The focal account: Indirect lie detection need not access unconscious, implicit knowledge.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
People are poor lie detectors, but accuracy can be improved by making the judgment indirectly. In a typical demonstration, participants are not told that the experiment is about deception at all. Instead, they judge whether the speaker appears, say, tense or not. Surprisingly, these indirect judgments better reflect the speaker's veracity. A common explanation is that participants have an implicit awareness of deceptive behavior, even when they cannot explicitly identify it. We propose an alternative explanation. Attending to a range of behaviors, as explicit raters do, can lead to conflict: A speaker may be thinking hard (indicating deception) but not tense (indicating honesty). In 2 experiments, we show that the judgment (and in turn the correct classification rate) is the result of attending to a single behavior, as indirect raters are instructed to do. Indirect lie detection does not access implicit knowledge, but simply focuses the perceiver on more useful cues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it