Advertisement

The Perception-Conceptualisation-Knowledge Representation-Reasoning Representation-Action Cycle: The View from the Brain

  • John G. Taylor
Chapter
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS)

Abstract

We consider new and important aspects of brain processing in which it is shown how perception, attention, reward, working memory, long-term memory, spatial and object recognition, conceptualisation and action can be melded together in a coherent manner. The approach is based mainly on work done in the EU GNOSYS project to create a reasoning robot using brain guidance, starting with the learning of object representations and associated concepts (as long-term memory), with the inclusion of attention. Additional material on actions and internal simulation is taken from the EU MATHESIS project. The framework is thereby extended to the affordances of objects, so that effective action can be taken on the objects. The knowledge gained and the related rewards associated with the representations of the objects involved are used to guide reasoning, through the co-operation of internal models, to attain one or other of the objects. This approach is based on attention as a control system to be exploited to allow high level processing (in conscious thought) or lower level processing (in creative but unconscious thought); creativity is also considered as part of the abilities of the overall system.

Keywords

Attention Control Forward Model Internal Model Inverse Model Object Representation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. .
    Bhattacharya J et al. (2009) Posterior beta and anterior gamma oscillations predict cognitive insight. Journal of Cognitive Neuroscience 21(7):1269–1279CrossRefPubMedGoogle Scholar
  2. .
    Christoff K et al. (2009) Experience sampling during fMRI reveals default network and executive system contributions to mind wandering. Proceedings of the National Academy of Sciences of the United States of America 106(21):8719–8724CrossRefPubMedGoogle Scholar
  3. .
    Clarke A (2004) Being There. Cambridge MA: MIT PressGoogle Scholar
  4. .
    Corbetta M & Shulman GL (2002) Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience 3:201–215CrossRefPubMedGoogle Scholar
  5. .
    Corbetta M, Tansy AP, Stanley CM, Astafiev SV, Snyder AZ, & Shulman GL (2005) A functional MRI study of preparatory signals for spatial location and objects. Neuropsychologia 43:2041–2056CrossRefPubMedGoogle Scholar
  6. .
    Deco G & Rolls ET (2005) Attention, short-term memory, and action selection: a unifying theory. Progress in Neurobiology 76, 236–256PubMedGoogle Scholar
  7. .
    Desmurget M, Grafton S (2000) Forward modeling allows feedback control for fast reaching movements. Trends in Cognitive Sciences 6(11):423–431CrossRefGoogle Scholar
  8. .
    Diamond MR, Ross J, & Morrone MC (2000) Extraretinal control of saccadic suppression. The Journal of Neuroscience 20(9):3449–3455PubMedGoogle Scholar
  9. .
    Fang F, Boyaci H, Kersten D & Murray SO (2008) Attention-dependent representations of a size illusion in V1. Current Biology 18(21):1707–1712CrossRefPubMedGoogle Scholar
  10. .
    Friedman-Hill SR, Robertson LC, Desimone R & Ungerleider LG (2003) Posterior parietal cortex and the filtering of distracters. Proceedings of the National Academy of Sciences of the United States of America 1999(7):4263–4268CrossRefGoogle Scholar
  11. .
    Gibson JJ (1979) The Ecological Approach to Visual Perception. Boston: Houghton-MifflinGoogle Scholar
  12. .
    Gurney K, Prescott TJ, Wickens JR & Redgrave P (2004) Computational models of the basal ganglia: from robots to membranes. Trends in Neurosciences 27:453–459CrossRefPubMedGoogle Scholar
  13. .
