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The rapid and fluent nature of human communicative interactions strongly suggests the existence of an online mechanism for intention recognition. We motivate and outline a mathematical model that addresses these requirements. Our model provides a way of integrating knowledge about the relationship between linguistic expressions and communicative intentions, through a rapid process of Bayesian update. It enables us to frame predictions about the processes of intention recognition, utterance planning and other-repair mechanisms, and contributes towards a broader theory of communication.

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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 col- laborative 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, coda- bility, 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.

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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 col- laborative 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, coda- bility, 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.

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Human social interaction is studied by researchers in conversation analysis (CA) and psychology, but the dominant methodologies within these two disciplines are very different. Analyzing methodological differences in rela- tion to major developments in the philosophy of science, we suggest that a central difference is that psychologists tend to follow Popper’s falsification- ism in dissociating the context of discovery and the context of justification. In CA, following Garfinkel’s ethnomethodology, these two contexts are much closer to one another, if not inextricable. While this dissociation allows the psychologist a much larger theoretical freedom, because psy- chologists “only” need to validate their theories by generating confirmed predictions from experiments, it also carries the risk of generating theories that are less robust and pertinent to everyday interaction than the body of knowledge accumulated by CA. However, as long as key philosophical differences are well understood, it is not an inherently bad idea to generate predictions from theories and use quantitative and experimental methods to test them. It is both desirable and achievable to find a synthesis between methodologies that combines their strengths and avoids their weaknesses. We discuss a number of challenges that would need to be met and some opportunities that may arise from creating such a synthesis.

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A number of studies from the last decades have demonstrated that the iconic gestures are shaped not only by our mental imagery but also, quite strongly, by structural properties of the accompanying speech. These findings are problematic for the central assumption in the Sketch Model (De Ruiter, 2000) about the function of representational gesture. I suggest a seemingly small but fundamental modification to the processing assumptions in the Sketch Model that not only accommodates the discussed empirical findings, but also explains many other well-known gesture phenomena. The new model also generates new and testable predictions regarding the relationship between gesture and speech.  

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Interaction researchers often tend to identify themselves and others with the methods they are using, rather than with the topic they are studying. I survey
a number of commonly used research methods in human interaction research, and indicate some of their strengths, weaknesses, and relative advantages. I will argue that human interaction is a topic of such massive complexity that it will require not only a multidisciplinary approach, but also, and more importantly, a multimethodological approach. This requires open-mindedness about the use of methods that we happen not to be familiar with. Combining different qualitative and quantitative methods to address similar and converging research questions is essential for gaining progress in the complex field of human interaction.

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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.

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The pragmatics of natural language poses a challenge both at a theoretical level and at a practical level, in part because of the absence of simple one–one mappings between form and meaning. This is exemplified by the recognition of speech act or dialogue act types. The linguistic tradi- tion of research in this area has been primarily taxonomic in its focus and has had relatively little to say about the processes underpinning speech act recognition in real time. Similarly, the rich body of applied computational research on dialogue has chief ly addressed the practical consid- erations of how to build working artificial systems that can handle natural language. Neverthe- less, both strands of research have the potential to offer useful psycholinguistic insights, which have only recently begun to be explored. This course presents some of the relevant background and discusses the relevance of computational and theoretical dialogue work to active research questions in linguistics.

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Computational Approaches to the Pragmatics Problem

Unlike many aspects of human language, pragmatics involves a systematic many-to-many mapping between form and meaning. This renders the computational problems of encoding and decoding meaning especially challenging, both for humans in normal conversation and for artificial dialogue systems that need to understand their users’ input. A particularly striking example of this difficulty is the recognition of speech act or dialogue act types. In this review, we discuss why this is a problem and why its solution is potentially relevant both for our understanding of human interaction and for the implementation of artificial systems. We examine some of the theoretical and practical attempts that have been made to overcome this problem and consider how the field might develop in the near future.

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Abstract. Despite its importance for human interaction, dialogue act recognition has attracted little psycholinguistic attention. We argue that the ability of humans to solve this problem, rapidly and online, speaks to the existence of specific mechanisms. We discuss a model of dialogue act recognition that accommodates multiple sources of information (from multiple modalities) in a single probabilistic and Bayesian framework, and consider some of the implications of how this model treats classically pragmatic aspects of interaction.