Cover not available
Article published In:
Empirical Studies of Literariness
Edited by Massimo Salgaro and Paul Sopčák
[Scientific Study of Literature 8:1] 2018
► pp. 165–208

(Neuro-)Cognitive poetics and computational stylistics

| Freie Universität Berlin

This perspective paper discusses four general desiderata of current computational stylistics and (neuro-)cognitive poetics concerning the development of (a) appropriate databases/training corpora, (b) advanced qualitative-quantitative narrative analysis (Q2NA) and machine learning tools for feature extraction, (c) ecologically valid literary test materials, and (d) open-access reader-response data banks. In six explorative computational stylistics studies, it introduces a number of tools that provide QNA indices of the foregrounding potential at the sublexical, lexical, inter- and supralexical levels for poems by Shakespeare, Blake, or Dickens. These concern lexical diversity and aesthetic potential, sentiment analysis, sublexical sonority scores or phrase structure, and topics analysis. The results illustrate the complex interplay of stylistic features and the necessity for theoretical guidance and interdisciplinary cooperation in selecting adequate training corpora, QNA tools, test texts, and response measures.

Keywords: neurocognitive poetics, quantitative narrative analysis, computational stylistics, digital literary studies, neuroaesthetics, affective-aesthetic processes, literary reading
Article outline
  • Towards representative and reliable training corpora in several languages
  • The training corpus as a statistical reader model
  • QNA and machine learning tools for extracting and determining the importance of text features
  • Well selected, ecologically valid literary test materials and reliable open-access reader-response data banks
  • Illustrative studies merging (neuro-)cognitive poetics and computational stylistics
    • Study 1. Lexical distinctiveness
    • Study 2. Lexical diversity and concreteness
    • Study 3. Sentiment analysis and lexical aesthetic potential
    • Study 4. Affective sublexical indices: Tender vs. aggressive consonants and sonority score
    • Study 5. Interlexical indices: Bigram novelty, tenor vehicle similarity, and arousal span
    • Study 6. Supralexical indices (lines, sentences, stanzas, poem)
      • Lines
      • Sentences
      • Stanza dynamics
      • Topics
  • Discussion and outlook
  • Notes
  • References
Published online: 17 January 2019
DOI logo
https://doi.org/10.1075/ssol.18002.jac
References
Abramo, F., Gambino, R., Pulvirenti, G., Xue, S., Sylvester, T., Mangen, A., Papp-Zipernovszky, O., Lüdtke, J. & Jacobs, A. M.
(2018) A qualitative-quantitative analysis of Shakespeare’s Sonnet 60. Style, in revision.Google Scholar
Altmann, U., Bohrn, I. C., Lubrich, O., Menninghaus, W., & Jacobs, A. M.
(2012) The power of emotional valence-from cognitive to affective processes in reading. Frontiers in Human Neuroscience, 6(192). DOI logoGoogle Scholar
(2014) Fact vs fiction: How paratextual information shapes our reading processes. Social Cognitive and Affective Neuroscience, 9(1), 22–29. DOI logoGoogle Scholar
Andrzejewski, D., Zhu, X., & Craven, M.
(2009) Incorporating domain knowledge into topic modeling via dirichlet forest priors. ICML ’09: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 25–32, New York, NY: ACM. DOI logoGoogle Scholar
Armeni, K., Willems, R. M., & Frank, S. L.
(2017) Probabilistic language models in cognitive neuroscience: promises and pitfalls. Neuroscience and Biobehavioral Reviews, 831, 579–588. DOI logoGoogle Scholar
Aryani, A., Jacobs, A. M., & Conrad, M.
(2013) Extracting salient sublexical units from written texts: “Emophon,” a corpus-based approach to phonological iconicity. Frontiers in Psychology, 4(654). DOI logoGoogle Scholar
Aryani, A., Kraxenberger, M., Ullrich, S., Jacobs, A. M., & Conrad, M.
(2016) Measuring the basic affective tone of poems via phonological saliency and iconicity. Psychology of Aesthetics, Creativity, and the Arts, 101, 191–204. DOI logoGoogle Scholar
Bambini, V., Canal, P., Resta, D., & Grimaldi, M.
(2018) Time course and neurophysiological underpinnings of metaphor in literary context. Discourse Processes. DOI logoGoogle Scholar
Baroni, M., Bernardini, S., Ferraresi, A., & Zanchetta, E.
