Cognitive modeling in linguistics: conceptual metaphors

Cognitive modeling in linguistics: conceptual metaphors

Nathan Labhart's picture

“The concepts that govern our thought are not just matters of the intellect. They also govern our everyday functioning, down to the most mundane details. Our concepts structure what we perceive, how we get around in the world, and how we relate to other people. Our conceptual system thus plays a central role in defining our everyday realities. If we are right in suggesting that our conceptual system is largely metaphorical, then the way we think, what we experience, and what we do every day is very much a matter of metaphor…” (Lakoff & Johnson, 1980)

The lecture will address the integration challenge facing cognitive science as an interdisciplinary endeavor. The interconnection between the AI and Linguistics will be highlighted. Cognitive modeling in Linguistics will be exemplified by conceptual metaphors.

Conceptual metaphor (CM) is a schema underlying identification of an abstract concept with a more basic or concrete concept (or a mapping between a concrete domain and an abstract domain). CM is based on analogy, which is a universal mental operation. In accessing new information humans create mental models by proceeding from existing knowledge. I will try to show how in accordance with Johnson-Laird’s theory of mental models the speakers achieve their semantic representations. Based on the experience situation, the speakers build analogous representations from which they can infer implicit information. I will follow the original schema theories as introduced by Barlett and Piaget as they are still valid in cognitive psychology. I will explain how in accordance with Piaget during individuals’ active information processing he\she integrates new information into their existing assimilating schemata. In connection with this I will pose the question of relevance of new information.

I will also touch upon the embodied cognition as the bases for conceptual metaphors. And finally I will try to shed some light on the method of qualia-structure analysis of the word meaning introduced by James Pustejovsky which helps to decompose the word meaning into the four main components according to the four generative factors or qualia roles. This theory (method) proves useful in machine translation and studies of AI.


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