Dynamic Range Adaptation to Spectral Stimulus Statistics in Human Auditory Cortex

Herrmann, Björn; Schlichting, Nadine; Obleser, Jonas

doi:10.1523/jneurosci.3974-13.2014

Cited by 50 publications

(45 citation statements)

References 28 publications

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“…While our findings provide no support for the neural adaptation model, they also underscore the distinction between the attenuation of the N1 response to the second sound of a pair (or train) and the amplitude decreases associated with the degree of similarity between sounds that have been reported in studies using long sequences of alternating (Näätänen et al, ; Yagcioglu & Ungan, ) or randomly varying (Herrmann et al, ) tones. It would be of interest that future investigations address differences between the suppressive mechanisms at work in these two cases.…”

Section: Resultscontrasting

confidence: 79%

“…In studies using pure tones alternating in frequency (Näätänen et al, ; Yagcioglu & Ungan, ) or perceived spatial location (Butler, ; Näätänen et al, ), the N1 has been shown to decrease in amplitude with decreasing differences between the two alternating tones. Herrmann et al recorded N1 responses to pure tones varying randomly in frequency and found that the amplitudes were lower the closer the tone frequency was to the center of the variation range (Herrmann, Henry, Scharinger, & Obleser, ; Herrmann, Schlichting, & Obleser, ). Whereas such results have been interpreted as evidencing stimulus‐specific adaptation, they are not necessarily in conflict with ours.…”

Section: Discussionmentioning

confidence: 99%

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N1 response attenuation and the mismatch negativity (MMN) to within‐ and across‐category phonetic contrasts

2017

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According to the neural adaptation model of the mismatch negativity (MMN), the sensitivity of this event-related response to both acoustic and categorical information in speech sounds can be accounted for by assuming that (a) the degree of overlapping between neural representations of two sounds depends on both the acoustic difference between them and whether or not they belong to distinct phonetic categories, and (b) a release from stimulus-specific adaptation causes an enhanced N1 obligatory response to infrequent deviant stimuli. On the basis of this view, we tested in Experiment 1 whether the N1 response to the second sound of a pair (S ) would be more attenuated in pairs of identical vowels compared with pairs of different vowels, and in pairs of exemplars of the same vowel category compared with pairs of exemplars of different categories. The psychoacoustic distance between S and S was the same for all within-category and across-category pairs. While N1 amplitudes decreased markedly from S to S , responses to S were quite similar across pair types, indicating that the attenuation effect in such conditions is not stimulus specific. In Experiment 2, a pronounced MMN was elicited by a deviant vowel sound in an across-category oddball sequence, but not when the exact same deviant vowel was presented in a within-category oddball sequence. This adds evidence that MMN reflects categorical phonetic processing. Taken together, the results suggest that different neural processes underlie the attenuation of the N1 response to S and the MMN to vowels.

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Section: Resultscontrasting

confidence: 79%

Section: Discussionmentioning

confidence: 99%

N1 response attenuation and the mismatch negativity (MMN) to within‐ and across‐category phonetic contrasts

2017

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“…2. Instead of assuming the involvement of different types and numbers of detectors, the neural adaptation hypothesis may offer an alternative explanation for the current results based on the frequency-specific adaptation pattern of the N1 response (e.g., Butler, 1968;Herrmann, Schlichting, & Obleser, 2014;Lanting, Briley, Sumner, & Krumbholz, 2013;May et al, 1999;Näätänen et al, 1988). Although N1 generator structures are frequency specific, the level of specificity is low; that is, for example, a 1000 Hz tone also activates N1 generators that have a "best frequency" at 500 Hz.…”

Section: Discussionmentioning

confidence: 87%

The detection of higher‐order acoustic transitions is reflected in the N1 ERP

2018

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The auditory system features various types of dedicated change detectors enabling the rapid parsing of auditory stimulation into distinct events. The activity of such detectors is reflected by the N1 ERP. Interestingly, certain acoustic transitions show an asymmetric N1 elicitation pattern: whereas first-order transitions (e.g., a change from a segment of constant frequency to a frequency glide [c-to-g change]) elicit N1, higher-order transitions (e.g., glide-to-constant [g-to-c] changes) do not. Consensus attributes this asymmetry to the absence of any available sensory mechanism that is able to rapidly detect higher-order changes. In contrast, our study provides compelling evidence for such a mechanism. We collected electrophysiological and behavioral data in a transient-detection paradigm. In each condition, a random (50%-50%) sequence of two types of tones occurred, which did or did not contain a transition (e.g., c-to-g and constant stimuli or g-to-c and glide tones). Additionally, the rate of pitch change of the glide varied (i.e., 10 vs. 40 semitones per second) in order to increase the number of responding neural assemblies. The rate manipulation modulated transient ERPs and behavioral detection performance for g-to-c transitions much stronger than for c-to-g transitions. The topographic and tomographic analyses suggest that the N1 response to c-to-g and also to g-to-c transitions emerged from the superior temporal gyrus. This strongly supports a sensory mechanism that allows the fast detection of higher-order changes.

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“…Herrmann et al 2014). In our implementation, participants listened to a series of 10 tone sequences with a maximum duration of 15 s per sequence.…”

Section: Introductionmentioning

confidence: 99%

Audio-visual synchrony and feature-selective attention co-amplify early visual processing

Keitel

Müller

2015

Exp Brain Res

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2 ABSTRACT Our brain relies on neural mechanisms of selective attention and converging sensory processing to efficiently cope with rich and unceasing multisensory inputs. One prominent assumption holds that audio-visual synchrony can act as a strong attractor for spatial attention. Here, we tested for a similar effect of audio-visual synchrony on feature-selective attention. We presented two superimposedGabor patches that differed in colour and orientation. On each trial participants were cued to selectively attend to one of the two patches. Over time, spatial frequencies of both patches varied sinusoidally at distinct rates (3.14 and 3.63 Hz) giving rise to pulse-like percepts. A simultaneously presented pure tone carried a frequency modulation at the pulse rate of one of the two visual stimuli to introduce audio-visual synchrony. Pulsed stimulation elicited distinct time-locked oscillatory electrophysiological brain responses. These steady-state responses (SSRs) were quantified in the spectral domain to examine individual stimulus processing under conditions of synchronous vs.asynchronous tone presentation and when respective stimuli were attended vs. unattended. We found that both, attending to the colour of a stimulus and its synchrony with the tone, enhanced its processing. Moreover, both gain effects combined linearly for attended in-sync stimuli. Our results suggest that audio-visual synchrony can attract attention to specific stimulus features when stimuli overlap in space.

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Dynamic Range Adaptation to Spectral Stimulus Statistics in Human Auditory Cortex

Cited by 50 publications

References 28 publications

N1 response attenuation and the mismatch negativity (MMN) to within‐ and across‐category phonetic contrasts

N1 response attenuation and the mismatch negativity (MMN) to within‐ and across‐category phonetic contrasts

The detection of higher‐order acoustic transitions is reflected in the N1 ERP

Audio-visual synchrony and feature-selective attention co-amplify early visual processing

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