Previous research with speech and non-speech stimuli suggested that in audiovisual perception, visual information starting prior to the onset of corresponding sound can provide visual cues, and form a prediction about the upcoming auditory sound. This prediction leads to audiovisual interaction. Auditory and visual perception interact and induce suppression and speeding up of the early auditory event-related potentials such as N1 and P2. To investigate AV interaction, previous research examined N1 and P2 amplitudes and latencies in response to audio only, (...) video only, audiovisual, and control stimuli, and compared AV with auditory perception based on four AV interaction models. The current study addresses how different models of AV interaction express N1 and P2 suppression in music perception. Furthermore, the current study took one step further and examined whether previous musical experience, which can potentially lead to higher N1 and P2 amplitudes in auditory perception, influenced AV interaction in different models. Musicians and non-musicians were presented the recordings of a keyboard /C4/ key being played, as well as CO stimuli. Results showed that AV interaction models differ in their expression of N1 and P2 amplitude and latency suppression. The calculation of model and has consequences for the resulting N1 and P2 difference waves. Furthermore, while musicians, compared to non-musicians, showed higher N1 amplitude in auditory perception, suppression of amplitudes and latencies for N1 and P2 was similar for the two groups across the AV models. Collectively, these results suggest that when visual cues from finger and hand movements predict the upcoming sound in AV music perception, suppression of early ERPs is similar for musicians and non-musicians. Notably, the calculation differences across models do not lead to the same pattern of results for N1 and P2, demonstrating that the four models are not interchangeable and are not directly comparable. (shrink)

What role do linguistic cues on a surface and contextual level have in identifying the intention behind an utterance? Drawing on the wealth of studies and corpora from the computational task of dialog act classification, we studied this question from a cognitive science perspective. We first reviewed the role of linguistic cues in dialog act classification studies that evaluated model performance on three of the most commonly used English dialog act corpora. Findings show that frequency‐based, machine learning, and deep learning (...) methods all yield similar performance. Classification accuracies, moreover, generally do not explain which specific cues yield high performance. Using a cognitive science approach, in two analyses, we systematically investigated the role of cues in the surface structure of the utterance and cues of the surrounding context individually and combined. By comparing the explained variance, rather than the prediction accuracy of these cues in a logistic regression model, we found that (1) while surface and contextual linguistic cues can complement each other, surface linguistic cues form the backbone in human dialog act identification, (2) with word frequency statistics being particularly important for the dialog act, and (3) the similar trends across corpora, despite differences in the type of dialog, corpus setup, and dialog act tagset. The importance of surface linguistic cues in dialog act classification sheds light on how both computers and humans take advantage of these cues in speech act recognition. (shrink)

Despite increased focus on emotional language, research lacks for the most emotional language: Swearing. We used event-related potentials to investigate whether swear words have content dist...

Conversation is a time-pressured environment. Recognizing a social action (the ‘‘speech act,’’ such as a question requesting information) early is crucial in conversation to quickly understand the intended message and plan a timely response. Fast turns between interlocutors are especially relevant for responses to questions since a long gap may be meaningful by itself. Human language is multimodal, involving speech as well as visual signals from the body, including the face. But little is known about how conversational facial signals contribute (...) to the communication of social actions. Some of the most prominent facial signals in conversation are eyebrow movements. Previous studies found links between eyebrow movements and questions, suggesting that these facial signals could contribute to the rapid recognition of questions. Therefore, we aimed to investigate whether early eyebrow movements (eyebrow frown or raise vs. no eyebrow movement) facilitate question identification. Participants were instructed to view videos of avatars where the presence of eyebrow movements accompanying questions was manipulated. Their task was to indicate whether the utterance was a question or a statement as accurately and quickly as possible. Data were collected using the online testing platform Gorilla. Results showed higher accuracies and faster response times for questions with eyebrow frowns, suggesting a facilitative role of eyebrow frowns for question identification. This means that facial signals can critically contribute to the communication of social actions in conversation by signaling social action-specific visual information and providing visual cues to speakers’ intentions. (shrink)