Cover not available

Article published In: Scientific Study of Literature
Vol. 7:1 (2017) ► pp.4–51

Get fulltext from our e-platform
JBE logo with link
PDF logo with download linkDownload PDF
authorization required

What’s in the brain that ink may character ….

A quantitative narrative analysis of Shakespeare’s 154 sonnets for use in (Neuro-)cognitive poetics

 | Department of Experimental and Neurocognitive Psychology, Freie Universität, Berlin, Germany | Dahlem Institute for Neuroimaging of Emotion (D.I.N.E.), Berlin, Germany | Center for Cognitive Neuroscience Berlin (CCNB), Berlin, Germany
 | Department of Experimental and Neurocognitive Psychology, Freie Universität, Berlin, Germany | Universität Salzburg, Centre for Cognitive Neuroscience, Salzburg, Austria
 | Department of Experimental and Neurocognitive Psychology, Freie Universität, Berlin, Germany
 | Department of Experimental and Neurocognitive Psychology, Freie Universität, Berlin, Germany | Dahlem Institute for Neuroimaging of Emotion (D.I.N.E.), Berlin, Germany
Published online: 23 November 2017
DOI logo with link
https://doi.org/10.1075/ssol.7.1.02jac
Abstract
In this theoretical paper, we would like to pave the ground for future empirical studies in Neurocognitive Poetics by describing relevant properties of Shakespeare’s 154 sonnets extracted via Quantitative Narrative Analysis. In the first two parts, we quantify aspects of the sonnets’ cognitive and affective-aesthetic features, as well as indices of their thematic richness, symbolic imagery, and semantic association potential. In the final part, we first demonstrate how the results of these quantitative narrative analyses can be used for generating testable predictions for empirical studies of literature. Second, we feed the quantitative narrative analysis data into a machine learning algorithm which successfully classifies the 154 sonnets into two main categories, i.e. the young man and dark lady poems. This shows how quantitative narrative analysis data can be combined with computational modeling for identifying those of the many quantifiable sonnet features that may play a key role in their reception.
Article outline
  • General features of sonnets
  • Cognitive, evolutionary, and quantitative narrative analyses of Shakespeare’s dramas and sonnets
  • The present study
    • Part I.Cognitive quantitative narrative analyses: Readability and easability analyses
      • Surprisal
    • Part II.Affective-aesthetic quantitative narrative analyses
      • Emotion and mood potential
      • Thematic richness
      • Symbolic imagery
      • Semantic association potential
    • Can quantitative narrative analysis capture sonnet dynamics?
    • Part III.Hypotheses for neurocognitive poetics studies
      • What can the present quantitative narrative analyses be used for?
      • Predictions based on present results
      • Poem category
      • Across-poem contrasts
      • Within-poem contrasts (e.g., quatrain 1–3 vs. couplet, octave vs. sestet, or linewise)
    • Part IV.Machine-learning-based computational modeling
      • Classifying the sonnets via machine-learning assisted quantitative narrative analysis
  • Discussion, limitations and outlook
  • Notes
  • References
References (153)
References
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). Google Scholar logo with link to Google Scholar
(2014). Fact vs fiction-how paratextual information shapes our reading processes. Social Cognitive and Affective Neuroscience, 9(1), 22–29. Google Scholar logo with link to Google Scholar
Anderson, T., & Crossley, S. (2011). “Rue with a Difference: A Stylistic Analysis of the Rhetoric of Suicide in Hamlet ” (pp. 192–214). In Jonathan Culpepper and Mireille Ravassat (Eds.), Stylistics and Shakespeare’s language: Transdisciplinary approaches. Bloomsbury.Google Scholar logo with link to Google 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, 41:654. Google Scholar logo with link to Google Scholar
Aryani, A., Kraxenberger, M., Ullrich, S., Jacobs, A. M., & Conrad, M. (2016). Measuring the ba- sic a ective tone of poems via phonological saliency and iconicity. Psychology of Aesthetics, Creativity, and the Arts, 101, 191–204. Google Scholar logo with link to Google Scholar
Beaty, R. E., Benedek, M., Silvia, P. J., & Schacter, D. L. (2016). Creative cognition and brain network dynamics. Trends in Cognitive Sciences, 201, 87–95. Google Scholar logo with link to Google Scholar
