Comparing performance in discrete and continuous comparison tasks.

The approximate number system (ANS) theory suggests that all magnitudes, discrete (i.e., number of items) or continuous (i.e., size, density, etc.), are processed by a shared system and comply with Weber's law. The current study reexamined this notion by comparing performance in discrete (comparing...

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Publicado en:Quarterly Journal of Experimental Psychology Vol. 67; no. 5; pp. 899 - 918
Autores principales: Leibovich, Tali, Henik, Avishai
Formato: Artículo
Publicado: Sage Publications Inc. May2014
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Comparing performance in discrete and continuous comparison tasks.
      aug:
        au:
          Leibovich, Tali
          Henik, Avishai
        affil:
          Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
          Department of Psychology and the Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
      su:
        Cognition
        Task performance
        Psychophysics
        Number systems
        Number theory
        Weber-Fechner law
      sug:
        subj:
          Cognition
          Task performance
          Psychophysics
          Number systems
          Number theory
          Weber-Fechner law
      keyword:
        Approximate number system
        Continuous magnitudes.
        Numerical cognition
        Approximate number system
        Continuous magnitudes.
        Numerical cognition
      ab: The approximate number system (ANS) theory suggests that all magnitudes, discrete (i.e., number of items) or continuous (i.e., size, density, etc.), are processed by a shared system and comply with Weber's law. The current study reexamined this notion by comparing performance in discrete (comparing numerosities of dot arrays) and continuous (comparisons of area of squares) tasks. We found that: (a) threshold of discrimination was higher for continuous than for discrete comparisons; (b) while performance in the discrete task complied with Weber's law, performance in the continuous task violated it; and (c) performance in the discrete task was influenced by continuous properties (e.g., dot density, dot cumulative area) of the dot array that were not predictive of numerosities or task relevant. Therefore, we propose that the magnitude processing system (MPS) is actually divided into separate (yet interactive) systems for discrete and continuous magnitude processing. Further subdivisions are discussed. We argue that cooperation between these systems results in a holistic comparison of magnitudes, one that takes into account continuous properties in addition to numerosities. Considering the MPS as two systems opens the door to new and important questions that shed light on both normal and impaired development of the numerical system.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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