Article published In:
Scientific Study of Literature
Vol. 7:1 (2017) ► pp.451
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). 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
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
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. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle Scholar
Berlyne, D. E.
(1971) Aesthetics and Psychobiology. New York: Appleton-Century-Crofts.Google Scholar
Bestgen, Y.
(1994) Can emotional valence in stories be determined from words?. Cognition & Emotion, 8(1), 21–36. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle Scholar
(2013) When we like what we know – A parametric fMRI analysis of beauty and familiarity. Brain and Language, 124(1), 1–8. DOI logoGoogle 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. DOI logoGoogle Scholar
Boyd, B.
(2012) Why lyrics last: Evolution, Cognition, and Shakespeare’s Sonnets. Harvard University Press. DOI logoGoogle 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
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. DOI logoGoogle Scholar
(2014) Emotion word recognition: Discrete information effects first, continuous later?. Brain Research, 15641, 62–71. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle Scholar
Burke, M.
(2011) Literary reading, cognition and emotion: An exploration of the oceanic mind. London: Routledge.Google Scholar
(2015) The neuroaesthetics of prose fiction: Pitfalls, parameters and prospects. Frontiers in Human Neuroscience, 91. DOI logoGoogle Scholar
Chatterjee, A., & Vartanian, O.
(2014) Neuroaesthetics. Trends in Cognitive Sciences, 18(7), 370–375. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle Scholar
Cook, A.
(2010) Shakespearean neuroplay: Reinvigorating the study of dramatic texts and performance through cognitive science. NY: Palgrave Macmillan DOI logoGoogle Scholar
Crane, M. T.
(2001) Shakespeare’s Brain: Reading with Cognitive Theory. Princeton University Press.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. DOI logoGoogle 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. DOI logoGoogle Scholar
Crystal, D.
(2012) Think on my Words: Exploring Shakespeare's Language (Canto Classics). Cambridge: Cambridge University Press. DOI logoGoogle Scholar
Cupchik, G. C.
(1986) A decade after Berlyne: New directions in experimental aesthetics. Poetics, 15(4–6), 345–369. DOI logoGoogle Scholar
De Beaugrande, R. A.
(1979) Toward a general theory of creativity. Poetics, 8(3), 269–306. DOI logoGoogle Scholar
Delmonte, R.
(2016) Exploring Shakespeare’s Sonnets with SPARSAR. Linguistics and Literature Studies, 4(1), 61–95. DOI logoGoogle 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. DOI logoGoogle Scholar
Dixon, P., & Bortolussi, M.
(2015) Measuring literary experience. Scientific Study of Literature, 5(2), 178–182. DOI logoGoogle Scholar
Fellbaum, C.
(Ed.) (1998) WordNet: An electronic lexical database. Cambridge: MIT Press. DOI logoGoogle 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. DOI logoGoogle Scholar
Frank, S. L.
(2013) Uncertainty reduction as a measure of cognitive load in sentence comprehension. Topics in Cognitive Science, 5(3), 475–494. DOI logoGoogle 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. DOI logoGoogle Scholar
Goldman, A. I.
(2006) Simulating minds: The philosophy, psychology, and neuroscience of mindreading. Oxford University Press. 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, 36(2), 193–202. DOI logoGoogle Scholar
Graesser, A. C., & McNamara, D. S.
(2010) Computational analyses of multilevel discourse comprehension. Topics in Cognitive Science, 3(2), 371–398. DOI logoGoogle Scholar
Graesser, A. C., Dowell, N., & Moldovan, C.
(2011) A computer’s understanding of literature. Scientific Study of Literature, 1(1), 24–33. DOI logoGoogle 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. DOI logoGoogle 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
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. DOI logoGoogle 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
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
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
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. DOI logoGoogle 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. DOI logoGoogle Scholar
Hutcheon, L.
(2012) A Theory of Adaptation. 2nd edition with Siobhan O’Flynn. London and New York: Routledge. DOI logoGoogle Scholar
Hutchins, R.
(Ed.) (1952) Great books of the Western world (541 vols.), Chicago: University of Chicago Press.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
(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. 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
(2015c) The scientific study of literary experience. Scientific Study of Literature, 5(2), 139–170. DOI logoGoogle 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
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. DOI logoGoogle 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
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. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle Scholar
Jacobsen, T.
