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Soltanlou, Mojtaba; Coldea, Andra; Artemenko, Christina; Ehlis, Ann-Christine; Fallgatter, Andreas J.; Nuerk, Hans-Christoph; Dresler, Thomas – Mind, Brain, and Education, 2019
It is under debate whether the neural representation of numbers and letters might rely on distinct neural correlates, or on a mostly shared neural network. In the present study, a total of 47 children in fifth grade (Experiment 1) and sixth grade (Experiment 2) simply copied numbers and letters on a touch screen while brain activation changes were…
Descriptors: Brain Hemisphere Functions, Spectroscopy, Numbers, Alphabets
Kleinberg, Bennett; Warmelink, Lara; Arntz, Arnoud; Verschuere, Bruno – Applied Cognitive Psychology, 2018
Verbal deception detection has gained momentum as a technique to tell truth-tellers from liars. At the same time, researchers' degrees of freedom make it hard to assess the robustness of effects. Replication research can help evaluate how reproducible an effect is. We present the first replication in verbal deception research whereby ferry…
Descriptors: Deception, Credibility, Verbal Communication, Bayesian Statistics
Casabianca, Jodi M.; Lewis, Charles – Journal of Educational and Behavioral Statistics, 2018
The null hypothesis test used in differential item functioning (DIF) detection tests for a subgroup difference in item-level performance--if the null hypothesis of "no DIF" is rejected, the item is flagged for DIF. Conversely, an item is kept in the test form if there is insufficient evidence of DIF. We present frequentist and empirical…
Descriptors: Test Bias, Hypothesis Testing, Bayesian Statistics, Statistical Analysis
Ames, Allison J. – Measurement: Interdisciplinary Research and Perspectives, 2018
Bayesian item response theory (IRT) modeling stages include (a) specifying the IRT likelihood model, (b) specifying the parameter prior distributions, (c) obtaining the posterior distribution, and (d) making appropriate inferences. The latter stage, and the focus of this research, includes model criticism. Choice of priors with the posterior…
Descriptors: Bayesian Statistics, Item Response Theory, Statistical Inference, Prediction
Yuxi Qiu – ProQuest LLC, 2018
Research in education has become increasingly reliant on statistical modeling frameworks to be reflective of the subject matter, to accurately assess what students know and can do, to assist instructors with curriculum design by supplementing informative feedback, and to support policy-makers when making evidence-based decisions. A common feature…
Descriptors: Goodness of Fit, Learning Theories, Bayesian Statistics, Curriculum Design
Hayes, Brett K.; Hawkins, Guy E.; Newell, Ben R. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2016
Four experiments examined the locus of impact of causal knowledge on consideration of alternative hypotheses in judgments under uncertainty. Two possible loci were examined; overcoming neglect of the alternative when developing a representation of a judgment problem and improving utilization of statistics associated with the alternative…
Descriptors: Knowledge Level, Evaluative Thinking, Influences, Bias
Rafferty, Anna N.; Jansen, Rachel A.; Griffiths, Thomas L. – Cognitive Science, 2020
Online educational technologies offer opportunities for providing individualized feedback and detailed profiles of students' skills. Yet many technologies for mathematics education assess students based only on the correctness of either their final answers or responses to individual steps. In contrast, examining the choices students make for how…
Descriptors: Computer Assisted Testing, Mathematics Tests, Mathematics Skills, Student Evaluation
Ossewaarde, Roelant; Jonkers, Roel; Jalvingh, Fedor; Bastiaanse, Roelien – Journal of Speech, Language, and Hearing Research, 2020
Purpose: Corpus analyses of spontaneous language fragments of varying length provide useful insights in the language change caused by brain damage, such as caused by some forms of dementia. Sample size is an important experimental parameter to consider when designing spontaneous language analyses studies. Sample length influences the confidence…
Descriptors: Speech Communication, Dementia, Computational Linguistics, Neurological Impairments
Cutter, Michael G.; Martin, Andrea E.; Sturt, Patrick – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2020
We investigated whether readers use the low-level cue of proper noun capitalization in the parafovea to infer syntactic category, and whether this results in an early update of the representation of a sentence's syntactic structure. Participants read sentences containing either a subject relative or object relative clause, in which the relative…
Descriptors: Nouns, Phrase Structure, Syntax, Eye Movements
Mao, Ye; Marwan, Samiha; Price, Thomas W.; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2020
Modeling student learning processes is highly complex since it is influenced by many factors such as motivation and learning habits. The high volume of features and tools provided by computer-based learning environments confounds the task of tracking student knowledge even further. Deep Learning models such as Long-Short Term Memory (LSTMs) and…
Descriptors: Time, Models, Artificial Intelligence, Bayesian Statistics
Xie, Xuanqian; Sinclair, Alison; Dendukuri, Nandini – Research Synthesis Methods, 2017
Background: "Streptococcus pneumoniae" (SP) pneumonia is often treated empirically as diagnosis is challenging because of the lack of a perfect test. Using BinaxNOW-SP, a urinary antigen test, as an add-on to standard cultures may not only increase diagnostic yield but also increase costs. Objective: To estimate the sensitivity and…
Descriptors: Accuracy, Cost Effectiveness, Medical Services, Diseases
Rehder, Bob – Cognitive Science, 2017
This article assesses how people reason with categories whose features are related in causal cycles. Whereas models based on causal graphical models (CGMs) have enjoyed success modeling category-based judgments as well as a number of other cognitive phenomena, CGMs are only able to represent causal structures that are acyclic. A number of new…
Descriptors: Abstract Reasoning, Logical Thinking, Causal Models, Graphs
Page, Robert; Satake, Eiki – Journal of Education and Learning, 2017
While interest in Bayesian statistics has been growing in statistics education, the treatment of the topic is still inadequate in both textbooks and the classroom. Because so many fields of study lead to careers that involve a decision-making process requiring an understanding of Bayesian methods, it is becoming increasingly clear that Bayesian…
Descriptors: Probability, Bayesian Statistics, Hypothesis Testing, Statistical Inference
Kim, Nana; Bolt, Daniel M. – Educational and Psychological Measurement, 2021
This paper presents a mixture item response tree (IRTree) model for extreme response style. Unlike traditional applications of single IRTree models, a mixture approach provides a way of representing the mixture of respondents following different underlying response processes (between individuals), as well as the uncertainty present at the…
Descriptors: Item Response Theory, Response Style (Tests), Models, Test Items
Starns, Jeffrey J.; Cohen, Andrew L.; Bosco, Cara; Hirst, Jennifer – Applied Cognitive Psychology, 2019
We tested a method for solving Bayesian reasoning problems in terms of spatial relations as opposed to mathematical equations. Participants completed Bayesian problems in which they were given a prior probability and two conditional probabilities and were asked to report the posterior odds. After a pretraining phase in which participants completed…
Descriptors: Visualization, Bayesian Statistics, Problem Solving, Probability