fingertip skin is particularly sensitive to shear generated during both object manipulation and tactile exploration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".