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...

Humor plays an essential role in human interactions. Precisely what makes something funny, however, remains elusive. While research on natural language understanding has made significant advancements in recent years, there has been little direct integration of humor research with computational models of language understanding. In this paper, we propose two information‐theoretic measures—ambiguity and distinctiveness—derived from a simple model of sentence processing. We test these measures on a set of puns and regular sentences and show that they correlate significantly with human judgments of funniness. Moreover, within a set of puns, the distinctiveness measure distinguishes exceptionally funny puns from mediocre ones. Our work is the first, to our knowledge, to integrate a computational model of general language understanding and humor theory to quantitatively predict humor at a fine‐grained level. We present it as an example of a framework for applying models of language processing to understand higher level linguistic and cognitive phenomena.

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We used double electron-beam coevaporation to fabricate TiO(2)-SiO(2) mixed films. The deposition process included oxygen partial pressure, substrate temperature, and deposition rate, all of which were real-time computer controlled. The optical properties of the mixed films varied from pure SiO(2) to pure TiO(2) as the composition of the films varied accordingly. X-ray diffraction showed that the mixed films all have amorphous structure with a SiO(2) content of as low as 11%. Atomic force microscopy showed that the mixed film has a smoother surface than pure TiO(2) film because of its amorphous structure.

Linear and Bruggeman's effective medium approximation models fit the experimental data better than other models.

We used the electron-beam evaporation method in various oxygen partial pressure environments to deposit TiO(2) thin films on various glass substrates at 300 degrees C. We found the threshold oxygen partial pressures above which the film is transparent are different for films on various substrates. Below the threshold oxygen partial pressure, the refractive index and the extinction coefficient of the films varied from substrate to substrate. The films on substrates with higher threshold oxygen partial pressure were associated with a higher extinction coefficient and a higher growth rate. These phenomena are correlated with the appearance of rutile phase in the anatase phase, which is also correlated with variations in the Al(2)O(3) and Na(2)O content in the substrates. The Al(2)O(3) content in the substrate tends to enhance the formation of rutile phase in the film and to give a higher extinction coefficient for the film, while the Na(2)O content in the substrate tends to retard the rutile formation in the film and to give a lower extinction coefficient for the film.