Auditory perceptual decision-making based on semantic categorization of environmental sounds

Abstract

Discriminating complex sounds relies on multiple stages of differential brain activity. The specific roles of these stages and their links to perception were the focus of the present study. We presented 250 ms duration sounds of living and man-made objects while recording 160-channel electroencephalography (EEG). Subjects categorized each sound as that of a living, man-made or unknown item. We tested whether/when the brain discriminates between sound categories even when not transpiring behaviorally. We applied a single-trial classifier that identified voltage topographies and latencies at which brain responses are most discriminative. For sounds that the subjects could not categorize, we could successfully decode the semantic category based on differences in voltage topographies during the 116-174 ms post-stimulus period. Sounds that were correctly categorized as that of a living or man-made item by the same subjects exhibited two periods of differences in voltage topographies at the single-trial level. Subjects exhibited differential activity before the sound ended (starting at 112 ms) and on a separate period at~270 ms post-stimulus onset. Because each of these periods could be used to reliably decode semantic categories, we interpreted the first as being related to an implicit tuning for sound representations and the second as being linked to perceptual decision-making processes. Collectively, our results show that the brain discriminates environmental sounds during early stages and independently of behavioral proficiency and that explicit sound categorization requires a subsequent processing stage.

Prediction of awakening from hypothermic postanoxic coma based on auditory discrimination

“…32,33 The models are computed through an expectation-maximization algorithm 36 for each patient and recording (TH/NT) separately, using only one part of the available data (training data set, consisting of 90% of the artifact-free single trials). They are then fitted back to the single trials of the training data set by computing posterior probabilities.…”

“…Full details about this algorithm have been reported elsewhere. 32,33 Here, we applied this algorithm with the same parameters as in our preliminary study based on the same paradigm and type of patients. 21 Outcome prediction was based on the change of decoding performance during NT (AUC NT ) versus during TH (AUC TH ) and specifically on the percentage change in AUC values: 100 3 (AUC NT 2 AUC TH )/AUC TH .…”

Experience-based Auditory Predictions Modulate Brain Activity to Silence as Do Real Sounds

“…This approach has been successfully used in the past to decode EEG responses to auditory stimuli (Cossy, Tzovara, Simonin, Rossetti, & De Lucia, 2014;Tzovara et al, 2013;De Lucia, Tzovara, Bernasconi, Spierer, & Murray, 2012;Bernasconi et al, 2011). Here, we applied this algorithm in the groups of passive and active participants separately.…”

Quantifying the time for accurate EEG decoding of single value-based decisions

“…The employed approach consists of modeling single-trial voltage topographies (i.e. the configuration of electric activity across all electrodes at any given instant) based on Mixture of Gaussians models (GMM) (see similar applications of the GMM for single-trial EEG analysis in (Bernasconi et al, 2011;Cossy et al, 2014;De Lucia et al, 2012). The probability distribution of a GMM model with Q Gaussians in total was computed in an N-dimensional space (N = total number of electrodes) as:…”