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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
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
Tutku Öztel; Fuat Balci – Cognitive Science, 2024
One of the most prominent social influences on human decision making is conformity, which is even more prominent when the perceptual information is ambiguous. The Bayes optimal solution to this problem entails weighting the relative reliability of cognitive information and perceptual signals in constructing the percept from self-sourced/endogenous…
Descriptors: Bayesian Statistics, Computation, Social Influences, Decision Making
Valentina Gliozzi – Cognitive Science, 2024
We propose a simple computational model that describes potential mechanisms underlying the organization and development of the lexical-semantic system in 18-month-old infants. We focus on two independent aspects: (i) on potential mechanisms underlying the development of taxonomic and associative priming, and (ii) on potential mechanisms underlying…
Descriptors: Infants, Computation, Models, Cognitive Development
Cheng, Chen; Kibbe, Melissa M. – Cognitive Science, 2023
Young children with limited knowledge of formal mathematics can intuitively perform basic arithmetic-like operations over nonsymbolic, approximate representations of quantity. However, the algorithmic rules that guide such nonsymbolic operations are not entirely clear. We asked whether nonsymbolic arithmetic operations have a function-like…
Descriptors: Young Children, Mathematics Skills, Arithmetic, Problem Solving
Kun Sun; Rong Wang – Cognitive Science, 2025
The majority of research in computational psycholinguistics on sentence processing has focused on word-by-word incremental processing within sentences, rather than holistic sentence-level representations. This study introduces two novel computational approaches for quantifying sentence-level processing: sentence surprisal and sentence relevance.…
Descriptors: Reading Rate, Reading Comprehension, Sentences, Computation
Ramotowska, Sonia; Steinert-Threlkeld, Shane; Maanen, Leendert; Szymanik, Jakub – Cognitive Science, 2023
According to logical theories of meaning, a meaning of an expression can be formalized and encoded in truth conditions. Vagueness of the language and individual differences between people are a challenge to incorporate into the meaning representations. In this paper, we propose a new approach to study truth-conditional representations of vague…
Descriptors: Computation, Models, Semantics, Decision Making
Paape, Dario; Vasishth, Shravan – Cognitive Science, 2022
What is the processing cost of being garden-pathed by a temporary syntactic ambiguity? We argue that comparing average reading times in garden-path versus non-garden-path sentences is not enough to answer this question. Trial-level contaminants such as inattention, the fact that garden pathing may occur non-deterministically in the ambiguous…
Descriptors: Computation, Language Processing, Syntax, Ambiguity (Semantics)
Brehm, Laurel; Cho, Pyeong Whan; Smolensky, Paul; Goldrick, Matthew A. – Cognitive Science, 2022
Subject-verb agreement errors are common in sentence production. Many studies have used experimental paradigms targeting the production of subject-verb agreement from a sentence preamble ("The key to the cabinets") and eliciting verb errors (… "*were shiny"). Through reanalysis of previous data (50 experiments; 102,369…
Descriptors: Sentences, Sentence Structure, Grammar, Verbs
Fusaroli, Riccardo; Weed, Ethan; Rocca, Roberta; Fein, Deborah; Naigles, Letitia – Cognitive Science, 2023
Linguistic repetitions in children are conceptualized as negative in children with autism -- echolalia, without communicative purpose -- and positive in typically developing (TD) children -- linguistic alignment involved in shared engagement, common ground and language acquisition. To investigate this apparent contradiction we analyzed spontaneous…
Descriptors: Computation, Autism Spectrum Disorders, Young Children, Repetition
Kim, Dan; Opfer, John E. – Cognitive Science, 2021
Perceptual judgments result from a dynamic process, but little is known about the dynamics of number-line estimation. A recent study proposed a computational model that combined a model of trial-to-trial changes with a model for the internal scaling of discrete numbers. Here, we tested a surprising prediction of the model--a situation in which…
Descriptors: Numbers, Computation, Children, Adults
Chu, Junyi; Cheung, Pierina; Schneider, Rose M.; Sullivan, Jessica; Barner, David – Cognitive Science, 2020
By around the age of 5½, many children in the United States judge that numbers never end, and that it is always possible to add 1 to a set. These same children also generally perform well when asked to label the quantity of a set after one object is added (e.g., judging that a set labeled "five" should now be "six"). These…
Descriptors: Preschool Children, Numeracy, Number Concepts, Knowledge Level
Do Additional Features Help or Hurt Category Learning? The Curse of Dimensionality in Human Learners
Vong, Wai Keen; Hendrickson, Andrew T.; Navarro, Danielle J.; Perfors, Amy – Cognitive Science, 2019
The curse of dimensionality, which has been widely studied in statistics and machine learning, occurs when additional features cause the size of the feature space to grow so quickly that learning classification rules becomes increasingly difficult. How do people overcome the curse of dimensionality when acquiring real-world categories that have…
Descriptors: Learning Processes, Classification, Models, Performance
Perkins, Laurel; Feldman, Naomi H.; Lidz, Jeffrey – Cognitive Science, 2022
Learning in any domain depends on how the data for learning are represented. In the domain of language acquisition, children's representations of the speech they hear determine what generalizations they can draw about their target grammar. But these input representations change over development as a function of children's developing linguistic…
Descriptors: Persuasive Discourse, Language Acquisition, Form Classes (Languages), Verbs
Litwin, Piotr; Milkowski, Marcin – Cognitive Science, 2020
Predictive processing (PP) has been repeatedly presented as a unificatory account of perception, action, and cognition. In this paper, we argue that this is premature: As a unifying theory, PP fails to deliver general, simple, homogeneous, and systematic explanations. By examining its current trajectory of development, we conclude that PP remains…
Descriptors: Prediction, Cognitive Processes, Epistemology, Theories

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