Abstract

This study explores the embodied conceptualisation of emotion metaphors in English, Hindi, Khortha, and Marwari, examining how abstract emotional experiences are conceptually mapped through concrete bodily source domains. Drawing on the Conceptual Metaphor Theory framework of embodiment-based cognitive semantics, the study identifies and analyses metaphorical expressions of anger, happiness, sadness, social emotions (pride, honour, shame), and Heart as an embodied source domain collected through elicitation from native speakers above 25 years of age and from secondary textual sources, including folk narratives, proverbs, and idioms. English is included as a comparison language because of its extensive documentation in cognitive linguistic research. However, besides Hindi and English, Khortha and Marwari are largely unexplored in a cognitive-semantic context. Findings show that all four languages conceptualise anger through heat, pressure, fluid containment, and explosive release; happiness through upward movement, brightness, and expansion; sadness through downward movement, darkness, and submersion; and social emotions through bodily posture and body-part symbolism involving the head, chest, eyes, and nose. These patterns cluster into recurring oppositional cognitive schemas — upwardness versus downwardness, brightness versus darkness, expansion versus contraction, and fullness versus emptiness — that organise emotional cognition across the languages, lending support to the embodiment hypothesis of shared physiological grounding. At the same time, Hindi, Khortha, and Marwari display a wider range of body-part lexicalised metaphorical expressions, particularly involving blood, the heart, and the nose, along with culturally specific elaborations such as nose-based honour and heart-centred flourishing imagery that are comparatively less salient in English and are interpreted qualitatively. The study argues that emotion metaphors are simultaneously embodied and culturally mediated, and it contributes to cross-linguistic metaphor theory, Indo-Aryan cognitive semantics, and applications in translation studies, multilingual education, and computational modelling of emotion in low-resource regional languages.

Keywords

Conceptual Metaphor Theory, Embodiment, Emotion Metaphors, Khortha, Marwari, Hindi, Cognitive Semantics,

