Skip to main content
Log in

Emergence of scientific understanding in real-time ecological research practice

  • Original Paper
  • Published:
History and Philosophy of the Life Sciences Aims and scope Submit manuscript

Abstract

Scientific understanding as a subject of inquiry has become widely discussed in philosophy of science and is often addressed through case studies from history of science. Even though these historical reconstructions engage with details of scientific practice, they usually provide only limited information about the gradual formation of understanding in ongoing processes of model and theory construction. Based on a qualitative ethnographic study of an ecological research project, this article shifts attention from understanding in the context of historical case studies to evidence of current case studies. By taking de Regt’s (Understanding scientific understanding. Oxford University Press, New York, 2017) contextual theory of scientific understanding into the field, it confirms core tenets of the contextual theory (e.g. the crucial role of visualization and visualizability) suggesting a normative character with respect to scientific activities. However, the case study also shows the limitations of de Regt’s latest version of this theory as an attempt to explain the development of understanding in current practice. This article provides a model representing the emergence of scientific understanding that exposes main features of scientific understanding such as its gradual formation, its relation to skills and imagination, and its capacity for knowledge selectivity. The ethnographic evidence presented here supports the claim that something unique can be learned by looking into ongoing research practices that can’t be gained by studying historical case studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Source: Coutinho (2018)

Fig. 3

Source: Poliseli (2018)

Fig. 4

Source: Coutinho (2018)

Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. I would like to draw special attention to the fact that not all investigations concerning understanding are based on historical case studies, there are increasingly investigations such as those provided by Leonelli (2009) that provide an account of scientific understanding via modelling practices and skills in contemporary biological research.

  2. The contextual theory of understanding makes no difference between theories, hypothesis, and principles. The starting point is that all of them are statements, and these statements can be reliable according to its intelligibility (de Regt 2017). This is in agreement with Giere (2004) when he says that there is no reason for such analysis because the terms ‘theory’, ‘laws’ and ‘principles are used broadly in scientific practice and in metalevel discussions about sciences.

  3. The statements exposed are authorized.

References

  • Ankeny, R., Chang, H., Boumans, M., & Boon, M. (2011). Introduction: Philosophy of science in practice. European Journal for Philosophy of Science, 3(1), 303–307.

    Google Scholar 

  • Baumberger, C. (2014). Types of understanding: Their nature and their relation to knowledge. Conceptus, 40, 67–88.

    Google Scholar 

  • Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 421–441.

    Google Scholar 

  • Bishop, M. A., & Trout, J. D. (2002). Reason in the balance: An inquiry approach to critical thinking (2nd ed.). Indianopolis: Hackett Publish Company.

    Google Scholar 

  • Boccara, N. (2004). Modeling complex systems. New York, NY: Springer.

    Google Scholar 

  • Boehm, B. W. (1988). A spiral model of software development and enhancement. Computer, 21(5), 61–72.

    Google Scholar 

  • Bolinsca, A., & Martin, J. D. (2019). Negotiating history: Contingency, canonicity, and case studies. Studies in History and Philosophy of Science Part A. https://doi.org/10.1016/j.shpsa.2019.05.003.

    Article  Google Scholar 

  • Boscolo, D., Tokumoto, P. M., Ferreira, P. A., Ribeiro, J. W., & Santos, J. S. (2017). Positive responses of flower visiting bees to landscape heterogeneity depends on functional connectivity levels. Perspectives in Ecology and Conservation, 15, 18–24.

    Google Scholar 

  • Cadotte, M. W., Carscadden, K., & Mirotchnick, N. (2011). Beyond species: Functional diversity and the maintenance of ecological processes and services. Journal of Applied Ecology, 48, 1079–1087.

    Google Scholar 

  • Coutinho, J. G. E. (2018). Diversidade funcional de abelhas em sistemas agrícolas: aportes teóricos, empíricos e epistêmicos. Originally presented as doctorate dissertation, Universidade Federal da Bahia, Salvador, BA.

  • Craik, K. J. W. (1967). The nature of explanation. CUP Archive.

  • De Mey, T. (2006). Imagination’s grip on science. Metaphilosophy, 37(2), 222–239.

    Google Scholar 

  • de Regt, H. W. (2009). Understanding and scientific explanation. In H. W. de Regt, S. Leonelli, & K. Eigner (Eds.), Scientific understanding: Philosophical perspectives (pp. 21–42). Pittsburgh: University of Pittsburgh Press.

    Google Scholar 

  • de Regt, H. W. (2017). Understanding scientific understanding. New York: Oxford University Press.

    Google Scholar 

  • de Regt, H. W., & Dieks, D. (2005). A contextual approach to scientific understanding. Synthese, 144(1), 137–170.

