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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
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Cheng, Peter C.-H.; van Genuchten, Erlijn – Cognitive Science, 2018
Individual differences in the strategies that control sequential behavior were investigated in an experiment in which participants memorized sentences and then wrote them by hand, in a non-cursive style. Thirty-two participants each wrote eight sentences, which had hierarchical structures with five levels. The dataset included over 31,000 letters.…
Descriptors: Sentences, Short Term Memory, Cues, Stimuli
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Evans, Nathan J.; Steyvers, Mark; Brown, Scott D. – Cognitive Science, 2018
Understanding individual differences in cognitive performance is an important part of understanding how variations in underlying cognitive processes can result in variations in task performance. However, the exploration of individual differences in the components of the decision process--such as cognitive processing speed, response caution, and…
Descriptors: Cognitive Ability, Models, Individual Differences, Cognitive Processes
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Lloyd, Kevin; Sanborn, Adam; Leslie, David; Lewandowsky, Stephan – Cognitive Science, 2019
Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the…
Descriptors: Short Term Memory, Bayesian Statistics, Cognitive Ability, Individual Differences
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Siegelman, Noam; Bogaerts, Louisa; Kronenfeld, Ofer; Frost, Ram – Cognitive Science, 2018
From a theoretical perspective, most discussions of statistical learning (SL) have focused on the possible "statistical" properties that are the object of learning. Much less attention has been given to defining what "learning" is in the context of "statistical learning." One major difficulty is that SL research has…
Descriptors: Statistics, Learning Processes, Visual Learning, Learning Modalities
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Thomas, Michael S. C.; Forrester, Neil A.; Ronald, Angelica – Cognitive Science, 2016
In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such…
Descriptors: Cognitive Development, Artificial Intelligence, Networks, Models
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Patil, Umesh; Hanne, Sandra; Burchert, Frank; De Bleser, Ria; Vasishth, Shravan – Cognitive Science, 2016
Individuals with agrammatic Broca's aphasia experience difficulty when processing reversible non-canonical sentences. Different accounts have been proposed to explain this phenomenon. The Trace Deletion account (Grodzinsky, 1995, 2000, 2006) attributes this deficit to an impairment in syntactic representations, whereas others (e.g., Caplan,…
Descriptors: Aphasia, Language Processing, Sentences, Language Impairments
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Navarro, Daniel J.; Dry, Matthew J.; Lee, Michael D. – Cognitive Science, 2012
Inductive generalization, where people go beyond the data provided, is a basic cognitive capability, and it underpins theoretical accounts of learning, categorization, and decision making. To complete the inductive leap needed for generalization, people must make a key "sampling" assumption about how the available data were generated.…
Descriptors: Logical Thinking, Generalization, Sampling, Learning
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Weaver, Rhiannon – Cognitive Science, 2008
Model validation in computational cognitive psychology often relies on methods drawn from the testing of theories in experimental physics. However, applications of these methods to computational models in typical cognitive experiments can hide multiple, plausible sources of variation arising from human participants and from stochastic cognitive…
Descriptors: Models, Prediction, Cognitive Psychology, Computation
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Jones, Gary; Gobet, Fernand; Pine, Julian M. – Cognitive Science, 2008
Increasing working memory (WM) capacity is often cited as a major influence on children's development and yet WM capacity is difficult to examine independently of long-term knowledge. A computational model of children's nonword repetition (NWR) performance is presented that independently manipulates long-term knowledge and WM capacity to determine…
Descriptors: Short Term Memory, Computer Simulation, Child Development, Models
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Shiffrin, Richard M.; Lee, Michael D.; Kim, Woojae; Wagenmakers, Eric-Jan – Cognitive Science, 2008
This article reviews current methods for evaluating models in the cognitive sciences, including theoretically based approaches, such as Bayes factors and minimum description length measures; simulation approaches, including model mimicry evaluations; and practical approaches, such as validation and generalization measures. This article argues…
Descriptors: Bayesian Statistics, Generalization, Sciences, Models