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UBC Theses and Dissertations

Transfer of acquired rule to conditional reasoning as a function of content similarity, attribute dimension size, and acquisition mode Vavrik, John


In this study, deductive reasoning with conditional statements of the form IF x THEN y was investigated in a transfer of training paradigm. Subjects induced conditional rules under different training conditions and then solved a series of deductive syllogisms based on conditional rules the content of which was either related (near-transfer) or unrelated (far-transfer) to that of the previously induced rules. The conditions under which the conditional rules were induced varied in terms of the acquisition mode (prediction/diagnosis) and in terms of the size of the attribute dimensions from which rule instances were drawn (binary/trinary). The deductive reasoning task, as the criterion task for transfer, included all four types of conditional syllogisms (Modus Ponens, MP; Denying the Antecedent, DA, Affirming the Consequent, AC, and Modus Tollens, MT). The accuracy of deductive performance on near-transfer was significantly higher than response accuracy on far-transfer on the DA & AC but not on the MP & MT argument forms. Transfer of conditional rule knowledge induced in a predictive mode was significantly better compared to a diagnostic mode on the AC form. Overall, near as well as far-transfer performance was similar for the binary and trinary conditions. In addition, neither the binary nor the trinary condition resulted in improved far-transfer performance compared to controls. The results provide evidence against purely instance-based as well as purely rule-based models of deductive reasoning. Instead, they suggest that conditional knowledge is represented at an intermediate level of abstraction, and that conditional reasoning is adaptive to the form of the deductive argument. Different types of deductive argument types may trigger different reasoning strategies.

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