Laughter and Tickles: Toward Novel Approaches for Emotion and Behavior Elicitation
Bibliographic record
Abstract
Considerable effort has been invested in the development of effective emotion and behavior recognition techniques. In comparison, little work has been devoted to technologies that can be used to induce specific emotional and behavioral responses, with most such research relying on the presentation of video or images. In this article, we propose a novel technique for the elicitation of emotion based on audio-tactile stimulation. Taking advantage of the relationship between tickling, laughter and emotional states, we conducted an experiment to map the perception of the tickle sensation as a function of vibrotactile stimulation frequency, quantify the effect of hearing laughter stimulus on the perceived intensity of the tactile experience, and assess the potential of the proposed multimodal approach to induce observable mirthful responses. Experimental evidence shows that the perceived intensity of the auditory laughter stimulus has a repeatable scaling effect on the tickle sensation and that the proposed audio-tactile stimulation is a promising approach to laughter elicitation. These findings may inform the design of future multimodal affective interfaces by allowing a more informed prediction of induced emotional and behavioral responses.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".