Berlyne, D. E. (1971). Aesthetics and Psychobiology. New York: Appleton-Century-Crofts.Google Scholar logo with link to Google Scholar
Bestgen, Y. (1994). Can emotional valence in stories be determined from words?. Cognition & Emotion, 8(1), 21–36. Google Scholar logo with link to Google Scholar
Bohrn, I. C., Altmann, U., & Jacobs, A. M. (2012a). Looking at the brains behind figurative language – A quantitative meta-analysis of neuroimaging studies on metaphor, idiom and irony processing. Neuropsychologia, 501, 2669–2683. Google Scholar logo with link to Google Scholar
Bohrn, I. C., Altmann, U., Lubrich, O., Menninghaus, W., & Jacobs, A. M. (2012b). Old proverbs in new skins – an FMRI study on defamiliarization. Frontiers in Psychology, 31:204. Google Scholar logo with link to Google Scholar
(2013). When we like what we know – A parametric fMRI analysis of beauty and familiarity. Brain and Language, 124(1), 1–8. Google Scholar logo with link to Google Scholar
Boston, M. F., Hale, J., Kliegl, R., Patil, U., & Vasishth, S. (2008). Parsing costs as predictors of reading difficulty: An evaluation using the Potsdam Sentence Corpus. Journal of Eye Movement Research, 2(1), 1–12. Google Scholar logo with link to Google Scholar
Boyd, B. (2012). Why lyrics last: Evolution, Cognition, and Shakespeare’s Sonnets. Harvard University Press. Google Scholar logo with link to 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, FL: University of Florida.Google Scholar logo with link to Google Scholar
Briesemeister, B. B., Kuchinke, L., & Jacobs, A. M. (2011). Discrete emotion norms for nouns: Berlin affective word list (DENN – BAWL). Behavior Research Methods, 43(2), 441–448. Google Scholar logo with link to Google Scholar
(2014). Emotion word recognition: Discrete information effects first, continuous later?. Brain Research, 15641, 62–71. Google Scholar logo with link to Google Scholar
Briesemeister, B. B., Kuchinke, L., Jacobs, A. M., & Braun, M. (2015). Emotions in reading: Dissociation of happiness and positivity. Cognitive, Affective, & Behavioral Neuroscience, 15(2), 287–298. Google Scholar logo with link to Google Scholar
Brown, S., Gao, X., Tisdelle, L., Eickhoff, S. B., & Liotti, M. (2011). Naturalizing aesthetics: brain areas for aesthetic appraisal across sensory modalities. Neuroimage, 58(1), 250–258. Google Scholar logo with link to Google Scholar
Brysbaert, M., & New, B. (2009). Moving beyond Kučera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behavior Research Methods, 41(4), 977–990. Google Scholar logo with link to Google Scholar
Burke, M. (2011). Literary reading, cognition and emotion: An exploration of the oceanic mind. London: Routledge.Google Scholar logo with link to Google Scholar
(2015). The neuroaesthetics of prose fiction: Pitfalls, parameters and prospects. Frontiers in Human Neuroscience, 91. Google Scholar logo with link to Google Scholar
Chatterjee, A., & Vartanian, O. (2014). Neuroaesthetics. Trends in Cognitive Sciences, 18(7), 370–375. Google Scholar logo with link to Google Scholar
Chen, Q., Zhang, J., Xu, X., Scheepers, C., Yang, Y., & Tanenhaus, M. K. (2016). Prosodic expectations in silent reading: ERP evidence from rhyme scheme and semantic congruence in classic Chinese poems. Cognition, 1541, 11–21. Google Scholar logo with link to Google Scholar
Citron, F. M. (2012). Neural correlates of written emotion word processing: a review of recent electrophysiological and hemodynamic neuroimaging studies. Brain and Language, 122(3), 211–226. Google Scholar logo with link to Google Scholar
Cook, A. (2010). Shakespearean neuroplay: Reinvigorating the study of dramatic texts and performance through cognitive science. NY: Palgrave Macmillan Google Scholar logo with link to Google Scholar
Crane, M. T. (2001). Shakespeare’s Brain: Reading with Cognitive Theory. Princeton University Press.Google Scholar logo with link to Google Scholar
Crossley, S. A., Kyle, K., & McNamara, D. S. (2016). The tool for the automatic analysis of text cohesion (TAACO): Automatic assessment of local, global, and text cohesion. Behavior Research Methods, 48(4), 1227–1237. Google Scholar logo with link to Google Scholar
(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. Google Scholar logo with link to Google Scholar
