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Cheng, Patricia W.; Sandhofer, Catherine M.; Liljeholm, Mimi – Cognitive Science, 2022
The present paper examines a type of abstract domain-general knowledge required for the process of constructing useable domain-specific causal knowledge, the evident goal of causal learning. It tests the hypothesis that analytic knowledge of "causal-invariance decomposition functions" is essential for this process. Such knowledge…
Descriptors: Preschool Children, Learning Processes, Generalization, Heuristics
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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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Johnson, Tamar; Siegelman, Noam; Arnon, Inbal – Cognitive Science, 2020
Over the last decade, iterated learning studies have provided compelling evidence for the claim that linguistic structure can emerge from non-structured input, through the process of transmission. However, it is unclear whether individuals differ in their tendency to add structure, an issue with implications for understanding who are the agents of…
Descriptors: Individual Differences, Cognitive Ability, Learning Processes, Language Acquisition
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Zhang, Yayun; Yurovsky, Daniel; Yu, Chen – Cognitive Science, 2021
Recent laboratory experiments have shown that both infant and adult learners can acquire word-referent mappings using cross-situational statistics. The vast majority of the work on this topic has used unfamiliar objects presented on neutral backgrounds as the visual contexts for word learning. However, these laboratory contexts are much different…
Descriptors: Cognitive Mapping, Language Acquisition, Linguistic Input, Generalization
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Ito, Chiyuki; Feldman, Naomi H. – Cognitive Science, 2022
Iterated learning models of language evolution have typically been used to study the emergence of language, rather than historical language change. We use iterated learning models to investigate historical change in the accent classes of two Korean dialects. Simulations reveal that many of the patterns of historical change can be explained as…
Descriptors: Diachronic Linguistics, Sociolinguistics, Comparative Analysis, Models
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Chin-Parker, Seth; Cantelon, Julie – Cognitive Science, 2017
This paper provides evidence for a contrastive account of explanation that is motivated by pragmatic theories that recognize the contribution that context makes to the interpretation of a prompt for explanation. This study replicates the primary findings of previous work in explanation-based category learning (Williams & Lombrozo, 2010),…
Descriptors: Context Effect, Prompting, Generalization, Classification
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Fedzechkina, Maryia; Newport, Elissa L.; Jaeger, T. Florian – Cognitive Science, 2017
Across languages of the world, some grammatical patterns have been argued to be more common than expected by chance. These are sometimes referred to as (statistical) "language universals." One such universal is the correlation between constituent order freedom and the presence of a case system in a language. Here, we explore whether this…
Descriptors: Grammar, Diachronic Linguistics, English, Old English
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Baer-Henney, Dinah; Kügler, Frank; van de Vijver, Ruben – Cognitive Science, 2015
Using the artificial language paradigm, we studied the acquisition of morphophonemic alternations with exceptions by 160 German adult learners. We tested the acquisition of two types of alternations in two regularity conditions while additionally varying length of training. In the first alternation, a vowel harmony, backness of the stem vowel…
Descriptors: Morphology (Languages), Phonemics, Generalization, German
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Tabor, Whitney; Cho, Pyeong W.; Dankowicz, Harry – Cognitive Science, 2013
Human participants and recurrent ("connectionist") neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular ("strong") classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the…
Descriptors: Learning Processes, Task Analysis, Systems Approach, Geometric Concepts
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Ahn, Woo-Young; Busemeyer, Jerome R.; Wagenmakers, Eric-Jan; Stout, Julie C. – Cognitive Science, 2008
It is a hallmark of a good model to make accurate "a priori" predictions to new conditions (Busemeyer & Wang, 2000). This study compared 8 decision learning models with respect to their generalizability. Participants performed 2 tasks (the Iowa Gambling Task and the Soochow Gambling Task), and each model made a priori predictions by estimating the…
Descriptors: Prediction, Generalization, Models, Comparative Analysis