(2009) The WaCky wide web: A collection of very large linguistically processed web-crawled corpora. Language Resources and Evaluation, 431, 209–226. DOI logoGoogle Scholar
Berlyne, D. E.
(1971) Aesthetics and psychobiology. New York, NY: Appleton-Century-Crofts.Google Scholar
Bestgen, Y., & Vincze, N.
(2012) Checking and boot-strapping lexical norms by means of word similarity indexes. Behavior Research Methods, 44(4), 998–1006. DOI logoGoogle Scholar
Bird, S., Klein, E., & Loper, E.
(2009) Natural language processing with Python. O’Reilly Media.Google Scholar
Bohrn, I. C., Altmann, U., Lubrich, O., Menninghaus, W., & Jacobs, A. M.
(2013) When we like what we know – a parametric fMRI analysis of beauty and familiarity. Brain Language, 1241, 1–8. DOI logoGoogle Scholar
Bornet, C., & Kaplan, F.
(2017) A simple set of rules for characters and place recognition in French novels. Frontiers in Digital Humanities, 4(6). DOI logoGoogle Scholar
Braun, M., Jacobs, A. M., Hahne, A., Ricker, B., Hofmann, M., & Hutzler, F.
(2006) Model-generated lexical activity predicts graded ERP amplitudes in lexical decision. Brain Research, 1073–1074, 431–439.Google Scholar
Bradley, M. M., & Lang, P. J.
(1999) Affective norms for English words (ANEW): Stimuli instruction and affective ratings technical report C-1. Gainesville: University of Florida.Google Scholar
Braun, M., Hutzler, F., Ziegler, J. C., Dambacher, M., & Jacobs, A. M.
(2009) Pseudo homophone effects provide evidence of early lexico-phonological processing in visual word recognition. Human Brain Mapping, 30(7), 1977–1989. DOI logoGoogle Scholar
Braun, M., Hutzler, F., Munte, T. F., Rotte, M., Dambacher, M., Richlan, F., & Jacobs, A. M.
(2015) The neural bases of the pseudohomophone effect: Phonological constraints on lexico-semantic processing in reading. Neuroscience, 2951, 151–163. DOI logoGoogle Scholar
Breen, M.
(2014) Empirical investigations of the role of implicit prosody in sentence processing. Language and Linguistics Compass, 8(2), 37–50. DOI logoGoogle Scholar
Briesemeister, B. B., Kuchinke, L., & Jacobs, A. M.
(2011) Discrete emotion norms for nouns: Berlin a ective word list (DENN – BAWL). Behavior Research Methods, 43(2), 441–448. DOI logoGoogle Scholar
Brunswik, E.
(1956) Perception and the representative design of psychological experiments (2nd ed.). Berkeley: University of California Press.Google Scholar
Burke, M.
(2013) The rhetorical neuroscience of style: On the primacy of style elements during literary discourse processing. Journal of Literary Semantics, 421, 199–215. DOI logoGoogle Scholar
Burrows, J. F.
(1986) Modal verbs and moral principles: an aspect of Jane Austen’s style, Literary and Linguistic Computing, 11, 9–23. DOI logoGoogle Scholar
Burrows, J.
(2002) ‘Delta’: A measure of stylistic difference and a guide to likely authorship. Literary and Linguistic Computing, 17(3), 267–87. DOI logoGoogle Scholar
Busemann, A.
(1948) Stil und Charakter. Untersuchungen zur Psychologie der individuellen Redeform, Meisenheim.Google Scholar
Castiglione, D.
(2017) Difficult poetry processing: Reading times and the narrativity hypothesis. Language and Literature, 261, 99–121. DOI logoGoogle Scholar
Citron, F. M. M., Weekes, B. S., & Ferstl, E. C.
(2012) How are affective word ratings related to lexico-semantic properties? Evidence from the Sussex Affective Word List (SAWL). Applied Psycholinguist, 351, 313–331. DOI logoGoogle Scholar
Crossley, S. A., Kyle, K., & McNamara, D. S.
(2017) Sentiment Analysis and Social Cognition Engine (SEANCE): An automatic tool for sentiment, social cognition, and social-order analysis. Behavior Research Methods, 49(3), 803–821. DOI logoGoogle Scholar
Dalvean, M.
