Thinking forensics: Cognitive science for forensic practitioners
Open accessAbstract
Human factors and their implications for forensic science have attracted increasing levels of interest across criminal justice communities in recent years. Initial interest centred on cognitive biases, but has since expanded such that knowledge from psychology and cognitive science is slowly infiltrating forensic practices more broadly. This article highlights a series of important findings and insights of relevance to forensic practitioners. These include research on human perception, memory, context information, expertise, decision-making, communication, experience, verification, confidence, and feedback. The aim of this article is to sensitise forensic practitioners (and lawyers and judges) to a range of potentially significant issues, and encourage them to engage with research in these domains so that they may adapt procedures to improve performance, mitigate risks and reduce errors. Doing so will reduce the divide between forensic practitioners and research scientists as well as improve the value and utility of forensic science evidence.
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The participatory approach to communication has been criticized by some commentators due to the potential for bias it introduces.
“This participatory approach to communication has been criticized by some commentators due to the potential for bias that it introduces (Ditrich, 2015;Dror, Kukucka, Kassin, & Zapf, 2018;Edmond et al., 2017).”
From Communication, forensic science, and the law (2021) · cites this paper as Edmond et al., 2017
The nature of errors in forensic science is wide and varied, including scientific validity of methodologies, human factors such as bias and competency, and communication to criminal justice decision-makers.
“Cases in both the media and literature demonstrate that the nature of such errors is wide and varied, from matters of scientific validity of methodologies [1], to errors influenced by human factors such as bias and competency [2, 3], to how results and information are communicated effectively to criminal justice decision‐makers [4].”
From Quality issue management and disclosure in forensic science: A survey of practice and perceptions (2025) · cites this paper as [2]
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Osborne, N. K. P., Woods, S. R., Kieser, J. A., & Zajac, R. (2014) · Science & Justice · doi:10.1016/j.scijus.2013.12.005 · Cited by 59 · see citing works
The Influence of Race on Eyewitness Memory
Brigham, J. C., Bennett, L. B., Meissner, C. A., et al. (2007) · doi:10.4324/9781315805535.ch11 · Cited by 58 · see citing works
Context Effects and Observer Bias—Implications for Forensic Odontology
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Self-citationUsing Comprehensive Feature Lists to Bias Medical Diagnosis.
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The Cool Crucible: Forensic Science and the Frailty of the Criminal Trial
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Self-citationThe psychology of interpreting expert evaluative opinions
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Putting bias into context: The role of familiarity in identification.
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Self-citationOptical Illusions and the Visual Arts
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Legal versus non-legal approaches to forensic science evidence
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Self-citationA critical examination of the psychological and practical implications of eyewitness research.
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Unsound law: Issues with ('expert') voice comparison evidence
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Self-citationTeaching posttraining: Influencing diagnostic strategy with instructions at test.
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Understanding expertise and non-analytic cognition in fingerprint discriminations made by humans
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GAITKEEPING IN CANADA: MIS-STEPS IN ASSESSING THE RELIABILITY OF EXPERT TESTIMONY
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Communicating the Results of Forensic Science ExaminationsPreprint
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Four principles of explainable artificial intelligence
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Quality issue management and disclosure in forensic science: A survey of practice and perceptions
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““Each corrective action is handled individually – there is no review for trends, no meaningful management review for similar incidents or similar causes that could point to larger, system level preventative actions” 2.”
- Authors
- Edmond, G., Towler, A., Growns, B., Ribeiro, G., Found, B., White, D., Ballantyne, K., Searston, R. A., Thompson, M. B., Tangen, J. M., Kemp, R. I., & Martire, K.
- Affiliations
- UNSW Sydney (AU)
- The University of Queensland (AU)
- Victoria Police (AU)
- Corresponding author
- Edmond, Gary
- ORCID
- Venue
- Science & Justice
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 57
- Issue
- 2
- Pages
- 144-154
- ISSN
- 1355-0306 (ISSN-L)
- OpenAlex
- W2559702471
- Cited by
- 95
- References
- 148
- Citations by year
- 2026: 10 · 2025: 8 · 2024: 10 · 2023: 4 · 2022: 11 · 2021: 14 · 2020: 11 · 2019: 12 · 2018: 6 · 2017: 7 · 2016: 1 · 2013: 1
- FWCI
- 1.36
- Type
- journal article
- Language
- en
- Open access
- green
- handle.unsw.edu.au
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Science & Justice (publishedVersion)
- PubMed (publishedVersion)
- Murdoch University Research Portal (submittedVersion)
- UNSWorks (UNSW Sydney) (submittedVersion, cc-by-nc-nd)
- Northumbria Research Link (Northumbria University) (submittedVersion)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Psychology › Social Psychology › Deception detection and forensic psychology
- Social Sciences › Law › Legal processes and jurisprudence
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Keywords
- forensic science, cognitive science, human factors, cognitive biases, human perception, memory, contextual information, expertise, decision-making, communication, experience, verification, confidence, feedback, forensic practitioners, forensic evidence, error reduction
- MeSH terms
- Expert Testimony, Feedback, Humans, Memory, Professional Competence, Cognitive Science, Forensic Sciences
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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