Neural responses in novice learners’ perceptual learning and generalization of lexical tones
The effect of training variability
Bibliographic Data
| ID | 21425244 |
|---|---|
| Authors | W Scott Cleveland (0000-0002-9688-6728), Zhen Qin (0000-0002-6117-7809), Minzhi Gong, Lorraine Denby, Caicai Zhang (0000-0002-7687-0518), Chuanhai Liu (0000-0003-1919-7280) |
| Year | 2021 |
| Volume | 223 |
| Pages | 105029 |
| Publication date | 2021-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Brain and Language (JOURNAL) |
| Journal identifiers | ISSN: 0093-934X • E-ISSN: 1090-2155 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.bandl.2021.105029 |
| OpenAlex | W34624686 |
| Language | EN |
| Citations received | 8 |
| References cited | 48 |
The acoustics of lexical tones are highly variable across talkers, and require second-language (L2) learners' flexibility in accommodating talker-specific tonal variations for successful learning. This study investigated how tone training with high vs. low talker-variability modulated novice learners' neural responses to non-native tones. A passive oddball paradigm tested Mandarin-speaking participants' neural responses to Cantonese low-high and low-mid tonal contrasts in the pretest and posttest. Participants were trained using a tone identification task with feedback, either with high or low talker-variability. The results of mismatch negativity (MMN) showed no group difference in the pretest whereas the high-variability group demonstrated greater neural sensitivity to the low-high tonal contrast produced by a novel talker and a trained talker in the posttest. The finding provides (tentative) novel evidence that training variability may benefit perceptual learning of the relatively easy tone pair and facilitate the formation of talker-independent representations of non-native tones by novice learners
Cartography · Class (philosophy) · Data mining · Geography · Scale (ratio) · Sequence (biology) · Artificial Intelligence · Bayesian Methods and Mixture Models · Computer Science · Soil Geostatistics and Mapping · Statistical Methods and Bayesian Inference
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| Unique citing works | 8 |
|---|---|
| Citations per year | 1 |
| Citation span | 2018 - 2026 (9) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 6 |