(2015) Ranking contemporary American poems. Digital Scholarship in the Humanities, 30, 1. DOI logoGoogle Scholar
Davies, M.
(2009) The 385+ million word Corpus of Contemporary American English (1990–2008+): Design, architecture, and linguistic insights. International Journal of Corpus Linguistics, 141, 159–190. DOI logoGoogle Scholar
Davis, P.
(2013) Reading and the reader. Oxford, United Kingdom: Oxford University Press.Google Scholar
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R.
(1990) Indexing by latent semantic analysis. Journal of the American Society for Information Science, 411, 391–407. DOI logoGoogle Scholar
Delmonte, R.
(2016) Exploring Shakespeare’s Sonnets with SPARSAR. Linguistics and Literature Studies, 4(1), 61–95. DOI logoGoogle Scholar
De Smedt, T., & Daelemans, W.
(2012) Pattern for Python. Journal of Machine Learning Research, 131, 2031–2035.Google Scholar
Eder, M.
(2013) Mind your corpus: Systematic errors in authorship attribution. Literary and Linguistic Computing, 28(4), 604–614. DOI logoGoogle Scholar
Eder, M., Rybicki, J., & Kestemont, M.
(2016) Stylometry with R: A package for computational text analysis. R Journal, 8(1), 107–121. DOI logoGoogle Scholar
Epstein, R.
(2004) Consciousness, art and the brain: Lessons from Marcel Proust. Consciousness and Cognition, 131, 213–240. DOI logoGoogle Scholar
Fechner, G. T.
(1876) Vorschule der Ästhetik, Vol. 21. Leipzig: Breitkopf & Härtel.Google Scholar
Fónagy, I.
(1961) Communication in poetry. Word, 171, 194–218. DOI logoGoogle Scholar
(1983/1991) La vive voix: essais de psycho-phonetique. Paris: Payot.Google Scholar
Forsyth, R. S.
(2000) Pops and flops: Some properties of famous English poems. Empirical Studies of the Arts, 18(1), 49–67. DOI logoGoogle Scholar
Forsyth, R. S., & Holmes, D. I.
(1996) Feature-finding for text classification. Literary and Linguistic Computing, 111, 163–174. DOI logoGoogle Scholar
Fucks, W.
(1955) Mathematische Analyse von Sprachelementen, Sprachstil und Sprachen, Köln. DOI logoGoogle Scholar
Graesser, A. C., McNamara, D. S., Louwerse, M. M., & Cai, Z.
(2004) Coh-Metrix: Analysis of text on cohesion and language. Behavior Research Methods, Instruments, & Computers, 361, 193–202. DOI logoGoogle Scholar
Hanauer, D.
(2017) Towards a critical mass of accumulated knowledge in the field of scientific literary studies. Scientific Study of Literature, 7(1), 1–3. DOI logoGoogle Scholar
Hayward, M.
(1991) A connectionist model of poetic meter. Poetics, 201, 303–317. DOI logoGoogle Scholar
Henrich, V., Hinrichs, E., & Suttner, K.
(2012) Automatically linking germanet to Wikipedia for harvesting corpus examples for germanet senses. Journal for Language Technology and Computational Linguistics, 271, 1–19.Google Scholar
Herrmann, J. B., van Dalen-Oskam, K., & Schöch, C.
(2015) Revisiting style, a key concept in literary studies. Journal of Literary Theory, 9(1), 25–52.Google Scholar
Hoffstaedter, P.
(1987) Poetic text processing and its empirical investigation. Poetics, 161, 75–91. DOI logoGoogle Scholar
Hofmann, M. J., Kuchinke, L., Biemann, C., Tamm, S., & Jacobs, A. M.
(2011) Remembering words in context as predicted by an associative read-out model. Frontiers in Psychology, 2(252). DOI logoGoogle Scholar
Hofmann, M. J., & Jacobs, A. M.
(2014) Interactive activation and competition models and semantic context: From behavioral to brain data. Neuroscience & Biobehavioral Reviews, 461, 85–104. DOI logoGoogle Scholar
Hogan, P. C.
(2003) Cognitive science, literature and the arts. New York, NY: Routledge.Google Scholar
Hollis, G., Westbury, C., & Lefsrud, L.
(2017) Extrapolating human judgments from skip-gram vector representations of word meaning. The Quarterly Journal of Experimental Psychology, 1–45. DOI logoGoogle Scholar
Hoorn, J., Frank, S., Kowalczyk, W., & van der Ham, F.
