Bernardo Autonomous Emotional Agents Increase Perception of VR Stimuli
Bibliographic record
Abstract
Video games are high emotional vectors. They play with the emotions of players by eliciting and increasing them. The importance of the induction of basic emotions has been a long forestay and is favoured by video game publishers, as they are quite easily mobilized. Video game publishers look to produce more complex social emotions like empathy, and compassion. In games framework with narrative context, designers frequently use cinema movies methods, like cinematic non-interactive Cutscenes. These methods temporarily exclude the player from interactivity to leave his first viewpoint view and move the camera focusing on the narrative stimuli. Cutscenes were used abundantly and are now rejected, the new development wave is often trying to develop in a “zero cinematic” way. For the same reason, cinematics are also not usable in new Virtual Reality. If VR games and simulations provides a high level of presence, VR environments needs certain rules related in particular to the continuation of free will and the avoidance of possible Break in Presence. We propose in this paper a concept of Emotionally Intelligent Virtual Avatars, which when they perceive an important narrative stimulus, share their emotions through, gestures, facial nonverbal expressions, and declarative sentences to stimulate the player's attention. This will lead players to focus on the narrative stimuli. Our research studies the impact of the use of Bernardo Agents Emotional Avatars involving n = 51 users. The statistical analysis of the results shows a significant difference in the narrative perception of the stimuli and in Presence, correlated to the use of Agents Bernardo. Overall, our emotional Agent Bernardo is a unique concept for increasing the perception of narrative stimuli in virtual environments using HMD, and may be useful in all virtual environments using an emotional narrative process.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".