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Related Experiment Video

Updated: Jul 9, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Where do the hypotheses come from? Data-driven learning in science and the brain.

Barton L Anderson¹, Katherine R Storrs², Roland W Fleming^3,4

¹School of Psychology, University of Sydney, Sydney, Australia barton.anderson@sydney.edu.au.

The Behavioral and Brain Sciences

|December 6, 2023

Summary

Hypotheses for perception and brain function research should stem from understanding how information is learned through evolution and development. Deep neural networks offer a valuable tool for exploring these origins.

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Area of Science:

Cognitive Neuroscience
Computational Neuroscience
Evolutionary Psychology

Background:

Hypothesis generation in perception and brain function research lacks clear origins.
Existing research often overlooks evolutionary and developmental learning processes.

Purpose of the Study:

To propose a framework for deriving hypotheses in perception and brain function research.
To highlight the role of evolutionary and developmental learning in hypothesis formation.
To introduce deep neural networks as a tool for this research.

Main Methods:

Conceptual framework development.
Literature review on learning, evolution, and neuroscience.
Application of deep neural networks (DNNs) as a modeling tool.

Main Results:

Suggests that hypotheses should originate from understanding information acquisition over evolutionary and developmental timescales.
Demonstrates the potential of DNNs to model these learning processes.

Conclusions:

Integrating evolutionary and developmental learning provides a robust foundation for generating testable hypotheses.
Deep neural networks are a promising methodology for investigating the origins of perception and brain function.