1987 — 1991 |
Cheng, Patricia |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Inference, Instruction, and Pragmatic Reasoning Schemas @ University of California-Los Angeles
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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0.915 |
1998 — 2001 |
Cheng, Patricia |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Natural Causal Induction @ University of California-Los Angeles
Causal induction, the process of identifying causes for events, plays a central role in everyday life as well as in science. The goal of this project is to find out how people discover that one thing causes another, in daily life and in the laboratory. Because relations between causes and effects are neither deducible nor directly observable, they must be induced from observable events. Inferring causes is far from trivial; whereas effects regularly follow their causes, not all events that regularly follow one another are causally related. Cheng recently proposed a theory of causal induction in which the reasoner explains regularity of succession (a function defined in terms of observable events) by a general notion of causal power (an unobservable theoretical entity), just as scientists explain data with theories. Cheng's theory explains (1) the boundary conditions under which regularity implies causality and (2) a diverse set of robust psychological phenomena concerning judgments of what causes, or prevents, an effect. In addition the theory makes many novel predictions. The goal of the proposed research is to test these predictions against alternative accounts. The experiments involve asking human subjects to make causal judgments when given information about the occurrence of candidate causes for an effect. The project directly aims at gaining a deeper understanding of the process of causal induction; it indirectly aims at providing a basis for the development of an effective program for teaching scientific methodology.
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0.915 |
2002 — 2004 |
Cheng, Patricia W |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Natural Causal Discovery: Simple and Complex Causes @ University of California Los Angeles
DESCRIPTION (provided by applicant): The goal of the proposed research is to study how an untutored reasoner comes to know that one thing causes another. The process of causal discovery concerns the mechanism by which an intelligent system learns to differentiate sequences on which the system bases explanation and control (i.e., causal relations) from the indefinitely many that are incidental or merely covariational. Previous psychological research reveals that natural causal discovery is remarkably rational, in fact, more so than the current explicit statistical methods that scientists and lawyers use to help them infer causality. An analysis of the two previous dominant approaches to the psychological process of causal discovery -- the pure causal-power view and the pure covariation view -- shows that they respectively specify an incorrect input and an incorrect output. The latter approach is that adopted by standard statistics. Cheng (1997) proposed an integration of these approaches that overcomes their individual problems. Her theory concerns the discovery of relations involving causes and effects that each can be represented as a single binary variable. Novick and Cheng (accepted) have extended it to apply to conjunctive causes. The proposed research (1) tests the assumptions underlying the derivation of simple and conjunctive causal powers, (2) tests the novel predictions made by the two components of the theory, (3) examines whether there is a dissociation between implicit and explicit causal discovery, (4) examines why some previous findings deviated from the predictions according to causal power, and (5) tests the implications of this theory for causal attribution in the law. The method involves presenting adult human participants with information regarding the presence and absence of candidate causes and the presence and absence of effects, and asking them to make causal judgments. The project directly aims at gaining a deeper understanding of the process of causal discovery; it indirectly aims at providing a basis for the development of an effective program for teaching statistics and scientific methodology.
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