Abstract
In this paper we argue that the debate between representational and anti-representational cognitive theories cannot be reduced to a difference between the types of model respectively employed. We show that, on the one side, models standardly used in representational theories, such as computational ones, can be analyzed in the context of dynamical systems theory and, on the other, non-representational theories such as Gibson’s ecological psychology can be formalized with the use of computational models. Given these considerations, we propose that the true conceptual difference between representational and anti-representational cognitive descriptions should be characterized in terms of style of explanation, which indicates the particular stance taken by a theory with respect to its explanatory target.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
An example is a dynamical system DS in which any state transition moves the system to a fixed point. Let the time set T of DS be the set of reals, and its state space M be any finite or countably infinite set. Let \(y \in M\) be a fixed point, that is to say, a state y such that, for any t, \(g^{t}(y) = y\). The state transitions of DS are then defined as follows. Let \(y \in M\); for any \(t \ne 0\), for any state \(x \in M\), \(g^{t}(x) = y\); \(g^{0}\) is the identity function on M.
- 3.
See, e.g., Fodor (1980).
- 4.
The main components of a TM are the following:
-
1.
a finite automaton (Minsky 1967; Wells 2005) consisting of
-
a simple input-output device that implements a specific set of instructions (machine table);
-
an internal memory that holds only one discrete element at each step (internal state) and
-
an internal device (read/write head) that can scan and change the content of the internal memory.
-
-
2.
An external memory consisting of a tape divided into squares, potentially extendible in both directions ad infinitum;
-
3.
an external device (read/write/move head) that scans the content of a cell at a time and allows the finite automaton to work on the memory tape.
-
1.
- 5.
This view is also consistent with Wilson’s proposal of a wide computationalism (Wilson 1994).
- 6.
The authors define the concept of duality as follows: “[A] duality relation between two structures X and Z is specified by any symmetrical rule, operation, transformation or ‘mapping’, T, where T applies to map X onto Z and Z onto X: that is, where \(T(X) \rightarrow Z\) and \(T(Z) \rightarrow X\) such that for any relation \(r_{1}\) in X, there exist some relation \(r_{2}\) in Z such that \(T: r_{1} \rightarrow r_{2}\) and \(T: r_{2} \rightarrow r_{1}\); hence, \(X R Z = Z R X\) under transformation T” (Shaw and Turvey 1981, p. 381). They also highlight the importance of this concept in logic, mathematics and geometry, showing as examples dualities in logic between DeMorgan laws, theorems in point and line geometries, open and closed sets in topology, etc. (Shaw and Turvey 1981, pp. 382–384).
- 7.
In situation theory a constraint is defined as a “dependency relation between situation types” (Greeno 1994, p. 338).
References
Beer R (1998) Framing the debate between computational and dynamical approaches to cognitive science. Behav Brain Sci 21:630
Bremner JG (1978) Egocentric versus allocentric spatial coding in nine-month-old infants: factors influencing the choice of code. Dev Psychol 14(4):346
Diamond A (1998) Understanding the a-not-b error: working memory vs. reinforced response, or active trace vs. latent trace. Dev Sci 1(2):185–189
Fodor JA (1980) Methodological solipsism considered as a research strategy in cognitive psychology. Behav Brain Sci 3(01):63–73
van Gelder T (1995) What might cognition be, if not computation? J Philos 92(7):345–381
van Gelder T (1998) The dynamical hypothesis in cognitive science. Behav Brain Sci 21:615–665
Gibson J (1966) The senses considered as perceptual systems. Houghton Mifflin, Boston
Gibson J (1977) The theory of affordances. In: Shaw R, Bransfordk J (eds) Perceiving, acting, and knowing, toward an ecological psychology. Lawrence Erlbaum Associates, Hillsdale
Gibson J (1979) The ecological approach to visual perception. Houghton Mifflin, Boston
Giunti M (1995) Dynamical models of cognition. In: Port R, van Gelder T (eds) Mind as motion. The MIT Press, Cambridge, pp 71–75
Giunti M, Pinna S (2016) For a dynamical approach to human computation. Logic J IGPL 24(4):557–569
Greeno J (1994) Gibson’s affordances. Psychol Rev 101:336–342
Kelso JAS (1995) Dynamic patterns: the self-organization of brain and behavior. The MIT Press, Cambridge
Minsky ML (1967) Computation, finite and infinite machines. Prentice-Hall, Englewood Cliffs
Munakata Y (1998) Infant perseveration and implications for object permanence theories: a PDP model of the AB task. Dev Sci 1(2):161–184
Piaget J (1952) The origins of intelligence in children. International Universities Press, New York
Pinna S (2017) Extended cognition and the dynamics of algorithmic skills. Springer, Cham
Port R, van Gelder T (eds) (1995) Mind as motion. The MIT Press, Cambridge
Shaw R, Turvey M (1981) Coalitions as models for ecosystems: a realist perspective on perceptual organization. In: Kubovy M, Pomerantz J (eds) Perceptual organization. Lawrence Erlbaum Associates, Hillsdale
Smith L, Thelen E (eds) (1993) A dynamic systems approach to development. MIT Press, Cambridge
Smith L, Thelen E, Titzer R, McLin D (1999) Knowing in the context of acting: the task dynamics of the A-not-B error. Psychol Rev 106:235–260
Smith LB, Thelen E (2003) Development as a dynamic system. Trends Cogn Sci 7(8):343–348
Thelen E, Smith L (eds) (1994) A dynamic systems approach to the development of cognition and action. MIT Press, Cambridge
Tschacher W, Dauwalder J (eds) (2003) The dynamical systems approach to cognition. World Scientific, Singapore
Turvey M (1992) Affordances and prospective control: an outline of the ontology. Ecol Psychol 4:173–187
van Gelder T (1999) Dynamic approaches to cognition. In: Wilson RA, Keil FC (eds) The MIT encyclopedia of the cognitive sciences. MIT Press, Cambridge, pp 244–246
van Gelder T, Port R (1995) It’s about time: an overview of the dynamical approach to cognition. In: Port R, van Gelder T (eds) Mind as motion. MIT Press, Cambridge
Wells A (1998) Turing’s analysis of computation and theories of cognitive architecture. Cogn Sci 22:269–294
Wells A (2002) Gibson’s affordances and Turing’s theory of computation. Ecol Psychol 14:140–180
Wells A (2005) Rethinking cognitive computation: Turing and the science of the mind. Palgrave Macmillan, Basingstoke
Wilson RA (1994) Wide computationalism. Mind 103(411):351–372
Acknowledgements
This work is supported by Fondazione di Sardegna and Regione Autonoma della Sardegna, research project “Science and its Logics: the Representation’s Dilemma,” Cagliari, CUP F72F16003220002.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Pinna, S., Giunti, M. (2019). Model Types and Explanatory Styles in Cognitive Theories. In: Nepomuceno-Fernández, Á., Magnani, L., Salguero-Lamillar, F., Barés-Gómez, C., Fontaine, M. (eds) Model-Based Reasoning in Science and Technology. MBR 2018. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-030-32722-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-32722-4_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32721-7
Online ISBN: 978-3-030-32722-4
eBook Packages: Religion and PhilosophyPhilosophy and Religion (R0)