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A Cognitive Control Architecture for the Perception–Action Cycle in Robots and Agents

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Abstract

We show aspects of brain processing on how visual perception, recognition, attention, cognitive control, value attribution, decision-making, affordances and action can be melded together in a coherent manner in a cognitive control architecture of the perception–action cycle for visually guided reaching and grasping of objects by a robot or an agent. The work is based on the notion that separate visuomotor channels are activated in parallel by specific visual inputs and are continuously modulated by attention and reward, which control a robot’s/agent’s action repertoire. The suggested visual apparatus allows the robot/agent to recognize both the object’s shape and location, extract affordances and formulate motor plans for reaching and grasping. A focus-of-attention signal plays an instrumental role in selecting the correct object in its corresponding location as well as selects the most appropriate arm reaching and hand grasping configuration from a list of other configurations based on the success of previous experiences. The cognitive control architecture consists of a number of neurocomputational mechanisms heavily supported by experimental brain evidence: spatial saliency, object selectivity, invariance to object transformations, focus of attention, resonance, motor priming, spatial-to-joint direction transformation and volitional scaling of movement.

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References

  1. Alexander GE, Crutcher MD. Neural representations of the target (goal) of visually guided arm movements in three motor areas of the monkey. J Neurophysiol. 1990;64(1):164–78.

    CAS  PubMed  Google Scholar 

  2. Braver TS, Cohen JD. On the control of control: the role of dopamine in regulating prefrontal function and working memory. In: Monsell S, Driver J, editors. Control of cognitive processes: attention and performance XVIII. Cambridge: MIT Press; 2000. p. 713–38.

    Google Scholar 

  3. Bullock D, Grossberg S. Neural dynamics of planned arm movements: emergent invariants and speed-accuracy properties during trajectory formation. Psychol Rev. 1988;95:49–90.

    Article  CAS  PubMed  Google Scholar 

  4. Bullock D, Grossberg S, Guenther F. A self organizing neural model for motor equivalent reaching and tool use by a multijoint arm. J Cogn Neurosci. 1993;5(4):408–35.

    Article  CAS  PubMed  Google Scholar 

  5. Burt PJ, Adelson EH. The Laplacian pyramid as a compact image code. IEEE Trans Commun. 1983;31:532–40.

    Article  Google Scholar 

  6. Camintini R, Johnson P, Urbano A. Making arm movements within different parts of space: dynamic aspects in the primate motor cortex. J Neurosci. 1990;10:2039–58.

    Google Scholar 

  7. Carpenter GA, Grossberg S. Adaptive resonance theory. In: Arbib MA, editor. The handbook of brain theory and neural networks. 2nd ed. Cambridge: MIT Press; 2003. p. 87–90.

    Google Scholar 

  8. Chelazzi L, Duncan J, Miller EK, Desimone R. Responses of neurons in the inferior temporal cortex during memory guided visual search. J Neurophysiol. 1998;80(6):2918–40.

    CAS  PubMed  Google Scholar 

  9. Coizet V, Comoli E, Westby GW, Redgrave P. Phasic activation of Substantia Nigra and the ventral tegmental area by chemical stimulation of the superior colliculus: an electrophysiological investigation in the rat. Eur J Neurosci. 2003;17(1):28–40.

    Article  PubMed  Google Scholar 

  10. Colby CL, Goldberg ME. Space and attention in parietal cortex. Ann Rev Neurosci. 1999;22:319–49.

    Article  CAS  PubMed  Google Scholar 

  11. Comoli E, Coizet V, Boyes J, Bolam JP, Canteras NS, Quirk RH, Overton PG, Redgrave P. A direct projection from the Superior Colliculus to substantia Nigra for detecting salient visual events. Nat Neurosci. 2003;6(9):974–80.

    Article  CAS  PubMed  Google Scholar 

  12. Cutsuridis V. Does abnormal reciprocal inhibition lead to co-contraction of antagonist muscles? A modeling study. Int J Neural Syst. 2007;17(4):319–27.

    Article  PubMed  Google Scholar 

  13. Cutsuridis V. A cognitive model of saliency, overt attention and picture scanning. Cognit Comput. 2009;1:292–9.

    Article  Google Scholar 

  14. Cutsuridis V. Neural network modeling of voluntary single joint movement organization. I. Normal conditions. In: Chaovalitwongse WA, Pardalos P, Xanthopoulos P, editors. Computational neuroscience. Berlin: Springer; 2010. p. 181–92.

    Chapter  Google Scholar 

  15. Cutsuridis V. Neural network modeling of voluntary single joint movement organization. II. Parkinson’s disease. In: Chaovalitwongse WA, Pardalos P, Xanthopoulos P, editors. Computational neuroscience. Berlin: Springer; 2010. p. 193–212.

