This target article presents a new computational theory of explanatory coherence that applies to the acceptance and rejection of scientific hypotheses as well as to reasoning in everyday life, The theory consists of seven principles that establish relations of local coherence between a hypothesis and other propositions. A hypothesis coheres with propositions that it explains, or that explain it, or that participate with it in explaining other propositions, or that offer analogous explanations. Propositions are incoherent with each other if they (...) are contradictory, Propositions that describe the results of observation have a degree of acceptability on their own. An explanatory hypothesis is acccpted if it coheres better overall than its competitors. The power of the seven principles is shown by their implementation in a connectionist program called ECHO, which treats hypothesis evaluation as a constraint satisfaction problem. Inputs about the explanatory relations are used to create a network of units representing propositions, while coherende and incoherence relations are encoded by excitatory and inbihitory links. ECHO provides an algorithm for smoothly integrating theory evaluation based on considerations of explanatory breadth, simplicity, and analogy. It has been applied to such important scientific cases as Lovoisier's argument for oxygen against the phlogiston theory and Darwin's argument for evolution against creationism, and also to cases of legal reasoning. The theory of explanatory coherence has implications for artificial intelligence, psychology, and philosophy. (shrink)

In this article, I argue against a widely accepted model of utterance interpretation, namely the LS model (literality‐based serial model), according to which the literal interpretation of an utterance (the proposition literally expressed by that utterance) must be computed before nonliteral interpretations can be entertained. Alleged arguments in favor of this model are shown to be fallacious, counter‐examples are provided, and alternative models are sketched.

In this paper I argue against a widely accepted model of utterance interpretation, namely the LS model, according to which the literal interpretation of an utterance (the proposition literally expressed by that utterance) must be computed before non-literal interpretations can be entertained. Alleged arguments in favor of this model are shown to be fallacious, counterexamples are provided, and alternative models are sketched.

The problems of access—retrieving linguistic structure from some mental grammar —and disambiguation—choosing among these structures to correctly parse ambiguous linguistic input—are fundamental to language understanding. The literature abounds with psychological results on lexical access, the access of idioms, syntactic rule access, parsing preferences, syntactic disambiguation, and the processing of garden‐path sentences. Unfortunately, it has been difficult to combine models which account for these results to build a general, uniform model of access and disambiguation at the lexical, idiomatic, and syntactic levels. (...) For example, psycholinguistic theories of lexical access and idiom access and parsing theories of syntactic rule access have almost no commonality in methodology or coverage of psycholinguistic data. This article presents a single probabilistic algorithm which models both the access and disambiguation of linguistic knowledge. The algorithm is based on a parallel parser which ranks constructions for access, and interpretations for disambiguation, by their conditional probability. Low‐ranked constructions and interpretations are pruned through beam‐search; this pruning accounts, among other things, for the garden‐path effect. I show that this motivated probabilistic treatment accounts for a wide variety of psycholinguistic results, arguing for a more uniform representation of linguistic knowledge and for the use of probabilistically‐enriched grammars and interpreters as models of human knowledge of and processing of language. (shrink)

In spite of alleged differences in purpose, descriptive and computational linguistics share many problems, due to the fact that any precise study on language needs some form of knowledge representation. This constraint is mostly apparent when interpretation of sentences takes into account elements of the so-called “context”. The parametrization of context, i.e. the explicit listing of features relevant to some intepretation task, is difficult because it requires flexible formal structures for understanding or simulating inferential behaviour, as well as a large (...) amount of information about conventional structures in the given language. This paper aims at illustrating major difficulties in these two fields, in relation with the necessity of a contextual approach. It offers a (clearly partial) enumeration of open problems inthe reperesentation of commonsense knowledge and languages-dependent structures, with some attempt to delineate future solutions. (shrink)

What role do speakers'/authors’ communicative intentions play in language interpretation? Cognitive scientists generally assume that listeners'/readers’ recognitions of speakers'/authors’ intentions is a crucial aspect of utterance interpretation. Various philosophers, literary theorists and anthropologists criticize this intentional view and assert that speakers'/authors’ intentions do not provide either the starting point for linguistic interpretation or constrain how texts should be understood. Until now, cognitive scientists have not seriously responded to the current challenges regarding intentions in communication. My purpose in this article is (...) to provide just such a response. I briefly describe some of the empirical evidence in cognitive science on the importance of intentions in communication. I then discuss some of the criticisms of the intentional view that have arisen in the humanities and social sciences. Following this, I offer a partial resolution to the intentionalist controversy that recognizes the differing views on the idea of linguistic interpretation. I specifically suggest a way of thinking about linguistic interpretation in terms of the time‐course of understanding that provides a metric for evaluating the role of intentions in communication. This time‐course perspective on linguistic understanding provides for the plurality of ideas that now exist both in cognitive science and in postmodern philosophical thought. (shrink)

