Currently, production and comprehension are regarded as quite distinct in accounts of language processing. In rejecting this dichotomy, we instead assert that producing and understanding are interwoven, and that this interweaving is what enables people to predict themselves and each other. We start by noting that production and comprehension are forms of action and action perception. We then consider the evidence for interweaving in action, action perception, and joint action, and explain such evidence in terms of prediction. Specifically, we assume (...) that actors construct forward models of their actions before they execute those actions, and that perceivers of others' actions covertly imitate those actions, then construct forward models of those actions. We use these accounts of action, action perception, and joint action to develop accounts of production, comprehension, and interactive language. Importantly, they incorporate well-defined levels of linguistic representation. We show how speakers and comprehenders use covert imitation and forward modeling to make predictions at these levels of representation, how they interweave production and comprehension processes, and how they use these predictions to monitor the upcoming utterances. We show how these accounts explain a range of behavioral and neuroscientific data on language processing and discuss some of the implications of our proposal. (shrink)

The human sentence processor is able to make rapid predictions about upcoming linguistic input. For example, upon hearing the verb eat, anticipatory eye-movements are launched toward edible objects in a visual scene. However, the cognitive mechanisms that underlie anticipation remain to be elucidated in ecologically valid contexts. Previous research has, in fact, mainly used clip-art scenes and object arrays, raising the possibility that anticipatory eye-movements are limited to displays containing a small number of objects in a visually impoverished context. In (...) Experiment 1, we confirm that anticipation effects occur in real-world scenes and investigate the mechanisms that underlie such anticipation. In particular, we demonstrate that real-world scenes provide contextual information that anticipation can draw on: When the target object is not present in the scene, participants infer and fixate regions that are contextually appropriate. Experiment 2 investigates whether such contextual inference requires the co-presence of the scene, or whether memory representations can be utilized instead. The same real-world scenes as in Experiment 1 are presented to participants, but the scene disappears before the sentence is heard. We find that anticipation occurs even when the screen is blank, including when contextual inference is required. We conclude that anticipatory language processing is able to draw upon global scene representations to make contextual inferences. These findings are compatible with theories assuming contextual guidance, but posit a challenge for theories assuming object-based visual indices. (shrink)

Pickering & Garrod's (P&G's) framework assumes an efference copy based on the interlocutor's intentions. Yet, elaborate attribution of intentions may not always be necessary for online prediction. Instead, contextual cues such as speaker gaze can provide similar information with a lower demand on processing resources.

Our target article proposed that language production and comprehension are interwoven, with speakers making predictions of their own utterances and comprehenders making predictions of other people's utterances at different linguistic levels. Here, we respond to comments about such issues as cognitive architecture and its neural basis, learning and development, monitoring, the nature of forward models, communicative intentions, and dialogue.

Gaze following occurs automatically in social interactions, but the degree to which gaze is followed depends on whether an agent is perceived to have a mind, making its behavior socially more relevant for the interaction. Mind perception also modulates the attitudes we have towards others, and deter-mines the degree of empathy, prosociality and morality invested in social interactions. Seeing mind in others is not exclusive to human agents, but mind can also be ascribed to nonhuman agents like robots, as long (...) as their appearance and/or behavior allows them to be perceived as intentional beings. Previous studies have shown that human appearance and reliable behavior induce mind perception to robot agents, and positively affect attitudes and performance in human-robot interaction. What has not been investigated so far is whether different triggers of mind perception have an independent or interactive effect on attitudes and performance in human-robot interaction. We examine this question by manipulating agent appearance (human vs. robot) and behavior (reliable vs. random) within the same paradigm and examine how congruent (human/reliable vs. robot/random) versus incongruent (human/random vs. robot/reliable) combinations of these triggers affect performance (i.e., gaze fol-lowing) and attitudes (i.e., agent ratings) in human-robot interaction. The results show that both ap-pearance and behavior affect human-robot interaction but that the two triggers seem to operate in iso-lation, with appearance more strongly impacting attitudes, and behavior more strongly affecting per-formance. The implications of these findings for human-robot interaction are discussed. (shrink)

Beyond the observation that both speakers and listeners rapidly inspect the visual targets of referring expressions, it has been argued that such gaze may constitute part of the communicative signal. In this study, we investigate whether a speaker may, in principle, exploit listener gaze to improve communicative success. In the context of a virtual environment where listeners follow computer-generated instructions, we provide two kinds of support for this claim. First, we show that listener gaze provides a reliable real-time index of (...) understanding even in dynamic and complex environments, and on a per-utterance basis. Second, we show that a language generation system that uses listener gaze to provide rapid feedback improves overall task performance in comparison with two systems that do not use gaze. Aside from demonstrating the utility of listener gaze in situated communication, our findings open the door to new methods for developing and evaluating multi-modal models of situated interaction. (shrink)

Referential gaze has been shown to benefit language processing in situated communication in terms of shifting visual attention and leading to shorter reaction times on subsequent tasks. The present study simultaneously assessed both visual attention and, importantly, the immediate cognitive load induced at different stages of sentence processing. We aimed to examine the dynamics of combining visual and linguistic information in creating anticipation for a specific object and the effect this has on language processing. We report evidence from three visual‐world (...) eye‐tracking experiments, showing that referential gaze leads to a shift in visual attention toward the cued object, which consequently lowers the effort required for processing the linguistic reference. Importantly, perceiving and following the gaze cue did not prove costly in terms of cognitive effort, unless the cued object did not fit the verb selectional preferences. (shrink)

In this paper, we analyze what effects indicators of a shared situation have on a speaker’s persuasiveness by investigating how a robot’s advice is received when it indicates that it is sharing the situational context with its user. In our experiment, 80 participants interacted with a robot that referred to aspects of the shared context: Face tracking indicated that the robot saw the participant, incremental feedback suggested that the robot was following their actions, and comments about, and gestures towards, the (...) shared physical situation and linguistic references to the dialog history indicated to participants that the robot had learned from the interaction and perceived its surroundings. The results show that especially the linguistic and gestural references to the shared context have a significant influence on participants’ compliance with the robot’s suggestions. Thus, indicating that it is ‘in the same boat’ with the user, i.e. that it is sharing the situational context, increases a robot’s persuasiveness during advice giving. (shrink)