dc.contributor.author | Siegelman, Noam | |
dc.contributor.author | Bogaerts, Louisa | |
dc.contributor.author | Frost, Ram | |
dc.date.accessioned | 2017-11-16T13:24:01Z | |
dc.date.available | 2017-11-16T13:24:01Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Siegelman, N., Bogaerts, L. & Frost, R. Behav Res (2017) 49: 418. https://doi.org/10.3758/s13428-016-0719-z | es_ES |
dc.identifier.issn | 1554-351X | |
dc.identifier.uri | http://hdl.handle.net/10810/23506 | |
dc.description | Published online: 4 March 2016 | es_ES |
dc.description.abstract | Most research in statistical learning (SL) has focused
on the mean success rates of participants in detecting
statistical contingencies at a group level. In recent years, however,
researchers have shown increased interest in individual
abilities in SL, either to predict other cognitive capacities or as
a tool for understanding the mechanism underlying SL. Most
if not all of this research enterprise has employed SL tasks that
were originally designed for group-level studies. We argue
that from an individual difference perspective, such tasks are
psychometrically weak, and sometimes even flawed. In particular,
the existing SL tasks have three major shortcomings:
(1) the number of trials in the test phase is often too small (or,
there is extensive repetition of the same targets throughout the
test); (2) a large proportion of the sample performs at chance
level, so that most of the data points reflect noise; and (3) the
test items following familiarization are all of the same type
and an identical level of difficulty. These factors lead to high
measurement error, inevitably resulting in low reliability, and
thereby doubtful validity. Here we present a novel method
specifically designed for the measurement of individual differences
in visual SL. The novel task we offer displays substantially
superior psychometric properties. We report data
regarding the reliability of the task and discuss the importance
of the implementation of such tasks in future research. | es_ES |
dc.description.sponsorship | This article was supported by the Israel Science
Foundation (ISF Grant No. 217/14 awarded to R.F.), and by the
NICHD (Grant No. RO1 HD 067364 awarded to Ken Pugh and R.F.,
and Grant No. PO1-HD 01994 awarded to Haskins Laboratories). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Behavior Research Methods | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | Statistical learning | es_ES |
dc.subject | Individual differences | es_ES |
dc.subject | Psychometrics | es_ES |
dc.title | Measuring individual differences in statistical learning: Current pitfalls and possible solutions | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © Psychonomic Society, Inc. 2016 | es_ES |
dc.relation.publisherversion | https://link.springer.com/journal/13428 | es_ES |
dc.identifier.doi | 10.3758/s13428-016-0719-z | |