Cognitive Load as a Key to Lie Detection: A Psycholinguistic Exploration of Pause and Hesitation in True and Fabricated Narratives
Bibliographic record
Abstract
This study investigates the role of pauses and hesitations as crucial non-verbal cues in deception detection, emphasizing the influence of cognitive load, as framed by the theoretical framework established by Memon et al. (2003). By examining cognitive indicators such as filled and silent pauses, repetitions, slips of the tongue, word omissions, speech rearrangements, sentence incompletions, and drawl, this research sheds light on the mental processes underlying language production in deceptive communication. Utilizing a comprehensive experimental design, we collected narratives from 30 native Persian speakers who recounted both truthful experiences and fabricated scenarios over a one-week period. This design aimed to minimize situational cognitive load and emotional anxiety by allowing participants ample preparation time and enabling them to record their narratives in a familiar home setting, thus isolating the cognitive demands inherent in the act of fabrication itself. Our dataset comprised 60 transcribed video recordings, which were meticulously coded for the specified disfluency types and analyzed using Praat software for phonetic detail where necessary (e.g., pause duration). Statistical analysis via paired sample t-tests in SPSS, with Bonferroni correction for multiple comparisons, revealed a significant increase ( p < .006) in the frequency of silent pauses (≥200 ms in duration) during the narration of fabricated stories compared to truthful ones. This finding suggests that the cognitive effort required for deception persists and manifests measurably, even when lies are premeditated and delivered in a low-stakes environment. Despite the controlled setting designed to reduce anxiety and planning demands, our findings underscore the potential reliability of specific non-verbal cues, particularly silent pauses, as indicators reflecting the cognitive architecture of deception. The study emphasizes the importance of integrating analyses of both verbal content and non-verbal behaviours in developing more nuanced and effective lie detection methodologies.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".