Abstract
When presenting his enthralling talk on TED, Daniel [2] put forth the following hypothesis on the evolutionary justification for the existence of the brain in Nature:
I’m a neuroscientist. And in neuroscience, we have to deal with many difficult questions about the brain. But I want to start with the easiest question and the question you really should have all asked yourselves at some point in your life, because it’s a fundamental question if we want to understand brain function. And that is, why do we and other animals have brains? Not all species on our planet have brains, so if we want to know what the brain is for, let’s think about why we evolved one.
Now you may reason that we have one to perceive the world or to think, and that’s completely wrong. If you think about this question for any length of time, it’s blindingly obvious why we have a brain. We have a brain for one reason and one reason only, and that’s to produce adaptable and complex movements.
There is no other reason to have a brain. Think about it. Movement is the only way you have of affecting the world around you. [...] So think about communication – speech, gestures, writing, sign language – they’re all mediated through contractions of your muscles. So it’s really important to remember that sensory, memory and cognitive processes are all important, but they’re only important to either drive or suppress future movements.
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References
Gilet, E., Diard, J., Bessiére, P.: Bayesian Action–Perception Computational Model: Interaction of Production and Recognition of Cursive Letters. PLoS ONE 6(6), e20387 (2011), doi:10.1371/journal.pone.0020387
Wolpert, D.: The real reason for brains. Video on TED.com (2011), http://www.ted.com/talks/daniel_wolpert_the_real_reason_for_brains.html
Colas, F., Diard, J., Bessiére, P.: Common Bayesian Models For Common Cognitive Issues. Acta Biotheoretica 58(2-3), 191–216 (2010)
Diard, J., Bessiére, P.: Bayesian maps: probabilistic and hierarchical models for mobile robot navigation. In: Bessiére, P., Laugier, C., Siegwart, R. (eds.) Probabilistic Reasoning and Decision Making in Sensory-motor Systems. STAR, vol. 46, pp. 153–176. Springer, Heidelberg (2008)
Koike, C.C., Bessiére, P., Mazer, E.: Bayesian Approach to Action Selection and Attention Focusing. In: Bessiére, P., Laugier, C., Siegwart, R. (eds.) Probabilistic Reasoning and Decision Making in Sensory-motor Systems. STAR, vol. 46, pp. 177–201. Springer, Heidelberg (2008)
Ernst, M.O.: A Bayesian view on multimodal cue integration. In: Human Body Perception From The Inside Out, ch. 6, pp. 105–131. Oxford University Press, New York (2006)
Thrun, S., Burgard, W., Fox, D.: Probabilistic robotics. MIT Press, Cambridge (2005)
Ernst, M.O., Bülthoff, H.H.: Merging the senses into a robust percept. Trends in Cognitive Sciences 8(4), 162–169 (2004)
Kersten, D., Mamassian, P., Yuille, A.: Object perception as Bayesian inference. Annual Review of Psychology 55, 271–304 (2004)
Diard, J., Bessiere, P., Mazer, E.: A survey of probabilistic models using the Bayesian programming methodology as a unifying framework. In: International Conference on Computational Intelligence, Robotics and Autonomous Systems (IEEE-CIRAS), Singapore (2003)
Longcamp, M., Anton, J.L., Roth, M., Velay, J.L.: Visual presentation of single letters activates a premotor area involved in writing. Neuroimage 19(4), 1492–1500 (2003)
Pradalier, C., Colas, F., Bessiere, P.: Expressing Bayesian fusion as a product of distributions: Applications in robotics. In: Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), vol. 2, pp. 1851–1856 (2003)
Pineau, J., Thrun, S.: High-level robot behavior control using POMDPs. In: AAAI 2002 Workshop on Cognitive Robotics, vol. 107 (2002)
Boutilier, C., Dean, T., Hanks, S.: Decision-theoretic planning: Structural assumptions and computational leverage. Journal of Artificial Intelligence Research 11, 1–94 (1999)
Fox, D., Burgard, W., Thrun, S.: Markov Localization for Mobile Robots in Dynamic Environments. Journal of Artificial Intelligence Research 11, 391–427 (1999)
Hauskrecht, M., Meuleau, N., Kaelbling, L.P., Dean, T., Boutilier, C.: Hierarchical solution of Markov decision processes using macro-actions. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 220–229 (1998)
Yuille, A.L., Bülthoff, H.H.: Bayesian decision theory and psychophysics. In: Knill, D., Richards, W. (eds.) Perception as Bayesian Inference, pp. 123–161. Cambridge University Press, Cambridge (1996)
Russell, S.J., Norvig, P., Canny, J.F., Malik, J.M., Edwards, D.D.: Artificial intelligence: a modern approach, 3rd edn. Prentice Hall, Englewood Cliffs (1995)
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Ferreira, J.F., Dias, J. (2014). Bayesian Decision Theory and the Action-Perception Loop. In: Probabilistic Approaches to Robotic Perception. Springer Tracts in Advanced Robotics, vol 91. Springer, Cham. https://doi.org/10.1007/978-3-319-02006-8_5
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