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Digital Twin Studies for Reverse Engineering the Origins of Visual Intelligence
Justin N Wood1,2,3, Lalit Pandey1, Samantha M W Wood1,2,3
1Informatics Department, Indiana University Bloomington, Bloomington, Indiana, USA; email: woodjn@indiana.edu, lpandey@iu.edu, sw113@iu.edu.
Annual Review of Vision Science
|September 18, 2024
Summary
Digital twin studies reveal that domain-general learning algorithms can explain both innate knowledge and learned abilities in brains. This suggests a universal principle of "space-time fitting" underlies intelligence across species and machines.
Area of Science:
- Cognitive Science
- Neuroscience
- Artificial Intelligence
Background:
- The debate on intelligence origins centers on innate knowledge versus learning from experience.
- Nativism posits innate, domain-specific systems, while empiricism favors domain-general learning systems.
Purpose of the Study:
- To investigate the core learning algorithms in newborn brains.
- To address the nativism vs. empiricism debate using digital twin studies.
Main Methods:
- Digital twin studies comparing newborn animals and artificial agents in identical environments and tasks.
- Reverse engineering of learning algorithms in simulated newborn brains.
Main Results:
- Domain-general algorithms acquired animal-like perception from postnatal visual experiences, supporting empiricism.
- Domain-general algorithms generated innate, domain-specific knowledge from prenatal experiences (retinal waves), supporting nativism.
Conclusions:
- A universal principle, termed "space-time fitting," unifies nativist and empiricist findings.
- Space-time fitting offers a framework for understanding the origins of intelligence in humans, animals, and machines.
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