Predicting Pragmatic Reasoning in Language Games

Frank, Michael C.; Goodman, Noah D.

doi:10.1126/science.1218633

Cited by 656 publications

(778 citation statements)

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“…[94] -are central in pragmatic inferences for both adults and children [95][96] and can be derived from the naïve utility calculus. Minimizing utterance length (communication costs) while maximizing information transfer to the listener (communicative rewards) can be seen as optimizing an overall utility function trading off these costs and rewards.…”

Section: Communication and Pedagogymentioning

confidence: 99%

The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology

Jara‐Ettinger¹,

Gweon²,

Schulz³

et al. 2016

Trends in Cognitive Sciences

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We propose that human social cognition is structured around a basic understanding of ourselves and others as intuitive utility maximizers: From a young age, humans implicitly assume that agents choose goals and actions to maximize the rewards they expect to obtain relative to the costs they expect to incur. This "naïve utility calculus" lets both children and adults observe others' behavior and infer their beliefs and desires, their longer-term knowledge and preferences, and even their character: who is knowledgeable or competent, who is praiseworthy or blameworthy, who is friendly, indifferent or an enemy. We review studies providing support for the naïve utility calculus, and we show how it captures much of the rich social reasoning humans engage in from infancy. Commonsense PsychologyTheories of decision-making have been at the heart of psychology since the field's inception, but only recently has the field turned to the study of how humans -especially the youngest humansthink humans make decisions. When we watch someone make a choice, we explain it in terms of their goals, preferences, personalities, and moral beliefs. This capacity -our commonsense psychology -is the cognitive foundation of human society. It lets us share what we have and know, with those from whom we expect the same in return, and it guides how we evaluate those who deviate from our expectations.The representations and inferential power underlying commonsense psychology trace back to early childhood -before children begin kindergarten, and often even in infancy. Work on how children reason about other agents' goals [1][2][3][4][5][6][7][8], desires [9][10][11], beliefs [12][13][14][15][16][17][18], and pro-social behavior [19][20][21][22][23][24][25][26][27][28][29] has advanced our understanding of what in our commonsense psychology is at work in early infancy [30][31][32] and what develops [16][17][33][34][35]. Nonetheless, major theoretical questions remain unresolved. What computations underlie our commonsense psychology, and to what extent are they specific to the social domain? Are there a small number of general principles by which humans reason about and evaluate other agents, or do we instead learn a large number of special case rules and heuristics? To what extent is there continuity between the computations supporting commonsense psychology in infancy and later ages? Is children's social-cognitive development a progressive refinement of a computational system in place from birth, or are there fundamentally new computational principles coming into play?In this article we advance a hypothesis that offers answers to each of these questions, and provides a unifying framework in which to understand the diverse social-cognitive capacities we see even in young children. We propose that human beings, from early infancy, interpret others' intentional actions through the lens of a naïve utility calculus: that is, people assume that others choose actions to maximize utilities -the rewards they expect to obtain relative to the costs...

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Section: Communication and Pedagogymentioning

confidence: 99%

The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology

Jara‐Ettinger¹,

Gweon²,

Schulz³

et al. 2016

Trends in Cognitive Sciences

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“…First, speakers and listeners communicate with the assumption that their interlocutors are rational and cooperative agents; second, listeners assume that speakers choose utterances to maximize informativeness with respect to their communicative goals; third, speaker and listener use common ground-their shared knowledge of the world-to communicate effectively. The first principle has been formalized by a recent body of work on rational speech act (RSA) models, which views pragmatic language understanding as probabilistic inference over recursive social models and explains a range of phenomena in human pragmatic reasoning (5)(6)(7)(8). We go beyond the previous formal work and address the second principle by extending the RSA framework.…”

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confidence: 99%

Nonliteral understanding of number words

Kao¹,

Bergen

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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124

