Noam Siegelman
Noam Siegelman
Postdoctoral fellow, Haskins Laboratories
Geverifieerd e-mailadres voor yale.edu
Titel
Geciteerd door
Geciteerd door
Jaar
Domain generality versus modality specificity: the paradox of statistical learning
R Frost, BC Armstrong, N Siegelman, MH Christiansen
Trends in cognitive sciences 19 (3), 117-125, 2015
2712015
What predicts successful literacy acquisition in a second language?
R Frost, N Siegelman, A Narkiss, L Afek
Psychological science 24 (7), 1243-1252, 2013
1702013
Statistical learning as an individual ability: Theoretical perspectives and empirical evidence
N Siegelman, R Frost
Journal of memory and language 81, 105-120, 2015
1272015
Measuring individual differences in statistical learning: Current pitfalls and possible solutions
N Siegelman, L Bogaerts, R Frost
Behavior research methods 49 (2), 418-432, 2017
752017
Towards a theory of individual differences in statistical learning
N Siegelman, L Bogaerts, MH Christiansen, R Frost
Philosophical Transactions of the Royal Society B: Biological Sciences 372 …, 2017
752017
Redefining “learning” in statistical learning: What does an online measure reveal about the assimilation of visual regularities?
N Siegelman, L Bogaerts, O Kronenfeld, R Frost
Cognitive science 42, 692-727, 2018
332018
Linguistic entrenchment: Prior knowledge impacts statistical learning performance
N Siegelman, L Bogaerts, A Elazar, J Arciuli, R Frost
Cognition 177, 198-213, 2018
302018
Transposed-letter priming effect in Hebrew in the same–different task
S Kinoshita, D Norris, N Siegelman
The Quarterly Journal of Experimental Psychology 65 (7), 1296-1305, 2012
222012
The advantage of starting big: Learning from unsegmented input facilitates mastery of grammatical gender in an artificial language
N Siegelman, I Arnon
Journal of Memory and Language 85, 60-75, 2015
202015
Splitting the variance of statistical learning performance: A parametric investigation of exposure duration and transitional probabilities
L Bogaerts, N Siegelman, R Frost
Psychonomic bulletin & review 23 (4), 1250-1256, 2016
182016
Is the Hebb repetition task a reliable measure of individual differences in sequence learning?
L Bogaerts, N Siegelman, T Ben-Porat, R Frost
The Quarterly Journal of Experimental Psychology, 1-35, 2017
72017
What exactly is learned in visual statistical learning? Insights from Bayesian modeling
N Siegelman, L Bogaerts, BC Armstrong, R Frost
Cognition 192, 104002, 2019
32019
Using information-theoretic measures to characterize the structure of the writing system: the case of orthographic-phonological regularities in English
N Siegelman, DM Kearns, JG Rueckl
Behavior Research Methods, 1-21, 2020
12020
The role of information in visual word recognition: A perceptually-constrained connectionist account
RG Alhama, N Siegelman, R Frost, BC Armstrong
the 41st Annual Meeting of the Cognitive Science Society (CogSci 2019), 83-89, 2019
12019
Statistical learning abilities and their relation to language
N Siegelman
Language and Linguistics Compass 14 (3), e12365, 2020
2020
What Determines Visual Statistical Learning Performance? Insights From Information Theory
N Siegelman, L Bogaerts, R Frost
Cognitive Science 43 (12), e12803, 2019
2019
Statistical learning shapes proficient reading: A cross-linguistic information-theoretic study
RG Alhama, N Siegelman, R Frost, BC Armstrong
the International Conference on Interdisciplinary Advances in Statistical …, 2019
2019
A perceptually-constrained visual word recognition model
RG Alhama, N Siegelman, R Frost, BC Armstrong
Architectures and Mechanisms for Language Processing (AMLaP 2019), 2019
2019
Prediction and uncertainty in an artificial language.
T Linzen, N Siegelman, L Bogaerts
CogSci, 2017
2017
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Artikelen 1–19