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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
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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
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Erdin Mujezinovic; Vsevolod Kapatsinski; Ruben van de Vijver – Cognitive Science, 2024
A word often expresses many different morphological functions. Which part of a word contributes to which part of the overall meaning is not always clear, which raises the question as to how such functions are learned. While linguistic studies tacitly assume the co-occurrence of cues and outcomes to suffice in learning these functions (Baer-Henney,…
Descriptors: Morphology (Languages), Phonology, Morphemes, Cues
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Francisco Barbosa Escobar; Qian Janice Wang – Cognitive Science, 2024
The interest in crossmodal correspondences, including those involving sounds and involving tastes, has experienced rapid growth in recent years. However, the mechanisms underlying these correspondences are not well understood. In the present study (N = 302), we used an associative learning paradigm, based on previous literature using simple sounds…
Descriptors: Foreign Countries, Learning Modalities, Adults, Acoustics
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Fangli Xia; Mitchell J. Nathan; Kelsey E. Schenck; Michael I. Swart – Cognitive Science, 2025
Task-relevant actions can facilitate mathematical thinking, even for complex topics, such as mathematical proof. We investigated whether such cognitive benefits also occur for action predictions. The action-cognition transduction (ACT) model posits a reciprocal relationship between movements and reasoning. Movements--imagined as well as real ones…
Descriptors: Undergraduate Students, Geometry, Mathematical Concepts, Mathematics Instruction
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Eliza L. Congdon; Elizabeth M. Wakefield; Miriam A. Novack; Naureen Hemani-Lopez; Susan Goldin-Meadow – Cognitive Science, 2024
Gestures--hand movements that accompany speech and express ideas--can help children learn how to solve problems, flexibly generalize learning to novel problem-solving contexts, and retain what they have learned. But does it matter who is doing the gesturing? We know that producing gesture leads to better comprehension of a message than watching…
Descriptors: Nonverbal Communication, Predictor Variables, Learning Processes, Generalization
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Susan Wagner Cook; Elle M. D. Wernette; Madison Valentine; Mary Aldugom; Todd Pruner; Kimberly M. Fenn – Cognitive Science, 2024
Although children learn more when teachers gesture, it is not clear "how" gesture supports learning. Here, we sought to investigate the nature of the memory processes that underlie the observed benefits of gesture on lasting learning. We hypothesized that instruction with gesture might create memory representations that are particularly…
Descriptors: Prior Learning, Nonverbal Communication, Grade 2, Grade 3
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Aislinn Keogh; Simon Kirby; Jennifer Culbertson – Cognitive Science, 2024
General principles of human cognition can help to explain why languages are more likely to have certain characteristics than others: structures that are difficult to process or produce will tend to be lost over time. One aspect of cognition that is implicated in language use is working memory--the component of short-term memory used for temporary…
Descriptors: Language Variation, Learning Processes, Short Term Memory, Schemata (Cognition)
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Yanjun Liu; Ben R. Newell; Jaimie E. Lee; Brett K. Hayes – Cognitive Science, 2025
A simple-rule learning trap occurs when people show suboptimal category learning due to insufficient exploration of the learning environment. By combining experimental methods and computational modeling, the current study investigated the impact of two key factors believed to play essential roles in the development of a simple-rule learning trap:…
Descriptors: Early Experience, Attention Control, Educational Environment, Barriers
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Péter Rácz; Ágnes Lukács – Cognitive Science, 2024
People learn language variation through exposure to linguistic interactions. The way we take part in these interactions is shaped by our lexical representations, the mechanisms of language processing, and the social context. Existing work has looked at how we learn and store variation in the ambient language. How this is mediated by the social…
Descriptors: Foreign Countries, Native Speakers, Hungarian, Language Processing
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Alicia M. Chen; Andrew Palacci; Natalia Vélez; Robert D. Hawkins; Samuel J. Gershman – Cognitive Science, 2024
How do teachers learn about what learners already know? How do learners aid teachers by providing them with information about their background knowledge and what they find confusing? We formalize this collaborative reasoning process using a hierarchical Bayesian model of pedagogy. We then evaluate this model in two online behavioral experiments (N…
Descriptors: Bayesian Statistics, Models, Teaching Methods, Evaluation
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Tanja C. Roembke; Bob McMurray – Cognitive Science, 2025
Computational and animal models suggest that the unlearning or pruning of incorrect meanings matters for word learning. However, it is currently unclear how such pruning occurs during word learning and to what extent it depends on supervised and unsupervised learning. In two experiments (N[subscript 1] = 40; N[subscript 2] = 42), adult…
Descriptors: Vocabulary Development, Computation, Models, Accuracy
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Ilona Bass; Cristian Espinoza; Elizabeth Bonawitz; Tomer D. Ullman – Cognitive Science, 2024
When people make decisions, they act in a way that is either automatic ("rote"), or more thoughtful ("reflective"). But do people notice when "others" are behaving in a rote way, and do they care? We examine the detection of rote behavior and its consequences in U.S. adults, focusing specifically on pedagogy and…
Descriptors: Teaching Methods, Learning Processes, Rote Learning, Critical Thinking
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Sebastian Holt; David Barner – Cognitive Science, 2025
Humans count to indefinitely large numbers by recycling words from a finite list, and combining them using rules--for example, combining sixty with unit labels to generate sixty-one, sixty-two, and so on. Past experimental research has focused on children learning base-10 systems, and has reported that this rule learning process is highly…
Descriptors: Computation, Numbers, Adult Students, Number Concepts
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Stephen Man-Kit Lee; Nicole Sin Hang Law; Shelley Xiuli Tong – Cognitive Science, 2024
Statistical learning enables humans to involuntarily process and utilize different kinds of patterns from the environment. However, the cognitive mechanisms underlying the simultaneous acquisition of multiple regularities from different perceptual modalities remain unclear. A novel multidimensional serial reaction time task was developed to test…
Descriptors: Statistics Education, Hypothesis Testing, Cognitive Processes, Reaction Time
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