Written in an outstandingly clear and lively style, this 1969 book provokes its readers to rethink issues they may have regarded as long since settled.

This article described three heuristics that are employed in making judgements under uncertainty: representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and adjustment from an anchor, which is usually employed in numerical prediction when a relevant value (...) is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty. (shrink)

Traditional mechanistic accounts of language processing derive almost entirely from the study of monologue. Yet, the most natural and basic form of language use is dialogue. As a result, these accounts may only offer limited theories of the mechanisms that underlie language processing in general. We propose a mechanistic account of dialogue, the interactive alignment account, and use it to derive a number of predictions about basic language processes. The account assumes that, in dialogue, the linguistic representations employed by the (...) interlocutors become aligned at many levels, as a result of a largely automatic process. This process greatly simplifies production and comprehension in dialogue. After considering the evidence for the interactive alignment model, we concentrate on three aspects of processing that follow from it. It makes use of a simple interactive inference mechanism, enables the development of local dialogue routines that greatly simplify language processing, and explains the origins of self-monitoring in production. We consider the need for a grammatical framework that is designed to deal with language in dialogue rather than monologue, and discuss a range of implications of the account. Key Words: common ground; dialogue; dialogue routines; language comprehension; language production; monitoring; perception-behavior link. (shrink)

Humans and animals make inferences about the world under limited time and knowledge. In contrast, many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time, knowledge, and computational might. Following H. Simon's notion of satisficing, the authors have proposed a family of algorithms based on a simple psychological mechanism: one-reason decision making. These fast and frugal algorithms violate fundamental tenets of classical rationality: They neither look up nor integrate all information. By computer simulation, the (...) authors held a competition between the satisficing "Take The Best" algorithm and various "rational" inference procedures. The Take The Best algorithm matched or outperformed all competitors in inferential speed and accuracy. This result is an existence proof that cognitive mechanisms capable of successful performance in the real world do not need to satisfy the classical norms of rational inference. (shrink)

No one denies that people adapt what they say and how they interpret what is said to them, depending on their interactive partners. What is controversial is when and how they do so. Several psycholinguistics research programs have found what appear to be failures to adapt to partners in the early moments of processing and have used this evidence to argue for modularity in the language processing architecture, claiming that the system cannot take into account a partner’s distinct needs or (...) knowledge early in processing. We review the evidence for both early and delayed partner‐specific adaptations, and we identify some challenges and difficulties with interpreting this evidence. We then discuss new analyses from a previously published referential communication experiment (Metzing & Brennan, 2003) demonstrating that partner‐specific effects need not occur late in processing. In contrast to Pickering and Garrod (2004) and Keysar, Barr, and Horton (1998b), we conclude that there is no good evidence that early processing has to be be “egocentric,”“dumb,” or encapsulated from social knowledge or common ground, but that under some circumstances, such as when one partner has made an attribution about another’s knowledge or needs, processing can be nimble enough to adapt quite early to a perspective different from one’s own. (shrink)

We examine the problem of generating definite noun phrases that are appropriate referring expressions; that is, noun phrases that (a) successfully identify the intended referent to the hearer whilst (b) not conveying to him or her any false conversational implicatures (Grice, 1975). We review several possible computational interpretations of the conversational implicature maxims, with different computational costs, and argue that the simplest may be the best, because it seems to be closest to what human speakers do. We describe our recommended (...) algorithm in detail, along with a specification of the resources a host system must provide in order to make use of the algorithm, and an implementation used in the natural language generation component of the IDAS system. (shrink)

This article offers a survey of computational research on referring expressions generation (REG). It introduces the REG problem and describes early work in this area, discussing what basic assumptions lie behind it, and showing how its remit has widened in recent years. We discuss computational frameworks underlying REG, and demonstrate a recent trend that seeks to link up REG algorithms with well-established Knowledge Representation traditions. Considerable attention is given to recent efforts at evaluating REG algorithms and the lessons that they (...) allow us to learn. The article concludes with a discussion of the way forward in REG, focussing on references in larger and more realistic settings. (shrink)

The notion of common ground is important for the production of referring expressions: In order for a referring expression to be felicitous, it has to be based on shared information. But determining what information is shared and what information is privileged may require gathering information from multiple sources, and constantly coordinating and updating them, which might be computationally too intensive to affect the earliest moments of production. Previous work has found that speakers produce overinformative referring expressions, which include privileged names, (...) violating Grice’s Maxims, and concluded that this is because they do not mark the distinction between shared and privileged information. We demonstrate that speakers are in fact quite effective in marking this distinction in the form of their utterances. Nonetheless, under certain circumstances, speakers choose to overspecify privileged names. (shrink)

