Abstract
In this article, I propose some elements for a conceptual foundation for a negative answer to the titular question, based on a historical conceptual analysis of some definitions of “learning” in the specialized literature. I intend such a foundation to include learning in living organisms as well as inorganic machines. After analyzing several behavioral and nonbehavioral definitions, I argue that although most of the former favor a negative answer, they tend to be restricted to living organisms and thus exclude inorganic machine learning. They also face the yet-unresolved issue of behavioral silence, which makes behavior not defining of learning. Some nonbehavioral neurobiological definitions favor an affirmative, others a negative answer, but still exclude inorganic machines. Nonneurobiological definitions are more suitable, but they commit us to some form of computationalism (Turing machine or connectionist) about learning, which is premature. I thus propose elements for an alternative definition of “learning” without such commitment. The elements are elaborations of the notions of learning as a kind of causal interaction between causal stochastic environmental and internal processes, and minimal learner as a kind of abstract system that shares certain internal structural and functional features with animals, spinal vertebrates, bacteria, plants, and inorganic machines.
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Notes
A thoughtful reviewer asked, “How can one observe learning where there is no change in behavior?” Nonbehavioral definitions allow for this answer: Observe the internal processes in behaving as well as nonbehaving subjects; if comparable, the latter can be said to have learned. The research of Eric Kandel and colleagues (e.g., Carew, Walters, & Kandel, 1981) suggests how it could be done. Train various specimens of Aplysia, immobilize some, and observe the internal processes (postsynaptic excitatory potentials on the motor neurons that mediate the response, transmitter release from sensory neurons, and concentration of intracellular calcium and second messengers) in all the specimens. The immobilized ones can be said to have learned if such processes are comparable to those observed in the behaving subjects. Moore’s theorem (also mentioned by the reviewer) becomes inapplicable, as it is restricted to black boxes, whereas organisms whose internal processes have been observed are not black boxes. There is no need to include IML anywhere here. As clarified in the introduction, I intend the view that IML is true learning as a conjecture in its own right, regardless of whether and how it benefits the study of animal learning, although I do not exclude the possibility of some benefit either.
These last two definitions, and others quoted earlier (Thorpe’s) and later (Bolles’s), conceive learning as a process, predating Lachman’s (1997) alleged “new” definition by several decades. Here are others: “. . . learning is a process in the behavior of the individual” (Skinner, 1950, pp. 195–196); “Learning is a process by which an organism benefits from experience so that its future behavior is better adapted to its environment” (Rescorla, 1988, p. 329); “. . . learning as a process of accumulation where incorrect response tendencies remain constant and correct response tendencies increase with practice” (Mazur & Hastie, 1978, p. 1256). In general, it seems plausible to conceive learning as a process, and I will adopt this view later on.
Such continuity does not necessarily skew the present proposal towards analog computation, as the continuity could be viewed as just an approximation implemented by binary computations.
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I thank two anonymous reviewers for useful comments to a previous draft.
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Burgos, J.E. Is a Nervous System Necessary for Learning?. Perspect Behav Sci 41, 343–368 (2018). https://doi.org/10.1007/s40614-018-00179-7
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DOI: https://doi.org/10.1007/s40614-018-00179-7