Skip to main content
SpringerLink
Log in

Theories and Models: Realism and Objectivity in Cognitive Science

  • Chapter
  • First Online:
Varieties of Scientific Realism

Abstract

Scientific realism is often analyzed in the context of natural sciences theory. How does it behave in cognitive science theories? Some philosophers of science have proposed a pragmatic approach to the concept of scientific theory where models form an essential part of its construction. They play a role of mediation. Three distinct classes of models play such a role in cognitive science: (a) formal models, (b) physical models, and (c) conceptual models. Each of these classes challenges the realist thesis in specific ways.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Many of the ideas were quite present with Duhem (1906) and Bachelard (1979).

  2. 2.

    “If in an investigation explicit reference is made to the speaker, or, to put it in more general terms, to the user of the language, then we assign it to the field of pragmatics. (Whether in this case reference to designata is made or not makes no difference for this classification.) If we abstract from the user of the language and analyze only the expressions and their designata, we are in the field of semantics. And if, finally, we abstract from the designata and analyze only the relations between the expressions, we are in (logical) syntax. The whole science of language, consisting of the three parts mentioned, is called semiotic” (Carnap 1937, 3–5, 16).

  3. 3.

    Davis and many others have proven that computational machines that are parallel exist. And computational dynamicity will depend on the nature of the equation itself.

  4. 4.

    There are significant formal problems with this position, for not all causal explanations allow predictions. For instance, a chaotic explanation is not necessarily predictive and all predictive explanations are not necessarily causal. Correlation is predictive, but not necessarily causal.

  5. 5.

    This could be reformulated in set-theoretical terms and constitute a formal Tarskian model.

  6. 6.

    The devil’s details for the realist and objectivist thesis applied to the physical models hide under the hood of the words phenomenon and features of the phenomenon.

References

  • Armstrong, D. M., (1968). A Materialist theory of Mind. Routledge and Kegan Paul, London.

    Google Scholar 

  • Bachelard, R., (1979). Quelques aspects historiques des notions de modèle et de justification des modèles. In P. Delattre & M. Thellier, (Éd.), Elaboration et justification des modèles. Paris: Maloine.

    Google Scholar 

  • Baetu, T. M., (2013). When Is a Mechanistic Explanation Satisfactory? Reductionism and Antireductionism in the Context of Mechanistic Explanations. Romanian Studies in Philosophy of Science. Volume 313 of the series Boston Studies in the Philosophy and History of Science, 255–268.

    Google Scholar 

  • Bechtel, W., (1998). Representations and Cognitive Explanations: Assessing the Dynamicist’s Challenge in Cognitive Science, Cognitive Science, 22, (3), pp. 295–318.

    Google Scholar 

  • Bechtel, W., (2008). Mental Mechanisms: Philosophical Perspectives on Cognitive Neuroscience, London: Routledge.

    Google Scholar 

  • Brachman, R., Levesque, H., eds., (1985). Readings in Knowledge Representation, Los Altos: Morgan Kaufmann.

    Google Scholar 

  • Braillard, P., Malaterre, C., (2015). Explanation in Biology, Springer.

    Google Scholar 

  • Brandom, R. B., (1994). Making It Explicit: Reasoning, Representing, and Discursive Commitment, Cambridge, MA, Harvard University Press.

    Google Scholar 

  • Brooks, R. A., (1991). Intelligence without representation. In Haugeland, J., (ed.) Mind Design II, Routledge, (1997), pp. 395–420.

    Google Scholar 

  • Brown, H. L., (2007). Conceptual Systems, Routledge Studies in the Philosophy of Science, Routledge.

    Google Scholar 

  • Burch, T. K., (1999). Computer Modelling of Theory: Explanation for the 21st Century. PSC Discussion Papers Series 3-1.

    Google Scholar 

  • Carnap, R., (1937). The Logical Syntax of Language, London: Kegan Paul, Trench, Trübner & Co.

    Google Scholar 

  • Cartwright, N., Shomar, T., Suárez, M., (1995). “The Tool Box of Science: Tools for the Building of Models with a Superconductivity Example,” in Theories and Models in Scientific Processes, (Poznan Studies in the Philosophy of the Sciences and the Humanities, Volume 44), Amsterdam: Rodopi, pp. 137–149.

