Have a personal or library account? Click to login

Cognitive Artifacts and Their Virtues in Scientific Practice

Open Access
|Dec 2022

References

  1. Afeltowicz, Ł. and Wachowski, W. (2015). “How far we can go without looking under the skin: The bounds of cognitive science”. Studies in Logic, Grammar and Rhetoric, 40(1), 91–109. doi: 10.1515/slgr-2015-0005
  2. Becvar, A., Hollan, J. and Hutchins, E. (2008). “Representational gestures as cognitive artifacts for developing theories in a scientific laboratory”. In: Ackerman, M.S., Halverson, C.A., Erickson, T. and Kellogg, W.A. (eds.), Resources, Co-Evolution and Artifacts: Theory in CSCW. London: Springer, 117–143. doi: 10.1007/978-1-84628-901-9_5
  3. Brey, P. A. E. (2005). “The epistemology and ontology of human-computer interaction”. Minds and Machines, 15(3), 383–398. doi: 10.1007/s11023-005-9003-1
  4. Brown, G., Wyatt, J., Harris, R. and Yao, X. (2005). “Diversity creation methods: A survey and categorisation”. Information Fusion, 6(1), 5–20. doi: 10.1016/j.inffus.2004.04.004
  5. Campbell, D. T. and Fiske, D. W. (1959). “Convergent and discriminant validation by the multitrait-multimethod matrix”. Psychological Bulletin, 56(2), 81–105. doi: 10.1037/h0046016
  6. Clark, A. (2016). Surfing Uncertainty: Prediction, Action, and the Embodied Mind. New York: Oxford University Press.10.1093/acprof:oso/9780190217013.001.0001
  7. Clark, A. and Chalmers, D. J. (1998). “The extended mind”. Analysis, 58(1), 7–19.10.1093/analys/58.1.7
  8. Davidson, D. (1991). “Epistemology externalized”. Dialectica, 45(2–3), 191–202. doi: 10.1111/j.1746-8361.1991.tb00986.x
  9. Facchin, M. (2021). “Structural representations do not meet the job description challenge”. Synthese. doi: 10.1007/s11229-021-03032-8
  10. Fasoli, M. (2018). “Substitutive, complementary and constitutive cognitive artifacts: Developing an interaction-centered approach”. Review of Philosophy and Psychology, 9(3), 671–687. doi: 10.1007/s13164-017-0363-2
  11. Fodor, J. A. (1991). “The dogma that didn’t bark (A fragment of a naturalized epistemology)”. Mind, 100(2), 201–220. doi: 10.1093/mind/LI.202.200
  12. French, S. (2020). There Are No Such Things As Theories. Oxford: Oxford University Press. doi: 10.1093/oso/9780198848158.001.0001
  13. Frixione, M. (2001). “Tractable competence”. Minds and Machines, 379–397.10.1023/A:1017503201702
  14. Gablasova, D., Brezina, V. and McEnery, T. (2017). “Collocations in corpus-based language learning research: identifying, comparing, and interpreting the evidence”. Language Learning, 67(S1), 155–179. doi: 10.1111/lang.12225
  15. Gitelman, L. (ed.). (2013). “Raw Data” Is an Oxymoron. Cambridge Mass.: The MIT Press.10.7551/mitpress/9302.001.0001
  16. Gładziejewski, P. (2016). “Predictive coding and representationalism”. Synthese, 193(2), 559–582. doi: 10.1007/s11229-015-0762-9
  17. Haugeland, J. (1998). Having Thought. Essays in the Metaphysics of Mind. Cambridge Mass./London: Harvard University Press.
  18. Heersmink, R. (2013). “A taxonomy of cognitive artifacts: function, information, and categories. Review of Philosophy and Psychology, (4), 465–481. doi: 10.1007/s13164-013-0148-1
  19. Heersmink, R. (2021). “Varieties of artifacts: embodied, perceptual, cognitive, and affective”. Topics in Cognitive Science, 13(4), 573–596. doi: 10.1111/tops.12 549
  20. Hellman, M. (1980). “A cryptanalytic time-memory trade-off”. IEEE Transactions on Information Theory, 26(4), 401–406. doi: 10.1109/TIT.1980.1056220
  21. Hochstein, E. (2016). “One mechanism, many models: A distributed theory of mechanistic explanation”. Synthese, 193(5), 1387–1407. doi: 10.1007/s11229-015-0844-8
  22. Hohol, M. (2020). Foundations of Geometric Cognition. New York: Routledge.
  23. Hohol, M. and Miłkowski, M. (2019). “Cognitive artifacts for geometric reasoning”. Foundations of Science, 24(4), 657–680. doi: 10.1007/s10699-019-09603-w
  24. Hutchins, E. (1995). Cognition in the Wild. Cambridge Mass.: MIT Press.
