Evidence map›Paper›PMID 37980697›Full record

ArticleCognitive research: principles and implications2023

Supporting detection of hostile intentions: automated assistance in a dynamic decision-making context.

Colleen E Patton, Christopher D Wickens, C A P Smith, Kayla M Noble, Benjamin A Clegg

Abstract read
In one paragraph

Article in Cognitive research: principles and implications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Colleen E PattonDepartment of Psychology, Colorado State University, Fort Collins, USA. colleenpatton10@gmail.com.ORCID 0000-0002-2841-4526
Christopher D WickensDepartment of Psychology, Colorado State University, Fort Collins, USA.
C A P SmithDepartment of Psychology, Colorado State University, Fort Collins, USA.
Kayla M NobleDepartment of Psychology, Colorado State University, Fort Collins, USA.
Benjamin A CleggMontana State University, Bozeman, MT, 59717, USA.

Funding

Office of Naval Research N00014-20-1-2518
6 · The paper itself

Abstract

In a dynamic decision-making task simulating basic ship movements, participants attempted, through a series of actions, to elicit and identify which one of six other ships was exhibiting either of two hostile behaviors. A high-performing, although imperfect, automated attention aid was introduced. It visually highlighted the ship categorized by an algorithm as the most likely to be hostile. Half of participants also received automation transparency in the form of a statement about why the hostile ship was highlighted. Results indicated that while the aid's advice was often complied with and hence led to higher accuracy with a shorter response time, detection was still suboptimal. Additionally, transparency had limited impacts on all aspects of performance. Implications for detection of hostile intentions and the challenges of supporting dynamic decision making are discussed.

Indexed as

AlgorithmsIntentionAutomationHostilityHumansHydrolasesHydrolasesAutomationDynamic decision makingTransparencyTrust

Identifiers

PMID37980697
PMCPMC10657914

What OpenQuestion holds

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LicenceCC BY
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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.