    Chapter  Google Scholar 

  16. Cutsuridis V. Origins of a repetitive and co-contractive pattern of muscle activation in Parkinson’s disease. Neural Networks. 2011;24(6):592–601.

    Article  PubMed  Google Scholar 

  17. Cutsuridis V. (2012). The perception-…-action cycle cognitive architecture and autonomy: a view from the brain. J Artif General Intell (in press).

  18. Cutsuridis V, Perantonis S. A neural model of Parkinson’s disease bradykinesia. Neural Netw. 2006;19(4):354–74.

    Article  PubMed  Google Scholar 

  19. Cutsuridis V, Heida T, Duch W, Doya K. Neurocomputational models of brain disorders. Neural Netw. 2011;24(6):513–4.

    Article  Google Scholar 

  20. Cutsuridis V, Hussain A, Taylor JG. Perception-action cycle: Models, architectures and hardware. USA: Springer; 2011.

    Book  Google Scholar 

  21. Cutsuridis V, Smyrnis N, Evdokimidis I, Perantonis S. A neural network model of decision making in an antisaccade task by the superior colliculus. Neural Networks. 2007;20(6): 690–704.

    Article  PubMed  Google Scholar 

  22. Desimone R, Duncan J. Neural mechanisms of selective visual attention. Ann Rev Neurosci. 1995;18:193–222.

    Article  CAS  PubMed  Google Scholar 

  23. DeValois RL, Albrecht DG, Thorell LG. Spatial-frequency selectivity of cells in macaque visual cortex. Vis Res. 1982;22:545–59.

    Article  CAS  Google Scholar 

  24. Dommett E, Coizet V, Blaha CD, Martindale J, Lefebre V, Walton N, Mayhew JE, Overton PG, Redgrave P. How visual stimuli activate dopaminergic neurons at short latency. Science. 2005;307(5714):1476–9.

    Article  CAS  PubMed  Google Scholar 

  25. Egner T, Hirsch J. Cognitive control mechanisms resolve conflict through cortical amplification of task relevant information. Nat Neurosci. 2005;8(12):1784–90.

    Article  CAS  PubMed  Google Scholar 

  26. Fagg AH, Arbib M. Modelling parietal-premotor interactions in a primate control of grasping. Neural Netw. 1998;11(7–8):1277–303.

    Article  PubMed  Google Scholar 

  27. Fuster JM. Upper processing stages of the perception-action cycle. TICS. 2004;8(4):143–5.

    Google Scholar 

  28. Gallese V, Fadiga L, Fogassi L, Luppino G, Murata A. A parietal-frontal circuit for hand grasping movements in the monkey: evidence from reversible inactivation experiments. In: Their P, Karnath HO, editors. Parietal lobe contributions to orientation in 3D space. Berlin: Springer; 1997. p. 255–70.

    Chapter  Google Scholar 

  29. Gawne TJ, Martin JM. Responses of primate visual cortical V4 neurons to simultaneously presented stimuli. J Neurophys. 2002;88:1128–35.

    Google Scholar 

  30. Georgopoulos AP, Kalaska JF, Crutcher MD, Camintini R, Massey JT. The representation of movement direction in the motor cortex: single-cell and population. In: Edelman GM, Gall WE, Cowan WM, editors. Dynamic aspects of cortical function. New York: Wiley; 1984. p. 501–24.

    Google Scholar 

  31. Georgopoulos AP, Schwartz AB, Ketter RE. Neuronal population coding of movement direction. Science. 1986;233:1416–9.

    Article  CAS  PubMed  Google Scholar 

  32. Gonzalez RC, Woods RE. Digital image processing. New Jersey: Prentice Hall; 2002.

    Google Scholar 

  33. Gottlieb JP, Kusunoki M, Goldberg ME. The representation of visual salience in monkey parietal cortex. Nature. 1998;391(6666):481–4.

    Article  CAS  PubMed  Google Scholar 

  34. Horak FB, Anderson ME. Influence of globus pallidus on arm movements in monkeys. I. Effects of kainic acid induced lesions. J Neurophysiol. 1984;52:290–304.

    CAS  PubMed  Google Scholar 

  35. Horak FB, Anderson ME. Influence of globus pallidus on arm movements in monkeys. I. Effects of stimulations. J Neurophysiol. 1984;52:305–22.

    CAS  PubMed  Google Scholar 

  36. Hubel DH, Wiesel TN. Receptive fields and functional architecture of monkey striate cortex. J Phys. 1968;195:215–43.

    CAS  Google Scholar 

  37. Itti L, Koch C. A saliency based search mechanism for overt and covert shifts of visual attention. Vis Res. 2000;40:1489–506.