  25. Ioannidis, J. P. A. (2005). “Why most published research findings are false. PLOS Medicine, 2(8), e124. doi: 10.1371/journal.pmed.0020124
  26. Jurgens, D., Kumar, S., Hoover, R., McFarland, D. and Jurafsky, D. (2018). “Measuring the evolution of a scientific field through citation frames”. Transactions of the Association for Computational Linguistics, 6, 391–406. doi: 10.1162/tacl a 00028
  27. Keas, M. N. (2018). “Systematizing the theoretical virtues”. Synthese, 195(6), 2761–2793. doi: 10.1007/s11229-017-1355-6
  28. Kilgarriff, A., Baisa, V., Bušta, J., Jakubíček, M., Kovář, V., Michelfeit, J., Rychly, P. and Suchomel, V. (2014). “The Sketch Engine: Ten years on”. Lexicography, 1, 7–36. doi: 10.1007/s40607-014-0009-9
  29. Kuhn, T. S. (1977). The Essential Tension: Selected Studies in Scientific Tradition and Change. Chicago: The University of Chicago Press.10.7208/chicago/9780226217239.001.0001
  30. Larkin, J. and Simon, H. A. (1987). “Why a diagram is (sometimes) worth ten thousand words”. Cognitive Science, 11(1), 65–100. doi: 10.1016/S0364-0213(87)80026-5
  31. Laudan, L. (1977). Progress and Its Problem: Towards a Theory of ScientificGrowth. Berkeley Ca.: University of California Press.
  32. Lean, O. M., Rivelli, L. and Pence, C. H. (2021, accepted). “Digital literature analysis for empirical philosophy of science”. The British Journal for the Philosophy of Science. doi: 10.1086/715049
  33. Litwin, P. and Miłkowski, M. (2020). “Unification by fiat: arrested development of predictive processing”. Cognitive Science, 44(7), e12867. doi: 10.1111/cogs.12867
  34. Marghetis, T. and Núñez, R. (2013). “The motion behind the symbols: a vital role for dynamism in the conceptualization of limits and continuity in expert mathematics”. Topics in Cognitive Science, 5(2), 299–316. doi: 10.1111/tops.12013
  35. Medawar, P. (1963). “Is scientific paper a fraud?” The Listener, 70, 377–378.
  36. Miłkowski, M. (2010). “Making naturalised epistemology (slightly) normative”. In: Miłkowski, M. and Talmont-Kamiński, K. (eds.), Beyond Description: Naturalism and Normativity. London: College Publications, 72–84.
  37. Miłkowski, M., Clowes, R. W., Rucińska, Z., Przegalińska, A., Zawidzki, T., Gies, A., ... Hohol, M. (2018). “From wide cognition to mechanisms: a silent revolution”. Frontiers in Psychology, 9, 2393. doi: 10.3389/fpsyg.2018.02393
  38. Miłkowski, M., Hensel, W. M., and Hohol, M. (2018). “Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail”. Journal of Computational Neuroscience, 45(3), 163–172. doi: 10.1007/s10827-018-0702-z
  39. Mizrahi, M. (2021, accepted). “Theoretical virtues in scientific practice: an empirical study”. The British Journal for the Philosophy of Science. doi: 10.1086/714790
  40. Moretti, F. (2000). “Conjectures on World Literature”. New Left Review, 1, 54–68.
  41. Morgan, A. (2013). “Representations gone mental”. Synthese, 191(2), 213–244. doi: 10.1007/s11229-013-0328-7
  42. Nersessian, N. J. (2006). “The cognitive-cultural systems of the research laboratory”. Organization Studies, 27(1), 125–145. doi: 10.1177/0170840606061842
  43. Netz, R. (2011). The Shaping of Deduction in Greek Mathematics: A Study in Cognitive History. Cambridge: Cambridge University Press.
  44. Nirshberg, G. and Shapiro, L. (2021). “Structural and indicator representations: A difference in degree, not kind”. Synthese, 198, 7647–7664. doi: 10.1007/s11229-020-02537-y
  45. Norman, D. A. (1991). “Cognitive artifacts”. In: Carroll, J. M. (ed.), Designing Interaction: Psychology at the Human-Computer Interface. Cambridge: Cambridge University Press, 17–38.
  46. Norman, D. A. (1993). Things That Make Us Smart: Defending Human Attributes in the Age of the Machine. Reading, Mass.: Addison-Wesley Pub. Co.
  47. Pence, C. H., and Ramsey, G. (2018). “How to do digital philosophy of science”. Philosophy of Science, 85(5), 930–941. doi: 10.1086/699697