    Google Scholar 

  • de Regt, H. W., & Gijbergs, V. (2017). How false theories can yield genuine understanding. In S. Grimm, C. Baumberger, & S. Ammon (Eds.), Explaining understanding: New perspectives from epistemology and philosophy of science (pp. 50–57). Pittsburgh: University of Pittsburgh Press.

    Google Scholar 

  • de Regt, H. W., Leonelli, S., & Eigner, K. (Eds.). (2009). Scientific understanding: Philosophical perspectives. Pittsburgh: University of Pittsburgh Press.

    Google Scholar 

  • Ebert, E. S. (1994). The cognitive spiral: Creative thinking and cognitive processing. Journal of Creative Behavior, 28(4), 275–290.

    Google Scholar 

  • Filotas, E., Parrott, L., Burton, P. J., Chazdon, R. L., Coates, K. D., Coll, L., et al. (2014). Viewing forests through the lens of complexity systems science. Ecosphere, 5(1), 1–23.

    Google Scholar 

  • Giere, R. N. (2004). How models are used to represent reality. Philosophy of Science, 71(5), 742–752.

    Google Scholar 

  • Gigerenzer, G. (2007). Gut feelings: The intelligence of the unconscious. London: Penguin Books.

    Google Scholar 

  • Gijsbers, V. (2013). Understanding, explanation, and unification. Studies in History and Philosophy of Science Part A, 44(3), 516–522.

    Google Scholar 

  • Gopnik, A. (1988). Explanation as orgasm. Minds and Machines, 8, 101–118.

    Google Scholar 

  • Grimm, S. R. (2006). Is understanding a species of knowledge? The British Journal for the Philosophy of Science, 57(3), 515–535.

    Google Scholar 

  • Gupta, S., & Bhatia, P. K. (2012). Cognitive spiral model: A framework approach. International Journal of Advances in Engineering, Science and Technology (IJAEST), 1(2), 194–199.

    Google Scholar 

  • Hawley, K. (2003). Success and knowledge-how. American Philosophical Quarterly, 40(1), 19–31.

    Google Scholar 

  • Hawthorn, G. (1991). Plausible worlds: Possibility and understanding in history and the social sciences. Cambridge: Cambridge University Press.

    Google Scholar 

  • Johnson-Laird, P. N. (1980). Mental models in cognitive science. Cognitive Science, 4(1), 71–115.

    Google Scholar 

  • Johson-Laird, P. N. (1983). Mental models. Cambridge: Harvard University Press.

    Google Scholar 

  • Kahneman, D. (2011). Thinking fast and slow. London: Allen Lane.

    Google Scholar 

  • Kelp, C. (2015). Understanding phenomena. Synthese, 192(12), 3799–3816.

    Google Scholar 

  • Khalifa, K., & Gadomski, M. (2013). Understanding as explanatory knowledge: The case of Bjorken scaling. Studies in History and Philosophy of Science Part A, 44(3), 384–392.

    Google Scholar 

  • Klein, A.-M., Vaissière, B. E., Cane, J. H., Steffan-Dewenter, I., Cunningham, S. A., Kremen, C., et al. (2007). Importance of pollinators in changing landscapes for world crops. Proceedings of the Royal Society, 274, 303–313.

    Google Scholar 

  • Knuuttila, T., & Loettgers, A. (2016). Contrasting cases: The Lotka–Volterra model times three. In T. Sauer & R. Scholl (Eds.), The philosophy of historical case studies (pp. 151–178). Basel: Springer.

    Google Scholar 

  • Knuuttila, T., & Merts, M. (2009). Understanding by modeling: An objectual approach. In H. W. De Regt, S. Leonelli, & K. Eigner (Eds.), Scientific understanding: Philosophical perspectives (pp. 146–168). Pittsburgh: University of Pittsburgh Press.

    Google Scholar 

  • Kvanvig, J. (2003). The value of knowledge and the pursuit of understanding. New York: Oxford University Press.

    Google Scholar 

  • Leonelli, S. (2009). Understanding in biology: The impure nature of biological knowledge. In H. W. De Regt, S. Leonelli, & K. Eigner (Eds.), Scientific understanding: Philosophical perspectives (pp. 189–209). Pittsburgh: University of Pittsburgh Press.

    Google Scholar 

  • Levin, S. A. (1992). The problem of pattern and scale in ecology: The Robert H. MacArthur award lecture. Ecology, 73, 1943–1967.

    Google Scholar 

  • Lipton, P. (2009). Understanding without explanation. In H. W. De Regt, S. Leonelli, & K. Eigner (Eds.), Scientific understanding: Philosophical perspectives (pp. 43–63). Pittsburgh: University of Pittsburgh Press.

    Google Scholar 

  • Machamer, P., Darden, L., & Craver, F. C. (2000). Thinking about mechanisms. Philosophy of Science, 67, 1–25.

    Google Scholar 

  • Mitchell, M. (2009). Complexity: A guided tour. New York, NY: Oxford University Press.

