Who can satisfy themselves with mere cognition through experience in all the cosmological questions, of the duration and size of the world, of freedom or natural necessity, since, wherever we may begin, any answer given according to principles of experience always begets a new question which also requires an answer, and for that reason clearly proves the insufficiency of all physical modes of explanation for the satisfaction of reason?
Immanuel Kant. Prolegomena to Any Future Metaphysics. Cambridge University Press, 1997/2004 (p. 103).
Abstract
In this paper, we aim at explaining the relevance of thought experiments (TEs) in philosophy and the history of science by describing them as particular instances of two categories of creative thinking: metaphorical reasoning and abductive cognition. As a result of this definition, we will claim that TEs hold an ignorance-preserving trait that is evidenced in both TEs inferential structure and in the process of scenario creation they presuppose. Elaborating this thesis will allow us to explain the wonder that philosophers of science have consistently shown for TEs, as well as the high functionality of TEs in the creative aspects of scientific and philosophical praxis.
Similar content being viewed by others
Notes
On the history of metaphors, see cf. Ortony (1993).
Of course, the terms that canalize the metaphorical reasoning are not random, but the authors of the metaphors choose them because they structurally fit in the domains connection. The functional adequacy of the metaphorical terms will be further discussed in the next subsection where we will analyze the construction of TEs scenario as a metaphorical construction.
Structure-mapping mechanisms are present in the so-called analogical metaphors, that share the relational and inferential structure of analogical reasoning. As already reported by Gentner (1982), analogical relationships do not support all metaphorical reasonings: metaphors can range from mere relational to attributional comparisons and even elude the definition of domains relationship in terms of alignment. In this article we focus on conceptual analogical metaphors because we claim that this particular type of metaphorical reasoning is at the core of scenario creation of TEs and can shed some lights on their specific functionality.
Also Brown (1991) highlighted the reframing power of TEs.
To be clear on this point, we refer to the form of abduction as fallacious just because we discuss its value within a classical deductive framework. That, of course, goes without saying that deduction does not exhaust the range of legitimate inferences.
\(K^*\) is an accessible successor of K to the degree that an agent has the know-how to construct it in a timely way; i.e., in ways that are of service in the attainment of targets linked to K. For example, I want to know how to spell ‘accommodate’, and have forgotten, then my target can’t be hit on the basis of K, what I now know. But I might go to my study and consult the dictionary. This is \(K^*\). It solves a problem originally linked to K.
The description of TEs as normally more comprehensible than the theories they explain or describe still makes a compelling case when considering the “illusion of depth of understanding or IDU” discussed by Ylikosky (2009), which strongly differentiates the sense of understanding from the actual state of understanding. TEs can actually improve the state of understanding in the readers of the theories they refer to because, even if TEs are created through an abductive reasoning, they explain or describe their argument adopting deductive schemas (Arfini 2016). With this overturning, TEs become efficient means to explain more clearly the theories or phenomena they are based upon, with respect to linear argumentations of the theories from which they emerge. As argued by Ylikosky (2009, p. 16) “Explanations are not deductive arguments, but they can be reconstructed as such. The idea is that an (even partial) attempt at deductive reconstruction leads to improvements in the process of articulating explanations by forcing one to explicate both many of the background assumptions and the intended explanandum.” Thus, the point of the publication and discussion of TEs alongside of the relative theories is not only to focus on the particular framework the authors aim at presenting, but also to ease the comprehension for the outsiders of this point of view.
Representing TEs as models that can help us face our ignorance without actually resolve it, does not merely mean that we help ourselves to thought experiments when we do not, for lack of complete knowledge, know better. TEs are conceptual tools that let the agents access levels of understanding that are not available through linear argumentation (that are “tacit”, we could say, borrowing Polanyi’s terminology) and need a visual/narrative scenario to become apparent. The abductive reasoning that forms the inferential pattern of this knowledge-attainment plays an active role in determining both the explanatory force of the designated solution and the ignorance that is preserved in the inferential performance.
References
Achinstein, P. (1964). Models, analogies, and theories. Philosophy of Science, 31(4), 328–350.
Aliseda, A. (2005). The logic of abduction in the light of Peirce’s pragmatism. Semiotica, 1/4(153):363–374.
Arfini, S. (2016). Thought experiments as model-based abductions. In L. Magnani & C. Casadio (Eds.), Model-based reasoning in science and technology. Logical, epistemological, and cognitive issues. Berlin: Springer.
Bishop, M. (1999). Why thought experiments are not arguments. Philosophy of Science, 66(4), 534–541.
Black, M. (1955). Metaphor. Proceedings of the Aristotelian Society, 55, 273–294.
Black, M. (1962). Models and metaphors. Studies in language and philosophy. Ithaca: Cornell University Press.
Boyd, R. (1979). Metaphors and theory change. What is “metaphor” a metaphor for? In A. Ortony (Ed.), Metaphors and thought. Cambridge: Cambridge University Press.
Brown, J. R. (1991). The laboratory of the mind: Thought experiments in the natural sciences. London: Routledge.
Brown, T. (2003). Making truth metaphors in science. Urbana: The Board of Trustees of the University of Illinois.
Cartwright, N. (1983). How the laws of physics lie. Oxford: Oxford University Press.
