Evidence map›Paper›PMID 38806791›Full record

ReviewPsychonomic bulletin & review2024

A hierarchical signal detection model with unequal variance for binary responses.

Martin Lages

Abstract readReview
In one paragraph

Review in Psychonomic bulletin & review, 2024. 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

1 author.

Martin LagesSchool of Psychology and Neuroscience, University of Glasgow, 62 Hillhead Street, Glasgow G12 8QQ, Glasgow, UK. martin.lages@glasgow.ac.uk.ORCID http://orcid.org/0000-0002-4962-3116

Funding

European Commission Erasmus+ QHELP/ 2019-1-EE01-KA203-051708, Erasmus+ TquanT/ 2015-1-UK01-KA203-013819
6 · The paper itself

Abstract

Gaussian signal detection models with equal variance are commonly used in simple yes-no detection and discrimination tasks whereas more flexible models with unequal variance require additional information. Here, a hierarchical Bayesian model with equal variance is extended to an unequal-variance model by exploiting variability of hit and false-alarm rates in a random sample of participants. This hierarchical model is investigated analytically, in simulations and in applications to existing data sets. The results suggest that signal variance and other parameters can be accurately estimated if plausible assumptions are met. It is concluded that the model provides a promising alternative to the ubiquitous equal-variance model for binary data.

Indexed as

Bayes TheoremSignal Detection, PsychologicalHumansModels, PsychologicalModels, StatisticalBayesian inferenceBivariate normal distributionLatent parameter estimationMCMC sampling

Identifiers

PMID38806791
PMCPMC11680650

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.