Evidence map›Paper›PMID 35708846›Full record

ArticleAttention, perception & psychophysics2023

Variability of dot spread is overestimated.

Jessica K Witt, Mengzhu Fu, Michael D Dodd

Abstract read
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In one paragraph

Article in Attention, perception & psychophysics, 2023. 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. Overestimation of variability in ensembles of color value and size.Attention, perception & psychophysics · 2025
    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

3 authors.

Jessica K WittColorado State University, Fort Collins, CO, USA. Jessica.Witt@colostate.edu.ORCID http://orcid.org/0000-0003-1139-1599
Mengzhu FuUniversity of Nebraska-Lincoln, Lincoln, NE, USA.
Michael D DoddUniversity of Nebraska-Lincoln, Lincoln, NE, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Previous research has demonstrated that individuals exhibit a tendency to overestimate the variability of both low-level features (e.g., color, orientation) and mid-level features (e.g., size) when items are presented dynamically in a sequential order, a finding we will refer to as the variability overestimation effect. Because previous research on this bias used sequential displays, an open question is whether the effect is due to a memory-related bias or a vision-related bias. To assess whether the bias would also be apparent with static, simultaneous displays, and to examine whether the bias generalizes to spatial properties, we tested participants' perception of the variability of a cluster of dots. Results showed a consistent overestimation bias: Participants judged the dots as being more spread than they actually were. The variability overestimation effect was observed when there were 10 or 20 dots but not when there were 50 dots. Taken together, the results of the current study contribute to the ensemble perception literature by providing evidence that simultaneously presented stimuli are also susceptible to the variability overestimation effect. The use of static displays further demonstrates that this bias is present in both dynamic and static contexts, suggesting an inherent bias existent in the human visual system. A potential theoretical account-boundary effect-is discussed as a potential underlying mechanism. Moreover, the present study has implications for common visual tasks carried out in real-world scenarios, such as a radiologist making judgments about distribution of calcification in breast cancer diagnoses.

Indexed as

JudgmentBiasHumansEnsemble perceptionPerceived spreadVariabilityVisual biases

Identifiers

PMID35708846

What OpenQuestion holds

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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.