On Chomsky and the Two Cultures of Statistical Learning

Developmental Dyslexia, Reading Acquisition, and Statistical Learning: A Sceptic’s Guide

Many theories have been put forward that propose that developmental dyslexia is caused by low-level neural, cognitive, or perceptual deficits. For example, statistical learning is a cognitive mechanism that allows the learner to detect a probabilistic pattern in a stream of stimuli and to generalise the knowledge of this pattern to similar stimuli. The link between statistical learning and reading ability is indirect, with intermediate skills, such as knowledge of frequently co-occurring letters, likely being causally dependent on statistical learning skills and, in turn, causing individual variation in reading ability. We discuss theoretical issues regarding what a link between statistical learning and reading ability actually means and review the evidence for such a deficit. We then describe and simulate the “noisy chain hypothesis”, where each intermediary link between a proposed cause and the end-state of reading ability reduces the correlation coefficient between the low-level deficit and the end-state outcome of reading. We draw the following conclusions: (1) Empirically, there is evidence for a correlation between statistical learning ability and reading ability, but there is no evidence to suggest that this relationship is causal, (2) theoretically, focussing on a complete causal chain between a distal cause and developmental dyslexia, rather than the two endpoints of the distal cause and reading ability only, is necessary for understanding the underlying processes, (3) statistically, the indirect nature of the link between statistical learning and reading ability means that the magnitude of the correlation is diluted by other influencing variables, yielding most studies to date underpowered, and (4) practically, it is unclear what can be gained from invoking the concept of statistical learning in teaching children to read.

Tracking human skill learning with a hierarchical Bayesian sequence model

Author summary A central function of the brain is to predict. One challenge of prediction is that both external events and our own actions can depend on a variably deep temporal context of previous events or actions. For instance, in a short motor routine, like opening a door, our actions only depend on a few previous ones (e.g., push the handle if the key was turned). In longer routines such as coffee making, our actions require a deeper context (e.g., place the moka pot on the hob if coffee is ground, the pot is filled and closed, and the hob is on). We adopted a model from the natural language processing literature that matches humans’ ability to learn variable-length relationships in sequences. This model explained the gradual emergence of more complex sequence knowledge and individual differences in an experiment where humans practiced a perceptual-motor sequence over 10 weekly sessions.

Linear Regression Using R: An Introduction to Data Modeling

Linear Regression Using R: An Introduction to Data Modeling presents one of the fundamental data modelling techniques in an informal