Evidence map›Paper›PMID 34570551›Full record

ArticlePsychological review2021

Estimating systematic and random sources of variability in perceptual decision-making: A reply to Evans, Tillman, & Wagenmakers (2020).

Roger Ratcliff, Philip L Smith

Abstract read
In one paragraph

Article in Psychological review, 2021. 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

2 authors.

Roger RatcliffDepartment of Psychology, The Ohio State University.ORCID 0000-0001-9657-0814
Philip L SmithMelbourne School of Psychological Sciences, The University of Melbourne.

Funding

Toward using comprehensive decision-making models in neuropsychological testingR01AG041176 · NIA · OHIO STATE UNIVERSITY · PI MCKOON, GAIL A, RATCLIFF, ROGER · 2012 to 2021
$3.8M
Decision Making on Continuous Scales in Aging and Alzheimers DiseaseR01AG057841 · NIA · OHIO STATE UNIVERSITY · PI MCKOON, GAIL A, RATCLIFF, ROGER · 2020 to 2024
$2.5M
NIA NIH HHS R01 AG041176NIA NIH HHS R01 AG057841
6 · The paper itself

Abstract

Ratcliff, Voskuilen, and McKoon (2018) presented data and model-based analyses that provided strong evidence for across-trial variability in evidence entering the decision process in several perceptual tasks. They did this using a double-pass procedure in which exactly the same stimuli are presented on two widely-separated trials. If there were only random variability (i.e., the first and second presentations of a stimulus were independent), then the agreement in the choice made on the two trials would be a function of accuracy: as accuracy increases from chance to 100% correct, then the probability of agreement increases. In the experiments, agreement was greater than that predicted from independence which means that there was systematic variability in items from trial to trial. Evans et al. (2020) criticized this by arguing that because of possible tradeoffs among parameters, the evidence did not support two sources of across-trial variability, but rather the results could be explained by only a systematic (item) component of variability. However, their own analysis showed that parameter estimates were accurate enough to support identification of the two sources of variability. We present a new analysis of possible sources of across-trial variability in evidence and show that systematic variability can be estimated from accuracy-agreement functions with a functional form that depends on only two diffusion model parameters. We also point out that size of the estimates of these two sources are model-dependent. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Indexed as

ProbabilityHumans

Identifiers

PMID34570551
PMCPMC9428907

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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.