Volume 10, Issue 1
  • ISSN 2210-4372
  • E-ISSN: 2210-4380
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In the present study, we sought to clarify how differences in contextualized experience influence the performance of participants engaged in genre decision-making. Using a simple learning algorithm, we ran a series of computational simulations to model the effects that context and cue competition have on the way readers of different backgrounds make genre decisions. Next, we used the results of those simulations as predictions for our behavioural genre decision experiment. Differences in test performance were strongly influenced by the factors that have long been known to influence learning: Cue competition and its embedding in a specific context jointly modulate what gets learned and that inevitably affects later performance. We discuss our findings in the context of learning and literary genres.


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  1. Abernethy, B., & Russell, D. G.
    (1987) The relationship between expertise and visual search strategy in a racquet sport. Human Movement Science, 6(4), 283–319. doi:  10.1016/0167‑9457(87)90001‑7
    https://doi.org/10.1016/0167-9457(87)90001-7 [Google Scholar]
  2. Adam, J. -M., & Heidmann, U.
    (2004) Des genres à la généricité. L’exemple des contes (Perrault et les Grimm). Langages(1), 62–72. 10.3917/lang.153.0062
    https://doi.org/10.3917/lang.153.0062 [Google Scholar]
  3. Ambridge, B., & Lieven, E. V. M.
    (2011) Child language acquisition : contrasting theoretical approaches. 10.1017/CBO9780511975073
    https://doi.org/10.1017/CBO9780511975073 [Google Scholar]
  4. Baayen, R. H., Milin, P., Đurđević, D. F., Hendrix, P., & Marelli, M.
    (2011) An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological review, 118(3), 438–481. 10.1037/a0023851
    https://doi.org/10.1037/a0023851 [Google Scholar]
  5. Bilalić, M., Grottenthaler, T., Nägele, T., & Lindig, T.
    (2014) The faces in radiological images: fusiform face area supports radiological expertise. Cerebral Cortex, 26(3), 1004–1014. 10.1093/cercor/bhu272
    https://doi.org/10.1093/cercor/bhu272 [Google Scholar]
  6. Bilalić, M., Langner, R., Erb, M., & Grodd, W.
    (2010) Mechanisms and neural basis of object and pattern recognition: a study with chess experts. Journal of Experimental Psychology: General, 139(4), 728–742. 10.1037/a0020756
    https://doi.org/10.1037/a0020756 [Google Scholar]
  7. Bilalić, M., McLeod, P., & Gobet, F.
    (2008) Why good thoughts block better ones: The mechanism of the pernicious Einstellung (set) effect. Cognition, 108(3), 652–661. 10.1016/j.cognition.2008.05.005
    https://doi.org/10.1016/j.cognition.2008.05.005 [Google Scholar]
  8. Blough, D. S.
    (1975) Steady state data and a quantitative model of operant generalization and discrimination. Journal of Experimental Psychology: Animal Behavior Processes, 1(1), 3–21.
    [Google Scholar]
  9. Bortolussi, M., & Dixon, P.
    (2003) Psychonarratology: Foundations for the empirical study of literary response. Cambridge: Cambridge University Press.
    [Google Scholar]
  10. Bouton, M. E.
    (1993) Context, time, and memory retrieval in the interference paradigms of Pavlovian learning. Psychological Bulletin, 114(1), 80–99. 10.1037/0033‑2909.114.1.80
    https://doi.org/10.1037/0033-2909.114.1.80 [Google Scholar]
  11. (1997) Signals for whether versus when an event will occur. InM. E. Bouton & M. S. Fanselow (Eds.), Learning, motivation, and cognition: The functional behaviorism of Robert C. Bolles (pp.385–409). Washington, DC: American Psychological Association. 10.1037/10223‑019
    https://doi.org/10.1037/10223-019 [Google Scholar]
  12. Bradshaw, T., & Nichols, B.
    (2004) Reading At Risk: A Survey of Literary Reading in America (Research Division Report #46). Retrieved fromWashington, DC:
    [Google Scholar]
  13. Chase, W. G., & Simon, H. A.
    (1973) Perception in chess. Cognitive Psychology, 4(1), 55–81. 10.1016/0010‑0285(73)90004‑2
    https://doi.org/10.1016/0010-0285(73)90004-2 [Google Scholar]
  14. Chen, Z., Haykin, S., Eggermont, J. J., & Becker, S.
    (2008) Correlative learning: a basis for brain and adaptive systems. New Jersey: John Wiley & Sons.
