Did You Just Lie to Me? Deception Detection in Face to Face versus Computer Mediated Communication
Bibliographic record
Abstract
Individuals' deception detection ability during either a face-to-face (FtF) interaction or through computer-mediated communication (CMC) was explored under more naturalistic conditions where they were not forewarned that deception may be involved. Participants discussed a social issue either in a room together or by instant messaging from separate rooms. Prior to discussion, some participants were asked to deceive their partner regarding their actual opinion on the issue. Results showed that mode of communication did not influence participants' deception detection accuracy rate, nor their truth bias. Regardless of mode of communication, deceptive participants experienced the same level of physiological arousal as the non-deceivers. In contrast, deceivers reported experiencing higher levels of anxiety but only in the FtF condition. Findings highlight how for different communication modalities, a multitude of interactive factors may influence deception detection.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".