The mental model theory predicts variations in the percentage of errors in meta-propositional reasoning tasks but does not specify the nature of these errors. In the present study, we drew predictions concerning the nature of errors in a meta-propositional reasoning task by importing and elaborating the distinction between implicit and explicit models previously applied by the mental model theory to the domain of propositional reasoning. An experiment was conducted in which participants were asked to solve problems concerning the truth or (...) falsity of propositional assertions. The task was to determine the truth-status of two persons making a propositional assertion about their own truth-status and/or the truth-status of the other person. The results were consistent with the hypothesis that people may reason in ways that differ in the extent to which initially implicit models are made explicit. As such, the results corroborate a basic principle of the mental model theory: the errors that reasoners make are consistent with at least one of the mental models they construct during the process of reasoning. (shrink)

Working memory limits are best defined in terms of the complexity of the relations that can be processed in parallel. Complexity is defined as the number of related dimensions or sources of variation. A unary relation has one argument and one source of variation; its argument can be instantiated in only one way at a time. A binary relation has two arguments, two sources of variation, and two instantiations, and so on. Dimensionality is related to the number of chunks, because (...) both attributes on dimensions and chunks are independent units of information of arbitrary size. Studies of working memory limits suggest that there is a soft limit corresponding to the parallel processing of one quaternary relation. More complex concepts are processed by or In segmentation, tasks are broken into components that do not exceed processing capacity and can be processed serially. In conceptual chunking, representations are to reduce their dimensionality and hence their processing load, but at the cost of making some relational information inaccessible. Neural net models of relational representations show that relations with more arguments have a higher computational cost that coincides with experimental findings on higher processing loads in humans. Relational complexity is related to processing load in reasoning and sentence comprehension and can distinguish between the capacities of higher species. The complexity of relations processed by children increases with age. Implications for neural net models and theories of cognition and cognitive development are discussed. (shrink)

Two experiments investigated inferences based on suppositions. In Experiment 1, the subjects decided whether suppositions about individuals' veracity were consistent with their assertions—for example, whether the supposition “Ann is telling the truth and Beth is telling a lie”, is consistent with the premises: “Ann asserts: I am telling the truth and Beth is telling the truth. Beth asserts: Ann is telling the truth”. It showed that these inferences are more difficult than ones based on factual premises: “Ann asserts: I live (...) in Dublin and Beth lives in Dublin”. There was no difference between problems about truthtellers and liars, who always told the truth or always lied, and normals, who sometimes told the truth and sometimes lied. In Experiment 2, the subjects made inferences about factual matters set in three contexts: a truth-inducing context in which friends confided their personality characteristics, a lie-inducing context in which business rivals advertised their products, and a neutral context in which computers printed their program characteristics. Given the supposition that the individuals were lying, it was more difficult to make inferences in a truth-inducing context than in the other two contexts. We discuss the implications of our results for everyday reasoning from suppositions, and for theories of reasoning based on models or inference rules. (shrink)

Deductive reasoning shares with other forms of thinking a reliance on strategies, as shown by the results of three experiments on the nature and development of control strategies to solve suppositional deductions. These puzzles are based on assertors who may or may not be telling the truth, and their assertions about their status as truthtellers and liars. The first experiment shows that reasoners make backward inferences as well as forward inferences, to short-cut their way through the alternatives, and the generation (...) of suppositions is a source of difficulty. The second experiment establishes that the elimination of the suppositional status of an individual does not render problems easier. The third experiment shows that reasoners can improve their reasoning accuracy and speed spontaneously, without feedback, and it clarifies the transfer of strategies and their development. We discuss the implications of these data for alternative theories of suppositional deduction and for the relationship between reasoning and other forms of thinking such as problem solving. (shrink)