    Hamker FH & Zirnsak M (2006) V4 receptive field dynamics as predicted by a systems-level model of visual attention using feedback form the frontal eye field. Neural Networks 19(9):1371–1382CrossRefPubMedGoogle Scholar
  14. .
    Hartley M, Fagard J, Esseily R & Taylor JG (2008) Observational versus trial and error effects in an infant learning paradigm – modelling and experimental data. Proceedings of the International Conference on Artificial Neural Networks 5164:277–289Google Scholar
  15. .
    Hartley M & Taylor JG (2009) A neural network model of creativity. Proceedings of the International Conference on Artificial Neural Networks (in press)Google Scholar
  16. .
    Heft H (1989) Affordances and the body: an intentional analysis of Gibson’s ecological approach to visual perception. Journal for the Theory of Social Behaviour 19(1):1–30CrossRefGoogle Scholar
  17. .
    Kandel ER, Schwartz JH & Jessell TM (2000) Principles of Neuroscience (4th edition) New York: McGraw HillGoogle Scholar
  18. .
    Kanwisher N & Wojciulik E (2000) Visual attention: insights from brain imaging. Nature Reviews Neuroscience 1:91–100CrossRefPubMedGoogle Scholar
  19. .
    Kasderidis S (2007) Developing concept representations In Proceedings of International Conference on Artificial Neural Network (ICANN 2007), Porto, 10–14 Sep. 2007, pp. 922–933Google Scholar
  20. .
    Kounios J, Fleck JI, Green DL, Payne L, Stevenson JL, Bowdend EM & Jung-Beeman M (2008) The origin of insight in resting state behaviour. Neuropsychologia 46(1):281–291CrossRefPubMedGoogle Scholar
  21. .
    Mehta AD, Ulbert I, Schroeder CD (2000) Intermodal selective attention in monkeys. Cerebral Cortex 10:343–358CrossRefPubMedGoogle Scholar
  22. .
    Miall RC (2003) Connecting mirror neurons and forward models. Neuroreport 14(17):2135–2137CrossRefPubMedGoogle Scholar
  23. .
    Milner AD & Goodale MA (1995) The Visual Brain in Action Oxford: Oxford University PressGoogle Scholar
  24. .
    Mohan V & Morasso P (2007) Towards reasoning and coordinating action in the mental space. International Journal of Neural Systems 17(4):1–13CrossRefGoogle Scholar
  25. .
    Morasso P, Bottaro A, Casadio M & Sanguineti V (2005) Preflexes and internal models in biomimetic robot systems. Cognitive Processing 6(1):25–36CrossRefGoogle Scholar
  26. .
    Mozer MC & Sitton M (1998) Computational modelling of spatial attention In H. Pashler (Ed.) Attention (pp. 341–393). New York: Taylor & FrancisGoogle Scholar
  27. .
    Natsoulas T (2004) “To see is to perceive what they afford” James J Gibson’s concept of affordance. Mind and Behaviour 2(4):323–348Google Scholar
  28. .
    Pasupathy A & Connor CE (2001) Shape representation in area V4: position-specific tuning for boundary configuration. Journal of Neurophysiology 86:2505–2519PubMedGoogle Scholar
  29. .
    Raos V, Evangeliou MN & Savaki HE (2004) “Observation of action: grasping with the mind’s hand”. Neuroimage 23:191–201CrossRefGoogle Scholar
  30. .
    Reynolds JH, Chelazzi I & Desimone R (1999) Competitive mechanisms subserve attention in macaque areas V2 and V4 Journal of Neuroscience 19:1736–1753Google Scholar
  31. .
    Santelli M, Flanders M & Soechting JF (2002) Patterns of hand motion during grasping and the influence of sensory guidance. Journal of Neuroscience 22(4):1426–1435Google Scholar
  32. .
    Schultz W (1998) Predictive reward signal of dopamine neurons Journal of Neurophysiology 80 (1):1–27Google Scholar
  33. .
    Schultz W (2004) Neural coding of basic reward terms of animal learning theory, game theory, microeconomics and behavioural ecology. Current Opinion in Neurobiology 14:139–147CrossRefPubMedGoogle Scholar
  34. .
    Schultz W, Dayan P & Montague PR (1997) A neural substrate of prediction and reward. Science 275(5306): 1593–1599CrossRefPubMedGoogle Scholar
