Concepts are the elementary units of reason and linguistic meaning. They are conventional and relatively stable. As such, they must somehow be the result of neural activity in the brain. The questions are: Where? and How? A common philosophical position is that all concepts—even concepts about action and perception—are symbolic and abstract, and therefore must be implemented outside the brain’s sensory-motor system. We will argue against this position using (1) neuroscientific evidence; (2) results from neural computation; and (3) results about the nature of concepts from cognitive linguistics. We will propose that the sensory-motor system has the right kind of structure to characterise both sensory-motor and more abstract concepts. Central to this picture are the neural theory of language and the theory of cogs, according to which, brain structures in the sensory-motor regions are exploited to characterise the so-called “abstract ” concepts that constitute the meanings of grammatical constructions and general inference patterns.

This paper describes the results of an implemented computational model that cashes out the belief that metaphor interpretation is grounded in embodied primitives. The speci c task addressed is the interpretation of simple causal narratives in the domains of Politics and Economics. The stories are taken from newspaper articles in these domains. When presented with a preparsed version of these narratives as input, the system described is able to generate commonsense inferences consistent with the input.

markj @ oregon.uoregon.edu and rohrer @ cogsci.ucsd.edu © 2003-2007 by the authors, pre-press final draft 4/7/07 citation information:

Language learning is one of the most complicated feats that human beings accomplish. Any number of very real reasons exist as to why L2 learning presents tremendous challenges. However, instructed L2 learning has been further complicated by the fact that important elements of systematicity that

The role of metaphor and embodiment in the development of mathematical

Abstract: This essay offers an explicit application of the mental spaces and blending framework to account for the dynamic nature of meaning construction in a public art museum. The mental spaces and blending framework show great utility for modelling the use of diverse sign systems in specific social and institutional settings. The objects of inquiry are audio presentations on particular paintings, sculptures, and decorative artifacts on display in the Cleveland Museum of Art. As a preserved record of human meaning as it unfolds in time and space, these ‘texts ’ are prime candidates for testing the mental spaces and blending framework as a sufficient interpretive model of how we map conceptualization onto expression within a well-defined social-interactive context. [Todd Oakley: Mapping the Museum Space: Verbal and

Cognitive research on metaphoric concepts of time has focused on differences between moving Ego and moving time models, but even more basic is the contrast between Ego- and temporal-reference-point models. Dynamic models appear to be quasi-universal cross-culturally, as does the generalization that in Ego-reference-point models, FUTURE IS IN FRONT OF EGO and PAST IS IN BACK OF EGO. The Aymara language instead has a major static model of time wherein FUTURE IS BEHIND EGO and PAST IS IN FRONT OF EGO; linguistic and gestural data give strong confirmation of this unusual culture-specific cognitive pattern. Gestural data provide crucial information unavailable to purely linguistic analysis, suggesting that when investigating conceptual systems both forms of expression should be analyzed complementarily. Important issues in embodied cognition are raised: how fully shared are bodily grounded motivations for universal cognitive patterns, what makes a rare pattern emerge, and what are the cultural entailments of such patterns?

There is mounting evidence that language comprehension involves the activation of mental imagery

The heading figurative language subsumes multiple phenomena that can be used to perform most linguistic functions including predication, modification, and reference. Figurative language can tap into conceptual and linguistic knowledge (as in the case of idioms, metaphor, and some metonymies) as well as evoke pragmatic factors in interpretation (as in indirect speech acts, humor, irony, or

Metaphor is highly frequent in language, which makes its computational processing indispensable for real-world NLP applications addressing semantic tasks. Previous approaches to metaphor modelling rely on task-specific hand-coded knowledge and operate on a limited domain or a subset of phenomena. We present the first integrated open-domain statistical model of metaphor processing in unrestricted text. Our method first identifies metaphorical expressions in running text and then paraphrases them with their literal paraphrases. Such a text-to-text model of metaphor interpretation is compatible with other NLP applications that can benefit from metaphor resolution. Our approach is minimally supervised, it relies on the state-of-the-art parsing and lexical acquisition technologies (distributional clustering and selectional preference induction) and operates with a high accuracy. 1.