Introduction
Neurophysiological and evolutionary theories focus on developments in the brain as the fundamental part of the body that controls all other body parts and activities. While the brain plays a critical role in major processes of an individual, the concept of learning has occurred seamlessly throughout the lives of species. Evolutionary psychology explores the concept that organized functions and complex processes in all species take place due to evolution that results from natural selection. On the other hand, neurophysiological theories focus on the relation between functions of nervous systems and learning. These theories would show the association between learning and changes in the neurons.
The contributions that Donald O. Hebb and Robert C. Bolles made to the field of learning and cognition
Hebb associated the concept of cell assemblies to learning processes. He noted that the process of cell assembly influenced early learning processes in organisms. However, Hebb noted that the arrangement of cells was a slow process. According to Hebb, learning that took place in the early period of development was slow. However, after the establishment of the neuron cells, learning of complex functions could take place at high speed based on the moves of an individual. Later and complex learning depended on the development that took place between cell assemblies. This would result in sequences of activities. Critics have decried a lack of sufficient data to support Hebbian learning theory.
Robert Bolles had interests in behavior and function. Specifically, Bolles looked at the effect of cognition on motivation. From Bolles’ views, one can note that motivation processes do not depend on simple processes of cause and effect relationships. Bolles noted that organisms’ thought processes affected how they responded to specific situations. Schultz noted the relationship between rewards and the subsequent behaviors, but he showed that such functions were “less directly governed by the physics and chemistry of input events as in sensory systems” (Schultz, 2006). He concluded that one can understand the neural processes through behavioral theories in order to demonstrate “different effects of rewards on behavior” (Schultz, 2006).
The models associated with Hebb and Bolles
Hebb developed the Hebbian learning model. According to this model, neuro-physiological functions affected the manner in which learning took place in an ideal environment and a restricted one. The first theory of Hebbian learning showed that learning could take place in a restricted environment. Specifically, Hebb concentrated on how learning occurred during early periods of a species’ life. From this view, Hebb was able to demonstrate that learning in formative years was slow due to slow assemblies of the neuron cells. In addition, he showed that when there was deprivation of a certain function for an organism, then the development of specific part of the brain would be hindered (Cooper, 2005). Ultimately, this would also hinder learning.
Robert Bolles’ experimental psychology resulted in the species-specific defense reaction theory. This theory posits that a number of avoidance behaviors result from reactions rather than operant conditioning. For instance, an organism would learn responses that are easy to learn in a given situation in order to avoid aversive conditions. In this regard, one can note that the main factor that influences learning is the kind of response that an organism requires in a given situation. Therefore, an organism may find it easier to learn other concepts than others because of several choices. Learning such easier concepts led to suppression of other behaviors.
A framework for the theoretical concepts associated Hebb and Bolles models
Hebbian learning model originated from neuron assemblies and the speed of synaptic activities. According to Hebb, Hebb synapse results from association and efficacy of cells in a repeated or persistent manner. Changes may result in the metabolic or growth processes among cells and create learning. However, transmissions may be not be rapid and could disappear before reaching cells. Such slow processes may inhibit learning. A rapid pace of transmission, which could take place later after cell assemblies, may make cells to receive arousal in sequences, which could result in fast processes of learning.
Bolles’ model of avoidance learning rests on the notion that learning can occur faster in certain conditions than in others. However, this remains a technique that an organism may apply in aversive conditions. The use of reinforcement to elicit a behavior is imperative in this theoretical framework. However, in some cases, it could be difficult to predict a behavior in response to an aversive situation because an organism could choose a behavior that fits a given situation. Hence, learning in this situation may depend on the power of the reinforcement that induces a behavior in an organism. Overall, if an organism can acquire a given avoidance response fast, then the response is avoidance learning.
The modern-day relevancy of the models in education
Scholars have noted that Hebb’s learning model focused on behaviors and ideas and concepts in brains of organisms. This has provided a solid base for contemporary neuroscience and other studies in modern computer and neural studies. Neurophysiological theories show how one can control adult intelligence (Sevelinges et al., 2011). These studies originate from the Hebb’s concept of cell assemblies. This comes from the observation that organisms, which operated in enriched and complex conditions would later perform better than their counterparts in other normal or bare environments. Today, educators have resorted to evolutionary concepts in order to facilitate learning among children (Blais, Terkel and Goldblatt, 2006).
In this case, some educators have argued that curricula should have certain abstract concepts, which would allow children to learn properly. From evolutionary concepts, people have predisposition genetic abilities to learn given concepts. For instance, children’s abilities to learn their languages result from predisposition processes. They naturally learn how to talk by watching and listening to other people. A problem that could hinder learning of language in this manner could originate from the neuro-physiological composition of the child.
However, many critics have noted that neurophysiological and evolutionary theories do not have sufficient evidence to support their claims.
Conclusion
Neurophysiological and evolutionary theories show how learning activities relate to functions and changes of the brain over a given period. Learning has taken place seamlessly throughout the lives of organisms. The brain remains at the center of an organism’s learning because it causes behaviors. Multifaceted and functional processes that occur in all living species result from natural selection of evolution. Such behaviors show functional aspects of organisms that result from evolution. Neurophysiological and evolutionary theories show that one can analyze functional components and parts of the brain by breaking them down into different functional units. Evolutionary theorists have assumed that one can understand functions of the brain and learning by studying different components. In addition, neuroscientific concepts show the relationships between stimuli and responses.
References
Blais, I., Terkel, J., and Goldblatt, A. (2006). Long-term impact of early olfactory experience on later olfactory conditioning. Developmental Psychobiology, 48(7), 501-507.
Cooper, S. J. (2005). Donald O. Hebb’s synapse and learning rule: a history and commentary. Neuroscience Biobehavioral Review, 28(8), 851-74.
Schultz, W. (2006). Behavioral theories and the neurophysiology of reward. Annual Review of Psychology, 57, 87-115.
Sevelinges, Y., Mouly, A-M., Raineki, C., Moriceau, S., Forest, C., and Sullivan, R. (2011). Adult depression-like behavior, amygdala and olfactory cortex functions are restored by odor previously paired with shock during infant’s sensitive period attachment learning. Developmental Cognitive Neuroscience, 1(1), 77-87.