Bibliographic record
Abstract
Abstract The modality of touch encompasses distinct cutaneous, kinesthetic, and haptic systems that are distinguished on the basis of the underlying neural inputs. The cutaneous receptors are embedded in the skin; the kinesthetic receptors lie in muscles, tendons, and joints; and the haptic system uses combined inputs from both. Topics in this chapter range from sensory phenomena, such as threshold‐level responses, to cognitive and memory processes associated with the haptic system. Haptic perception extracts properties of objects and surfaces that lead to recognition of objects. Material properties are highly accessible, relative to geometric properties, providing a contrast between haptics and vision. The haptic system provides a map of space within reach of the body that provides the basis for recognition of two‐dimensional patterns and outline drawings. The spatial map is subject to systematic distortion, particularly as a result of the movements used in exploration. Interactions between haptic and visual perception are described with respect to attention, representation, and memory. As do other modalities, the sense of touch gives rise to implicit and explicit forms of memory. The chapter concludes with applications of research on touch, including aids for the blind and deaf and virtual environments that provide haptic feedback.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.187 | 0.062 |
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".