It makes sense that the more information people share, the better they communicate. To evaluate the effect of knowledge overlap on the effectiveness of communication, participants played a communication game where the “director” identified objects to the “addressee”. Pairs either shared information about most objects' names (high overlap), or about the minority of objects' names (low overlap). We found that high-overlap directors tended to use more names than low overlap directors. High overlap directors also used more names with objects whose (...) names only they knew, thereby confusing their addressees more often than low-overlap directors. We conclude that while sharing more knowledge can be beneficial to communication overall, it can cause communication to be locally ineffective. Sharing more information reduces communication effectiveness precisely when there is an opportunity to inform—when people communicate information only they themselves know. (shrink)

The tradeoff hypothesis in the speech–gesture relationship claims that (a) when gesturing gets harder, speakers will rely relatively more on speech, and (b) when speaking gets harder, speakers will rely relatively more on gestures. We tested the second part of this hypothesis in an experimental collaborative referring paradigm where pairs of participants (directors and matchers) identified targets to each other from an array visible to both of them. We manipulated two factors known to affect the difficulty of speaking to assess (...) their effects on the gesture rate per 100 words. The first factor, codability, is the ease with which targets can be described. The second factor, repetition, is whether the targets are old or new (having been already described once or twice). We also manipulated a third factor, mutual visibility, because it is known to affect the rate and type of gesture produced. None of the manipulations systematically affected the gesture rate. Our data are thus mostly inconsistent with the tradeoff hypothesis. However, the gesture rate was sensitive to concurrent features of referring expressions, suggesting that gesture parallels aspects of speech. We argue that the redundancy between speech and gesture is communicatively motivated. (shrink)

A substantial amount of recent work in natural language generation has focused on the generation of ‘‘one-shot’’ referring expressions whose only aim is to identify a target referent. Dale and Reiter's Incremental Algorithm (IA) is often thought to be the best algorithm for maximizing the similarity to referring expressions produced by people. We test this hypothesis by eliciting referring expressions from human subjects and computing the similarity between the expressions elicited and the ones generated by algorithms. It turns out that (...) the success of the IA depends substantially on the ‘‘preference order’’ (PO) employed by the IA, particularly in complex domains. While some POs cause the IA to produce referring expressions that are very similar to expressions produced by human subjects, others cause the IA to perform worse than its main competitors; moreover, it turns out to be difficult to predict the success of a PO on the basis of existing psycholinguistic findings or frequencies in corpora. We also examine the computational complexity of the algorithms in question and argue that there are no compelling reasons for preferring the IA over some of its main competitors on these grounds. We conclude that future research on the generation of referring expressions should explore alternatives to the IA, focusing on algorithms, inspired by the Greedy Algorithm, which do not work with a fixed PO. (shrink)

How do people decide what to say in context? Many theories of pragmatics assume that people have specialized knowledge that drives them to utter certain words in different situations. But these theories are mostly unable to explain both the regularity and variability in people’s speech behaviors. Our purpose in this article is to advance a view of pragmatics based on complexity theory, which specifically explains the pragmatic choices speakers make in conversations. The concept of self-organized criticality sheds light on how (...) a history of utterances and subtle details of a situation surrounding a conversation may directly specify language behavior. Under this view, pragmatic choice in discourse does not reflect the output of any dedicated pragmatic module but arises from a complex coordination or coupling between speakers and their varying communicative tasks. (shrink)

Psycholinguistic studies often look at the production of referring expressions in interactive settings, but so far few referring expression generation algorithms have been developed that are sensitive to earlier references in an interaction. Rather, such algorithms tend to rely on domain-dependent preferences for both content selection and linguistic realization. We present three experiments showing that humans may opt for dispreferred attributes and dispreferred modifier orderings when these were primed in a preceding interaction (without speakers being consciously aware of this). In (...) addition, we show that speakers are more likely to produce overspecified references, including dispreferred attributes (although minimal descriptions with preferred attributes would suffice), when these were similarly primed. (shrink)

A model is presented that addresses both the distribution and comprehension of different forms of referring expressions in language. This model is expressed in a formalism (Kamp & Reyle, 1993) that uses interpretive rules to map syntactic representations onto representations of discourse. Basic interpretive rules are developed for names, pronouns, definite descriptions, and quantified descriptions. These rules are triggered by syntactic input and interact dynamically with representations of discourse to establish reference and coreference. This interaction determines the ease with which (...) coreference can be established for different linguistic forms given the existing discourse context. The performance of the model approximates that observed in studies of intuitive judgments of grammaticality and studies using online measures of language comprehension. The model uses the same basic interpretive mechanisms for coreference within and between sentences, thereby linking the domain traditionally studied by generative linguists to domains that have been of concern primarily to psychologists and computational linguists. (shrink)

