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The status of the simulative method in cognitive science: current debates and future prospects
Titolo Rivista: PARADIGMI 
Autori/Curatori: Vieri Giuliano Santucci, Dalia Nicole Cilia, Giovanni Pezzulo 
Anno di pubblicazione:  2015 Fascicolo: 3 Lingua: Inglese 
Numero pagine:  20 P. 47-66 Dimensione file:  88 KB
DOI:  10.3280/PARA2015-003004
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In questo lavoro sarà esaminato lo status attuale dell’approccio simulativo nelle scienze cognitive e nelle neuroscienze. Attraverso esempi specifici, discuteremo come idee, teorie e previsioni derivanti dalla ricerca sui sistemi artificiali abbiano contribuito significativamente al progresso scientifico nelle scienze cognitive e delle neuroscienze, spesso lavorando in sinergia con la ricerca empirica e teorica. Inoltre, l’approccio simulativo è oggi presente in un numero crescente di conferenze interdisciplinari ed eventi, e sta influenzando politiche di finanziamento dell’UE e degli USA. Gli esempi qui menzionati suggeriscono che il metodo simulativo è più efficace quando inserito in un contesto interdisciplinare, quando offre una base normativa / meccanicistica per generare predizioni empiriche e quando fornisce un quadro unitario di fenomeni tradizionalmente studiati separatamente. Nonostante i numerosi "success cases" qui menzionati, comunque, lo status epistemologico dell’approccio simulativo - e specialmente della robotica - è ancora oggetto di discussione, ed il suo impatto non ancora pienamente realizzato.


Keywords: Metodo simulativo, Modelli nelle neuroscienze, Scienza dell’artificiale, Neuroscienze computazionali, Scienza cognitiva, Simulazioni a larga scala del cervello.

  1. Anderson J.R. (1983). The architecture of cognition. Cambridge (MA): Harvard University Press.
  2. Arbib M.A. (1992). Schema theory. In: Shapiro S., ed. Encyclopedia of artificial intelligence. 2nd Edition. New York: Wiley: 1427-1443.
  3. Arbib M.A. (2003). The handbook of brain theory and neural networks. Cambridge: MIT Press.
  4. Arkin R.C. (1998). Behavior-based robotics. Cambridge (MA): MIT Press.
  5. Asada M., Hosoda K., Kuniyoshi Y., Ishiguro H., Inui T., Yoshikawa Y., Ogino M. and Yoshida C. (2009). Cognitive developmental robotics: a survey. IEEE Transactions on Autonomous Mental Development, 1, 1: 12-34.
  6. Baranes A. and Oudeyer P.Y. (2010). Maturationally-constrained competence-based intrinsically motivated learning. Proceedings of ICDL: 197-203., DOI: 10.1109/DEVLRN.2010.5578842
  7. Barsalou L.W. (1999). Perceptual symbol system. Behavioral and Brain Science, 22: 577-600.
  8. Barsalou L.W. (2008). Grounded cognition. Annual Review of Psychology, 59: 617-645.
  9. Bayer H.M. and Glimcher P.W. (2005). Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47: 129-141.
  10. Bednar J.A. (2009). Topographica: building and analyzing map-level simulations from Python, C/C++, MATLAB, NEST, or NEURON components. Frontiers in Neuroinformatics, 3, 8: 1-9., DOI: 10.3389/neuro.11.008.2009
  11. Beer R.D. (1997). The dynamics of adaptive behavior: A research program. Robotics and Autonomous Systems, 20: 257-289.
  12. Brooks R.A. (1991). Intelligence without representation. Artificial Intelligence, 47: 139-159.
  13. Carpenter G.A. and Grossberg S. (1988). The ART of adaptive pattern recognition by a self-organizing neural network. Computer, 21: 77-88., DOI: 10.1109/2.33
  14. Churchland M.M., Cunningham J.P., Kaufman M.T., Ryu S.I. and Shenoy K.V. (2010). Cortical preparatory activity: representation of movement or first cog in a dynamical machine? Neuron, 68: 387-400., DOI: 10.1016/j.neuron.2010.09.015
  15. Cisek P. (2007). Cortical mechanisms of action selection: the affordance competition hypothesis. Philosophical Transactions of Royal Society B, 362: 1585-1599.
  16. Cisek P. and Kalaska J.F. (2005). Neural correlates of reaching decisions in dorsal premotor cortex: specification of multiple direction choices and final selection of action. Neuron, 45: 801-814.
