Naive probability: A mental model theory of extensional reasoning.

Johnson‐Laird, P. N.; Legrenzi, Paolo; Girotto, Vittorio; Legrenzi, Maria Sonino; Caverni, Jean-Paul

doi:10.1037/0033-295x.106.1.62

Cited by 245 publications

(279 citation statements)

References 42 publications

(99 reference statements)

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“…If there were an ace in the hand, then two of the assertions would be true, contrary to the rubric that only one of them is true. Analogous illusions arise in reasoning about necessary conclusions (Johnson-Laird & Savary, 1999), probable conclusions (Johnson-Laird, Legrenzi, Girotto, Legrenzi, & Caverni, 1999), and quantified conclusions (Yang & Johnson-Laird, 2000). They also occur in reasoning about whether sets of assertions are consistent (Johnson-Laird, Legrenzi, Girotto, & Legrenzi, 2000).…”

Section: Discussionmentioning

confidence: 99%

On imagining what is true (and what is false)

Barres

Johnson‐Laird

2003

Thinking & Reasoning

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How do people imagine the possibilities in which an assertion would be true and the possibilities in which it would be false? We argue that the mental representation of the meanings of connectives, such as "and", "or", and "if", specify how to construct the true possibilities for simple assertions containing just a single connective. It follows that the false possibilities are constructed by inference from the true possibilities. We report converging evidence supporting this account from four experiments in which the participants had to list the true and the false possibilities for various sorts of assertion. Their systematic errors, response times, and think-aloud protocols corroborated the theory's predictions. Not even true possibilities are immediately available for complex assertions containing two or more different connectives. The task of listing the true possibilities then becomes difficult, and the task of listing the false possibilities almost impossible.A fundamental semantic ability is to imagine the circumstances in which an assertion is true. If you understand an assertion, then you should be able to imagine such circumstances; if you cannot imagine any circumstance in which an assertion is true, then you are hardly able to understand it. As Wittgenstein (1922, 4

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Section: Discussionmentioning

confidence: 99%

On imagining what is true (and what is false)

Barres

Johnson‐Laird

2003

Thinking & Reasoning

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“…The training methods were based on three different theoretical approaches: Lecoutre's (1985) theory of the equiprobability bias; the theory of naïve probability (Johnson-Laird, Legrenzi, Girotto, Sonino-Legrenzi, & Caverni, 1999); and the fast and frugal heuristics approach (e.g., Gigerenzer, Gaissmaier, Kurz-Milcke, Schwartz, & Woloshin, 2008). The study was conducted with first-, second-and third-year psychology students from the University of Plymouth.…”

Section: The Use Of Virtual Summer Schools To Sustain Engagement and mentioning

confidence: 99%

PLAT2010 Conference Abstracts

2011

Psychology Learning & Teaching

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In order for students to take control of their learning and in effect become self-regulated learners they need to understand what good performance is, be able to judge accurately how their performance compares to good performance, and understand what they need to do to improve performance. Higher-achieving students are more likely to engage actively in and self-regulate their learning, but we argue that even 'at risk' students have the potential to become self-regulated learners. However, anecdotal and research evidence has indicated that low-and high-performing students differ in their capacity to understand the requirements of assessment within academic tasks.This discrepancy has been attributed to differences in motivation but also in cognitive processing, in that poorer-performing students have difficulty in understanding and objectively applying set criteria to their own work. Consequently, an understanding of assessment criteria and student cognitive processing is important for aiding students in their course performance. This project investigated differences in ability to understand and apply assessment criteria among a cohort of final-year psychology students within the School of Social Sciences and Psychology at Victoria University, Melbourne, Australia.The aim in this field study was to assess differences in the cognitive grasp of performance measurement between students with high academic ability and students with relatively lower academic ability. In particular, the project examined students' capacity to reflect upon and assess their own and other students' work against set marking criteria. Specifically, the hypothesis was that weaker-performing students were more generous or tended to grade themselves higher in comparison to stronger-performing students.Participants included 64 third-(and final-) year psychology students, 52 females and 12 males, completing the core unit History and Theories of Psychology within the Bachelor of Psychology degree. Results indicated that higher-performing students were significantly more accurate in predicting their academic performance than lower-performing students. Analysing the Influence of Tutoring in Problem-BasedLearning: the role of tutors' and students' expertise Joerg Zumbach, University of Salzburg, Austria (joerg.zumbach@sbg.ac.at) In this session we will examine the role of tutoring behaviour and learners' expertise in problembased learning. Problem-based learning (PBL) is an interdisciplinary, collaborative, and situated learning approach that contributes to meaningful and successful learning and teaching within authentic learning environments. Nevertheless, several factors directly and indirectly affect problem-based learning, such as case or problem design, learning group characteristics, the role and expertise of tutors, and learner characteristics.In one study we investigated the role of tutors' content expertise during a problem-based learning scenario. We examined whether or not a tutor should use content expertise in facilitating

