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Getting it: A predictive processing approach to irony comprehension

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Abstract

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.

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Notes

  1. By talking about the pragma-linguistic component, I would like to emphasise two points: first, pragmatics is an important part of language comprehension. Second, lexical, semantic, and syntactical sub-processes also play an important role in irony comprehension. However, since irony comprehension shares these sub-processes with other types of language processing, I will focus on the pragmatic peculiarities of irony comprehension. In what follows, I will use pragma-linguistic and pragmatic interchangeably to label this component.

  2. Giora’s (1997, 1999) notion of salience is non-technical and clearly distinct from the notion of salience that is frequently used in empirical research on visual attention (see, e.g., Carrasco 2011; Krüger et al. 2017).

  3. Another theoretical account of irony comprehension that is sometimes mentioned (e.g., by Kaakinen et al. 2014) is Pexman’s (2008) constraint satisfaction model. The key idea of this model is that irony comprehension is a function of the satisfaction of constraints on linguistic communication. However, given that the relevant constraints remain largely underdetermined and that it remains unclear how irony comprehension proceeds according to this model, I will not further consider this account.

  4. In the study by Nieuwland and van Berkum (2005), a discourse context is created by introducing a speaker, an interlocutor (e.g., a tourist), and an inanimate object (e.g., a suitcase). In the continuation of the discourse, the interlocutor (e.g., the tourist) is replaced by the inanimate object (e.g., the suitcase), such that the speaker addresses that object rather than another human being. This can give rise to a semantic illusion.

  5. According to Cornejo et al. (2007), the analytic strategy is “characterized by the search and detection of logical semantic incongruities” (p. 422). By contrast, the holistic strategy is “characterized by the search for an expression’s sense in a general context” (ibid.). The participants are asked to categorise each target sentence as either coherent or incoherent. They were instructed to use the analytic strategy by telling them that “it is important you think of the meaning of the sentence and if it is congruent or not with the story” (ibid., 416; italics in original). By contrast, the holistic strategy is induced by asking the participants “to consider if the sentence would make sense in real life, that is, if you can understand what the character means by it” (ibid.; italics in original).

  6. Experiment 1 reported by Filik et al. (2014) will be reviewed below in Sect. 2.2.2.

  7. An example of a familiar ironical target word is ‘tactful’ and an example of an unfamiliar ironical target word is ‘meticulous’ (see Filik et al. 2014, Table 1).

  8. Another notable exception is the fMRI study reported by Akimoto et al. (2014), which uses a presentation format that integrates pictures and written sentences.

  9. For a systematic exploration of the differences between oral and typographic/chirographic communication (see Fabry 2018b).

  10. Activations in the temporo-parietal junction are also frequently associated with perspective taking (Aichhorn et al. 2006), the detection of biological motion (Grossman et al. 2000), and action observation (Blakemore and Decety 2001).

  11. It is an interesting research question in its own right how we should characterise the relationship between irony and humour. See Dynel (2018) for a very elucidating discussion on this question. For current purposes, it is sufficient to assume that at least some instances of ironical utterances are humorous.

  12. The debate concerns the question whether the left vOT area is selectively recruited for visual word recognition only (Dehaene 2010), or whether it still makes important neurofunctional contributions to perceptual and cognitive processes in other domains (Price and Devlin 2003, 2004; Vogel et al. 2012, 2013, 2014). See Fabry (2018a) for a discussion on this issue.

  13. For a preliminary model of irony comprehension in a Bayesian framework, see Kao and Goodman (2015).

  14. I am grateful to an anonymous reviewer for drawing my attention to the commensurability of my interpretation of the word frequency and word predictability effects and the simulations reported by Friston et al. (2018).

  15. I am grateful to an anonymous reviewer for pressing me on this point.

  16. For simulations of epistemic insight directed by epistemic value within the PP framework, see Friston et al. (2017).

  17. The present account of emotional valence can be equally applied to instances of negatively valenced (epistemic) emotions. Negative valence would be associated with a prediction error minimisation rate that is slower (and less efficient) than expected. This could be the case if cognitive agents realise that they have failed to understand a certain utterance, for example. For current purposes, however, I am only interested in providing a preliminary account of positively valenced epistemic emotions (i.e., mirth) within the predictive processing framework.

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Acknowledgements

I am greatly indebted to Markus Pantsar and two anonymous reviewers for their constructive feedback on previous versions of this paper. I would also like to thank the audience of the second workshop of the ERC-funded project X-Spect: Embodied prediction and the construction of conscious experience on Predictive processing: State of the art (and just beyond) (University of Edinburgh, November 2018), as well as Peter Brössel, Insa Lawler, Matthias Unterhuber, and Markus Werning for insightful comments on earlier versions of this work.

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    Regina E. Fabry

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Fabry, R.E. Getting it: A predictive processing approach to irony comprehension. Synthese 198, 6455–6489 (2021). https://doi.org/10.1007/s11229-019-02470-9

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