    [Google Scholar]
  15. Dayan, P., & Niv, Y.
    (2008) Reinforcement learning: the good, the bad and the ugly. Current Opinion in Neurobiology, 18(2), 185–196. 10.1016/j.conb.2008.08.003
    https://doi.org/10.1016/j.conb.2008.08.003 [Google Scholar]
  16. De Geest, D., & Van Gorp, H.
    (1999) Literary genres from a systemic-functionalist perspective. European Journal of English Studies, 3(1), 33–50. 10.1080/13825579908574428
    https://doi.org/10.1080/13825579908574428 [Google Scholar]
  17. Derrida, J., & Ronell, A.
    (1980) The law of genre. Critical Inquiry, 7(1), 55–81. 10.1086/448088
    https://doi.org/10.1086/448088 [Google Scholar]
  18. Divjak, D., Milin, P., & Medimorec, S.
    (2020) Construal in language: A visual-world approach to the effects of linguistic alternations on event perception and conception. Cognitive Linguistics, 31(1), 37–72. 10.1515/cog‑2018‑0103
    https://doi.org/10.1515/cog-2018-0103 [Google Scholar]
  19. Enquist, M., Lind, J., & Ghirlanda, S.
    (2016) The power of associative learning and the ontogeny of optimal behaviour. Royal Society open science, 3(11), 160734. 10.1098/rsos.160734
    https://doi.org/10.1098/rsos.160734 [Google Scholar]
  20. Fox, J.
    (2002) An R and S-Plus companion to applied regression. Thousand Oaks, CA: Sage.
    [Google Scholar]
  21. Gallistel, C. R., & Gibbon, J.
    (2000) Time, rate, and conditioning. Psychological Review, 107(2), 289–344. 10.1037/0033‑295X.107.2.289
    https://doi.org/10.1037/0033-295X.107.2.289 [Google Scholar]
  22. Hanauer, D.
    (1996) Integration of phonetic and graphic features in poetic text categorization judgements. Poetics, 23(5), 363–380. 10.1016/0304‑422X(95)00010‑H
    https://doi.org/10.1016/0304-422X(95)00010-H [Google Scholar]
  23. (1998a) The genre-specific hypothesis of reading: Reading poetry and encyclopedic items. Poetics, 26(2), 63–80. 10.1016/S0304‑422X(98)00011‑4
    https://doi.org/10.1016/S0304-422X(98)00011-4 [Google Scholar]
  24. (1998b) Reading poetry: An empirical investigation of formalist, stylistic, and conventionalist claims. Poetics Today, 565–580. 10.2307/1773260
    https://doi.org/10.2307/1773260 [Google Scholar]
  25. (2001) What we know about reading poetry. Theoretical positions and empirical research. InD. Schram & G. Steen (Eds.), The psychology and sociology of literature (pp.107–128). Amsterdam: John Benjamins. 10.1075/upal.35.08han
    https://doi.org/10.1075/upal.35.08han [Google Scholar]
  26. Hintzman, D. L.
    (1984) MINERVA 2: A simulation model of human memory. Behavior Research Methods, Instruments, & Computers, 16(2), 96–101. 10.3758/BF03202365
    https://doi.org/10.3758/BF03202365 [Google Scholar]
  27. (1988) Judgments of frequency and recognition memory in a multiple-trace memory model. Psychological Review, 95(4), 528–551. 10.1037/0033‑295X.95.4.528
    https://doi.org/10.1037/0033-295X.95.4.528 [Google Scholar]
  28. Hoffstaedter, P.
    (1987) Poetic text processing and its empirical investigation. Poetics, 16(1), 75–91. 10.1016/0304‑422X(87)90037‑4
    https://doi.org/10.1016/0304-422X(87)90037-4 [Google Scholar]
  29. Hull, C. L.
    (1943) Principles of behavior: An introduction to behavior theory. New York: Appleton-Century-Crofts.
    [Google Scholar]
  30. James, W.
    (1909) The meaning of truth. New York: Longmans, Green and Company.
