Neural Decoding of Bistable Sounds Reveals an Effect of Intention on Perceptual Organization

Abstract

Auditory signals arrive at the ear as a mixture that the brain must decompose into distinct sources based to a large extent on acoustic properties of the sounds. An important question concerns whether listeners have voluntary control over how many sources they perceive. This has been studied using pure high (H) and low (L) tones presented in the repeating pattern HLH-HLH-, which can form a bistable percept heard either as an integrated whole (HLH-) or as segregated into high (H-H-) and low (-L-) sequences. Although instructing listeners to try to integrate or segregate sounds affects reports of what they hear, this could reflect a response bias rather than a perceptual effect. We had human listeners (15 males, 12 females) continuously report their perception of such sequences and recorded neural activity using MEG. During neutral listening, a classifier trained on patterns of neural activity distinguished between periods of integrated and segregated perception. In other conditions, participants tried to influence their perception by allocating attention either to the whole sequence or to a subset of the sounds. They reported hearing the desired percept for a greater proportion of time than when listening neutrally. Critically, neural activity supported these reports; stimulus-locked brain responses in auditory cortex were more likely to resemble the signature of segregation when participants tried to hear segregation than when attempting to perceive integration. These results indicate that listeners can influence how many sound sources they perceive, as reflected in neural responses that track both the input and its perceptual organization.SIGNIFICANCE STATEMENT Can we consciously influence our perception of the external world? We address this question using sound sequences that can be heard either as coming from a single source or as two distinct auditory streams. Listeners reported spontaneous changes in their perception between these two interpretations while we recorded neural activity to identify signatures of such integration and segregation. They also indicated that they could, to some extent, choose between these alternatives. This claim was supported by corresponding changes in responses in auditory cortex. By linking neural and behavioral correlates of perception, we demonstrate that the number of objects that we perceive can depend not only on the physical attributes of our environment, but also on how we intend to experience it.

Neural Signatures of Auditory Perceptual Bistability Revealed by Large-Scale Human Intracranial Recordings

“…A relatively weak classification performance for bistable auditory streaming of triplets was recently reported for MEG recordings (Billig et al, 2018; Sanders et al, 2018). Those studies also found accuracies <60%, despite comparing three different classifiers and using a larger pool of subjects and more experimental blocks.…”

“…Noninvasive recordings in humans found neural correlates of persistent perceptual states in streaming of triplets in AC and the intraparietal sulcus (Cusack, 2005; Gutschalk et al, 2005; Snyder et al, 2006; Hill et al, 2011; Billig et al, 2018). Percept-related MEG responses were localized to AC, occurring ∼60 ms after the onset of tone B and had larger amplitudes at the 2-stream percept (Gutschalk et al, 2005).…”

“…The literature on nonvisual perceptual transitions in humans has identified several sensory and frontoparietal areas, including bilateral activation of auditory cortex (AC), posterior insular cortex, supramarginal gyrus (Kondo and Kashino, 2009), intraparietal sulcus (Cusack, 2005), inferior frontal gyrus (IFG), and anterior cingulate cortex (Kondo and Kashino, 2007; Basirat et al, 2008). On the other hand, evoked responses during the perceptual states induced by streaming of triplets, and their corresponding differences, have been localized to AC by MEG and EEG recordings (Gutschalk et al, 2005; Snyder et al, 2006; Hill et al, 2012; Billig et al, 2018; Sanders et al, 2018), and to AC and intraparietal sulcus by fMRI research (Cusack, 2005; Hill et al, 2011).…”

Decoding continuous variables from event-related potential (ERP) data with linear support vector regression (SVR) using the Decision Decoding Toolbox (DDTBOX)

“…DDTBOX uses a moving-window approach in which the trial data (usually containing a baseline period and epoched and truncated, depending on the individual research question) is analysed within an analysis time window , which is moved through the entire trial in small (overlapping or non-overlapping) steps, each time containing the next step’s data. It is also possible to use a pre-defined time-period of interest instead (e.g., Billing et al, 2018), but we will focus on the moving-window approach in this paper. Each analysis step/window is treated as an independent analysis.…”

Switching perception of musical meters by listening to different acoustic cues of biphasic sound stimulus

“…Listeners can perceive sound series as multiple streams when their duration and frequencies are in a certain range. The number of streams can be switched according to the interplay of attention and acoustic features [19][20][21]. These studies on the interaction between top-down and bottom-up processes motivated us to ask whether meter perception might also switch depending on the attribute of sound events that we focus on.…”