摘要 :
How do reasoners understand and formulate denials of compound assertions, such as conjunctions and disjunctions? A theory based on mental models postulates that individuals enumerate models of the various possibilities consistent ...
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How do reasoners understand and formulate denials of compound assertions, such as conjunctions and disjunctions? A theory based on mental models postulates that individuals enumerate models of the various possibilities consistent with the assertions. It therefore predicts a novel interaction: in affirmations, conjunctions,A and B, which refer to one possibility, should be easier to understand than disjunctions, A or B, which refer to more than one possibility; in denials conjunctions, not (A and B), which refer to more than one possibility, should be harder to understand than disjunctions not (A or B), which do not. Conditionals are ambiguous and they should be of intermediate difficulty. Experiment 1 corroborated this trend with a task in which the participants selected which possibilities were consistent with assertions, such as: Bob denied that he wore a yellow shirt and he wore blue pants on Tuesday. Experiment 2 likewise showed that participants' own formulations of verbal denials yielded the same trend in which denials of conjunctions were harder than denials of conditionals,which in turn were harder than denials of disjunctions.
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In cognitive modeling, it is routine to report a goodness-of-fit index (e.g., R2 or RMSE) between a putative model's predictions and an observed dataset. However, there exist no standard index values for what counts as "good" or "...
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In cognitive modeling, it is routine to report a goodness-of-fit index (e.g., R2 or RMSE) between a putative model's predictions and an observed dataset. However, there exist no standard index values for what counts as "good" or "bad", and most indices do not take into account the number of data points in an observed dataset. These limitations impair the interpretability of goodness-of-fit indices. We propose a generalized methodology, percentile analysis, which contextualizes goodness-of-fit measures in terms of performance that can be achieved by chance alone. A series of Monte Carlo simulations showed that the indices of randomized models systematically decrease as the number of data points to be fit increases, and that the relationship is nonlinear. We discuss the results of the simulation and how computational cognitive modelers can use them to place commonly used fit indices in context.
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Many theorists argue that deduction is based on the construction of mental models or simulations of descriptions. Individuals tend to reason intuitively from a single mental model, but on occasion they make a deliberate search for...
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Many theorists argue that deduction is based on the construction of mental models or simulations of descriptions. Individuals tend to reason intuitively from a single mental model, but on occasion they make a deliberate search for alternative models. Previous computer implementations of the theory were deterministic, but evidence from empirical studies suggested that a stochastic algorithm would have greater predictive power. We present such a system for inferences from assertions with single quantifiers, such as ???All the agents are lawyers???. This system implements constraints on the size of model, the sorts of individual it represents, and on the likelihood of a search for alternative models. We show that the system yields quantitative predictions at a fine-grained level, and that they fit the data from two experiments better than previous accounts.
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In daily conversations, what information do people use to assess their conversational partner's explanations? We explore how a metacognitive cue, in particular the partner's confidence or uncertainty, can modulate the credibility ...
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In daily conversations, what information do people use to assess their conversational partner's explanations? We explore how a metacognitive cue, in particular the partner's confidence or uncertainty, can modulate the credibility of an explanation. Two experiments showed that explanations are accepted more often when delivered by an uncertain conversational partner. Participants in Experiment 1 demonstrated the general effect by interacting with a pseudoautonomous robotic confederate. Experiment 2 used the same methodology to show that the effect was applicable to explanatory reasoning and not other sorts of inferences. Results are consistent with an account in which reasoners use relative confidence as a metacognitive cue to infer their conversational partner's depth of processing.
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We examine whether reasoning is improved by evaluative feedback, i.e., the information of whether a reasoner's answer was correct or incorrect, and report two studies that show that evaluative feedback increases the chances that p...
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We examine whether reasoning is improved by evaluative feedback, i.e., the information of whether a reasoner's answer was correct or incorrect, and report two studies that show that evaluative feedback increases the chances that participants will produce normatively correct responses for deductive reasoning problems. In Experiment 1, participants who were given feedback about their performance did better on problems based on disjunctions that were designed to elicit illusory inferences. In Experiment 2, participants answered difficult syllogisms with more accuracy when they were provided with feedback. We conclude by contrasting the rule- , heuristics-, and model-based accounts of deduction on their ability to explain the effects of evaluative feedback.
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摘要 :
The mReactr system is a computational implementation of the mental model theory of reasoning (Johnson-Laird, 1983) that is embedded within the ACT- R cognitive architecture (Anderson, 1990). We show how the memory-handling mechani...
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The mReactr system is a computational implementation of the mental model theory of reasoning (Johnson-Laird, 1983) that is embedded within the ACT- R cognitive architecture (Anderson, 1990). We show how the memory-handling mechanisms of the architecture can be leveraged to store and handle discrete representations of possibilities, i.e., mental models, efficiently. Namely, the iconic representation of a mental model can be distributed, in which each component of a model is represented by a 'chunk' in ACT-R's declarative memory. Those chunks can be merged to create minimal mental models, i.e., reduced representations that do not contain redundant information. Minimal models can then be modified and inspected rapidly. We describe three separate versions of the mReactr software that minimize models at different stages of the system's inferential processes. Only one of the versions provides an acceptable model of data from an immediate inference task. The resulting system suggests that reasoners minimize mental models only when they initiate deliberative mental processes such as a search for alternative models.
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摘要 :
How do reasoners negate compound sentences, such as conjunctions of the form A and B and disjunctions of the form A or B or both. A theory based on mental models posits that reasoners negate each clause independently, and enumerat...
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How do reasoners negate compound sentences, such as conjunctions of the form A and B and disjunctions of the form A or B or both. A theory based on mental models posits that reasoners negate each clause independently, and enumerate the various possibilities consistent with the negation. It makes a novel prediction: negations of conjunctions should be more difficult to comprehend than negations of disjunctions. Two experiments corroborate the prediction. Experiment 1 tested participants' ability to comprehend sentential negations by giving them assertions of the form: Bob denied that he wore a yellow shirt and he wore blue pants on Tuesday. Participants selected the clothing options that Bob possibly wore on Tuesday. Experiment 2 gave participants descriptions such as Bob loves Mary or Mary loves John or both, and they were required to formulate a denial by completing a sentence that started with 'No, '. In both studies, participants' responses were more accurate for denials of disjunctions than denials of conjunctions.
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