Rapid Serial Auditory Presentation

Franco, Ana; Eberlen, Julia; Destrebecqz, Arnaud; Cleeremans, Axel; Bertels, Julie

doi:10.1027/1618-3169/a000295

Cited by 42 publications

(46 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast to the RT pattern we observed, both these studies found a graded RT effect as a function of syllable position. In addition, a recent auditory statistical learning study using syllable triplets in which the second and third syllables were deterministically predicted by the first syllable found the same pattern of results: RTs were slowest to target syllables occurring in the first position, intermediate to syllables occurring in the second position, and fastest to syllables occurring in the final position (Franco et al 2015). Taken together, these results suggest that significant RT differences may emerge only when an item can be uniquely predicted from the preceding context.…”

Section: Reaction-time Taskmentioning

confidence: 60%

“…The function of statistical learning was then assessed using an online, speeded, performance-based measure. Known as the target-detection task, this task was originally used in visual statistical learning studies (Turk-Browne et al 2005;Kim et al 2009) and more recently has been adapted to assess statistical learning in the auditory domain (Batterink et al 2015;Franco et al 2015). The task requires participants to respond to target syllables occurring in a continuous syllable stream and provides an indirect measure of statistical learning (Fig.…”

mentioning

confidence: 99%

“…1). The target-detection task assesses the extent to which learners use their acquired statistical knowledge to optimize online processing, with faster reaction times (RTs) indexing more efficient processing of predictable targets (e.g., Turk-Browne et al 2005;Kim et al 2009;Batterink et al 2015;Franco et al 2015).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Functional differences between statistical learning with and without explicit training

2015

View full text Add to dashboard Cite

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.

show abstract

Section: Reaction-time Taskmentioning

confidence: 60%

mentioning

confidence: 99%

See 1 more Smart Citation

Functional differences between statistical learning with and without explicit training

2015

View full text Add to dashboard Cite

show abstract

“…Knowledge of words in the structured stream was assessed directly using a rating task, in which participants provided familiarity ratings to words from the stream and foil items made up of recombined syllables. Statistical learning was also assessed indirectly using a target detection task, an online, speeded, performance-based measure (Batterink et al, 2015a, 2015b, Franco et al, 2015). This task requires participants to respond to target syllables occurring in a continuous syllable stream and assesses the extent to which learners use their acquired statistical knowledge to optimize online processing.…”

Section: Introductionmentioning

confidence: 99%

“…This task requires participants to respond to target syllables occurring in a continuous syllable stream and assesses the extent to which learners use their acquired statistical knowledge to optimize online processing. We expected to observe faster reaction times (RTs) to relatively predictable targets (i.e., those occurring in later syllable positions compared to the first position), indexing more efficient processing (e.g., Turk-Browne et al 2005; Kim et al 2009; Batterink et al 2015a, 2015b; Franco et al 2015). Whereas the rating task is presumably most sensitive to explicit memory, the target detection task has the potential to capture knowledge above and beyond what is reflected by explicit memory and may partially reflect contributions from implicit memory (Batterink et al, 2015a).…”

Section: Introductionmentioning

confidence: 99%

Online neural monitoring of statistical learning

Batterink

Paller

2017

Cortex

133

228

View full text Add to dashboard Cite

The extraction of patterns in the environment plays a critical role in many types of human learning, from motor skills to language acquisition. This process is known as statistical learning. Here we propose that statistical learning has two dissociable components: (1) perceptual binding of individual stimulus units into integrated composites and (2) storing those integrated representations for later use. Statistical learning is typically assessed using post-learning tasks, such that the two components are conflated. Our goal was to characterize the online perceptual component of statistical learning. Participants were exposed to a structured stream of repeating trisyllabic nonsense words and a random syllable stream. Online learning was indexed by an EEG-based measure that quantified neural entrainment at the frequency of the repeating words relative to that of individual syllables. Statistical learning was subsequently assessed using conventional measures in an explicit rating task and a reaction-time task. In the structured stream, neural entrainment to trisyllabic words was higher than in the random stream, increased as a function of exposure to track the progression of learning, and predicted performance on the RT task. These results demonstrate that monitoring this critical component of learning via rhythmic EEG entrainment reveals a gradual acquisition of knowledge whereby novel stimulus sequences are transformed into familiar composites. This online perceptual transformation is a critical component of learning.

show abstract

Familiar units prevail over statistical cues in word segmentation

Poulin-Charronnat

Perruchet

Tillmann

et al. 2016

Psychological Research

View full text Add to dashboard Cite

In language acquisition research, the prevailing position is that listeners exploit statistical cues, in particular transitional probabilities between syllables, to discover words of a language. However, other cues are also involved in word discovery. Assessing the weight learners give to these different cues leads to a better understanding of the processes underlying speech segmentation. The present study evaluated whether adult learners preferentially used known units or statistical cues for segmenting continuous speech. Before the exposure phase, participants were familiarized with part-words of a three-word artificial language. This design allowed the dissociation of the influence of statistical cues and familiar units, with statistical cues favoring word segmentation and familiar units favoring (nonoptimal) part-word segmentation. In Experiment 1, performance in a two-alternative forced choice (2AFC) task between words and part-words revealed part-word segmentation (even though part-words were less cohesive in terms of transitional probabilities and less frequent than words). By contrast, an unfamiliarized group exhibited word segmentation, as usually observed in standard conditions. Experiment 2 used a syllable-detection task to remove the likely contamination of performance by memory and strategy effects in the 2AFC task. Overall, the results suggest that familiar units overrode statistical cues, ultimately questioning the need for computation mechanisms of transitional probabilities (TPs) in natural language speech segmentation.

show abstract

Rapid Serial Auditory Presentation

Cited by 42 publications

References 29 publications

Functional differences between statistical learning with and without explicit training

Functional differences between statistical learning with and without explicit training

Online neural monitoring of statistical learning

Familiar units prevail over statistical cues in word segmentation

Contact Info

Product

Resources

About