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• W162928091 abstract "Language-induced Biases on Human Sequential Learning Luca Onnis (lucao@hawaii.edu) Department of Second Language Studies & Center for Second Language Research University of Hawai‘i at Manoa, 1890 East-West Road, Honolulu, Hawaii 96822 USA Erik Thiessen (thiessen@andrew.cmu.edu) Department of Psychology Carnegie Mellon University, Baker Hall, Pittsburgh, Pennsylvania 15213 USA Abstract regularities that learners detect in ways that are consistent with the predominant statistical structure in their native language. What are the effects of experience on subsequent learning? We explored the effects of language-specific word order knowledge on the acquisition of sequential conditional information. Korean and English adults were engaged in a sequence learning task involving three different sets of stimuli: auditory linguistic (nonsense syllables), visual non- linguistic (nonsense shapes), and auditory non-linguistic (pure tones). The forward and backward probabilities between adjacent elements generated two equally probable and orthogonal perceptual parses of the elements, such that any significant preference at test must be due to either general cognitive biases, or prior language-induced biases. We found that language modulated parsing preferences with the linguistic stimuli only. Intriguingly, these preferences are congruent with the dominant word order patterns of each language, as corroborated by corpus analyses. These findings suggest that mechanisms of statistical sequential learning are implicated in language, and experience with language may affect cognitive processes and later learning. Prediction and retrodiction in sequential learning Recent studies have shown that learners can exploit both predictive and retrodictive relations, operationalized as forward and backward transitional probabilities respectively. For instance, Jones & Pashler (2007) showed participants sequences of shapes governed by probabilistic relations, and then asked them to choose which shape reliably came after a probe shape (prediction test) or before a probe shape (retrodiction test). In experiments where forward and backward probabilities were made informative, they found that both prediction and retrodiction were used effectively for recalling memories. In a similar experiment using a continuous sequence of nonsense syllables, Perruchet & Desaulty (2008) found that participants perceived word boundaries based on backward transitional probabilities as well as forward probabilities equally well. Likewise, Pelucchi, Hay, & Saffran (2009) provided evidence that infants can track backward statistics in speech. The three studies above tested cases in which forward and backward probabilities were never in conflict. Rather, each cue was made maximally informative in a given experiment, while the other was made uninformative. Yet in naturalistic circumstances, prediction and retrodiction may need to be effectively combined, as when learning the word order of a language. In this respect, a comparison between English and Korean seems particularly appropriate because several word order relations of English are reversed in Korean. Keywords: corpus analyses; experience-dependent learning; implicit learning; prediction; retrodiction; second language acquisition; sensitive periods; sequential learning; statistical learning; transitional probabilities; word order; linguistic typology. Introduction Statistical information has been argued to be an important cue to linguistic structure. For example, sounds within a word are more predictable than sounds across boundaries, which may help infants discover words in fluent speech. Because this type of statistical information is present in all languages, statistical information may be a particularly important cue early in development, one that can be used without requiring prior experience with the native language (e.g., Thiessen & Saffran, 2003). But while statistical learning may be a universal cue to linguistic structure, it is also the case that the statistical structure across languages differs. If statistical learning fails to adapt to these differences, it is unlikely to be an optimal learning strategy. While much research has examined how statistical learning helps learners adapt to the structure of their native language (e.g., Maye, Werker, & Gerken, 2002; Thiessen & Saffran, 2007), it is unknown whether statistical learning itself adapts to the characteristics of the native language. In this series of experiments, we ask whether experience with language alters the kinds of statistical Prediction and retrodiction in natural languages: Typology and word order tendencies. In English, the head elements in a phrase come first, while in Korean the head follows the phrase (e.g., ‘[Door- OBJECT] [close-IMPERATIVE]’ = ‘[You close] [the door]’, where square brackets indicate phrase groupings). The English sentence “I saw him go there” is glossed as “I him there go saw”. Likewise “Give me the ball” is glossed as “Ball me give”; “Let’s go get some food” is glossed as “Food get go let’s”. Thus, frequent constructions such as transitives, imperatives, and exortatives in English have a reversed word-order in Korean. English is also prepositional (‘to school’), while Korean is postpositional (‘school to’). We conjectured that since the" @default.
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• W162928091 title "Language-induced Biases on Human Sequential Learning" @default.
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