Splitting the variance of statistical learning performance: A parametric investigation of exposure duration and transitional probabilities
Ikusi/ Ireki
Data
2016Egilea
Bogaerts, Louisa
Siegelman, Noam
Frost, Ram
Bogaerts, L., Siegelman, N. & Frost, R. Psychon Bull Rev (2016) 23: 1250. https://doi.org/10.3758/s13423-015-0996-z
Laburpena
What determines individuals’ efficacy in detecting
regularities in visual statistical learning? Our theoretical
starting point assumes that the variance in performance of
statistical learning (SL) can be split into the variance related
to efficiency in encoding representations within a modality
and the variance related to the relative computational efficiency
of detecting the distributional properties of the encoded
representations. Using a novel methodology, we dissociated
encoding from higher-order learning factors, by independently
manipulating exposure duration and transitional probabilities
in a stream of visual shapes. Our results show that the
encoding of shapes and the retrieving of their transitional
probabilities are not independent and additive processes, but
interact to jointly determine SL performance. The theoretical
implications of these findings for a mechanistic explanation of
SL are discussed.