Linking Cognitive Decision-Making with Brain Activity During Haptic Interactions in Virtual Environments
Bibliographic record
Abstract
Haptic feedback is essential for creating immersive and intuitive user experiences in remote and virtual environments (VEs). However, the cognitive processes underlying haptic interactions and their connection to brain activity remain underexplored. This study investigates the relationship between behavioral modeling through the Drift-Diffusion Model (DDM) and brain responses measured by event-related potentials (ERPs), focusing on the P300 and N200 components.A user study was conducted in which users were tasked to trace a helical curve while perceiving force and vibrotactile cues in a 3D stereoscopic VE. Participants wore an EEG cap to record their brain activity, and reaction times (RTs) were recorded for each haptic interaction. The study analyzed the DDM’s drift rate parameter, which corresponds to the speed of evidence accumulation, and compared it with the slopes from the cue onset to P300 (P300 slope) and slope from N200 to P300 components (NP300 slope), obtained from ERPs.Results revealed an agreement between DDM drift rates and ERP slopes, particularly with the NP300 slope, suggesting that NP300 slope may more accurately represent the cognitive processes reflected by DDM when perceiving haptic cues. The agreement between behavioral data (RTs) and brain responses (ERPs) suggests that the DDM could be an useful tool for inferring underlying brain activity by analyzing RTs during haptic interactions.
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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 | 0.000 | 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) | 0.000 | 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 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".