Evidence map›Paper›PMID 31825997›Full record

ArticlePloS one2019

Detecting false intentions using unanticipated questions.

Glynis Bogaard, Joyce van der Mark, Ewout H Meijer

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 77% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. Investigative fatigue: how sleep-circadian factors shape criminal investigations.Sleep advances : a journal of the Sleep Research Society · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Glynis BogaardDepartment of Clinical Psychological Science, Section Forensic Psychology, Maastricht University, Maastricht, The Netherlands.ORCID 0000-0001-6795-9433
Joyce van der MarkDepartment of Clinical Psychological Science, Section Forensic Psychology, Maastricht University, Maastricht, The Netherlands.
Ewout H MeijerDepartment of Clinical Psychological Science, Section Forensic Psychology, Maastricht University, Maastricht, The Netherlands.
Maastricht University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The present study investigated whether measurable verbal differences occur when people vocalize their true and false intentions. To test potential differences, we used an experimental set-up where liars planned a criminal act (i.e., installing a virus on a network computer) and truth-tellers a non-criminal act (i.e., installing a new presentation program "SlideDog" on a network computer). Before they could carry out these acts, a confederate intercepted the participant and interviewed them about their intentions and the planning phase by using both anticipated and unanticipated questions. Liars used a cover story to mask their criminal intentions while truth-tellers told the entire truth. In contrast to our hypotheses, both human and automated coding did not show any evidence that liars and truth-tellers differed in plausibility or detailedness. Furthermore, results showed that asking unanticipated questions resulted in lengthier answers than anticipated questions. These results are in line with the mixed findings in the intention literature and suggest that plausibility and detailedness are less diagnostic cues for deception about intentions.

Indexed as

IntentionAnticipation, PsychologicalDeceptionFemaleHumansInterviews as TopicLie DetectionMaleYoung Adult

Identifiers

PMID31825997
PMCPMC6905579
OpenAlexW2996445545

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.