Advertisement

Embodiment in Emotional Learning, Decision Making and Behaviour: The ‘What’ and the ‘How’ of Action

  • Robert Lowe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8515)

Abstract

Connectionist and bio-inspired approaches to the study of emotional learning and decision making often emphasize, or imply, an executive role for the brain whilst paying only lip service to the role of the non-neural body. In this short paper I will discuss approaches to modelling emotions that have attempted to take into account, in one form or another, the role of the body in emotional learning and decision making. More specifically, I will argue that the ‘how’ of behavioural responding and not just the ‘what’ must be factored into any learning algorithm that purports to be emotional. Furthermore, I will refer to research that has utilized abstract artificial environments designed to explore the relevance of how behaviours are carried out with a view to scaling performance to more complex, including human-based, environments.

Keywords

Emotions Neural Networks Homeostatic grounding Abstract environments 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alexander, W.H., Sporns, O.: An Embodied Model of Learning Plasticity, and Reward. Adaptive Behavior (3-4), 143–159 (2002)Google Scholar
  2. 2.
    Armony, J.L.: Computational Models of Emotion. In: Proceedings of the IEEE Int. Joint Conf. on Neural Networks, pp. 1598–1602 (2005)Google Scholar
  3. 3.
    Avila-Garcia, O., Canamero, L.: Hormonal modulation of perception in motivation-based action selection architectures. In: Proceedings of Agents that Want and Like: Motivational and Emotional Roots of Cognition and Action, Symposium of the AISB05 Convention, pp. 9–17. University of Hertfordshire, Hatfield (2005)Google Scholar
  4. 4.
    Balkenius, C.: Emotional Learning: A Computational Model of the Amygdala. Cybernetics and Syst. 32, 611–636 (2001)CrossRefzbMATHGoogle Scholar
  5. 5.
    Balkenius, C., Förster, A., Johansson, B., Thorsteinsdottir, V.: Anticipation in attention. In: Pezzulo, G., Butz, M.V., Castelfranchi, C., Falcone, R. (eds.) The Challenge of Anticipation. LNCS (LNAI), vol. 5225, pp. 65–83. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Balkenius, C., Morén, J., Winberg, S.: Interactions between Motivation, Emotion and Attention: From Biology to Robotics. In: Cañamero, L., Oudeyer, P.-Y., Balkenius, C. (eds.) Proceedings of the Ninth International Conference on Epigenetic Robotics, vol. 145. Lund Univeristy Cognitive Studies (2009)Google Scholar
  7. 7.
    Boureau, Y.-L., Dayan, P.: Opponency revisited: competition and cooperation between dopamine and serotonin. Neuropsychopharmacol. Rev. 1, 1–24 (2010)Google Scholar
  8. 8.
    Itti, L., Koch, C.: Feature combination strategies for saliency-based visual attention systems. Journal of Electronic Imaging 10(1), 161–169 (2001)CrossRefGoogle Scholar
  9. 9.
    LeDoux, J.E.: The Emotional Brain. Simon & Schuster, NewYork (1996)Google Scholar
  10. 10.
    Kiryazov, K., Lowe, R.: The role of arousal in embodying the cue-deficit model in multi-resource human-robot interaction. In: European Conference of Artificial Life (ECAL) (2013a) (accepted)Google Scholar
  11. 11.
    Kiryazov, K., Lowe, R., Becker-Asano, C., Randazzo, M.: The role of arousal in two resource problem tasks for humanoid service robots. In: 22nd IEEE International Symposium on Robot and Human Interactive Communication (Ro-Man) (2013) (in press)Google Scholar
  12. 12.
    Lowe, R., Humphries, M., Ziemke, T.: The dual-route hypothesis: evaluating a neurocomputational model of fear conditioning in rats. Connection Science 21(1), 15–37 (2009)CrossRefGoogle Scholar
  13. 13.
    Lowe, R., Montebelli, A., Ieropoulos, I., Greenman, J., Melhuish, C., Ziemke, T.: Grounding motivation in energy autonomy: a study of artificial metabolism constrained robot dynamics. In: Fellermann, H., Drr, M., Hanczyc, M., Laursen, L., Maurer, S., Merkle, D., Monnard, P.-A., Sty, K., Rasmussen, S. (eds.) Artificial Life XII, pp. 725–732. The MIT Press, Odense (2010)Google Scholar
  14. 14.
    McFarland, D., Spier, E.: Basic cycles, utility and opportunism in self-sufficient robots. Rob. Auton. Syst. 20, 179–190 (1997)CrossRefGoogle Scholar
  15. 15.
    Montebelli, A., Lowe, R., Ziemke, T.: Toward Metabolic Robotics: Insights from Modeling Embodied Cognition in a Biomechatronic Symbiont. Artificial Life 19, 299–315 (2013)CrossRefGoogle Scholar
  16. 16.
    Morén, J.: LearningandEmotion. Ph.D. thesis, Lund University (2002)Google Scholar
  17. 17.
    Niv, Y.: Reinforcement learning in the brain. J. Math. Psychol. 53, 139–154 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  18. 18.
    Rescorla, R.A., Wagner, A.R.: A theory of pavloviancon- ditioning: variations in the effectiveness of reinforcement and non- reinforcement. In: Black, A.H., Prokasy, W.F. (eds.) Classical Conditioning II: Current Research and Theory, Appleton- Century-Crofts, New York (1972)Google Scholar
  19. 19.
    Roesch, E.B., Korsten, N.: I, Fragopanagos, J.G, Taylor. Emotions in artificial neural networks. In: Scherer, K.R., Baenziger, T., Roesch, E.B. (eds.) Blueprint for Affective Computing: a Sourcebook. Oxford University Press, Oxford (2010)Google Scholar
  20. 20.
    Rolls, E.: Précis of the brain and emotion. Behavioral and Brain Sciences 23, 177–234 (2001)CrossRefGoogle Scholar
  21. 21.
    Rolls, E.T.: Emotion Explained. Oxford University Press, Oxford (2005)CrossRefGoogle Scholar
  22. 22.
    Schultz, W., Dayan, P., Montague, P.R.: A neural substrate of prediction and reward. Science 275, 1593–1599 (1997)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Robert Lowe
    • 1
  1. 1.Interaction LabUniversity of SkövdeSweden

Personalised recommendations