Incremental implicit learning of bundles of statistical patterns

Abstract

Forming an accurate representation of a task environment often takes place incrementally as the information relevant to learning the representation only unfolds over time. This incremental nature of learning poses an important problem: it is usually unclear whether a sequence of stimuli consists of only a single pattern, or multiple patterns that are spliced together. In the former case, the learner can directly use each observed stimulus to continuously revise its representation of the task environment. In the latter case, however, the learner must first parse the sequence of stimuli into different bundles, so as to not conflate the multiple patterns. We created a video-game statistical learning paradigm and investigated 1) whether learners without prior knowledge of the existence of multiple “stimulus bundles” — subsequences of stimuli that define locally coherent statistical patterns — could detect their presence in the input, and 2) whether learners are capable of constructing a rich representation that encodes the various statistical patterns associated with bundles. By comparing human learning behavior to the predictions of three computational models, we find evidence that learners can handle both tasks successfully. In addition, we discuss the underlying reasons for why the learning of stimulus bundles occurs even when such behavior may seem irrational.

Robust speech perception: Recognize the familiar, generalize to the similar, and adapt to the novel.

“…Rather, learners can benefit from inferring the underlying structure to cross-situation variation, in order to recognize familiar situations and generalize to similar situations. In speech perception, the major source of variation across situations is the talker , but the same logic can be applied to other domains (Qian et al, 2012; Qian, Jaeger, and Aslin, submitted ). The ideal adapter highlights the potential of speech perception to serve as a model organism for understanding perception in a variable—but structured—world, and suggests that superficially unrelated phenomena from non-linguistic perceptual/motor domains might be informative about language processing and acquisition and vice-versa.…”

Re-examining selective adaptation: Fatiguing feature detectors, or distributional learning?

“…Elsewhere (Kleinschmidt & Jaeger, 2015b), we have proposed that such cross-situational learning effects can be modeled as distributional learning that is indexed to particular indexical variables like a talker, groups of talkers, or environment. Such hierarchical distributional learning enables previously learned cue distributions to be re-learned very quickly when the associated indexical variable is encountered again (for similar arguments applied to domain-general sensory/motor learning, see Qian et al, 2012, 2015). If selective adaptation is due to the same sort of distributional learning, then it follows that listeners should re-adapt to previously encountered distributions more quickly than the initial adaptation.…”

Sociophonetics: The Role of Words, the Role of Context, and the Role of Words in Context

“…Individuals learn to segment units from the speech stream based on co-occurence statistics (Saffran, Aslin, & Newport, 1996), and also group non-linguistic stimuli based on temporal (Brady & Oliva, 2008;Fiser & Aslin, 2002) or spatial (Fiser & Aslin, 2001) co-occurrence. Individuals also use statistical cooccurrence patterns of visual cues to guide attention and learning (Chun, 2000;Chun & Jiang, 1998;Qian, Jaeger, & Aslin, 2016).…”