Crystal, D. (2012). Think on my Words: Exploring Shakespeare's Language (Canto Classics). Cambridge: Cambridge University Press. Google Scholar logo with link to Google Scholar
Cupchik, G. C. (1986). A decade after Berlyne: New directions in experimental aesthetics. Poetics, 15(4–6), 345–369. Google Scholar logo with link to Google Scholar
De Beaugrande, R. A. (1979). Toward a general theory of creativity. Poetics, 8(3), 269–306. Google Scholar logo with link to Google Scholar
Delmonte, R. (2016). Exploring Shakespeare’s Sonnets with SPARSAR. Linguistics and Literature Studies, 4(1), 61–95. Google Scholar logo with link to Google Scholar
Dietterich, T. G. (2000). An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40(2), 139–157. Google Scholar logo with link to Google Scholar
Dixon, P., & Bortolussi, M. (2015). Measuring literary experience. Scientific Study of Literature, 5(2), 178–182. Google Scholar logo with link to Google Scholar
Fellbaum, C. (Ed.). (1998). WordNet: An electronic lexical database. Cambridge: MIT Press. Google Scholar logo with link to Google Scholar
Forgács, B., Bohrn, I., Baudewig, J., Hofmann, M. J., Pléh, C., & Jacobs, A. M. (2012). Neural correlates of combinatorial semantic processing of literal and figurative noun noun compound words. Neuroimage, 63(3), 1432–1442. Google Scholar logo with link to Google Scholar
Frank, S. L. (2013). Uncertainty reduction as a measure of cognitive load in sentence comprehension. Topics in Cognitive Science, 5(3), 475–494. Google Scholar logo with link to Google Scholar
Frank, S. L., Otten, L. J., Galli, G., & Vigliocco, G. (2015). The ERP response to the amount of information conveyed by words in sentences. Brain and Language, 1401, 1–11. Google Scholar logo with link to Google Scholar
Goldman, A. I. (2006). Simulating minds: The philosophy, psychology, and neuroscience of mindreading. Oxford University Press. Google Scholar logo with link to Google 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, 36(2), 193–202. Google Scholar logo with link to Google Scholar
Graesser, A. C., & McNamara, D. S. (2010). Computational analyses of multilevel discourse comprehension. Topics in Cognitive Science, 3(2), 371–398. Google Scholar logo with link to Google Scholar
Graesser, A. C., Dowell, N., & Moldovan, C. (2011). A computer’s understanding of literature. Scientific Study of Literature, 1(1), 24–33. Google Scholar logo with link to Google Scholar
Green, M. C., & Brock, T. C. (2000). The role of transportation in the persuasiveness of public narratives. Journal of Personality and Social Psychology, 79(5), 701–721. Google Scholar logo with link to Google Scholar
Grünbein, D. (1996). Katze und Mond. In D. Grünbein (Eds) Galilei Vermisst Dantes Hölle und Bleibt an den Maßen Hängen (pp. 116–128). Frankfurt AM: Suhrkamp.Google Scholar logo with link to Google Scholar
Habermas, T., & de Silveira, C. (2008). The development of global coherence in life narratives across adolescence: Temporal, causal, and thematic aspects. Developmental Psychology, 44(3), 707–721. Google Scholar logo with link to Google Scholar
Harker, W. J. (1997). Literary texts as models: Implications for the empirical study of literature. In S. Tötösy de Zepetnek & I. Sywenky (Eds.), The systemic and empirica lapproach to literature and culture as theory and application (pp. 51–65). Edmonton, Canada: University of Alberta.Google Scholar logo with link to Google 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. Google Scholar logo with link to Google Scholar
Hope, J., & Witmore, M. (2004). The very large textual object: a prosthetic reading of Shakespeare. Early Modern Literary Studies, 9(3), 1–36.Google Scholar logo with link to Google Scholar
Hsu, C. T., Conrad, M., & Jacobs, A. M. (2014). Fiction feelings in Harry Potter: Haemodynamic response in the mid-cingulate cortex correlates with immersive reading experience. Neuroreport, 25(17), 1356–1361. Google Scholar logo with link to Google Scholar
Hsu, C. T., Jacobs, A. M., Citron, F. M., & Conrad, M. (2015). The emotion potential of words and passages in reading Harry Potter – An fMRI study. Brain and Language, 1421, 96–114. Google Scholar logo with link to Google Scholar
Hutcheon, L. (2012). A Theory of Adaptation. 2nd edition with Siobhan O’Flynn. London and New York: Routledge. Google Scholar logo with link to Google Scholar
