Structural priming offers a powerful method for experimentally investigating the mental representation of linguistic structure. We clarify the nature of our proposal, justify the versatility of priming, consider alternative approaches, and discuss how our specific account can be extended to new questions as part of an interdisciplinary programme integrating linguistics and psychology as part of the cognitive sciences of language.

Interpretation is the process whereby a hearer reasons to an interpretation of a speaker's discourse. The hearer normally adopts a credulous attitude to the discourse, at least for the purposes of interpreting it. That is to say the hearer tries to accommodate the truth of all the speaker's utterances in deriving an intended model. We present a nonmonotonic logical model of this process which defines unique minimal preferred models and efficiently simulates a kind of closed‐world reasoning of particular interest for (...) human cognition. Byrne's “suppression” data (Byrne, 1989) are used to illustrate how variants on this logic can capture and motivate subtly different interpretative stances which different subjects adopt, thus indicating where more fine‐grained empirical data are required to understand what subjects are doing in this task. We then show that this logical competence model can be implemented in spreading activation network models. A one pass process interprets the textual input by constructing a network which then computes minimal preferred models for (3‐valued) valuations of the set of propositions of the text. The neural implementation distinguishes easy forward reasoning from more complex backward reasoning in a way that may be useful in explaining directionality in human reasoning. (shrink)

In the late summer of 1998, the authors, a cognitive scientist and a logician, started talking about the relevance of modern mathematical logic to the study of human reasoning, and we have been talking ever since. This book is an interim report of that conversation. It argues that results such as those on the Wason selection task, purportedly showing the irrelevance of formal logic to actual human reasoning, have been widely misinterpreted, mainly because the picture of logic current in psychology (...) and cognitive science is completely mistaken. We aim to give the reader a more accurate picture of mathematical logic and, in doing so, hope to show that logic, properly conceived, is still a very helpful tool in cognitive science. The main thrust of the book is therefore constructive. We give a number of examples in which logical theorizing helps in understanding and modeling observed behavior in reasoning tasks, deviations of that behavior in a psychiatric disorder (autism), and even the roots of that behavior in the evolution of the brain. (shrink)

Is the ability to speak a language an acquired skill? Leading proponents of the generative approach to human language—notably Chomsky (2000) and Pinker (2003)—have argued that the thesis that language capacities are skills is hopelessly confused and at odds with a range of empirical evidence, which suggests that human language capacities are grounded in a biologically inherited set of language instincts or a Universal Grammar (UG). In this paper, we argue that resistance to the claim that human language capacities are (...) skills has been fostered by naïve and implausible conceptions of the nature of skilled action. Correcting these misconceptions about the nature of skilled action breathes new life into the old idea that human language competences are particular kinds of skills and does so in a way that is fully consistent with appeals to the central importance of UG, broadly construed. More generally, we argue that by taking human language capacities to be skills, we capture what is right about the generative program while at the same time avoiding its most serious pitfalls. (shrink)

Each of our theories of mental representation provides some insight into how the mind works. However, these insights often seem incompatible, as the debates between symbolic, dynamical, emergentist, sub-symbolic, and grounded approaches to cognition attest. Mental representations—whatever they are—must share many features with each of our theories of representation, and yet there are few hypotheses about how a synthesis could be possible. Here, I develop a theory of the underpinnings of symbolic cognition that shows how sub-symbolic dynamics may give rise (...) to higher-level cognitive representations of structures, systems of knowledge, and algorithmic processes. This theory implements a version of conceptual role semantics by positing an internal universal representation language in which learners may create mental models to capture dynamics they observe in the world. The theory formalizes one account of how truly novel conceptual content may arise, allowing us to explain how even elementary logical and computational operations may be learned from a more primitive basis. I provide an implementation that learns to represent a variety of structures, including logic, number, kinship trees, regular languages, context-free languages, domains of theories like magnetism, dominance hierarchies, list structures, quantification, and computational primitives like repetition, reversal, and recursion. This account is based on simple discrete dynamical processes that could be implemented in a variety of different physical or biological systems. In particular, I describe how the required dynamics can be directly implemented in a connectionist framework. The resulting theory provides an “assembly language” for cognition, where high-level theories of symbolic computation can be implemented in simple dynamics that themselves could be encoded in biologically plausible systems. (shrink)

As of March 2012, students with concealed carry permits attending public colleges and universities in the state of Colorado may carry their weapons on campus. Colorado is one of six states with legal provisions permitting guns on public campuses. An additional twenty-two states leave it up to the governing bodies of individual colleges and universities to determine their institution's gun policy, while twenty-two states ban concealed weapons on campuses. The NRA often asserts that "an armed society is a polite society." (...) They and those who favorably quote them take this as a positive result of an armed citizenry. People won't be rude. They won't argue. They won't say anything offensive, for fear of being shot. And that may be right. But we do not want a polite campus. If an armed campus is a polite campus, then students at such campuses will miss a fundamentally important aspect of their college experience. Students ought to be able to voice their opinions, to argue with others, and to test new ideas without fear. The threat of violence that guns create challenges the most fundamental liberty we have: the freedom of speech. (shrink)

