Publication Date
In 2025 | 0 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 4 |
Since 2016 (last 10 years) | 9 |
Since 2006 (last 20 years) | 17 |
Descriptor
Bayesian Statistics | 18 |
Logical Thinking | 18 |
Inferences | 14 |
Probability | 10 |
Prediction | 7 |
Models | 6 |
Statistical Inference | 6 |
Causal Models | 3 |
Decision Making | 3 |
Prior Learning | 3 |
Theories | 3 |
More ▼ |
Source
Author
Tenenbaum, Joshua B. | 3 |
Griffiths, Thomas L. | 2 |
Holyoak, Keith J. | 2 |
Lu, Hongjing | 2 |
Austerweil, Joseph L. | 1 |
Binder, Karin | 1 |
Blokpoel, Mark | 1 |
Bowers, Jeffrey S. | 1 |
Braun, Leah T. | 1 |
Brisson, Janie | 1 |
Chen, Dawn | 1 |
More ▼ |
Publication Type
Journal Articles | 17 |
Reports - Research | 9 |
Reports - Evaluative | 5 |
Reports - Descriptive | 3 |
Dissertations/Theses -… | 1 |
Opinion Papers | 1 |
Education Level
Higher Education | 4 |
Postsecondary Education | 4 |
Audience
Location
California | 1 |
Canada | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
J. E. Borgert – ProQuest LLC, 2024
Foundations of statistics research aims to establish fundamental principles guiding inference about populations under uncertainty. It is concerned with the process of learning from observations, notions of uncertainty and induction, and satisfying inferential objectives. The growing interest in predictive methods in high-stakes fields like…
Descriptors: Statistics, Research, Logical Thinking, Statistical Inference
Hayes, Brett K.; Liew, Shi Xian; Desai, Saoirse Connor; Navarro, Danielle J.; Wen, Yuhang – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2023
The samples of evidence we use to make inferences in everyday and formal settings are often subject to selection biases. Two property induction experiments examined group and individual sensitivity to one type of selection bias: sampling frames - causal constraints that only allow certain types of instances to be sampled. Group data from both…
Descriptors: Logical Thinking, Inferences, Bias, Individual Differences
Binder, Karin; Krauss, Stefan; Schmidmaier, Ralf; Braun, Leah T. – Advances in Health Sciences Education, 2021
When physicians are asked to determine the positive predictive value from the a priori probability of a disease and the sensitivity and false positive rate of a medical test (Bayesian reasoning), it often comes to misjudgments with serious consequences. In daily clinical practice, however, it is not only important that doctors receive a tool with…
Descriptors: Clinical Diagnosis, Efficiency, Probability, Bayesian Statistics
Austerweil, Joseph L.; Sanborn, Sophia; Griffiths, Thomas L. – Cognitive Science, 2019
Generalization is a fundamental problem solved by every cognitive system in essentially every domain. Although it is known that how people generalize varies in complex ways depending on the context or domain, it is an open question how people "learn" the appropriate way to generalize for a new context. To understand this capability, we…
Descriptors: Generalization, Logical Thinking, Inferences, Bayesian Statistics
Hinterecker, Thomas; Knauff, Markus; Johnson-Laird, P. N. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2019
Individuals draw conclusions about possibilities from assertions that make no explicit reference to them. The model theory postulates that assertions such as disjunctions refer to possibilities. Hence, a disjunction of the sort, "A or B or both," where "A" and "B" are sensible clauses, yields mental models of an…
Descriptors: Logical Thinking, Abstract Reasoning, Inferences, Probability
Lu, Yonggang; Zheng, Qiujie; Quinn, Daniel – Journal of Statistics and Data Science Education, 2023
We present an instructional approach to teaching causal inference using Bayesian networks and "do"-Calculus, which requires less prerequisite knowledge of statistics than existing approaches and can be consistently implemented in beginner to advanced levels courses. Moreover, this approach aims to address the central question in causal…
Descriptors: Bayesian Statistics, Learning Motivation, Calculus, Advanced Courses
Mayrhofer, Ralf; Waldmann, Michael R. – Cognitive Science, 2016
Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when…
Descriptors: Causal Models, Bayesian Statistics, Inferences, Probability
Chen, Dawn; Lu, Hongjing; Holyoak, Keith J. – Cognitive Science, 2017
A key property of relational representations is their "generativity": From partial descriptions of relations between entities, additional inferences can be drawn about other entities. A major theoretical challenge is to demonstrate how the capacity to make generative inferences could arise as a result of learning relations from…
Descriptors: Inferences, Abstract Reasoning, Learning Processes, Models
Blokpoel, Mark; Wareham, Todd; Haselager, Pim; Toni, Ivan; van Rooij, Iris – Journal of Problem Solving, 2018
The ability to generate novel hypotheses is an important problem-solving capacity of humans. This ability is vital for making sense of the complex and unfamiliar world we live in. Often, this capacity is characterized as an inference to the best explanation--selecting the "best" explanation from a given set of candidate hypotheses.…
Descriptors: Hypothesis Testing, Logical Thinking, Inferences, Computation
Markovits, Henry; Brisson, Janie; de Chantal, Pier-Luc – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2015
One of the major debates concerning the nature of inferential reasoning is between counterexample-based theories such as mental model theory and probabilistic theories. This study looks at conclusion updating after the addition of statistical information to examine the hypothesis that deductive reasoning cannot be explained by probabilistic…
Descriptors: Logical Thinking, Theories, Bayesian Statistics, Probability
Sun, Shuyan; Pan, Wei – Educational Psychology Review, 2011
From the perspectives of the philosophy of science and statistical inference, we discuss the challenges of making prescriptive statements in quantitative research articles. We first consider the prescriptive nature of educational research and argue that prescriptive statements are a necessity in educational research. The logic of deduction,…
Descriptors: Evidence, Educational Research, Logical Thinking, Bayesian Statistics
Fenton, Norman; Neil, Martin; Lagnado, David A. – Cognitive Science, 2013
A Bayesian network (BN) is a graphical model of uncertainty that is especially well suited to legal arguments. It enables us to visualize and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs…
Descriptors: Networks, Bayesian Statistics, Persuasive Discourse, Models
Kemp, Charles; Tenenbaum, Joshua B. – Psychological Review, 2009
Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. This article presents a Bayesian framework that attempts to meet…
Descriptors: Logical Thinking, Inferences, Statistical Inference, Models
Murphy, Gregory L.; Ross, Brian H. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2010
Two experiments investigated how people perform category-based induction for items that have uncertain categorization. Whereas normative considerations suggest that people should consider multiple relevant categories, much past research has argued that people focus on only the most likely category. A new method is introduced in which responses on…
Descriptors: Logical Thinking, Classification, Inferences, Prediction
Holyoak, Keith J.; Lee, Hee Seung; Lu, Hongjing – Journal of Experimental Psychology: General, 2010
A fundamental issue for theories of human induction is to specify constraints on potential inferences. For inferences based on shared category membership, an analogy, and/or a relational schema, it appears that the basic goal of induction is to make accurate and goal-relevant inferences that are sensitive to uncertainty. People can use source…
Descriptors: Inferences, Logical Thinking, Bayesian Statistics, Causal Models
Previous Page | Next Page ยป
Pages: 1 | 2