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neurophysiological theory
Neurophysiological and Evolutionary Theories Paper

For one who may be interested in the neurophysiological or evolutionary theories of psychology, one need not look any further than Donald Olding Hebb who has been described as the father of neuropsychology and Robert C. Bolles who did most of his work in experimental psychology. Hebb is best known for his theory of Hebbian Learning which was introduced in his 1949 work: The Organization of Behavior. As far as his contributions to learning and cognition, his most important contribution was his conceptual demonstration that we could study higher cognitive processes by using single neurons and synapses as our fundamental tools (Olson & Hergenhahn, 2013). This theory was a first in that it differed from theories relying on stimulus-response bonds. Even today, it continues to be a profound influence in the neurosciences, as well as in computer research and neural networks. With all of his research concerning the nature of arousal, sensory deprivation, reinforcement, and fear, Hebb had an important influence on the study of motivation as well as the study of learning (Olson & Hergenhahn, 2013). The most important contribution that Bolles made is the insistence that proximate explanations must be considered simultaneously with ultimate explanations. Proximate explanations include “references to deprivation conditions, observable environmental stimuli, reinforcement contingencies, and the immediate learning history of the organism.” Ultimate explanations emphasize “traits and behaviors of organisms that have been shaped by natural selection” (Olson & Hergenhahn, 2013), This type of learning has helped bring us closer to a more complete understanding of learning that includes both non-humans and humans. Hebb’s theory, also known as ‘Hebbian Learning’ is a theory in biological neuroscience which explains how neurons adjust and



Cited: Olson, M. H. & Hergenhahn, B. R. (2013). An Introduction to theories of learning (9th ed.). Upper Saddle River, NJ: Pearson.

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