"It is difficult to distinguish deduction from what in other circumstances is called problem-solving. And concept learning, inference, and reasoning by analogy are all instances of inductive reasoning." (Detectives typically induce, rather than deduce.) None of these things can be done separately from each other, or from anything else. They are pseudo-categories." (Frank Smith, "To Think: In Language, Learning and Education", 1990)
"The rise of statistical reasoning was a key step in the birth of many empirical sciences, especially epidemiology. The ability to focus on the aggregate behavior amidst apparently chaotic variation across autonomous individuals has dramatically increased our understanding of disease processes that affect the health of the public. Simple statistical models based upon the laws of probability provide the language for this population perspective." (Scott Zeger, "Statistical reasoning in epidemiology", American Journal of Epidemiology, 1991)
"Common sense is merely unaided intuition, and unaided intuition is reasoning performed in the absence of instruments and the tested knowledge of science." (Edward O Wilson, "The Diversity of Life", 1992)
"All this points to an appealing intuition: that a faculty for analogical reasoning is an innate part of human cognition." (Dedre Gentner & Michael Jeziorski,"Western science", 1993)
"The deep paradox uncovered by AI research: the only way to deal efficiently with very complex problems is to move away from pure logic. [...] Most of the time, reaching the right decision requires little reasoning.[...] Expert systems are, thus, not about reasoning: they are about knowing. [...] Reasoning takes time, so we try to do it as seldom as possible. Instead we store the results of our reasoning for later reference." (Daniel Crevier, "The Tree of Knowledge", 1993)
"The pinball machine is one of those rare dynamical systems whose chaotic nature we can deduce by pure qualitative reasoning, with fair confidence that we have not wandered astray. Nevertheless, the angles in the paths of the balls that are introduced whenever a ball strikes a pin and rebounds […] render the system some what inconvenient for detailed quantitative study." (Edward N Lorenz, "The Essence of Chaos", 1993)
“Mathematicians apparently don’t generally rely on the formal rules of deduction as they are thinking. Rather, they hold a fair bit of logical structure of a proof in their heads, breaking proofs into intermediate results so that they don’t have to hold too much logic at once. In fact, it is common for excellent mathematicians not even to know the standard formal usage of quantifiers" (for all and there exists), yet all mathematicians certainly perform the reasoning that they encode." (William P Thurston,"On Proof and Progress in Mathematics", 1994)
“Mathematicians apparently don’t generally rely on the formal rules of deduction as they are thinking. Rather, they hold a fair bit of logical structure of a proof in their heads, breaking proofs into intermediate results so that they don’t have to hold too much logic at once. In fact, it is common for excellent mathematicians not even to know the standard formal usage of quantifiers" (for all and there exists), yet all mathematicians certainly perform the reasoning that they encode." (William P Thurston,"On proof and progress in mathematics", Bulletin of the American Mathematical Society Vol. 30" (2), 1994)
“Mathematics is not the study of an ideal, preexisting nontemporal reality. Neither is it a chess-like game with made-up symbols and formulas. Rather, it is the part of human studies which is capable of achieving a science-like consensus, capable of establishing reproducible results. The existence of the subject called mathematics is a fact, not a question. This fact means no more and no less than the existence of modes of reasoning and argument about ideas which are compelling an conclusive, ‘noncontroversial when once understood’." (Philip J Davis & Rueben Hersh,"The Mathematical Experience", 1995)
"Scientists reach their conclusions for the damnedest of reasons: intuition, guesses, redirections after wild-goose chases, all combing with a dollop of rigorous observation and logical reasoning to be sure […] This messy and personal side of science should not be disparaged, or covered up, by scientists for two major reasons. First, scientists should proudly show this human face to display their kinship with all other modes of creative human thought […] Second, while biases and references often impede understanding, these mental idiosyncrasies may also serve as powerful, if quirky and personal, guides to solutions." (Stephen J Gould, "Dinosaur in a Haystack: Reflections in natural history", 1995)
"Scientists reach their conclusions for the damnedest of reasons: intuition, guesses, redirections after wild-goose chases, all combing with a dollop of rigorous observation and logical reasoning to be sure […]" (Stephen J Gould, "Dinosaur in a Haystack: Reflections in natural history", 1995)
"To select an appropriate fuzzy implication for approximate reasoning under each particular situation is a difficult problem. Although some theoretically supported guidelines are now available for some situations, we are still far from a general solution to this problem." (George Klir, "Fuzzy sets and fuzzy logic", 1995)
"We can assemble big pieces of information and little pieces, but we can never get all the pieces together. We never know for sure how good our sample is. That uncertainty is what makes arriving at judgments so difficult and acting on them so risky. […] When information is lacking, we have to fall back on inductive reasoning and try to guess the odds." (Peter L Bernstein, "Against the Gods: The Remarkable Story of Risk", 1996)
