Midfrontal theta power encodes the value of haptic delay
Bibliographic record
Abstract
The use of haptic technologies in modern life scenarios is becoming the new normal particularly in rehabilitation, medical training, and entertainment applications. An evident challenge in haptic telepresence systems is the delay in haptic information. How humans perceive delayed visual and audio information has been extensively studied, however, the same for haptically delayed environments remains largely unknown. Here, we develop a visuo-haptic experimental setting that simulates pick and place task and involves continuous haptic feedback stimulation with four possible haptic delay levels. The setting is built using a haptic device and a computer screen. We use electroencephalography (EEG) to study the neural correlates that could be used to identify the amount of the experienced haptic delay. EEG data were collected from 34 participants. Results revealed that midfrontal theta oscillation plays a pivotal role in quantifying the amount of haptic delay while parietal alpha showed a significant modulation that encodes the presence of haptic delay. Based on the available literature, these results suggest that the amount of haptic delay is proportional to the neural activation that is associated with conflict detection and resolution as well as for multi-sensory divided attention.
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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 | 0.003 |
| 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 | 0.000 | 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".