    [Google Scholar]
  31. Kakade, S., & Dayan, P.
    (2002) Acquisition and extinction in autoshaping. Psychological Review, 109(3), 533–544. 10.1037/0033‑295X.109.3.533
    https://doi.org/10.1037/0033-295X.109.3.533 [Google Scholar]
  32. Kalman, R. E.
    (1960) A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35–45. 10.1115/1.3662552
    https://doi.org/10.1115/1.3662552 [Google Scholar]
  33. Kimmel, H. D.
    (1959) Amount of conditioning and intensity of conditioned stimulus. Journal of Experimental Psychology, 58(4), 283–288. 10.1037/h0048529
    https://doi.org/10.1037/h0048529 [Google Scholar]
  34. Konorski, J.
    (1967) Integrative activity of the brain; an interdisciplinary approach. Chicago: University of Chicago Press.
    [Google Scholar]
  35. Kruschke, J. K., & Hullinger, R. A.
    (2010) Evolution of attention in learning. InN. A. Schmajuk (Ed.), Computational models of conditioning (pp.10–52). Cambridge: Cambridge University Press. 10.1017/CBO9780511760402.002
    https://doi.org/10.1017/CBO9780511760402.002 [Google Scholar]
  36. Landeira-Fernandez, J.
    (1996) Context and Pavlovian conditioning. Brazilian Journal of Medical and Biological Research, 29(2), 149–173.
    [Google Scholar]
  37. Linderholm, T.
    (2006) Reading with purpose. Journal of College Reading and Learning, 36(2), 70–80. 10.1080/10790195.2006.10850189
    https://doi.org/10.1080/10790195.2006.10850189 [Google Scholar]
  38. Mathôt, S., Schreij, D., & Theeuwes, J.
    (2012) OpenSesame: An open-source, graphical experiment builder for the social sciences. Behavior Research Methods, 44(2), 314–324. 10.3758/s13428‑011‑0168‑7
    https://doi.org/10.3758/s13428-011-0168-7 [Google Scholar]
  39. Miall, D. S.
    (2002) Literary discourse. InA. Graesser, M. Gernsbacher, & S. Goldman (Eds.), Handbook of discourse processes (pp.321–355). New Jersey: Lawrence Erlbaum
    [Google Scholar]
  40. Miall, D. S., & Kuiken, D.
    (1998) The form of reading: Empirical studies of literariness. Poetics, 25(6), 327–341. 10.1016/S0304‑422X(98)90003‑1
    https://doi.org/10.1016/S0304-422X(98)90003-1 [Google Scholar]
  41. Milin, P., Feldman, L. B., Ramscar, M., Hendrix, P., & Baayen, R. H.
    (2017) Discrimination in lexical decision. PloS one, 12(2), e0171935. 10.1371/journal.pone.0171935
    https://doi.org/10.1371/journal.pone.0171935 [Google Scholar]
  42. Milin, P., Madabushi, H. T., Croucher, M., & Divjak, D.
    (2020) Keeping it simple: Implementation and performance of the proto-principle of adaptation and learning in the language sciences. arXiv, arXiv:2003.03813.
    [Google Scholar]
  43. Miller, C. R., & Kelly, A. R.
    (2016) Discourse genresInA. Rocci & L. d. Saussure (Eds.), Verbal communication (pp.269–286). Berlin: Walter de Gruyter. 10.1515/9783110255478‑015
    https://doi.org/10.1515/9783110255478-015 [Google Scholar]
  44. Myers, C. E., & Gluck, M. A.
    (1994) Context, conditioning, and hippocampal rerepresentation in animal learning. Behavioral Neuroscience, 108(5), 835–847. 10.1037/0735‑7044.108.5.835
    https://doi.org/10.1037/0735-7044.108.5.835 [Google Scholar]
  45. Nadel, L., & Willner, J.
    (1980) Context and conditioning: A place for space. Physiological Psychology, 8(2), 218–228. 10.3758/BF03332853
    https://doi.org/10.3758/BF03332853 [Google Scholar]
  46. Niv, Y.
    (2009) Reinforcement learning in the brain. Journal of Mathematical Psychology, 53(3), 139–154. 10.1016/j.jmp.2008.12.005
    https://doi.org/10.1016/j.jmp.2008.12.005 [Google Scholar]
