Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 1 |
| Since 2017 (last 10 years) | 1 |
| Since 2007 (last 20 years) | 2 |
Descriptor
| Prediction | 2 |
| Probability | 2 |
| Sequential Learning | 2 |
| Accuracy | 1 |
| Alphabets | 1 |
| Cognitive Processes | 1 |
| Cues | 1 |
| Discrimination Learning | 1 |
| English | 1 |
| Executive Function | 1 |
| Language Processing | 1 |
| More ▼ | |
Source
| Cognitive Science | 2 |
Author
| Fabian Tomaschek | 1 |
| Jessie S. Nixon | 1 |
| Michael Ramscar | 1 |
| Sperduti, Alessandro | 1 |
| Stoianov, Ivilin | 1 |
| Testolin, Alberto | 1 |
| Zorzi, Marco | 1 |
Publication Type
| Journal Articles | 2 |
| Reports - Evaluative | 1 |
| Reports - Research | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Fabian Tomaschek; Michael Ramscar; Jessie S. Nixon – Cognitive Science, 2024
Sequence learning is fundamental to a wide range of cognitive functions. Explaining how sequences--and the relations between the elements they comprise--are learned is a fundamental challenge to cognitive science. However, although hundreds of articles addressing this question are published each year, the actual learning mechanisms involved in the…
Descriptors: Sequential Learning, Learning Processes, Serial Learning, Executive Function
Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco – Cognitive Science, 2016
Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in…
Descriptors: Orthographic Symbols, Neurological Organization, Models, Probability

Peer reviewed
Direct link
