Measuring the cognitive resources consumed per second for real-time lie-production and recollection: a dual-tasking paradigm
Bibliographic record
Abstract
This research report presents a novel method of dual-tasking lie-detection. Novel software "Follow Me" was invented for a concurrent eye-hand coordination task during truth-telling/lying. Undergraduate participants were instructed to tell truths on questions about undergraduate school whereas they were instructed to tell lies on interview questions about graduate school, pretending they were graduate students. Throughout the experiment, they operated the "Follow Me" software: moving the mouse pointer to follow a randomly-moving dot on a computer screen. The distance between the mouse pointer tip and the dot center was measured by the software every 50 ms. Frequency of distance fluctuation was analyzed as the index of cognitive effort consumed per second (i.e., "degree of cognitive effort"). The results revealed that the dominant frequency of distance fluctuation was significantly lower during encoding than during retrieving responses; and lower during lying than truth-telling. Thus, dominant frequency of distance fluctuation may be an effective index of cognitive effort. Moreover, both encoding and retrieving bald-faced lies were more cognitively effortful than truth-telling. This novel definition and measurement of degree of cognitive effort may contribute to the research field of deception as well as to many other fields in social cognition.
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".