107

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One of the most puzzling and important facts about communication is that people do not always mean what they say; speakers often use imprecise, exaggerated, or otherwise literally false descriptions to communicate experiences and attitudes. Here, we focus on the nonliteral interpretation of number words, in particular hyperbole (interpreting unlikely numbers as exaggerated and conveying affect) and pragmatic halo (interpreting round numbers imprecisely). We provide a computational model of number interpretation as social inference regarding the communicative goal, meaning, and affective subtext of an utterance. We show that our model predicts humans' interpretation of number words with high accuracy. Our model is the first to our knowledge to incorporate principles of communication and empirically measured background knowledge to quantitatively predict hyperbolic and pragmatic halo effects in number interpretation. This modeling framework provides a unified approach to nonliteral language understanding more generally.pragmatics | computational modeling I magine a friend describing a new restaurant where she recently dined. Your friend says, "It took 30 minutes to get a table." You are likely to interpret this to mean she waited ∼30 min. Suppose she says: "It took 32 minutes to get a table." You are more likely to interpret this to mean exactly 32 min. Now, suppose she says: "It took a million years to get a table." You will probably interpret this to mean that the wait was shorter than a million years, but importantly that she thinks it took much too long. One of the most fascinating facts about communication is that people do not always mean what they say-a crucial part of the listener's job is to understand an utterance even when its literal meaning is false. People's ability to interpret nonliteral language poses a critical puzzle for research on language understanding.A rich body of literature in psychology and linguistics has examined how people use and understand nonliteral language (1-4). However, most of the work has been qualitative, with little focus on analyzing aspects of an utterance that predict the quantitative details of people's figurative interpretations. Here, we present a computational model that formalizes and integrates three general principles of language and communication to explain the basis of nonliteral language understanding. First, speakers and listeners communicate with the assumption that their interlocutors are rational and cooperative agents; second, listeners assume that speakers choose utterances to maximize informativeness with respect to their communicative goals; third, speaker and listener use common ground-their shared knowledge of the world-to communicate effectively. The first principle has been formalized by a recent body of work on rational speech act (RSA) models, which views pragmatic language understanding as probabilistic inference over recursive social models and explains a range of phenomena in human pragmatic reasoning (5-8). We go beyond the previous formal wor...

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“…one benefit of the RSA formalism is that probabilities can be put to each of these inferences 120 and so the strength of the interpretive judgment can be predicted (Frank & Goodman, 2012 …”

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confidence: 99%

“…Both classic theories of 91 communication (e.g., Sperber & Wilson, 1995) and more recent probabilistic models of 92 pragmatic inference (e.g., Frank & Goodman, 2012; see Goodman & Frank, 2016 for review) 93 describe the processes that language users use to compute such implicatures in different ways.…”

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confidence: 99%

The role of salience in young children's processing of ad-hoc implicatures

Yoon¹,

Frank²

2016

Preprint

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Language comprehension often requires making implicatures. For example, inferring that "I ate some of the cookies" implicates the speaker ate some but not all (scalar implicatures); and "I ate the chocolate-chip cookies" where there are both chocolate chip cookies and raisin cookies in the context implicates that the speaker ate the chocolate chip, but not both the chocolate chip and raisin cookies (ad-hoc implicatures). Children’s ability to make scalar implicatures develops around age five, with ad-hoc implicatures emerging somewhat earlier. In the current work, using a time-sensitive tablet paradigm, we examined developmental gains in children’s ad-hoc implicature processing, and found evidence for successful pragmatic inferences by children as young as 3 years in a supportive context and substantial developmental gains in inference computation from 2 to 5 years. We also tested whether one cause of younger children (2-year-olds)'s consistent failure to make pragmatic inferences is their difficulty in inhibiting an alternative interpretation that is more salient than the target meaning (the salience hypothesis). Our findings supported this hypothesis: Younger children’s failures with pragmatic inferences were related to effects of the salience mismatch between possible interpretations.

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Predicting Pragmatic Reasoning in Language Games

Cited by 656 publications

References 28 publications

The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology

The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology

Nonliteral understanding of number words

The role of salience in young children's processing of ad-hoc implicatures

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