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Where do the hypotheses come from? Data-driven learning in science and the brain
Barton L Anderson1, Katherine R Storrs2, Roland W Fleming3,4
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.
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.
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