Detecting and representing predictable structure during auditory scene analysis

Abstract

We use psychophysics and MEG to test how sensitivity to input statistics facilitates auditory-scene-analysis (ASA). Human subjects listened to ‘scenes’ comprised of concurrent tone-pip streams (sources). On occasional trials a new source appeared partway. Listeners were more accurate and quicker to detect source appearance in scenes comprised of temporally-regular (REG), rather than random (RAND), sources. MEG in passive listeners and those actively detecting appearance events revealed increased sustained activity in auditory and parietal cortex in REG relative to RAND scenes, emerging ~400 ms of scene-onset. Over and above this, appearance in REG scenes was associated with increased responses relative to RAND scenes. The effect of temporal structure on appearance-evoked responses was delayed when listeners were focused on the scenes relative to when listening passively, consistent with the notion that attention reduces ‘surprise’. Overall, the results implicate a mechanism that tracks predictability of multiple concurrent sources to facilitate active and passive ASA.DOI: http://dx.doi.org/10.7554/eLife.19113.001

Contributions of local speech encoding and functional connectivity to audio-visual speech perception

“…Predictions modulate auditory evoked responses in an area specific manner, involve both the ventral and dorsal pathways (Kandylaki et al, 2016; Sohoglu and Chait, 2016), and affect both feedforward and feedback connections (Auksztulewicz and Friston, 2016; Chennu et al, 2016). While an informative visual context facilitates the correction of predictions about expected speech using incoming multisensory evidence, we can only speculate about a direct link between the reported effects and predictive processes.…”

Detecting changes in dynamic and complex acoustic environments

“…Further, a series of recent studies investigated detection of change occurring in first- and second-order sound statistics (Sohoglu and Chait, 2016; Barascud et al, 2016). In particular, these authors probed the detection of appearing or disappearing regular sound sources in an acoustic scene (Sohoglu and Chait, 2016).…”

“…In particular, these authors probed the detection of appearing or disappearing regular sound sources in an acoustic scene (Sohoglu and Chait, 2016). This type of changes featured modifications of first- and second-order sound statistics, which also included an increase in the overall sound level.…”

A Corticothalamic Circuit for Dynamic Switching between Feature Detection and Discrimination

“…Afferent sensory traces undergo another set of transformations upon reaching forebrain sensory areas, where they are contextualized according to internal state, recent stimulus histories, long-term sensory experience and top-down predictions of behavioral relevance (David et al, 2012; McGinley et al, 2015; Mesgarani and Chang, 2013; Polley et al, 2006; Shuler and Bear, 2006; Sohoglu and Chait, 2016). Adaptive modulation of forebrain sensory traces is accomplished through the interaction of long-range neuromodulatory inputs with local excitatory-inhibitory microcircuits (Fu et al, 2014; Kuchibhotla et al, 2016; Letzkus et al, 2011; Marlin et al, 2015; Pi et al, 2013; Pinto et al, 2013; Schneider et al, 2014; Zhou et al, 2014).…”

“…Enhancement or suppression of cortical sensory representations has an immediate and direct impact on perceptual salience, as studied behaviorally (Froemke et al, 2013; Pinto et al, 2013; Sohoglu and Chait, 2016). Studies of sensory processing in humans and non-human primates suggest that cortical networks can enhance detection, segregate stimulus sources and suppress distracting stimuli by organizing the frequency and phase of low-frequency oscillations in the cortical electrical field (Giraud and Poeppel, 2012; Schroeder and Lakatos, 2009).…”