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From biological vision to artificial intelligence: The role of foundational predictive processing
Fernando Marmolejo-Ramos1, Alejandra Ciria2
1College of Education, Psychology, and Social Work, Flinders University, Adelaide, Australiafernando.marmolejoramos@flinders.edu.aualejandra.ciria@unam.mx.
The Behavioral and Brain Sciences
|November 3, 2025
Summary
High-resolution vision acts as an intrinsic predictive mechanism, integrating sensory input to enhance perception. This framework offers a biologically inspired basis for developing advanced artificial intelligence (AI) systems.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Cognitive Science
Background:
- The trait-linkage hypothesis by Coombs and Trestman offers a framework for understanding how traits are linked.
- Teufel and Fletcher's neurocomputational predictive framework models brain function as a predictive system.
Purpose of the Study:
- To propose that high-resolution visual systems function as intrinsic bottom-up predictive mechanisms.
- To integrate existing hypotheses into a novel neurocomputational model.
- To provide a biologically inspired foundation for artificial intelligence (AI) development.
Main Methods:
- Conceptual integration of the trait-linkage hypothesis and the neurocomputational predictive framework.
- Theoretical analysis of high-resolution visual systems as predictive mechanisms.
Main Results:
- High-resolution visual systems can be understood as intrinsic bottom-up predictive mechanisms.
- Early sensory prediction plays a crucial role in perception.
- The integrated framework highlights the significance of predictive processing in vision.
Conclusions:
- The proposed model offers a new perspective on visual perception.
- This work provides a foundation for developing more adaptive, embodied, and cognitively resilient AI systems.
- Emphasizes the importance of biologically inspired AI.
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