Academia.eduAcademia.edu

Outline

A Model of Emotion as Patterned Metacontrol

Abstract

Adaptive agents use feedback as a key strategy to cope with uncertainty and change in their environments. The information fed back from the sensorimotor loop into the control subsystem can be used to change four different elements of the controller: parameters associated to the control model, the control model itself, the functional organization of the agent and the functional realization of the agent. There are many change alternatives and hence the complexity of the agent's space of potential configurations is daunting. The only viable alternative for space-and time-constrained agents-in practical, economical, evolutionary terms-is to achieve a reduction of the dimensionality of this configuration space. Emotions play a critical role in this reduction. The reduction is achieved by functionalization, interface minimization and by patterning, i.e. by selection among a predefined set of organizational configurations. This analysis lets us state how autonomy emerges from the integration of cognitive, emotional and autonomic systems in strict functional terms: autonomy is achieved by the closure of functional dependency. Emotion-based morphofunctional systems are able to exhibit complex adaptation patterns at a reduced cognitive cost. In this article we show a general model of how emotion supports functional adaptation and how the emotional biological systems operate following this theoretical model. We will also show how this model is also of applicability to the construction of a wide spectrum of artificial systems 1 .

References (140)

  1. Adolphs, R., Tranel, D., Damasio, H., R.Damasio, A., 1995. Fear and the human amygdala. The Journal of Neuroscience 15, 5879-5891.
  2. Aldridge, H., Bluethmann, B., Ambrose, R., Diftler, M., 2000. Control architecture for the robonaut space humanoid, in: First IEEE-RAS International Conference on Humanoid Robots, Cambridge, Massachusetts.
  3. Alexander, C., Ishikawa, S., Silverstein, M., 1977. A Pattern Language: Towns, Buildings, Construction. Oxford University Press.
  4. Anderson, J.R., Bothell, D., Byrne, M.D., 2004. An integrated theory of the mind. Psychological Review 111, 1036-1060.
  5. Andrew, A.M., 2009. A missing link in cybernetics: logic and continuity. volume v. 26 of International Federation for Systems Research international series on systems science and engineering. Springer, New York.
  6. Arbib, M.A., Fellous, J.M., 2004. Emotions: from brain to robot. Trends in Cognitive Sciences 8, 554-561.
  7. Ashby, A.R., 1956. An Introduction to Cybernetics. Chapman and Hall, London.
  8. Baars, B.J., Gage, N.M., 2007. Cognition, Brain and Consciousness: Introduction to Cognitive Neuroscience. Academic Press. 1 edition.
  9. Bajuelos, A., 2011. Improving robustness in robotic navigation by using a self- reconfigurable control system. Master's thesis. Escuela Técnica Superior de In- genieros Industriales -Universidad Politécnica de Madrid.
  10. Balmelli, L., Brown, D., Cantor, M., Mott, M., 2006. Model-driven systems devel- opment. IBM Systems journal 45, 569-585.
  11. Barker, F.I., 1995. Phineas among the phrenologists: the american crowbar case and nineteenth-century theories of cerebral localization. Journal of Neurosurgery 82, 672-682.
  12. Bartneck, C., 2002. Integrating the occ model of emotions in embodied characters. Design , 1-5.
  13. Bartneck, C., Lyons, M., Saerbeck, M., 2008. The relationship between emotion models and artificial intelligence, in: Proceedings of the Workshop on The Role Of Emotion In Adaptive Behavior And Cognitive Robotics, in affiliation with the 10th International Conference on Simulation of Adaptive Behavior: From Animals to Animates (SAB 2008), Osaka.
  14. Bass, L., Clements, P., Kazman, R., 2013. Software Architecture in Practice. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA. third edition.
  15. Bass, L., Cohen, S., Northrop, L., 1996. Product line architectures, in: Proceedings for the International Workshop on Development and Evolution of Software Ar- chitectures for Product Families, Las Navas del Marqués, Ávila, Spain.
  16. Bayar, S., Yurdakul, A., 2012. A dynamically reconfigurable communication ar- chitecture for multicore embedded systems. Journal of Systems Architecture 58, 140-159.
