Marking prosodic prominence for voice assistant and human addressees.

Prosodic prominence (realized with phonetic features such as increased intensity, duration, and pitch, among others) is thought to guide listeners' attention by focusing new information. This study investigates production and perception of prosodic prominence toward two types of addressees: a human and a voice assistant interlocutor. We examine how the language system adapts to this increasingly common technology, by testing whether prosodic prominence is subject to when addressing an interlocutor that is consistently rated as having less communicative ability.

Decoding semantic relatedness and prediction from EEG: A classification method comparison.

Machine-learning (ML) decoding methods have become a valuable tool for analyzing information represented in electroencephalogram (EEG) data. However, a systematic quantitative comparison of the performance of major ML classifiers for the decoding of EEG data in neuroscience studies of cognition is lacking. Using EEG data from two visual word-priming experiments examining well-established N400 effects of prediction and semantic relatedness, we compared the performance of three major ML classifiers that each use different algorithms: support vector machine (SVM), linear discriminant analysis (LDA), and random forest (RF).