Publications

Abstract

We here demonstrate that face-to-face spatial orientation induces a special ‘social mode’ for neurocognitive processing during conversation, even in the absence of visibility. Participants conversed face-to-face, face-to-face but visually occluded, and back-to-back to tease apart effects caused by seeing visual communicative signals and by spatial orientation. Using dual-EEG, we found that 1) listeners’ brains engaged more strongly while conversing in face-to-face than back-to-back, irrespective of the visibility of communicative signals, 2) listeners attended to speech more strongly in a back-to-back compared to a face-to-face spatial orientation without visibility; visual signals further reduced the attention needed; 3) the brains of interlocutors were more in sync in a face-to-face compared to a back-to-back spatial orientation, even when they could not see each other; visual signals further enhanced this pattern. Communicating in face-to-face spatial orientation is thus sufficient to induce a special ‘social mode’ which fine-tunes the brain for neurocognitive processing in conversation.

Abstract

Height-pitch associations are claimed to be universal and independent of language, but this claim remains controversial. The present study sheds new light on this debate with a multimodal analysis of individual sound and melody descriptions obtained in an interactive communication paradigm with speakers of Dutch and Farsi. The findings reveal that, in contrast to Dutch speakers, Farsi speakers do not use a height-pitch metaphor consistently in speech. Both Dutch and Farsi speakers’ co-speech gestures did reveal a mapping of higher pitches to higher space and lower pitches to lower space, and this gesture space-pitch mapping tended to co-occur with corresponding spatial words (high-low). However, this mapping was much weaker in Farsi speakers than Dutch speakers. This suggests that cross-linguistic differences shape the conceptualization of pitch and further calls into question the universality of height-pitch associations.

Abstract

Native listeners benefit from both visible speech and iconic gestures to enhance degraded speech comprehension (Drijvers & Ozyürek, 2017). We tested how highly proficient non-native listeners benefit from these visual articulators compared to native listeners. We presented videos of an actress uttering a verb in clear, moderately, or severely degraded speech, while her lips were blurred, visible, or visible and accompanied by a gesture. Our results revealed that unlike native listeners, non-native listeners were less likely to benefit from the combined enhancement of visible speech and gestures, especially since the benefit from visible speech was minimal when the signal quality was not sufficient.

Abstract

In human face-to-face communication, speech is frequently accompanied by visual signals, especially communicative hand gestures. Analyzing these visual signals requires detailed manual annotation of video data, which is often a labor-intensive and time-consuming process. To facilitate this process, we here present SPUDNIG (SPeeding Up the Detection of Non-iconic and Iconic Gestures), a tool to automatize the detection and annotation of hand movements in video data. We provide a detailed description of how SPUDNIG detects hand movement initiation and termination, as well as open-source code and a short tutorial on an easy-to-use graphical user interface (GUI) of our tool. We then provide a proof-of-principle and validation of our method by comparing SPUDNIG’s output to manual annotations of gestures by a human coder. While the tool does not entirely eliminate the need of a human coder (e.g., for false positives detection), our results demonstrate that SPUDNIG can detect both iconic and non-iconic gestures with very high accuracy, and could successfully detect all iconic gestures in our validation dataset. Importantly, SPUDNIG’s output can directly be imported into commonly used annotation tools such as ELAN and ANVIL. We therefore believe that SPUDNIG will be highly relevant for researchers studying multimodal communication due to its annotations significantly accelerating the analysis of large video corpora.