  17. Bermejo-Alonso, J., Sanz, R., Rodríguez, M., Hernández, C., 2010. An ontological framework for autonomous systems modelling. International Journal on Ad- vances in Intelligent Systems 3, 211-225.
  18. Bosse, T., Jonker, C.M., Treur, J., 2008. Formalisation of damasio's theory of emo- tion, feeling and core consciousness. Consciousness and Cognition In Press.
  19. Bradley, M.M., 2000. Emotion and motivation, in: Cacioppo, J., Tassinary, L., Bern- ston, G. (Eds.), Handbook of Psychophysiology. Cambridge Univ. Press, New York, pp. 602-642.
  20. Breazeal, C., 2004. Function meets style: insights from emotion theory applied to hri. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 34, 187-194.
  21. Brun, Y., Desmarais, R., Geihs, K., Litoiu, M., Lopes, A., Smit, M., 2013. A design space for self-adaptive systems, in: Lemos, R., Giese, H., M üller, H.A., Shaw, M. (Eds.), Software Engineering for Self-Adaptive Systems II. Springer Berlin Hei- delberg. volume 7475 of Lecture Notes in Computer Science, pp. 33-50.
  22. Bush, G., Luu, P., Posner, M.I., 2000. Cognitive and emotional influences in anterior cingulate cortex. Trends in cognitive sciences 4, 215-222.
  23. Cacioppo, J., Berntson, G., Larsen, J., Poehlmann, K., Ito, T., et al., 2000. The psy- chophysiology of emotion. Handbook of emotions 2, 173-191.
  24. Chaput, H.H., 2004. The Constructivist Learning Architecture: A Model of Cog- nitive Development for Robust Autonomous Robots. Ph.D. thesis. Computer Science Department, University of Texas at Austin. Technical Report TR-04-34.
  25. Cheng, S.W., Garlan, D., Schmerl, B., 2006. Architecture-based self-adaptation in the presence of multiple objectives, in: ICSE 2006 Workshop on Software Engi- neering for Adaptive and Self-Managing Systems (SEAMS), Shanghai, China.
  26. Cloutier, R.J., Verma, D., 2007. Applying the concept of patterns to systems archi- tecture. Systems Engineering 10, 138-154.
  27. Cosmides, L., Tooby, J., 2003. Evolutionary psychology: Theoretical foundations, in: Encyclopedia of Cognitive Science. Macmillan, London.
  28. Damasio, A., 1994. Descartes' Error: Emotion, Reason and the Human Brain. Put- man.
  29. Damasio, A.R., Everitt, B.J., Bishop, D., 1996. The somatic marker hypothesis and the possible functions of the prefrontal cortex [and discussion]. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 351, 1413-1420.
  30. Dario, P., Carrozza, M., Guglielmelli, E., Laschi, C., Menciassi, A., Micera, S., Vec- chi, F., 2005. Robotics as a future and emerging technology: biomimetics, cyber- netics, and neuro-robotics in european projects. Robotics Automation Magazine, IEEE 12, 29 -45.
  31. Davidson, R., Sutton, S., 1995. Affective neuroscience: The emergence of a disci- pline. Current Opinion in Neurobiology 5, 217-224.
  32. Davidson, R.J., Lewis, D., Alloy, L., Amaral, D., Bush, G., Cohen, J., Drevets, W., Farah, M., Kagan, J., McClelland, J., Nolen-Hoeksema, S., Peterson, B., 2002. Neural and behavioral substrates of mood and mood regulation. Biological Psy- chiatry 52, 478-502.
  33. Davidson, R.J., Pizzagalli, D., Nitschke, J.B., Kalin, N.H., 2003. Parsing the sub- components of emotion and disorders of emotion: Perspectives from affective neuroscience, in: R. J. Davidson, H. H. Goldsmith, .K.S. (Ed.), Handbook of Af- fective Science. Oxford University Press, New York, pp. 8-24.
