Naïve Point Estimation.

The capacity of short-term memory is a key constraint when people make online judgments requiring them to rely on samples retrieved from memory (e.g., Dougherty & Hunter, 2003). In this article, the authors compare 2 accounts of how people use knowledge of statistical distributions to make point est...

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Detalles Bibliográficos
Publicado en:Journal of Experimental Psychology. Learning, Memory & Cognition Vol. 39; no. 3; pp. 782 - 801
Autores principales: Lindskog, Marcus, Winman, Anders, Juslin, Peter
Formato: Artículo
Publicado: American Psychological Association May2013
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: May2013
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      pub: American Psychological Association
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        10.1037/a0029670
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        atl: Naïve Point Estimation.
      aug:
        au:
          Lindskog, Marcus
          Winman, Anders
          Juslin, Peter
        affil: Uppsala University
      su:
        Psychology
        Short-term memory
        Fix-point estimation
        Comparative studies
        Probability theory
        Statistical sampling
      sug:
        subj:
          Psychology
          Marketing Research and Public Opinion Polling
          Short-term memory
          Fix-point estimation
          Comparative studies
          Probability theory
          Statistical sampling
      keyword:
        intuitive statistics
        point estimation
        sampling model
        intuitive statistics
        point estimation
        sampling model
      ab: The capacity of short-term memory is a key constraint when people make online judgments requiring them to rely on samples retrieved from memory (e.g., Dougherty & Hunter, 2003). In this article, the authors compare 2 accounts of how people use knowledge of statistical distributions to make point estimates: either by retrieving precomputed large-sample representations or by retrieving small samples of similar observations post hoc at the time of judgment, as constrained by short-term memory capacity (the naive sampling model: Juslin, Winman, & Hansson, 2007). Results from four experiments support the predictions by the naive sampling model, including that participants sometimes guess values that they, when probed, demonstrably know have the lowest probability of occurring. Experiment 1 also demonstrated the operations of an unpredicted recognition-based inference. Computational modeling also incorporating this process demonstrated that the data from all 4 experiments were better predicted by assuming a post hoc sampling process constrained by short-term memory capacity than by assuming abstraction of large-sample representations of the distribution.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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