An evolutionary account of intermodality differences in statistical learning
Data
2021Egilea
Ordin, Mikhail
Polyanskaya, Leona
Samuel, Arthur G.
Ordin, M., Polyanskaya, L. and Samuel, A.G. (2021), An evolutionary account of intermodality differences in statistical learning. Ann. N.Y. Acad. Sci., 1486: 76-89. https://doi.org/10.1111/nyas.14502
Laburpena
The cognitive mechanisms underlying statistical learning are engaged for the purposes of speech processing and
language acquisition. However, these mechanisms are shared by a wide variety of species that do not possess the
language faculty.Moreover, statistical learning operates across domains, including nonlinguistic material. Ancient
mechanisms for segmenting continuous sensory input into discrete constituents have evolved for general-purpose
segmentation of the environment and been readopted for processing linguistic input. Linguistic input provides a
rich set of cues for the boundaries between sequential constituents. Such input engages a wider variety of more
specializedmechanisms operating on these language-specific cues, thus potentially reducing the role of conditional
statistics in tokenizing a continuous linguistic stream. We provide an explicit within-subject comparison of the
utility of statistical learning in language versus nonlanguage domains across the visual and auditorymodalities. The
results showed that in the auditory modality statistical learning ismore efficient with speech-like input, while in the
visual modality efficiency is higher with nonlanguage input.We suggest that the speech faculty has been important
for individual fitness for an extended period, leading to the adaptation of statistical learningmechanisms for speech
processing. This is not the case in the visual modality, in which linguistic material presents a less ecological type of
sensory input.