(1999) Neural network identification of poets using letter sequences. Literary and Linguistic Computing, 14(3), 311–338. DOI logoGoogle Scholar
Hoover, D. L.
(2007) Corpus stylistics, stylometry, and the styles of Henry James. Style, 41(2), 174–203.Google Scholar
Hoshi, H., & Menninghaus, W.
(2018) The eye tracks the aesthetic appeal of sentences. Journal of Vision, 18(3). DOI logoGoogle Scholar
Hsu, C. T., Jacobs, A. M., Citron, F., & Conrad, M.
(2015) The emotion potential of words and passages in reading harry potter: An fMRI study. Brain and Language, 1421, 96–114. DOI logoGoogle Scholar
Hutto, C. J., & Gilbert, E. E.
(2014) VADER: A parsimonious rule-based model for sentiment analysis of social media rext. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI.Google Scholar
Jacobs, A. M.
(2015a) Towards a neurocognitive poetics model of literary reading. In R. Willems (Ed.), Cognitive neuroscience of natural language use (pp. 135–159). Cambridge, United Kingdom: Cambridge University Press. DOI logoGoogle Scholar
(2015b) Neurocognitive poetics: Methods and models for investigating the neuronal and cognitive-affective bases of literature reception. Frontiers in Human Neuroscience, 9, 186. DOI logoGoogle Scholar
Jacobs, A.
(2015c) The scientific study of literary experience: Sampling the state of the art. Scientific Study of Literature, 5(2), 139–170. DOI logoGoogle Scholar
Jacobs, A. M.
(2017) Quantifying the beauty of words: A neurocognitive poetics perspective. Frontiers in Human Neuroscience, 11, 622. DOI logoGoogle Scholar
(2018) The Gutenberg English Poetry Corpus: Exemplary quantitative narrative analyses. Frontiers in Digital Humanities, 5(5). DOI logoGoogle Scholar
Jacobs, A. M., & Kinder, A.
(2017) The brain is the prisoner of thought: A machine-learning assisted quantitative narrative analysis of literary metaphors for use in Neurocognitive Poetics. Metaphor and Symbol, 32(3), 139–160. DOI logoGoogle Scholar
(2018) What makes a metaphor literary? Answers from two computational studies. Metaphor and Symbol, in press. DOI logoGoogle Scholar
Jacobs, A. M., & Lüdtke, J.
(2017) Immersion into narrative and poetic worlds: A neurocognitive poetics perspective. In F. Hakemulder, M. M. Kuijpers, E. S. Tan, K. Bálint, & M. M. Doicaru (Eds.), Narrative absorption (pp. 69–96). Amsterdam, Netherlands: John Benjamins. DOI logoGoogle Scholar
Jacobs, A. M., & Willems, R. M.
(2017) The fictive brain: Neurocognitive correlates of engagement in literature. Review of General Psychology. 221, 147–160. DOI logoGoogle Scholar
Jacobs, A. M., Võ, M. L. H., Briesemeister, B. B., Conrad, M., Hofmann, M. J., Kuchinke, L., Lüdtke, J., & Braun, M.
(2015) 10 years of BAWLing into a ective and aesthetic processes in reading: What are the echoes? Frontiers in Psychology, 6, 714. DOI logoGoogle Scholar
Jacobs, A. M., Hofmann, M. J., & Kinder, A.
(2016) On elementary affective decisions: To like or not to like, that is the question. Frontiers in Psychology, 7, 1836. DOI logoGoogle Scholar
Jacobs, A. M., Lüdtke, J., Aryani, A., Meyer-Sickendiek, B., & Conrad, M.
(2016) Mood- empathic and aesthetic responses in poetry reception: A model-guided, multilevel, multimethod approach. Scientific Study of Literature, 6(1), 87–130. DOI logoGoogle Scholar
Jacobs, A. M., Schuster, S., Xue, S., & Lüdtke, J.
(2017) What’s in the brain that ink may character.…: A quantitative narrative analysis of Shakespeare’s 154 sonnets for use in Neurocognitive Poetics. Scientific Study of Literature, 7(1), 4–51. DOI logoGoogle Scholar
James, W.
(1890/1950) The principles of psychology. New York, NY: Dover.Google Scholar
Jannidis, F., & Lauer, G.
