Evidence map›Paper›PMID 41748773›Full record

ArticlePsychonomic bulletin & review2026

The diffusion model's drift rate parameter primarily reflects efficiency, rather than speed, of evidence accumulation.

Alexander Weigard, M Fiona Molloy, Chandra Sripada, Andrew Heathcote

Abstract read
In one paragraph

Article in Psychonomic bulletin & review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

4 authors.

Alexander WeigardDepartment of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA. asweigar@med.umich.edu.ORCID http://orcid.org/0000-0003-3820-6461
M Fiona MolloyDepartment of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA.
Chandra SripadaDepartment of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA.
Andrew HeathcoteDepartment of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands.

Funding

ABCD-USA Consortium: Research ProjectU01DA041106 · NIDA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Mary M Heitzeg, Chandra Sekhar Sripada · 2015 to 2026
$24.9M
MULTIDISCIPLINARY ALCOHOLISM RESEARCH TRAINING PROGRAMT32AA007477 · NIAAA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Maureen A. Walton · 1990 to 2026
$7.4M
Building a multi-factor etiological model of the emergence of general psychopathology (the "P factor") in adolescence with multi-modal neuroimaging in ABCDR01MH123458 · NIMH · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI SRIPADA, CHANDRA SEKHAR · 2021 to 2025
$1.9M
Leveraging computational models of neurocognition to improve predictions about individual youths' risk for substance use disordersK23DA051561 · NIDA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI WEIGARD, ALEXANDER · 2021 to 2025
$983k
Efficiency of evidence accumulation (EEA) as a higher-order, computationally defined RDoc constructR21MH130939 · NIMH · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI WEIGARD, ALEXANDER · 2023 to 2024
$429k
Defence Science and Technology Group AUSMURIV000003NIAAA NIH HHS T32 AA007477NIDA NIH HHS K23 DA051561NIDA NIH HHS U01 DA041106NIMH NIH HHS R01 MH123458NIMH NIH HHS R21 MH130939Office of Naval Research N00014-23-1-2792
6 · The paper itself

Abstract

Applications of the diffusion decision model (DDM) to the study of cognitive individual differences consistently find that the model's drift rate (v) parameter forms a cohesive factor across many tasks and relates to measures of higher-order cognitive functioning, including general cognitive ability and working memory. This parameter is often interpreted as a measure of "processing speed," a traditional psychometric construct thought to reflect an individual's basic speed of information processing across tasks. However, conceptual differences between v and traditional notions of processing speed make this mapping far from straightforward. Racing accumulator models, which provide a more flexible and comprehensive account of behavioral data than the DDM, allow for the speed with which individuals accumulate evidence to be dissociated from the efficiency with which they accumulate task-relevant evidence (versus task-irrelevant evidence). We applied the DDM and a racing accumulator model to three tasks across three independent datasets to gauge the extent to which v parameter findings from the cognitive individual differences literature reflect speed of evidence accumulation (SEA) versus efficiency of evidence accumulation (EEA). Across all tasks, v was more strongly related to EEA than SEA. EEA was consistently related to measures of general cognitive ability, working memory, and executive function whereas SEA explained <1% of the variance in each. These findings suggest individual differences in the DDM's v parameter, and its relations with higher-order cognitive abilities, primarily reflect EEA rather than SEA and challenge the widespread practice of equating v with the traditional "processing speed" construct.

Indexed as

CognitionDecision MakingIndividualityModels, PsychologicalHumansMemory, Short-TermProcessing SpeedCognitive ontologiesIndividual differencesMathematical modelsProcessing speed

Identifiers

PMID41748773
PMCPMC12945898

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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.