Inference, Instruction, and Pragmatic Reasoning Schemas
{'Code': '04040500', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'BCS', 'LongName': 'Division Of Behavioral and Cognitive Sci'}}
{'SignBlockName': 'Jasmine Young', 'PO_EMAI': 'jyoung@nsf.gov', 'PO_PHON': '7032924833'}
The classical view of reasoning holds that people solve problems                by applying formal inferential rules such as the syntactic rules                of deductive logic.  Evidence based on previous work by Nisbett,                Cheng, and Holyoak shows that people typically do not reason                    using such rules; instead, they use what they term pragmatic                    reasoning schemas, which are clusters of abstract rules organized               according to goals and conditions of applicability.  The                        pragmatic schemas studied so far include regulation schemas, such               as permissions and obligations, and a qualitative version of the                law of large numbers.  Teaching such abstract pragmatic schemas                 improved reasoning, both immediately following training and after               a delay of a week or two, whereas teaching formal rules did not                 improve reasoning, even immediately following training (or, in                  other studies, improved reasoning immediately, but the                          improvement decayed rapidly).  One plausible explanation is that                pragmatic rules are organized according to the conditions under                 which they apply, and therefore can be applied and practiced once               they are learned.  In contrast, formal rules do not specify                     conditions of applicability and therefore are difficult to apply.                                                                                               The first set of experiments will extend the pragmatic schema                   approach to the evaluation of evidence on cause-and-effect                      relations.  The first experiment will test whether people use                   pragmatic rather than formal rules in evaluating causal                         relations.  It will also investigate the reasons for common                     fallacies about causal relations.  The second experiment will                   measure the effectiveness of current graduate training on causal                reasoning in psychology and chemistry.  Psychology students are                 exposed to a wider range of causal relations than chemistry                     students, and therefore should be more accurate in classifying                  events according to the type of causal relation involved.  The                  third experiment will test whether causal reasoning can be taught               effectively by abstract means, as predicted by the pragmatic                    hypothesis.  The second set of experiments will extend the study                of the duration of training effects of formal and pragmatic                     rules.  Pragmatic training will include rules on obligations and                causal relations.  The third set of experiments will examine                    whether it is the naturalness or the pragmatic nature of the                    rules that lead to effective training.  The last study will                     evaluate a new course on reasoning based on pragmatic rules.                                                                                                    This research will lead to the development of instructions on                   reasoning more effective than training in formal logic.  The new                instructions, being theoretically based, will be more systematic                as well as more general than the case-based methodnow prevalent                 in law, medical, and graduate schools.  The instructions will                   cover reasoning on social regulations, explanation of variability               based on the law of large numbers, and the evaluation of evidence               in accepting or rejecting scientific hypotheses.
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{'Code': '118000', 'Text': 'HUMAN COGNITION & PERCEPTION'}
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