Empirical analyses have provided some important constraints for computational theories of metaphor. Three such constraints relate to (1) the similar processing time for literal and metaphorical language, (2) the time‐limited processing of many metaphors, and (3) the dissociation of metaphor comprehension and appreciation. Indurkhya's (1986, 1987) model is discussed with respect to these issues.

This study documents and characterizes a phenomenon in naturally‐occurring conversation which I have termed interactional reconstruction. Interactional reconstruction involves retroactive reinterpretation of an earlier utterance (or set of utterances) on the basis of a more recent utterance (or set of utterances). This work is meant to serve two functions: first, to enrich our theories of human communication; and second, to explore directions and implications for theories of meaning and discourse modeling within cognitive science.

We introduce a novel experimental paradigm for eliciting metaphoric expressions in spontaneous speech, under controlled conditions. Participants were presented with a pair of words on a PC monitor and were asked to provide a verbal response describing a conceptual relation between the stimuli. The proportion of metaphoric responses depended on the stimuli in a predictable manner. A large proportion of metaphoric responses was obtained for stimuli that were derived from existing metaphors. The chronometric study of metaphor production in this paradigm (...) produced 3 major findings: (a) a figurative-literal difference: responses that included metaphorical expressions showed greater reaction times (RTs) than responses with only literal expressions; (b) familiarity effect: responses that included more-familiar metaphorical expressions showed smaller RTs than responses that included less-familiar metaphorical expressions; (c) degree of metaphoricity effect: responses that included highly metaphorical expressions showed greater RTs than responses that included less metaphorical expressions. We discuss the processing implications of these results. (shrink)

In this article we present the bases for a computational theory of the cognitive processes underlying human communication. The core of the article is devoted to the analysis of the phases in which the process of comprehension of a communicative act can be logically divided: (1) literal meaning, where the reconstruction of the mental states literally expressed by the actor takes place: (2) speaker's meaning, where the partner reconstructs the communicative intentions of the actor; (3) communicative effect, where the partner (...) possibly modifies his own beliefs and intentions; (4) reaction, where the intentions for the generation of the response are produced; and (5) response, where an overt response is constructed. The model appears to be compatible with relevant facts about human behavior. Our hypothesis is that, through communication, an actor tries to exploit the motivational structures of a partner so that the desired goal is generated. A second point is that social behavior requires that cooperation be maintained at some level. In the case of communication, cooperation is, in general, pursued even when the partner does not adhere to the actor's goals, and therefore no cooperation occurs at the behavioral level. This important distinction is reflected in the two kinds of game we introduce to account for communication. The main concept implied in communication is that two agents overtly reach a situation of shared mental states. Our model deals with sharedness through two primitives: shared beliefs and communicative intentions. (shrink)

Metaphors are considered as instruments crucial for persuasion. However, while their emotive, communicative and persuasive effects are the focus of different studies and discussions, the core of their persuasive function, namely their argumentative dimension, is almost neglected. This paper addresses the problem of explaining how metaphors can communicate arguments, and how it is possible to reconstruct and justify them. To this purpose, a distinction is drawn between the arguments that are communicated metaphorically and reconstructed “top down,” namely based on relevance (...) considerations, and the ones that are triggered implicitly by the use of a metaphorical expression. In both cases, metaphorical arguments are reconstructed through different patterns of argument, called argumentation schemes (Walton, Reed & Macagno 2008). However, while the purpose of a metaphorical sequence of discourse (called metaphorical move) can guide and justify the reconstruction of the argument that can sufficiently support the intended conclusion in a persuasive move, a more complex analysis is needed for analyzing the inferences that a metaphorical move can trigger. These inferences are claimed to represent part of the connotation of the metaphorical expression and can be captured through its most frequent collocations, determinable using some tools of the corpus linguistics. (shrink)