Neural evidence suggests phonological acceptability judgments reflect similarity, not constraint evaluation

Abstract

Acceptability judgments are a primary source of evidence in formal linguistic research. Within the generative linguistic tradition, these judgments are attributed to evaluation of novel forms based on implicit knowledge of rules or constraints governing well-formedness. In the domain of phonological acceptability judgments, other factors including ease of articulation and similarity to known forms have been hypothesized to influence evaluation. We used data-driven neural techniques to identify the relative contributions of these factors. Granger causality analysis of magnetic resonance imaging (MRI)-constrained magnetoencephalography (MEG) and electroencephalography (EEG) data revealed patterns of interaction between brain regions that support explicit judgments of the phonological acceptability of spoken nonwords. Comparisons of data obtained with nonwords that varied in terms of onset consonant cluster attestation and acceptability revealed different cortical regions and effective connectivity patterns associated with phonological acceptability judgments. Attested forms produced stronger influences of brain regions implicated in lexical representation and sensorimotor simulation on acoustic-phonetic regions, whereas unattested forms produced stronger influence of phonological control mechanisms on acoustic-phonetic processing. Unacceptable forms produced widespread patterns of interaction consistent with attempted search or repair. Together, these results suggest that speakers' phonological acceptability judgments reflect lexical and sensorimotor factors.

Keywords: Acceptability judgments; Effective connectivity; Lexical effects; MEG/EEG; Phonology/Phonotactics; Rules.

Figure 1.

Average proportion of acceptance (“Yes”) responses in the three conditions across the continuum of phonotactic attestation and phonological acceptability. Error bars indicate standard error of the mean, stars indicate the significance of pairwise comparisons (p<.0001).

Figure 2.

Regions of interests (ROIs) visualized over an inflated averaged cortical surface. Lateral (top) and medial (bottom) views of the left and right hemisphere are shown. For further description of the ROIs, see Table 1.

Figure 3.

Causal influences on L-STG₁ (yellow) by the other ROIs for data combined over the three stimulus conditions. The diameter of the green bubbles indicates the number of time points with significant GCI values in the 100–500 ms window. A time point was included in the count if the p-value determined by bootstrapping analysis for the GCI reached α = 0.05 (uncorrected).

Figure 4.

Differential influences of the other ROIs on L- STG₁ (shown in yellow) for AA-UA contrast. The bubbles indicate ROIs that showed significantly (p<0.05, with FDR correction) larger number of time points with significant GCI values in the 100–500 ms window in the AA (blue) or UA (purple) condition. The diameter of a bubble corresponds to the difference in the number of time points between conditions.

Figure 5.

Differential influences of the other ROIs on L- STG₁ (yellow) for UA-UU contrast. The bubbles indicate ROIs that showed significantly (p<0.05, with FDR correction) larger number of time points with significant GCI values in the 100–500 ms window in the UA (purple) or UU (orange) condition.

Figure 6.

Differential influences of the other ROIs on L-STG₁ (yellow) for AA-UU contrast. The bubbles indicate ROIs that showed significantly (p<0.05, with FDR correction) larger number of time points with significant GCI values in the 100–500 ms window in the AA (blue) or UU (orange) condition.