糖心视频

Metaphors are a fundamental aspect of human language and cognition, allowing us to understand complex concepts and relationships by mapping them onto more familiar and concrete domains. However, the nature of metaphors and how they work is still not well understood.

In a new paper in PLOS Complex Systems, Max-Planck-Institute for Mathematics in the Sciences researchers Marie Teich and Wilmer Leal together with director J眉rgen Jost have developed a formal framework and large-scale empirical methodology to analyze metaphors and their role in conceptual metaphor theory.

The study confirms the fundamental assumption in conceptual metaphor theory that metaphors are enduring linguistic and cognitive structures, not merely rhetorical figures. Using complex systems tools, the researchers identified a metaphor network with distinctions between abstract and concrete categories, and two significant metaphorical processes: mappings from concrete to abstract topics and the emergence of new mappings between concrete domains.

The study also found that metaphors concentrate on two small sets of everyday topics, with one within the concrete group serving as both strong source and target domains, and the other in the abstract group primarily acting as targets. These findings indicate that metaphor is a creative process driven by contrast and tension between topics which allow re-conceptualizations and the emergence of new similarities.

The study's findings have significant implications for researchers in cognitive linguistics and the philosophy of language, particularly those focused on conceptual metaphor theory, figurative language, and semantic structure. The large-scale empirical methodology proposed by the authors can be used to do fundamental research in conceptual metaphor theory providing new insights into the nature of metaphor and figurative thinking.

Beyond the humanities, this work opens interesting research questions for machine learning and artificial intelligence, particularly in areas concerned with analogy and representation learning. The methods also hold promise for the mathematics of cognition and formal epistemology, offering tools to study how abstract meaning arises from structure-preserving mappings across conceptual domains.