Becoming informed lie-catchers: classical findings and recent developments in deception detection
Bibliographic record
Abstract
Deception, a commonplace phenomenon, is particularly problematic in investigative and courtroom settings. Accordingly, the field of forensic psychology has recently made promising laboratory progress towards developing reliable lie detection techniques. The motivation of this narrative review is two-fold. First, for field researchers, a unifying evaluation of the scientific process by which we reach the current consensus is overdue. To facilitate a common understanding and to inspire continuous advancements, this review commenced by portraying the evolution of the field’s theoretical and practical understanding of lie detection. Second, the sheer number of proposed detection techniques – each with its own use case and caveats – leaves forensic practitioners potentially overwhelmed. The end goal of this narrative review is, therefore, to pinpoint what works and under what circumstances; by critically sifting through at times conflicting evidence, readers are presented with the state-of-the-art theories and detection techniques backed by contemporary research. As a novel contribution, an integrative decision tree is developed to guide practitioners in selecting the most appropriate technique for their circumstances. In parallel, by pointing out current weaknesses in the literature, avenues for future research are highlighted, possibly by applying past lessons learned as described in the article.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".