Probabilistic pragmatics, or why Bayes’ rule is probably important for pragmatics

Franke, Michael; Jäger, Gerhard

doi:10.1515/zfs-2016-0002

Cited by 124 publications

(89 citation statements)

References 52 publications

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\begin{matrix} \underset{posterior}{\underset{P_{L} (r ∣ u, C)}{true}} \propto \underset{likelihood}{\underset{P_{S} (u ∣ r, C)}{true}} \underset{prior}{\underset{P false(r ∣ C false)}{true}}, \end{matrix}

where P ( r ∣ C ) is the listener's prior degree of belief that the speaker wants to express (referential) meaning r . Since there are only two possible referents at stake, we can look at Margarethe's posterior odds in favor of referent r 1 over r 2 after observing a (possibly partial) utterance u .…”

Section: Rational Predictive Processing Of Intonational Cuesmentioning

confidence: 99%

“…Regardless of whether u is a partial or a complete utterance, we can quantify the evidential strength of u as the extent to which an observation of u changes the listener's beliefs about the relative probability (so-called odds) of the competing interpretations. Following recent work in probabilistic pragmatics (Frank & Goodman, 2012;Franke & J€ ager, 2016;Goodman & Frank, 2016), we assume that the listener's interpretation of an observed utterance u follows Bayes rule to assign probabilities to possible interpretations, like so: where P(r | C) is the listener's prior degree of belief that the speaker wants to express (referential) meaning r. Since there are only two possible referents at stake, we can look at Margarethe's posterior odds in favor of referent r 1 over r 2 after observing a (possibly partial) utterance u. These are calculated, by Bayes rule, as the product of the likelihood ratio (how likely a speaker produces u for r i ) and the prior odds (how likely a speaker refers to r i ): All else equal, if utterance u with its specific intonation contour is more likely to be produced for r 1 than for r 2 , an observation of u would shift the listener's beliefs towards r 1 and away from r 2 .…”

Section: Evidential Strength Of Intonational Cuesmentioning

confidence: 99%

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Evidential Strength of Intonational Cues and Rational Adaptation to (Un‐)Reliable Intonation

Roettger¹,

Franke

2019

Cognitive Science

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Intonation plays an integral role in comprehending spoken language. Listeners can rapidly integrate intonational information to predictively map a given pitch accent onto the speaker's likely referential intentions. We use mouse tracking to investigate two questions: (a) how listeners draw predictive inferences based on information from intonation? and (b) how listeners adapt their online interpretation of intonational cues when these are reliable or unreliable? We formulate a novel Bayesian model of rational predictive cue integration and explore predictions derived under a concrete linking hypothesis relating a quantitative notion of evidential strength of a cue to the moment in time, relative to the unfolding speech signal, at which mouse trajectories turn towards the eventually selected option. In order to capture rational belief updates after concrete observations of a speaker's behavior, we formulate and explore an extension of this model that includes the listener's hierarchical beliefs about the speaker's likely production behavior. Our results are compatible with the assumption that listeners rapidly and rationally integrate all available intonational information, that they expect reliable intonational information initially, and that they adapt these initial expectations gradually during exposition to unreliable input. All materials, data, and scripts can be retrieved here: https://osf.io/dnbuk/

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\begin{matrix} \underset{posterior}{\underset{P_{L} (r ∣ u, C)}{true}} \propto \underset{likelihood}{\underset{P_{S} (u ∣ r, C)}{true}} \underset{prior}{\underset{P false(r ∣ C false)}{true}}, \end{matrix}

Section: Rational Predictive Processing Of Intonational Cuesmentioning

confidence: 99%

Section: Evidential Strength Of Intonational Cuesmentioning

confidence: 99%

Evidential Strength of Intonational Cues and Rational Adaptation to (Un‐)Reliable Intonation