    Google Scholar 

  • Cartwright, N., (1983). How the Laws of Physics Lie. Oxford University Press.

    Google Scholar 

  • Chaitin G. J., Doria, F. A., da Costa N. C. A., (2012). Gödel’s Way: Exploits Into an Undecidable World, Boca Raton, CRC Press.

    Google Scholar 

  • Chang, H., (2011). “The Philosophical Grammar of Scientific Practice” in International Studies in the Philosophy of Science, 25, (3): 205–221.

    Google Scholar 

  • Chomsky, N., (1957). Syntactic Structures. The Hague: Mouton.

    Google Scholar 

  • Churchland, P. M., (1988). Matter and Consciousness. Cambridge, (Mass.): MIT Press.

    Google Scholar 

  • Churchland, P. S. & Sejnowski, T. J., (1992). The Computational Brain, The MIT Press.

    Google Scholar 

  • Copeland, B. J., (2000). “Narrow Versus Wide Mechanism Including a re-examination of Turing’s views on the mind-machine issue,” Journal of Philosophy 96, pp. 5–32.

    Google Scholar 

  • Craver, C. F., (2006). When mechanistic models explain, Synthese, 153(3), pp. 355–376.

    Google Scholar 

  • Curry, H. B., Feys, R., (1958). Combinatory logic, Vol.1, Amsterdam, North-Holland.

    Google Scholar 

  • Davis M., (1982). Computability and Unsolvability, Dover.

    Google Scholar 

  • Duhem, P., (1906). La théorie physique: Son objet et sa structure, Paris: Chevalier et Rivière ; transl. by P.W. Wiener, The Aim and Structure of Physical Theory, Princeton, NJ: Princeton University Press (1954).

    Google Scholar 

  • Dennett, D., (1978) Brains Storms. Cambridge: MIT Press.

    Google Scholar 

  • Desclés, J. P., & Cheong, K. S., (2006). Analyse critique de la notion de variable, Math. & Sci. hum. ~ Mathematics and Social Sciences, 44e année, n 173, 2006, (1), p. 43–.

    Google Scholar 

  • Eliasmith, C., Anderson, C. H., (2003). Neural engineering: Representation, Computation, and Dynamics in Neurobiological systems. Cambridge, Mass: MIT Press.

    Google Scholar 

  • Evans, V., (2009). How words means Lexical concepts, cognitive models, and meaning construction, Oxford University Press.

    Google Scholar 

  • Feyerabend. P., (1975) Against Method: Outline of an Anarchistic Theory of Knowledge, Verso Books, New York.

    Google Scholar 

  • Fodor J. A., Pylyshyn Z. W., (1988). Connectionism and Cognitive Architecture: A Critical Analysis. Cognition. Mar; 28 (1–2):3–71.

    Google Scholar 

  • Fodor J. A., (2008). LOT2: The Language of Thought Revisited, Oxford, Oxford University Press.

    Google Scholar 

  • Fodor, J. A., (1981). Representations. Cambridge: MIT Press.

    Google Scholar 

  • Fodor, J. A., (1983). Modularity of Mind: An essay on faculty psychology Cambridge: MIT Press.

    Google Scholar 

  • Frigg, R., Hartmann, S., (2012). “Models in Science”, The Stanford Encyclopedia of Philosophy, (Fall 2012 Edition), Edward N. Zalta, (ed.), URL = <http://plato.Stanford.edu/archives/fall2012/entries/models-science/>.

  • Gardenfors, P., (2000). Conceptual Spaces. Cambridge, (Mass.): MIT Press.

    Google Scholar 

  • Gelfert, A., (2011). Mathematical formalisms in scientific practice: From denotation to model-based representation, History and Philosophy of Science, 42(2), pp. 272–286.

    Google Scholar 

  • Genesereth, M., Nilsson, N. J., (1987). Logical Foundations of Artificial Intelligence, San Mateo, California: Morgan Kaufmann.

    Google Scholar 

  • Gentner, D., (1983). Structure mapping: A theoretical framework for analogy. Cognitive Science 7(2), pp. 155–170.

    Google Scholar 

  • Giere, R. N., (1988). Explaining Science: A Cognitive Approach, Chicago: University of Chicago Press.

