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Descending Marr's levels: Standard observers are no panacea

Published online by Cambridge University Press:  10 January 2019

Carlos Zednik
Affiliation:
Otto-von-Guericke-Universität Magdeburg, D-39016 Magdeburg, Germany. [email protected]://sites.google.com/site/czednik/
Frank Jäkel
Affiliation:
Technische Universität Darmstadt, Centre for Cognitive Science, D-64283 Darmstadt, Germany. [email protected]

Abstract

According to Marr, explanations of perceptual behavior should address multiple levels of analysis. Rahnev & Denison (R&D) are perhaps overly dismissive of optimality considerations at the computational level. Also, an exclusive reliance on standard observer models may cause neglect of many other plausible hypotheses at the algorithmic level. Therefore, as far as explanation goes, standard observer modeling is no panacea.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2018 

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References

Bechtel, W. (2009) Looking down, around, and up: Mechanistic explanation in psychology. Philosophical Psychology 22:543–64.Google Scholar
Bechtel, W. & Shagrir, O. (2015) The non-redundant contributions of Marr's three levels of analysis for explaining information-processing mechanisms. Topics in Cognitive Science 7(2):312–22.Google Scholar
Colombo, M. & Hartmann, S. (2017) Bayesian cognitive science, unification, and explanation. British Journal for the Philosophy of Science 68(2):451–84.Google Scholar
Craver, C. F. (2013) Functions and mechanisms: A perspectivalist view. In: Functions: Selection and mechanisms, ed. Huneman, P., pp. 133–58. Springer.Google Scholar
Danks, D. (2008) Rational analyses, instrumentalism, and implementations. In: The probabilistic mind: Prospects for rational models of cognition, ed. Chater, N. & Oaksford, M., pp. 5975. Oxford University Press.Google Scholar
Gigerenzer, G. (1991) From tools to theories: A heuristic of discovery in cognitive psychology. Psychological Review 98(2):254–67.Google Scholar
Jones, M. & Love, B. C. (2011) Bayesian fundamentalism or enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition. Behavioral and Brain Sciences 34(4):169–88. Available at: http://www.journals.cambridge.org/abstract_S0140525X10003134.Google Scholar
Marr, D. (1982) Vision: A computational investigation into the human representation and processing of visual information. W. H. Freeman.Google Scholar
Sanborn, A. N., Griffiths, T. L. & Navarro, D. J. (2010) Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review 4:1144–67.Google Scholar
Shagrir, O. (2010) Marr on computational-level theories. Philosophy of Science 77(4):477500.Google Scholar
Stüttgen, M. O., Schwarz, C. & Jäkel, F. (2011) Mapping spikes to sensations. Frontiers in Neuroscience 5:125.Google Scholar
Swets, J. A., Tanner, W. P. & Birdsall, T. G. (1961) Decision processes in perception. Psychological Review 68(5):301–40. Available at: http://www.ncbi.nlm.nih.gov/pubmed/13774292.Google Scholar
Zednik, C. (2017) Mechanisms in cognitive science. In: The Routledge handbook of mechanisms and mechanical philosophy, ed. Glennan, S. & Illari, P., pp. 389400. Routledge.Google Scholar
Zednik, C. & Jäkel, F. (2014) How does Bayesian reverse-engineering work? In: Proceedings of the 36th Annual Conference of the Cognitive Science Society, ed. Bello, P., Guarini, M., McShane, M. & Scassellati, B., pp. 666–71. Cognitive Science Society.Google Scholar
Zednik, C. & Jäkel, F. (2016) Bayesian reverse-engineering considered as a research strategy for cognitive science. Synthese 193:3951–85.Google Scholar