Towards an Internal Process Model for Haptic Interactions within Virtual Environments
Bibliographic record
Abstract
Interactive human-machine systems (HMS), such as compute-based virtual environments (VEs), have been increasingly relied upon for decision-making. Building trust between human users and machines is crucial to enable a cooperative relationship. One aspect of building trust requires modeling sensory feedback from virtual objects in VEs to the users for appropriate understanding and utilization. In current VEs, of interest is modelling the integration of vibrotactile and force cues for providing sensory feedback to stimulate the haptic modality of the users. Behavioral models, such as maximum likelihood estimation, have failed to interpret the integration. Underlying this failure might be subtle internal processes of the human brain. Hence, we conducted an experiment to investigate the feasibility of modeling the integration using a drift-diffusion model (DDM), which is known to bridge observed behavioral outcomes and internal processes. In the experiment, human participants undertook a navigation and detection task within a 3D VE. Their task execution was aided by vibrotactile or/and force cues. Analyses on task accuracy and response time to the cues confirmed that DDM was feasible to interpret behavioral outcomes of the participants. The interpretation implies a link between the outcomes and the internal processes, paving a potential way to use DDM for elucidating the integration of vibrotactile and force cues.
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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 | 0.000 |
| 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) | 0.004 | 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".