One of the most debated questions in psychology and cognitive science is the nature and the functioning of the mental processes involved in deductive reasoning. However, all existing theories refer to a specific deductive domain, like syllogistic, propositional or relational reasoning.Our goal is to unify the main types of deductive reasoning into a single set of basic procedures. In particular, we bring together the microtheories developed from a mental models perspective in a single theory, for which we provide a formal (...) foundation. We validate the theory through a computational model (UNICORE) which allows fine‐grained predictions of subjects' performance in different reasoning domains.The performance of the model is tested against the performance of experimental subjects—as reported in the relevant literature—in the three areas of syllogistic, relational and propositional reasoning. The computational model proves to be a satisfactory artificial subject, reproducing both correct and erroneous performance of the human subjects. Moreover, we introduce a developmental trend in the program, in order to simulate the performance of subjects of different ages, ranging from children (3–6) to adolescents (8–12) to adults (>21). The simulation model performs similarly to the subjects of different ages.Our conclusion is that the validity of the mental model approach is confirmed for the deductive reasoning domain, and that it is possible to devise a unique mechanism able to deal with the specific subareas. The proposed computational model (UNICORE) represents such a unifying structure. (shrink)

Two experiments investigated the mental representation of spatial and nonspatial two-dimensional problems. The experiments were designed to contrast opposite predictions of the model theory of reasoning and the formal rules of inference theories. Half of the problems required more inferential steps but only one model, whereas the other half required fewer inferential steps but two models. According to the inference rules, theory problems that require more inferential steps should be harder, whereas the model-based theory predicts that problems that require two (...) models should be harder. In Experiments 1a and 1b we measured the problem solving time and the percentage of errors. In Experiments 2a and 2b the problems were presented segmented in two different displays. We measured the comprehension time for each display, the question answering times, and the percentage of errors. The results of all experiments supported the model theory predictions in both spatial and nonspatial domains. (shrink)