Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 0 |
| Since 2017 (last 10 years) | 2 |
| Since 2007 (last 20 years) | 4 |
Descriptor
| Abstract Reasoning | 4 |
| Bayesian Statistics | 4 |
| Learning Processes | 3 |
| Models | 3 |
| Inferences | 2 |
| Causal Models | 1 |
| Child Language | 1 |
| Classification | 1 |
| Cognitive Science | 1 |
| Computation | 1 |
| Computational Linguistics | 1 |
| More ▼ | |
Author
| Beckers, Tom | 1 |
| Hartshorne, Joshua K. | 1 |
| Hinterecker, Thomas | 1 |
| Johnson-Laird, P. N. | 1 |
| Knauff, Markus | 1 |
| Lee, Michael D. | 1 |
| Lu, Hongjing | 1 |
| Rojas, Randall R. | 1 |
| Vanpaemel, Wolf | 1 |
| Yuille, Alan L. | 1 |
Publication Type
| Journal Articles | 4 |
| Reports - Evaluative | 4 |
| Opinion Papers | 2 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
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
Hartshorne, Joshua K. – First Language, 2020
Ambridge argues that the existence of exemplar models for individual phenomena (words, inflection rules, etc.) suggests the feasibility of a unified, exemplars-everywhere model that eschews abstraction. The argument would be strengthened by a description of such a model. However, none is provided. I show that any attempt to do so would immediately…
Descriptors: Models, Language Acquisition, Language Processing, Bayesian Statistics
Lu, Hongjing; Rojas, Randall R.; Beckers, Tom; Yuille, Alan L. – Cognitive Science, 2016
Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about…
Descriptors: Learning Processes, Causal Models, Sequential Learning, Abstract Reasoning
Lee, Michael D.; Vanpaemel, Wolf – Cognitive Science, 2008
This article demonstrates the potential of using hierarchical Bayesian methods to relate models and data in the cognitive sciences. This is done using a worked example that considers an existing model of category representation, the Varying Abstraction Model (VAM), which attempts to infer the representations people use from their behavior in…
Descriptors: Computation, Inferences, Cognitive Science, Models

Peer reviewed
Direct link