(2014) Burrows’s delta and its use in German literary history. In M. Erlin & L. Tatlock (Eds.), Distant readings: Topologies of German culture in the long nineteenth century (pp. 29–54). Rochester, NY: Camden House.Google Scholar
Jurafsky, D., & Martin, J. H.
(2016) N-grams (draft chapter). [URL]
Kao, J., & Jurafsky, D.
(2012) A computational analysis of style, affect, and imagery in contemporary poetry. NAACL Workshop on Computational Linguistics for Literature. [URL]
Kaplan, D., & Blei, D.
(2007) A computational approach to style in American poetry. IEEE Conference on Data Mining.Google Scholar
Katz, A. N., Paivio, A., & Marschark, M.
(1985) Poetic comparisons: Psychological dimensions of metaphoric processing. Journal of Psycholinguistic Research, 141, 365–383. DOI logoGoogle Scholar
Katz, A., Paivio, A., Marschark, M., & Clark, J.
(1988) Norms for 204 literary and 260 non- literary metaphors on psychological dimensions. Metaphor and Symbolic Activity, 3(4), 191–214. DOI logoGoogle Scholar
Keats, J.
(1958) Letter 199, To George and Georgiana Keats. In H. Rollins (Ed.), The letters of John Keats (Vol. 21). Cambridge, Massachusetts: Harvard University Press.Google Scholar
Keidel, J. L., Davis, P. M., Gonzalez-Diaz, V., Martin, C. D., & Thierry, G.
(2013) How Shakespeare tempests the brain: Neuroimaging insights. Cortex, 49(4). DOI logoGoogle Scholar
Kintsch, W.
(1980) Learning from text, levels of comprehension, or: why anyone would read a story anyway. Poetics, 91, 87–98. DOI logoGoogle Scholar
(2000) Metaphor comprehension: A computational theory. Psychonomic Bulletin & Review, 71, 257–266. DOI logoGoogle Scholar
Kintsch, W., & van Dijk, T. A.
(1978) Toward a model of text comprehension and production. Psychological Review, 851, 363–394. DOI logoGoogle Scholar
Kraxenberger, M.
(2017) On sound-emotion associations in poetry (Doctoral dissertation). Freie Universität Berlin.Google Scholar
Kraxenberger, M., & Menninghaus, W.
(2016) Emotional effects of poetic phonology, word positioning and dominant stress peaks in poetry. Scientific Study of Literature, 6(2), 298–313. DOI logoGoogle Scholar
(2017) Affinity for poetry and aesthetic appreciation of joyful and sad poems. Frontiers in Psychology, 7, 2051.Google Scholar
Kucera, H., & Francis, W. N.
(1967) Computational analysis of present-day American English. Providence, RI: Brown University Press.Google Scholar
Kutas, M.
(2006) One lesson learned: Frame language processing – literal and figurative – as a human brain function. Metaphor and Symbol, 211, 285–325. DOI logoGoogle Scholar
Larsen, S. E., & Seilman, U.
(1988) Personal remindings while reading literature. Text, 81, 411–429. DOI logoGoogle Scholar
Lea, R. B., Rapp, D. N., Elfenbein, A., Mitchel, A. D., & Romine, R. S.
(2008) Sweet silent thought: Alliteration and resonance in poetry comprehension. Psychological Science, 19(7), 709–716. DOI logoGoogle Scholar
Leech, G. N.
(1969) A linguistic guide to English poetry. London, United Kingdom: Longman.Google Scholar
Lehne, M., Engel, P., Menninghaus, W., Jacobs, A. M., & Koelsch, S.
(2015) Reading a suspenseful literary text activates brain areas related to social cognition and predictive inference. PLOS One, 10(5). DOI logoGoogle Scholar
Louwerse, M. M.
(2001) An analytic and cognitive parametrization of coherence relations. Cognitive Linguistics, 12(3), 291–315.Google Scholar
Lüdtke, J., Meyer-Sickendiek, B., & Jacobs, A. M.
(2014) Immersing in the stillness of an early morning: Testing the mood empathy hypothesis in poems. Psychology of Aesthetics, Creativity, and the Arts, 81, 363–377. DOI logoGoogle Scholar
Mandera, P., Keuleers, E., & Brysbaert, M.
