24-03-2006, 01:30 AM
20-04-2006, 02:22 PM
Traditional methods centered on explicit instruction assume that the brain learns like a computer in that rules are explicitly programmed. Spitzer (1999) debunks this common misconception and suggests that the brain more likely learns like neural networks, which do not explicitly learn rigid rules from direct inputting, but construct flexible rules from experience with multiple examples. The hypothesis that learning in the brain occurs by a mechanism similar to learning in neural networks is plausible. In the brain, synaptic connections between neurons that fire action potentials at the same time are strengthened, or as Hebb described, “cells that fire together, wire together." Conversely, synaptic connections that are not active together are weakened. Therefore, each input slightly modifies the strength of various synaptic connections. Consequently, reoccurring input associations progressively refine brain circuitry to reflect past experience. This learning mechanism parallels that of neural networks, which gradually adjust the strength of connections to reflect reoccurring input associations.
According to Spitzer’s (1999) hypothesis, this shared mechanism results in the gradual shaping of brain circuitry into configurations that reflect frequent patterns embedded in input. These patterns can be described by rules. However, these configurations do not exist because they were explicitly programmed by rules, but because they emerged in response to experience with inputs. In other words, the neural circuitry was not “told†the rule, but gradually constructed it because they were “shown†it many times embedded in inputs. As Spitzer (1999) states, “There was no explicit learning of any rule, just a gradual change in connections. Moreover, the rule does not exist except as a description of what has been learned†(p. 31-32). Therefore, if the brain learns like a neural network, it does not learn rules through explicit instruction, but by extracting them implicitly from inputs.
Spitzer, M. (1999). The mind within the net: Models of learning, thinking, and acting (pp. 1-63, 295-313). Cambridge: MIT Press.
According to Spitzer’s (1999) hypothesis, this shared mechanism results in the gradual shaping of brain circuitry into configurations that reflect frequent patterns embedded in input. These patterns can be described by rules. However, these configurations do not exist because they were explicitly programmed by rules, but because they emerged in response to experience with inputs. In other words, the neural circuitry was not “told†the rule, but gradually constructed it because they were “shown†it many times embedded in inputs. As Spitzer (1999) states, “There was no explicit learning of any rule, just a gradual change in connections. Moreover, the rule does not exist except as a description of what has been learned†(p. 31-32). Therefore, if the brain learns like a neural network, it does not learn rules through explicit instruction, but by extracting them implicitly from inputs.
Spitzer, M. (1999). The mind within the net: Models of learning, thinking, and acting (pp. 1-63, 295-313). Cambridge: MIT Press.