  17. Cisek P. and Kalaska J.F. (2010). Neural mechanisms for interacting with a world full of action choices. Annual Review of Neuroscience, 33: 269-298., DOI: 10.1146/annurev.neuro.051508.135409
  18. Clark A. (1998). Being there. putting brain, body, and world together. Cambridge (MA): MIT Press.
  19. Clark A. (1999). An embodied cognitive science? Trends in Cognitive Science, 3: 345-351.
  20. Clark A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36: 181-204., DOI: 10.1017/S0140525X12000477
  21. Clark A. and Grush R. (1999). Towards a cognitive robotics. Adaptive Behavior, 7: 5-16.
  22. Cordeschi R. (2002). The discovery of the artificial: behavior, mind and machines before and beyond cybernetics. Dordrecht: Kluwer.
  23. Cordeschi R. (2004). Cybernetics. In: Floridi L., ed. The Blackwell guide to philosophy of computing and information. Oxford: Blackwell: 186-196.
  24. Cordeschi R. (2006) Simulation models of organism behavior: some lessons from precybernetic and cybernetic approaches. In: Termini S., ed. Imagination and rigor: essays on Eduardo R. Caianiello’s scientific heritage. Berlin-Milan: Springer: 39-46.
  25. Cordeschi R. (2008). Steps toward the synthetic method: symbolic information processing and self-organization systems in early artificial intelligence. In: Husbands P., Holland O. and Wheeler M., eds. The mechanical mind in history. Cambridge (MA): MIT Press: 219-258.
  26. Datteri E. and Tamburrini G. (2007). Biorobotic experiments for the discovery of biological mechanisms. Philosophy of Science, 74, 3: 409-430.
  27. Daw N.D., Gershman S.J., Seymour B., Dayan P. and Dolan R.J., (2011). Model-based influences on humans’ choices and striatal prediction errors. Neuron, 69: 1204-1215.
  28. Demiris Y. and Khadhouri B. (2005). Hierarchical attentive multiple models for execution and recognition. Robotics and Autonomous Systems, 54: 361-369.
  29. Dindo H., Zambuto D. and Pezzulo G. (2011). Motor simulation via coupled internal models using sequential Monte Carlo. Proceedings of IJCAI: 2113-2119.
  30. Doya K., Ishii S., Pouget A. and Rao R.P.N., eds. (2007). Bayesian brain: probabilistic approaches to neural coding. Cambrigde (MA): MIT Press.
  31. Engel A.K., Maye A., Kurthen M. and König P. (2013). Where’s the action? The pragmatic turn in cognitive science. Trends in Cognitive Science, 17: 202-209., DOI: 10.1016/j.tics.2013.03.006
  32. Fiorillo C.D., Newsome W.T. and Schultz W. (2008). The temporal precision of reward prediction in dopamine neurons. Nature Neuroscience, 11: 966-973.
  33. Frackowiak R. (2014). Eyes on the prize. New Scientist, 223: 28-29.
  34. Frégnac Y. and Laurent G. (2014). Neuroscience: where is the brain in the Human Brain Project? Nature, 513: 27-29., DOI: 10.1038/513027a
  35. Friston K. (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience, 11: 127-138., DOI: 10.1038/nrn2787
  36. Friston K., Schwartenbeck P., FitzGerald T., Moutoussis M., Behrens T. and Dolan R.J. (2014). The anatomy of choice: dopamine and decision-making. Philosophical Transactions of the Royal Society B: Biological Sciences, 369, 1655: 20130481., DOI: 10.1098/rstb.2013.0481
  37. Glenberg A. (1997). What memory is for. Behavioral and Brain Sciences, 20: 1-55.
  38. Glimcher P.W. and Rustichini A. (2004). Neuroeconomics: the consilience of brain and decision. Science, 306: 447-452., DOI: 10.1126/science.1102566
  39. Gold J.I. and Shadlen M.N. (2007). The neural basis of decision making. Annual Review of Neuroscience, 30: 535-574., DOI: 10.1146/annurev.neuro.29.051605.113038
  40. Gottlieb J., Oudeyer P.-Y., Lopes M. and Baranes A. (2013). Information-seeking, curiosity, and attention: computational and neural mechanisms. Trends in Cognitive Science, 17: 585-593., DOI: 10.1016/j.tics.2013.09.001
  41. Griffiths T.L., Chater N., Kemp C., Perfors A. and Tenenbaum J.B. (2010). Probabilistic models of cognition: exploring representations and inductive biases. Trends in Cognitive Science, 14: 357-364.