    Google Scholar 

  • Mizrahi, M. (2020). The case study method in philosophy of science: An empirical study. Perspectives on Science, 28(1), 63–88.

    Google Scholar 

  • Morgan, M. S., & Morrison, M. (1999). Models as mediators: Perspectives, on natural and social science. Cambridge: Cambridge University Press.

    Google Scholar 

  • Mouquet, L., Lagadeus, Y., Devictor, V., Doyen, L., Duputié, A., Eveillard, D., et al. (2015). Predictive ecology in a changing world. Journal of Applied Ecology, 52, 1293–1310.

    Google Scholar 

  • Poliseli, L. (2018). When ecology and philosophy meet: Constructing explanations and assessing understanding in scientific practice. Originally presented as doctorate dissertation, Universidade Federal da Bahia, Salvador, BA.

  • Poliseli, L. (2020). Book review [Resenha]: de Regt, H. Understanding scientific understanding, OUP, 2017. Principia: An International of Epistemology, 24(1), 239–245.

    Google Scholar 

  • Poliseli, L., & El-Hani, C. N. (2020). Imagination in science. In L. Tateo (Ed.), A theory of imagining, knowing and understanding (pp. 65–84). Cham: Springer.

    Google Scholar 

  • Pritchard, D. (2009). Knowledge, understanding and epistemic value. Royal Institute of Philosophy Supplement, 64, 19–43.

    Google Scholar 

  • Pritchard, D. (2014). Knowledge and understanding. In A. Fairweather (Ed.), Virtue epistemology naturalized (Vol. 366)., Synthese library. Studies in epistemology, logic, methodology, and philosophy of science Berlin: Springer.

    Google Scholar 

  • Schleuning, M., Fründ, J., & García, D. (2015). Predicting ecosystems functions from biodiversity and mutualistic networks: An extension of trait-based concepts to plant–animal interactions. Ecography (Cop.), 38, 380–392.

    Google Scholar 

  • Sliwa, P. (2015). Understanding and knowing. Proceedings of the Aristotelian Society, 115, 57–74.

    Google Scholar 

  • Solé, R. V., & Goodwin, B. (2000). Signs of life: How complexity pervades biology. New York, NY: Basic Books.

    Google Scholar 

  • Tateo, L. (2016). What imagination can teach us about higher mental functions. In J. Valsiner, G. Marsico, N. Chaudhary, T. Sato, & V. Dazzani (Eds.), Psychology as the science of human being: The Yokohama manifesto (pp. 149–164). Cham: Springer.

    Google Scholar 

  • Tateo, L. (2020). A theory of imagining, knowing and understanding. Cham: Springer.

    Google Scholar 

  • Thagard, P. (2010). How brains make mental models. In L. Magnani, W. Carnieli, & C. Pizzi (Eds.), Model-based reasoning in science and technology: Abduction, logic and computational discovery (pp. 447–461). Berlin: Springer.

    Google Scholar 

  • Waskan, J. A. (2006). Models and cognition: Prediction and explanation in everyday life and in science. Cambridge: The MIT Press.

    Google Scholar 

  • Williamson, T. (2002). Knowledge and its limits. Oxford: Oxford University Press.

    Google Scholar 

  • Woodward, J. (2003). Making things happen: A theory of causal explanation. Oxford: Oxford University Press.

    Google Scholar 

  • Ylikoski, P. K. (2009). The illusion of depth of understanding in science. In H. D. Regt, S. Leonelli, & K. Eigner (Eds.), Scientific understanding: Philosophical perspectives (pp. 100–119). Pittsburgh: University of Pittsburgh Press.

    Google Scholar 

Download references

Acknowledgements

Thanks to Charbel Niño El-Hani, Federica Russo, David Ludwig, and Henk De Regt for helpful comments on earlier versions of this article. I would like to thank both anonymous reviewers, and the editor, Sabina Leonelli, for the suggestions to improve this article. I am also much obliged to Jeferson Coutinho’s precious collaboration. This work was supported by the Brazilian Coordination for the Improvement of Higher Educational Personnel (CAPES—Finance Code 001) and Programa de Doutorado Sanduíche no Exterior (PDSE—Grant Number 88881.123457/2016-01). I am grateful for the National Institute of Science and Technology, Inter- and Transdisciplinary Studies in Ecology and Evolution (INCT IN-TREE) for the support at an earlier version of this article presented at the “Workshop on Scientific Explanation and Understanding”, 2018, at the University of Ghent. I also thanks CNPQ (Grant Number 465767/2014-1) and CAPES (Grant Number 23038.000776/2017-54) for their support of INCT/IN-TREE.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Luana Poliseli.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Poliseli, L. Emergence of scientific understanding in real-time ecological research practice. HPLS 42, 51 (2020). https://doi.org/10.1007/s40656-020-00338-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40656-020-00338-7

Keywords

Navigation