That's what she (could have) said: How alternative utterances affect language use

That’s what she (could have) said: How alternative utterances affect language use Leon Bergen (bergen@mit.edu) 1 , Noah D. Goodman (ngoodman@stanford.edu) 2 , Roger Levy (rlevy@ucsd.edu) 3 1 Department of Brain and Cognitive Sciences, MIT, Cambridge MA 02139, of Psychology, Stanford University, Stanford CA 94305, 3 Department of Linguistics, UC San Diego, La Jolla CA 92093 2 Department Abstract language. The alternative is that such explanations merely provide a succinct way of summarizing pragmatic phenom- ena, and that pragmatic meanings are learned as part of the grammar, i.e. conventionalized as part of the language. Re- searchers have argued that some types of pragmatic mean- ings are computed from the grammar, and not from coopera- tive social reasoning (Chierchia, 2004). Intuitively, however, some pragmatic inferences generalize to settings in which they could not have been previously learned. Consider the salad/lobster inference described above. This inference is highly context-dependent, and must require a reasoning pro- cess that extends beyond what has been learned in the gram- mar. But then where is the boundary between conventional- ized and socially-derived implicatures? Here we investigate these questions using experiments and computational modeling. Despite the apparent simplicity of explanations in terms of the Q and R-principles, it has been notoriously difficult to develop a formal framework that cap- tures these principles (or the maxims of conversation). In addition, there has been little empirical work investigating whether pragmatic inferences rely on conventionalized mean- ings. We will be looking at the minimal ways for contrasts between alternative utterances to drive pragmatic interpreta- tion. Specifically, we will be looking at cases in which there are no linguistic conventions whatsoever. If people’s prag- matic interpretations show the same sensitivity to contrast in these settings, it will provide evidence that social reasoning explains more, rather than less, of their pragmatic abilities. We focus on two traditional examples of counterfactual reasoning in pragmatics. The first, scalar implicatures, arise because of the contrast between words that fall on an increas- ing scale of informativeness. The less informative meaning is typically strengthened to the complement of the more infor- mative meaning, as in the case of “some” vs. “all” above. Be- cause the term “scalar implicature” is sometimes reserved to refer to cases where lexical items fall on a canonical scale of informativeness, we will use the term specificity implicatures to describe the strengthening of less informative meanings even in the absence of such a canonical scale. The second, which we will call Horn implicatures, guides the interpreta- tion of utterances that differ in their complexity (Horn, 1984). Typically, more complex constructions receive marked (or less probable) interpretations. The car case above is an ex- ample of a Horn implicature, assuming (as is plausible) that the two expressions have the same literal content. These two kinds of implicatures will allow us to explore pragmatic contrast effects along two distinct dimensions: in- formativeness and cost. While we will model both of these ef- We investigate the effects of alternative utterances on prag- matic interpretation of language. We focus on two specific cases: specificity implicatures (less specific utterances im- ply the negation of more specific utterances) and Horn impli- catures (more complex utterances are assigned to less likely meanings). We present models of these phenomena in terms of recursive social reasoning. Our most sophisticated model is not only able to handle specificity implicature but is also the first formal account of Horn implicatures that correctly pre- dicts human behavior in signaling games with no prior conven- tions, without appeal to specialized equilibrium selection crite- ria. Two experiments provide evidence that these implicatures are generated in the absence of prior linguistic conventions or language evolution. Taken together, our modeling and experi- mental results suggest that the pragmatic effects of alternative utterances can be driven by cooperative social reasoning. Keywords: Pragmatics; Communication; Bayesian modeling Introduction A central observation in the field of pragmatics is that alter- native utterances affect our interpretation of language. If the teacher says, “Some of the students passed the test”, then this means that not all of them passed, because the teacher would have said so if they did. If someone is asked what they ate at the restaurant, and they say “salad”, then this means that they did not also get the lobster; otherwise they would have said so. If someone says, “I got the car to turn on,” then this means that turning on the car involved something more unusual than just turning the key. If it hadn’t, they could have just said, “I turned on the car. ” Many other cases like this were described in Grice’s (1975) classic. Horn (1984) proposed a unified account of these disparate cases in terms of his Q and R-Principles. These principles de- scribe how conversational partners are expected to communi- cate with each other. The Q-Principle states: Say as much as you can. The R-Principle states: Say no more than you must. These principles explain how counterfactual utterances like the ones above have their effect on meaning. If the speaker behaves according to the Q-Principle, then when she says that some of the students passed the test, this must mean that she said all that she could. In particular, she must not have been in a position to say that all of the students passed. Similarly, if the speaker is following the R-Principle, then when she re- ports that she got the car to turn on, this means that a simpler utterance would not have sufficed to convey her meaning. In particular, simply saying that she turned on the car would not have conveyed her meaning. A basic question about these principles (or Grice’s related maxims of conversation) is the extent to which they capture people’s online reasoning when they pragmatically interpret