  42. Grossberg S. (1978). A theory of human memory: self-organization and performance of sensory-motor codes, maps, and plans. In: Rosen R. and Snell F., eds. Progress in theoretical biology. New York: Academic Press: 233-374.
  43. Gurney K.N., Prescott T.J. and Redgrave P. (2001). A computational model of action selection in the basal ganglia in a new functional anatomy. Biological Cybernetics, 84: 401-410.
  44. Harnad S. (1990). The symbol grounding problem. Physica D: Nonlinear Phenomena, 42: 335-346.
  45. Hoffmann H. (2007). Perception through visuomotor anticipation in a mobile robot. Neural Networks, 20: 22-33.
  46. Insel T.R., Landis S.C. and Collins F.S. (2013). The NIH Brain initiative. Science, 340: 687-688., DOI: 10.1126/science.1239276
  47. Jeannerod M. (2006). Motor cognition. New York: Oxford University Press.
  48. Ljungberg T., Apicella, P. and Schultz W. (1992). Responses of monkey dopamine neurons during learning of behavioral reactions. Journal of Neurophysiology, 67, 1: 145-163.
  49. Lungarella M., Metta G., Pfeifer R. and Sandini G. (2003). Developmental robotics: a survey. Connection Science, 15: 151-190.
  50. Marcos E., Pani P., Brunamonti E., Deco G., Ferraina S. and Verschure P. (2013). Neural variability in premotor cortex is modulated by trial history and predicts behavioral performance. Neuron, 78: 249-255.
  51. Markram H. (2012). The Human Brain Project. Scientific Amenican, 306: 50-55.
  52. Marr D. (1982). Vision: a computational investigation into the human representation and processing of visual information. New York: Henry Holt and Co.
  53. McCarthy J., Minsky M., Rochester N. and Shannon C. (1955). A proposal for the Darmouth summer research project on artificial intelligence. Available at: http://www.formal. stanford.edu/jmc/his- tory/dartmouth/dartmouth.html.
  54. McClelland J.L., McNaughton B.L. and O’Reilly R.C. (1995). Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102: 419-457.
  55. McClelland J.L., Botvinick M.M., Noelle D.C., Plaut D.C., Rogers T.T., Seidenberg M.S. and Smith L.B. (2010). Letting structure emerge: connectionist and dynamical systems approaches to cognition. Trends in Cognitive Science, 14: 348-356.
  56. Metta G., Sandini G., Natale L. and Panerai F. (2001). Development and robotics. Proceedings of IEEE-RAS: 33-42.
  57. Mirolli M., Santucci V.G. and Baldassarre G. (2013). Phasic dopamine as a prediction error of intrinsic and extrinsic reinforcements driving both action acquisition and reward maximization: a simulated robotic study. Neural Networks, 39: 40-51.
  58. Montague P.R., Dolan R.J., Friston K.J. and Dayan P. (2012). Computational psychiatry. Trends in Cognitive Science, 16: 72-80., DOI: 10.1016/j.tics.2011.11.018
  59. Newell A., Shaw J.C. and Simon H.A. (1958). Elements of a theory of human problem solving. Psychological Review, 65, 3: 151-166.
  60. Newell A. and Simon H.A. (1972). Human problem solving. Englewood Cliffs (NJ): Prentice-Hall.
  61. Nolfi S. (2009). Behavior and cognition as a complex adaptive system: Insights from robotic experiments. In: Hooker C., ed. Handbook of the philosophy of science. Vol 10: Philosophy of complex systems. Amsterdam: Elsevier.
  62. O’Regan J.K. and Noe A. (2001). A sensorimotor account of vision and visual consciousness. Behavioral and Brain Sciences, 24: 883-917.
  63. O’Reilly R.C. and Munakata Y. (2000). Computational explorations in cognitive neuroscience: understanding the mind by simulating the brain. Cambridge (MA): MIT Press.
  64. Oudeyer P.Y. (2010). On the impact of robotics in behavioral and cognitive sciences: from insect navigation to human cognitive development. IEEE Transactions on Autonomous Mental Development, 2:2-16., DOI: 10.1109/TAMD.2009.2039057
  65. Oudeyer P.Y., Kaplan F. and Hafner V. (2007). Intrinsic motivation systems for autonomous mental development. IEEE Transactions on Evolutionary Computation, 11, 2: 265-286.
  66. Pezzulo G. (2011). Grounding procedural and declarative knowledge in sensorimotor anticipation. Mind and Language, 26: 78-114.
  67. Pezzulo G., Barsalou L.W, Cangelosi A., Fischer M.H., McRae K. and Spivey M. (2011). The mechanics of embodiment: a dialogue on embodiment and computational modeling. Frontiers in Psychology, 2: 1-21.
