The tradeoff hypothesis in the speech–gesture relationship claims that (a) when gesturing gets harder, speakers will rely relatively more on speech, and (b) when speaking gets harder, speakers will rely relatively more on gestures. We tested the second part of this hypothesis in an experimental collaborative referring paradigm where pairs of participants (directors and matchers) identified targets to each other from an array visible to both of them. We manipulated two factors known to affect the difficulty of speaking to assess (...) their effects on the gesture rate per 100 words. The first factor, codability, is the ease with which targets can be described. The second factor, repetition, is whether the targets are old or new (having been already described once or twice). We also manipulated a third factor, mutual visibility, because it is known to affect the rate and type of gesture produced. None of the manipulations systematically affected the gesture rate. Our data are thus mostly inconsistent with the tradeoff hypothesis. However, the gesture rate was sensitive to concurrent features of referring expressions, suggesting that gesture parallels aspects of speech. We argue that the redundancy between speech and gesture is communicatively motivated. (shrink)

A scientific claim is ageneralizationbased on a reported statistically significant effect. The reproducibility of that claim is its scientific meaning. Anything not explicitly mentioned in a scientific claim as a limitation of the claim's scope means that it implicitly generalizes over these unmentioned aspects. Hence, so-called “conceptual” replications that differ in these unmentioned aspects from the original study are legitimate, and necessary to test the generalization implied by the original study's claim.

Human communication is often thought about in terms of transmitted messages in a conventional code like a language. But communication requires a specialized interactive intelligence. Senders have to be able to perform recipient design, while receivers need to be able to do intention recognition, knowing that recipient design has taken place. To study this interactive intelligence in the lab, we developed a new task that taps directly into the underlying abilities to communicate in the absence of a conventional code. We (...) show that subjects are remarkably successful communicators under these conditions, especially when senders get feedback from receivers. Signaling is accomplished by the manner in which an instrumental action is performed, such that instrumentally dysfunctional components of an action are used to convey communicative intentions. The findings have important implications for the nature of the human communicative infrastructure, and the task opens up a line of experimentation on human communication. (shrink)

We used a new method called “Ghost-in-the-Machine” (GiM) to investigate social interactions with a robotic bartender taking orders for drinks and serving them. Using the GiM paradigm allowed us to identify how human participants recognize the intentions of customers on the basis of the output of the robotic recognizers. Specifically, we measured which recognizer modalities (e.g., speech, the distance to the bar) were relevant at different stages of the interaction. This provided insights into human social behavior necessary for the development (...) of socially competent robots. When initiating the drink-order interaction, the most important recognizers were those based on computer vision. When drink orders were being placed, however, the most important information source was the speech recognition. Interestingly, the participants used only a subset of the available information, focussing only on a few relevant recognizers while ignoring others. This reduced the risk of acting on erroneous sensor data and enabled them to complete service interactions more swiftly than a robot using all available sensor data. We also investigated socially appropriate response strategies. In their responses,the participants preferred to use the same modality as the customer’s requests, e.g., they tended to respond verbally to verbal requests. Also, they added redundancy to their responses, for instance by using echo questions. We argue that incorporating the social strategies discovered with the GiM paradigm in multimodal grammars of human–robot interactions improves the robustness and the ease-of-use of these interactions, and therefore provides a smoother user experience. (shrink)

People with aphasia use gestures not only to communicate relevant content but also to compensate for their verbal limitations. The Sketch Model (De Ruiter, 2000) assumes a flexible relationship between gesture and speech with the possibility of a compensatory use of the two modalities. In the successor of the Sketch Model, the AR‐Sketch Model (De Ruiter, 2017), the relationship between iconic gestures and speech is no longer assumed to be flexible and compensatory, but instead iconic gestures are assumed to express (...) information that is redundant to speech. In this study, we evaluated the contradictory predictions of the Sketch Model and the AR‐Sketch Model using data collected from people with aphasia as well as a group of people without language impairment. We only found compensatory use of gesture in the people with aphasia, whereas the people without language impairments made very little compensatory use of gestures. Hence, the people with aphasia gestured according to the prediction of the Sketch Model, whereas the people without language impairment did not. We conclude that aphasia fundamentally changes the relationship of gesture and speech. (shrink)

Any complete theory of speaking must take the dialogical function of language use into account. Pickering & Garrod (P&G) make some progress on this point. However, we question whether their interactive alignment model is the optimal approach. In this commentary, we specifically criticize (1) their notion of alignment being implemented through priming, and (2) their claim that self-monitoring can occur at all levels of linguistic representation.