  34. Deconinck, G., Vounckx, J., Cuyvers, R., Lauwereins, R., 1994. Fault tolerance in massively parallel systems. Transputer Communications 2, 241-257.
  35. Dennett, D.C., 1996. Kinds of minds: toward an understanding of consciousness. Science masters, Basic Books, New York, NY. 1st ed edition.
  36. Dolcos, F., Iordan, A.D., Dolcos, S., 2011. Neural correlates of emotion--cognition interactions: A review of evidence from brain imaging investigations. Journal of Cognitive Psychology 23, 669-694.
  37. Dyer, M.G., 1987. Emotions and their computations: Three computer models. Cog- nition & Emotion 1, 323-347.
  38. Edwards, C., Spurgeon, S., 1998. Sliding Mode Control: Theory and Applications. Taylor and Francis, London.
  39. Eibl-Eibesfeldt, I., 1971. Love and hate: on the natural history of basic behaviour patterns. Methuen, London.
  40. Famaey, J., Latré, S., Strassner, J., De Turck, F., 2010. An ontology-driven semantic bus for autonomic communication elements, in: Brennan, R., Fleck, J., van der Meer, S. (Eds.), Modelling Autonomic Communication Environments. Springer Berlin / Heidelberg. volume 6473 of Lecture Notes in Computer Science, pp. 37-50.
  41. Fellous, J.M., 2004. From human emotions to robot emotions, in: AAAI Spring Symposium on Architectures for Modeling Emotion, Stanford University, USA.
  42. Fellous, J.M., Arbib, M.A., 2005. Who needs emotions?: the brain meets the robot. Oxford University Press, Oxford.
  43. Fiadeiro, J.L., Lopes, A., 2010. A model for dynamic reconfiguration in service- oriented architectures, in: Proceedings of the 4th European conference on Soft- ware architecture, Springer-Verlag, Berlin, Heidelberg. pp. 70-85.
  44. Frijda, N.H., 1986. The Emotions. Cambridge University Press.
  45. Frijda, N.H., 2007. The laws of emotion. Lawrence Erlbaum Associates, Mahwah, N.J. Gamma, E., Helm, R., Johnson, R., Vlissides, J., 1995. Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley Professional Computing Series, Addison-Wesley Publishing Company, New York, NY.
  46. Gini, G.C., Folgheraiter, M., Scarfogliero, U., Moro, F., 2009. A biologically founded design and control of a humanoid biped, in: Choi, B. (Ed.), Humanoid Robots. InTech. chapter 3.
  47. Gleeson, P., Crook, S., Cannon, R.C., Hines, M.L., Billings, G.O., Farinella, M., Morse, T.M., Davison, A.P., Ray, S., Bhalla, U.S., Barnes, S.R., Dimitrova, Y.D., Silver, R.A., 2010. Neuroml: A language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS Comput Biol 6, e1000815.
  48. G ómez, J., Sanz, R., Hernández, C., 2008. Cognitive ontologies: Mapping structure and function of the brain from a systemic view, in: AAAI 2008 Fall Symposium on Biologically Inspired Cognitive Architectures.
  49. Gould, S.J., 2002. The structure of evolutionary theory. Belknap Press of Harvard University Press, Cambridge, Mass.
  50. Hamm, A.O., Cuthbert, B.N., Globisch, J., Vaitl, D., 1997. Fear and the startle re- flex: Blink modulation and autonomic response patterns in animal and mutila- tion fearful subjects. Psychophysiology 34, 97-107.
  51. Hansen, L.P., Sargent, T.J., 2007. Robustness. Princeton University Press.
  52. Hara, F., Pfeifer, R., 2003. Morpho-functional machines: the new species : designing embodied intelligence. Springer, Berlin.
  53. Hernández, C., L ópez, I., Sanz, R., 2009. The operative mind: a functional, compu- tational and modelling approach to machine consciousness. International Jour- nal of Machine Consciousness 1, 83-98.
  54. Hernández, C., Sanz, R., 2012. Three Patterns for Autonomous Systems. Technical Note ASLAB-A-2012-007.pdf. Autonomous Systems Laboratory ASLab. Univer- sidad Politécnica de Madrid.
