Emergence of neural encoding of auditory objects while listening to competing speakers

By Nai Ding, Jonathan Z. Simon. Proceedings of the National Academy of Sciences of the United States of America, 2012

838 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/4934ac57123efbfbde139ec846452acd95520b40

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. V. van Wassenhove
2. Anne Kösem
3. Nai Ding
4. Jonathan Z. Simon
5. David Poeppel
6. Charles E. Schroeder
7. John J. Foxe
8. Peter Lakatos

In Experimental and Cognitive Psychology

1. David Poeppel
2. V. van Wassenhove
3. Anne Kösem
4. John J. Foxe
5. Barbara G. Shinn-Cunningham
6. Nima Mesgarani
7. Edmund C. Lalor
8. Nai Ding

Citations in the corpus

• Distinct contributions of low- and high-frequency neural oscillations to speech comprehension (2017, background): “For instance, an increase in speech intelligibility would be the result of an increased excitability of the neural network encoding for the relevant speech features (Ding & Simon, 2012; Hillyard, Hink, Schwent, & Picton, 1973; Kerlin et al., 2010; Woldorff et al., 1993).”
• Mechanisms Underlying Selective Neuronal Tracking of Attended Speech at a ‘Cocktail Party’ (2013, methodology): “…in regions closer to auditory cortex are consistent with findings that sensory areasmaintain representations for both attended and ignored speech (Ding and Simon, 2012b), as well as with classic findings for modulation of simple sensory responses by attention (Hillyard et al., 1973; Woldorff et…”
• The Encoding of Speech Sounds in the Superior Temporal Gyrus (2019, neutral): “This is true at an acoustic level (Diehl et al., 2004), but it is also true at an algorithmic and representational level (Marr, 1982) and is based on the experience and statistical structure of the input training data to the network.”
• Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG. (2015, methodology): “…and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland, 2Department of Psychology, Centre for Neuroscience in Education, University of Cambridge, Cambridge, UK, 3Department of Neurological Surgery, 4Department of Physiology, UCSF Center for Integrative…”
• Human auditory cortex preferentially tracks speech over music without explicit attention (2026, unknown): “Interestingly, similar speech dominance effects have been reported in selective attention EEG and MEG studies, where participants actively attend to a sound (Ding & Simon, 2012; Kerlin et al., 2010; O’Sullivan et al., 2015; Zion Golumbic et al., 2013).”
• The human auditory cortex concurrently tracks syllabic and phonemic timescales via acoustic spectral flux (2024, unknown): “…on univariate and spectrally resolved estimates of phase coherence between acoustic and neural signals, we capitalized on multivariate linear encoding models ( 44 , 65 ) to further investigate the respective contributions of the acoustic amplitude envelope and spectral flux to the neural responses.”
• Intracranial Mapping of Response Latencies and Task Effects for Spoken Syllable Processing in the Human Brain (2024, unknown): “…[using functional magnetic resonance imaging (fMRI; Jäncke et al., 1999; De Martino et al., 2015) and MEG (Woldorff et al., 1993; Kauramäki et al., 2012; Ding and Simon, 2012)] suggest that task-85 dependent modulation of neural responses may be present as early as primary auditory cortex (HG).”