(2015) How useful are corpus-based methods for extrapolating psycholinguistic variables? The Quarterly Journal of Experimental Psychology, 68(8), 1623–1642. DOI logoGoogle Scholar
Marschark, M., Katz, A. N., & Paivio, A.
(1983) Dimensions of metaphor. Journal of Psycholinguistic Research, 121, 17–40. DOI logoGoogle Scholar
Martindale, C.
(1978) The evolution of English poetry. Poetics, 71, 231–248. DOI logoGoogle Scholar
Menninghaus, W., Bohrn, I. C., Altmann, U., Lubrich, O., & Jacobs, A. M.
(2014) Sounds funny? Humor effects of phonological and prosodic figures of speech. Psychology of Aesthetics, Creativity, and the Arts, 8(1), 71. DOI logoGoogle Scholar
Miall, D. S.
(2001) Sounds of contrast: An empirical approach to phonemic iconicity. Poetics, 291, 55–70. DOI logoGoogle Scholar
Miall, D. S., & Kuiken, D.
(1994) Foregrounding, defamiliarization and affect: Response to literary stories. Poetics, 221, 389–407. DOI logoGoogle Scholar
(2002) The effects of local phonetic contrasts in readers’ responses to a short story. Empirical Studies of the Arts, 201, 157–175. DOI logoGoogle Scholar
Michel, J. B., Shen, Y. K., Aiden, A. P., Veres, A., Gray, M. K., The Google Books Team, … Lieberman Aiden, E.
(2011) Quantitative analysis of culture using millions of digitized books. Science, 331(6014), 176–182. DOI logoGoogle Scholar
Mikolov, T., Chen, K., Corrado, G., & Dean, J.
(2013) Efficient estimation of word representations in vector space. Retrieved from [URL]
Miller, G. A.
(1995) WordNet: A lexical database for English. Communications of the ACM, 38(11), 39–41. DOI logoGoogle Scholar
Neuhäuser, R.
(1991) Sound and meaning in romantic poetry: Preseren’s Poeziie. Russian Literature, 85–108. DOI logoGoogle Scholar
O’Sullivan, N., Davis, P., Billington, J., Gonzalez-Diaz, V., & Corcoran, R.
(2015) “Shall I compare thee”: e neural basis of literary awareness, and its bene ts to cognition. Cortex, 731, 144–157. DOI logoGoogle Scholar
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Dubourg, V.
(2011) scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 121, 2825–2830.Google Scholar
Pennebaker, J. W., & Francis, M. E.
(1999) Linguistic inquiry and word count (LIWC). Mahwah, NJ: Erlbaum.Google Scholar
Petrie, K. J., Pennebaker, J. W., & Sivertsen, B.
(2008) Things we said today: A linguistic analysis of the Beatles. Psychology of Aesthetics, Creativity, and the Arts, 21, 197–202. DOI logoGoogle Scholar
Recchia, G., & Louwerse, M. M.
(2015) Reproducing affective norms with lexical co-occurrence statistics: Predicting valence, arousal, and dominance. The Quarterly Journal of Experimental Psychology, 68(8), 1584–1598. DOI logoGoogle Scholar
Rehuřek, R., & Sojka, P.
(2010) Software framework for topic modelling with large corpora. Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks (pp. 45–50). Valletta, Malta: ELRA.Google Scholar
Riegel, M., Wierzba, M., Wypych, M., Zurawski, L., Jednorog, K., Grabowska, A., & Marchewka, A.
(2015) Nencki Affective Word List (NAWL): Cultural adaptation of the Berlin Affective Word List-Reloaded (BAWL-R) for Polish. Behavior Research Methods, 471, 1222–1236. DOI logoGoogle Scholar
Rouse, B., & Bennett, J. R.
(1967) Editorial. Style, 1(1), v–vii.Google Scholar
Salgaro, M.
(2015) How literary can literariness be? Methodological problemsin the study of foregrounding. Scientific Study of Literature, 5(2), 229–249. DOI logoGoogle Scholar
Schmidtke, D. S., Schröder, T., Jacobs, A. M., & Conrad, M.
(2014a) ANGST: Affective norms for German sentiment terms, derived from the affective norms for English words. Behavior Research Methods, 461, 1108–1118. DOI logoGoogle Scholar
Schmidtke, D. S., Conrad, M., & Jacobs, A. M.
