Hierarchical Multinomial Modeling Approaches

Arnold, Nina R.; Bayen, Ute J.; Smith, Rebekah E.

doi:10.1027/1618-3169/a000287

Cited by 24 publications

(9 citation statements)

References 45 publications

(115 reference statements)

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“…These tasks likely overloaded WM and executive resources, particularly when participants were required to inhibit stereotypic sequences (e.g., 1-2-3, 2-4-6) while monitoring their output to comply with the randomness condition ( Baddeley et al, 1998 ). These findings are consistent with prior research indicating that poor PM performance is linked to impaired WM ( Arnold et al, 2015 ; Rose et al, 2010 ), planning (e.g., Shum et al, 2013 ), inhibition, or task-switching abilities (e.g., Schnitzspahn et al, 2013 ).…”

Section: Discussionsupporting

confidence: 91%

How Does Performing Demanding Activities Influence Prospective Memory? A Systematic Review

Matos¹,

Pereira²,

Albuquerque³

et al. 2020

ACP

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This paper is the first systematic review on the role of ongoing task load in prospective remembering, which was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). Forty articles published between 1995 and 2020 were included. They evaluated prospective memory (PM) performance (i.e., the ability to remember to execute a delayed intention) in adult samples aged between 19 and 50 years old when the PM cue appeared under cognitively demanding conditions. The results revealed that people are more likely to fail to remember to perform a delayed intention at the appropriate circumstances or time in the future when their cognitive resources are taxed by demanding ongoing activities. We conclude the review by highlighting that the degree of working memory and executive resources seems to account for some of the discrepant findings and by proposing directions for future research.

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Section: Discussionsupporting

confidence: 91%

How Does Performing Demanding Activities Influence Prospective Memory? A Systematic Review

Matos¹,

Pereira²,

Albuquerque³

et al. 2020

ACP

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“…These findings are consistent with previous research which has shown that state and trait rumination are related to impairments in working memory, inhibition and set-shifting (De Lissnyder et al, 2010;De Lissnyder et al, 2011;Watkins & Brown, 2002). While all these cognitive processes are involved in PM (Arnold et al, 2015;Kliegel et al, 2008), to our knowledge this is the first study to show a specific relationship between state rumination and impaired PM cue detection, and trait brooding and impaired response times to PM cues.…”

Section: Discussionsupporting

confidence: 92%

Rumination and Impaired Prospective Memory

Stein

Morys-Carter

Hinkley

2018

The Journal of General Psychology

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Prospective memory (PM), remembering to remember, is crucial to everyday functioning. Understanding factors associated with PM impairments is thus important. One likely factor is rumination: a common cognitive process comprising repetitive self-focused thoughts. We investigated whether rumination is associated with impaired PM, and whether any associated impairment is exacerbated with negative stimuli. A sentence-rating task with sentences varying in valence was used with embedded PM cues in a non-clinical sample (N = 60). State rumination, two trait rumination subtypes (reflective pondering and brooding), and mood were measured in relation to PM cue detection and response times. Results showed that state rumination was associated with impaired PM cue detection and slower response times to PM cues embedded in negative sentences (not positive or neutral). Trait brooding (not reflective pondering) was associated with slower PM response times. These findings indicate that state rumination and trait brooding are associated with dissociable PM impairments.

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“…In previous analyses, parameter estimates for the beta-MPT model were often similar to those for the latent-trait MPT model (e.g., Arnold, Bayen, & Smith, 2015 ). Table 1 shows the results of both analyses for the retrieval condition from Arnold et al ( 2013 ).…”

Section: Treebugsmentioning

confidence: 58%

TreeBUGS: An R package for hierarchical multinomial-processing-tree modeling

2017

Self Cite

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Multinomial processing tree (MPT) models are a class of measurement models that account for categorical data by assuming a finite number of underlying cognitive processes. Traditionally, data are aggregated across participants and analyzed under the assumption of independently and identically distributed observations. Hierarchical Bayesian extensions of MPT models explicitly account for participant heterogeneity by assuming that the individual parameters follow a continuous hierarchical distribution. We provide an accessible introduction to hierarchical MPT modeling and present the user-friendly and comprehensive R package TreeBUGS, which implements the two most important hierarchical MPT approaches for participant heterogeneity—the beta-MPT approach (Smith & Batchelder, Journal of Mathematical Psychology 54:167-183, 2010) and the latent-trait MPT approach (Klauer, Psychometrika 75:70-98, 2010). TreeBUGS reads standard MPT model files and obtains Markov-chain Monte Carlo samples that approximate the posterior distribution. The functionality and output are tailored to the specific needs of MPT modelers and provide tests for the homogeneity of items and participants, individual and group parameter estimates, fit statistics, and within- and between-subjects comparisons, as well as goodness-of-fit and summary plots. We also propose and implement novel statistical extensions to include continuous and discrete predictors (as either fixed or random effects) in the latent-trait MPT model.

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Hierarchical Multinomial Modeling Approaches

Cited by 24 publications

References 45 publications

How Does Performing Demanding Activities Influence Prospective Memory? A Systematic Review

How Does Performing Demanding Activities Influence Prospective Memory? A Systematic Review

Rumination and Impaired Prospective Memory

TreeBUGS: An R package for hierarchical multinomial-processing-tree modeling

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