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.

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)

We consider the ways humans engage in social epistemic actions, to guide each other's attention, prediction, and learning processes towards salient information, at the timescale of online social interaction and joint action. This parallels the active guidance of other's attention, prediction, and learning processes at the longer timescale of niche construction and cultural practices, as discussed in the target article.

Pickering & Garrod (P&G) explain dialogue dynamics in terms of forward modeling and prediction-by-simulation mechanisms. Their theory dissolves a strict segregation between production and comprehension processes, and it links dialogue to action-based theories of joint action. We propose that the theory can also incorporate intentional strategies that increase communicative success: for example, signaling strategies that help remaining predictable and forming common ground.

In this paper, we evaluate the proposal that a central function of commitments within joint action is to reduce various kinds of uncertainty, and that this accounts for the prevalence of commitments in joint action. While this idea is prima facie attractive, we argue that it faces two serious problems. First, commitments can only reduce uncertainty if they are credible, and accounting for the credibility of commitments proves not to be straightforward. Second, there are many other ways in which uncertainty (...) is commonly reduced within joint actions, which raises the possibility that commitments may be superfluous. Nevertheless, we argue that the existence of these alternative uncertainty reduction processes does not make commitments superfluous after all but, rather, helps to explain how commitments may contribute in various ways to uncertainty reduction. (shrink)

We studied collaborative skill acquisition in a dynamic setting with the game Co-op Space Fortress. While gaining expertise, the majority of subjects became increasingly consistent in the role they adopted without being able to communicate. Moreover, they acted in anticipation of the future task state. We constructed a collaborative skill acquisition model in the cognitive architecture ACT-R that reproduced subject skill acquisition trajectory. It modeled role adoption through reinforcement learning and predictive processes through motion extrapolation and learned relevant control parameters (...) using both a reinforcement learning procedure and a new to ACT-R supervised learning procedure. This is the first integrated cognitive model of collaborative skill acquisition and, as such, gives us valuable insights into the multiple cognitive processes that are involved in learning to collaborate. (shrink)

In this paper, we evaluate the proposal that a central function of commitments within joint action is to reduce various kinds of uncertainty, and that this accounts for the prevalence of commitments in joint action. While this idea is prima facie attractive, we argue that it faces two serious problems. First, commitments can only reduce uncertainty if they are credible, and accounting for the credibility of commitments proves not to be straightforward. Second, there are many other ways in which uncertainty (...) is commonly reduced within joint actions, which raises the possibility that commitments may be superfluous. Nevertheless, we argue that the existence of these alternative uncertainty reduction processes does not make commitments superfluous after all but, rather, helps to explain how commitments may contribute in various ways to uncertainty reduction. (shrink)