We consider a computational model comparing the possible roles of “association” and “simulation” in phonetic decoding, demonstrating that these two routes can contain similar information in some “perfect” communication situations and highlighting situations where their decoding performance differs. We conclude that optimal decoding should involve some sort of fusion of association and simulation in the human brain.

Although we agree with Pickering & Garrod (P&G) that prediction-by-simulation and prediction-by-association are important mechanisms of anticipatory language processing, this commentary suggests that they: (1) overlook other potential mechanisms that might underlie prediction in language processing, (2) overestimate the importance of prediction-by-association in early childhood, and (3) underestimate the complexity and significance of several factors that might mediate prediction during language processing.

Although Pickering & Garrod (P&G) argue convincingly for a unified system for language comprehension and production, they fail to explain how such a system might develop. Using a recent computational model of language acquisition as an example, we sketch a developmental perspective on the integration of comprehension and production. We conclude that only through development can we fully understand the intertwined nature of comprehension and production in adult processing.

Many authors have recently highlighted the importance of prediction for language comprehension. Pickering & Garrod (P&G) are the first to propose a central role for prediction in language production. This is an intriguing idea, but it is not clear what it means for speakers to predict their own utterances, and how prediction during production can be empirically distinguished from production proper.

The neurocognitive evidence that Pickering & Garrod (P&G) cite in favor of positing forward models in speech production is not compelling. The data to which they appeal either cannot be explained by forward models, or can be explained by a more parsimonious model.

Pickering & Garrod (P&G) consider the possibility that inner speech might be a product of forward production models. Here I consider the idea of inner speech as a forward model in light of empirical work from the past few decades, concluding that, while forward models could contribute to it, inner speech nonetheless requires activity from the implementers.

I evaluate the bottlenecks involved in the simulation mechanism underpinning superior predictive abilities for upcoming actions. This perceptual-motor state is characterized by a complex interrelationship designed to make predictions using a highly fine-tuned and constrained motor operation. The extension of such mechanisms to language may occur only in sensorimotor circuits devoted to the action domain.

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.

For action systems, the critical task is to predict what will happen next. In language, however, the critical task is not to predict the next auditory event but to extract meaning. Reducing language to an action system, and putting prediction at center, mistakenly marginalizes our core capacity to communicate the novel and unpredictable.

Pickering & Garrod's (P&G's) integrated model of production and comprehension includes no explicit role for nonlinguistic cognitive processes. Yet, how domain-general cognitive functions contribute to language processing has become clearer with well-specified theories and supporting data. We therefore believe that their account can benefit by incorporating functions like working memory and cognitive control into a unified model of language processing.

Pickering & Garrod (P&G) propose that inner speech monitoring is subserved by predictions stemming from fast forward modeling. In this commentary, we question this alignment of language prediction with the inner speech monitor. We wonder how the speech monitor can function so efficiently if it is based on incomplete representations.

Pickering & Garrod's (P&G's) theory could be modified to describe how listeners rapidly incorporate context to generate predictions about speech despite inter-talker variability. However, in order to do so, the content of “impoverished” predicted percepts must be expanded to include phonetic information. Further, the way listeners identify and represent inter-talker differences and subsequently determine which prediction method to use would require further specification.

We agree with Pickering & Garrod's (P&G's) claim that theories of language processing must address the interconnection of language production and comprehension. However, we have two concerns: First, the central notion of context when predicting what another person will say is underspecified. Second, it is not clear that P&G's dual-mechanism model captures the data better than a single-mechanism model would.

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.

In spite of the remarkable progress made in the burgeoning field of social neuroscience, the neural mechanisms that underlie social encounters are only beginning to be studied and could – paradoxically – be seen as representing the “dark matter” of social neuroscience. Recent conceptual and empirical developments consistently indicate the need for investigations that allow the study of real-time social encounters in a truly interactive manner. This suggestion is based on the premise that social cognition is fundamentally different when we are in interaction with others rather than merely observing them. In this article, we outline the theoretical conception of a second-person approach to other minds and review evidence from neuroimaging, psychophysiological studies, and related fields to argue for the development of a second-person neuroscience, which will help neuroscience to really “go social”; this may also be relevant for our understanding of psychiatric disorders construed as disorders of social cognition.

This commentary situates the second person account within a broader framework of ecological validity for experimental paradigms in social cognitive neuroscience. It then considers how individual differences at psychological and genetic levels can be integrated within the proposed framework.

The target article's approach is applauded, but it is suggested that the “dark matter” may be much larger than even the current authors suspect. Cartesian and mechanistic assumptions infuse not only the discipline of cognitive psychology, but all societal accounts of the person. A switch to dynamical accounts in which lawfulness is observed within a given systemic context is suggested.

Second person social cognition cannot be restricted to dyadic interactions between two persons (the “I” and the “you”). Many instances of social communication are triadic, and involve a third person (the “him/her/it”), which is the object of the interaction. We discuss neuropsychological and brain imaging data showing that triadic interactions involve dedicated brain networks distinct from those of dyadic interactions.

We question the idea that the mirror neuron system is the substrate of social affordances perception, and we suggest that most of the activity seen in the parietal and premotor cortex of the human brain is independent of mirroring activity as characterized in macaques, but rather reflects a process of one's own action specification in response to social signals.

As there is “dark matter” in the neuroscience of individuals engaged in dynamic interactions, similar dark matter is present in the domain of interaction between humans and cognitive robots. Progress in second-person neuroscience will contribute to the development of robotic cognitive systems, and such developed robotic systems will be used to test the validity of the underlying theories.