Biology subjects

Herbst, S. K.

Publications and source records attributed to Herbst, S. K..

3 recordsLinked to original sources

bioRxiv2018

Implicit temporal predictability biases slow oscillatory phase in auditory cortex and enhances pitch discrimination sensitivity

Can human listeners use strictly implicit temporal contingencies in auditory input to form temporal predictions, and if so, how are these predictions represented endogenously? To assess this question, we implicitly manipulated foreperiods in an auditory pitch discrimination task. Unbeknownst to participants, the pitch of the standard tone could either be deterministically predictive of the onset of the target tone, or convey no predictive information. Both conditions were presented interleaved in one stream, and separated by variable inter-stimulus intervals such that there was no dominant stimulus rhythm throughout. Even though participants were unaware of the implicit temporal contingencies, pitch discrimination sensitivity (i.e. the slope of the psychometric function) increased when the onset of the target tone was predictable in time (N = 49). Concurrently recorded EEG data (N = 24) revealed that standard tones which initiated temporal predictions evoked a more negative N1 component than non-predictive standards, and were followed by an increase in delta power during the foreperiod. Furthermore, the phase angle of delta oscillations (1-3Hz) evoked by the standard tone predicted pitch discrimination sensitivity at the target tone (1.75 s later on average), which suggests that temporal predictions can be initiated by an optimized delta phase reset. In sum, we show that auditory perception benefits from implicit temporal contingencies, and provide evidence for a role of slow neural oscillations in the endogenous representation of temporal predictions, in absence of exogenously driven entrainment to rhythmic input. Significance StatementTemporal contingencies are ubiquitous in sensory environments, especially in the auditory domain, and have been shown to facilitate perception and action. Yet, how these contingencies in exogenous inputs are transformed into an endogenous representation of temporal predictions is not known. Here, we implicitly induced temporal predictability in the absence of a rhythmic input structure, that is without exogenously driven entrainment of neural oscillations. Our results show that even implicit and non-rhythmic temporal predictions are extracted and used by human observers, underlining the role of timing in sensory processing. Furthermore, our EEG results point towards an instrumental role of delta oscillations in initiating temporal predictions by an optimized phase reset in response to a temporally predictive cue.

neuroscience↗

bioRxiv2017

Unfolding of a noise-invariant neural representation of attended speech

Listening requires selective neural processing of the incoming sound mixture, which in humans is borne out by a surprisingly clean representation of attended-only speech in auditory cortex. How this neural selectivity is achieved even at negative signal-to-noise ratios (SNR) remains unclear. We show that, under such conditions, a late cortical representation (i.e., neural tracking) of the ignored acoustic signal is key to successful separation of attended and distracting talkers (i.e., neural selectivity). We recorded and modelled the electroencephalographic response of 18 participants who attended to one of two simultaneously presented stories, while the SNR between the two talkers varied dynamically. The neural tracking showed an increasing early-to-late attention-biased selectivity. Importantly, acoustically dominant ignored talkers were tracked neurally by late involvement of fronto-parietal regions, which contributed to enhanced neural selectivity. This neural selectivity by way of representing the ignored talker poses a mechanistic neural account of attention under real-life acoustic conditions.

neuroscience↗

bioRxiv2017

Tracking temporal hazard in the human electroencephalogram using a forward encoding model

Human observers automatically extract temporal contingencies from the environment and predict the onset of future events. Temporal predictions are modelled by the hazard function, which describes the instantaneous probability for an event to occur given it has not occurred yet. Here, we tackle the question of whether and how the human brain tracks continuous temporal hazard on a moment-to-moment basis, and how flexibly it adjusts to strictly implicit variations in the hazard function. We applied an encoding-model approach to human electroencephalographic (EEG) data recorded during a pitch-discrimination task, in which we implicitly manipulated temporal predictability of the target tones by varying the interval between cue and target tone (the foreperiod). Critically, temporal predictability was either solely driven by the passage of time (resulting in a monotonic hazard function), or was modulated to increase at intermediate foreperiods (resulting in a modulated hazard function with a peak at the intermediate foreperiod). Forward encoding models trained to predict the recorded EEG signal from different temporal hazard functions were able to distinguish between experimental conditions, showing that implicit variations of temporal hazard bear tractable signatures in the human electroencephalogram. Notably, this tracking signal was reconstructed best from the supplementary motor area (SMA), underlining this areas link to cognitive processing of time. Our results underline the relevance of temporal hazard to cognitive processing, and show that the predictive accuracy of the encoding-model approach can be utilised to track abstract time-resolved stimuli.\n\nSignificance StatementExtracting temporal predictions from sensory input allows to process future input more efficiently and to prepare responses in time. In mathematical terms, temporal predictions can be described by the hazard function, modelling the probability of an event to occur over time. Here, we show that the human EEG tracks temporal hazard in an implicit foreperiod paradigm. Forward encoding models trained to predict the recorded EEG signal from different temporal-hazard functions were able to distinguish between experimental conditions that differed in their build-up of hazard over time. These neural signatures of tracking temporal hazard converge with the extant literature on temporal processing and provide new evidence that the supplementary motor area tracks hazard under strictly implicit timing conditions.

neuroscience↗