Processing Asymmetry of Transitions between Order and Disorder in Human Auditory Cortex

Abstract

Auditory environments vary as a result of the appearance and disappearance of acoustic sources, as well as fluctuations characteristic of the sources themselves. The appearance of an object is often manifest as a transition in the pattern of ongoing fluctuation, rather than an onset or offset of acoustic power. How does the system detect and process such transitions? Based on magnetoencephalography data, we show that the temporal dynamics and response morphology of the neural temporal-edge detection processes depend in precise ways on the nature of the change. We measure auditory cortical responses to transitions between "disorder," modeled as a sequence of random frequency tone pips, and "order," modeled as a constant tone. Such transitions embody key characteristics of natural auditory edges. Early cortical responses (from ϳ50 ms post-transition) reveal that order-disorder transitions, and vice versa, are processed by different neural mechanisms. Their dynamics suggest that the auditory cortex optimally adjusts to stimulus statistics, even when this is not required for overt behavior. Furthermore, this response profile bears a striking similarity to that measured from another order-disorder transition, between interaurally correlated and uncorrelated noise, a radically different stimulus. This parallelism suggests the existence of a general mechanism that operates early in the processing stream on the abstract statistics of the auditory input, and is putatively related to the processes of constructing a new representation or detecting a deviation from a previously acquired model of the auditory scene. Together, the data reveal information about the mechanisms with which the brain samples, represents, and detects changes in the environment.

Sustained responses for pitch and vowels map to similar sites in human auditory cortex

“…Alternatively, the response could reflect a more basic physical difference between the vowel and control stimuli. Chait et al (2007) found a negative transient at the transition from variable-to constant-frequency tones but not in the inverse direction, which was Fig. 7.…”

Auditory cortex tracks the temporal regularity of sustained noisy sounds

“…and disorder, our results might be compared to those of Chait et al (2005), who investigated the transition between interaurally correlated and uncorrelated noise. They found that brain responses to transitions from uncorrelated to correlated noise lag those from correlated to uncorrelated noise by about 80 ms. 2 In a subsequent study, the same authors investigated the transition between a sequence of constant-frequency tone pips and a sequence of tone pips where the frequency varied randomly between 222 and 2000 Hz (Chait et al, 2007a). The transition from order (constant frequency) to disorder (random frequency) was faster than the reciprocal transition.…”

Comparison of Regularity Detection between Individuals with and without Speech-in-Noise Problems using Electrophysiological Methods

“…In addition to repeating sound at a random frequency and repeating three-sound patterns, sound patterns decreasing and increasing in frequency may be recognized as auditory regularities as they represent fundamental frequency (Fo) trajectories in speech; tracking this is a mechanism for extracting the target message in the presence of masking sounds [16] . These regularities have the potential to evoke specific cortical responses that have been observed in a series of studies by Chait et al [5,6,17] . Besides, cortical responses evoked by regularity detection may have different amplitude and latency characteristics between individuals with varying SIN abilities because of the relation between auditory regularity detection and SIN performance.…”

“…Speech-in-noise (SIN) is related to detecting auditory regularities, a process that has been investigated via cortical responses [4] . For example, a repeating sound at a random frequency and a repeating three-sound pattern appearing after random frequency sounds have been found to be evoking magnetic 100 (M100) responses [electroencephalography (EEG) N1 response] in the root mean square amplitude waveform of magnetoencephalography recordings [5,6] . In fact, cortical implications of auditory processes, ranging from the detection of sound onset to the detection of frequency changes as well as the detection of embedded gaps in continuous noise, are reflected on N1 responses, the magnetic counterpart of which is the M100 response [7][8][9][10][11][12] .…”