Evidence map›Paper›PMID 36008508›Full record

ArticleScientific reports2022

Human preferences toward algorithmic advice in a word association task.

Eric Bogert, Nina Lauharatanahirun, Aaron Schecter

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Trial
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  3. Warning people about the risk of AI error mitigates human acquisition of AI bias.Cognitive research: principles and implications · 2026
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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.

Eric BogertDepartment of Supply Chain and Information Management, Northeastern University, Boston, MA, 02115, USA.
Nina LauharatanahirunDepartments of Biomedical Engineering and Biobehavioral Health, Pennsylvania State University, University Park, PA, 16802, USA.
Aaron SchecterDepartment of Management Information Systems, University of Georgia, Athens, GA, 30602, USA. aschecter@uga.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Algorithms provide recommendations to human decision makers across a variety of task domains. For many problems, humans will rely on algorithmic advice to make their choices and at times will even show complacency. In other cases, humans are mistrustful of algorithmic advice, or will hold algorithms to higher standards of performance. Given the increasing use of algorithms to support creative work such as text generation and brainstorming, it is important to understand how humans will respond to algorithms in those scenarios-will they show appreciation or aversion? This study tests the effects of algorithmic advice for a word association task, the remote associates test (RAT). The RAT task is an established instrument for testing critical and creative thinking with respect to multiple word association. We conducted a preregistered online experiment (154 participants, 2772 observations) to investigate whether humans had stronger reactions to algorithmic or crowd advice when completing multiple instances of the RAT. We used an experimental format in which subjects see a question, answer the question, then receive advice and answer the question a second time. Advice was provided in multiple formats, with advice varying in quality and questions varying in difficulty. We found that individuals receiving algorithmic advice changed their responses 13[Formula: see text] more frequently ([Formula: see text], [Formula: see text]) and reported greater confidence in their final solutions. However, individuals receiving algorithmic advice also were 13[Formula: see text] less likely to identify the correct solution ([Formula: see text], [Formula: see text]). This study highlights both the promises and pitfalls of leveraging algorithms to support creative work.

Indexed as

AlgorithmsHumansTime Factors

Identifiers

PMID36008508
PMCPMC9411628

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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.