  47. Peskin, J.
    (1998) Constructing meaning when reading poetry: An expert-novice study. Cognition and Instruction, 16(3), 235–263. 10.1207/s1532690xci1603_1
    https://doi.org/10.1207/s1532690xci1603_1 [Google Scholar]
  48. (2007) The genre of poetry: Secondary school students’ conventional expectations and interpretive operations. English in Education, 41(3), 20–36. 10.1111/j.1754‑8845.2007.tb01162.x
    https://doi.org/10.1111/j.1754-8845.2007.tb01162.x [Google Scholar]
  49. (2010) The development of poetic literacy during the school years. Discourse Processes, 47(2), 77–103. 10.1080/01638530902959653
    https://doi.org/10.1080/01638530902959653 [Google Scholar]
  50. R Core Team
    R Core Team (2017) R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. Retrieved fromwww.R-project.org/
  51. Ramscar, M., Sun, C. C., Hendrix, P., & Baayen, H.
    (2017) The mismeasurement of mind: Life-span changes in paired-associate-learning scores reflect the “cost” of learning, not cognitive decline. Psychological Science, 28(8), 1171–1179. 10.1177/0956797617706393
    https://doi.org/10.1177/0956797617706393 [Google Scholar]
  52. Ramscar, M., & Yarlett, D.
    (2007) Linguistic self-correction in the absence of feedback: A new approach to the logical problem of language acquisition. Cognitive Science, 31(6), 927–960. 10.1080/03640210701703576
    https://doi.org/10.1080/03640210701703576 [Google Scholar]
  53. Ramscar, M., Yarlett, D., Dye, M., Denny, K., & Thorpe, K.
    (2010) The effects of feature-label-order and their implications for symbolic learning. Cognitive Science, 34(6), 909–957. 10.1111/j.1551‑6709.2009.01092.x
    https://doi.org/10.1111/j.1551-6709.2009.01092.x [Google Scholar]
  54. Rayner, K., Chace, K. H., Slattery, T. J., & Ashby, J.
    (2006) Eye movements as reflections of comprehension processes in reading. Scientific Studies of Reading, 10(3), 241–255. 10.1207/s1532799xssr1003_3
    https://doi.org/10.1207/s1532799xssr1003_3 [Google Scholar]
  55. Reichle, E. D., & Reingold, E. M.
    (2013) Neurophysiological constraints on the eye-mind link. Frontiers in Human Neuroscience, 7, 361. 10.3389/fnhum.2013.00361
    https://doi.org/10.3389/fnhum.2013.00361 [Google Scholar]
  56. Rescorla, R. A.
    (1970) Reduction in the effectiveness of reinforcement after prior excitatory conditioning. Learning and Motivation, 1(4), 372–381. 10.1016/0023‑9690(70)90101‑3
    https://doi.org/10.1016/0023-9690(70)90101-3 [Google Scholar]
  57. (1988) Pavlovian conditioning: It’s not what you think it is. American psychologist, 43(3), 151–160. 10.1037/0003‑066X.43.3.151
    https://doi.org/10.1037/0003-066X.43.3.151 [Google Scholar]
  58. Rescorla, R. A., Durlach, P. J., & Grau, J. W.
    (1985) Contextual learning in Pavlovian conditioning. InP. D. B. A. Tomie (Ed.), Context and learning (pp.23–56). New Jersey: Lawrence Erlbaum.
    [Google Scholar]
  59. Rescorla, R. A., & Wagner, A. R.
    (1972) A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. InA. H. Black & W. F. Prokasy (Eds.), Classical conditioning II: Current research and theory (pp.64–99). New York: Appleton-Century-Crofts.
    [Google Scholar]
  60. Rosas, J. M., Aguilera, J. E. C., Álvarez, M. M. R., & Abad, M. J. F.
    (2006) Revision of retrieval theory of forgetting: What does make information context-specific?international Journal of Psychology and Psychological Therapy, 6(2), 147–166.
    [Google Scholar]
  61. Rosas, J. M., Todd, T. P., & Bouton, M. E.
    (2013) Context change and associative learning. Wiley Interdisciplinary Reviews: Cognitive Science, 4(3), 237–244.
    [Google Scholar]
  62. Spence, K. W.
    (1956) Behavior theory and conditioning. New Haven: Yale University Press. 10.1037/10029‑000
    https://doi.org/10.1037/10029-000 [Google Scholar]
  63. Spreat, S., & Spreat, S. R.
    (1982) Learning principles. The Veterinary clinics of North America. Small animal practice, 12(4), 593–606. 10.1016/S0195‑5616(82)50104‑0
    https://doi.org/10.1016/S0195-5616(82)50104-0 [Google Scholar]
  64. Sutton, R. S.
    (1992) Gain adaptation beats least squares. Paper presented at theProceedings of the 7th Yale workshop on adaptive and learning systems.
