Publication Date
In 2025 | 0 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 1 |
Since 2016 (last 10 years) | 3 |
Since 2006 (last 20 years) | 3 |
Descriptor
Prediction | 3 |
Sequential Learning | 3 |
Cues | 2 |
Probability | 2 |
Task Analysis | 2 |
Accuracy | 1 |
Alphabets | 1 |
Cognitive Processes | 1 |
Computer Peripherals | 1 |
Discrimination Learning | 1 |
English | 1 |
More ▼ |
Source
Cognitive Science | 3 |
Author
Fabian Tomaschek | 1 |
Hommel, Bernhard | 1 |
Jessie S. Nixon | 1 |
Kachergis, George | 1 |
Michael Ramscar | 1 |
Sperduti, Alessandro | 1 |
Stoianov, Ivilin | 1 |
Testolin, Alberto | 1 |
Zorzi, Marco | 1 |
de Kleijn, Roy | 1 |
Publication Type
Journal Articles | 3 |
Reports - Research | 2 |
Reports - Evaluative | 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
de Kleijn, Roy; Kachergis, George; Hommel, Bernhard – Cognitive Science, 2018
Sequential action makes up the bulk of human daily activity, and yet much remains unknown about how people learn such actions. In one motor learning paradigm, the serial reaction time (SRT) task, people are taught a consistent sequence of button presses by cueing them with the next target response. However, the SRT task only records keypress…
Descriptors: Sequential Learning, Reinforcement, Psychomotor Skills, Reaction Time
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