Mechanisms Underlying Selective Neuronal Tracking of Attended Speech at a ‘Cocktail Party’

By Elana Zion Golumbic, Nai Ding, S. Bickel, Peter Lakatos, C. Schevon, G. Mckhann, R. Goodman, R. Emerson, Ashesh D. Mehta, Jonathan Z. Simon and 2 more. Neuron, 2013

866 citations recorded by the source corpus.

Summary The ability to focus on and understand one talker in a noisy social environment is a critical social-cognitive capacity, whose underlying neuronal mechanisms are unclear. We investigated the manner in which speech streams are represented in brain activity and the way that selective attention governs the brain’s representation of speech using a ‘Cocktail Party’ Paradigm, coupled with direct recordings from the cortical surface in surgical epilepsy patients. We find that brain activity dynamically tracks speech streams using both low frequency phase and high frequency amplitude fluctuations, and that optimal encoding likely combines the two. In and near low level auditory cortices, attention ‘modulates’ the representation by enhancing cortical tracking of attended speech streams, but ignored speech remains represented. In higher order regions, the representation appears to become more ‘selective,’ in that there is no detectable tracking of ignored speech. This selectivity itself seems to sharpen as a sentence unfolds.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/8b370be84b939e56a3833ceebebb802a6dc9638d

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In Cognitive Neuroscience

Citations in the corpus

The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli (2016, methodology): “Alternative SI techniques (that ultimately yield the same solution) have also been used to investigate auditory scene analysis (Ding and Simon, 2012a; Mesgarani and Chang, 2012; Zion-Golumbic et al., 2013; O’Sullivan et al., 2015), speech-in-noise (Ding and Simon, 2013; Ding et al.”
Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG. (2015, methodology): “…studies have used recorded cortical population data to estimate the input stimulus using a mapping approach in the reverse direction (i.e., from the neural data back to the stimulus) (Rieke et al. 1995; Stanley et al. 1999; Mesgarani et al. 2009; Pasley et al. 2012; Zion Golumbic et al. 2013).”
The multisensory function of the human primary visual cortex. (2016, background): “…is provided by studies of cross- modal selective attention that have focused on cross-frequency coupling as a potential neural mechanism (e.g. Zion-Golumbic et al., 2013 for evidence concerning auditory cortex; Lakatos et al., 2009 for evidence from non-human primates concerning visual cortex).”
Distinct contributions of low- and high-frequency neural oscillations to speech comprehension (2017, background): “In addition, the power of high-frequency activity (>40 Hz) has also been observed to follow phrasal and syllabic rhythms (Ding et al., 2016; Kubanek, Brunner, Gunduz, Poeppel, & Schalk, 2013; Mesgarani & Chang, 2012; Nourski et al., 2009; Pasley et al., 2012; Zion Golumbic et al., 2013).”
Speech Rhythms and Multiplexed Oscillatory Sensory Coding in the Human Brain (2013, background): “well known cocktail party situation, they code mainly for the attended stimulus [26,30,31].”
Identifying and Quantifying Multisensory Integration: A Tutorial Review (2014, background): “Much interest of late is focused on the concept that visual inputs may also have the capacity to cause phase resets in auditory cortex, a possible mechanistic basis for the gains in speech intelligibility seen when the mouth of a speaker is viewed (Zion Golumbic et al. 2012, 2013b, 2013a).”
Cortical encoding of probabilistic temporal predictions during speech perception (2026, unknown): “Together, these results suggest that the brain exploits different levels of temporal structure in addition to content information to continuously shape attention in time and better process low-level input (55, 74).”
Oscillatory entrainment to our early social or physical environment and the emergence of volitional control (2022, background): “…entrainment (because, as discussed in Section 5.1, evoked responses to attended-to stimuli are larger), phase-shifts in entrainment were also observed which cannot be explained by simple bottom-up representations of stimulus properties (Golumbic et al., 2013; Lakatos et al., 2013) (see Fig.”
On Natural Attunement: Shared Rhythms Between the Brain and the Environment. (2023, unknown): “Alignment of neural rhythms to environmental regularities in the auditory domain, e.g., speech or music, is mainly observed at frequencies below 10 Hz (Doelling and Poeppel, 2015; Golumbic et al., 2013; Obleser and Kayser, 2019).”