    [Google Scholar]
  65. Sutton, R. S., & Barto, A. G.
    (1990) Time-derivative models of pavlovian reinforcement. InM. Gabriel & J. Moore (Eds.), Learning and computational neuroscience: Foundations of adaptive networks (pp.497–537). Cambridge, MA: The MIT Press.
    [Google Scholar]
  66. Todorov, T.
    (2000) The origin of genres. InD. Duff (Ed.), Modern genre theory. Harlow: Longman.
    [Google Scholar]
  67. Tolman, E. C.
    (1949) There is more than one kind of learning. Psychological Review, 56(3), 144–155. 10.1037/h0055304
    https://doi.org/10.1037/h0055304 [Google Scholar]
  68. Tomie, A.
    (1981) Effect of unpredictable food on the subsequent acquisition of autoshaping: Analysis of the context blocking hypothesis. InC. M. Locurto, H. S. Terrace, & J. Gibbon (Eds.), Autoshaping and conditioning theory (pp.181–215). New York: Academic Press.
    [Google Scholar]
  69. Trimmer, P. C., McNamara, J. M., Houston, A. I., & Marshall, J. A.
    (2012) Does natural selection favour the Rescorla–Wagner rule?Journal of Theoretical Biology, 302, 39–52. 10.1016/j.jtbi.2012.02.014
    https://doi.org/10.1016/j.jtbi.2012.02.014 [Google Scholar]
  70. Urcelay, G. P., & Miller, R. R.
    (2014) The functions of contexts in associative learning. Behavioural Processes, 104, 2–12. 10.1016/j.beproc.2014.02.008
    https://doi.org/10.1016/j.beproc.2014.02.008 [Google Scholar]
  71. Van den Broek, P., Lorch, R. F., Linderholm, T., & Gustafson, M.
    (2001) The effects of readers’ goals on inference generation and memory for texts. Memory & Cognition, 29(8), 1081–1087. 10.3758/BF03206376
    https://doi.org/10.3758/BF03206376 [Google Scholar]
  72. Van Rij, J., Wieling, M., Baayen, R. H., & van Rijn, H.
    (2015) itsadug: Interpreting time series and autocorrelated data using GAMMs (Version 1.0.1): R statistical group.
    [Google Scholar]
  73. Widrow, B., & Hoff, M. E.
    (1960) Adaptive switching circuits. Paper presented at theWESCON Convention Record Part IV. 10.21236/AD0241531
    https://doi.org/10.21236/AD0241531 [Google Scholar]
  74. Wood, S.
    (2007) The mgcv package: R statistical group.
    [Google Scholar]
  75. Wood, S. N., Pya, N., & Säfken, B.
    (2016) Smoothing parameter and model selection for general smooth models. Journal of the American Statistical Association, 111(516), 1548–1563. 10.1080/01621459.2016.1180986
    https://doi.org/10.1080/01621459.2016.1180986 [Google Scholar]
  76. Woollett, K., & Maguire, E. A.
    (2009) Navigational expertise may compromise anterograde associative memory. Neuropsychologia, 47(4), 1088–1095. 10.1016/j.neuropsychologia.2008.12.036
    https://doi.org/10.1016/j.neuropsychologia.2008.12.036 [Google Scholar]
  77. (2011) Acquiring “the Knowledge” of London’s layout drives structural brain changes. Current Biology, 21(24), 2109–2114. 10.1016/j.cub.2011.11.018
    https://doi.org/10.1016/j.cub.2011.11.018 [Google Scholar]
  78. Zwaan, R. A.
    (1991) Some parameters of literary and news comprehension: Effects of discourse-type perspective on reading rate and surface structure representation. Poetics, 20(2), 139–156. 10.1016/0304‑422X(91)90003‑8
    https://doi.org/10.1016/0304-422X(91)90003-8 [Google Scholar]
  79. (1993) Aspects of literary comprehension: A cognitive approach (Vol.29). Amsterdam: John Benjamins. 10.1075/upal.29
    https://doi.org/10.1075/upal.29 [Google Scholar]
  80. (1994) Effect of genre expectations on text comprehension. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20(4), 920–933.
    [Google Scholar]

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Keyword(s): context; cue competition; experience; genre; learning; poetry
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