References

  1. Augustin, M., Pollak, T.A., Morrin, H. (2026). Characterizing the spiral: potential mechanisms in AI-associated delusions. NPP—Digital Psychiatry and Neuroscience, 4(1), 14. https://doi.org/10.1038/s44277-026-00065-0
  2. Blevins, T., Schmalwieser, S., Roth, B. (2026). Do language models accommodate their users? A study of linguistic convergence. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics 1, 791-807. https://doi.org/10.18653/v1/2026.eacl-long.34
  3. DeVrio, A., Cheng, M., Egede, L., Olteanu, A., & Blodgett, S. L. (2025). A taxonomy of linguistic expressions that contribute to anthropomorphism of language technologies. In Proceedings of the 2025 CHI conference on human factors in computing systems, 1-18. https://doi.org/10.1145/3706598.3714038
  4. Duran, N. D., Paxton, A., & Fusaroli, R. (2019). ALIGN: Analyzing linguistic interactions with generalizable techNiques—A Python library. Psychological Methods, 24(4), 419. https://psycnet.apa.org/doi/10.1037/met0000206
  5. Glickman, M., Sharot, T. (2025). How human–AI feedback loops alter human perceptual, emotional and social judgements. Nature Human Behaviour, 9(2), 345-359. https://doi.org/10.1038/s41562-024-02077-2
  6. Gonen, H., Blevins, T., Liu, A., Zettlemoyer, L., Smith, N. A. (2025). Does liking yellow imply driving a school bus? Semantic leakage in language models. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, 1, 785-798. https://doi.org/10.18653/v1/2025.naacl-long.35
  7. Kandra, F., Demberg, V., Koller, A. (2025). LLMs syntactically adapt their language use to their conversational partner. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, 2, 873-886. https://doi.org/10.18653/v1/2025.acl-short.68
  8. Kim, S.S.Y, Liao, Q.V., Vorvoreanu, M., Ballard, S., Vaughan, J.W., (2024) "I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust. In Proceedings of the 2024 ACM conference on fairness, accountability, and transparency, 822-835. https://doi.org/10.1145/3630106.3658941
  9. Kirk, H.R., Gabriel, I., Summerfield, C., Vidgen, B., Hale, S.A. (2025). Why human–AI relationships need socioaffective alignment. Humanities and Social Sciences Communications, 12(1), 728. https://doi.org/10.1057/s41599-025-04532-5
  10. Laestadius, L., Bishop, A., Gonzalez, M., Illenčík, D., Campos-Castillo, C. (2024). Too human and not human enough: A grounded theory analysis of mental health harms from emotional dependence on the social chatbot Replika. New media & society, 26(10), 5923-5941. https://doi.org/10.1177/14614448221142007
  11. Li, J., Yang, Y., Liao, Q. V., Zhang, J., & Lee, Y. C. (2025). As confidence aligns: understanding the effect of AI confidence on human self-confidence in human-AI decision making. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1-6. https://doi.org/10.1145/3706598.3713336
  12. Perez, E., Ringer, S., Lukosiute, K., Nguyen, K., Chen, E., Heiner, S., Pettit, C., Olsson, C., Kundu, S., Kadavath, S., Jones, A., Chen, A., Mann, B., Israel, B., Seethor, B., McKinnon, C., Olah, C., Yan, D., Amodei, D., Amodei, D., Drain, D., Li, D., Tran-Johnson, E., Khundadze, G., Kernion, J., Landis, J., Kerr, J., Mueller, J., Hyun, J., Landau, J., Ndousse, N., Goldberg, K., Lovitt, L., Lucas, L., Sellitto, M., Zhang, M., Kingsland, M., Elhage, N., Joseph, N., Mercado, N., DasSarma, N., Rausch, O., Larson, R., McCandlish, S., Johnston, S., Kravec, S., El Showk, S., Lanham, T., Telleen-Lawton, T., Brown, T., Henighan, T., Hume, T., Bai, Y., Hatfield-Dodds, Z., Clark, J., Bowman, S.R., Askell, A., Grosse, R., Hernandez, D., Ganguli, D., Hubinger, E., Schiefer, N., Kaplan, J., (2023). Discovering language model behaviors with model-written evaluations. In Findings of the association for computational linguistics: ACL, 2023, 13387-13434. https://doi.org/10.18653/v1/2023.findings-acl.847
  13. Peter, S., Riemer, K., West, J.D. (2025). The benefits and dangers of anthropomorphic conversational agents. Proceedings of the National Academy of Sciences, 122(22), e2415898122. https://doi.org/10.1073/pnas.2415898122
  14. Pickering, M. J., & Ferreira, V. S. (2008). Structural priming: a critical review. Psychological bulletin, 134(3), 427. https://psycnet.apa.org/doi/10.1037/0033-2909.134.3.427
  15. Pickering, M.J., Garrod, S. (2004). Toward a mechanistic psychology of dialogue. Behavioral and brain sciences, 27(2), 169-190. https://doi.org/10.1017/S0140525X04000056
  16. Sharma, M., Tong, M., Korbak, T., Duvenaud, D., Askell, A., Bowman, S., Durmus, E., Dodds, Z.H., Johnston, S., Kravec, S., Maxwell, T., McCandlish, S., Ndousse, K., Rausch, O., Schiefer, N., Yan, D., Zhang, M., Perez. E., (2024). Towards understanding sycophancy in language models. In International Conference on Learning Representations, 2024, 110-144.
  17. Sharma, N., Liao, Q.V., Xiao, Z. (2024). Generative echo chamber? Effect of llm-powered search systems on diverse information seeking. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, 1-17. https://doi.org/10.1145/3613904.3642459
  18. Siddals, S., Torous, J., Coxon, A. (2024). “It happened to be the perfect thing”: experiences of generative AI chatbots for mental health. Npj mental health research, 3(1), 48. https://doi.org/10.1038/s44184-024-00097-4
  19. Tanguy, C., Janssens, R., Belpaeme, T., Dambre, J. (2025). Human Alignment: How Much Do We Adapt to LLMs?. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, 2, 603-613. https://doi.org/10.18653/v1/2025.acl-short.47