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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
Warren, Tiffani; Cagliani, Rachel R.; Whiteside, Erinn; Ayres, Kevin M. – Journal of Behavioral Education, 2021
This study compared effects of student choice of task sequence to two variations in teacher-manipulated task sequences on on-task behavior of elementary-aged students with disabilities. Researchers modified Call et al.'s (J Appl Behav Anal 42: 723-728, 2009) demand assessment to determine high-, moderate-, and low-probability tasks. Next,…
Descriptors: Task Analysis, Sequential Learning, Elementary School Students, Students with Disabilities
Janacsek, Karolina; Fiser, Jozsef; Nemeth, Dezso – Developmental Science, 2012
Implicit skill learning underlies obtaining not only motor, but also cognitive and social skills through the life of an individual. Yet, the ontogenetic changes in humans' implicit learning abilities have not yet been characterized, and, thus, their role in acquiring new knowledge efficiently during development is unknown. We investigated such…
Descriptors: Skill Development, Sequential Learning, Age Differences, Reaction Time
Hlavatý, Robert; Dömeová, Ludmila – International Education Studies, 2014
The paper is focused on students of Mathematical methods in economics at the Czech university of life sciences (CULS) in Prague. The idea is to create a model of students' progress throughout the whole course using the Markov chain approach. Each student has to go through various stages of the course requirements where his success depends on the…
Descriptors: Foreign Countries, Markov Processes, Mathematical Models, Progress Monitoring
Endress, Ansgar D.; Wood, Justin N. – Cognitive Psychology, 2011
When other individuals move, we interpret their movements as discrete, hierarchically-organized, goal-directed actions. However, the mechanisms that integrate visible movement features into actions are poorly understood. Here, we consider two sequence learning mechanisms--transitional probability-based (TP) and position-based encoding…
Descriptors: Memory, Probability, Sequential Learning, Visual Perception
Lew-Williams, Casey; Saffran, Jenny R. – Cognition, 2012
Infants have been described as "statistical learners" capable of extracting structure (such as words) from patterned input (such as language). Here, we investigated whether prior knowledge influences how infants track transitional probabilities in word segmentation tasks. Are infants biased by prior experience when engaging in sequential…
Descriptors: Infants, Language Acquisition, Linguistic Input, Prior Learning
Onnis, Luca; Thiessen, Erik – Cognition, 2013
What are the effects of experience on subsequent learning? We explored the effects of language-specific word order knowledge on the acquisition of sequential conditional information. Korean and English adults were engaged in a sequence learning task involving three different sets of stimuli: auditory linguistic (nonsense syllables), visual…
Descriptors: Cognitive Processes, Syllables, Stimuli, Probability
Marcovitch, Stuart; Lewkowicz, David J. – Developmental Science, 2009
The ability to perceive sequences is fundamental to cognition. Previous studies have shown that infants can learn visual sequences as early as 2 months of age and it has been suggested that this ability is mediated by sensitivity to conditional probability information. Typically, conditional probability information has covaried with frequency…
Descriptors: Infants, Children, Probability, Sequential Learning
French, Robert M.; Addyman, Caspar; Mareschal, Denis – Psychological Review, 2011
Individuals of all ages extract structure from the sequences of patterns they encounter in their environment, an ability that is at the very heart of cognition. Exactly what underlies this ability has been the subject of much debate over the years. A novel mechanism, implicit chunk recognition (ICR), is proposed for sequence segmentation and chunk…
Descriptors: Infants, Probability, Learning Processes, Pattern Recognition
Archbald, Doug; Farley-Ripple, Elizabeth N. – High School Journal, 2012
Educators and researchers have long been interested in determinants of access to honors level and college prep courses in high school. Factors influencing access to upper level mathematics courses are particularly important because of the hierarchical and sequential nature of this subject and because students who finish high school with only lower…
Descriptors: Academic Achievement, High Schools, Predictor Variables, Prediction
Brown, Scott D.; Steyvers, Mark – Cognitive Psychology, 2009
When required to predict sequential events, such as random coin tosses or basketball free throws, people reliably use inappropriate strategies, such as inferring temporal structure when none is present. We investigate the ability of observers to predict sequential events in dynamically changing environments, where there is an opportunity to detect…
Descriptors: Preschool Children, Probability, Learning Strategies, Prediction
Alvarado, Angelica; Jara, Elvia; Vila, Javier; Rosas, Juan M. – Learning and Motivation, 2006
Five experiments were conducted to explore trial order and retention interval effects upon causal predictive judgments. Experiment 1 found that participants show a strong effect of trial order when a stimulus was sequentially paired with two different outcomes compared to a condition where both outcomes were presented intermixed. Experiment 2…
Descriptors: Time, Retention (Psychology), Intervals, Stimuli

Losee, Robert M. – Journal of the American Society for Information Science, 1988
Describes probabilistic document retrieval systems as a sequential learning process, in which the system learns the parameters of probability distributions describing the frequencies of feature occurrences in relevant and nonrelevant documents. Several techniques for estimating the parameters of distributions are described and the results of tests…
Descriptors: Estimation (Mathematics), Feedback, Information Retrieval, Man Machine Systems