In the latter part of the twentieth century, there were attempts to simulate human reasoning with computers and to develop computers capable of humanlike reasoning. One notable attempt involved the work of Allen Newell and Herbert Simon, who provided human subjects with various sorts of problems to solve. Their human subjects would "think out loud," and transcripts of what they said became the basis of computer programs designed to mimic human problem solving and reasoning. Thus, the study of human logic and reasoning not only furthered the understanding of human cognitive processes but also gave guidance to those working in artificial intelligence. One caveat, however, is that even though such transcripts may serve as a model for computer intelligence, there remain important differences between human and machine "reasoning." For example, in humans, the correct application of some inference rules (for example, -modus tollens) depends upon the context (for example, the atmosphere hypothesis or the belief-bias effect). Furthermore, not all human reasoning may be strictly verbalizable, and to the extent that human reasoning relies on nonlinguistic processes (such as imagery), it might not be possible to mimic or re-create it on a computer.
After being assumed to be logical, or even being ignored by science, human reasoning is finally being studied for what it is. In solving logical problems, humans do not always comply with the dictates of logical theory; the solutions reached may be influenced by the context of the problem, previous knowledge or belief, and the particular heuristics used in reaching a solution. Discovery of the structures, processes, and strategies involved in reasoning promises to increase the understanding not only of how the human mind works but also of how to develop artificially intelligent machines.
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