Estimating Cognitive Processes Related to Haptic Interaction within Virtual Environments
Bibliographic record
Abstract
Efforts exist to combine a brain-machine interface (BMI) into a 3D virtual environment (VE) for visual tasks. User interaction via haptic stimuli within the VE is still unexplored for developing the BMI however, due to little understanding of cognitive processes related to such haptic interaction. Hence, we investigated a feasibility of estimating cognitive processes related to haptic interaction. Involved in the investigation, human participants undertook a task via different haptic stimuli (e.g., force and vibration) within a 3D VE . Their brain activities evoked by the stimuli were acquired as electroencephalography signals. Patterns of event-related potential and power spectral density were extracted from the signals, indicating activation in certain brain areas. The estimation of connectivity among these areas used directed transfer function, emphasizing on the middle of the β band (1/030 Hz) in the signals. The emphasis was due to the band's association with active focus and thinking. We found that, while behavioral differences were unapparent, all vibration-related stimuli yielded distinct active brain areas and connectivity to form certain cognitive processes. The finding implied a potential of localizing the processes for BMI-based haptic interaction.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".