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      Preattentive Extraction of Abstract Auditory Rules in Speech Sound Stream: A Mismatch Negativity Study Using Lexical Tones

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          Abstract

          Background

          Extraction of linguistically relevant auditory features is critical for speech comprehension in complex auditory environments, in which the relationships between acoustic stimuli are often abstract and constant while the stimuli per se are varying. These relationships are referred to as the abstract auditory rule in speech and have been investigated for their underlying neural mechanisms at an attentive stage. However, the issue of whether or not there is a sensory intelligence that enables one to automatically encode abstract auditory rules in speech at a preattentive stage has not yet been thoroughly addressed.

          Methodology/Principal Findings

          We chose Chinese lexical tones for the current study because they help to define word meaning and hence facilitate the fabrication of an abstract auditory rule in a speech sound stream. We continuously presented native Chinese speakers with Chinese vowels differing in formant, intensity, and level of pitch to construct a complex and varying auditory stream. In this stream, most of the sounds shared flat lexical tones to form an embedded abstract auditory rule. Occasionally the rule was randomly violated by those with a rising or falling lexical tone. The results showed that the violation of the abstract auditory rule of lexical tones evoked a robust preattentive auditory response, as revealed by whole-head electrical recordings of the mismatch negativity (MMN), though none of the subjects acquired explicit knowledge of the rule or became aware of the violation.

          Conclusions/Significance

          Our results demonstrate that there is an auditory sensory intelligence in the perception of Chinese lexical tones. The existence of this intelligence suggests that the humans can automatically extract abstract auditory rules in speech at a preattentive stage to ensure speech communication in complex and noisy auditory environments without drawing on conscious resources.

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          Neural mechanisms of involuntary attention to acoustic novelty and change.

          C Escera, K Alho, I Winkler … (1998)
          Behavioral and event-related brain potential (ERP) measures were used to elucidate the neural mechanisms of involuntary engagement of attention by novelty and change in the acoustic environment. The behavioral measures consisted of the reaction time (RT) and performance accuracy (hit rate) in a forced-choice visual RT task where subjects were to discriminate between odd and even numbers. Each visual stimulus was preceded by an irrelevant auditory stimulus, which was randomly either a "standard" tone (80%), a slightly higher "deviant" tone (10%), or a natural, "novel" sound (10%). Novel sounds prolonged the RT to successive visual stimuli by 17 msec as compared with the RT to visual stimuli that followed standard tones. Deviant tones, in turn, decreased the hit rate but did not significantly affect the RT. In the ERPs to deviant tones, the mismatch negativity (MMN), peaking at 150 msec, and a second negativity, peaking at 400 msec, could be observed. Novel sounds elicited an enhanced N1, with a probable overlap by the MMN, and a large positive P3a response with two different subcomponents: an early centrally dominant P3a, peaking at 230 msec, and a late P3a, peaking at 315 msec with a right-frontal scalp maximum. The present results suggest the involvement of two different neural mechanisms in triggering involuntary attention to acoustic novelty and change: a transient-detector mechanism activated by novel sounds and reflected in the N1 and a stimulus-change detector mechanism activated by deviant tones and novel sounds and reflected in the MMN. The observed differential distracting effects by slightly deviant tones and widely deviant novel sounds support the notion of two separate mechanisms of involuntary attention.
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            Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns.

            Rainer Goebel, Giancarlo Valente, Elia Formisano … (2008)
            In functional brain mapping, pattern recognition methods allow detecting multivoxel patterns of brain activation which are informative with respect to a subject's perceptual or cognitive state. The sensitivity of these methods, however, is greatly reduced when the proportion of voxels that convey the discriminative information is small compared to the total number of measured voxels. To reduce this dimensionality problem, previous studies employed univariate voxel selection or region-of-interest-based strategies as a preceding step to the application of machine learning algorithms. Here we employ a strategy for classifying functional imaging data based on a multivariate feature selection algorithm, Recursive Feature Elimination (RFE) that uses the training algorithm (support vector machine) recursively to eliminate irrelevant voxels and estimate informative spatial patterns. Generalization performances on test data increases while features/voxels are pruned based on their discrimination ability. In this article we evaluate RFE in terms of sensitivity of discriminative maps (Receiver Operative Characteristic analysis) and generalization performances and compare it to previously used univariate voxel selection strategies based on activation and discrimination measures. Using simulated fMRI data, we show that the recursive approach is suitable for mapping discriminative patterns and that the combination of an initial univariate activation-based (F-test) reduction of voxels and multivariate recursive feature elimination produces the best results, especially when differences between conditions have a low contrast-to-noise ratio. Furthermore, we apply our method to high resolution (2 x 2 x 2 mm(3)) data from an auditory fMRI experiment in which subjects were stimulated with sounds from four different categories. With these real data, our recursive algorithm proves able to detect and accurately classify multivoxel spatial patterns, highlighting the role of the superior temporal gyrus in encoding the information of sound categories. In line with the simulation results, our method outperforms univariate statistical analysis and statistical learning without feature selection.
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              "Who" is saying "what"? Brain-based decoding of human voice and speech.

              Milene Bonte, Rainer Goebel, Elia Formisano … (2008)
              Can we decipher speech content ("what" is being said) and speaker identity ("who" is saying it) from observations of brain activity of a listener? Here, we combine functional magnetic resonance imaging with a data-mining algorithm and retrieve what and whom a person is listening to from the neural fingerprints that speech and voice signals elicit in the listener's auditory cortex. These cortical fingerprints are spatially distributed and insensitive to acoustic variations of the input so as to permit the brain-based recognition of learned speech from unknown speakers and of learned voices from previously unheard utterances. Our findings unravel the detailed cortical layout and computational properties of the neural populations at the basis of human speech recognition and speaker identification.
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                Author and article information

                Contributors
                : Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2012
                Publication date (Electronic): 6 January 2012
                Volume: 7
                Issue: 1
                Electronic Location Identifier: e30027
                Affiliations
                [1 ]CAS Key Laboratory of Brain Function and Diseases, School of Life Sciences, University of Science and Technology of China, Hefei, China
                [2 ]Auditory Research Laboratory, University of Science and Technology of China, Hefei, China
                [3 ]iFlytek Speech Laboratory, School of Information Science and Technology, University of Science and Technology of China, Hefei, China
                University of Salamanca- Institute for Neuroscience of Castille and Leon and Medical School, Spain
                Author notes

                Conceived and designed the experiments: LC XDW. Performed the experiments: XDW FG KH. Analyzed the data: XDW FG. Contributed reagents/materials/analysis tools: LHC. Wrote the paper: LC XDW.

                Article
                Publisher ID: PONE-D-11-11684
                DOI: 10.1371/journal.pone.0030027
                PMC ID: 3253114
                PubMed ID: 22238691
                SO-VID: 3c747163-ebf2-4391-a5a0-0d108ebcbb7f
                Copyright © Wang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                Date received : 24 June 2011
                Date accepted : 12 December 2011
                Page count
                Pages: 7
                Categories
                Subject: Research Article
                Subject: Biology
                Subject: Neuroscience
                Subject: Sensory Systems
                Subject: Social and Behavioral Sciences
                Subject: Linguistics
                Subject: Psychology

                ScienceOpen disciplines: Uncategorized
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                ScienceOpen disciplines: Uncategorized

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