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Conceptualizing emotional distress in Late Middle English medical texts

Paper · 2015 · Javier E. Díaz Vera

Summary

Díaz-Vera asks whether late Middle English medical writing separated bodily suffering from mental suffering, and whether it distinguished different kinds of low mood. The question tests Leff's evolutionary claim that early English merged somatic and psychic distress, and that the split appeared only after seventeenth-century works such as Burton's The Anatomy of Melancholy.

The method has two steps. First, the author used the Historical Thesaurus of the OED (HTOED) to list every distress word current between 1330 and 1500. Second, he traced each word's occurrences in the Middle English Medical Texts (MEMT) corpus, using Wordsmith Tools together with manual reading. He then grouped the attested usages into conceptual metaphors in the tradition of Conceptual Metaphor Theory.

The main result is that the heart serves as the seat of distress, which is described as soreness, heaviness or bitterness of the heart. More abstract terms frame distress as sorrow, darkness, slowness and sinking. The same vocabulary applies to both mind and organs, so the body–mind boundary stays blurred. Even the borrowed term melancholie names both a humour and a mental state. This gives historical evidence against treating the modern body–mind dichotomy as universal.

Key points

  • Data source: The MEMT corpus (Taavitsainen, Pahta & Mäkinen, 2005) covers treatises from about 1375 to 1500, plus an appendix of texts from about 1330. It follows Voigts' three-way split:
    • surgical texts (15),
    • specialized texts (24),
    • remedies and materia medica.
  • HTOED category 2.1.1.1 ("Mind, soul, spirit, heart"): It has 49 entries.
    • 21 entries are attested only in Old English.
    • 7 entries survive into Middle English, such as womb, heart, bosom and soul.
    • 8 entries are new in Middle English, such as mind, conscience, inwit and spirit.
  • Literal body-part words: In the medical texts, body-part words are almost always literal. Only hert regularly extends to emotion and knowledge.
  • Dual-sense words: Sore/sorrow, heuynes, greve and affrey cover both mental distress and physical symptoms, including irregular heartbeat.
  • Unifying metaphor: Darkness, slowness and falling down are unified under DEPRESSION IS DOWN, which links to SADNESS IS DOWN and HAPPINESS IS UP.

Interesting ideas

  • I1. Thesaurus-driven lexicon: Build the target vocabulary from a thesaurus category restricted to a date window, then inspect every corpus hit rather than sampling, which gives a closed and auditable lexicon.
  • I2. Domain-specific sense preferences: The same word can lean strongly literal in one register (body-part terms in medical prose) while being figurative elsewhere, so sense preferences should depend on the domain.
  • I3. Coordination as a sense cue: When an ambiguous noun appears inside a coordinated list of emotion nouns (heaviness of heart next to sorrow and joy), its psychological reading is signalled by its list neighbours.
  • I4. Clustering into a schema: Many surface metaphors (darkness, slowness, sinking) can be grouped under one spatial schema (DOWN), which compresses varied wording into a single interpretable mapping.
  • I5. Register-stratified analysis: Splitting the corpus by tradition and audience (surgical, academic, remedy-book) gives a principled axis for comparing usage.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (world): Emotion or state concepts in the ontology could be seeded from a WordNet/FrameNet category and then checked against all corpus occurrences, so each concept has documented attestations.
  • I2 (world): Keep per-genre sense priors in compiled knowledge, so "heart" in a remedy text defaults to the organ but in a fable defaults to feeling.
  • I3 (frames): Use conj siblings in the UD tree as evidence for choosing between the physical and mental frame of a polysemous noun, and record that evidence in the explanation.
  • I4 (world): A small table mapping vertical and light expressions ("feeling low", "dark mood") to a sad or depressed entity state would let the text world record moods without bag-of-words guessing.
  • I5 (learn): Report parser and frame accuracy per register stratum, so that gains on one genre cannot mask regressions on another.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

📚 Shelves 2The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 works

🧭 Topics 3Research subjects and languages the work is about.English · 1,373 worksSentiment & emotion · 608 worksHistorical linguistics & language change · 159 works

Cite

Cite as Javier E. Díaz Vera. 2015. Conceptualizing emotional distress in Late Middle English medical texts. Revista de Lenguas para Fines Específicos 17.

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{vera2015conceptualizing,
  title = {{Conceptualizing emotional distress in Late Middle English medical texts}},
  author = {Javier E. Díaz Vera},
  booktitle = {Revista de Lenguas para Fines Específicos 17},
  year = {2015},
  url = {https://ojsspdc.ulpgc.es/ojs/index.php/LFE/article/view/99}
}

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