Learning Words While Listening to Syllables: Electrophysiological Correlates of Statistical Learning in Children and Adults

Abstract

From an early age, exposure to a spoken language has allowed us to implicitly capture the structure underlying the succession of speech sounds in that language and to segment it into meaningful units (words). Statistical learning (SL), the ability to pick up patterns in the sensory environment without intention or reinforcement, is thus assumed to play a central role in the acquisition of the rule-governed aspects of language, including the discovery of word boundaries in the continuous acoustic stream. Although extensive evidence has been gathered from artificial languages experiments showing that children and adults are able to track the regularities embedded in the auditory input, as the probability of one syllable to follow another syllable in the speech stream, the developmental trajectory of this ability remains controversial. In this work, we have collected Event-Related Potentials (ERPs) while 5-year-old children and young adults (university students) were exposed to a speech stream made of the repetition of eight three-syllable nonsense words presenting different levels of predictability (high vs. low) to mimic closely what occurs in natural languages and to get new insights into the changes that the mechanisms underlying auditory statistical learning (aSL) might undergo through the development. The participants performed the aSL task first under implicit and, subsequently, under explicit conditions to further analyze if children take advantage of previous knowledge of the to-be-learned regularities to enhance SL, as observed with the adult participants. These findings would also contribute to extend our knowledge of the mechanisms available to assist SL at each developmental stage. Although behavioral signs of learning, even under explicit conditions, were only observed for the adult participants, ERP data showed evidence of online segmentation in the brain in both groups, as indexed by modulations in the N100 and N400 components. A detailed analysis of the neural data suggests, however, that adults and children rely on different mechanisms to assist the extraction of word-like units from the continuous speech stream, hence supporting the view that SL with auditory linguistic materials changes through development.

Keywords: developmental changes; electrophysiological correlates; explicit learning; implicit learning; implicit statistical learning; speech segmentation; statistical learning; transitional probability.

FIGURE 1

A visual summary of the experimental design. Panels (A–G) illustrate the timeline of the experimental procedure in which the implicit and, subsequently, the explicit aSL tasks were administered. Each aSL task comprised three parts: instructions, familiarization phase, and test phase. Each task was initiated with specific instructions (A,E) that determined the conditions under which the aSL task was performed: (A) implicit instructions (i.e., without knowledge of the stimuli or the structure of the stream) or (E) explicit instructions (i.e., with explicit knowledge or pre-training on the “words” presented in the stream). In the familiarization phase of both tasks (B,F) during which EEG data were collected, the participants were presented with a continuous auditory stream of four high-TP and four low-TP “words,” with chirp sounds (depicted as a speaker icon in the figure) superimposed over specific syllables. The chirp sounds could emerge at any of the three syllabic positions of the “words,” which precluded its use as a cue for stream segmentation. During this phase, the participants had to perform a chirp detection task. Then, a test phase (C,G) consisting of a 2-AFC task asked the participants to indicate which of the two-syllable sequences (a “word” and a foil) sounded more familiar, considering the stream heard on the familiarization phase.

FIGURE 2

Grand-averaged waveforms (central ROI) and topographic maps for adults and children. “IMP” stands for the aSL task performed under implicit conditions, whereas “EXP” for the aSL performed under explicit instructions (first and second blocks collapsed). Gray-shaded rectangles indicate the analyzed time windows. For a better visualization of the effects, data depicted in this figure were low-pass filtered at 25 Hz after grand average.

FIGURE 3

Block effects in N100 and N400 components both in the adult and children groups. Grand-averaged waveforms correspond to central ROI in adults and fronto-central ROI in children. To assure the clarity of the graphical representation, the conditions of type of “word” and aSL task were collapsed. Gray-shaded rectangles indicate the time windows in which the block effect was significant. For a better visualization of the effects, data depicted in this figure were low-pass filtered at 25 Hz after grand average.

FIGURE 4

Graphical representation of the N400 triple interaction effect in the children group. Gray-shaded rectangles indicate the N400 time window. (A) Task effect in the low-TP condition, in the first block. (B) Task effect in the high-TP condition, in the second block. (C) Type of “word” effect under explicit instructions in the first block. (D) Effect of block in low-TP “words” under explicit instructions. For a better visualization of the effects, data depicted in this figure were low-pass filtered at 25 Hz after grand average.