  55. Hernández, C., Sanz, R., G ómez-Ramirez, J., Smith, L.S., Hussain, A., Chella, A., Aleksander, I. (Eds.), 2011. From Brains to Systems. Brain-Inspired Cognitive Systems 2010. Springer.
  56. Hernandez, C., Sanz, R., Hernando, A., Gomez, J., Sanchez, G., Salom, M., 2010. Integrated Cognitive Architecture Model. Deliverable D61. Universidad Politécnica de Madrid -European Integrated Project IST-027819 ICEA.
  57. Herrera, C., Sánchez, G., Sanz, R., 2012. The morphofunctional approach to emo- tion modelling in robotics. Adaptive Behavior 20, 388-404.
  58. Herrera-Perez, C., Sanz, R., 2013. Emotion as morphofunctionality. Artificial Life 19. Houwer, J.D., Hermans, D., 2010. Cognition & Emotion: Reviews of Current Re- search and Theories. Psychology Press.
  59. Klir, G.C., 1969. An Approach to General Systems Theory. Van Nostrand Reinhold.
  60. Kluwer, H., Bucy, P.C., 1939. Preliminary analysis of functions of the temporal lobe in monkeys. Archives of Neurology and Psychiatry 42, 979-1000.
  61. Krichmar, J.L., 2012. Design principles for biologically inspired cognitive robotics. Biologically Inspired Cognitive Architectures 1, 73-81.
  62. Kringelbach, M.L., Berridge, K.C., 2009. Towards a functional neuroanatomy of pleasure and happiness. Trends in Cognitive Sciences 13, 479 -487.
  63. Kushiro, K., Harada, Y., Takeno, J., 2013. Robot uses emotions to detect and learn the unknown. Biologically Inspired Cognitive Architectures This Issue.
  64. Laird, J.E., Newell, A., Rosenbloom, P.S., 1987. Soar: an architecture for general intelligence. Artificial Intelligence 33, 1-64.
  65. Laird, P.R.J., Newell, A. (Eds.), 1993. The Soar Papers: Research on Integrated Intelligence. MIT Press, Cambridge, MA.
  66. van Lamsweerde, A., 2003. From system goals to software architecture, in: Bernardo, M., Inverardi, P. (Eds.), Formal Methods for Software Architecture. Springer.
  67. Larue, O., Poirier, P., Nkambou, R., 2013. The emergence of (artificial) emotions from cognitive and neurological processes. Biologically Inspired Cognitive Ar- chitectures This Issue.
  68. Lazarus, R.S., 1991. Emotion and adaptation. Oxford University Press, New York.
  69. LeDoux, J., 1996. The Emotional Brain. Simon & Schuster, New York.
  70. LeDoux, J., 2012. Rethinking the emotional brain. Neuron 73, 653-676.
  71. Lind, M., 1990. Representing Goals and Functions of Complex Systems. An Intro- duction to Multilevel Flow Modelling. Technical Report. Institute of Automatic Control Systems. Technical University of Denmark.
  72. Lindquist, K.A., Barrett, L.F., 2012. A functional architecture of the human brain: emerging insights from the science of emotion. Trends in Cognitive Sciences 16.
  73. Lipson, H., Pollack, J.B., 2000. Automatic design and manufacture of robotic life- forms. Nature 406, 974-980.
  74. Liu, J.W., 2000. Real-time Systems. Prentice-Hall, Upper Saddle River, NJ.
  75. Lorini, E., 2011. Qualitative and quantitative aspects of expectation-based emo- tions: a logical approach, in: Broekens, J., Bosse, T., Marsella, S. (Eds.), Workshop on Standards in Emotion Modeling, Lorentz Center.
  76. MacLean, P.D., 1949. Psychosomatic disease and the 'visceral brain': recent devel- opments bearing on the papez theory of emotion. Psychosom. Med. 11, 38 -353.
  77. Marinier III, R.P., Laird, J.E., Lewis, R.L., 2009. A computational unification of cognitive behavior and emotion. Cognitive Systems Research 10, 48-69.
