Access the full text.
Sign up today, get DeepDyve free for 14 days.
Nicolas Riesterer, Daniel Brand, Marco Ragni (2020)
Predictive Modeling of Individual Human Cognition: Upper Bounds and a New Perspective on PerformanceTopics in cognitive science
M. Bucciarelli, P. Johnson-Laird (1999)
Strategies in syllogistic reasoningCogn. Sci., 23
P. Johnson-Laird, Mark Steedman (1978)
The psychology of syllogismsCognitive Psychology, 10
P. Johnson-Laird, S. Khemlani (2013)
Toward a Unified Theory of ReasoningPsychology of Learning and Motivation, 59
I. Begg, J. Denny (1969)
Empirical reconciliation of atmosphere and conversion interpretations of syllogistic reasoning errorsJournal of Experimental Psychology, 81
N. Chater, M. Oaksford (1999)
The Probability Heuristics Model of Syllogistic ReasoningCognitive Psychology, 38
L. Rips (1994)
The Psychology of Proof
S. Khemlani, P. Johnson-Laird (2016)
How people differ in syllogistic reasoningCognitive Science
S. Khemlani, P. Johnson-Laird (2012)
Theories of the syllogism: A meta-analysis.Psychological bulletin, 138 3
N. Wetherick, Kenneth Gilhooly (1995)
‘Atmosphere’, matching, and logic in syllogistic reasoningCurrent Psychology, 14
D. O'reilly (2018)
The Psychology of Pricing
T. Polk, A. Newell (1995)
Deduction as verbal reasoning.Psychological Review, 102
L. Chapman, J. Chapman (1959)
Atmosphere effect re-examined.Journal of experimental psychology, 58
R. Revlis (1975)
Two models of syllogistic reasoning: Feature selection and conversionJournal of Verbal Learning and Verbal Behavior, 14
B. Geurts (2003)
Reasoning with quantifiersCognition, 86
M. Ford (1995)
Two modes of mental representation and problem solution in syllogistic reasoningCognition, 54
R. Woodworth, S. Sells (1935)
An atmosphere effect in formal syllogistic reasoning.Journal of Experimental Psychology, 18
K. Stenning, P. Yule (1997)
Image and Language in Human Reasoning: A Syllogistic IllustrationCognitive Psychology, 34
A. Newell (1993)
Reasoning, problem solving, and decision processes: the problem space as a fundamental category
Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations
S. Khemlani, P. Johnson-Laird (2013)
The processes of inferenceArgument Comput., 4
How humans reason in general about syllogisms is, despite a century of research and many proposed cognitive theories, still an unanswered question. It is even more difficult, however, to answer how an individual human reasons. The goal of this article is twofold: First, it analyses the predictive quality of existing cognitive theories by providing a standardized (re-) implementation of existing theories. Towards this, theories are algorithmically formalized, including their potential capabilities for adaptation to an individual reasoner. The implementations are modular with regard to the underlying mental operations defined by the cognitive theories. Second, it proposes a novel composite approach based on existing cognitive theories, resulting in a cognitive model for predicting an individual reasoner before s/he draws a conclusion. This approach uses sequences of operations, inherited and combined from different theories, to form its predictions. Among the existing models, our implementations of PHM, mReasoner, and Verbal Models make the most accurate predictions of the conclusions drawn by individual reasoners. The designed composite model, however, is able to significantly surpass those implementations by exploiting synergies between different models. In particular, it successfully combines operations from PHM and Verbal Models. Therefore, the composite approach is a promising tool to model and study syllogistic reasoning and to generate tailored cognitive theories. At the same time it provides a general method that can potentially be applied to predict individual human reasoners in other domains, too.
Annals of Mathematics and Artificial Intelligence – Springer Journals
Published: Nov 1, 2021
Keywords: Syllogistic reasoning; Cognitive modeling; Model evaluation; Individual differences; Cognitive processes; 91E45; 68T37
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.