Children often refer to things ambiguously but learn not to from responding to clarification requests. We review and explore this learning process here. In Study 1, eighty-four 2- and 4-year-olds were tested for their ability to request stickers from either (a) a small array with one dissimilar distracter or (b) a large array containing similar distracters. When children made ambiguous requests, they received either general feedback or specific questions about which of two options they wanted. With training, children learned to (...) produce more complex object descriptions and did so faster in the specific feedback condition. They also tended to provide more information when requesting stickers from large arrays. In Study 2, we varied only distracter similarity during training and then varied array size in a generalization test. Children found it harder to learn in this case. In the generalization test, 4-year-olds were more likely to provide information (a) when it was needed because distracters were similar to the target and (b) when the array size was greater (regardless of need for information). We discuss how clear cues to potential ambiguity are needed for children to learn to tailor their referring expression to context and how several cues of heuristic value (e.g., more distracters > say more) can promote the efficiency of communication while language is developing. Finally, we consider whether it would be worthwhile drawing on the human learning process when developing algorithms for the production of referring expressions. (shrink)

This paper presents two cognitive models that simulate the production of referring expressions in the iMAP task—a task-oriented dialog. One general model is based on Dale and Reiter’s (1995)incremental algorithm, and the other is a simple template model that has a higher correlation with the data but is specifically geared toward the properties of the iMAP task. The property of the iMAP task environment that is modeled here is that the color feature is unreliable for identifying referents while other features (...) are reliable. The low computational cost of the incremental algorithm for generating referring expressions makes it an interesting starting point for a cognitive model. However, its explanatory power is limited, because it generates uniquely distinguishing referring expressions and because it considers features for inclusion in the referring expression in a fixed order. The first model extends the original incremental algorithm by an ability to adapt to feedback of whether a referring expression was used successfully, but it seems to overpredict the frequency with which distinguishing expressions are made and underpredict the frequency of overspecified referring expressions. The second model produces features for referring expressions purely based on its current estimate of a feature’s utility. Both models predict the observed human behavior of decreasing use of color terms and increasing use of useful feature terms. (shrink)

Within the Givenness Hierarchy framework of Gundel, Hedberg, and Zacharski (1993), lexical items included in referring forms are assumed to conventionally encode two kinds of information: conceptual information about the speaker’s intended referent and procedural information about the assumed cognitive status of that referent in the mind of the addressee, the latter encoded by various determiners and pronouns. This article focuses on effects of underspecification of cognitive status, establishing that, although salience and accessibility play an important role in reference processing, (...) the Givenness Hierarchy itself is not a hierarchy of degrees of salience/accessibility, contrary to what has often been assumed. We thus show that the framework is able to account for a number of experimental results in the literature without making additional assumptions about form-specific constraints associated with different referring forms. (shrink)

This book deals with a major problem in the study of language: the problem of reference. The ease with which we refer to things in conversation is deceptive. Upon closer scrutiny, it turns out that we hardly ever tell each other explicitly what object we mean, although we expect our interlocutor to discern it. Amichai Kronfeld provides an answer to two questions associated with this: how do we successfully refer, and how can a computer be programmed to achieve this? Beginning (...) with the major theories of reference, Dr Kronfeld provides a consistent philosophical view which is a synthesis of Frege's and Russell's semantic insights with Grice's and Searle's pragmatic theories. This leads to a set of guiding principles, which are then applied to a computational model of referring. The discussion is made accessible to readers from a number of backgrounds: in particular, students and researchers in the areas of computational linguistics, artificial intelligence and the philosophy of language will want to read this book. (shrink)

This article explores the role of surface ambiguities in referring expressions, and how the risk of such ambiguities should be taken into account by an algorithm that generates referring expressions, if these expressions are to be optimally effective for a hearer. We focus on the ambiguities that arise when adjectives occur in coordinated structures. The central idea is to use statistical information about lexical co-occurrence to estimate which interpretation of a phrase is most likely for human readers, and to avoid (...) generating phrases where misunderstandings are likely. Various aspects of the problem were explored in three experiments in which responses by human participants provided evidence about which reading was most likely for certain phrases, which phrases were deemed most suitable for particular referents, and the speed at which various phrases were read. We found a preference for ‘‘clear’’ expressions to ‘‘unclear’’ ones, but if several of the expressions are ‘‘clear,’’ then brief expressions are preferred over non-brief ones even though the brief ones are syntactically ambiguous and the non-brief ones are not; the notion of clarity was made precise using Kilgarriff's Word Sketches. We outline an implemented algorithm that generates noun phrases conforming to our hypotheses. (shrink)