On many occasions, irony is used to communicate emotions, to criticise or to tease other people. Irony comprehension consists in identifying an utterance as ironical and detecting its implied meaning. Existing research has investigated irony comprehension as a pragma-linguistic phenomenon, which has led to several theoretical accounts and interesting empirical results. However, given that irony comprehension is situated in a social context and has the purpose to communicate the mental states of the speaker/writer indirectly, it is reasonable to assume that (...) social cognition and emotional processes play an important role. Until very recently, this has been largely overlooked by research in the field. Furthermore, an overarching framework that can integrate theoretical insights and empirical data on the component processes of irony comprehension is still lacking. The purpose of this paper is to help close this gap. The positive proposal is that the predictive processing framework provides the theoretical resources and conceptual tools to describe relevant aspects of irony comprehension. According to predictive processing, perception, action, cognition, and emotion can be described as a continuous attempt to minimise prediction error. Irony comprehension, I will show, can be depicted as a special case of prediction error minimisation. The neuro-functional mechanism postulated by predictive processing is apt to account for the realisation of the pragma-linguistic, social, and emotional processes that jointly give rise to irony comprehension. The emerging perspective can elucidate why and how people comprehend ironical utterances. (shrink)

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 welcome the proposal to use forward models to understand predictive processes in language processing. However, Pickering & Garrod (P&G) miss the opportunity to provide a strong framework for future work. Forward models need to be pursued in the context of learning. This naturally leads to questions about what prediction error these models aim to minimize.

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.

A classic problem in cognitive science concerns how listeners perceive and understand speech as comprised of discrete words. We propose a Syllable Inference account of spoken word recognition and segmentation, under which alternative hierarchical models of syllables, words, and phonemes are dynamically posited from cues that include current and past speech rate, with a goal of maximal prediction of sensory input. Three experiments using the Visual World eye‐tracking paradigm provide evidence supporting our proposal.