Hutchins, R. (Ed.) (1952). Great books of the Western world (541 vols.), Chicago: University of Chicago Press.Google Scholar logo with link to Google Scholar
Jacobs, A. M. (2011). Neurokognitive Poetik: Elemente eines Modells des literarischen Lesens [Neurocognitive poetics: Elements of a model of literary reading]. In R. Schrott and A. M. Jacobs (Eds.), Gehirn und Gedicht: Wie wir unsere Wirklichkeiten konstruieren [Brain and poetry: How we construct our realities] (pp. 492–520). Munich: Carl Hanser.Google Scholar logo with link to Google Scholar
(2015a). Towards a neurocognitive poetics model of literary reading, in R. Willems (Ed.) Towards a Cognitive Neuroscience of Natural Language Use (pp. 135–159). Cambridge: Cambridge University Press. Google Scholar logo with link to Google Scholar
(2015b). Neurocognitive poetics: Methods and models for investigating the neuronal and cognitive-affective bases of literature reception. Frontiers in Human Neuroscience, 9:186. Google Scholar logo with link to Google Scholar
(2015c). The scientific study of literary experience. Scientific Study of Literature, 5(2), 139–170. Google Scholar logo with link to Google Scholar
Jacobs, A. M., and Kinder, A. (2015). Worte als Worte erfahren: wie erarbeitet das Gehirn Gedichte (Experience words as words: how the brain constructs poems), in A. Pompe (Ed.) Kind und Gedicht [Child and Poem] (pp. 57–76). Berlin: Rombach.Google Scholar logo with link to Google 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 & Symbol, in press. Google Scholar logo with link to Google Scholar
Jacobs, A. M., and Schrott, R. (2015). Gefesselt im Kopfkino: Von Kippschaltern, Madeleine Effekten und Don Quichote Syndromen bei der Immersion in Textwelten (Captivated in the mind’s cinema: Of trigger-switches, Don Quichote syndroms and immersion in text worlds). Available online at: FIKTION.CCGoogle Scholar logo with link to Google 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 affective and aesthetic processes in reading: what are the echoes?. Frontiers in Psychology, 61:714. Google Scholar logo with link to Google Scholar
Jacobs, A. M., Lüdtke, J., Aryani, A., Meyer-Sickendieck, B., & Conrad, M. (2016a). Mood-empathic and aesthetic responses in poetry reception. Scientific Study of Literature, 6(1), 87–130. Google Scholar logo with link to Google Scholar
Jacobs, A., Hofmann, M. J., & Kinder, A. (2016b). On elementary affective decisions: To like or not to like, that is the question. Frontiers in Psychology, 71:1836. Google Scholar logo with link to Google Scholar
Jacobsen, T. (2006). Bridging the arts and sciences: A framework for the psychology of aesthetics. Leonardo, 391, 155–162. Google Scholar logo with link to Google Scholar
Jakobson, R., & Jones, L. G. (1970). Shakespeare’s Verbal Art in Th’Expence of Spirit (No. 35). Walter de Gruyter. Google Scholar logo with link to Google Scholar
Jakobson, R., & Lévi-Strauss, C. (1962). “Les chats” de Charles Baudelaire. L’homme, 21, 5–21. Google Scholar logo with link to Google Scholar
Kintsch, W. (2000). Metaphor comprehension: A computational theory. Psychonomic Bulletin & Review, 7(2), 257–266. Google Scholar logo with link to Google Scholar
(2012). Musings about beauty. Cognitive Science, 36(4), 635–654. Google Scholar logo with link to Google Scholar
Kintsch, W., & Mangalath, P. (2011). The construction of meaning. Topics in Cognitive Science, 3(2), 346–370. Google Scholar logo with link to Google Scholar
Kiss, G. R., Armstrong, C., Milroy, R. and Piper, J. (1973). An associative thesaurus of English and its computer analysis. In Aitken, A. J., Bailey, R. W. and Hamiltonsmith, N. (Eds), The Computer and Literary Studies. Edinburgh: University Press.Google Scholar logo with link to Google Scholar
Kivy, P. (1991). Music alone: Philosophical reflections on the purely musical experience. Cornell University Press.Google Scholar logo with link to Google Scholar
Koelsch, S., Jacobs, A. M., Menninghaus, W., Liebal, K., Klann-Delius, G., von Scheve, C., & Gebauer, G. (2015). The quartet theory of human emotions: An integrative and neurofunctional model. Physics of Life Reviews, 131, 1–27. Google Scholar logo with link to Google Scholar
Krumhansl, C. L. (1997). An Exploratory Study of Musical Emotions and Psychophysiology. Canadian Journal of Experimental Psychology, 51(4), 336. Google Scholar logo with link to Google Scholar
