Abstract
Hoffman, Singh, and Prakash (2014) observe that perception evolves to serve as an interface between the perceiver and the world and proceed to reason that percepts need not, or even cannot, resemble their objects. I accept their premise, but argue that there are interesting ways in which perception can be truthful, with regard not to “objects” but to relations, and that evolutionary pressure is expected to favor rather than rule out such veridicality.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
A question that echoes the opening sentence of Francis Bacon’s essay On Truth (1601): “What is truth? said jesting Pilate, and would not stay for an answer.”
This qualifier and others like it below can be made precise with a bit of extra notation.
“Fortunate [is he] who was able to know the causes of things” (Georgics II:490).
References
Barlow, H.B. (1980). The absolute efficiency of perceptual decisions. Proceedings of the Royal Society of London B, 290, 71–82.
Clark, A. (1993). Sensory qualities. Oxford: Clarendon Press.
Cleeremans, A. (2008). Consciousness: The radical plasticity thesis. In R. Banerjee, & B. K. Chakrabarti (Eds.) Progress in Brain Research, Volume 168, Chapter 3, (pp. 19–33).
Cosmides, L., & Tooby, J. (1997). Evolutionary psychology: A primer. Available online at. http://www.psych.ucsb.edu/research/cep/primer.html.
De Pierris, G., & Friedman, M. (2013). Kant and Hume on causality In Zalta, E.N. (Ed.) The Stanford Encyclopedia of Philosophy. (Winter 2013 ed.)
Dobzhansky, T. (1973). Nothing in biology makes sense except in the light of evolution. The American Biology Teacher, 35, 125–129.
Edelman, S. (1998). Representation is representation of similarity. Behavioral and Brain Sciences, 21, 449–498.
Edelman, S. (1999). Representation and recognition in vision. Cambridge: MIT Press.
Edelman, S. (2008). Computing the mind: How the mind really works. New York: Oxford University Press.
Edelman, S. (2011). The metaphysics of embodiment. International Journal of Machine Consciousness, 3, 321–325. Part of a collective review of Embodiment and the Inner Life by M. Shanahan, Oxford University Press, 2010.
Edelman, S. (2014). Design for a brain? Submitted.
Edelman, S., & Shahbazi, R. (2012). Renewing the respect for similarity. Frontiers in Computational Neuroscience, 6, 45.
Fekete, T., & Edelman, S. (2011). Towards a computational theory of experience. Consciousness and Cognition, 20, 807–827.
Ginsburg, S., & Jablonka, E. (2010). Experiencing: A Jamesian approach. Journal of Consciousness Studies, 17, 102–124.
Harnad, S. (1987). Categorical Perception: The Groundwork of Cognition. New York: Cambridge University Press.
Hoffman, D.D., Singh, M., Prakash, C. (2014). The interface theory of perception. Psychonomic Bulletin and Review. In press.
Holyoak, K.J., & Cheng, P.W. (2011). Causal learning and inference as a rational process: The new synthesis. Annual Review of Psychology, 62, 135–163.
Hume, D. (1748). An enquiry concerning human understanding. Available online at http://eserver.org/18th/hume-enquiry.html.
Knill, D.C., & Kersten, D. (1991). Ideal perceptual observers for computation, psychophysics and neural networks In Watt, R. (Ed.), Vision and visual dysfunction, Volume 14, Chapter 7, (pp. 83–97). London: Macmillan.
Kolodny, O., & Edelman, S. (2014). The problem of concurrent multimodal serial order in behavior. Submitted.
Kushnir, T., & Gopnik, A. (2005). Young children infer causal strength from probabilities and interventions. Psychological Science, 16, 678–683.
Lagnado, D.A., & Speekenbrink, M. (2010). The influence of delays in real-time causal learning. The Open Psychology Journal, 3, 184–195.
Lagnado, D.A., Waldmann, M.R., Hagmayer, Y., Sloman, S.A. (2007). Beyond covariation: Cues to causal structure In Gopnik, A., & Schulz, L. (Eds.) Structure, (pp. 1–48): Oxford University Press.
Lashley, K.S. (1951). The problem of serial order in behavior In Jeffress, L.A. (Ed.), Cerebral Mechanisms in Behavior, (pp. 112–146). New York: Wiley.
Leising, K.J., Wong, J., Waldmann, M.R., Blaisdell, A.P. (2008). The special status of actions in causal reasoning in rats. Journal of Experimental Psychology: General, 137, 514–527.
Macmillan, N.A., Kaplan, H.L., Creelman, C.D. (1977). The psychophysics of categorical perception. Psychological Review, 84, 452–471.
Pearl, J. (2001). Causal inference in statistics: A gentle introduction. In Computing science and statistics: Proceedings of interface 01 (Vol. 33). Avaliable online at http://ftp.cs.ucla.edu/pub/stat_ser/R289.pdf.
Pearl, J. (2009). Causal inference in statistics: An overview. Statistics Surveys, 3, 96–146.
Pearl, J. (2013). Structural counterfactuals: A brief introduction. Cognitive Science, 37, 977–985.
Poggio, T., Mutch, J., Leibo, J., Rosasco, L., Tacchetti, A. (2012). The computational magic of the ventral stream: Sketch of a theory (and why some deep architectures work). CSAIL TR 035, MIT.
Quine, W.V.O. (1969). Natural kinds. In: Ontological relativity and other essays, (pp. 114–138). New York: Columbia University Press.
Schaffer, J. (2009). The metaphysics of causation In Zalta, E. N. (Ed.), The Stanford Encyclopedia of Philosophy. (Spring 2009 ed.)
Shepard, R.N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 1317–1323.
Shepard, R.N., & Chipman, S. (1970). Second-order isomorphism of internal representations: Shapes of states. Cognitive Psychology, 1, 1–17.
Singh, S., Lewis, R.L., Barto, A.G. (2010). Intrinsically motivated reinforcement learning: an evolutionary perspective. IEEE Transactions on Autonomous Mental Development, 2, 70–82.
Tenenbaum, J.B., & Griffiths, T.L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24, 629–641.
Watanabe, S. (1969). Knowing and Guessing: A Quantitative Study of Inference and Information. New York: Wiley.
Woergoetter, W., & Porr, B. (2007). Reinforcement learning. Scholarpedia, 3(3), 1448.
Yamins, D.L., Hong, H., Cadieu, C., Solomon, E.A., Seibert, D., Dicarlo, J.J. (2014). Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the National Academy of Science, 111, 8619–8624.
Author information
Authors and Affiliations
Corresponding author
Additional information
Author Note
A commentary on Hoffman, D., M. Singh, and C. Prakash, The interface theory of perception (2014).
Rights and permissions
About this article
Cite this article
Edelman, S. Varieties of perceptual truth and their possible evolutionary roots. Psychon Bull Rev 22, 1519–1522 (2015). https://doi.org/10.3758/s13423-014-0741-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.3758/s13423-014-0741-z