(2014b) Phonological iconicity. Frontiers in Psychology, 5, 80. DOI logoGoogle Scholar
Schrott, R., & Jacobs, A. M.
(2011) Gehirn und Gedicht: Wie wir unsere Wirklichkeiten konstruieren (Brain and Poetry: How We Construct Our Realities). München: Hanser.Google Scholar
Shen, Y.
(2008) Metaphor and poetic figures. In R. W. Gibbs, Jr. (Ed.), The Cambridge handbook of metaphor and thought (pp. 295–307). Cambridge, United Kingdom: Cambridge University Press. DOI logoGoogle Scholar
Simonton, D. K.
(1989) Shakespeare’s sonnets: A case of and for single-case historiometry. Journal of Personality, 57(3), 695–721. DOI logoGoogle Scholar
(1990) Lexical choices and aesthetic success: A computer content analysis of 154 Shakespeare sonnets. Computers and the Humanities, 241, 254–264.Google Scholar
Steen, G.
(2004) Can discourse properties of metaphor affect metaphor recognition? Journal of Pragmatics, 36(7), 1295–1313. DOI logoGoogle Scholar
Steyvers, M., Smyth, P., & Chemuduganta, C.
(2011) Combining background knowledge and learned topics. Topics in Cognitive Science, 31, 18–47. DOI logoGoogle Scholar
Strobl, C., Malley, J., & Tutz, G.
(2009) An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological Methods, 14(4), 323–348. DOI logoGoogle Scholar
Stockwell, P.
(2009) The cognitive poetics of literary resonance. Language and Cognition, 11, 25–44. DOI logoGoogle Scholar
Stone, P. J., Dunphy, D. C., Smith, M. S., & Ogilvie, D. M.
(1966) The general inquirer: A computer approach to content analysis. Cambridge: Massachusetts Institute of Technology Press.Google Scholar
Tsur, R.
(1992) What makes sound patterns expressive? The poetic mode of speech perception. Durham, NC: Duke University Press. DOI logoGoogle Scholar
(1997) Sound effects of poetry: Critical impressionism, reductionism and cognitive poetics. Pragmatics & Cognition, 51, 283–304. DOI logoGoogle Scholar
Turner, F., & Poeppel, E.
(1983) The neural lyre: Poetic meter, the brain and time. Poetry Magazine, 121, 277–309.Google Scholar
Turney, P. D., & Littman, M. L.
(2003) Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems (TOIS), 21(4), 315–346. DOI logoGoogle Scholar
Ullrich, S., Aryani, A., Kraxenberger, M., Jacobs, A. M., & Conrad, M.
(2017) On the relation between the general affective meaning and the basic sublexical, lexical, and interlexical features of poetic texts – A case study using 57 poems of H. M. Enzensberger. Frontiers in Psychology, 7, 2073. DOI logoGoogle Scholar
van den Hoven, E., Hartung, F., Burke, M., & Willems, R. M.
(2016) Individual differences in sensitivity to style during literary reading: Insights from eye-tracking. Collabra, 21, 1–16. DOI logoGoogle Scholar
van Peer, W., Hakemulder, J., & Zyngier, S.
(2007) Lines on feeling: Foregrounding, aesthetics and meaning. Language and Literature, 161, 197–213. DOI logoGoogle Scholar
Vendler, H.
(1997) The art of Shakespeare’s sonnets. Cambridge, MA: Harvard University Press.Google Scholar
Võ, M. L. H., Jacobs, A. M., & Conrad, M.
(2006) Cross-validating the Berlin affective word list. Behavioral Research Methods, 381, 606–609. DOI logoGoogle Scholar
Võ, M. L. H., Conrad, M., Kuchinke, L., Hartfeld, K., Hofmann, M. J., & Jacobs, A. M.
(2009) The Berlin Affective Word List reloaded (BAWL-R). Behavioral Research Methods, 411, 534–539. DOI logoGoogle Scholar
Warriner, A. B., Kuperman, V., & Brysbaert, M.
(2013) Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior Research Methods, 451, 1191–1207. DOI logoGoogle Scholar
Westbury, C., Keith, J., Briesemeister, B. B., Hofmann, M. J., & Jacobs, A. M.
(2015) Avoid violence, rioting, and outrage; approach celebration, delight, and strength: Using large text corpora to compute valence, arousal, and the basic emotions. Quarterly Journal of Experimental Psychology, 681, 1599–1622. DOI logoGoogle Scholar
Whissell, C.