Casadio, C. (2009). Effetto “framing”: come inquadriamo il mondo con le metafore. Paradigmi, 1, 55–68.
Craik, K. (1943). The nature of explanations. Cambridge: Cambridge University Press.
D’Angelosante, V., Tommasi, M., Casadio, C., & Verrotti, A. (2015). Seizure metaphors in children with epilepsy: A study based on a multiple-choice self-report questionnaire. Epilepsy and Behavior, 46, 167–172.
Davidson, D. (1978). What metaphors mean. Critical Inquiry, 5(1), 31–47.
Falkenhainer, B., Forbus, K. D., & Gentner, D. (1989). The structure-mapping engine: Algorithm and examples. Artificial Intelligence, 41, 1–63.
Feldman, J., & Narayanan, S. (2003). Embodied meaning in a neural theory of language. Brain and Language, 89, 385–392.
Firestein, S. (2012). Ignorance: How it drives science. Oxford: Oxford University Press.
Gabbay, D. M., & Woods, J. (2005). The reach of abduction: Insight and trial, Volume 1 of A practical logic of cognitive systems. Amsterdam: Elsevier.
Gendler, T. S. (1998). Galileo and the indispensability of scientific thought experiment. British Journal for the Philosophy of Science, 49(3), 397–424.
Gendler, T. S. (2000). Thought Experiment: On the powers and limits of imaginary cases. New York: Garland Press.
Gentner, D. (1982). Are scientific analogies metaphors? In D. S. Miall (Ed.), Metaphor: Problems and perspectives (pp. 106–132). Brighton: Harvester.
Gentner, D., & Bowdle, B. (2008). Metaphor as structure-mapping. In R. W. Gibbs (Ed.), The Cambridge handbook of metaphor and thought (pp. 109–128). Cambridge: Cambridge University Press.
Gentner, D., & Markman, A. B. (1997). Structure mapping in analogy and similarity. American Psychologist, 52, 45–56.
Gola, E. & Ervas, F. (2016). Metaphors we live twice: A communicative approach beyond the conceptual view? In E. G & F. E (Ed.), Metaphor and communication. Amsterdam: John Benjamins
Kovecses, A. (2002). Metaphor: A practical Introduction. Oxford: Oxford University Press.
Kuhn, T. (1979). Metaphors in science. In A. Ortony (Ed.), Metaphors and Thought. Cambridge, UK: Cambridge University Press.
Lakoff, G. (2004). Don’t think of an elephant! Know your values and frame the debate. Chelsa: Green Publishing.
Lakoff, G., & Johnson, M. (1981). Metaphors we live by. Chicago: University Chicago Press.
Madsen, M. W. (2016). Cognitive metaphor theory and the metaphysics of immediacy. Cognitive Science, 40, 881–908.
Magnani, L. (2009). Abductive Cognition. The epistemological and eco-cognitive dimensions of hypothetical reasoning. Berlin: Springer.
Magnani, L. (2013). Is abduction ignorance-preserving? Conventions, models, and fictions in science. Logic Journal of IGPL, 21, 882–914.
Magnani, L. (2015). The eco-cognitive model of abduction: Ἀπαγωγή now: Naturalizing the logic of abduction. Journal of Applied Logic, 13, 285–315.
Markman, A. B., & Gentner, D. (1993). Structural alignment during similarity comparisons. Cognitive Psychology, 25, 431–467.
Nersessian, N. (1992). In the theoretician’s laboratory: Thought experimenting as mental modeling. PSA, 2, 291–301.
Norton, J. D. (2004). On thought experiments: Is there more to the argument? Philosophy of Science, 71, 1391151.
Ortony, A. (1993). Metaphor and thought. Cambridge: Cambridge University Press.
Peirce, C. S. (1931–1958). Collected papers of Charles Sanders Peirce. Cambridge, MA: Harvard University Press. Vols. 1–6, C. Hartshorne & P. Weiss (Eds.); Vols. 7–8, Burks, A. W. (Ed.).
Peirce, C. S. (1992–1998). The Essential Peirce. Selected philosophical writings. Bloomington and Indianapolis: Indiana University Press. Vol. 1 (1867–1893), N. Houser & C. Kloesel (Eds.); Vol. 2 (1893–1913) The Peirce Edition Project (Ed.).
Schrödinger, E. (1983). The present situation in quantum mechanics. In J. Wheeler & W. Zurek (Eds.), Quantum Theory and Measurement, page Part I. Translated by J. D. Trimmer: Princeton University Press, New Jersey.
Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59, 433–460.
Wittgenstein, L. (1958). Philosophical investigations (G. E. M. Anscombe, Trans.). Oxford: Basil Blackwell Ltd.
Woods, J. (2009). Ignorance, inference and proof: Abductive logic meets the criminal law. In G. Tuzet & D. Canale (Eds.), The rules of inference: Inferentialism in law and philosophy. Utrecht: Egea.
Woods, J. (2013). Errors of reasoning naturalizing the logic of inference (Vol. 45). London: College Publications.
Ylikosky, P. (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). Pittsburg: University of Pittsburg Press.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Arfini, S., Casadio, C. & Magnani, L. Ignorance-Preserving Mental Models Thought Experiments as Abductive Metaphors. Found Sci 24, 391–409 (2019). https://doi.org/10.1007/s10699-018-9564-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10699-018-9564-0