Hearing Scenes: A Neuromagnetic Signature of Auditory Source and Reverberant Space Separation

Abstract

Perceiving the geometry of surrounding space is a multisensory process, crucial to contextualizing object perception and guiding navigation behavior. Humans can make judgments about surrounding spaces from reverberation cues, caused by sounds reflecting off multiple interior surfaces. However, it remains unclear how the brain represents reverberant spaces separately from sound sources. Here, we report separable neural signatures of auditory space and source perception during magnetoencephalography (MEG) recording as subjects listened to brief sounds convolved with monaural room impulse responses (RIRs). The decoding signature of sound sources began at 57 ms after stimulus onset and peaked at 130 ms, while space decoding started at 138 ms and peaked at 386 ms. Importantly, these neuromagnetic responses were readily dissociable in form and time: while sound source decoding exhibited an early and transient response, the neural signature of space was sustained and independent of the original source that produced it. The reverberant space response was robust to variations in sound source, and vice versa, indicating a generalized response not tied to specific source-space combinations. These results provide the first neuromagnetic evidence for robust, dissociable auditory source and reverberant space representations in the human brain and reveal the temporal dynamics of how auditory scene analysis extracts percepts from complex naturalistic auditory signals.

The Rapid Emergence of Auditory Object Representations in Cortex Reflect Central Acoustic Attributes

“…A related study (Teng et al, 2017) decoded neural representations of different impact sounds and reverberant spaces from MEG, finding earlier onsets and latencies in their sound source decoding than what we observe here. The slower latencies in our results are likely a function of the more varied stimulus onsets (amplitude envelopes) we employed in our set of sounds.…”

“…Relatively few studies have used decoding techniques to study neural responses to natural auditory stimuli over time in M/EEG (although see Khalighinejad, da Silva, & Mesgarani, 2017;Sankaran, Thompson, Carlile, & Carlson, 2018;Teng, Sommer, Pantazis, & Oliva, 2017). So far, the small body of work using these methods has focused only on specific subsets of stimuli (e.g., speech or musical pitches).…”

The effect of healthy aging on change detection and sensitivity to predictable sturcture in crowded acoustic scenes

“…The relevant predictive cues in our REG scenes involve rapidly unfolding information (over several concurrent streams) that precludes overt perceptual tracking and likely engages automatic statistical tracking mechanisms (Sohoglu and Chait, 2016b). Importantly, the rates used here-3-25Hzare all within the range that is considered to be most critical for hearing in natural environments (Kayser, 2019;Overath et al, 2015;Teng et al, 2017;Yi et al, 2019). That older listeners exhibited a benefit of regularity therefore indicates that the capacity to extract rapid temporal structure is largely maintained with healthy aging.…”

Enhancing multivariate pattern analysis for magnetoencephalography through relevant sensor selection

“…Temporal generalization or time-time decoding 1 is an extended version of decoding over time and explains the persistence or transience of neural representations. 55 While one-dimensional decoding time courses represent the accuracy of SVM classifier trained and tested with the MEG pattern vectors of the same time point, time-time decoding matrix represents the SVM accuracy which is trained on pattern vectors at time t x and is tested on the data at all other time points t y . Performing linear SVM classifier between every pair of stimuli yields a 92 × 92 decoding matrix for every pair of time points (t x , t y ).…”