    Google Scholar 

  • Giere, R. N., (1999). Using Models to Represent Reality, in L. Magnani, N. Nersessian and P. Thagard, (eds.), Model-Based Reasoning in Scientific Discovery. Plenum Publishers: New York, pp. 41–57.

    Google Scholar 

  • Givón, T. (Ed.), (1979). Syntax and Semantics 12: Discourse and Syntax, New York: Academic Press.

    Google Scholar 

  • Godfrey-Smith, P., (2009). Models and Fictions in Science. Philosophical Studies, 143(1), pp. 101–116.

    Google Scholar 

  • Graziano, M. S. A., (2013). Consciousness and the Social Brain, Oxford University Press.

    Google Scholar 

  • Green, S., (2013). When one model is not enough: Combining epistemic tools in systems biology. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 44(2), 170–180.

    Google Scholar 

  • Gruber, T. R., (1993). “A translation approach to portable ontology specifications”, (PDF), Knowledge Acquisition, 5(2): pp. 199–220.

    Google Scholar 

  • Harnad, S., (1994). Computation is Just Interpretable Symbol Manipulation; Cognition Isn’t, Minds and Machines, 4(4), pp. 379–390.

    Google Scholar 

  • Harnad, S., (2005). “To cognize is to categorize: Cognition is categorization”, Handbook of categorization in cognitive science, p. 20–45.

    Google Scholar 

  • Hartmann, S., (1995). “Models and Stories in Hadron Physics”, in Morgan and Morrison 1999, pp. 326–346.

    Google Scholar 

  • Hempel, C. G., (1965). Aspects of Scientific Explanation and other Essays in the Philosophy of Science. New York: Free Press.

    Google Scholar 

  • Hesse, M. B., (1963). Models and Analogies in Science. London: Sheed and Ward.

    Google Scholar 

  • Hohwy, J., (2014). The Predictive Mind, Oxford University, Press.

    Google Scholar 

  • Johnson-Laird, P. N., (1986). Mental models: Towards a cognitive science of language, inference, and consciousness, Harvard University Press.

    Google Scholar 

  • Knuuttila, T., (2011). Modelling and representing: An artefactual approach to model-based representation. Studies in History and Philosophy of Science, 42(2), pp. 262–271.

    Google Scholar 

  • Koppelman, E., (1971). The calculus of Operation and the Rise of abstract algebra. Arch. Hist. Exact Sci., 8(3), pp. 155–242.

    Google Scholar 

  • Langacker, R.W., (1987). Foundations of Cognitive Grammar. Stanford University Press.

    Google Scholar 

  • Lakoff, G., Johnson, M., (2003). Metaphors we Live by. The University of Chicago Press.

    Google Scholar 

  • Leonelli, S., (2007). What is in a Model? Combining Theoretical and Material Models to Develop Intelligible Theories. In Manfred Dietrich Laubichler, Gerd B. Mülle (eds.) Modeling Biology: Structures, Behaviors, Evolution, MIT Press.

    Google Scholar 

  • Leplin, J., (ed.), (1981). Scientific Realism. Berkeley: University of California Press.

    Google Scholar 

  • Levesque, H. J., Brachman, R. J., (1987), “Expressiveness and tractability in knowledge representation and reasoning”. Computational Intelligence, 3(1), pp. 78–93.

    Google Scholar 

  • Markov, A., (1960). The Theory of Algorithms. American Mathematical Society Translations, series 2, n 15, pp. 1–14.

    Google Scholar 

  • Marr, D., (1975). Computer vision, P. Winston, (Ed.), McGraw Hill.

    Google Scholar 

  • McClelland, J. L., (1998). Connectionist Models and Bayesian Inference. In Rational Models of Cognition, M. Oaksford & N. Chater (Eds.), Oxford UP.

    Google Scholar 

  • McDermott, D., Doyle, J., (1980). Non-monotonic logic I. Artificial intelligence, 13(1-2), pp. 41–72.

    Google Scholar 

  • Meunier J. G., (2013). Computers as Models of Computers and Vice Versa. Epistemologia, 2013:18–37.

    Google Scholar 

  • Michalski, R. S., (1983). A theory and methodology of inductive learning. Springer.

    Google Scholar 

  • Minsky, M., (1974). “A framework for representing knowledge”, Tech. Rep. 306, Artificial Intelligence Laboratory, MIT.

