Behavioral and Neurodynamic Effects of Word Learning on Phonotactic Repair

Abstract

Processes governing the creation, perception and production of spoken words are sensitive to the patterns of speech sounds in the language user's lexicon. Generative linguistic theory suggests that listeners infer constraints on possible sound patterning from the lexicon and apply these constraints to all aspects of word use. In contrast, emergentist accounts suggest that these phonotactic constraints are a product of interactive associative mapping with items in the lexicon. To determine the degree to which phonotactic constraints are lexically mediated, we observed the effects of learning new words that violate English phonotactic constraints (e.g., srigin) on phonotactic perceptual repair processes in nonword consonant-consonant-vowel (CCV) stimuli (e.g., /sre/). Subjects who learned such words were less likely to "repair" illegal onset clusters (/sr/) and report them as legal ones (/∫r/). Effective connectivity analyses of MRI-constrained reconstructions of simultaneously collected magnetoencephalography (MEG) and EEG data showed that these behavioral shifts were accompanied by changes in the strength of influences of lexical areas on acoustic-phonetic areas. These results strengthen the interpretation of previous results suggesting that phonotactic constraints on perception are produced by top-down lexical influences on speech processing.

Keywords: effective connectivity; emergence; magnetoencephalography; phonology; phonotactic; rule; speech perception; word learning.

Figure 1

Behavioral results. Percentage of trials in which the subjects’ responses produced onset clusters that were phonotactically unrepaired (illegal) in the /-_r-/ and /_l-/s contexts. Error bars show the SE. Results are broken down by context because context produced a significant main effect in addition to the effect of training condition.

Figure 2

Regions of interest (ROIs). For the effective connectivity analyses, ROIs identified by algorithm based on the estimated cortical activation pattern. The ROIs are visualized over an averaged inflated cortical surface. Further details of the ROIs are given in Supplementary Table S2.

Figure 3

Differential influences on left posterior superior temporal gyrus pSTG (shown in yellow) by the other ROIs in the Trained and Naïve conditions. Green bubbles indicate significant differences (p < 0.05) in which influences were stronger in the Trained condition. Orange bubbles indicate significant differences in which influences were stronger in the Naïve (untrained) condition. Bubble radius indicates the difference in the number of timepoints during 100–500 ms post stimulus onset in which Granger Causality Index (GCi) reached the significance threshold of α = 0.05 in the two conditions. No significant results were found for the ROIs in the medial cortical surfaces.

Figure 4

Differential influences by left supramarginal gyrus (SMG; A) and left posterior middle temporal gyrus (MTG; B, both shown in yellow) on other ROIs in the Trained vs. Naïve conditions. Blue and pink bubbles indicate significant differences (p < 0.05) in which influences are stronger in the Trained and in the Naïve conditions, respectively. No medial surfaces are shown in panel (A) because the left supramarginal exerted no significant influences on medial ROIs.

Figure 5

Differential influences on left SMG (A) and left posterior MTG (pMTG: B, both shown in yellow) by other ROIs in the Trained vs. Naïve conditions. Green and orange bubbles indicate significant differences (p < 0.05) in which influences are stronger in the Trained and in the Naïve conditions, respectively.