Biology subjects

Synigal, S. R.

Publications and source records attributed to Synigal, S. R..

3 recordsLinked to original sources

bioRxiv2026

Is that clear? Robust electrophysiological measures of the effects of prior knowledge on degraded speech perception.

Perception and sensation are not synonymous. Rather, perception is a process whereby sensory input is organized and interpreted in a behaviorally relevant way based on memory, experience, and context. One specific framework that is commonly invoked to explain perception is that of Bayesian inference. This framework casts perception as a probabilistic process whereby imprecise sensory data are combined with prior knowledge (prior) to determine what is consciously perceived (the posterior probability), which reflects the brains best guess as to the cause(s) of the sensory data. A striking behavioral example of how prior information can influence perception is seen in studies in which degraded speech is rendered intelligible by presenting information about the speech content in advance. Neurophysiological studies of this phenomenon have primarily focused on how it affects neural indices of low-level sensory encoding. The size of any reported effects on these indices tends to be much smaller - and much less consistent - than the notably large effects on perception that come with prior information. In the present study, we recorded EEG from 27 healthy adult participants (16 female) as they listened to degraded speech clips that were preceded by matching or mismatching text. Prior knowledge in the form of matching text led to a large perceptual pop-out effect when listening to degraded speech. Analyses of the resulting EEG revealed: 1) significant but relatively weak effects of prior information on EEG measures of the linguistic encoding of speech; and 2) a very large effect of prior information on an EEG signal that resembles a well-established neural index of perceptual evidence accumulation and that was strongly related to speech intelligibility ratings across participants. These EEG signals likely relate to separate components of a Bayesian inferential process during the predictive perception of degraded speech. As such, they have implications for understanding predictive perception more broadly and for future research on perceptual disturbances in clinical populations.

neuroscience↗

bioRxiv2023

Electrophysiological indices of hierarchical speech processing differentially reflect the comprehension of speech in noise

The past few years have seen an increase in the use of encoding models to explain neural responses to natural speech. The goal of these models is to characterize how the human brain converts acoustic energy into distinct linguistic representations that enable everyday speech comprehension. For example, researchers have shown that electroencephalography (EEG) data can be modeled in terms of acoustic features of speech, such as its amplitude envelope or spectrogram, linguistic features such as phonemes and phoneme probability, and higher-level linguistic features like context-based word predictability. However, it is unclear how reliably EEG indices of these speech feature representations reflect comprehension in different listening conditions. To address this, we recorded EEG from neurotypical adults who listened to segments of an audiobook in various levels of background noise. We modeled how their EEG responses reflected a range of acoustic and linguistic speech features and how this tracking varied with behavior across noise levels. EEG tracking of nearly all examined features showed SNR-dependent changes in unique variance explained, with the largest changes occurring for linguistic features. We hypothesized that only higher-level feature tracking would predict behavior but instead found that both high and low-level features were associated with behavioral scores depending on the noise level. EEG markers of the influence of top-down, context-based prediction on bottom-up acoustic processing also correlated with behavior. These findings help characterize the relationship between brain and behavior by comprehensively linking hierarchical indices of neural speech processing to language comprehension metrics. SIGNIFICANCE STATEMENTAcoustic and linguistic features of speech have been shown to be consistently tracked by neural activity even in noisy conditions. However, it is unclear how signatures of low- and high-level features covary with one another and relate to behavior across these listening conditions. Here, we find that linguistic (phonetic feature and word probability-based feature) processing is affected by noise more than low-level acoustic feature processing. We also find that behavioral performance is associated with acoustic, phonetic, and lexical surprisal tracking, and that these associations depend on background noise levels. These results extend our understanding of how various speech features are comparatively reflected in electrical brain activity and how they relate to perception in challenging listening conditions.

neuroscience↗

bioRxiv2019

Including measures of high gamma power can improve the decoding of natural speech from EEG

The human auditory system is adept at extracting information from speech in both single-speaker and multi-speaker situations. This involves neural processing at the rapid temporal scales seen in natural speech. Non-invasive brain imaging (electro-/magnetoencephalography [EEG/MEG]) signatures of such processing have shown that the phase of neural activity below 16 Hz tracks the dynamics of speech, whereas invasive brain imaging (electrocorticography [ECoG]) has shown that such rapid processing is even more strongly reflected in the power of neural activity at high frequencies (around 70-150 Hz; known as high gamma). The aim of this study was to determine if high gamma power in scalp recorded EEG carries useful stimulus-related information, despite its reputation for having a poor signal to noise ratio. Furthermore, we aimed to assess whether any such information might be complementary to that reflected in well-established low frequency EEG indices of speech processing. We used linear regression to investigate speech envelope and attention decoding in EEG at low frequencies, in high gamma power, and in both signals combined. While low frequency speech tracking was evident for almost all subjects as expected, high gamma power also showed robust speech tracking in a minority of subjects. This same pattern was true for attention decoding using a separate group of subjects who undertook a cocktail party attention experiment. For the subjects who showed speech tracking in high gamma power, the spatiotemporal characteristics of that high gamma tracking differed from that of low-frequency EEG. Furthermore, combining the two neural measures led to improved measures of speech tracking for several subjects. Overall, this indicates that high gamma power EEG can carry useful information regarding speech processing and attentional selection in some subjects and combining it with low frequency EEG can improve the mapping between natural speech and the resulting neural responses.

neuroscience↗