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“…There have been many previous one-process accounts of reasoning about both deductive and inductive problems (Chater & Oaksford 2000;Harman 1999; Heit 1998; JohnsonLaird 1994;Johnson-Laird et al 1999;Osherson et al 1990;Sloman 1993). For example, Johnson-Laird et al (1999) applied an account of deduction to a range of inductive problems, and Osherson et al proposed an account of induction that treats deductive problems as a special case. However, none of the previous oneprocess accounts are concerned with the issue of how many features are required for different reasoning problems.…”

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confidence: 99%

Two types of thought: Evidence from aphasia

Davidoff

2005

Behav Brain Sci

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Abstract:The distinction between rules and similarity is central to our understanding of much of cognitive psychology. Two aspects of existing research have motivated the present work. First, in different cognitive psychology areas we typically see different conceptions of rules and similarity; for example, rules in language appear to be of a different kind compared to rules in categorization. Second, rules processes are typically modeled as separate from similarity ones; for example, in a learning experiment, rules and similarity influences would be described on the basis of separate models. In the present article, I assume that the rules versus similarity distinction can be understood in the same way in learning, reasoning, categorization, and language, and that a unified model for rules and similarity is appropriate. A rules process is considered to be a similarity one where only a single or a small subset of an object's properties are involved. Hence, rules and overall similarity operations are extremes in a single continuum of similarity operations. It is argued that this viewpoint allows adequate coverage of theory and empirical findings in learning, reasoning, categorization, and language, and also a reassessment of the objectives in research on rules versus similarity.Keywords: categorization; cognitive explanation; language; learning; reasoning; rules; similarity Emmanuel Pothos received his D.Phil. in Experimental Psychology from the University of Oxford in 1998. Since then he has been a lecturer of psychology at the University of Wales at Bangor and at the University of Edinburgh; he is currently at the University of Crete. His research activity includes computational models in unsupervised categorization and statistical methods for disambiguating rules and similarity in learning. Additionally, he is looking at applications of learning models in the study of addictive behavior. A focal point of his work is the a priori comparability of theoretical accounts and explanatory concepts in cognitive psychology.partly because they uniquely have in common a particular set of properties or features, as research in basic level categorization and spontaneous classification shows (Pothos & Chater 2002;Rosch & Mervis 1975). I postulate that the object properties relevant in deciding how to categorize it amongst a number of candidate categories are the properties uniquely common to the instances of each category (cf. Aha & Goldstone 1992). For example, in order to decide whether my car keys are a member of the category 'things to take out of my house when it is on fire," I will have to consider only whether car keys match the uniquely common properties of the other members of that category ("credit cards," "my cat," "my university diploma") (Barsalou 1991). Note that the present proposal does not involve any commitment about the form of features or properties (cf. Marr 1982). We simply require that at some level it is possible to represent objects in terms of discrete entities (perceptual features, abstract proper...

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Naive probability: A mental model theory of extensional reasoning.

Cited by 245 publications

References 42 publications

On imagining what is true (and what is false)

On imagining what is true (and what is false)

PLAT2010 Conference Abstracts

Two types of thought: Evidence from aphasia

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