  78. Marsella, S., Gratch, J., Petta, P., 2010. Computational models of emotion, in: Scherer, K.R., Banziger, T., Roesch, E. (Eds.), A Blueprint for Affective Comput- ing: A sourcebook and manual. Oxford University Press, USA, Oxford, UK. Se- ries in affective science.
  79. de la Mata, J.L., Rodríguez, M., 2010. Accident prevention by control system recon- figuration. Computers and Chemical Engineering 34, 846-855.
  80. McClelland, J.L., 2009. The place of modeling in cognitive science. Topics in Cog- nitive Science 1, 11 -38.
  81. Miller, E., Cohen, J., 2001. An integrative theory of prefrontal cortex function. Annu Rev Neurosci. 24, 167-202.
  82. Mitchinson, B., Chan, T., Chambers, J., Humphries, M., Gurney, K., Prescott, T., 2008. Brahms: Novel middleware for integrated systems computation, in: Fron- tiers in Neuroinformatics. Conference Abstract: Neuroinformatics 2008.
  83. Moffat, D., Frijda, N., Phaf, H., 1995. Analysis of a model of emotions, in: Sloman, A., Hogg, D., Humphreys, G., Ramsay, A., Partridge, D. (Eds.), Prospects for Artificial Intelligence: Proceedings of AISB93.
  84. Morris, J.S., Ohman, A., Dolan, R.J., 1998. Conscious and uncon-scious emotional learning in the human amygdala. Nature 393, 467-470.
  85. Nolfi, S., Floreano, D., 2000. Evolutionary robotics: the biology, intelligence, and technology of self-organizing machines. MIT Press, Cambridge, Mass.
  86. Ochsner, K.N., Bunge, S.A., Gross, J.J., Gabrieli, J.D.E., 2002. Rethinking feelings: An fMRI study of the cognitive regulation of emotion. Journal of Cognitive Neu- roscience 14, 1215-1299.
  87. OMG, 2007. Data Distribution Service for Real-time Systems. OMG Available Spec- ification formal/07-01-01. Object Management Group.
  88. Panksepp, J., 1982. Toward a general psychobiological theory of emotions. Behav- ioral and Brain Sciences 5, 407-422.
  89. Panksepp, J., 1998. Affective neuroscience: the foundations of human and animal emotions. Oxford University Press, New York.
  90. Papez, J.W., 1995. A proposed mechanism of emotion. 1937. The Journal of Neu- ropsychiatry and Clinical Neurosciences 7, 103-112.
  91. Phelps, E., 2006. Emotion and cognition: Insights from studies of the human amyg- dala. Annual Review of Psychology 24, 27-53.
  92. Phelps, E., LeDoux, J., 2005. Contributions of the amygdala to emotion processing: From animal models to human behavior. Neuron 48, 175-187.
  93. Picard, R.W., 1997. Affective Computing. The MIT Press.
  94. Quigley, M., Conley, K., Gerkey, B.P., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., 2009. Ros: an open-source robot operating system, in: ICRA Workshop on Open Source Software.
  95. Ritter, F.E., 1993. Three types of emotional effects that will occur in a cognitive architecture like SOAR, in: Workshop on architectures underlying motivation and emotion, University of Birmingham.
  96. Rolls, E.T., 2005. Emotion explained. Oxford University Press, Oxford.
  97. Rosen, J.B., Donley, M.P., 2006. Animal studies of amygdala function in fear and uncertainty: Relevance to human research. Biological Psychology 73, 49-60.
  98. Rosen, R., 1970. Structural And Functional Considerations In The Modelling Of Biological Organization. Report 77-25. Center for the Study of Democratic Insti- tutions.
  99. Rosen, R., 2012. Anticipatory Systems. Philosophical, Mathematical, and Method- ological Foundations. volume 1 of IFSR International Series on Systems Science and Engineering. Springer. 2nd edition.
  100. Rosenthal, D.M., 2009. Higher-order theories of consciousness, in: McLaughlin, B., Beckermann, A., Walter, S. (Eds.), Oxford Handbook on the Philosophy of Mind. Oxford University Press, Oxford. chapter 13.
