Mental models and temporal reasoning.

Department of Psychology, University of Leuven, Belgium.
Cognition (Impact Factor: 3.16). 10/1996; 60(3):205-34. DOI:10.1016/0010-0277(96)00708-1
Source: PubMed

ABSTRACT We report five experiments investigating reasoning based on temporal relations, such as: "John takes a shower before he drinks coffee". How individuals make temporal inferences has not been studied hitherto, but we conjectured that they construct mental models of events, and we developed a computer program that reasons in this way. As the program shows, a problem of the form: a before b b before c d while b e while c What is the relation between d and e? where a, b, c, etc. refer to everyday events, calls for just one model, whereas a problem in which the second premise is modified to c before b calls for multiple models because a may occur before c, after c, or at the same time as c. Experiments 1-3 showed that problems requiring one mental model elicited more correct responses than problems requiring multiple models, which in turn elicited more correct answers than multiple model problems with no valid answers. Experiment 4 contrasted the predictions of the model theory with those based on formal rules of inference; its results corroborated the model theory. Experiment 5 confirmed that a premise leading to multiple models took longer to read than the corresponding premise in one-model problems, and that latency to respond correctly was greater for multiple-model problems than for one-model problems. We conclude that the experiments corroborate the mental model theory.

0 0
  • [show abstract] [hide abstract]
    ABSTRACT: Previous studies in spatial propositional reasoning showed that adults use a particular strategy for making representations and inferences from indeterminate descriptions (those consistent with different alternatives). They do not initially represent all the alternatives, but construct a unified mental representation that includes a kind of mental footnote. Only when the task requires access to alternatives is the unified representation re-inspected. The degree of generalisation of this proposal to other perceptual situations was evaluated in three experiments with children, adolescents and adults, using a perceptual inference task with diagrammatic premises that gave information about the location of one of three possible objects. Results obtained with this very quick perceptual task support the kind of representation proposed from propositional spatial reasoning studies. However, children and adults differed in accuracy, with the results gradually changing with age: indeterminacy leads adults to require extra time for understanding and inferring alternatives, whereas children commit errors. These results could help inform us of how people can make inferences from diagrammatic information and make wrong interpretations.
    Acta psychologica 03/2014; 148C:216-225. · 2.19 Impact Factor
  • [show abstract] [hide abstract]
    ABSTRACT: 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.
    Cognitive Science A Multidisciplinary Journal 01/2013; · 2.59 Impact Factor
  • [show abstract] [hide abstract]
    ABSTRACT: We present a theory, and its computer implementation, of how mental simulations underlie the abductions of informal algorithms and deductions from these algorithms. Three experiments tested the theory's predictions, using an environment of a single railway track and a siding. This environment is akin to a universal Turing machine, but it is simple enough for nonprogrammers to use. Participants solved problems that required use of the siding to rearrange the order of cars in a train (experiment 1). Participants abduced and described in their own words algorithms that solved such problems for trains of any length, and, as the use of simulation predicts, they favored "while-loops" over "for-loops" in their descriptions (experiment 2). Given descriptions of loops of procedures, participants deduced the consequences for given trains of six cars, doing so without access to the railway environment (experiment 3). As the theory predicts, difficulty in rearranging trains depends on the numbers of moves and cars to be moved, whereas in formulating an algorithm and deducing its consequences, it depends on the Kolmogorov complexity of the algorithm. Overall, the results corroborated the use of a kinematic mental model in creating and testing informal algorithms and showed that individuals differ reliably in the ability to carry out these tasks.
    Proceedings of the National Academy of Sciences 09/2013; · 9.74 Impact Factor

Full-text (2 Sources)

Available from
Sep 12, 2013