  35. .
    Sommer MA & Wurtz RH (2002) A pathway in primate brain for internal monitoring of movements Science 296(5572):1480–1482Google Scholar
  36. .
    Sutton R (1988) Learning to predict by the methods of temporal differences Machine Learning 3(1):9–44Google Scholar
  37. .
    Sutton R & Barto A (1998) Reinforcement Learning Cambridge: MITGoogle Scholar
  38. .
    Taylor JG (2000a) A control model for attention and consciousness. Society for Neuroscience Abstract 26:2231#839.3Google Scholar
  39. .
    Taylor JG (2000b) Attentional movement: the control basis for consciousness. Society for Neuroscience Abstracts 26:2231#839.3Google Scholar
  40. .
    Taylor JG (2005) Mind and consciousness: towards a final answer? Physics of Life Reviews 2(1):1–45CrossRefGoogle Scholar
  41. .
    Taylor JG (2007) CODAM: a model of attention leading to the creation of consciousness. Scholarpedia 2(11):1598CrossRefGoogle Scholar
  42. .
    Taylor JG (2009) A neural model of the loss of self in schizophrenia. Schizophrenia Bulletin (in press)Google Scholar
  43. .
    Taylor JG & Hartley MR (2007) Through reasoning to cognitive machines. IEEE Computational Intelligence Magazine 2(3):12–24CrossRefGoogle Scholar
  44. .
    Taylor JG & Hartley M (2008) Exploring cognitive machines – neural models of reasoning, illustrated through the 2-sticks paradigm. Neurocomputing 71:2411–2419CrossRefGoogle Scholar
  45. .
    Taylor JG & Taylor NR (2000a) Analysis of recurrent cortico-basal ganglia-thalamic loops for working memory. Biological Cybernetics 82:415–432CrossRefPubMedGoogle Scholar
  46. .
    Taylor NR & Taylor JG (2000b) Hard-wired models of working memory and temporal sequence storage and generation. Neural Networks 13:201–224CrossRefPubMedGoogle Scholar
  47. .
    Taylor NR & Taylor JG (2007) A novel novelty detector, in: J. Marques de Sa, L. A. Alexandre, W. Duch and D. Mandic (eds), Proceedings of the International Conference on Artificial Neural Networks, Lecture Notes in Computer Science #4669, Springer, Berlin, pp 973–983Google Scholar
  48. .
    Taylor NR, Panchev C, Kasderidis S, Hartley M & Taylor JG (2006) “Occlusion, attention and object representations”, ICANN’06Google Scholar
  49. .
    Taylor NR, Panchev C, Hartley M, Kasderidis S, Taylor JG (2007a) Occlusion, attention and object representations. Integrated Computer-Aided Engineering 14(4):283–306Google Scholar
  50. .
    Taylor JG, Freeman W, Cleeremans A (eds) (2007b) Brain and consciousness. Neural Networks 20(9):929–1060Google Scholar
  51. .
    Taylor JG, Hartley M, Taylor NR, Panchev C & Kasderidis S (2009) A hierarchical attention-based neural network architecture, based on human brain guidance, for perception, conceptualisation, Action and Reasoning. Image and Vision Computing 27(11):1641–1657CrossRefGoogle Scholar
  52. .
    Toda S (2009) Joint Attention between Mother and Infant in Play Situations Paper Presented at the Annual Meeting of the XVth Biennial International Conference on Infant StudiesGoogle Scholar
  53. .
    Vandervert L (2003) How working memory and cognitive modelling functions of the cerebellum contribute to discoveries in mathematics. New Ideas in Psychology 21:159–175CrossRefGoogle Scholar
  54. .
    Wallas G (1926) The Art of Thought. New York: HarperGoogle Scholar
  55. .
    Wertheimer M (1945) Productive Thinking. New York: HarperGoogle Scholar
  56. .
    Wolpert DM & Kawato M (1998) Multiple paired forward and inverse models for motor control. Neural Networks 11:1317–29CrossRefPubMedGoogle Scholar
  57. .
    Young G (2006) Are different affordances subserved by different neural pathways? Brain and Cognition 62:134–142CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of MathematicsKing’s CollegeLondonUK

Personalised recommendations