Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 0 |
| Since 2017 (last 10 years) | 3 |
| Since 2007 (last 20 years) | 5 |
Descriptor
| Abstract Reasoning | 5 |
| Bayesian Statistics | 5 |
| Models | 5 |
| Inferences | 3 |
| Learning Processes | 3 |
| Decision Making | 2 |
| Logical Thinking | 2 |
| Prediction | 2 |
| Child Language | 1 |
| Classification | 1 |
| Cognitive Science | 1 |
| More ▼ | |
Author
| Chen, Dawn | 1 |
| Hartshorne, Joshua K. | 1 |
| Hinterecker, Thomas | 1 |
| Holyoak, Keith J. | 1 |
| Homaei, Hadjar | 1 |
| Johnson-Laird, P. N. | 1 |
| Knauff, Markus | 1 |
| Lee, Michael D. | 1 |
| Lu, Hongjing | 1 |
| Mozer, Michael C. | 1 |
| Pashler, Harold | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 5 |
| Reports - Evaluative | 3 |
| Opinion Papers | 2 |
| Reports - Research | 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
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
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
Mozer, Michael C.; Pashler, Harold; Homaei, Hadjar – Cognitive Science, 2008
Griffiths and Tenenbaum (2006) asked individuals to make predictions about the duration or extent of everyday events (e.g., cake baking times), and reported that predictions were optimal, employing Bayesian inference based on veridical prior distributions. Although the predictions conformed strikingly to statistics of the world, they reflect…
Descriptors: Models, Individual Activities, Group Activities, Prediction

Peer reviewed
Direct link
