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

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.

SUBMITTER: Szegedi-Hallgato E

PROVIDER: S-EPMC6752858 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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

Szegedi-Hallgató Emese E Janacsek Karolina K Nemeth Dezso D

PloS one 20190919 9

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 quan ...[more]

PMID: 31536512

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