Kuchinke, L., Fritzemeier, S., Hofmann, M. J., & Jacobs, A. M. (2013). Neural correlates of episodic memory: Associative memory and confidence drive hippocampus activations. Behavioural Brain Research, 2541, 92–101. Google Scholar logo with link to Google Scholar
Kuhlmann, M., Hofmann, M. J., Briesemeister, B. B., & Jacobs, A. M. (2016). Mixing positive and negative valence: Affective-semantic integration of bivalent words. Scientific Reports, 6:30718. Google Scholar logo with link to Google Scholar
Kuzmičová, A. (2014). Literary narrative and mental imagery: A view from embodied cognition. Style, 48(3), 275–293.Google Scholar logo with link to Google Scholar
Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104(2), 211. Google Scholar logo with link to Google Scholar
Lasswell, H. D., & Namenwirth, J. Z. (1969). The Lasswell value dictionary. New Haven: Yale University Press.Google Scholar logo with link to Google Scholar
Leder, H., Belke, B., Oeberst, A., & Augustin, D. (2004). A model of aesthetic appreciation and aesthetic judgments. British Journal of Psychology, 95(4), 489–508. Google Scholar logo with link to Google Scholar
Leder, H., & Nadal, M. (2014). Ten years of a model of aesthetic appreciation and aesthetic judgments: The aesthetic episode – developments and challenges in empirical aesthetics. British Journal of Psychology, 105(4), 443–464. Google Scholar logo with link to Google Scholar
Leder, H., Markey, P. S., & Pelowski, M. (2015). Aesthetic emotions to art-What they are and what makes them special: Comment on “The quartet theory of human emotions: An integrative and neurofunctional model” by S. Koelsch et al. Physics of Life Reviews, 131, 67–70. Google Scholar logo with link to Google Scholar
Lehne, M., Engel, P., Rohrmeier, M., 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), e0124550. Google Scholar logo with link to Google Scholar
Levinson, J. (1997). Music and negative emotion. In J. Robinson (Ed.), Music and Meaning (pp. 215–41). Ithaca, NY: Cornell University Press.Google Scholar logo with link to Google Scholar
Lindquist, K. A., MacCormack, J. K., & Shablack, H. (2015). The role of language in emotion: Predictions from psychological constructionism. Frontiers in Psychology, 6:444. Google Scholar logo with link to Google Scholar
Liu, S., Erkkinen, M. G., Healey, M. L., Xu, Y., Swett, K. E., Chow, H. M., & Braun, A. R. (2015). Brain activity and connectivity during poetry composition: Toward a multidimensional model of the creative process. Human Brain Mapping, 36(9), 3351–3372. Google Scholar logo with link to Google Scholar
Loh, W. Y. (2011). Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(1), 14–23. Google Scholar logo with link to Google Scholar
Lüdtke, J., & Jacobs, A. M. (2015). The emotion potential of simple sentences: Additive or interactive effects of nouns and adjectives?. Frontiers in Psychology, 6:1137. Google Scholar logo with link to Google Scholar
Lüdtke, J., Meyer-Sickendieck, B., & Jacobs, A. M. (2014). Immersing in the stillness of an early morning: Testing the mood empathy hypothesis of poetry reception. Psychology of Aesthetics, Creativity, and the Arts, 8(3), 363. Google Scholar logo with link to Google Scholar
Marin, M. M. (2015). Crossing boundaries: Toward a general model of neuroaesthetics. Frontiers in Human Neuroscience, 91:443. Google Scholar logo with link to Google Scholar
Marin, M. M., Lampatz, A., Wandl, M., & Leder, H. (2016). Berlyne revisited: Evidence for the multifaceted nature of hedonic tone in the appreciation of paintings and music. Frontiers in Human Neuroscience, 10:536. Google Scholar logo with link to Google Scholar
Martindale, C. (1975). The romantic progression: The psychology of literary history. Halsted Press.Google Scholar logo with link to Google Scholar
McNamara, D. S., Louwerse, M. M., McCarthy, P. M., & Graesser, A. C. (2010). Coh-Metrix: Capturing linguistic features of cohesion. Discourse Processes, 47(4), 292–330. Google Scholar logo with link to Google Scholar
McNamara, D. S., Graesser, A. C., McCarthy, P. M., & Cai, Z. (2014). Automated evaluation of text and discourse with Coh-Metrix. Cambridge University Press. Google Scholar logo with link to Google Scholar
McQuarrie, E. F., & Mick, D. G. (1996). Figures of rhetoric in advertising language. Journal of Consumer Research, 22(4), 424–438. Google Scholar logo with link to Google Scholar
Mcquire, M., Mccollum, L., & Chatterjee, A. (2017). Aptness and beauty in metaphor. Language and Cognition, 9(2), 316–331. Google Scholar logo with link to Google Scholar
Meireles, R. C. (2005). The hermeneutics of symbolical imagery in Shakespeare’s sonnets. Unpublished Dissertation, University. Porto Alegre, Bresil.Google Scholar logo with link to Google 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), 71–76. Google Scholar logo with link to Google Scholar
Menninghaus, W., Bohrn, I. C., Knoop, C. A., Kotz, S. A., Schlotz, W., & Jacobs, A. M. (2015). Rhetorical features facilitate prosodic processing while handicapping ease of semantic comprehension. Cognition, 1431, 48–60. Google Scholar logo with link to Google Scholar
Meyer-Sickendiek, B. (2011). Lyrisches Gespür: Vom Geheimen Sensorium Moderner Poesie [The lyrical sense of feeling. About the secret sensorium of modern poetry]. Paderborn, Germany: Fink.Google Scholar logo with link to Google Scholar
Miall, D. S. (1989). Beyond the schema given: Affective comprehension of literary narratives. Cognition & Emotion, 3(1), 55–78. Google Scholar logo with link to Google Scholar
Miall, D. S., & Kuiken, D. (1994). Foregrounding, defamiliarization, and affect: Response to literary stories. Poetics, 22(5), 389–407. Google Scholar logo with link to Google Scholar
Miall, D. S., & Dissanayake, E. (2003). The poetics of babytalk. Human Nature, 14(4), 337–364. Google Scholar logo with link to Google Scholar
Miller, G. A. (1993). “Images and models, similes and metaphors”. In A. Ortony (ed), Metaphor and thought, 2nd edition (pp. 357–400). Cambridge: Cambridge University Press. Google Scholar logo with link to Google Scholar
(1993). Wörter: Streifzüge durch die Psycholinguistik. Frankfurt: Zweitausendeins.Google Scholar logo with link to Google Scholar
Millis, K., & Larson, M. (2008). Applying the construction-integration framework to aesthetic responses to representational artworks. Discourse Processes, 45(3), 263–287. Google Scholar logo with link to Google Scholar
Nicklas, P., & Jacobs, A. M. (2017). Rhetorics, neurocognitive poetics and the aesthetics of adaptation. Poetics Today, 381, 393–412. .Google Scholar logo with link to Google Scholar
Oatley, K. (1994). A taxonomy of the emotions of literary response and a theory of identification in fictional narrative. Poetics, 23(1–2), 53–74. Google Scholar logo with link to Google Scholar
(2016). Fiction: Simulation of social worlds. Trends in Cognitive Sciences, 20(8), 618–628. Google Scholar logo with link to Google Scholar
O’Sullivan, N., Davis, P., Billington, J., Gonzalez-Diaz, V., & Corcoran, R. (2015). “Shall I compare thee”: The neural basis of literary awareness, and its benefits to cognition. Cortex, 731, 144–157. Google Scholar logo with link to Google Scholar
Panksepp, J. (2008). The power of the word may reside in the power of affect. Integrative Psychological and Behavioral Science, 42(1), 47–55. Google Scholar logo with link to Google Scholar
Pelowski, M., Markey, P. S., Lauring, J. O., & Leder, H. (2016). Visualizing the impact of art: An update and comparison of current psychological models of art experience. Frontiers in Human Neuroscience, 10:160. Google Scholar logo with link to Google Scholar
Pléh, C. (2003). Narrativity in text construction and self construction. Neohelicon, 30(1), 187–205. Google Scholar logo with link to Google Scholar
Pragglejaz Group (2007). MIP: A method for identifying metaphorically used words in discourse. Metaphor and Symbol, 22(1), 1–39. Google Scholar logo with link to Google Scholar
Richards, I. A. (1929). Practical criticism: A study of literary judgment. New York: Harcourt Brace Jovanovich.Google Scholar logo with link to Google Scholar
Rosenblatt, L. M. (1978). The reader, the text, the poem: The transactionaltheoryof the literarywork. Carbondale, IL: Southern Illinois University Press.Google Scholar logo with link to Google Scholar
Ryan, M. L. (2001). Narrative as virtual reality: Immersion and interactivity in literature and electronic media. Johns Hopkins University Press.Google Scholar logo with link to Google Scholar
Schmidtke, D. S., Schröder, T., Jacobs, A. M., & Conrad, M. (2014). ANGST: Affective norms for German sentiment terms, derived from the affective norms for English words. Behavior Research Methods, 46(4), 1108–1118. Google Scholar logo with link to Google 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 logo with link to Google Scholar
Shakespeare online. n.d. Retrieved from [URL])
Shimron, J. (1980). Psychological processes behind the comprehension of a poetic text. Instructional Science, 9(1), 43–66. Google Scholar logo with link to Google Scholar
Simonto, D. K. (1989). Shakespeare’s Sonnets: A Case of and for Single – Case Historiometry. Journal of Personality, 57(3), 695–721. Google Scholar logo with link to Google Scholar
Simonton, D. K. (1990). Lexical choices and aesthetic success: A computer content analysis of 154 Shakespeare sonnets. Computers and the Humanities, 24(4), 251–264. Google Scholar logo with link to Google Scholar
Smith, N. J., & Levy, R. (2013). The effect of word predictability on reading time is logarithmic. Cognition, 128(3), 302–319. Google Scholar logo with link to Google Scholar
Steen, G. (1999). Analyzing metaphor in literature: With examples from William Wordsworth’s “I wandered lonely as a cloud”. Poetics Today, 20(3), 499–522.Google Scholar logo with link to Google Scholar
(2002). Metaphor in Bob Dylan’s ’Hurricane’: Genre, Language, and Style. In E. Semino, & J. Culpepper (eds): Cognitive Stylistics: Language and Cognition in Text Analysis (pp. 183–210). Amsterdam: John Benjamins. Google Scholar logo with link to Google Scholar
(2004). Can discourse properties of metaphor affect metaphor recognition?. Journal of Pragmatics, 36(7), 1295–1313. Google Scholar logo with link to Google Scholar
Stockwell, P. (2009). The cognitive poetics of literary resonance. Language and Cognition, 1(1), 25–44. Google Scholar logo with link to Google Scholar
Stone, P. J., Bales, R. F., Namenwirth, J. Z., & Ogilvie, D. M. (1962). The general inquirer: A computer system for content analysis and retrieval based on the sentence as a unit of information. Behavioral Science, 7(4), 484–498. Google Scholar logo with link to Google Scholar
Sylvester, T., Braun, M., Schmidtke, D., & Jacobs, A. M. (2016). The Berlin affective word list for children (kidBAWL): Exploring processing of affective lexical semantics in the visual and auditory modalities. Frontiers in Psychology, 7:969. Google Scholar logo with link to Google Scholar
Taruffi, L. (2016). Why We Listen to Sad Music: Effects on Emotion and Cognition. (Unpublished doctoral dissertation), Freie Universität Berlin.Google Scholar logo with link to Google Scholar
The Complete Works of William Shakespeare. n.d. Retrieved from: [URL].
Tsur, R. (1998). Poetic Rhythm: Structure and Performance – An Empirical Study in Cognitive Poetics. Bern: Peter Lang.Google Scholar logo with link to Google Scholar
(2006). Delivery style and listener response in the rhythmical performance of Shakespeare’s sonnets. College Literature, 33(1), 170–196. Google Scholar logo with link to Google Scholar
Turner, F., & Pöppel, E. (1983). The neural lyre: Poetic meter, the brain, and time. Poetry, 277–309.Google Scholar logo with link to Google Scholar
Ullrich, S., Aryani, A., Kraxenberger, M., Jacobs, A. M., & Conrad, M. (2016). Textual features as basis of the general affective meaning: Exploring the interplay of lexical affective features, dynamic inter-lexical shifts, and the basic affective tone in poetry. In revision.Google Scholar logo with link to Google Scholar
Van Peer, W. (1986). Stylistics and psychology: Investigations of foregrounding. London, UK: Croom Helm.Google Scholar logo with link to Google Scholar
van den Hoven, E., Hartung, F., Burke, M., & Willems, R. (2016). Individual differences in sensitivity to style during literary reading: Insights from eye-tracking. Collabra: Psychology, 2(1), 1–16. Google Scholar logo with link to Google Scholar
Vaughan-Evans, A., Trefor, R., Jones, L., Lynch, P., Jones, M. W., & Thierry, G. (2016). Implicit detection of poetic harmony by the naïve brain. Frontiers in Psychology, 7:1859. Google Scholar logo with link to Google Scholar
Vendler, H. (1997). The art of Shakespeare’s sonnets. Cambridge, MA: Harvard University Press.Google Scholar logo with link to Google Scholar
Võ, M. L., Conrad, M., Kuchinke, L., Urton, K., Hofmann, M. J., & Jacobs, A. M. (2009). The Berlin affective word list reloaded (BAWL-R). Behavior Research Methods, 41(2), 534–538. Google Scholar logo with link to Google Scholar
Võ, M. L., Jacobs, A. M., & Conrad, M. (2006). Cross-validating the Berlin affective word list. Behavior Research Methods, 38(4), 606–609. Google Scholar logo with link to Google Scholar
