Abstract
BackgroundNumerous studies have investigated the neural underpinnings of passive and active deviance and target detection in the well-known auditory oddball paradigm by means of event-related potentials (ERPs) or functional magnetic resonance imaging (fMRI). The present auditory oddball study investigates the spatio-temporal dynamics of passive versus active deviance and target detection by analyzing amplitude modulations of early and late ERPs while at the same time exploring the neural sources underling this modulation with standardized low-resolution brain electromagnetic tomography (sLORETA).MethodsA 64-channel EEG was recorded from twelve healthy right-handed participants while listening to ‘standards’ and ‘deviants’ (500 vs. 1000 Hz pure tones) during a passive (block 1) and an active (block 2) listening condition. During passive listening, participants had to simply listen to the tones. During active listening they had to attend and press a key in response to the deviant tones.ResultsPassive and active listening elicited an N1 component, a mismatch negativity (MMN) as difference potential (whose amplitudes were temporally overlapping with the N1) and a P3 component. N1/MMN and P3 amplitudes were significantly more pronounced for deviants as compared to standards during both listening conditions. Active listening augmented P3 modulation to deviants significantly compared to passive listening, whereas deviance detection as indexed by N1/MMN modulation was unaffected by the task. During passive listening, sLORETA contrasts (deviants > standards) revealed significant activations in the right superior temporal gyrus (STG) and the lingual gyri bilaterally (N1/MMN) as well as in the left and right insulae (P3). During active listening, significant activations were found for the N1/MMN in the right inferior parietal lobule (IPL) and for the P3 in multiple cortical regions (e.g., precuneus).DiscussionThe results provide evidence for the hypothesis that passive as well as active deviance and target detection elicit cortical activations in spatially distributed brain regions and neural networks including the ventral attention network (VAN), dorsal attention network (DAN) and salience network (SN). Based on the temporal activation of the neural sources underlying ERP modulations, a neurophysiological model of passive and active deviance and target detection is proposed which can be tested in future studies.
Highlights
Numerous studies have investigated the neural underpinnings of passive and active deviance and target detection in the well-known auditory oddball paradigm by means of event-related potentials (ERPs) or functional magnetic resonance imaging
Participants responded to all trials with deviant pure tones with high accuracy (M = 74.8, SD = 0.7)
Passive tone oddball paradigm—ERPs (N1, MMN and P3) Passive listening to deviant and standard pure tones elicited an N1 component as well as a P300 component
Summary
Numerous studies have investigated the neural underpinnings of passive and active deviance and target detection in the well-known auditory oddball paradigm by means of event-related potentials (ERPs) or functional magnetic resonance imaging (fMRI). The essential ability of deviance detection has been studied mostly in the so-called oddball paradigm [2] In this experimental paradigm two auditory stimuli (e.g., pure tones) are presented as targets (deviants) or as non-targets (standards). Deviants are usually embedded in a continuous stream of standards and differ from standards in at least one perceptual dimension (for instance, a difference in frequency, pitch or loudness). This difference in stimulus presentation frequency and in physical stimulus properties between deviants and standards seems sufficient to prioritize processing of deviants over standards during passive and involuntary (bottom-up) stimulus processing. Detection of deviant stimuli may benefit from active, i.e., voluntary (top-down) controlled stimulus processing, for instance, when deviants are actively attended by the participants as targets for task-related voluntary discrimination of deviants and standards [3]
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