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University of Ontario Institute of Technology

The impact of thin(ner) slicing on deception detection

Abstract

dc:description.abstract

I examined whether the length of thin slices (i.e., observations of behavior less than five minutes) affected deception detection. Participants (N = 262) were randomly assigned to one of seven exposure length conditions (i.e., 5-second, 10-second, 15-second, 20-second, 25-second, 30-second, or full-length clips). They attempted to detect the deception of 12 speakers. Participants’ ability to discriminate between truth- and lie-tellers did not significantly differ across conditions. Response biases, decision-making processes, and response times were similarly unaffected by exposure. However, there was some indication that confidence differed for truth- and lie-tellers across exposure lengths. That is, significant differences in confidence for truth- and lie-tellers were observed in the 5- and 10-second conditions, although the direction of effects differed. Therefore, while researchers can be confident that the decision to use long or short thin slices will not affect deception detection, it could affect participants’ confidence in those judgments.

Degree

thesis:*
Name thesis:degree_name
Master of Science (MSc)
Discipline thesis:degree_discipline
Electrical and Computer Engineering
Grantor
University of Ontario Institute of Technology
Year dc:date.issued
2022

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Villeneuve, Katrina
Advisor dc:contributor.advisor
  • Leach, Amy-May

Subjects

dc:subject × 4

Rights

Language dc:language.iso
en

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/10155/1503
OAI identifier oai:identifier
oai:ontariotechu.scholaris.ca:10155/1503

Chain of custody

source
Harvested from
Ontario Institute of Technology
Base URL
ontariotechu.scholaris.ca/server/oai/request
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Villeneuve, Katrina. The impact of thin(ner) slicing on deception detection. University of Ontario Institute of Technology, 2022. https://hdl.handle.net/10155/1503