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Different levels of statistical learning - Hidden potentials of sequence learning tasks

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  • Emese Szegedi-Hallgató
  • Karolina Janacsek
  • Dezso Nemeth

Abstract

In this paper, we reexamined the typical analysis methods of a visuomotor sequence learning task, namely the ASRT task (J. H. Howard & Howard, 1997). We pointed out that the current analysis of data could be improved by paying more attention to pre-existing biases (i.e. by eliminating artifacts by using new filters) and by introducing a new data grouping that is more in line with the task’s inherent statistical structure. These suggestions result in more types of learning scores that can be quantified and also in purer measures. Importantly, the filtering method proposed in this paper also results in higher individual variability, possibly indicating that it had been masked previously with the usual methods. The implications of our findings relate to other sequence learning tasks as well, and opens up opportunities to study different types of implicit learning phenomena.

Suggested Citation

  • Emese Szegedi-Hallgató & Karolina Janacsek & Dezso Nemeth, 2019. "Different levels of statistical learning - Hidden potentials of sequence learning tasks," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-32, September.
    • Handle: RePEc:plo:pone00:0221966
    DOI: 10.1371/journal.pone.0221966
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    1. Ilana J. Bennett & James H. Howard & Darlene V. Howard, 2007. "Age-Related Differences in Implicit Learning of Subtle Third-Order Sequential Structure," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 62(2), pages 98-103.
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