(2006) Bridging the arts and sciences: A framework for the psychology of aesthetics. Leonardo, 391, 155–162. DOI logoGoogle Scholar
Jakobson, R., & Jones, L. G.
(1970) Shakespeare’s Verbal Art in Th’Expence of Spirit (No. 35). Walter de Gruyter. DOI logoGoogle Scholar
Jakobson, R., & Lévi-Strauss, C.
(1962) “Les chats” de Charles Baudelaire. L’homme, 21, 5–21. DOI logoGoogle Scholar
Kintsch, W.
(2000) Metaphor comprehension: A computational theory. Psychonomic Bulletin & Review, 7(2), 257–266. DOI logoGoogle Scholar
(2012) Musings about beauty. Cognitive Science, 36(4), 635–654. DOI logoGoogle Scholar
Kintsch, W., & Mangalath, P.
(2011) The construction of meaning. Topics in Cognitive Science, 3(2), 346–370. DOI logoGoogle 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
Kivy, P.
(1991) Music alone: Philosophical reflections on the purely musical experience. Cornell University Press.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. DOI logoGoogle Scholar
Krumhansl, C. L.
(1997) An Exploratory Study of Musical Emotions and Psychophysiology. Canadian Journal of Experimental Psychology, 51(4), 336. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle Scholar
Kuzmičová, A.
(2014) Literary narrative and mental imagery: A view from embodied cognition. Style, 48(3), 275–293.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. DOI logoGoogle Scholar
Lasswell, H. D., & Namenwirth, J. Z.
(1969) The Lasswell value dictionary. New Haven: Yale University Press.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. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle 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
Lindquist, K. A., MacCormack, J. K., & Shablack, H.
(2015) The role of language in emotion: Predictions from psychological constructionism. Frontiers in Psychology, 6:444. DOI logoGoogle 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. DOI logoGoogle Scholar
Loh, W. Y.
(2011) Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(1), 14–23. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle Scholar
Marin, M. M.
(2015) Crossing boundaries: Toward a general model of neuroaesthetics. Frontiers in Human Neuroscience, 91:443. DOI logoGoogle 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. DOI logoGoogle Scholar
Martindale, C.
(1975) The romantic progression: The psychology of literary history. Halsted Press.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. DOI logoGoogle 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. DOI logoGoogle Scholar
McQuarrie, E. F., & Mick, D. G.
(1996) Figures of rhetoric in advertising language. Journal of Consumer Research, 22(4), 424–438. DOI logoGoogle Scholar
Mcquire, M., Mccollum, L., & Chatterjee, A.
(2017) Aptness and beauty in metaphor. Language and Cognition, 9(2), 316–331. DOI logoGoogle Scholar
Meireles, R. C.
(2005) The hermeneutics of symbolical imagery in Shakespeare’s sonnets. Unpublished Dissertation, University. Porto Alegre, Bresil.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. DOI logoGoogle 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. DOI logoGoogle 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
Miall, D. S.
(1989) Beyond the schema given: Affective comprehension of literary narratives. Cognition & Emotion, 3(1), 55–78. DOI logoGoogle Scholar
Miall, D. S., & Kuiken, D.
(1994) Foregrounding, defamiliarization, and affect: Response to literary stories. Poetics, 22(5), 389–407. DOI logoGoogle Scholar
Miall, D. S., & Dissanayake, E.
(2003) The poetics of babytalk. Human Nature, 14(4), 337–364. DOI logoGoogle 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. DOI logoGoogle Scholar
(1993) Wörter: Streifzüge durch die Psycholinguistik. Frankfurt: Zweitausendeins.Google Scholar
Millis, K., & Larson, M.
(2008) Applying the construction-integration framework to aesthetic responses to representational artworks. Discourse Processes, 45(3), 263–287. DOI logoGoogle Scholar
Nicklas, P., & Jacobs, A. M.
(2017) Rhetorics, neurocognitive poetics and the aesthetics of adaptation. Poetics Today, 381, 393–412. DOI logo.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. DOI logoGoogle Scholar
(2016) Fiction: Simulation of social worlds. Trends in Cognitive Sciences, 20(8), 618–628. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle Scholar
Pléh, C.
