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...
| Publicado en: | Memory & Cognition Vol. 50; no. 7; pp. 1530 - 1546 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Springer Nature
Oct2022
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=159303723&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 159303723 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0090502X MEG jtl: Memory & Cognition issn: 0090502X maglogo: N pubinfo: dt: Oct2022 vid: 50 iid: 7 pid: 237 pub: Springer Nature artinfo: ui: 159303723 10.3758/s13421-022-01302-5 ppf: 1530 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.5MB tig: atl: Online measurement of learning temporal statistical structure in categorization tasks. aug: au: 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 su: 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 refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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