Representation and strategy in reasoning : an individual differences approach
Individual differences in reasoning have been observed in a wide variety of tasks. Descriptions of the variation in response have been framed in terms of use of different strategies that invoke different representations. This thesis argues that in order to convert descriptions into <i>explanations </i>of performance it is necessary to compare and combine psychometric accounts with computational accounts of the processes underlying representation selection and use. Descriptions of strategies, representations and algorithms and their inter-relationships are necessary for a full account of reasoning behaviour. Two large-scale studies of deductive reasoning are presented to illustrate this approach in action, and the inadequacy of accounts that do not provide accounts at all these levels. The first compares two theoretically motivated methods for solving categorical syllogisms, the second study assesses learning from and learning within a multimodal logic course called Hyperproof. These studies are compared to measures of spatial ability, field-independence/dependence, and serial/holist learning style. The interaction of students' styles of learning with different presentations of information generalises across
Monaghan, Padraic
Representation and strategy in reasoning : an individual differences approach. Individual differences in reasoning have been observed in a wide variety of tasks. Descriptions of the variation in response have been framed in terms of use of different strategies that invoke different representations. This thesis argues that in order to convert descriptions into <i>explanations </i>of performance it is necessary to compare and combine psychometric accounts with computational accounts of the processes underlying representation selection and use. Descriptions of strategies, representations and algorithms and their inter-relationships are necessary for a full account of reasoning behaviour. Two large-scale studies of deductive reasoning are presented to illustrate this approach in action, and the inadequacy of accounts that do not provide accounts at all these levels. The first compares two theoretically motivated methods for solving categorical syllogisms, the second study assesses learning from and learning within a multimodal logic course called Hyperproof. These studies are compared to measures of spatial ability, field-independence/dependence, and serial/holist learning style. The interaction of students' styles of learning with different presentations of information generalises across
representation strategy reasoning individual_difference approach individual_difference reason observed wide_variety task descriptions variation response frame term use different strategy invoke different representation argues order convert description explanation performance necessary compare combine account computational account process underlying representation selection use descriptions strategy representation inter_relationships necessary full_account reason behaviour large_scale study deductive reason present illustrate action inadequacy account provide_account level first compare theoretically motivate method solve categorical syllogism second study ass learn learn within multimodal logic course call study compare measure spatial ability field independence dependence serial learn style interaction student style learn different presentation information across