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Hearing meanings: the revenge of context

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Abstract

According to the perceptual view of language comprehension, listeners typically recover high-level linguistic properties such as utterance meaning without inferential work. The perceptual view is subject to the Objection from Context: since utterance meaning is massively context-sensitive, and context-sensitivity requires cognitive inference, the perceptual view is false. In recent work, Berit Brogaard provides a challenging reply to this objection. She argues that in language comprehension context-sensitivity is typically exercised not through inferences, but rather through top-down perceptual modulations or perceptual learning. This paper provides a complete formulation of the Objection from Context and evaluates Brogaards reply to it. Drawing on conceptual considerations and empirical examples, we argue that the exercise of context-sensitivity in language comprehension does, in fact, typically involve inference.

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Notes

  1. Notice that this advantage rests on the prior assumption that the capacity to “immediately justify” exhibited by appearances of utterance meaning is best accounted for within a perceptual model of utterance comprehension. Recent work by Balcerak Jackson (2019) has argued that this assumption can be resisted: even non-perceptual models of utterance comprehension can account for the fact that the experience as of a speaker asserting that p normally confers immediate justification for believing that the speaker has asserted that p. In what follows, we won’t dwell on this issue, though we briefly return to epistemological matters in discussing the notion of inference. For more on the epistemology of language comprehension, see, e.g., Longworth (2008).

  2. Earlier formulations of the challenge can be found, a.o., in Stanley (2005) and Pettit (2010). In what follows, we provide an independent formulation of the objection, and lay it out in much more detail than previous authors. For the purposes of our argument, we will use the expression “context-sensitivity” in a broad sense: in addition to paradigm cases of dependence on non-linguistic context, we will file under the general rubric of “context-sensitivity” also cases where the interpretation of an expression depends primarily on the linguistic environment where the expression occurs, and cases where the interpretation is primarily guided by commonsense reasoning and world knowledge.

  3. Though of central importance, this is not the only challenge faced by the Perceptual View. For a helpful recent discussion of other challenges to the view, see Drożdżowicz (2019).

  4. The generic scope is explicitly endorsed by Brogaard (2019): “[the view] is thus consistent with the occasional reliance on inference in order to derive the meaning of what the speaker intended to convey” (our emphasis).

  5. Unless otherwise specified, by ‘module’ we mean peripheral (Fodorian) modules (Fodor 1983, 2000), rather than the central modules posited by the massive modularity view (Sperber 2001). It is worth noting, in this regard, that our defense of the inferential nature of language comprehension is compatible with multiple different views concerning how specifically such an inferential capacity is implemented in our cognitive architecture (e.g., the traditional view according to which inferences are carried out by domain general central systems, as well as the massive modularity view defended by Relevance theorists). As we will argue, what matters for Inferentialism is that meaning recovery requires reason-responsive integration of wide swaths of common-sense knowledge and information about the context. This view does not contradict the possibility that the flexible way in which we typically understand discourse results from the interaction and integration of various domain-specific modules. It is neutral, in particular, on the issue of whether utterance interpretation typically involves a mind-reading module or a specialized pragmatics submodule (Wilson 2005). We thank a referee for inviting us to be more explicit on this point.

  6. Though see Kiefer (2017), for a defense of “literal” perceptual inference.

  7. By “utterance meaning”, we have in mind the truth-conditional level of content expressed by sentences in their context of utterance, and corresponding roughly to Grice’s notion of “what is said”. Some such notion is, as far as we can tell, presupposed by all parties to the debate.

  8. We address Brogaard’s additional argument that meaning recovery is fast, automatic, and (purportedly) evidence-insensitive below. Brogaard also invokes neuro-anatomical evidence, in particular the role of Wernicke’s area in speech comprehension. We will not dwell on this particular point, except to note that the exact role of this area in speech comprehension is debated in neuroscience (e.g., Binder 2017). For critical discussion of aspects of Brogaard’s empirical arguments which fall outside the scope of our article, see Connolly (2019) and Drożdżowicz (2019).

  9. This is consonant with Brogaard’s general view of language learning as a form of perceptual learning. See Brogaard and Gatzia (2015, 2017).

  10. In saying as much, we do not mean to say, nor to attribute to Brogaard, the view that the rules must be explicitly or consciously represented by the agent performing the inference. They may, of course, be implicit in the architecture.

  11. For useful clarifications of the personal/subpersonal distinction, see Drayson (2014) and Lyons (2016). Lyons (2016: pp. 250–251) lists several reasons why “subpersonal assumptions” are to be contrasted with those attributable to the agent, including that the agent needn’t have the concepts involved to specify their content (e.g., zero-crossing), and that they do not tend to conflict with other beliefs of the agent, nor with other intra-modular assumptions.

  12. We believe that the primary reason why intra-modular transitions should not be characterized as inferences is that they are characteristically informationally encapsulated. Hence, that they are not reason-responsive, which we have noted is a plausible necessary feature of inference. E.g., giving a subject reasons to judge that the arrows in the Müller-Lyer illusion are of equal length does not make the illusion disappear. Note that transitions within central modules, if there are such, may count as personal-level by our criteria. Indeed, an advantage of the moderately permissive view of inference we defend is that it would be strange if the massive modularity view led to the conclusion that, since much of cognition is modular, nearly no inferences ever occur. By contrast, it seems this could be an unwelcome consequence of adopting a more restrictive view of inference like Brogaard’s.

  13. The same inference might be conscious in a strong sense (i.e., such that its premise- and conclusion-states are conscious and the subject endorses the transition between them, forming the judgment that the conclusion follows from the premises), or conscious in a weaker sense (i.e., such that its premise- and conclusion-states are conscious but the transition between them is not consciously endorsed), or completely unconscious. For example, see Reverberi et al. (2012) for empirical evidence that modus ponens inferences can be carried out unconsciously.

  14. We are grateful to an anonymous referee for raising this worry.

  15. Connolly (2019: pp. 154–178) develops this argument at length.

  16. We thank a referee for pushing this point.

  17. In the field of Artificial Intelligence, for example, pairs similar to (7)–(12) above are known as “Winograd Schemas”. They are currently used as an alternative to the Turing Test to test machine comprehension (Levesque et al. 2012). Tellingly, the Winograd Schema Challenge is trivial for humans, but very hard for computer programs (Davis, Morgenstern and Ortiz 2017). Levesque et al. note that this is because succeeding above chance requires one to “figure out what is going on”, as they nicely put it. Winograd Schemas are “Google-proof”: access to a huge corpus of text (which actual humans typically lack) and powerful statistical methods don’t help much. To the extent that these methods bear some resemblance to processes of associative learning, the difficulty of designing a program that comes at all close to human performance on the Winograd Schema Challenge using such techniques provides a practical illustration of the challenges facing any associative account.

  18. For more on the distinction between pragmatic processes that involve mental state attribution and those that do not, see Kissine (2016).

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Acknowledgements

We would like to thank Joulia Smortchkova and three anonymous reviewers for extremely helpful comments on the manuscript. The usual disclaimer applies. Luca Gasparri gratefully acknowledges financial support from the Alexander von Humboldt Foundation.

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Gasparri, L., Murez, M. Hearing meanings: the revenge of context. Synthese 198, 5229–5252 (2021). https://doi.org/10.1007/s11229-019-02398-0

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