Dataset Information

One Way or Another: Evidence for Perceptual Asymmetry in Pre-attentive Learning of Non-native Contrasts.

ABSTRACT: Research investigating listeners' neural sensitivity to speech sounds has largely focused on segmental features. We examined Australian English listeners' perception and learning of a supra-segmental feature, pitch direction in a non-native tonal contrast, using a passive oddball paradigm and electroencephalography. The stimuli were two contours generated from naturally produced high-level and high-falling tones in Mandarin Chinese, differing only in pitch direction (Liu and Kager, 2014). While both contours had similar pitch onsets, the pitch offset of the falling contour was lower than that of the level one. The contrast was presented in two orientations (standard and deviant reversed) and tested in two blocks with the order of block presentation counterbalanced. Mismatch negativity (MMN) responses showed that listeners discriminated the non-native tonal contrast only in the second block, reflecting indications of learning through exposure during the first block. In addition, listeners showed a later MMN peak for their second block of test relative to listeners who did the same block first, suggesting linguistic (as opposed to acoustic) processing or a misapplication of perceptual strategies from the first to the second block. The results also showed a perceptual asymmetry for change in pitch direction: listeners who encountered a falling tone deviant in the first block had larger frontal MMN amplitudes than listeners who encountered a level tone deviant in the first block. The implications of our findings for second language speech and the developmental trajectory for tone perception are discussed.

SUBMITTER: Liu L

PROVIDER: S-EPMC5869940 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Sleep-mediated overnight consolidation has been found to facilitate perceptual learning by promoting learners' generalization across talkers in their perception of novel segmental categories. Lexical tone is characterized by high variability across talkers, and displays dynamic change over time. For this reason, it remains unclear whether a similar effect of overnight consolidation would be found for perceptual learning of novel tonal contrasts. Thus, this study aims to examine whether overnight consolidation facilitates talker-independent learning of lexical tones in the identification and discrimination of novel Cantonese level tones by Mandarin listeners. Two groups of Mandarin listeners were perceptually trained either in the morning or in the evening. Listeners were trained in a tone identification (ID) task with feedback using stimuli produced by a trained talker. Their post-training changes and generalization to a novel talker were then tested in the ID and AX discrimination tasks using stimuli produced by trained and untrained talkers in three posttests following training: immediately after training, 12-hour delay, and 24-hour delay. While the evening group slept between the first and second posttests, the morning group did not. The accuracy rates in the ID task showed that the evening group showed an improved trend, predicted by their individual sleep time, in identifying the level tones produced by both the trained and untrained talkers; in contrast, the morning group showed a declining trend. The d-prime scores in the AX discrimination task did not show different patterns between the two groups. The finding of sleep-related identification changes over time suggests that overnight consolidation might have facilitated tone learning of stimuli produced by the novel talker and eventually facilitated the formation of a more talker-independent representation of novel tone categories in long-term memory. The results are discussed in light of the features of lexical tones to shed light on the mechanism of phonetic learning.

Project description:BackgroundA family of parsimonious Gaussian mixture models for the biclustering of gene expression data is introduced. Biclustering is accommodated by adopting a mixture of factor analyzers model with a binary, row-stochastic factor loadings matrix. This particular form of factor loadings matrix results in a block-diagonal covariance matrix, which is a useful property in gene expression analyses, specifically in biomarker discovery scenarios where blood can potentially act as a surrogate tissue for other less accessible tissues. Prior knowledge of the factor loadings matrix is useful in this application and is reflected in the one-way supervised nature of the algorithm. Additionally, the factor loadings matrix can be assumed to be constant across all components because of the relationship desired between the various types of tissue samples. Parameter estimates are obtained through a variant of the expectation-maximization algorithm and the best-fitting model is selected using the Bayesian information criterion. The family of models is demonstrated using simulated data and two real microarray data sets. The first real data set is from a rat study that investigated the influence of diabetes on gene expression in different tissues. The second real data set is from a human transcriptomics study that focused on blood and immune tissues. The microarray data sets illustrate the biclustering family's performance in biomarker discovery involving peripheral blood as surrogate biopsy material.ResultsThe simulation studies indicate that the algorithm identifies the correct biclusters, most optimally when the number of observation clusters is known. Moreover, the biclustering algorithm identified biclusters comprised of biologically meaningful data related to insulin resistance and immune function in the rat and human real data sets, respectively.ConclusionsInitial results using real data show that this biclustering technique provides a novel approach for biomarker discovery by enabling blood to be used as a surrogate for hard-to-obtain tissues.

Dataset Information

One Way or Another: Evidence for Perceptual Asymmetry in Pre-attentive Learning of Non-native Contrasts.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets