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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
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
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
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
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
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
Luthra, Sahil; You, Heejo; Rueckl, Jay G.; Magnuson, James S. – Cognitive Science, 2020
Visual word recognition is facilitated by the presence of "orthographic neighbors" that mismatch the target word by a single letter substitution. However, researchers typically do not consider "where" neighbors mismatch the target. In light of evidence that some letter positions are more informative than others, we investigate…
Descriptors: Visual Stimuli, Word Recognition, Orthographic Symbols, Alphabets
Reichle, Erik D.; Yu, Lili – Cognitive Science, 2018
Our understanding of the cognitive processes involved in reading has been advanced by computational models that simulate those processes (e.g., see Reichle, 2015). Unfortunately, most of these models have been developed to explain the reading of English and other alphabetic languages, with relatively fewer efforts to examine whether or not the…
Descriptors: Cognitive Processes, Reading Processes, Chinese, Computation
Kangasrääsiö, Antti; Jokinen, Jussi P. P.; Oulasvirta, Antti; Howes, Andrew; Kaski, Samuel – Cognitive Science, 2019
This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional…
Descriptors: Inferences, Computation, Cognitive Processes, Models
Lake, Brenden M.; Lawrence, Neil D.; Tenenbaum, Joshua B. – Cognitive Science, 2018
Both scientists and children make important structural discoveries, yet their computational underpinnings are not well understood. Structure discovery has previously been formalized as probabilistic inference about the right structural form--where form could be a tree, ring, chain, grid, etc. (Kemp & Tenenbaum, 2008). Although this approach…
Descriptors: Discovery Learning, Intuition, Bias, Computation
Lazaridou, Angeliki; Marelli, Marco; Baroni, Marco – Cognitive Science, 2017
By the time they reach early adulthood, English speakers are familiar with the meaning of thousands of words. In the last decades, computational simulations known as distributional semantic models (DSMs) have demonstrated that it is possible to induce word meaning representations solely from word co-occurrence statistics extracted from a large…
Descriptors: English, Language Acquisition, Semantics, Models
Jarecki, Jana B.; Meder, Björn; Nelson, Jonathan D. – Cognitive Science, 2018
Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference…
Descriptors: Classification, Conditioning, Inferences, Novelty (Stimulus Dimension)
Pritchard, Stephen C.; Coltheart, Max; Marinus, Eva; Castles, Anne – Cognitive Science, 2018
The self-teaching hypothesis describes how children progress toward skilled sight-word reading. It proposes that children do this via phonological recoding with assistance from contextual cues, to identify the target pronunciation for a novel letter string, and in so doing create an opportunity to self-teach new orthographic knowledge. We present…
Descriptors: Computation, Models, Independent Study, Reading
Calamaro, Shira; Jarosz, Gaja – Cognitive Science, 2015
Phonological rules create alternations in the phonetic realizations of related words. These rules must be learned by infants in order to identify the phonological inventory, the morphological structure, and the lexicon of a language. Recent work proposes a computational model for the learning of one kind of phonological alternation, allophony…
Descriptors: Language Acquisition, Phonology, Models, Indo European Languages
Chen, Qi; Mirman, Daniel – Cognitive Science, 2015
Computational modeling and eye-tracking were used to investigate how phonological and semantic information interact to influence the time course of spoken word recognition. We extended our recent models (Chen & Mirman, 2012; Mirman, Britt, & Chen, 2013) to account for new evidence that competition among phonological neighbors influences…
Descriptors: Phonology, Semantics, Interaction, Eye Movements
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