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Nina R Arnold

Showing results (1-10 of 10) with videos related to

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Quarterly Journal of Experimental Psychology (2006)|March 23, 2017
Item-method directed forgetting and working memory capacity: A hierarchical multinomial modeling approachIvan Marevic, Nina R Arnold, Jan Rummel
Behavior Research Methods|April 5, 2017
TreeBUGS: An R package for hierarchical multinomial-processing-tree modelingDaniel W Heck, Nina R Arnold, Denis Arnold
Psychological Research|October 5, 2014
Empirical validation of the diffusion model for recognition memory and a comparison of parameter-estimation methodsNina R Arnold, Arndt Bröder, Ute J Bayen
Experimental Psychology|March 26, 2015
Hierarchical Multinomial Modeling Approaches: An Application to Prospective Memory and Working MemoryNina R Arnold, Ute J Bayen, Rebekah E Smith
Memory (Hove, England)|October 23, 2014
Is prospective memory related to depression and anxiety? A hierarchical MPT modelling approachNina R Arnold, Ute J Bayen, Mateja F Böhm
Cognition & Emotion|July 13, 2021
A multinomial modelling approach to face identity recognition during instructed threatNina R Arnold, Hernán González Cruz, Sabine Schellhaas, et al.
Psychonomic Bulletin & Review|December 11, 2012
Hierarchical modeling of contingency-based source monitoring: a test of the probability-matching accountNina R Arnold, Ute J Bayen, Beatrice G Kuhlmann, et al.
Memory & Cognition|September 5, 2024
Connecting working and long-term memory: Bayesian-hierarchical multinomial model-based analyses reveal storage next to retrieval differencesCarolin Streitberger, Beatrice G Kuhlmann, Matt E Meier, et al.
Experimental Psychology|May 18, 2019
Testing Hypotheses About Binding in Context Memory With a Hierarchical Multinomial Modeling ApproachNina R Arnold, Daniel W Heck, Arndt Bröder, et al.
Psychological Bulletin|June 27, 2024
Evaluating the robustness of parameter estimates in cognitive models: A meta-analytic review of multinomial processing tree models across the multiverse of estimation methodsHenrik Singmann, Daniel W Heck, Marius Barth, et al.
Pageof 1

Showing results (1-10 of 10) with videos related to

Sort By:
Pageof 1
Quarterly Journal of Experimental Psychology (2006)|March 23, 2017
Item-method directed forgetting and working memory capacity: A hierarchical multinomial modeling approachIvan Marevic, Nina R Arnold, Jan Rummel
Behavior Research Methods|April 5, 2017
TreeBUGS: An R package for hierarchical multinomial-processing-tree modelingDaniel W Heck, Nina R Arnold, Denis Arnold
Psychological Research|October 5, 2014
Empirical validation of the diffusion model for recognition memory and a comparison of parameter-estimation methodsNina R Arnold, Arndt Bröder, Ute J Bayen
Experimental Psychology|March 26, 2015
Hierarchical Multinomial Modeling Approaches: An Application to Prospective Memory and Working MemoryNina R Arnold, Ute J Bayen, Rebekah E Smith
Memory (Hove, England)|October 23, 2014
Is prospective memory related to depression and anxiety? A hierarchical MPT modelling approachNina R Arnold, Ute J Bayen, Mateja F Böhm
Cognition & Emotion|July 13, 2021
A multinomial modelling approach to face identity recognition during instructed threatNina R Arnold, Hernán González Cruz, Sabine Schellhaas, et al.
Psychonomic Bulletin & Review|December 11, 2012
Hierarchical modeling of contingency-based source monitoring: a test of the probability-matching accountNina R Arnold, Ute J Bayen, Beatrice G Kuhlmann, et al.
Memory & Cognition|September 5, 2024
Connecting working and long-term memory: Bayesian-hierarchical multinomial model-based analyses reveal storage next to retrieval differencesCarolin Streitberger, Beatrice G Kuhlmann, Matt E Meier, et al.
Experimental Psychology|May 18, 2019
Testing Hypotheses About Binding in Context Memory With a Hierarchical Multinomial Modeling ApproachNina R Arnold, Daniel W Heck, Arndt Bröder, et al.
Psychological Bulletin|June 27, 2024
Evaluating the robustness of parameter estimates in cognitive models: A meta-analytic review of multinomial processing tree models across the multiverse of estimation methodsHenrik Singmann, Daniel W Heck, Marius Barth, et al.
Pageof 1