  68. Pezzulo G., Barsalou L.W., Cangelosi A., Fischer M.H., McRae K. and Spivey M.J. (2013). Computational grounded cognition: a new alliance between grounded cognition and computational modelling. Frontiers in Psychology, 3, 612., DOI: 10.3389/fpsyg.2012.00612
  69. Pezzulo G. and Castelfranchi C. (2009). Thinking as the control of imagination: a conceptual framework for goal-directed systems. Psychological Research, 73: 559-577.
  70. Pfeifer R. and Bongard J.C. (2006). How the body shapes the way we think. Cambridge (MA): MIT Press.
  71. Pfeifer R., Lungarella M. and Iida F. (2007). Self-organization, embodiment, and biologically inspired robotics. Science, 318: 1088-1093.
  72. Poo M. (2014). Whereto the mega brain projects? National Science Review, 1, 1: 12-14., DOI: 10.1093/nsr/nwt019
  73. Rao R.P. and Ballard D.H. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2: 79-87., DOI: 10.1038/4580
  74. Ratcliff R. (1978). A theory of memory retrieval. Psychological Review, 85: 59-108.
  75. Redgrave P., Prescott T.J. and Gurney K. (1999). The basal ganglia: a vertebrate solution to the selection problem? Neuroscience, 89: 1009-1023.
  76. Rennó-Costa C., Lisman J.E. and Verschure P.F.M.J. (2010). The mechanism of rate remapping in the dentate gyrus. Neuron, 68: 1051-1058., DOI: 10.1016/j.neuron.2010.11.024
  77. Rolf M., Steil J.J. and Gienger M. (2010). Goal babbling permits direct learning of inverse kinematics. IEEE Transactions on Autonomous Mental Development, 2, 3: 216-229.
  78. Romo R. and Schultz W. (1990). Dopamine neurons of the monkey midbrain: contingencies of responses to active touch during self-initiated arm movements. Journal of Neurophysiology, 63, 3: 592-606.
  79. Rose N. (2014). The Human Brain Project: social and ethical challenges. Neuron, 82, 6:1212-1215., DOI: 10.1016/j.neuron.2014.06.00
  80. Rosenbaum D.A. (2005). The Cinderella of psychology: the neglect of motor control in the science of mental life and behaviour. American Psychologist, 60: 308-317., DOI: 10.1037/0003-066X.60.4.308
  81. Rosenbloom P.S., Laird J.E. and Newell A. (1992). The Soar papers: research on integrated intelligence. Cambridge (MA): MIT Press.
  82. Roy D. (2005). Semiotic schemas: a framework for grounding language in action and perception. Artificial Intelligence, 167: 170-205., DOI: 10.1016/j.artint.2005.04.007
  83. Rumelhart D.E., McClelland J.L. and the P.R Group. (1986). Parallel distributed processing: explorations in the microstructure of cognition. Cambridge (MA): MIT Press.
  84. Ryan R.M. and Deci E.L. (2000). Intrinsic and extrinsic motivations: classic definitions and new directions. Contemporary Educational Psychology, 25, 1: 54-67.
  85. Salamone J.D. and Correa M. (2012). The mysterious motivational functions of mesolimbic dopamine. Neuron, 76: 470-485., DOI: 10.1016/j.neuron.2012.10.021
  86. Sanborn A.N., Griffiths T.L. and Shiffrin R.M. (2010). Uncovering mental representations with Markov chain Monte Carlo. Cognitive Psychology, 60: 63-106., DOI: 10.1016/j.cogpsych.2009.07.001
  87. Sandamirskaya Y., Zibner S.K., Schneegans S. and Schöner G. (2013). Using dynamic field theory to extend the embodiment stance toward higher cognition. New Ideas Psychology, 31: 322-339.
  88. Santucci V.G., Baldassarre G. and Mirolli M. (2014). Autonomous selection of the “what” and the “how” of learning: an intrinsically motivated system tested with a two armed robot. Proceedings of ICDL-Epirob, IEEE International Conferences: 434-439.
  89. Schoener G. (2008). Dynamical systems approaches to cognition. In: Spencer P., Thomas M.S. and McClelland J.L., eds. Toward a unified theory of development: connectionism and dynamic systems theory re-considered. Oxford: Oxford University Press.
