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Abstract

Our perceptual systems are products of evolution and have been shaped, in part, by natural selection. It is widely assumed that natural selection favors veridical perceptions—that is, perceptions that accurately describe aspects of the objective world relevant to fitness. This assumption has been tested using the mathematics of evolutionary game theory. It is false. Monte Carlo simulations reveal that veridical perceptions are never more fit, and generically are less fit, than nonveridical perceptions of equal complexity that are tuned to fitness. Veridical perceptions go extinct, and their extinction rate increases as complexity increases. These results motivate a new theory of perceptual systems—as species-specific interfaces shaped by natural selection to hide objective reality and guide adaptive behavior. For Homo sapiens, space-time is the desktop of the interface and physical objects are icons on the desktop. The shapes and colors of physical objects no more resemble objective reality than the shapes and colors of desktop icons resemble files in a computer.

Keywords

evolution natural selection evolutionary game theory perception Bayesian inference veridical perception illusion

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References

Hoffman

D. D.

Singh

Prakash

(2015a). (See References). A comprehensive introduction to the interface theory of perception.

Hoffman

D. D.

Singh

Prakash

(2015b). (See References). Replies to ten commentaries that canvas a wide range of natural objections to the interface theory of perception.

Mermin

N. D.

(1985). (See References). A gentle introduction to research that investigates whether physical objects have definite values for dynamical physical properties when they are not observed.

Nowak

(2006). (See References). The most accessible introduction to evolutionary game theory that I have found.

Bell

J. S.

(1964). On the Einstein Podolsky Rosen Paradox. Physics, 1(3), 195–200.

Chemero

(2009). Radical embodied cognitive science. Cambridge, MA: MIT Press.

Frisby

J. P.

Stone

J. V.

(2010). Seeing: The computational approach to biological vision. Cambridge, MA: MIT Press.

Fuchs

(2010). QBism: The perimeter of quantum Bayesianism. arXiv:1003.5209 [quant-ph]. Retrieved from http://arxiv.org/pdf/1003.5209v1.pdf

Giustina

Mech

Ramelow

Wittmann

Kofler

Beyer

. . . Zeilinger

(2013). Bell violation using entangled photons without the fair-sampling assumption. Nature, 497, 227–230.

10.

Hensen

Bernien

Dréau

A. E.

Reiserer

Kalb

Blok

. . . Hanson

(2015). Loophole-free Bell inequality violation using electron spins separated by 1.3 kilometers. Nature, 526, 682–686.

11.

Hoffman

D. D.

(2000). Visual intelligence: How we create what we see. New York, NY: W.W. Norton.

12.

Hoffman

D. D.

(2009). The interface theory of perception. In Dickinson

Tarr

Leonardis

Schiele

(Eds.), Object categorization: Computer and human vision perspectives (pp. 148–165). New York, NY: Cambridge University Press.

13.

Hoffman

D. D.

Singh

(2012). Computational evolutionary perception. Perception, 41, 1073–1091.

14.

Hoffman

D. D.

Singh

Prakash

(2015a). The interface theory of perception. Psychonomic Bulletin & Review, 22, 1480–1506.

15.

Hoffman

D. D.

Singh

Prakash

(2015b). Probing the interface theory of perception: Replies to commentaries. Psychonomic Bulletin & Review, 22, 1551–1576.

16.

Klimov

P. V.

Falk

A. L.

Christle

D. J.

Dobrovitski

V. V.

Awschalom

D. D.

(2015). Quantum entanglement at ambient conditions in a macroscopic solid-state spin ensemble. Science Advances, 1, Article e1501015. doi:10.1126/sciadv.1501015

17.

Knill

Richards

W. A.

(Eds.). (1996). Perception as Bayesian inference. New York, NY: Cambridge University Press.

18.

Koenderink

J. J.

(2015). Esse est percipi & verum factum est. Psychonomic Bulletin & Review, 22, 1530–1534.

19.

Mark

J. T.

Marion

B. B.

Hoffman

D. D.

(2010). Natural selection and veridical perceptions. Journal of Theoretical Biology, 266, 504–515.

20.

Marr

(1982). Vision. San Francisco, CA: Freeman Press.

21.

Mausfeld

(2015). Notions such as “truth” or “correspondence to the objective world” play no role in explanatory accounts of perception. Psychonomic Bulletin & Review, 22, 1535–1540.

22.

Mermin

N. D.

(1985, April). Is the moon there when nobody looks? Reality and the quantum theory. Physics Today, 38(4), 38–47.

23.

Mermin

N. D.

(1990, June). What’s wrong with these elements of reality? Physics Today, 43(6), 9–11.

24.

Mitchell

(1998). An introduction to genetic algorithms. Cambridge, MA: Bradford Books MIT Press.

25.

Nowak

(2006). Evolutionary dynamics: Exploring the equations of life. Cambridge, MA: Belknap Press of Harvard University Press.

26.

Palmer

S. E.

(1999). Vision science: Photons to phenomenology. Cambridge, MA: MIT Press.

27.

Szeliski

(2010). Computer vision: Algorithms and applications. New York, NY: Springer.

28.

Werner

J. S.

Chalupa

L. M.

(2014). The new visual neurosciences. Cambridge, MA: MIT Press.

The Interface Theory of Perception

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