Publications

Abstract

In face-to-face conversation, people use speech and gesture to convey meaning. Seeing gestures alongside speech facilitates comprehenders’ language processing, but crucially, the mechanisms underlying this facilitation remain unclear. We investigated whether comprehenders use the semantic information in gestures, typically preceding related speech, to predict upcoming meaning. Dutch adults listened to questions asked by a virtual avatar. Questions were accompanied by an iconic gesture (e.g., typing) or meaningless control movement (e.g., arm scratch) followed by a short pause and target word (e.g., “type”). A Cloze experiment showed that gestures improved explicit predictions of upcoming target words. Moreover, an EEG experiment showed that gestures reduced alpha and beta power during the pause, indicating anticipation, and reduced N400 amplitudes, demonstrating facilitated semantic processing. Thus, comprehenders use iconic gestures to predict upcoming meaning. Theories of linguistic prediction should incorporate communicative bodily signals as predictive cues to capture how language is processed in face-to-face interaction.

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.

Abstract

In face-to-face conversation, recipients might use the bodily movements of the speaker (e.g. gestures) to facilitate language processing. It has been suggested that one way through which this facilitation may happen is prediction. However, for this to be possible, gestures would need to precede speech, and it is unclear whether this is true during natural conversation.
In a corpus of Dutch conversations, we annotated hand gestures that represent semantic information and occurred during questions, and the word(s) which corresponded most closely to the gesturally depicted meaning. Thus, we tested whether representational gestures temporally precede their lexical affiliates. Further, to see whether preceding gestures may indeed facilitate language processing, we asked whether the gesture-speech asynchrony predicts the response time to the question the gesture is part of.
Gestures and their strokes (most meaningful movement component) indeed preceded the corresponding lexical information, thus demonstrating their predictive potential. However, while questions with gestures got faster responses than questions without, there was no evidence that questions with larger gesture-speech asynchronies get faster responses. These results suggest that gestures indeed have the potential to facilitate predictive language processing, but further analyses on larger datasets are needed to test for links between asynchrony and processing advantages.

Abstract

Research into sound-symbolism has shown that people can
consistently associate certain pseudo-words with certain referents;
for instance, pseudo-words with rounded vowels and
sonorant consonants are linked to round shapes, while pseudowords
with unrounded vowels and obstruents (with a noncontinuous
airflow), are associated with sharp shapes. Such
sound-symbolic associations have been proposed to arise from
cross-modal abstraction processes. Here we assess the link between
sound-symbolism and cross-modal abstraction by testing
dyslexic individuals’ ability to make sound-symbolic associations.
Dyslexic individuals are known to have deficiencies
in cross-modal processing. We find that dyslexic individuals
are impaired in their ability to make sound-symbolic associations
relative to the controls. Our results shed light on the cognitive
underpinnings of sound-symbolism by providing novel
evidence for the role —and disruptability— of cross-modal abstraction
processes in sound-symbolic eects.