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Marius Barth
Marius Barth
Verified email at uni-koeln.de - Homepage
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papaja: Prepare reproducible APA journal articles with R Markdown (R package version 0.1.1)
F Aust, M Barth
https://doi.org/10.32614/CRAN.package.papaja, 2022
687*2022
tinylabels: Lightweight variable labels
M Barth
https://doi.org/10.32614/CRAN.package.tinylabels, 2020
972020
Distorted estimates of implicit and explicit learning in applications of the process-dissociation procedure to the SRT task
C Stahl, M Barth, H Haider
Consciousness and cognition 37, 27-43, 2015
132015
Assumptions of the process-dissociation procedure are violated in implicit sequence learning.
M Barth, C Stahl, H Haider
Journal of Experimental Psychology: Learning, Memory, and Cognition 45 (4), 641, 2019
92019
Evaluating the robustness of parameter estimates in cognitive models: A meta-analytic review of multinomial processing tree models across the multiverse of estimation methods.
H Singmann, DW Heck, M Barth, E Erdfelder, NR Arnold, F Aust, ...
Psychological Bulletin 150 (8), 965, 2024
22024
A Bayesian and Frequentist multiverse pipeline for MPT models—applications to recognition memory
H Singmann, DW Heck, M Barth, J Groß, BG Kuhlmann
22019
Parallel acquisition of uncorrelated sequences does not provide firm evidence for a modular sequence-learning system
M Barth, C Stahl, H Haider
Journal of Cognition 6 (1), 2023
12023
Toward a Questionnaire to Assess Biology Student Teachers’ Knowledge of the Nature of Scientific Inquiry (NOSI)
CC Wacker, M Barth, C Stahl, K Schlüter
Current Research in Biology Education: Selected Papers from the ERIDOB …, 2022
12022
Parallel acquisition of uncorrelated sequences is hard to find
M Barth, C Stahl
OSF, 2024
2024
Relational EC MPT-Experiment 2 (RR)
KC Bading, K Rothermund, M Barth
OSF, 2023
2023
TeaP 2022-Abstracts of the 64th Conference of Experimental Psychologists
S Malejka, M Barth, H Haider, C Stahl
Tagung experimentell arbeitender Psychologen (TeaP), 2022, Cologne, Germany, 2022
2022
MPTmultiverse: Multiverse analysis of multinomial processing tree models (R package version 0.4-2)
H Singmann, DW Heck, M Barth
https://doi.org/10.32614/CRAN.package.MPTmultiverse, 2020
2020
Measuring Implicit and Explicit Sequence Learning
M Barth
Universität zu Köln, 2018
2018
How Implicit Sequence Learning and Explicit Sequence Knowledge Are Expressed in a Serial Response Time Task
M Barth, C Stahl, H Haider
OSF, 0
Sequence Learning and the Process Dissociation Procedure: How estimates of implicit and explicit knowledge are biased in the absence of associative learning
M Barth, H Haider, C Stahl
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Articles 1–15