Music can be described as sequences of events that are structured in pitch and time. Studying music processing provides insight into how complex event sequences are learned, perceived, and represented by the brain. Given the temporal nature of sound, expectations, structural integration, and cognitive sequencing are central in music perception (i.e., which sounds are most likely to come next and at what moment should they occur?). This paper focuses on similarities in music and language cognition research, showing that music cognition (...) research provides insight into the understanding of not only music processing but also language processing and the processing of other structured stimuli. The hypothesis of shared resources between music and language processing and of domain-general dynamic attention has motivated the development of research to test music as a means to stimulate sensory, cognitive, and motor processes. (shrink)

Both formal semantics and cognitive semantics are the source of important insights about language. By developing precise statements of the rules of meaning in fragmentary, abstract languages, formalists have been able to offer perspicuous accounts of how we might come to know such rules and use them to communicate with others. Conversely, by charting the overall landscape of interpretations, cognitivists have documented how closely interpretations draw on the commonsense knowledge that lets us make our way in the world. There is (...) no opposition between these insights. Sooner or later we will have a semantics that responds to both. However, developing such a semantics is profoundly difficult, because there are certain tensions to be overcome in reconciling the two perspectives. For one thing, the overall landscape of meaning does seem to be characterized by a much richer ontology and more dynamic categories than are exhibited by the fragments typically studied in the formal tradition. One sign of strain is the recent tendency to talk of “procedural”, “non-compositional”, or “computational” semantics, as in Hamm, Kamp and van Lambalgen 2006, hereafter HK&vL. We think such locutions can serve as useful reminders to keep semantics fixed on the central question of how language allows us to share information that some have and others need to get. However, there is some danger that formalists will merely by put off by an idea that, taken literally, may not be such a good one. In this short article, we want to explore and defend the traditional realist view attributed by HK&vL to Lewis among others. In fact, this view offers a well-developed, extremely straightforward and robust account of the relation between semantics and cognition. Moreover, while the realist view has ways of accommodating the representationalist insights of DRT (Lewis 1979; Thomason 1990; Stalnaker 1998), it remains unclear how “computational” semantics can account for the key data for the realist view: cases where we judge interlocutors to be ignorant about aspects of meaning in their native language (Kripke 1972; Putnam 1975; Stalnaker 1979; Williamson 1994).. (shrink)

I show how a conversational process that takes simple, intuitively meaningful steps may be understood as a sophisticated computation that derives the richly detailed, complex representations implicit in our knowledge of language. To develop the account, I argue that natural language is structured in a way that lets us formalize grammatical knowledge precisely in terms of rich primitives of interpretation. Primitives of interpretation can be correctly viewed intentionally, as explanations of our choices of linguistic actions; the model therefore fits our (...) intuitions about meaning in conversation. Nevertheless, interpretations for complex utterances can be built from these primitives by simple operations of grammatical derivation. In bridging analyses of meaning at semantic and symbol‐processing levels, this account underscores the fundamental place for computation in the cognitive science of language use. (shrink)

This paper argues that the faculty of language comes essentially for free in evolutionary terms, by grace of a capacity shared with some evolutionarily quite distantly related animals for deliberatively planning action in the world. The reason humans have language of a kind that animals do not is because of a qualitative difference in the nature of human plans rather than anything unique to language.

Interpretation is the process whereby a hearer reasons to an interpretation of a speaker's discourse. The hearer normally adopts a credulous attitude to the discourse, at least for the purposes of interpreting it. That is to say the hearer tries to accommodate the truth of all the speaker's utterances in deriving an intended model. We present a nonmonotonic logical model of this process which defines unique minimal preferred models and efficiently simulates a kind of closed-world reasoning of particular interest for (...) human cognition. Byrne's “suppression” data (Byrne, 1989) are used to illustrate how variants on this logic can capture and motivate subtly different interpretative stances which different subjects adopt, thus indicating where more fine-grained empirical data are required to understand what subjects are doing in this task. We then show that this logical competence model can be implemented in spreading activation network models. A one pass process interprets the textual input by constructing a network which then computes minimal preferred models for (3-valued) valuations of the set of propositions of the text. The neural implementation distinguishes easy forward reasoning from more complex backward reasoning in a way that may be useful in explaining directionality in human reasoning. (shrink)

The present paper argues that when thematic roles are restricted to judgments about causal properties of events, it falls short of accounting for cases where thematic roles reflect judgments about dispositional properties of objects. I develop my argument with a case study on a class of verbs that have been called ‘Emission Verbs’ and which are difficult to bring in line with the unaccusativity hypothesis put forward by Perlmutter. Reviewing two diametrically opposed accounts of Emission Verbs in the literature, I (...) show that the thematic-semantic relation between the events described by Emission Verbs and their single arguments cannot be characterized unambiguously in terms of causal properties of events but pertains to dispositional properties residing in the emitter argument. The paper develops a lexical-semantic analysis of Emission Verbs according to which the event described by an Emission Verb is the manifestation of the dispositional property of the emitter argument when appropriate external circumstances obtain. The paper concludes by outlining how the proposed dispositional analysis of Emission Verbs may inform the analysis of the causative alternation. (shrink)

We describe how Intuitionistic Linear Logic can be used to provide a unified logical account for agents to find and execute plans. This account supports the modelling of agent interaction, including dialogue; allows agents to be robust to unexpected events and failures; and supports significant reuse of agent specifications. The framework has been implemented and several case studies have been considered. Further applications include human–computer interfaces as well as agent interaction in the semantic web.