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  1. Elementary probabilistic operations: a framework for probabilistic reasoning.Siegfried Macho & Thomas Ledermann - 2024 - Thinking and Reasoning 30 (2):259-300.
    The framework of elementary probabilistic operations (EPO) explains the structure of elementary probabilistic reasoning tasks as well as people’s performance on these tasks. The framework comprises three components: (a) Three types of probabilities: joint, marginal, and conditional probabilities; (b) three elementary probabilistic operations: combination, marginalization, and conditioning, and (c) quantitative inference schemas implementing the EPO. The formal part of the EPO framework is a computational level theory that provides a problem space representation and a classification of elementary probabilistic problems based (...)
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  • How to Improve Performance in Bayesian Inference Tasks: A Comparison of Five Visualizations.Katharina Böcherer-Linder & Andreas Eichler - 2019 - Frontiers in Psychology 10:375260.
    Bayes’ formula is a fundamental statistical method for inference judgments in uncertain situations used by both laymen and professionals. However, since people often fail in situations where Bayes’ formula can be applied, how to improve their performance in Bayesian situations is a crucial question. We based our research on a widely accepted beneficial strategy in Bayesian situations, representing the statistical information in the form of natural frequencies. In addition to this numerical format, we used five visualizations: a 2×2-table, a unit (...)
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  • Natural frequencies improve Bayesian reasoning in simple and complex inference tasks.Ulrich Hoffrage, Stefan Krauss, Laura Martignon & Gerd Gigerenzer - 2015 - Frontiers in Psychology 6.
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  • An Eye-Tracking Study of Statistical Reasoning With Tree Diagrams and 2 × 2 Tables.Georg Bruckmaier, Karin Binder, Stefan Krauss & Han-Min Kufner - 2019 - Frontiers in Psychology 10:436373.
    Changing the information format from probabilities into frequencies as well as employing appropriate visualizations such as tree diagrams or 2 × 2 tables are important tools that can facilitate people’s statistical reasoning. Previous studies have shown that despite their widespread use in statistical textbooks, both of those visualization types are only of restricted help when they are provided with probabilities, but that they can foster insight when presented with frequencies instead. In the present study, we attempt to replicate this effect (...)
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  • Tversky and Kahneman’s Cognitive Illusions: Who Can Solve Them, and Why?Georg Bruckmaier, Stefan Krauss, Karin Binder, Sven Hilbert & Martin Brunner - 2021 - Frontiers in Psychology 12:584689.
    In the present paper we empirically investigate the psychometric properties of some of the most famous statistical and logical cognitive illusions from the “heuristics and biases” research program by Daniel Kahneman and Amos Tversky, who nearly 50 years ago introduced fascinating brain teasers such as the famous Linda problem, the Wason card selection task, and so-called Bayesian reasoning problems (e.g., the mammography task). In the meantime, a great number of articles has been published that empirically examine single cognitive illusions, theoretically (...)
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  • A New Visualization for Probabilistic Situations Containing Two Binary Events: The Frequency Net.Karin Binder, Stefan Krauss & Patrick Wiesner - 2020 - Frontiers in Psychology 11:506040.
    In teaching statistics in secondary schools and at university, two visualizations are primarily used when situations with two dichotomous characteristics are represented: 2×2 tables and tree diagrams. Both visualizations can be depicted either with probabilities or with frequencies. Visualizations with frequencies have been shown to help students significantly more in Bayesian reasoning problems than probability visualizations do. Because tree diagrams or double-trees (which are largely unknown in school) are node-branch-structures, these two visualizations (compared to the 2×2 table) can even simultaneously (...)
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  • Perspectives on the 2 × 2 Matrix: Solving Semantically Distinct Problems Based on a Shared Structure of Binary Contingencies. [REVIEW]Hansjörg Neth, Nico Gradwohl, Dirk Streeb, Daniel A. Keim & Wolfgang Gaissmaier - 2021 - Frontiers in Psychology 11:567817.
    Cognition is both empowered and limited by representations. The matrix lens model explicates tasks that are based on frequency counts, conditional probabilities, and binary contingencies in a general fashion. Based on a structural analysis of such tasks, the model links several problems and semantic domains and provides a new perspective on representational accounts of cognition that recognizes representational isomorphs as opportunities, rather than as problems. The shared structural construct of a 2 × 2 matrix supports a set of generic tasks (...)
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  • Different Visualizations Cause Different Strategies When Dealing With Bayesian Situations.Andreas Eichler, Katharina Böcherer-Linder & Markus Vogel - 2020 - Frontiers in Psychology 11:506184.
    People often struggle with Bayesian reasoning. However, research showed that people’s performance (and rationality) can be supported by the way of representing the statistical information. First, research showed that using natural frequencies instead of probabilities as format of statistical information increases people’s performance in Bayesian situations thoroughly. Second, research also yielded that people’s performance increases through using visualization. We build our paper on existing research in this field. The main aim is to analyse people’s strategies in Bayesian situations that are (...)
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  • Evidencing How Experience and Problem Format Affect Probabilistic Reasoning Through Interaction Analysis.Manuele Reani, Alan Davies, Niels Peek & Caroline Jay - 2019 - Frontiers in Psychology 10.
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  • Why Can Only 24% Solve Bayesian Reasoning Problems in Natural Frequencies: Frequency Phobia in Spite of Probability Blindness.Patrick Weber, Karin Binder & Stefan Krauss - 2018 - Frontiers in Psychology 9:375246.
    For more than 20 years, research has proven the beneficial effect of natural frequencies when it comes to solving Bayesian reasoning tasks (Gigerenzer & Hoffrage, 1995). In a recent meta-analysis, McDowell & Jacobs (2017) showed that presenting a task in natural frequency format increases performance rates to 24% compared to only 4% when the same task is presented in probability format. Nevertheless, on average three quarters of participants in their meta-analysis failed to obtain the correct solution for such a task (...)
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  • Editorial: Improving Bayesian Reasoning: What Works and Why?David R. Mandel & Gorka Navarrete - 2015 - Frontiers in Psychology 6.
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