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Decoding Natural Behavior from Neuroethological Embedding

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Uncertainty and Prior Assumptions, Rather Than Innate Logarithmic Encoding, Explain Nonlinear Number-to-Space

Guido Marco Cicchini¹, Giovanni Anobile², Eleonora Chelli²

¹Institute of Neuroscience, National Research Council, Pisa, Italy.

Psychological Science

|December 22, 2021

Summary

This summary is machine-generated.

Human number and color mapping show non-linear patterns. This study suggests these non-linearities arise from contextual Bayesian inference, not inherent logarithmic encoding.

Keywords:

Bayesian processes dyscalculia number line number sense numerical cognition open data

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Area of Science:

Cognitive Psychology
Neuroscience
Computational Modeling

Background:

Number-to-space mapping is often non-veridical, exhibiting compressive nonlinearity.
This nonlinearity has been attributed to intrinsic logarithmic encoding of numerical values.

Purpose of the Study:

To investigate the underlying mechanisms of non-veridical number-to-space mapping.
To test whether contextual Bayesian inference explains observed nonlinearities, challenging the intrinsic logarithmic encoding hypothesis.

Main Methods:

Adult participants (N=78) mapped dot arrays onto a number line over nine trials.
A separate group (N=90) performed a color-line task, mapping noise-perturbed colors to a color line.

Main Results:

Initial number mapping was compressed, becoming more linear with practice.
Both logarithmic and a parameter-free Bayesian model of central tendency described the data.
Color mapping exhibited logarithmic or exponential nonlinearity depending on noise distribution, supporting Bayesian inference.

Conclusions:

Nonlinearity in number and color mapping reflects contextual Bayesian inference.
This challenges the notion of intrinsic logarithmic encoding for numerical values.