  101. Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986. Learning representations by back-propagating errors. Nature 323, 533-536.
  102. Sabatinelli, D., Bradley, M.M., Lang, P.J., 2001. Affective startle modulation in an- ticipation and perception. Psychophysiology 38, 719-72.
  103. Samsonovich, A.V., 2012. On a roadmap for the bica challenge. Biologically In- spired Cognitive Architectures 1, 100-107.
  104. Sanchez-Escribano, M.G., Sanz, R., 2012. Value by architectural transversality, emo- tion and consciousness, in: 16th Meeting of the Association for the Scientific Study of Consciousness, Brighton, UK.
  105. Sanz, R., 2003. Against biologism. contribution to the panel on intelligent control imitating biology: Promises, challenges and lessons., in: IEEE International Con- ference on Intelligent Control, IEEE Press, Houston, USA.
  106. Sanz, R., Alonso, M., L ópez, I., García, C.A., 2001. Enhancing robot control ar- chitectures using CORBA, in: Proceedings of IEEE International Symposium on Intelligent Control, Mexico D.F.
  107. Sanz, R., Hernández, C., G ómez, J., Bedia, M.G., 2010a. Against animats, in: Pro- ceedings of CogSys 2010 -4th International Conference on Cognitive Systems, Zurich, Switzerland.
  108. Sanz, R., Hernández, C., G ómez, J., Bermejo-Alonso, J., Rodríguez, M., Hernando, A., Sánchez, G., 2009. Systems, models and self-awareness: Towards architec- tural models of consciousness. International Journal Of Machine Consciousness 1, 255-279.
  109. Sanz, R., Hernández, C., G ómez, J., Sánchez, G., Hernando, A., 2010b. About the validity of computer models in cognitive science, in: Proceedings of the 32nd Annual Meeting of the Cognitive Science Society, Portland, USA.
  110. Sanz, R., Hernández, C., Rodriguez, M., 2010c. The epistemic control loop, in: Proceedings of CogSys 2010 -4th International Conference on Cognitive Systems, Zurich, Switzerland.
  111. Sanz, R., Hernandez, C., Sanchez-Escribano, M.G., 2012. Consciousness, action se- lection, meaning and phenomenic anticipation. International Journal of Machine Consciousness 4, 383-399.
  112. Sanz, R., L ópez, I., 2000. Minds, MIPS and structural feedback, in: Performance Metrics for Intelligent Systems, PerMIS '2000, Gaithersburg, USA.
  113. Sanz, R., L ópez, I., Bermejo-Alonso, J., 2007a. A rationale and vision for machine consciousness in complex controllers, in: Chella, A., Manzotti, R. (Eds.), Artificial Consciousness. Imprint Academic, pp. 141-155.
  114. Sanz, R., Lopez, I., Hernandez, C., Gomez, J., 2008. Emotion in the ASys framework, in: Brain-Inspired Cognitive Systems Conference, Sao Luis, Brazil.
  115. Sanz, R., L ópez, I., Rodríguez, M., Hernández, C., 2007b. Principles for conscious- ness in integrated cognitive control. Neural Networks 20, 938-946.
  116. Sanz, R., Matía, F., Galán, S., 2000. Fridges, elephants and the meaning of auton- omy and intelligence, in: IEEE International Symposium on Intelligent Control, ISIC'2000, Patras, Greece.
  117. Scherer, K.R., 2000. Emotions as episodes of subsystem synchronization driven by nonlinear appraisal processes, in: (Eds.), M.D.L..I.G. (Ed.), Emotion, develop- ment, and self-organization:Dynamic systems approaches to emotional develop- ment. Cambridge University Press, New York / Cambridge, pp. 70-99.
  118. Scherer, K.R., 2009. Emotions are emergent processes: they require a dynamic com- putational architecture. Philosophical Transactions of the Royal Society B: Bio- logical Sciences 364, 3459-3474.
