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Is a Nervous System Necessary for Learning?
1Centro de Estudios e Investigaciones en Comportamiento, Universidad de Guadalajara, 180 Fco. De Quevedo, Arcos Vallarta, 44130 Guadalajara, Mexico.
This study explores the definition of learning, proposing a new conceptual foundation that includes both living organisms and inorganic machines. It argues against current definitions that exclude machine learning or rely on premature computationalism.
Area of Science:
- Philosophy of Mind
- Artificial Intelligence
- Cognitive Science
Background:
- Existing definitions of learning often exclude inorganic machines or rely on behavioral observations.
- Neurobiological definitions of learning are limited to living organisms.
- Current computationalist approaches to machine learning are considered premature.
Purpose of the Study:
- To establish a conceptual foundation for a negative answer to whether machines can learn.
- To propose elements for an alternative definition of learning applicable to both biological and artificial systems.
- To analyze historical definitions of learning in specialized literature.
Main Methods:
- Historical conceptual analysis of learning definitions.
- Examination of behavioral and nonbehavioral definitions of learning.
- Critique of neurobiological and computationalist definitions.
Main Results:
- Behavioral definitions of learning are restricted to living organisms and suffer from 'behavioral silence'.
- Neurobiological definitions exclude inorganic machines.
- Non-neurobiological definitions necessitate premature computationalism.
Conclusions:
- A new definition of learning is proposed, focusing on causal interactions between environmental and internal processes.
- This definition accommodates learning in diverse systems, including living organisms and inorganic machines.
- The proposed framework avoids commitment to specific computational models.

