Online measurement of learning temporal statistical structure in categorization tasks.

The ability to grasp relevant patterns from a continuous stream of environmental information is called statistical learning. Although the representations that emerge during visual statistical learning (VSL) are well characterized, little is known about how they are formed. We developed a sensitive b...

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Publicado en:Memory & Cognition Vol. 50; no. 7; pp. 1530 - 1546
Autores principales: Sáringer, Szabolcs, Fehér, Ágnes, Sáry, Gyula, Kaposvári, Péter
Formato: Artículo
Publicado: Springer Nature Oct2022
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Oct2022
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      pub: Springer Nature
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        10.3758/s13421-022-01302-5
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        atl: Online measurement of learning temporal statistical structure in categorization tasks.
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          Sáringer, Szabolcs
          Fehér, Ágnes
          Sáry, Gyula
          Kaposvári, Péter
        affil: Department of Physiology, Albert Szent-Györgyi Medical School, Faculty of Medicine, University of Szeged, 10. Dóm tér, 6720, Szeged, Hungary
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        Internet
        Task performance
        Paradigms (Social sciences)
        Motor ability
        Experimental design
        Research methodology
        Regression analysis
        Learning strategies
        Sensory stimulation
        Visual perception
        Statistical models
        Reaction time
      sug:
        subj:
          Internet
          Task performance
          Paradigms (Social sciences)
          Motor ability
          Internet Publishing and Broadcasting and Web Search Portals
          Wired Telecommunications Carriers
          Experimental design
          Research methodology
          Regression analysis
          Learning strategies
          Sensory stimulation
          Visual perception
          Statistical models
          Reaction time
      keyword:
        Anticipation
        Motor learning
        Priming
        Temporal dynamics
        Visual statistical learning
        Anticipation
        Motor learning
        Priming
        Temporal dynamics
        Visual statistical learning
      ab: The ability to grasp relevant patterns from a continuous stream of environmental information is called statistical learning. Although the representations that emerge during visual statistical learning (VSL) are well characterized, little is known about how they are formed. We developed a sensitive behavioral design to characterize the VSL trajectory during ongoing task performance. In sequential categorization tasks, we assessed two previously identified VSL markers: priming of the second predictable image in a pair manifested by a reduced reaction time (RT) and greater accuracy, and the anticipatory effect on the first image revealed by a longer RT. First, in Experiment 1A, we used an adapted paradigm and replicated these VSL markers; however, they appeared to be confounded by motor learning. Next, in Experiment 1B, we confirmed the confounding influence of motor learning. To assess VSL without motor learning, in Experiment 2 we (1) simplified the categorization task, (2) raised the number of subjects and image repetitions, and (3) increased the number of single unpaired images. Using linear mixed-effect modeling and estimated marginal means of linear trends, we found that the RT curves differed significantly between predictable paired and control single images. Further, the VSL curve fitted a logarithmic model, suggesting a rapid learning process. These results suggest that our paradigm in Experiment 2 seems to be a viable online tool to monitor the behavioral correlates of unsupervised implicit VSL.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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