Researchers currently working on relational reasoning typically argue that mental model theory (MMT) is a better account than the inference rule approach (IRA). They predict and observe that determinate (or one-model) problems are easier than indeterminate (or two-model) problems, whereas according to them, IRA should lead to the opposite prediction. However, the predictions attributed to IRA are based on a mistaken argument. The IRA is generally presented in such a way that inference rules only deal with determinate relations and not (...) with indeterminate ones. However, (a) there is no reason to presuppose that a rule-based procedure could not deal with indeterminate relations, and (b) applying a rule-based procedure to indeterminate relations should result in greater difficulty. Hence, none of the recent articles devoted to relational reasoning currently presents a conclusive case for discarding IRA by using the well-known determinate vs indeterminate problems comparison. (shrink)

The aim of this article is to strengthen links between cognitive brain research and formal logic. The work covers three fundamental sorts of logical inferences: reasoning in the propositional calculus, i.e. inferences with the conditional “if...then”, reasoning in the predicate calculus, i.e. inferences based on quantifiers such as “all”, “some”, “none”, and reasoning with n-place relations. Studies with brain-damaged patients and neuroimaging experiments indicate that such logical inferences are implemented in overlapping but different bilateral cortical networks, including parts of the (...) fronto-temporal cortex, the posterior parietal cortex, and the visual cortices. I argue that these findings show that we do not use a single deterministic strategy for solving logical reasoning problems. This account resolves many disputes about how humans reason logically and why we sometimes deviate from the norms of formal logic. (shrink)

Inferences about spatial, temporal, and other relations are ubiquitous. This article presents a novel model-based theory of such reasoning. The theory depends on 5 principles. The structure of mental models is iconic as far as possible. The logical consequences of relations emerge from models constructed from the meanings of the relations and from knowledge. Individuals tend to construct only a single, typical model. They spontaneously develop their own strategies for relational reasoning. Regardless of strategy, the difficulty of an inference depends (...) on the process of integration of the information from separate premises, the number of entities that have to be integrated to form a model, and the depth of the relation. The article describes computer implementations of the theory and presents experimental results corroborating its main principle. (shrink)

When is a conclusion worth deriving? We claim that a conclusion is worth deriving to the extent that it is relevant in the sense of relevance theory (Sperber & Wilson, 1995). To support this hypothesis, we experiment with ''indeterminate relational problems'' where we ask participants what, if anything, follows from premises such as A is taller than B, A is taller than C . With such problems, the indeterminate response that nothing follows is common, and we explain why. We distinguish (...) several types of determinate conclusions and show that their rate is a function of their relevance. We argue that by appropriately changing the formulation of the premises, the relevance of determinate conclusions can be increased, and the rate of indeterminate responses thereby reduced. We contrast these relevance-based predictions with predictions based on linguistic congruence. (shrink)

Many researchers have suggested that premise interpretation errors can account, at least in part, for errors on categorical syllogisms. However, although it is possible to show that people make such errors in simple inference tasks, the evidence for them is far less clear when actual syllogisms are administered. Part of the problem is due to the lack of clear predictions for the solutions that would be expected when using modified quantifiers, assuming that correct inferences are made from them. This paper (...) presents the expected solutions for Gricean, reversible, and reversible Gricean interpretations, and evaluates these using three datasets (two currently available, and one new). The evidence supported the adoption of reversible and reversible Gricean interpretations, but not Gricean interpretations on their own. These results suggest that the categorical syllogism task tends to induce different quantifier interpretations from those identified in simple inference tasks. (shrink)

A novel explanation of belief bias in relational reasoning is presented based on the role of working memory and retrieval in deductive reasoning, and the influence of prior knowledge on this process. It is proposed that belief bias is caused by the believability of a conclusion in working memory which influences its activation level, determining its likelihood of retrieval and therefore its effect on the reasoning process. This theory explores two main influences of belief on the activation levels of these (...) conclusions. First, believable conclusions have higher activation levels and so are more likely to be recalled during the evaluation of reasoning problems than unbelievable conclusions, and therefore, they have a greater influence on the reasoning process. Secondly, prior beliefs about the conclusion have a base level of activation and may be retrieved when logically irrelevant, influencing the evaluation of the problem. The theory of activation and memory is derived from the Atomic Components of Thought-Rational (ACT-R) cognitive architecture and so this account is formalized in an ACT-R cognitive model. Two experiments were conducted to test predictions of this model. Experiment 1 tested strength of belief and Experiment 2 tested the impact of a concurrent working memory load. Both of these manipulations increased the main effect of belief overall and in particular raised belief-based responding in indeterminately invalid problems. These effects support the idea that the activation level of conclusions formed during reasoning influences belief bias. This theory adds to current explanations of belief bias by providing a detailed specification of the role of working memory and how it is influenced by prior knowledge. (shrink)

This article describes a study on capacity limitations that affect the construction of spatial mental models. A process model is presented, according to which the construction of a mental model in Ehrlich and Johnson-Laird's (1982) spatial descriptions task places a workload of six information chunks for continuous and semi-continuous descriptions, and seven chunks for discontinuous descriptions. Participants (48 undergraduate students) performed the spatial descriptions task and the figural intersections test (FIT), which yields a capacity score. The pattern of errors and (...) sentence reading times in the spatial descriptions task confirmed that participants were using mental models. The FIT score was positively correlated with accuracy in the spatial descriptions task. Prediction analysis of cross-classification tables showed that, for successful performance in continuous and semi-continuous descriptions, a FIT score of 6 was necessary, and a score of 7 in discontinuous descriptions. These results are in agreement with theoretical predictions. The issue of integrating mental models theory with analyses of capacity limitations in performance is discussed. (shrink)

Relational reasoning is a complex form of human cognition involving the evaluation of relations between mental representations of information. Prior studies have modified stimulus properties of relational reasoning problems and examined differences in difficulty between different problem types. While subsets of these stimulus properties have been addressed in separate studies, there has not been a comprehensive study, to our knowledge, which investigates all of these properties in the same set of stimuli. This investigative gap has resulted in different findings across (...) studies which vary in task design, making it challenging to determine what stimulus properties make relational reasoning—and the putative formation of mental models underlying reasoning—difficult. In this article, we present the Multidimensional Relational Reasoning Task, a task which systematically varied an array of stimulus properties within a single set of relational reasoning problems. Using a mixed-effects framework, we demonstrate that reasoning problems containing a greater number of the premises as well as multidimensional relations led to greater task difficulty. The MRRT has been made publicly available for use in future research, along with normative data regarding the relative difficulty of each problem. (shrink)