Evidence map›Paper›PMID 32737317›Full record

ArticleNature communications2020

The suboptimality of perceptual decision making with multiple alternatives.

Jiwon Yeon, Dobromir Rahnev

Abstract read
In one paragraph

Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Think twice: Re-assessing confidence improves visual metacognition.Attention, perception & psychophysics · 2024
    Article
  5. Article
  6. Article
  7. Review
  8. Perception is rich and probabilistic.Scientific reports · 2022
    Article
  9. Decision neuroscience and neuroeconomics: Recent progress and ongoing challenges.Wiley interdisciplinary reviews. Cognitive science · 2022
    Review
  10. Article
  11. 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.

Jiwon YeonSchool of Psychology, Georgia Institute of Technology, Atlanta, GA, USA. j.yeon@gatech.edu.ORCID http://orcid.org/0000-0003-2737-4830
Dobromir RahnevSchool of Psychology, Georgia Institute of Technology, Atlanta, GA, USA. rahnev@psych.gatech.edu.ORCID http://orcid.org/0000-0002-5265-2559

Funding

Uncovering the Architecture of MetacognitionR01MH119189 · NIMH · GEORGIA INSTITUTE OF TECHNOLOGY · PI RAHNEV, DOBROMIR · 2020 to 2024
$1.5M
NIMH NIH HHS R01 MH119189
6 · The paper itself

Abstract

It is becoming widely appreciated that human perceptual decision making is suboptimal but the nature and origins of this suboptimality remain poorly understood. Most past research has employed tasks with two stimulus categories, but such designs cannot fully capture the limitations inherent in naturalistic perceptual decisions where choices are rarely between only two alternatives. We conduct four experiments with tasks involving multiple alternatives and use computational modeling to determine the decision-level representation on which the perceptual decisions are based. The results from all four experiments point to the existence of robust suboptimality such that most of the information in the sensory representation is lost during the transformation to a decision-level representation. These results reveal severe limits in the quality of decision-level representations for multiple alternatives and have strong implications about perceptual decision making in naturalistic settings.

Indexed as

Models, PsychologicalAdolescentAdultChoice BehaviorColor PerceptionDecision MakingFemaleHumansMaleMotion PerceptionPattern Recognition, VisualPhotic Stimulation

Identifiers

PMID32737317
PMCPMC7395091

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.