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Analyzing the misperception of exponential growth in graphs
Lorenzo Ciccione1, Mathias Sablé-Meyer1, Stanislas Dehaene1
1Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France; Collège de France, Université Paris Sciences Lettres (PSL), 11 Place Marcelin Berthelot, 75005 Paris, France.
Cognition
|April 2, 2022
Summary
People consistently underestimate exponential growth, leading to significant social costs during epidemics. This bias can be reduced through mathematical knowledge and logarithmic scales.
Area of Science:
- Cognitive psychology
- Epidemiology
- Data visualization
Background:
- Exponential growth is often underestimated, posing risks in epidemic contexts.
- Accurate trend extrapolation is crucial for public health interventions.
Purpose of the Study:
- To investigate human capacity for extrapolating linear versus exponential trends.
- To identify factors influencing the underestimation of exponential growth.
Main Methods:
- Experiment involving 521 participants extrapolating data from scatterplots.
- Manipulation of data function (linear/exponential), response modality (pointing/numerical), y-axis scale (linear/logarithmic), and data noise.
- Analysis of extrapolation accuracy and bias.
Main Results:
- Linear extrapolation was accurate and unbiased.
- Consistent underestimation of noisy exponential growth was observed across response types.
- A potential misperception of exponential curves as quadratic was identified.
Conclusions:
- Human underestimation of exponential growth is a significant cognitive bias.
- Mathematical knowledge, logarithmic scales, and noiseless data mitigate this bias.
- Findings suggest interventions to improve understanding of exponential trends.
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