Statistical Learning of Two Artificial Languages Presented Successively: How Conscious?

Franco, Ana; Cleeremans, Axel; Destrebecqz, Arnaud

doi:10.3389/fpsyg.2011.00229

Cited by 39 publications

(55 citation statements)

References 56 publications

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“…By this account, learners who most strongly acquired the word structure of L1 would most egregiously mis-segment L2, at least initially. On the other hand, the observed negative relationship between L1 and L2 learning is contrary to evidence collected by Franco, Cleeremans, and Destrebecqz (2011) that the weakest L1 learners tended to also be the weakest L2 learners. Despite their dissimilar conclusions, both Perruchet et al (2014) and Franco et al (2011) investigated learning only when participants were explicitly cued to the presence of two languages (i.e., in instruction, voicing, or a combination of the two).…”

Section: Discussioncontrasting

confidence: 99%

“…On the other hand, the observed negative relationship between L1 and L2 learning is contrary to evidence collected by Franco, Cleeremans, and Destrebecqz (2011) that the weakest L1 learners tended to also be the weakest L2 learners. Despite their dissimilar conclusions, both Perruchet et al (2014) and Franco et al (2011) investigated learning only when participants were explicitly cued to the presence of two languages (i.e., in instruction, voicing, or a combination of the two). In the present case, learners were informed only of a single language, suggesting that the negative correlation between L1 and L2 knowledge may be influenced by learners’ expectations about the number of structures in their environment.…”

Section: Discussioncontrasting

confidence: 99%

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Sampling over Nonuniform Distributions: A Neural Efficiency Account of the Primacy Effect in Statistical Learning

Karuza

Weiss

et al. 2016

Journal of Cognitive Neuroscience

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Successful knowledge acquisition requires a cognitive system that is both sensitive to statistical information and able to distinguish among multiple structures (i.e., to detect pattern shifts and form distinct representations). Extensive behavioral evidence has highlighted the importance of cues to structural change, demonstrating how, without them, learners fail to detect pattern shifts and are biased in favor of early experience. Here, we seek a neural account of the mechanism underpinning this primacy effect in learning. During fMRI scanning, adult participants were presented with two artificial languages: a familiar language (L1) on which they had been pretrained followed by a novel language (L2). The languages were composed of the same syllable inventory organized according to unique statistical structures. In the absence of cues to the transition between languages, posttest familiarity judgments revealed that learners on average more accurately segmented words from the familiar language compared with the novel one. Univariate activation and functional connectivity analyses showed that participants with the strongest learning of L1 had decreased recruitment of fronto-subcortical and posterior parietal regions, in addition to a dissociation between downstream regions and early auditory cortex. Participants with a strong new language learning capacity (i.e., higher L2 scores) showed the opposite trend. Thus, we suggest that a bias toward neural efficiency, particularly as manifested by decreased sampling from the environment, accounts for the primacy effect in learning. Potential implications of this hypothesis are discussed, including the possibility that “inefficient” learning systems may be more sensitive to structural changes in a dynamic environment.

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Section: Discussioncontrasting

confidence: 99%

Section: Discussioncontrasting

confidence: 99%

Sampling over Nonuniform Distributions: A Neural Efficiency Account of the Primacy Effect in Statistical Learning

Karuza

Weiss

et al. 2016

Journal of Cognitive Neuroscience

View full text Add to dashboard Cite

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“…The same procedure but with the streams of the other two conditions was used in the second session. Franco, Cleeremans, and Destrebecqz (2011) showed that people are able to learn two artificial languages sequentially and to easily differentiate between them. Gebhart, Aslin, and Newport (2009) found interference between statistically coherent languages when they were presented sequentially, but the interference disappears if either the exposure to the second language was long enough, or the presence of two different structures was marked explicitly in the instructions, or when the two subsequent languages were separated by a pause.…”

Section: Methodsmentioning

confidence: 99%

Cross-linguistic differences in the use of durational cues for the segmentation of a novel language

et al. 2017

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It is widely accepted that duration can be exploited as phonological phrase final lengthening in the segmentation of a novel language, i.e., in extracting discrete constituents from continuous speech. The use of final lengthening for segmentation and its facilitatory effect has been claimed to be universal. However, lengthening in the world languages can also mark lexically stressed syllables. Stress-induced lengthening can potentially be in conflict with right edge phonological phrase boundary lengthening. Thus the processing of durational cues in segmentation can be dependent on the listener's linguistic background, e.g. on the specific correlates and unmarked location of lexical stress in the native language of the listener. We tested this prediction and found that segmentation by both German and Basque speakers is facilitated when lengthening is aligned with the word final syllable and is not affected by lengthening on either the penultimate or the antepenultimate syllables. Lengthening of the word final syllable, however, does not help Italian and Spanish speakers to segment continuous speech, and lengthening of the antepenultimate syllable impedes their performance. We have also found a facilitatory effect of penultimate lengthening on segmentation by Italians.These results confirm our hypothesis that processing of lengthening cues is not universal, and interpretation of lengthening as a phonological phrase final boundary marker in a novel language of exposure can be overridden by the phonology of lexical stress in the native language of the listener.3

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“…First, there is evidence that although statistical learning occurs incidentally or implicitly, it produces knowledge that is at least partially explicit in nature, at least in adult learners (Franco et al 2011;Bertels et al 2012Bertels et al , 2013Batterink et al 2015). Whereas implicit knowledge, such as abstract grammar knowledge (Reber 1967) or perceptual-motor skills (Cohen and Squire 1980) must be acquired over time through direct experience, one feature of explicit knowledge is that it can be provided to learners through explicit instruction (i.e., by verbally transmitting information from one person to another).…”

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confidence: 99%

Functional differences between statistical learning with and without explicit training

2015

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Humans are capable of rapidly extracting regularities from environmental input, a process known as statistical learning. This type of learning typically occurs automatically, through passive exposure to environmental input. The presumed function of statistical learning is to optimize processing, allowing the brain to more accurately predict and prepare for incoming input. In this study, we ask whether the function of statistical learning may be enhanced through supplementary explicit training, in which underlying regularities are explicitly taught rather than simply abstracted through exposure. Learners were randomly assigned either to an explicit group or an implicit group. All learners were exposed to a continuous stream of repeating nonsense words. Prior to this implicit training, learners in the explicit group received supplementary explicit training on the nonsense words. Statistical learning was assessed through a speeded reaction-time (RT) task, which measured the extent to which learners used acquired statistical knowledge to optimize online processing. Both RTs and brain potentials revealed significant differences in online processing as a function of training condition. RTs showed a crossover interaction; responses in the explicit group were faster to predictable targets and marginally slower to less predictable targets relative to responses in the implicit group. P300 potentials to predictable targets were larger in the explicit group than in the implicit group, suggesting greater recruitment of controlled, effortful processes. Taken together, these results suggest that information abstracted through passive exposure during statistical learning may be processed more automatically and with less effort than information that is acquired explicitly.

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Statistical Learning of Two Artificial Languages Presented Successively: How Conscious?

Cited by 39 publications

References 56 publications

Sampling over Nonuniform Distributions: A Neural Efficiency Account of the Primacy Effect in Statistical Learning

Sampling over Nonuniform Distributions: A Neural Efficiency Account of the Primacy Effect in Statistical Learning

Cross-linguistic differences in the use of durational cues for the segmentation of a novel language

Functional differences between statistical learning with and without explicit training

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