| Literature DB >> 30643156 |
Mickael L D Deroche1, Hui-Ping Lu2, Aditya M Kulkarni3, Meredith Caldwell4, Karen C Barrett4, Shu-Chen Peng5, Charles J Limb4, Yung-Song Lin2, Monita Chatterjee3.
Abstract
In tonal languages, voice pitch inflections change the meaning of words, such that the brain processes pitch not merely as an acoustic characterization of sound but as semantic information. In normally-hearing (NH) adults, this linguistic pressure on pitch appears to sharpen its neural encoding and can lead to perceptual benefits, depending on the task relevance, potentially generalizing outside of the speech domain. In children, however, linguistic systems are still malleable, meaning that their encoding of voice pitch information might not receive as much neural specialization but might generalize more easily to ecologically irrelevant pitch contours. This would seem particularly true for early-deafened children wearing a cochlear implant (CI), who must exhibit great adaptability to unfamiliar sounds as their sense of pitch is severely degraded. Here, we provide the first demonstration of a tonal language benefit in dynamic pitch sensitivity among NH children (using both a sweep discrimination and labelling task) which extends partially to children with CI (i.e., in the labelling task only). Strong age effects suggest that sensitivity to pitch contours reaches adult-like levels early in tonal language speakers (possibly before 6 years of age) but continues to develop in non-tonal language speakers well into the teenage years. Overall, we conclude that language-dependent neuroplasticity can enhance behavioral sensitivity to dynamic pitch, even in extreme cases of auditory degradation, but it is most easily observable early in life.Entities:
Mesh:
Year: 2019 PMID: 30643156 PMCID: PMC6331606 DOI: 10.1038/s41598-018-36393-1
Source DB: PubMed Journal: Sci Rep ISSN: 2045-2322 Impact factor: 4.379
Demographics of the four groups of children.
| Chronological | Age at | Duration of | Age at profound hearing loss | |
|---|---|---|---|---|
| NH – US | 11.0 (2.8) | |||
| NH – Taiwan | 11.0 (2.9) | |||
| CI – US | 12.5 (3.3) | 2.8 (2.8) | 9.7 (3.7) | 0.7 (2.0) |
| CI – Taiwan | 10.5 (3.3) | 2.9 (1.8) | 7.6 (3.4) | 1.0 (0.7) |
Results of the statistical analysis of the d′ data obtained in the labelling task, for isolated sweep rates (shown in the top panels of Figs 1 and 2).
| Sweep rate | 0.5 | 1 | 2 | 4 | 8 | 16 | 32 | 64 |
|---|---|---|---|---|---|---|---|---|
| language | F(1,156) = 21.1 | F(1,160) = 35.3 | F(1,166) = 49.7 | F(1,168) = 37.3 | ||||
| hearing | F(1,156) = 49.4 | F(1,160) = 81.6 | F(1,166) = 209.3 | F(1,168) = 157.7 | ||||
| language × hearing | F(1,156) = 12.9 | F(1,160) = 10.9 | F(1,166) = 30.6 | F(1,168) = 19.4 | ||||
| NH-US vs. NH-Taiwan | t(60) = 0.9, | t(93) = 2.0, | F(1,156) = 41.7 | F(1,160) = 51.7 | F(1,166) = 93.3 | F(1,168) = 64.6 | ||
| CI-US vs. CI-Taiwan | F(1,156) = 0.4 | F(1,160) = 3.0 | F(1,166) = 1.0 | F(1,168) = 1.3 | t(75) = 4.6, | t(75) = 3.5, |
Results of the statistical analysis of the d′ data obtained in the discrimination task, for isolated sweep rates (shown in the bottom panels of Figs 1 and 2).
| Sweep rate | 0.5 | 1 | 2 | 4 | 8 | 16 | 32 | 64 |
|---|---|---|---|---|---|---|---|---|
| language | F(1,130) = 8.7 | F(1,132) = 20.8 | F(1,135) = 14.1 | F(1,137) = 25.7 | ||||
| hearing | F(1,130) = 49.7 | F(1,132) = 111.0 | F(1,135) = 163.7 | F(1,137) = 138.9 | ||||
| language × hearing | F(1,130) = 6.5 | F(1,132) = 6.6 | F(1,135) = 9.7 | F(1,137) = 7.8 | ||||
| NH-US vs. NH-Taiwan | t(52) = 0.5, | t(81) = 1.3, | F(1,130) = 22.2 | F(1,132) = 36.8 | F(1,135) = 33.7 | F(1,137) = 43.8 | ||
| CI-US vs. CI-Taiwan | F(1,130) < 0.1 | F(1,132) = 1.5 | F(1,135) = 0.2 | F(1,137) = 2.0 | t(57) = 0.4, | t(57) = 0.7, |
Figure 1d′ data across children for 300-ms sweeps of ±2 (most-left) ±4 (middle-left), ±8 (middle-right), and ±16 (most-right) semitones/sec, in two tasks where the child was asked to label the direction of a single sweep (top panels) or discriminate between sweeps of opposite direction (bottom panels). Means are on the right-hand side of each panel, and error bars represent one standard error. A higher d′ reflects a more acute sensitivity. At these rates, none of the regressions reached significance among children with CIs, and age effects were stronger for NH children in the US than in Taiwan.
Figure 2d′ data across children for 300-ms sweeps of ±0.5 (most-left) ±1 (middle-left), ±32 (middle-right), and ±64 (most-right) semitones/sec, in the labelling (top) or discrimination (bottom) task. Means are on the right-hand side of each panel, and error bars represent one standard error. At the shallow rates, none of the correlations reached significance among NH children, and at steep rates, age effects were more consistent for implanted children in the US than in Taiwan.
Results of the statistical analysis of the thresholds extracted at d′ = 0.77 (shown in Figs 4 and 5).
| Task | 1I-2AFC | 3I-2AFC |
|---|---|---|
| language | F(1,148) = 41.6 | F(1,118) = 5.9 |
| hearing | F(1,148) = 143.0 | F(1,118) = 151.3 |
| language × hearing | F(1,148) = 8.3 | F(1,118) = 11.5 |
| NH-US vs. NH-Taiwan | F(1,148) = 53.2 | F(1,118) = 26.6 |
| CI-US vs. CI-Taiwan | F(1,148) = 5.4 | F(1,118) = 0.3 |
Figure 4Individual thresholds across children and means on the right-hand side with one standard error, for the labelling task. Here, a better sensitivity is reflected by a lower threshold. Children who could not perform at this level of d′ are reported as chance on the top-end of each panel.
Figure 5Same as Fig. 4 for the discrimination task.
Figure 3Psychometric functions expressed as percent correct for up- and down-sweeps and subsequently converted into d′ values, in the 1I-2AFC labelling task (top) and the 3I-2AFC discrimination task (bottom), as a function of the rate of F0-sweeps expressed in semitones per second. Symbols represent the weighted mean and error bars indicate one weighted standard error of the mean. Note that the size of symbols relates to the weights on each condition reflecting the number of trials collected across all subjects. Lines and surfaces are the Weibull fits with one standard error.