(1999) Phonosymbolism and the emotional nature of sounds: Evidence of the preferential use of particular phonemes in texts of differing emotional tone. Perceptual and Motor Skills, 891, 19–48. DOI logoGoogle Scholar
Willems, R. M., Frank, S. L., Nijhof, A. D., Hagoort, P., & Van den Bosch, A.
(2016) Prediction during natural language comprehension. Cerebral Cortex, 261, 2506–2516. DOI logoGoogle Scholar
Wu, Z., & Palmer, M.
(1994) Verbs semantics and lexical selection. Proceedings of the 32nd annual meeting on Association for Computational Linguistics, ACL ’94, pages 133–138, Stroudsburg, PA, USA. Association for Computational Linguistics. DOI logoGoogle Scholar
Wundt, W. M.
(1874) Grundzüge der Physiologischen Psychologie. Leipzig: Engelmann.Google Scholar
Xue, X., Giordano, D., Lüdtke, J., Gambino, R., Pulvirenti, G., Spampinato, C., & Jacobs, A. M.
(2017) Weary with toil, I haste me to my bed: Eye tracking Shakespeare sonnets. Talk given at the 19th European Conference on Eye Movements, Wuppertal, Germany, 2017.
Yaron, I.
(2008) What is a “difficult” poem? Towards a definition. Journal of Literary Semantics, 37(2), 129–150. DOI logoGoogle Scholar
Ziegler, J. C., Jacobs, A. M., & Klueppel, D.
(2001) Pseudohomophone effects in lexical decision: Still a challenge for current word recognition models. Journal of Experimental Psychology: Human Perception & Performance, 271, 547–559.Google Scholar
Cited by

Cited by 12 other publications

Castano, Emanuele, Jessica Zanella, Fatemeh Saedi, Lisa Zunshine & Luca Ducceschi
2024. On the Complexity of Literary and Popular Fiction. Empirical Studies of the Arts 42:1 ► pp. 281 ff. DOI logo
Gracheva, Marianna
2022. Style of creative nonfiction. Scientific Study of Literature 12:1-2 ► pp. 49 ff. DOI logo
Jacobs, Arthur M.
2019. Sentiment Analysis for Words and Fiction Characters From the Perspective of Computational (Neuro-)Poetics. Frontiers in Robotics and AI 6 DOI logo
Jacobs, Arthur M., Berenike Herrmann, Gerhard Lauer, Jana Lüdtke & Sascha Schroeder
2020. Sentiment Analysis of Children and Youth Literature: Is There a Pollyanna Effect?. Frontiers in Psychology 11 DOI logo
Jacobs, Arthur M. & Annette Kinder
2019. Computing the Affective-Aesthetic Potential of Literary Texts. AI 1:1 ► pp. 11 ff. DOI logo
Jacobs, Arthur M. & Annette Kinder
2022. Computational Models of Readers' Apperceptive Mass. Frontiers in Artificial Intelligence 5 DOI logo
Mendhakar, Akshay
2022. Linguistic Profiling of Text Genres: An Exploration of Fictional vs. Non-Fictional Texts. Information 13:8 ► pp. 357 ff. DOI logo
Nishihara, Takayuki
2022. EFL learners’ reading traits for lexically easy short poetry. Cogent Education 9:1 DOI logo
Papp-Zipernovszky, Orsolya, Anne Mangen, Arthur Jacobs & Jana Lüdtke
2022. Shakespeare sonnet reading: An empirical study of emotional responses. Language and Literature: International Journal of Stylistics 31:3 ► pp. 296 ff. DOI logo
Usée, Franziska, Arthur M. Jacobs & Jana Lüdtke
2020. From Abstract Symbols to Emotional (In-)Sights: An Eye Tracking Study on the Effects of Emotional Vignettes and Pictures. Frontiers in Psychology 11 DOI logo
Xue, Shuwei, Arthur M. Jacobs & Jana Lüdtke
2020. What Is the Difference? Rereading Shakespeare’s Sonnets —An Eye Tracking Study. Frontiers in Psychology 11 DOI logo
[no author supplied]
2022. Poetic Metaphors [Figurative Thought and Language, 15], DOI logo

This list is based on CrossRef data as of 14 april 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.