(2003) Narrativity in text construction and self construction. Neohelicon, 30(1), 187–205. DOI logoGoogle Scholar
Pragglejaz Group
(2007) MIP: A method for identifying metaphorically used words in discourse. Metaphor and Symbol, 22(1), 1–39. DOI logoGoogle Scholar
Richards, I. A.
(1929) Practical criticism: A study of literary judgment. New York: Harcourt Brace Jovanovich.Google Scholar
Rosenblatt, L. M.
(1978) The reader, the text, the poem: The transactionaltheoryof the literarywork. Carbondale, IL: Southern Illinois University Press.Google Scholar
Ryan, M. L.
(2001) Narrative as virtual reality: Immersion and interactivity in literature and electronic media. Johns Hopkins University Press.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. 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
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. DOI logoGoogle Scholar
Simonto, D. K.
(1989) Shakespeare’s Sonnets: A Case of and for Single – Case Historiometry. Journal of Personality, 57(3), 695–721. DOI logoGoogle 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. DOI logoGoogle Scholar
Smith, N. J., & Levy, R.
(2013) The effect of word predictability on reading time is logarithmic. Cognition, 128(3), 302–319. DOI logoGoogle 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
(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. DOI logoGoogle Scholar
(2004) Can discourse properties of metaphor affect metaphor recognition?. Journal of Pragmatics, 36(7), 1295–1313. DOI logoGoogle Scholar
Stockwell, P.
(2009) The cognitive poetics of literary resonance. Language and Cognition, 1(1), 25–44. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle Scholar
Taruffi, L.
(2016) Why We Listen to Sad Music: Effects on Emotion and Cognition. (Unpublished doctoral dissertation), Freie Universität Berlin.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
(2006) Delivery style and listener response in the rhythmical performance of Shakespeare’s sonnets. College Literature, 33(1), 170–196. DOI logoGoogle Scholar
(2008) Deixis in literature: What isn’t cognitive poetics?. Pragmatics & Cognition, 16(1), 119–150. DOI logoGoogle Scholar
Turner, F., & Pöppel, E.
(1983) The neural lyre: Poetic meter, the brain, and time. Poetry, 277–309.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
Van Peer, W.
(1986) Stylistics and psychology: Investigations of foregrounding. London, UK: Croom Helm.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. DOI logoGoogle 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. DOI logoGoogle Scholar
Vendler, H.
(1997) The art of Shakespeare’s sonnets. Cambridge, MA: Harvard University Press.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. DOI logoGoogle Scholar
Võ, M. L., Jacobs, A. M., & Conrad, M.
(2006) Cross-validating the Berlin affective word list. Behavior Research Methods, 38(4), 606–609. DOI logoGoogle Scholar
Wainwright, J.
(2015) Poetry: The basics. Routledge. DOI logoGoogle Scholar
Wallace, W. T., & Rubin, D. C.
(1991) Characteristics and constraints in ballads and their effect on memory. Discourse Processes, 141, 181–202. DOI logoGoogle 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. 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, 45(4), 1191–1207. DOI logoGoogle 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. DOI logoGoogle Scholar
Willems, R. M.
(Ed.) (2015) Cognitive Neuroscience of Natural Language Use. Cambridge: Cambridge University Press. DOI logoGoogle 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. DOI logoGoogle 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. DOI logoGoogle Scholar
Yaron, I.
(2002) Processing of obscure poetic texts: Mechanisms of selection. Journal of Literary Semantics, 31(2), 133–170. DOI logoGoogle Scholar
(2008) What is a “difficult poem”? Towards a definition. Journal of Literary Semantics, 37(2), 129–150. DOI logoGoogle 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
Cited by

Cited by 15 other publications

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
Bruhn, Mark J.
2018. Citation analysis. Scientific Study of Literature 8:1  pp. 77 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
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
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
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
Jacobs, Arthur M. & Annette Kinder
2019. Computing the Affective-Aesthetic Potential of Literary Texts. AI 1:1  pp. 11 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

This list is based on CrossRef data as of 16 june 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.