  48. Pessoa, L., Medina, L. and Desfilis, E. (2022). “Refocusing neuroscience: Moving away from mental categories and towards complex behaviours”. Philosophical Transactions of the Royal Society B: Biological Sciences, 377(1844), 20200534. doi: 10.1098/rstb.2020.0534
  49. Piccinini, G. and Anderson, N. G. (2018). “Ontic pancomputationalism”. In: Cuffaro, M.E and Fletcher, S.C. (eds.), Physical Perspectives on Computation, Computational Perspectives on Physics. Cambridge: Cambridge University Press, 23–38. doi: 10.1017/9781316759745.002
  50. Piper, A. (2020). Can We Be Wrong? The Problem of Textual Evidence in a Time of Data. Cambridge: Cambridge University Press.10.1017/9781108922036
  51. Pitcher, D. and Ungerleider, L. G. (2021). “Evidence for a third visual pathway specialized for social perception”. Trends in Cognitive Sciences, 25(2), 100–110. doi: 10.1016/j.tics.2020.11.006
  52. Poldrack, R. A., Kittur, A., Kalar, D., Miller, E., Seppa, C., Gil, Y., ... Bilder, R. M. (2011). “The cognitive atlas: toward a knowledge foundation for cognitive neuroscience”. Frontiers in Neuroinformatics, 5. doi: 10.3389/fninf.2011.00017
  53. Regt, H. W. de. (2017). Understanding Scientific Understanding. New York: Oxford University Press.10.1093/oso/9780190652913.001.0001
  54. Rupert, R. D. (2013). “Distributed cognition and extended-mind theory”. In: Kaldis, B. (ed.), Encyclopedia of Philosophy and the Social Sciences. Los Angeles: SAGE Publications.
  55. Rychlý, P. (2008). “A lexicographer-friendly association score”. Proceedings of Second Workshop on Recent Advances in Slavonic Natural Languages Processing, 6–9. Brno: Masaryk University.
  56. Short, T. L. (2007). Peirce’s Theory of Signs. Cambridge/New York: Cambridge University Press.10.1017/CBO9780511498350
  57. Soo Ko, B. (2019). ImageNet Classification Leaderboard. Retrieved March 13, 2022, from Computer-Vision-Leaderboard website: https://kobiso.github.io/Computer-Vision-Leaderboard/imagenet.html
  58. Stenning, K. and Lambalgen, M. V. (2008). Human Reasoning and Cognitive Science. Cambridge Mass.: The MIT Press.10.7551/mitpress/7964.001.0001
  59. Tan, M. and Le, Q. V. (2020). “EfficientNet: rethinking model scaling for convolutional neural networks”. ArXiv:1905.11946 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1905.11946
  60. Vaccari, A. P. (2017). “Against cognitive artifacts: Extended cognition and the problem of defining ‘artifact’”. Phenomenology and the Cognitive Sciences, 16(5), 879–892. doi: 10.1007/s11097-016-9484-9
  61. Van Rooij, I. (2008). “The tractable cognition thesis”. Cognitive Science, 32(6), 939–984. doi: 10.1080/03640210801897856
  62. Wachowski, W. M. (2018). “Commentary: distributed cognition and distributed morality: agency, artifacts and systems”. Frontiers in Psychology, 9. doi: 10/gdcbs5
  63. Wimsatt, W. C. (2007). Re-Engineering Philosophy for Limited Beings: Piecewise Approximations to Reality. Cambridge, Mass.: Harvard University Press.10.2307/j.ctv1pncnrh
  64. Wray, A. (2012). “What do we (think we) know about formulaic language? An evaluation of the current state of play”. Annual Review of Applied Linguistics, 32, 231–254. doi: 10.1017/S026719051200013X
  65. Zhang, J. (1997). “The nature of external representations in problem solving”. Cognitive Science, 21(2), 179–217. doi: 10.1016/S0364-0213(99)80022-6
  66. Zhang, J. and Norman, D. A. (1994). “Representations in distributed cognitive tasks”. Cognitive Science, 18(1), 87–122. doi: 10.1207/s15516709cog1801_3
DOI: https://doi.org/10.2478/slgr-2022-0012 | Journal eISSN: 2199-6059 | Journal ISSN: 0860-150X
Language: English
Page range: 219 - 246
Published on: Dec 30, 2022
Published by: University of Białystok, Department of Pedagogy and Psychology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Related subjects:

© 2022 Marcin Miłkowski, published by University of Białystok, Department of Pedagogy and Psychology
This work is licensed under the Creative Commons Attribution 4.0 License.