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Paape, Dario; Avetisyan, Serine; Lago, Sol; Vasishth, Shravan – Cognitive Science, 2021
We present computational modeling results based on a self-paced reading study investigating number attraction effects in Eastern Armenian. We implement three novel computational models of agreement attraction in a Bayesian framework and compare their predictive fit to the data using k-fold cross-validation. We find that our data are better…
Descriptors: Computational Linguistics, Indo European Languages, Grammar, Bayesian Statistics
Dorambari, Diedon – International Journal of Education and Practice, 2022
This study examined whether instructional humor (IH) was not just another type of seductive detail when covariates such as humor pre-disposition, prior-knowledge, and working memory capacity were controlled. Participants were students (N = 228) from universities who were randomly assigned two stimuli conditions in the classic experimental design.…
Descriptors: Humor, Multimedia Instruction, Prior Learning, Short Term Memory
Evans, William S.; Cavanaugh, Robert; Quique, Yina; Boss, Emily; Starns, Jeffrey J.; Hula, William D. – Journal of Speech, Language, and Hearing Research, 2021
Purpose: The purpose of this study was to develop and pilot a novel treatment framework called "BEARS" (Balancing Effort, Accuracy, and Response Speed). People with aphasia (PWA) have been shown to maladaptively balance speed and accuracy during language tasks. BEARS is designed to train PWA to balance speed-accuracy trade-offs and…
Descriptors: Accuracy, Semantics, Aphasia, Reaction Time
Davidson, Lisa; Wilson, Colin – Second Language Research, 2016
Recent research has shown that speakers are sensitive to non-contrastive phonetic detail present in nonnative speech (e.g. Escudero et al. 2012; Wilson et al. 2014). Difficulties in interpreting and implementing unfamiliar phonetic variation can lead nonnative speakers to modify second language forms by vowel epenthesis and other changes. These…
Descriptors: Second Language Learning, Acoustics, Phonetics, Speech
Gelman, Andrew; Hill, Jennifer; Yajima, Masanao – Journal of Research on Educational Effectiveness, 2012
Applied researchers often find themselves making statistical inferences in settings that would seem to require multiple comparisons adjustments. We challenge the Type I error paradigm that underlies these corrections. Moreover we posit that the problem of multiple comparisons can disappear entirely when viewed from a hierarchical Bayesian…
Descriptors: Intervals, Comparative Analysis, Inferences, Error Patterns
Williams, Joseph J.; Griffiths, Thomas L. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2013
Errors in detecting randomness are often explained in terms of biases and misconceptions. We propose and provide evidence for an account that characterizes the contribution of the inherent statistical difficulty of the task. Our account is based on a Bayesian statistical analysis, focusing on the fact that a random process is a special case of…
Descriptors: Experimental Psychology, Bias, Misconceptions, Statistical Analysis

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