Benefits of multinomial processing tree models with discrete and continuous variables in memory research: an alternative modeling proposal to Juola et al. (2019).

Signal detection theory (SDT) and two-high threshold models (2HT) are often used to analyze accuracy data in recognition memory paradigms. However, when reaction times (RTs) and/or confidence levels (CLs) are also measured, they usually are analyzed separately or not at all as dependent variables (D...

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Publicado en:Memory & Cognition Vol. 52; no. 4; pp. 793 - 826
Autores principales: Gutkin, Anahí, Suero, Manuel, Botella, Juan, Juola, James F.
Formato: Artículo
Publicado: Springer Nature May2024
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Benefits of multinomial processing tree models with discrete and continuous variables in memory research: an alternative modeling proposal to Juola et al. (2019).
      aug:
        au:
          Gutkin, Anahí
          Suero, Manuel
          Botella, Juan
          Juola, James F.
        affil:
          https://ror.org/01rdrb571 Department of Psychological Methods, Philipps-Universität Marburg, Marburg, Germany
          https://ror.org/01cby8j38 Department of Social Psychology and Methodology, Universidad Autónoma de Madrid, Madrid, Spain
          https://ror.org/001tmjg57 Department of Social Psychology, University of Kansas, Lawrence, Kansas, USA
      su:
        Recognition (Psychology)
        Confidence
        Psychology
        Memory
        Statistical models
        Conceptual models
        Psychophysics
        Experimental design
        Mathematical models
        Reaction time
        Theory
      sug:
        subj:
          Recognition (Psychology)
          Confidence
          Psychology
          Memory
          Statistical models
          Conceptual models
          Psychophysics
          Experimental design
          Mathematical models
          Reaction time
          Theory
      keyword:
        Confidence ratings
        Multinomial processing tree
        Recognition memory models
        Confidence ratings
        Multinomial processing tree
        Recognition memory models
      ab: Signal detection theory (SDT) and two-high threshold models (2HT) are often used to analyze accuracy data in recognition memory paradigms. However, when reaction times (RTs) and/or confidence levels (CLs) are also measured, they usually are analyzed separately or not at all as dependent variables (DVs). We propose a new approach to include these variables based on multinomial processing tree models for discrete and continuous variables (MPT-DC) with the aim to compare fits of SDT and 2HT models. Using Juola et al.'s (2019, Memory & Cognition, 47[4], 855–876) data we have found that including CLs and RTs reduces the standard errors of parameter estimates and accounts for interactions among accuracy, CLs, and RTs that classical versions of SDT and 2HT models do not. In addition, according to the simulations, there is an increase in the proportion of correct model selections when relevant DV are included. We highlight the methodological and substantive advantages of MPT-DC in the disentanglement of contributing processes in recognition memory.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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