The long road of statistical learning research: past, present and future

Published 21 November 2016 http://rstb.royalsocietypublishing.org/content/372/1711/20160047http://rstb.royalsocietypublishing.org/content/372/1711/20160047This paper was supported by the Israel Science Foundation (grant no. 217/14 awarded to R.F.), by the National Institute of Child Health and Human Development (RO1 HD 067364 awarded to Ken Pugh and R.F., PO1-HD 01994 awarded to Haskins Laboratories) and by the European Research Council (project ERC-ADG- 692502 awarded to R.F.)

Towards a theory of individual differences in statistical learning

Published 21 November 2016http://rstb.royalsocietypublishing.org/content/372/1711/20160059In recent years, statistical learning (SL) research has seen a growing interest in tracking individual performance in SL tasks, mainly as a predictor of linguistic abilities. We review studies from this line of research and outline three presuppositions underlying the experimental approach they employ: (i) that SL is a unified theoretical construct; (ii) that current SL tasks are interchangeable, and equally valid for assessing SL ability; and (iii) that performance in the standard forced-choice test in the task is a good proxy of SL ability. We argue that these three critical presuppositions are subject to a number of theoretical and empirical issues. First, SL shows patterns of modality- and informational-specificity, suggesting that SL cannot be treated as a unified construct. Second, different SL tasks may tap into separate sub-components of SL that are not necessarily interchangeable. Third, the commonly used forced-choice tests in most SL tasks are subject to inherent limitations and confounds. As a first step, we offer a methodological approach that explicitly spells out a potential set of different SL dimensions, allowing for better transparency in choosing a specific SL task as a predictor of a given linguistic outcome. We then offer possible methodological solutions for better tracking and measuring SL ability. Taken together, these discussions provide a novel theoretical and methodological approach for assessing individual differences in SL, with clear testable predictions. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’.This article was supported by the Israel Science Foundation (Grant No. 217/14, awarded to R.F.), by the National Institute of Child Health and Human Development (Grant Nos. RO1 HD 067364, awarded to Ken Pugh and R.F., and PO1-HD 01994, awarded to Haskins Laboratories), and by the ERC (project 692502, awarded to R.F.). L.B. is a research fellow of the Fyssen Foundation

Is there such a thing as a ‘good statistical learner’?

Available online 19 November 2021A growing body of research investigates individual differences in the learning of statistical structure, tying them to variability in cognitive (dis)abilities. This approach views statistical learning (SL) as a general individual ability that underlies performance across a range of cognitive domains. But is there a general SL capacity that can sort individuals from ‘bad’ to ‘good’ statistical learners? Explicating the suppositions underlying this approach, we suggest that current evidence supporting it is meager. We outline an alternative perspective that considers the variability of statistical environments within different cognitive domains. Once we focus on learning that is tuned to the statistics of real-world sensory inputs, an alternative view of SL computations emerges with a radically different outlook for SL research.This article was supported by the European Research Council (ERC) Advanced Grant Project 692502-L2STAT and the Israel Science Foundation (ISF) Grant Project 705/20, awarded to R.F. L.B. received funding from the ERC Advanced Grant Project 833029-LEARNATTEND. N.S. received funding from the ISF, grant number 48/2

Statistically based chunking of nonadjacent dependencies.

How individuals learn complex regularities in the environment and generalize them to new instances is a key question in cognitive science. Although previous investigations have advocated the idea that learning and generalizing depend upon separate processes, the same basic learning mechanisms may account for both. In language learning experiments, these mechanisms have typically been studied in isolation of broader cognitive phenomena such as memory, perception, and attention. Here, we show how learning and generalization in language is embedded in these broader theories by testing learners on their ability to chunk nonadjacent dependencies—a key structure in language but a challenge to theories that posit learning through the memorization of structure. In two studies, adult participants were trained and tested on an artificial language containing nonadjacent syllable dependencies, using a novel chunking-based serial recall task involving verbal repetition of target sequences (formed from learned strings) and scrambled foils. Participants recalled significantly more syllables, bigrams, trigrams, and nonadjacent dependencies from sequences conforming to the language’s statistics (both learned and generalized sequences). They also encoded and generalized specific nonadjacent chunk information. These results suggest that participants chunk remote dependencies and rapidly generalize this information to novel structures. The results thus provide further support for learning-based approaches to language acquisition, and link statistical learning to broader cognitive mechanisms of memory

Case report:vitamin D-dependent rickets type 1 caused by a novel CYP27B1 mutation

Vitamin D‐dependent rickets type 1 VDDR‐1 is a recessive inherited disorder with impaired activation of vitamin D, caused by mutations in CYP27B1. We present long‐time follow‐up of a case with a novel mutation including high‐resolution peripheral quantitative computed tomography of the bone. Adequate treatment resulted in a normalized phenotype

Bone structure in two adult subjects with impaired minor spliceosome function resulting from RNU4ATAC mutations causing microcephalic osteodysplastic primordial dwarfism type 1 (MOPD1)

AbstractMicrocephalic osteodysplastic primordial dwarfism type 1 (MOPD1), or Taybi-Linder syndrome is characterized by distinctive skeletal dysplasia, severe intrauterine and postnatal growth retardation, microcephaly, dysmorphic features, and neurological malformations. It is an autosomal recessive disorder caused by homozygous or compound heterozygous mutations in the RNU4ATAC gene resulting in impaired function of the minor spliceosome. Here, we present the first report on bone morphology, bone density and bone microstructure in two adult MOPD1 patients and applied radiographs, dual energy X-ray absorptiometry, high-resolution peripheral quantitative computed tomography and biochemical evaluation.The MOPD1 patients presented with short stature, low BMI but normal macroscopic bone configuration. Bone mineral density was low. Compared to Danish reference data, total bone area, cortical bone area, cortical thickness, total bone density, cortical bone density, trabecular bone density and trabecular bone volume per tissue volume (BV/TV) were all low. These findings may correlate to the short stature and low body weight of the MOPD1 patients. Our findings suggest that minor spliceosome malfunction may be associated with altered bone modelling