Evidence map›Paper›PMID 36874812›Full record

ArticleFrontiers in psychology2023

Is my visualization better than yours? Analyzing factors modulating exponential growth bias in graphs.

Gerda Ana Melnik-Leroy, Linas Aidokas, Gintautas Dzemyda, Giedrė Dzemydaitė, Virginijus Marcinkevičius, Vytautas Tiešis, Ana Usovaitė

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychology, 2023. 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
3.0field-weighted citation impact, top 8% of its field
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, 9 citations in OpenAlex.

  1. Article
  2. Observational
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

7 authors at 1 institution in 1 country.

Gerda Ana Melnik-LeroyInstitute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania.
Linas AidokasInstitute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania.
Gintautas DzemydaInstitute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania.
Giedrė DzemydaitėInstitute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania.
Virginijus MarcinkevičiusInstitute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania.
Vytautas TiešisInstitute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania.
Ana UsovaitėInstitute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania.
Vilnius University · LT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Humans tend to systematically underestimate exponential growth and perceive it in linear terms, which can have severe consequences in a variety of fields. Recent studies attempted to examine the origins of this bias and to mitigate it by using the logarithmic vs. the linear scale in graphical representations. However, they yielded conflicting results as to which scale induces more perceptual errors. In the current study, in an experiment with a short educational intervention, we further examine the factors modulating the exponential bias in graphs and suggest a theoretical explanation for our findings. Specifically, we test the hypothesis that each of the scales can induce misperceptions in a particular context. In addition to this, we explore the effect of mathematical education by testing two groups of participants (with a background in humanities vs. formal sciences). The results of this study confirm that when used in an inadequate context, these scales can have a dramatic effect on the interpretation of visualizations representing exponential growth. In particular, while the log scale leads to more errors in graph description tasks, the linear scale misleads people when they have to make predictions on the future trajectory of exponential growth. The second part of the study revealed that the difficulties with both scales can be reduced by means of a short educational intervention. Importantly, while no difference between participants groups was observed prior to the intervention, participants with a better mathematical education showed a stronger learning effect at posttest. The findings of this study are discussed in light of a dual-process model.

Indexed as

cognitive biasdual-process modelexponential growthgraph perceptionlogarithmic scalingmathematical literacy

Identifiers

PMID36874812
PMCPMC9977824
OpenAlexW4321103314

What OpenQuestion holds

Textmetadata
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.