    Google Scholar 

  • Mitchell, T., (1997). Machine Learning, McGraw Hill.

    Google Scholar 

  • Morgan, M. S., & Morrison, M. (1999). Models as mediators: Perspectives on natural and social science (Vol. 52). Cambridge University Press.

    Google Scholar 

  • Morris, C. W., (1971). Writings on the General Theory of Signs. The Hague: Mouton.

    Google Scholar 

  • McCarthy, J., and Hayes, P. J., (1969). Some philosophical problems from the standpoint of artificial intelligence. In Meltzer, B., and Michie, D., eds., Machine Intelligence volume = 4. Edinburgh University Press. 463–502.

    Google Scholar 

  • Mundici, D., & Sieg, W., (1995). “Paper Machines”, Philosophia Mathematica, 3(1), pp. 5–30.

    Google Scholar 

  • Nagel, E., (1961). The Structure of Science: Problems in the Logic of Scientific Explanation, New York: Harcourt, Brace & World.

    Google Scholar 

  • Newell, A., Simon, H., (1976). Symbol Manipulation. In Ralston A., Meek C. L., (eds.), (1976), pp. 1384–1389.

    Google Scholar 

  • Newell, A., (1994). Unified Theories of Cognition. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Neumann J. V., (1945). First Draft of a Report on the EDVAC Published in IEEE Annals of the History of Computing, Volume 15 Issue 4, October 1993, pp 27–75.

    Google Scholar 

  • Nilsson, N. J., (2007). The Physical Symbol System Hypothesis: Status and Prospects in M. Lungarella et al., (Eds.): 50 Years of AI, Festschrift, LNAI 4, 85, 9–17.

    Google Scholar 

  • Papineau, D., (2010). “Realism, Ramsey Sentences and the Pessimistic Meta-Induction”, Studies in History and Philosophy of Science, 41(4), pp. 375–385.

    Google Scholar 

  • Pearl, J., (2000). Causality: Models, Reasoning, and Inference, Cambridge, England: Cambridge University Press, ISBN 0–521-77362-8.

    Google Scholar 

  • Peirce, C. S., (1931-58). Collected Papers C. Hortshorne, P. Weiss, and A. Burks, Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Penfield. W., (1959). “The interpretive cortex”, in: Science, 129(3365), pp. 1719–1725.

    Google Scholar 

  • Petitot J., (2009). Morphogéométrie de la vision, Paris, École Polytechnique.

    Google Scholar 

  • Piccinini, G., (2007). “Computing Mechanisms”, Philosophy of Science, 74(4), pp. 501–526.

    Google Scholar 

  • Polanyi, M., (1967). The Tacit Dimension, New York: Anchor Books.

    Google Scholar 

  • Post, E. L., (1936). Finite combinatory processes—formulation. The Journal of Symbolic Logic, 1(03), pp. 103–105.

    Google Scholar 

  • Pylyshyn Z. W., (1984). Computation and Cognition: Toward a Foundation for Cognitive Science. Cambridge, MA, MIT Press.

    Google Scholar 

  • Reichenbach, H., (1938). Experience and Prediction: An Analysis of the Foundations and the Structure of Knowledge, Chicago: The University of Chicago Press.

    Google Scholar 

  • Rheinberger, H. J., (1997). Toward a history of epistemic things: Synthesizing proteins in the test tube, Stanford: Stanford University Press.

    Google Scholar 

  • Rizzolatti, G., Fadiga, L., (1999). “Resonance Behaviors and Mirror Neurons”. Archives Italiennes de Biologie. 137: 85–100.

    Google Scholar 

  • Rumelhart, D. E. & J. L. McClelland, (Eds.), (1987). Parallel distributed processing: Explorations in the microstructure of cognition, Vol. 1, (Chap. 7, pp. 282–317), Cambridge, MA: MIT Press.

    Google Scholar 

  • Salmon, W. C., (1984). Scientific Explanation and the Causal Structure of the World, Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Scott, D. S., & Strachey, C., (1971). Toward a mathematical semantics for computer languages. Oxford University Computing Laboratory Programming Research Group-Library 8–11 Keble Road, Oxford OX, 3QD Oxford, (0865) 54, 141.