  119. Scherer, K.R., Banziger, T., Roesch, E.B., 2010. Blueprint for affective computing: a sourcebook and manual. Series in affective science, Oxford University Press, Oxford.
  120. Scherer, K.R., Shorr, A., Johnstone, T. (Eds.), 2001. Appraisal processes in emotion: theory, methods, research. Oxford University Press, Canary, NC.
  121. Selfridge, O.G., 1958. Pandemonium: A paradigm for learning, in: Blake, D.V., Ut- tley, A.M. (Eds.), Proceedings of the Symposium on Mechanisation of Thought Processes, National Physical Laboratory. Her Majesty's Stationery Office, Lon- don. pp. 511-529.
  122. Sellers, M., 2013. Toward a general theory of emotion for biological and artificial agents. Biologically Inspired Cognitive Architectures This Issue.
  123. Serugendo, G.D.M., Gleizes, M.P., Karageorgos, A., 2011. Self-organising software: from natural to artificial adaptation. Natural computing series, Springer Berlin Heidelberg, New York, NY.
  124. Shackman, A.J., Salomons, T.V., Slagter, H.A., Fox, A.S., Winter, J.J., Davidson, R.J., 2011. The integration of negative affect, pain, and cognitive control in the cingu- late cortex. Nature Reviews Neuroscience 12, 154-167.
  125. Shaw, M., Clements, P.C., 1997. A field guide to boxology: Preliminary classifi- cation of architectural styles for software systems, in: Proceedings of the 21st International Computer Software and Applications Conference, IEEE Computer Society, Washington, DC, USA. pp. 6-13.
  126. Simon, H.A., 1996. The Sciences of the Artificial. MIT Press, Cambridge,USA. third edition.
  127. Sloman, A., 1995. Exploring design space and niche space, in: Proceedings 5th Scandinavian Conf. on AI, Trondheim, IOS Press, Amsterdam.
  128. Stewart, T.C., West, R.L., 2007. Deconstructing and reconstructing ACT-R: Explor- ing the architectural space. Cognitive Systems Research 8, 227 -236. Cognitive Modeling.
  129. Sun, R. (Ed.), 2008. The Cambridge Handbook of Computational Psychology. Cam- bridge University Press, New York.
  130. Sztipanovits, J., Karsai, G., 1997. Model-integrated computing. Computer 30, 110- 111.
  131. Tamburrini, G., Datteri, E., 2005. Machine experiments and theoretical modelling: from cybernetic methodology to neuro-robotics. Minds and Machines 15, 335- 358.
  132. Tchao, A.E., Risoldi, M., Di Marzo Serugendo, G., 2011. Modeling self-* sys- tems using chemically-inspired composable patterns, in: Self-Adaptive and Self- Organizing Systems (SASO), 2011 Fifth IEEE International Conference on, pp. 109 -118.
  133. Th órisson, K.R., 2009. From constructionist to constructivist A.I., in: AAAI Fall Symposium on Biologically-Inspired Cognitive Architectures, AAAI press, Washington DC, USA. pp. 175-183.
  134. Treur, J., 2013. An integrative dynamical systems perspective on emotions. Biolog- ically Inspired Cognitive Architectures This Issue.
  135. Webb, B., 2009. Animals versus animats: Or why not model the real iguana? Adap- tive Behavior 17, 269-286.
  136. Webb, B., Consi, T.R. (Eds.), 2001. Biorobotics. Methods and Applications. The MIT Press, Cambridge, MA, USA.
  137. Weston, R.H., 1999. Reconfigurable, component-based systems and the role of en- terprise engineering concepts. Computers in Industry 40, 321-343.
  138. Wilson-Mendenhall, C.D., Barrett, L.F., Simmons, W.K., Barsalou, L.W., 2011. Grounding emotion in situated conceptualization. Neuropsychologia 49, 1105- 1127.
  139. Ziemke, T., 2008. On the role of emotion in biological and robotic autonomy. Biosys- tems 91, 401-408.
  140. Zinober, A.S. (Ed.), 1994. Variable Structure and Lyapunov Control. Springer- Verlag, London.