"Free will is an idealization of human beings that makes the ethics game playable. Euclidean geometry requires idealizations like infinite straight lines and perfect circles, and its deductions are sound and useful even though the world does not really have infinite straight lines or perfect circles. The world is close enough to the idealization that the theorems can usefully be applied. Similarly, ethical theory requires idealizations like free, sentient, rational, equivalent agents whose behavior is uncaused, and its conclusions can be sound and useful even though the world, as seen by science, does not really have uncaused events. As long as there is no outright coercion or gross malfunction of reasoning, the world is close enough to the idealization of free will that moral theory can meaningfully be applied to it." (Steven Pinker, "How the Mind Works", 1997)
"Suppose the reasoning centers of the brain can get their hands on the mechanisms that plop shapes into the array and that read their locations out of it. Those reasoning demons can exploit the geometry of the array as a surrogate for keeping certain logical constraints in mind. Wealth, like location on a line, is transitive: if A is richer than B, and B is richer than C, then A is richer than C. By using location in an image to symbolize wealth, the thinker takes advantage of the transitivity of location built into the array, and does not have to enter it into a chain of deductive steps. The problem becomes a matter of plop down and look up. It is a fine example of how the form of a mental representation determines what is easy or hard to think." (Steven Pinker, "How the Mind Works", 1997)
“Suppose that we think of the integers lined up like dominoes. The inductive step tells us that they are close enough for each domino to knock over the next one, the base case tells us that the first domino falls over; the conclusion is that they all fall over. The fault in this analogy is that it takes time for each domino to fall and so a domino which is a long way along the line won't fall over fora long time. Mathematical implication is outside time." (Peter J Eccles,"An Introduction to Mathematical Reasoning", 1997)
"Suppose the reasoning centers of the brain can get their hands on the mechanisms that plop shapes into the array and that read their locations out of it. Those reasoning demons can exploit the geometry of the array as a surrogate for keeping certain logical constraints in mind. Wealth, like location on a line, is transitive: if A is richer than B, and B is richer than C, then A is richer than C. By using location in an image to symbolize wealth, the thinker takes advantage of the transitivity of location built into the array, and does not have to enter it into a chain of deductive steps. The problem becomes a matter of plop down and look up. It is a fine example of how the form of a mental representation determines what is easy or hard to think." (Steven Pinker, "How the Mind Works", 1997)
"'Garbage in, garbage out' is a sound warning for those in the computer field; it is every bit as sound in the use of statistics. Even if the 'garbage' which comes out leads to a correct conclusion, this conclusion is still tainted, as it cannot be supported by logical reasoning. Therefore, it is a misuse of statistics. But obtaining a correct conclusion from faulty data is the exception, not the rule. Bad basic data (the 'garbage in') almost always leads to incorrect conclusions (the 'garbage out'). Unfortunately, incorrect conclusions can lead to bad policy or harmful actions." (Herbert F Spirer et al, "Misused Statistics" 2nd Ed, 1998)
"Know the subject matter, learn it fast, or get a trustworthy expert. To identify the unknown, you must know the known. But don't be afraid to challenge experts on the basis of your logical reasoning. Sometimes a knowledge of the subject matter can blind the expert to the novel or unexpected." (Herbert F Spirer et al, "Misused Statistics" 2nd Ed, 1998)
"Statistical thinking is a general, fundamental, and independent mode of reasoning about data, variation, and chance." (David S Moore, 1998)
"For most problems found in mathematics textbooks, mathematical reasoning is quite useful. But how often do people find textbook problems in real life? At work or in daily life, factors other than strict reasoning are often more important. Sometimes intuition and instinct provide better guides; sometimes computer simulations are more convenient or more reliable; sometimes rules of thumb or back-of-the-envelope estimates are all that is needed." (Lynn A Steen,"Twenty Questions about Mathematical Reasoning", 1999)
"Under the label 'cognitive maps', mental models have been conceived of as the mental representation of spatial aspects of the environment. A mental model, in this sense, comprises the topology of an area, including relevant districts, landmarks, and paths. [...] Under the label 'naive physics', mental models have been conceived of as the mental representation of natural or technical systems. A mental model, in this sense, comprises the effective determinants, true or not, of the functioning of a physical system. [...] Under the label 'model based reasoning', the mental models notion is featured in yet another area of cognitive science - deductive reasoning. In contrast to the commonly held view that logical competence depends on formal rules of deduction, it has been argued that reasoning is a semantic process based on the manipulation of mental models. [...] Finally, under terms like 'discourse model', 'situation model', or 'scenario', mental models have been conceived of as the mental representation of a verbal description of some real or fictional state of affairs. The role of mental models in the comprehension of discourse is discussed in more detail below." (Gert Rickheit & Lorenz Sichelschmidt, "Mental Models: Some Answers, Some Questions, Some Suggestions", 1999)
"What it means for a mental model to be a structural analog is that it embodies a representation of the spatial and temporal relations among, and the causal structures connecting the events and entities depicted and whatever other information that is relevant to the problem-solving talks. […] The essential points are that a mental model can be nonlinguistic in form and the mental mechanisms are such that they can satisfy the model-building and simulative constraints necessary for the activity of mental modeling." (Nancy J Nersessian, "Model-based reasoning in conceptual change", 1999)
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