Influence of auditory attention on sentence recognition captured by the neural phase

The aim of this study was to investigate whether attentional influences on speech recognition are reflected in the neural phase entrained by an external modulator. Sentences were presented in 7 Hz sinusoidally modulated noise while the neural response to that modulation frequency was monitored by electroencephalogram (EEG) recordings in 21 participants. We implemented a selective attention paradigm including three different attention conditions while keeping physical stimulus parameters constant. The participants’ task was either to repeat the sentence as accurately as possible (speech recognition task), to count the number of decrements implemented in modulated noise (decrement detection task), or to do both (dual task), while the EEG was recorded. Behavioural analysis revealed reduced performance in the dual task condition for decrement detection, possibly reflecting limited cognitive resources. EEG analysis revealed no significant differences in power for the 7 Hz modulation frequency, but an attention‐dependent phase difference between tasks. Further phase analysis revealed a significant difference 500 ms after sentence onset between trials with correct and incorrect responses for speech recognition, indicating that speech recognition performance and the neural phase are linked via selective attention mechanisms, at least shortly after sentence onset. However, the neural phase effects identified were small and await further investigation.

[1]  G. Valente,et al.  Sustained Selective Attention to Competing Amplitude-Modulations in Human Auditory Cortex , 2014, PloS one.

[2]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[3]  John J. Foxe,et al.  Ready, Set, Reset: Stimulus-Locked Periodicity in Behavioral Performance Demonstrates the Consequences of Cross-Sensory Phase Reset , 2011, The Journal of Neuroscience.

[4]  C. Schroeder,et al.  Low-frequency neuronal oscillations as instruments of sensory selection , 2009, Trends in Neurosciences.

[5]  Mounya Elhilali,et al.  Competing Streams at the Cocktail Party: Exploring the Mechanisms of Attention and Temporal Integration , 2010, The Journal of Neuroscience.

[6]  Benedikt Zoefel,et al.  EEG oscillations entrain their phase to high-level features of speech sound , 2016, NeuroImage.

[7]  Maarten De Vos,et al.  Cross-modal reorganization in cochlear implant users: Auditory cortex contributes to visual face processing , 2015, NeuroImage.

[8]  R. VanRullen,et al.  At What Latency Does the Phase of Brain Oscillations Influence Perception? , 2017, eNeuro.

[9]  John J. Foxe,et al.  Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG. , 2015, Cerebral cortex.

[10]  Ankoor S. Shah,et al.  An oscillatory hierarchy controlling neuronal excitability and stimulus processing in the auditory cortex. , 2005, Journal of neurophysiology.

[11]  E. C. Cmm,et al.  on the Recognition of Speech, with , 2008 .

[12]  Anna Warzybok,et al.  A simulation framework for auditory discrimination experiments: Revealing the importance of across-frequency processing in speech perception. , 2016, The Journal of the Acoustical Society of America.

[13]  Tim Jürgens,et al.  Influence of noise type on speech reception thresholds across four languages measured with matrix sentence tests , 2015, International journal of audiology.

[14]  D. Mathalon,et al.  Event-related EEG time-frequency analysis: an overview of measures and an analysis of early gamma band phase locking in schizophrenia. , 2008, Schizophrenia bulletin.

[15]  B. Kollmeier Overcoming language barriers: Matrix sentence tests with closed speech corpora , 2015, International journal of audiology.

[16]  Education Division Studies in social psychology in World War II , 1949 .

[17]  T. Sejnowski,et al.  Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects , 2000, Clinical Neurophysiology.

[18]  R. VanRullen How to Evaluate Phase Differences between Trial Groups in Ongoing Electrophysiological Signals , 2016, bioRxiv.

[19]  A. M. Mimpen,et al.  Improving the reliability of testing the speech reception threshold for sentences. , 1979, Audiology : official organ of the International Society of Audiology.

[20]  W. Singer,et al.  Modulation of Neuronal Interactions Through Neuronal Synchronization , 2007, Science.

[21]  David Poeppel,et al.  Cortical oscillations and speech processing: emerging computational principles and operations , 2012, Nature Neuroscience.

[22]  Maarten De Vos,et al.  Decoding the attended speech stream with multi-channel EEG: implications for online, daily-life applications , 2015, Journal of neural engineering.

[23]  Birger Kollmeier,et al.  Efficient adaptive procedures for threshold and concurrent slope estimates for psychophysics and speech intelligibility tests. , 2002, The Journal of the Acoustical Society of America.

[24]  Tom Eichele,et al.  Semi-automatic identification of independent components representing EEG artifact , 2009, Clinical Neurophysiology.

[25]  Anna Warzybok,et al.  The multilingual matrix test: Principles, applications, and comparison across languages: A review , 2015, International journal of audiology.

[26]  P. Heil,et al.  Detection of Near-Threshold Sounds is Independent of EEG Phase in Common Frequency Bands , 2013, Front. Psychol..

[27]  Andy P. Field,et al.  Discovering Statistics Using Ibm Spss Statistics , 2017 .

[28]  T. Robbins,et al.  Risk-Sensitive Decision-Making in Patients with Posterior Parietal and Ventromedial Prefrontal Cortex Injury , 2013, Cerebral cortex.

[29]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[30]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[31]  S. Stouffer Adjustment during army life , 1977 .

[32]  K. S. Rhebergen,et al.  Extended speech intelligibility index for the prediction of the speech reception threshold in fluctuating noise. , 2006, The Journal of the Acoustical Society of America.

[33]  Matthew H. Davis,et al.  Neural Oscillations Carry Speech Rhythm through to Comprehension , 2012, Front. Psychology.

[34]  H. Levitt Transformed up-down methods in psychoacoustics. , 1971, The Journal of the Acoustical Society of America.

[35]  G. Karmos,et al.  Entrainment of Neuronal Oscillations as a Mechanism of Attentional Selection , 2008, Science.

[36]  Manuel R. Mercier,et al.  Cortical cross-frequency coupling predicts perceptual outcomes , 2013, NeuroImage.

[37]  F. Fröhlich,et al.  Experiments and models of cortical oscillations as a target for noninvasive brain stimulation. , 2015, Progress in brain research.

[38]  Terrence J. Sejnowski,et al.  Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis , 2007, NeuroImage.

[39]  Thomas Brand,et al.  Comparison of Different Short-Term Speech Intelligibility Index Procedures in Fluctuating Noise for Listeners with Normal and Impaired Hearing , 2013 .

[40]  Q. Summerfield Book Review: Auditory Scene Analysis: The Perceptual Organization of Sound , 1992 .

[41]  R. Plomp,et al.  Effects of fluctuating noise and interfering speech on the speech-reception threshold for impaired and normal hearing. , 1990, The Journal of the Acoustical Society of America.

[42]  Michael X Cohen,et al.  Analyzing Neural Time Series Data: Theory and Practice , 2014 .

[43]  U. Lemke,et al.  Behavioral Assessment of Listening Effort Using a Dual-Task Paradigm , 2017, Trends in hearing.

[44]  D. Poeppel,et al.  Temporal context in speech processing and attentional stream selection: A behavioral and neural perspective , 2012, Brain and Language.