Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 1 |
Since 2016 (last 10 years) | 2 |
Since 2006 (last 20 years) | 6 |
Descriptor
Memory | 6 |
Probability | 6 |
Models | 4 |
Bayesian Statistics | 2 |
Cognitive Processes | 2 |
Computation | 2 |
Experiments | 2 |
Generalization | 2 |
Language Processing | 2 |
Sciences | 2 |
Bias | 1 |
More ▼ |
Source
Cognitive Science | 6 |
Author
Anderson, John R. | 1 |
Betts, Shawn | 1 |
Brown, Gordon D. A. | 1 |
Chater, Nick | 1 |
Gray, Wayne D. | 1 |
Jaeger, T. Florian | 1 |
Janssen, Christian P. | 1 |
Kim, Woojae | 1 |
Lee, Hee Seung | 1 |
Lee, Michael D. | 1 |
Qian, Ting | 1 |
More ▼ |
Publication Type
Journal Articles | 6 |
Reports - Research | 3 |
Reports - Evaluative | 2 |
Reports - Descriptive | 1 |
Education Level
Audience
Location
Hungary | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Ronai, Eszter; Xiang, Ming – Cognitive Science, 2023
Memory limitations and probabilistic expectations are two key factors that have been posited to play a role in the incremental processing of natural language. Relative clauses (RCs) have long served as a key proving ground for such theories of language processing. Across three self-paced reading experiments, we test the online comprehension of…
Descriptors: Memory, Expectation, Language Processing, Syntax
Lee, Hee Seung; Betts, Shawn; Anderson, John R. – Cognitive Science, 2016
Learning to solve a class of problems can be characterized as a search through a space of hypotheses about the rules for solving these problems. A series of four experiments studied how different learning conditions affected the search among hypotheses about the solution rule for a simple computational problem. Experiment 1 showed that a problem…
Descriptors: Problem Solving, Hypothesis Testing, Experiments, Cognitive Processes
Janssen, Christian P.; Gray, Wayne D. – Cognitive Science, 2012
Reinforcement learning approaches to cognitive modeling represent task acquisition as learning to choose the sequence of steps that accomplishes the task while maximizing a reward. However, an apparently unrecognized problem for modelers is choosing when, what, and how much to reward; that is, when (the moment: end of trial, subtask, or some other…
Descriptors: Rewards, Reinforcement, Models, Memory
Qian, Ting; Jaeger, T. Florian – Cognitive Science, 2012
Recent years have seen a surge in accounts motivated by information theory that consider language production to be partially driven by a preference for communicative efficiency. Evidence from discourse production (i.e., production beyond the sentence level) has been argued to suggest that speakers distribute information across discourse so as to…
Descriptors: Language Processing, Information Transfer, Evidence, Contrastive Linguistics
Chater, Nick; Brown, Gordon D. A. – Cognitive Science, 2008
The remarkable successes of the physical sciences have been built on highly general quantitative laws, which serve as the basis for understanding an enormous variety of specific physical systems. How far is it possible to construct universal principles in the cognitive sciences, in terms of which specific aspects of perception, memory, or decision…
Descriptors: Sciences, Scientific Principles, Models, Memory
Shiffrin, Richard M.; Lee, Michael D.; Kim, Woojae; Wagenmakers, Eric-Jan – Cognitive Science, 2008
This article reviews current methods for evaluating models in the cognitive sciences, including theoretically based approaches, such as Bayes factors and minimum description length measures; simulation approaches, including model mimicry evaluations; and practical approaches, such as validation and generalization measures. This article argues…
Descriptors: Bayesian Statistics, Generalization, Sciences, Models