    Google Scholar 

  • Scriven, M., (1962). “Explanations, Predictions, and Laws”, in Scientific Explanation, Space, and Time (Minnesota Studies in the Philosophy of Science: Vol. 3), H. Feigl and G. Maxwell (eds), 170–230. Minneapolis: University of Minnesota Press.

    Google Scholar 

  • Sejnowski, T. J., & Churchland, P. S., (1992). The Computational Brain. Cambridge, Mass: MIT Press.

    Google Scholar 

  • Sellars, W., (1948). Concepts as Involving Laws and Inconceivable Without Them. Philosophy of Science, 15(4), pp. 287–315.

    Google Scholar 

  • Schiffrin, R. M., (2009). Perspectives on Modeling in Cognitive Science 2, Topics in Cognitive Science, 2(4), pp. 736–750.

    Google Scholar 

  • Smart, J. J. C., (1959). “Sensations and Brain Processes”, Philosophical Review, 68(2), pp. 141–56.

    Google Scholar 

  • Smolensky P., (1986). Information Processing in Dynamical Systems: Foundations of Harmony Theory. In Rumelhart D. E., McClelland J. L., (eds.), pp. 194–281.

    Google Scholar 

  • Sowa, J. F., (2000). Knowledge Representation: Logical, Philosophical, and Computational Foundations, Brooks Cole Publishing Co., Pacific Grove, CA.

    Google Scholar 

  • Suárez, M., (2003). “Scientific Fictions as Rules of Inference” In: Mauricio Suárez, (ed.): Fictions in Science. Philosophical Essays on Modelling and Idealisation, Routledge: London, pp. 158–178.

    Google Scholar 

  • Suppes, P., (1967). “What is a Scientific Theory?”, in S. Morgenbesser, (ed.), Philosophy of Science Today. New York: Basic Books, pp. 55–67.

    Google Scholar 

  • Thagard, P., (19xx). The Cognitive Science of Science: Explanation, Discovery, and Conceptual Change, MIT Press.

    Google Scholar 

  • Talmy L., (2000) Toward a Cognitive Semantics Cambridge, MA: MIT Press 2000.

    Google Scholar 

  • Turing A. M., (1936). On Computable Numbers, with an Application to the Entscheidungs problem, Proceedings of the London Mathematical Society, 42(2), pp. 230–265.

    Google Scholar 

  • Uttal, W. R., (2011). Mind and brain: A critical appraisal of cognitive neuroscience. Cambridge, MA: MIT Press.

    Google Scholar 

  • van Fraassen, B. C., (1970). “On the Extension of Beth’s Semantics of Physical Theories,” Philosophy of Science, 37(3), pp. 325–339.

    Google Scholar 

  • van Fraassen, B. C., (1989). Laws and Symmetry, New York: Oxford University Press.

    Google Scholar 

  • van Gelder, T., (1997). Dynamics of Cognition. In J. Haugeland (ed), Mind design II, MIT Press.

    Google Scholar 

  • Weisberg, M., (2015) Simulation and Similarity: Using Models to Understand the World Oxford: Oxford University Press.

    Google Scholar 

  • Winther, R. G., (2015). “The Structure of Scientific Theories”, The Stanford Encyclopedia of Philosophy, (Spring 2016 Edition), Edward N. Zalta, (ed.), URL = <http://plato.stanford.edu/archives/spr2016/entries/structure-scientific-theories>.

  • Woodhouse, R., (1803). The Principles of Analytical Calculation. Cambridge 1803: 212.

    Google Scholar 

  • Wright, C., (1993). Realism, Meaning and Truth, Oxford: Blackwell. Aaron Stump, Programming Language Foundations. Wiley, 2014.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Université du Québec à Montréal, Quebec, Canada

    Jean-Guy Meunier

Authors
  1. Jean-Guy Meunier
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jean-Guy Meunier .

Editor information

Editors and Affiliations

  1. Universidad Panamericana , Mexico City, Mexico

    Evandro Agazzi

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Meunier, JG. (2017). Theories and Models: Realism and Objectivity in Cognitive Science. In: Agazzi, E. (eds) Varieties of Scientific Realism. Springer, Cham. https://doi.org/10.1007/978-3-319-51608-0_18

Download citation

Publish with us

Policies and ethics

Search

Navigation