Evidence map›Paper›PMID 32929205›Full record

ArticleNature human behaviour2020

A unified account of numerosity perception.

Samuel J Cheyette, Steven T Piantadosi

Abstract read
PubMed Publisher
In one paragraph

Article in Nature human behaviour, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Efficient numerosity estimation under limited time.PLoS computational biology · 2025
    Article
  8. Article
  9. Approximate planning in spatial search.PLoS computational biology · 2024
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. The cultural origins of symbolic number.Psychological review · 2022
    Article
  17. Visual field asymmetries in numerosity processing.Attention, perception & psychophysics · 2022
    Article
  18. Article
  19. Prior Expectations in Visual Speed Perception Predict Encoding Characteristics of Neurons in Area MT.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022
    Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Samuel J CheyetteDepartment of Psychology, UC Berkeley, Berkeley, CA, USA. sam_cheyette@berkeley.edu.ORCID http://orcid.org/0000-0002-7052-5369
Steven T PiantadosiDepartment of Psychology, UC Berkeley, Berkeley, CA, USA.

Funding

Origins and Logic of Counting AlgorithmsR01HD085996 · NICHD · UNIVERSITY OF ROCHESTER · PI CANTLON, JESSICA F · 2016 to 2020
$1.6M
NICHD NIH HHS R01 HD085996
6 · The paper itself

Abstract

People can identify the number of objects in sets of four or fewer items with near-perfect accuracy but exhibit linearly increasing error for larger sets. Some researchers have taken this discontinuity as evidence of two distinct representational systems. Here, we present a mathematical derivation showing that this behaviour is an optimal representation of cardinalities under a limited informational capacity, indicating that this behaviour can emerge from a single system. Our derivation predicts how the amount of information accessible to viewers should influence the perception of quantity for both large and small sets. In a series of four preregistered experiments (N = 100 each), we varied the amount of information accessible to participants in number estimation. We find tight alignment between the model and human performance for both small and large quantities, implicating efficient representation as the common origin behind key phenomena of human and animal numerical cognition.

Indexed as

JudgmentMathematicsPerceptionCognitionHumansModels, Theoretical

Identifiers

What OpenQuestion holds

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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.