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Metaphor - Key Sources

Metaphor Theory and Source–Target Mapping​

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This work in cognitive linguistics provided the foundational concept: metaphors operate by mapping structure from a familiar source domain (e.g., human cognition) onto a less familiar target domain (e.g., algorithmic processes). The mapping isn't arbitrary, it imports specific inferences and hides others.

Key insight for prompt design: To analyze metaphor, I needed to instruct the model to identify both domains, describe the structural mapping between them, and articulate what the mapping conceals.

Adam, A. (1995). Artificial intelligence and women’s knowledge: What can feminist epistemologies tell us? Women’s Studies International Forum, 18(4), 407–415

Agre, P. (1997). Computation and Human Experience. United Kingdom: Cambridge University Press.

ALEXANDER, P. A., SCHALLERT, D. L., & REYNOLDS, R. E. (2009). What Is Learning Anyway? A Topographical Perspective Considered. Educational Psychologist, 44(3), 176–192. https://doi.org/10.1080/00461520903029006

Bones, H., Ford, S., Hendery, R., Richards, K., & Swist, T. (2021). In the frame: The language of AI. Philosophy and Technology, 34(1), 23–44.

Charteris-Black, J. (2004). Corpus approaches to critical metaphor analysis. Palgrave Macmillan.

Chown, E., & Nascimento, F. (2023). Meaningful Technologies: How Digital Metaphors Change the Way We Think and Live. Lever Press. https://doi.org/10.3998/mpub.12668201

Clark, K. M. (2024). Embodied Imagination: Lakoff and Johnson’s Experientialist View of Conceptual Understanding. Review of General Psychology, 28(2), 166-183. https://doi.org/10.1177/10892680231224400 (Original work published 2024)

Colburn, T. R., & Shute, G. M. (2008). Metaphor in computer science. Journal of applied logic, 6(4), 526-533.

Durt, C., Froese, T., & Fuchs, T. (2023). Against AI understanding and sentience: large language models, meaning, and the patterns of human language use. Preprint.

Gerber, Y., & Sander, E. (2025). Promoting a shift in perspective in argumentative thinking: Metaphorical framing for orienting attention. Journal of Applied Research in Memory and Cognition. https://doi.org/10.1037/mac0000226

Group, P. (2007). MIP: A Method for Identifying Metaphorically Used Words in Discourse. Metaphor and Symbol, 22(1), 1–39. https://doi.org/10.1080/10926480709336752

Hicke, R. M. M., & Kristensen-McLachlan, R. D. (2024). Science is Exploration: Computational Frontiers for Conceptual Metaphor Theory. https://doi.org/10.48550/arxiv.2410.08991

Johnson, D. G., & Verdicchio, M. (2017). Reframing AI discourse. Minds & Machines, 27, 575–590.

Johnson, M. (2022). Embodied mind, meaning, and reason: How our bodies give rise to understanding. University of Chicago Press

Kövecses, Z. (2002). Metaphor: A practical introduction. New York: Oxford University Press.

Lakoff, G., & Johnson, M. (2003). Metaphors we live by. The University of Chicago Press.

Littlemore, J. (2019). Metaphors in the mind : sources of variation in embodied metaphor. Cambridge University Press.

Long, D., & Magerko, B. (2020, April). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1-16).

Mattson, G. (2020). Weaponization: Ubiquity and Metaphorical Meaningfulness. Metaphor and Symbol, 35(4), 250–265. https://doi.org/10.1080/10926488.2020.1810577

McCarthy, J. (1955). Dartmouth Summer Research Project on Artificial Intelligence.

Mitchell, M. (2021). Why AI is Harder Than We Think (No. arXiv:2104.12871). arXiv. https://doi.org/10.48550/arXiv.2104.12871

Musolff, A. (2006). Metaphor Scenarios in Public Discourse. Metaphor and Symbol, 21(1), 23–38. https://doi.org/10.1207/s15327868ms2101_2

O'Gieblyn, M. (2022). God, Human, Animal, Machine: Technology, Metaphor, and the Search for Meaning. United States: Knopf Doubleday Publishing Group.

Pierce, A. E., & Garrison, S. T. (2011). The metaphorical horizon: Between facts and fictions. The International Journal of Interdisciplinary Social Sciences, 5(9), 95–105. https://doi.org/10.18848/1833-1882/cgp/v05i09/59308

Pope, R. (1994). Textual intervention : critical and creative strategies for literary studies. United Kingdom: Routledge.

Quattrociocchi, W., Capraro, V., & Perc, M. (2025). Epistemological Fault Lines Between Human and Artificial Intelligence. arXiv preprint arXiv:2512.19466.

Ramsay, S. (2011). Reading machines : toward an algorithmic criticism (1st ed.). University of Illinois Press.

Rehak, R. (2021). The language labyrinth: Constructive critique on the terminology used in the AI discourse. In P. Verdegem (ed.), AI for everyone?. London: University of Westminster Press.

Samuels, L., & McGann, J. J. (1999). Deformance and interpretation. New Literary History, 30(1), 25-56.

Searle, J. R. (1992). The rediscovery of mind. Cambridge, MA: MIT Press.

Thibodeau, P. H., Matlock, T., & Flusberg, S. J. (2019). The role of metaphor in communication and thought. Language and Linguistics Compass, 13(5), Article e12327. https://doi.org/10.1111/lnc3.12327

Wilkinson, C., Yawney, J., Gadsde, S.A. (2026). , Explaining explainability: A comprehensive survey on explainable artificial intelligence and relevant industry applications. Intelligent Systems with Applications. https://doi.org/10.1016/j.iswa.2026.200647.

Winston, P. H., & Horn, B. (1975). The psychology of computer vision (Vol. 67). New York: McGraw-Hill.

Wyatt, S. (2021). Metaphors in critical Internet and digital media studies. New Media & Society, 23(2), 406-416. https://doi.org/10.1177/1461444820929324 (Original work published 2021)

Typologies of Explanation​

  • Brown, R. (1963). Explanation and Experience in Social Science. Routledge.
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Robert Brown's classic work distinguishes between different modes of explanation: genetic (how it came to be), functional (how it works), intentional (why it "wants" something), dispositional (why it "tends" to act), and so on.

The System Instructions are provided with examples using the following table:

TypeDefinitionLens
GeneticTraces development or origin.How it came to be.
FunctionalDescribes purpose within a system.How it works (as a mechanism).
EmpiricalCites patterns or statistical norms.How it typically behaves.
TheoreticalEmbeds behavior in a larger framework.How it's structured to work.
IntentionalExplains actions by referring to goals/desires.Why it "wants" something.
DispositionalAttributes tendencies or habits.Why it "tends" to act a certain way.
Reason-BasedExplains using rationales or justifications.Why it "chose" an action.
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