Evidence map›Paper›PMID 40417078›Full record

ArticlePNAS nexus2025

Metacognitive sensitivity: The key to calibrating trust and optimal decision making with AI.

Doyeon Lee, Joseph Pruitt, Tianyu Zhou, Jing Du, Brian Odegaard

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PNAS nexus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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.

Doyeon LeeDepartment of Psychology, University of Florida, 945 Center Dr., P.O. Box 112250, Gainesville, FL 32611, USA.ORCID https://orcid.org/0000-0001-7013-7254
Joseph PruittDepartment of Psychology, University of Florida, 945 Center Dr., P.O. Box 112250, Gainesville, FL 32611, USA.
Tianyu ZhouDepartment of Civil and Coastal Engineering, University of Florida, Weil Hall 360, 1949 Stadium Road, Gainesville, FL 32611, USA.ORCID https://orcid.org/0009-0009-1999-2883
Jing DuDepartment of Civil and Coastal Engineering, University of Florida, Weil Hall 360, 1949 Stadium Road, Gainesville, FL 32611, USA.
Brian OdegaardDepartment of Psychology, University of Florida, 945 Center Dr., P.O. Box 112250, Gainesville, FL 32611, USA.ORCID https://orcid.org/0000-0002-5459-1884

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Knowing when to trust and incorporate the advice from artificially intelligent (AI) systems is of increasing importance in the modern world. Research indicates that when AI provides high confidence ratings, human users often correspondingly increase their trust in such judgments, but these increases in trust can occur even when AI fails to provide accurate information on a given task. In this piece, we argue that measures of metacognitive sensitivity provided by AI systems will likely play a critical role in (1) helping individuals to calibrate their level of trust in these systems and (2) optimally incorporating advice from AI into human-AI hybrid decision making. We draw upon a seminal finding in the perceptual decision-making literature that demonstrates the importance of metacognitive ratings for optimal joint decisions and outline a framework to test how different types of information provided by AI systems can guide decision making.

Indexed as

artificial intelligencejoint decision makingmetacognitive sensitivityoptimal decisionstrust calibration

Identifiers

PMID40417078
PMCPMC12103939

What OpenQuestion holds

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LicenceCC BY-NC
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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.