Wainwright, J. (2015). Poetry: The basics. Routledge. Google Scholar logo with link to Google Scholar
Wallace, W. T., & Rubin, D. C. (1991). Characteristics and constraints in ballads and their effect on memory. Discourse Processes, 141, 181–202. Google Scholar logo with link to Google Scholar
Wallentin, M., Nielsen, A. H., Vuust, P., Dohn, A., Roepstorff, A., & Lund, T. E. (2011). Amygdala and heart rate variability responses from listening to emotionally intense parts of a story. Neuroimage, 58(3), 963–973. Google Scholar logo with link to Google Scholar
Warriner, A. B., Kuperman, V., & Brysbaert, M. (2013). Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior Research Methods, 45(4), 1191–1207. Google Scholar logo with link to Google Scholar
Westbury, C., Keith, J., Briesemeister, B. B., Hofmann, M. J., & Jacobs, A. M. (2014). Avoid violence, rioting, and outrage; approach celebration, delight, and strength: Using large text corpora to compute valence, arousal, and the basic emotions. The Quarterly Journal of Experimental Psychology, 68(8), 1599–1622. Google Scholar logo with link to Google Scholar
Willems, R. M. (Ed.). (2015). Cognitive Neuroscience of Natural Language Use. Cambridge: Cambridge University Press. Google Scholar logo with link to Google Scholar
Willems, R. M., Frank, S. L., Nijhof, A. D., Hagoort, P., & Van den Bosch, A. (2015). Prediction during natural language comprehension. Cerebral Cortex, 26(6), 2506–2516. Google Scholar logo with link to Google Scholar
Willems, R. M., & Jacobs, A. M. (2016). Caring about Dostoyevsky: The untapped potential of studying literature. Trends in Cognitive Sciences, 20(4), 243–245. Google Scholar logo with link to Google Scholar
Yaron, I. (2002). Processing of obscure poetic texts: Mechanisms of selection. Journal of Literary Semantics, 31(2), 133–170. Google Scholar logo with link to Google Scholar
(2008). What is a “difficult poem”? Towards a definition. Journal of Literary Semantics, 37(2), 129–150. Google Scholar logo with link to Google Scholar
Zeman, A., Milton, F., Smith, A., & Rylance, R. (2013). By heart an fMRI study of brain activation by poetry and prose. Journal of Consciousness Studies, 20(9–10), 132–158.Google Scholar logo with link to Google Scholar
Cited by (15)

Cited by 15 other publications

Knoop, Christine A. & Stefan Blohm
2025. Literarinesses—A Bag of Three-Sided Coins. Literature 5:3 ► pp. 21 ff. DOI logo
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
Al Mamun, Md Habib, Pantea Keikhosrokiani, Moussa Pourya Asl, Nur Ain Nasuha Anuar, Nurfarah Hadira Abdul Hadi & Thasnim Humida
2022. Sentiment Analysis of the Harry Potter Series Using a Lexicon-Based Approach. In Handbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media [Advances in Web Technologies and Engineering, ], ► pp. 263 ff. DOI logo
Barbado, Alberto, Víctor Fresno, Ángeles Manjarrés Riesco & Salvador Ros
2022. DISCO PAL: Diachronic Spanish sonnet corpus with psychological and affective labels. Language Resources and Evaluation 56:2 ► pp. 501 ff. 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
Crossley, Scott A., Kristopher Kyle & Mihai Dascalu
2019. The Tool for the Automatic Analysis of Cohesion 2.0: Integrating semantic similarity and text overlap. Behavior Research Methods 51:1 ► pp. 14 ff. DOI logo
Bruhn, Mark J.
2018. Citation analysis. Scientific Study of Literature 8:1 ► pp. 77 ff. DOI logo
Jacobs, Arthur M. & Annette Kinder
2018. What makes a metaphor literary? Answers from two computational studies. Metaphor and Symbol 33:2 ► pp. 85 ff. DOI logo
Hanauer, David Ian
2017. Becoming an undergraduate scientific researcher of literature. Scientific Study of Literature 7:2 ► pp. 262 ff. DOI logo
Jacobs, Arthur M.
2017. Quantifying the Beauty of Words: A Neurocognitive Poetics Perspective. Frontiers in Human Neuroscience 11 DOI logo
Jacobs, Arthur M.
2018. The Gutenberg English Poetry Corpus: Exemplary Quantitative Narrative Analyses. Frontiers in Digital Humanities 5 DOI logo
Jacobs, Arthur M.
2018. (Neuro-)Cognitive poetics and computational stylistics. Scientific Study of Literature 8:1 ► pp. 165 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

This list is based on CrossRef data as of 4 december 2025. 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.

Mobile Menu Logo with link to supplementary files background Layer 1 prag Twitter_Logo_Blue