Reduction of Uncertainty in Human Sequential Learning: Evidence from Artificial Grammar Learning
Luca Onnis, Morten Christiansen, Nick Chater & Rebecca Gomez
Abstract
Research on statistical learning in adults and infants has shown that humans are particularly sensitive to statistical properties of the input.
Early experiments in artificial grammar learning, for instance, show a sensitivity for transitional n-gram probabilities. It has been
argued, however, that this source of information may not help in detecting nonadjacent dependencies, in the presence of substantial variability
of the intervening material, thus suggesting a different focus of attention involving change versus non-change (Gomez, 2002). Following Gomez proposal,
we contend that alternative sources of information may be attended to simultaneously by learners, in an attempt to reduce uncertainty. With several
potential cues in competition, performance crucially depends on which cue is strong enough to be relied upon. By carefully manipulating the statistical
environment it is possible to weigh the contribution of each cue. Several implications for the field of statistical learning and language development are drawn.
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