Trends in Cognitive Sciences
OpinionDomain generality versus modality specificity: the paradox of statistical learning
Section snippets
The promise of statistical learning
Humans and other animals are constantly bombarded by streams of sensory information. SL (the extraction of distributional properties from sensory input across time and space) provides a mechanism by which cognitive systems discover the underlying structure of such stimulation. Therefore, SL has a key role in the detection of regularities and quasi-regularities in the environment, results in discrimination, categorization, and segmentation of continuous information, allows prediction of upcoming
Domain generality versus domain specificity
Originally, domain generality was invoked to argue against language modularity; therefore, its definition implicitly implied ‘something that is not language specific’. Consequently, within this context, ‘domain’ implies a range of stimuli that share physical and structural properties (e.g., spoken words, musical tones, or tactile input), whereas ‘generality’ is taken to be ‘something that does not operate along principles restricted to language learning’. However, this approach says what domain
Towards a mechanistic model of SL
Our approach construes SL as involving a set of domain-general neurobiological mechanisms for learning, representation, and processing that detect and encode a range of distributional properties within different modalities or types of input (see [13], for a related approach). Crucially, however, in our account, these principles are not instantiated by a unitary learning system but, rather, by separate neural networks in different cortical areas (e.g., visual, auditory, and somatosensory
The neurobiological bases of SL
Recent neuroimaging studies have shown that statistical regularities of visual shapes result in activation in higher-level visual networks (e.g., lateral occipital cortex and inferior temporal gyrus 40, 41), whereas statistical regularities in auditory stimuli result in activation in analogous auditory networks (e.g., left temporal and inferior parietal cortices; frontotemporal networks including portions of the inferior frontal gyrus, motor areas involved in speech production [42]; and the
Individual and group differences in SL
The proposed framework leads us to argue that individual differences provide key evidence for understanding the mechanism of SL. In past work, it has often been assumed that individual variance in implicit learning tasks is significantly smaller than that of explicit learning (e.g., [58]). Consequently, the source of variability in performance in SL has been largely overlooked, and has led researchers to focus on average success rate (but see 19, 59, 60, 61).
However, in the context of SL,
Concluding remarks
Here, we offer a novel theoretical perspective on SL that considers computational and neurobiological constraints. Previous work on SL offered a possible cognitive mechanistic account of how distributional properties are computed, with explicit demonstrations being provided only within the domain of language 65, 67. Our perspective has the advantage of providing a unifying neurobiological account of SL across domains, modalities, neural, and cognitive investigations, and cross-species studies,
Acknowledgments
This paper was supported by The Israel Science Foundation (Grant 217/14 awarded to R.F.), by the NICHD (RO1 HD 067364 awarded to Ken Pugh and R.F., and PO1 HD 01994 awarded to Haskins Laboratories), and by a Marie Curie IIF award (PIIF-GA-2013-627784 awarded to B.C.A.).
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