The effect of stimulus context on the buildup to stream segregation

Abstract

Stream segregation is the process by which the auditory system disentangles the mixture of sound inputs into discrete sources that cohere across time. The length of time required for this to occur is termed the “buildup” period. In the current study, we used the buildup period as an index of how quickly sounds are segregated into constituent parts. Specifically, we tested the hypothesis that stimulus context impacts the timing of the buildup and, therefore, affects when stream segregation is detected. To measure the timing of the buildup we recorded the Mismatch Negativity component (MMN) of event-related brain potentials (ERPs), during passive listening, to determine when the streams were neurophysiologically segregated. In each condition, a pattern of repeating low (L) and high (H) tones (L-L-H) was presented in trains of stimuli separated by silence, with the H tones forming a simple intensity oddball paradigm and the L tones serving as distractors. To determine the timing of the buildup, probe tones occurred in two positions of the trains, early (within the buildup period) and late (past the buildup period). The context was manipulated by presenting roving vs. non-roving frequencies across trains in two conditions. MMNs were elicited by intensity probe tones in the Non-Roving condition (early and late positions) and the Roving condition (late position only) indicating that neurophysiologic segregation occurred faster in the Non-Roving condition. This suggests a shorter buildup period when frequency was repeated from train to train. Overall, our results demonstrate that the dynamics of the environment influence the way in which the auditory system extracts regularities from the input. The results support the hypothesis that the buildup to segregation is highly dependent upon stimulus context and that the auditory system works to maintain a consistent representation of the environment when no new information suggests that reanalyzing the scene is necessary.

Selective entrainment of brain oscillations drives auditory perceptual organization

“…The overall pattern of results fits with previous research revealing that the auditory system is able to hold parallel acoustic regularities in multiple time-scales (Costa-Faidella et al 2011; Horvath et al 2001; Pannese et al 2015; Sussman et al 2014; Ulanovsky et al 2004; Winkler et al 2009). Our results support the idea that auditory streaming builds-up over time (Bregman 1978; Sussman et al 2007; Sussman-Fort and Sussman 2014; cf., Deike et al 2012). Further, as revealed by a decrease in PLF and oscillatory power to the rate of task-irrelevant organizations, our results suggest that attention inhibits the representation of the unattended organization rather than enhancing that of the attended one, consistent with behavioral results on spontaneous switching (Pressnitzer and Hupe 2006).…”

Effects of task-switching on neural representations of ambiguous sound input

“…The neural representation or model of the auditory environment must account for the dynamics of such situations as the population of sounds in the auditory scene can change from moment to moment. Most studies investigating the neurobiological basis of auditory scene analysis (the process that allows us to hear distinct auditory sound events in noisy environments) have focused on how the brain disentangles two fixed sets of sound that differ in frequency (Bregman, 1978; Brochard et al, 1999; Carlyon et al, 2001; Cusack et al, 2004; Micheyl et al, 2005; Müller et al, 2005; Rahne et al, 2007; Rahne and Sussman, 2009; Shamma et al, 2011; Sussman, 2005; Sussman et al, 2007a, 2007b; Sussman-Fort and Sussman, 2014; Sussman et al, 1999; Sussman and Steinschneider, 2006; Szalárdy et al, 2013). However, most of the input to our ears overlaps dynamically in time and is often not clearly disambiguated.…”

Linking Man and Machine Through Adaptive Sensory Processing

“…In addition to finalizing the collection and analysis of this data set, we conducted parallel experiments to determine how adaptation contributes to novelty detection. In one project, recently published, we showed that adaptation occurs on different time scales depending upon the dynamics of the listening environment, affecting the time it takes to detect new events in the input (Sussman-Fort & Sussman, 2014). We further demonstrated that attention sped up event detection, but only in more stable listening environments (Sussman-Fort & Sussman, in preparation).…”