Bayesian inference for stationary points in Gaussian process regression models for event-related potentials analysis.

Stationary points embedded in the derivatives are often critical for a model to be interpretable and may be considered as key features of interest in many applications. We propose a semiparametric Bayesian model to efficiently infer the locations of stationary points of a nonparametric function, which also produces an estimate of the function. We use Gaussian processes as a flexible prior for the underlying function and impose derivative constraints to control the function's shape via conditioning. We develop an inferential strategy that intentionally restricts estimation to the case of at least one stationary point, bypassing possible mis-specifications in the number of stationary points and avoiding the varying dimension problem that often brings in computational complexity. We illustrate the proposed methods using simulations and then apply the method to the estimation of event-related potentials derived from electroencephalography (EEG) signals. We show how the proposed method automatically identifies characteristic components and their latencies at the individual level, which avoids the excessive averaging across subjects that is routinely done in the field to obtain smooth curves. By applying this approach to EEG data collected from younger and older adults during a speech perception task, we are able to demonstrate how the time course of speech perception processes changes with age.

Early lexical influences on sublexical processing in speech perception: Evidence from electrophysiology.

Contextual information influences how we perceive speech, but it remains unclear at which level of processing contextual information merges with acoustic information. Theories differ on whether early stages of speech processing, like sublexical processing during which articulatory features and portions of speech sounds are identified, are strictly feed-forward or are influenced by semantic and lexical context. In the current study, we investigate the time-course of lexical context effects on judgments about the individual sounds we perceive by recording electroencephalography as an online measure of speech processing while subjects engage in a lexically biasing phoneme categorization task. We find that lexical context modulates the amplitude of the N100, an ERP component linked with sublexical processes in speech perception. We demonstrate that these results can be modeled in an interactive speech perception model and are not well fit by any established feed-forward mechanisms of lexical bias. These results support interactive speech perception theories over feed-forward theories in which sublexical speech perception processes are only driven by bottom-up information.