Lies, Deceit, and Hallucinations: Player Perception and Expectations Regarding Trust and Deception in Games
Bibliographic record
Abstract
Lying and deception are important parts of social interaction; when applied to storytelling mediums such as video games, such elements can add complexity and intrigue. We developed a game, “AlphaBetaCity”, in which non-playable characters (NPCs) made various false statements, and used this game to investigate perceptions of deceptive behaviour. We used a mix of human-written dialogue incorporating deliberate falsehoods and LLM-written scripts with (human-approved) hallucinated responses. The degree of falsehoods varied between believable but untrue statements to outright fabrications. 29 participants played the game and were interviewed about their experiences. Participants discussed methods for developing trust and gauging NPC truthfulness. Whereas perceived intentional false statements were often attributed towards narrative and gameplay effects, seemingly unintentional false statements generally mismatched participants’ mental models and lacked inherent meaning. We discuss how the perception of intentionality, the audience demographic, and the desire for meaning are major considerations when designing video games with falsehoods.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".