Roettger¹,

Franke

2019

Cognitive Science

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“…This work generalizes the Rational Speech Act (RSA) modeling framework, originally developed to explain contextual effects in verbal communication (Frank & Goodman, 2012;N. D. Goodman & Stuhlmüller, 2013;Franke & Jäger, 2016;Bergen et al, 2016), to the domain of visual communication. RSA models take inspiration from the insights of Paul Grice (Grice et al, 1975), and incorporate ideas from decision theory, probabilistic models of cognition, bounded rationality, and linguistics, to understand how substantial variance in natural language use can be explained by general principles of social cognition.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, it suggests an analogy to how shared context influences what people choose to say during verbal communication, a key target of recent advances in computational models of pragmatic language use (Frank & Goodman, 2012;N. D. Goodman & Stuhlmüller, 2013;Franke & Jäger, 2016;Bergen, Levy, & Goodman, 2016). Leveraging these advances, we propose that human sketchers determine what kind of sketch to produce in context by deploying two main faculties: visual abstraction, which here refers to the ability to judge how well a sketch evokes a real object, and pragmatic inference, which here refers to the ability to judge which sketches will be sufficiently detailed to be informative about the target object in context, but no more detailed than necessary.…”

Section: Computational Model Of Contextual Flexibility In Visual Commmentioning

confidence: 99%

Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication

Fan

Hawkins

et al. 2019

Comput Brain Behav

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Visual modes of communication are ubiquitous in modern life -from maps to data plots to political cartoons. Here we investigate drawing, the most basic form of visual communication. Participants were paired in an online environment to play a drawing-based reference game. On each trial, both participants were shown the same four objects, but in different locations. The sketcher's goal was to draw one of these objects so that the viewer could select it from the array. On 'close' trials, objects belonged to the same basic-level category, whereas on 'far' trials objects belonged to different categories. We found that people exploited shared information to efficiently communicate about the target object: on far trials, sketchers achieved high recognition accuracy while applying fewer strokes, using less ink, and spending less time on their drawings than on close trials. We hypothesized that humans succeed in this task by recruiting two core faculties: visual abstraction, the ability to perceive the correspondence between an object and a drawing of it; and pragmatic inference, the ability to judge what information would help a viewer distinguish the target from distractors. To evaluate this hypothesis, we developed a computational model of the sketcher that embodied both faculties, instantiated as a deep convolutional neural network nested within a probabilistic program. We found that this model fit human data well and outperformed lesioned variants. Together, this work provides the first algorithmically explicit theory of how visual perception and social cognition jointly support contextual flexibility in visual communication.

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“…A very influential recent idea in formal pragmatics is that the approach that we adopt to analyze human reasoning is the one that is found in the Bayesian / probabilistic approach to cognitive science (see Tenenbaum et al. ; Zeevat and Schmitz ; Franke and Jäger , for recent overviews of Bayesian pragmatics). More specifically, as discussed in Tenenbaum et al.…”

Section: Game Theory and Bayesian Reasoningmentioning

confidence: 99%

Sociolinguistic interaction and identity construction: The view from game‐theoretic pragmatics

Burnett

2017

Journal of Sociolinguistics

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Understanding the dynamics that characterize interaction between conversational participants is a fundamental goal of most theories of socially conditioned language use and identity construction through language. In this paper, I outline a class of formal tools that, I suggest, can be helpful in making progress towards this goal. More precisely, this paper explores how Bayesian signalling game models can be used to formalize key aspects of current sociolinguistic theories, and, in doing so, contribute to our knowledge of how speakers use their linguistic resources to communicate information and carve out their place in the social world. The Bayesian framework has become increasingly popular for the analysis of pragmatic phenomena of many different types, and, more generally, these models have become a dominant paradigm for the explanation of non‐linguistic cognitive processes. As such, I argue that this approach has the potential to yield a formalized theory of personal and social identity construction and to situate the study of sociolinguistic interaction within a broader theory of rationalistic cognition.

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Probabilistic pragmatics, or why Bayes’ rule is probably important for pragmatics

Cited by 124 publications

References 52 publications

Evidential Strength of Intonational Cues and Rational Adaptation to (Un‐)Reliable Intonation

Evidential Strength of Intonational Cues and Rational Adaptation to (Un‐)Reliable Intonation

Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication

Sociolinguistic interaction and identity construction: The view from game‐theoretic pragmatics

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