Noise in Brain Activity Engenders Perception and Influences Discrimination Sensitivity

Bernasconi, Fosco; Lucia, Marzia De; Tzovara, Athina; Manuel, Aurélie L.; Murray, Micah M.; Spierer, Lucas

doi:10.1523/jneurosci.3715-11.2011

Cited by 31 publications

(47 citation statements)

References 59 publications

(67 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A multivariate pattern analysis based on Bayesian logistic regression performed poorly in comparison to a similar analysis applied to visual stimuli; and in about half of the subjects they obtained chance-level classification accuracy. Despite that, in a more challenging experimental paradigm, Bernasconi et al (2011) could show that the same single-trial analysis as applied in the present study lead to an above-chance prediction of subjects auditory percepts while listening to acoustically identical auditory stimuli that were erroneously perceived as differing in pitch or duration.…”

Section: Multivariate Decodingmentioning

confidence: 69%

“…We decoded semantic categories at a single-trial level by identifying modulations of the voltage topographies, previously normalized by the strength of their activity (Bernasconi et al, 2011;De Lucia et al, 2007, 2010bMurray et al, 2009;Tzovara et al, 2012). Through multivariate decoding, we could establish whether specific differential activity in the EEG response to living and man-made sounds can be exploited to accurately decode a sound's semantic category.…”

Section: Discussionmentioning

confidence: 99%

“…We based our analyses on a multivariate decoding approach revealing at which latency it is possible to accurately decode the semantic category of the sounds in presence or not of correct categorization. Importantly, our method is based on modulation in voltage topographies (Bernasconi et al, 2011;De Lucia et al, 2007, 2010bMurray et al, 2009;Tzovara et al, 2012;Tzovara et al, in press) which takes advantage of the overall voltage configuration at the scalp without a priori selection of specific electrode locations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2012

View full text Add to dashboard Cite

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.

show abstract

Section: Multivariate Decodingmentioning

confidence: 69%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2012

View full text Add to dashboard Cite

show abstract

“…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.…”

Section: Single-trial Classificationmentioning

confidence: 99%

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

Chouiter

Tzovara

Diéguez

et al. 2015

Journal of Cognitive Neuroscience

View full text Add to dashboard Cite

Abstract■ Interactions between stimuli's acoustic features and experience-based internal models of the environment enable listeners to compensate for the disruptions in auditory streams that are regularly encountered in noisy environments. However, whether auditory gaps are filled in predictively or restored a posteriori remains unclear. The current lack of positive statistical evidence that internal models can actually shape brain activity as would real sounds precludes accepting predictive accounts of filling-in phenomenon. We investigated the neurophysiological effects of internal models by testing whether single-trial electrophysiological responses to omitted sounds in a rule-based sequence of tones with varying pitch could be decoded from the responses to real sounds and by analyzing the ERPs to the omissions with data-driven electrical neuroimaging methods.The decoding of the brain responses to different expected, but omitted, tones in both passive and active listening conditions was above chance based on the responses to the real sound in active listening conditions. Topographic ERP analyses and electrical source estimations revealed that, in the absence of any stimulation, experience-based internal models elicit an electrophysiological activity different from noise and that the temporal dynamics of this activity depend on attention. We further found that the expected change in pitch direction of omitted tones modulated the activity of left posterior temporal areas 140-200 msec after the onset of omissions. Collectively, our results indicate that, even in the absence of any stimulation, internal models modulate brain activity as do real sounds, indicating that auditory filling in can be accounted for by predictive activity. ■

show abstract

“…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:…”

Section: Eeg Analysis 251 Multivariate Eeg Decoding Based On Accummentioning

confidence: 99%

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

Tzovara

Chavarriaga

Lucia

2015

Journal of Neuroscience Methods

View full text Add to dashboard Cite

h i g h l i g h t s• We outline a novel method for assessing time-unlocked topographic EEG activity.• We apply this method for accurately predicting accept vs. reject decisions.• The time when decisions have been made is modulated by task difficulty.• It occurs well before subjects' button press (∼340 ms).• This time is compatible with a diffusion process, evaluated at behavioral level. a r t i c l e i n f o b s t r a c tBackground: Recent neuroimaging studies suggest that value-based decision-making may rely on mechanisms of evidence accumulation. However no studies have explicitly investigated the time when single decisions are taken based on such an accumulation process. New method: Here, we outline a novel electroencephalography (EEG) decoding technique which is based on accumulating the probability of appearance of prototypical voltage topographies and can be used for predicting subjects' decisions. We use this approach for studying the time-course of single decisions, during a task where subjects were asked to compare reward vs. loss points for accepting or rejecting offers. Results: We show that based on this new method, we can accurately decode decisions for the majority of the subjects. The typical time-period for accurate decoding was modulated by task difficulty on a trial-bytrial basis. Typical latencies of when decisions are made were detected at ∼500 ms for 'Easy' vs. ∼700 ms for 'Hard' decisions, well before subjects' response (∼340 ms). Importantly, this decision time correlated with the drift rates of a diffusion model, evaluated independently at the behavioral level. Comparison with existing method(s):We compare the performance of our algorithm with logistic regression and support vector machine and show that we obtain significant results for a higher number of subjects than with these two approaches. We also carry out analyses at the average event-related potential level, for comparison with previous studies on decision-making. Conclusions: We present a novel approach for studying the timing of value-based decision-making, by accumulating patterns of topographic EEG activity at single-trial level.

show abstract

Noise in Brain Activity Engenders Perception and Influences Discrimination Sensitivity

Cited by 31 publications

References 59 publications

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

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

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

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

Contact Info

Product

Resources

About