Evidence map›Paper›PMID 38361149›Full record

ArticleCognitive research: principles and implications2024

How do humans learn about the reliability of automation?

Luke Strickland, Simon Farrell, Micah K Wilson, Jack Hutchinson, Shayne Loft

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In one paragraph

Article in Cognitive research: principles and implications, 2024. 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
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.

  1. 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

5 authors.

Luke StricklandThe Future of Work Institute, Curtin University, 78 Murray Street, Perth, 6000, Australia. luke.strickland@curtin.edu.au.ORCID 0000-0002-6071-6022
Simon FarrellThe School of Psychological Science, The University of Western Australia, Crawley, Perth, Australia.
Micah K WilsonThe Future of Work Institute, Curtin University, 78 Murray Street, Perth, 6000, Australia.
Jack HutchinsonThe School of Psychological Science, The University of Western Australia, Crawley, Perth, Australia.
Shayne LoftThe School of Psychological Science, The University of Western Australia, Crawley, Perth, Australia.

Funding

Australian Research Council DE230100171Defence Science and Technology Group MyIP8433
6 · The paper itself

Abstract

In a range of settings, human operators make decisions with the assistance of automation, the reliability of which can vary depending upon context. Currently, the processes by which humans track the level of reliability of automation are unclear. In the current study, we test cognitive models of learning that could potentially explain how humans track automation reliability. We fitted several alternative cognitive models to a series of participants' judgements of automation reliability observed in a maritime classification task in which participants were provided with automated advice. We examined three experiments including eight between-subjects conditions and 240 participants in total. Our results favoured a two-kernel delta-rule model of learning, which specifies that humans learn by prediction error, and respond according to a learning rate that is sensitive to environmental volatility. However, we found substantial heterogeneity in learning processes across participants. These outcomes speak to the learning processes underlying how humans estimate automation reliability and thus have implications for practice.

Indexed as

LearningTask Performance and AnalysisAutomationHumansJudgmentReproducibility of ResultsAutomation reliabilityCognitive modelHuman-automation teamingLearning

Identifiers

PMID38361149
PMCPMC10869332

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Registered trials

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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.