Publication Date
In 2025 | 0 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 1 |
Since 2016 (last 10 years) | 1 |
Since 2006 (last 20 years) | 3 |
Descriptor
Stimulus Generalization | 4 |
Models | 2 |
Artificial Intelligence | 1 |
Associative Learning | 1 |
Child Language | 1 |
Computational Linguistics | 1 |
Criticism | 1 |
Cues | 1 |
Error Correction | 1 |
Error Patterns | 1 |
Evidence | 1 |
More ▼ |
Source
Cognitive Science | 4 |
Author
Bertenthal, Bennett I. | 1 |
Christiansen, Morten H. | 1 |
Dry, Matthew J. | 1 |
Elizabeth Wonnacott | 1 |
Eva Viviani | 1 |
Lee, Michael D. | 1 |
Michael Ramscar | 1 |
Navarro, Daniel J. | 1 |
Reali, Florencia | 1 |
Scheutz, Matthias | 1 |
Publication Type
Journal Articles | 4 |
Reports - Research | 3 |
Opinion Papers | 1 |
Reports - Descriptive | 1 |
Education Level
Higher Education | 1 |
Postsecondary Education | 1 |
Audience
Location
Australia | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Eva Viviani; Michael Ramscar; Elizabeth Wonnacott – Cognitive Science, 2024
Ramscar, Yarlett, Dye, Denny, and Thorpe (2010) showed how, consistent with the predictions of error-driven learning models, the order in which stimuli are presented in training can affect category learning. Specifically, learners exposed to artificial language input where objects preceded their labels learned the discriminating features of…
Descriptors: Symbolic Learning, Learning Processes, Artificial Intelligence, Prediction
Bertenthal, Bennett I.; Scheutz, Matthias – Cognitive Science, 2013
Cooper et al. (this issue) develop an interactive activation model of spatial and imitative compatibilities that simulates the key results from Catmur and Heyes (2011) and thus conclude that both compatibilities are mediated by the same processes since their single model can predict all the results. Although the model is impressive, the…
Descriptors: Models, Test Validity, Test Reliability, Reader Response
Navarro, Daniel J.; Dry, Matthew J.; Lee, Michael D. – Cognitive Science, 2012
Inductive generalization, where people go beyond the data provided, is a basic cognitive capability, and it underpins theoretical accounts of learning, categorization, and decision making. To complete the inductive leap needed for generalization, people must make a key "sampling" assumption about how the available data were generated.…
Descriptors: Logical Thinking, Generalization, Sampling, Learning
Reali, Florencia; Christiansen, Morten H. – Cognitive Science, 2005
The poverty of stimulus argument is one of the most controversial arguments in the study of language acquisition. Here we follow previous approaches challenging the assumption of impoverished primary linguistic data, focusing on the specific problem of auxiliary (AUX) fronting in complex polar interrogatives. We develop a series of corpus analyses…
Descriptors: Language Acquisition, Grammar, Sentence Structure, Stimulus Generalization