  90. Schultz W. (1998). Predictive reward signal of dopamine neurons. Journal of Neurophysiology, 80, 1: 1-27.
  91. Schultz W., Apicella P. and Ljungberg T. (1993). Responses of monkey dopamine neurons to reward and conditioned stimuli during successive steps of learning a delayed response task. Journal of Neuroscience, 13, 3: 900-913.
  92. Schultz W., Dayan P. and Montague P.R. (1997). A neural substrate of prediction and reward. Science, 275: 1593-1599.
  93. Schwartenbeck P., FitzGerald T.H., Mathys C., Dolan R. and Friston K. (2014). The dopaminergic midbrain encodes the expected certainty about desired outcomes. Cerebral Cortex, 25, 10:3434-3445., DOI: 10.1093/cercor/bhu159
  94. Shadlen M.N., Kiani R., Hanks T.D. and Churchland A.K. (2008). Neurobiology of decision making: sn intentional framework. In: Engel C. and Singer W., eds., Better than conscious? Decision making, the human mind, and implications for institutions. Cambridge (MA): MIT Press: 72-101.
  95. Shiffrin R.M. and Steyvers M. (1997). A model for recognition memory: REM-retrieving effectively from memory. Psychonomic Bullettin Review, 4: 145-166., DOI: 10.3758/BF03209391
  96. Simon H.A. (1996). The sciences of the artificial. Cambridge (MA): MIT Press.
  97. Spivey M. (2007). The continuity of mind. New York: Oxford University Press.
  98. Steels L. (2003). Intelligence with representation. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 361: 2381-2395.
  99. Stone R. and Lavine M. (2014). The social life of robots. Science, 346: 178-179., DOI: 10.1126/science.346.6206.178
  100. Sutton, R.S. and Barto A.G. (1998). Reinforcement learning: an introduction. Cambridge (MA): MIT Press.
  101. Tenenbaum J.B., Griffiths T.L. and Kemp C. (2006). Theory-based bayesian models of inductive learning and reasoning. Trends in Cognitive Science, 10: 309-318.
  102. Tenenbaum J.B., Kemp C., Griffiths T.L. and Goodman N.D. (2011). How to grow a mind: statistics, structure, and abstraction. Science, 331: 1279-1285., DOI: 10.1126/science.1192788
  103. Thelen E. and Smith L.B. (1996). A dynamic systems approach to the development of cognition and action. Cambridge (MA): MIT Press.
  104. Thelen E., Schöner G., Scheier C. and Smith L. (2001). The dynamics of embodiment: a field theory of infant perseverative reaching. Behavioral and Brain Sciences, 24: 1-33.
  105. Thompson E. and Varela F.J. (2001). Radical embodiment: neural dynamics and consciousness. Trends in Cognitive Science, 5: 418-425.
  106. Usher M. and McClelland J.L. (2001). On the time course of perceptual choice: the leaky, competing accumulator model. Psychological Review, 108, 3: 550-592.
  107. Verschure P.F.M.J. (2012). Distributed adaptive control: a theory of the mind, brain, body nexus, Biologically Inspired Cognitive Architectures, 1: 55-72., DOI: 10.1016/j.bica.2012.04.005
  108. Verschure P.F.M.J., Pennartz C.M.A. and Pezzulo G. (2014). The why, what, where, when and how of goal-directed choice: neuronal and computational principles. Philosophical Transactions of the Royal Society B: Biological Sciences, 369: 20130483., DOI: 10.1098/rstb.2013.048
  109. Von Hofsten C. (2004). An action perspective on motor development. Trends in Cognitive Sciences, 8, 6: 266-272.
  110. Webb B. (2000). What does robotics offer animal behaviour? Animal Behavior, 60: 545-558.
  111. Webb B. (2001). Can robots make good models of biological behaviour? Behavioral and Brain Sciences, 24: 1033-1050., DOI: 10.1017/S0140525X01000127
  112. Weng J. (2004). Developmental robotics: theory and experiments. International Journal of Humanoid Robotics, 1, 2: 199-236.
  113. White R. (1959). Motivation reconsidered: the concept of competence. Psychological Review, 66: 297-333.
  114. Wiener N. (1950). The human use of human beings. Boston: Houghton Mifflin.
  115. Wolpert D.M. and Ghahramani Z. (2000). Computational principles of movement neuroscience. Nature Neuroscience, 3: 1212-1217.

Vieri Giuliano Santucci, Dalia Nicole Cilia, Giovanni Pezzulo, Lo status del metodo simulativo nella scienza cognitiva: dibattito attuale e prospettive future in "PARADIGMI" 3/2015, pp. 47-66, DOI:10.3280/PARA2015-003004

   

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