    Article  CAS  PubMed  Google Scholar 

  38. Jordan MI. Motor learning and the degrees of freedom problem. In: Jeannerod M, editor. Attention and performance XIII: Motor representation and control. Hillsdale: Erlbaum; 1990. p. 796–836.

    Google Scholar 

  39. Jordan MI, Rumelhard DE. Forward models: supervised learning with a distal teacher. Cogn Sci. 1992;16:307–54.

    Article  Google Scholar 

  40. Kalaska JF, Cohen DA, Hyde ML, Prud’homme M. A comparison of movement direction related versus load direction related activity in primate motor cortex using a two dimensional reaching task. J Neurosci. 1989;9(6):2080–102.

    CAS  PubMed  Google Scholar 

  41. Ketter RE, Schwartz AB, Georgopoulos AP. Primate motor cortex and free arm movements to visual targets in three dimensional space. III. Positional gradients and population coding of movement direction from various movement origins. J Neurosci. 1988;8(8):2938–47.

    Google Scholar 

  42. Kravitz DJ, Saleem KS, Baker CI, Mishkin M. A new neural framework for visuospatial processing. Nat Rev Neurosci. 2011;12:217–30.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  43. Kusunoki M, Gottlieb J, Goldberg ME. The lateral intraparietal area as a salience map: the representation of abrupt onset, stimulus motion and task relevance. Vis Res. 2000;40:1459–68.

    Article  CAS  PubMed  Google Scholar 

  44. Lampl I, Ferster D, Poggio T, Riesenhuber M. Intracellular measurements of spatial integration and the MAX operation in complex cells of the cat primary visual cortex. J Neurophys. 2004;92:2704–13.

    Article  Google Scholar 

  45. Logothetis NK, Pauls J, Poggio T. Shape representation in the inferior temporal cortex of monkeys. Curr Biol. 1995;5:552–63.

    Article  CAS  PubMed  Google Scholar 

  46. MacDonald AWI, Cohen J, Stegner V, Carter CS. Dissociating the role of dorsolateral prefrontal and anterior cingulated cortex in cognitive control. Science. 2000;288(5472):1838–935.

    Article  Google Scholar 

  47. McHaffie JG, Jiang H, May PJ, Coizet V, Overton PG, Stein BE, Redgrave P. A direct projection from superior colliculus to substantia Nigra Pars compacta in the cat. Neurosci. 2006;138(1):221–34.

    Article  CAS  Google Scholar 

  48. Miller E. The prefrontal cortex and cognitive control. Nat Rev Neurosci. 2000;1:59–65.

    Article  CAS  PubMed  Google Scholar 

  49. Murata A, Gallese V, Kaseda K, Sakata H. Parietal neurons related to memory guided hand manipulation. J Neurophys. 1996;75:2180–6.

    CAS  Google Scholar 

  50. Murata A, Gallese V, Luppino G, Kaseda K, Sakata H. Selectivity for the shape, size and orientation of objects for grasping in neurons of monkey parietal area AIP. J Neurophysiol. 2000;83:339–65.

    Google Scholar 

  51. O’Reilly R, Braver T, Cohen J. A biologically based computational model of working memory. In: Miyake A, Shah P, editors. Models of working memory: mechanisms of active maintenance and executive control. Cambridge: Cambridge University Press; 1999.

    Google Scholar 

  52. Palmer S. Vision science: photons to phenomenology. USA: MIT Press; 1999.

    Google Scholar 

  53. Purves D, Augustine GJ, Fitzpatrick D, Hall WC, LaMantia AS, McNamara JO, White LE. Neuroscience. USA: Sinauer Associates Inc; 2004.

    Google Scholar 

  54. Redgrave P, Gurney K. The short latency dopamine signal: a role in discovering novel actions. Nat Neurosci. 2006;7:967–75.

    Article  CAS  Google Scholar 

  55. Reynolds JH, Desimone R. The role of neural mechanisms of attention in solving the binding problem. Neuron. 1999;24(1):19–29.

    Article  CAS  PubMed  Google Scholar 

  56. Rizzolatti G, Sinigaglia C. The functional role of the parieto-frontal mirror circuit: interpretations and misinterpretations. Nat Rev Neurosci. 2010;11(4):264–74.

    Article  CAS  PubMed  Google Scholar 

  57. Robinson DL, Petersen SE. The pulvinar and visual salience. TINS. 1992;15(4):127–32.

    CAS  PubMed  Google Scholar 

  58. Rolls E. Memory, attention and decision making: a unifying computational neuroscience approach. Oxford: Oxford University Press; 2008.

    Google Scholar 

  59. Sakata H, Taira M, Murata A, Mine S. Neural mechanisms of visual guidance of hand action in the parietal cortex of the monkey. Cereb Cortex. 1995;5:429–38.

    Article  CAS  PubMed  Google Scholar 

  60. Schall JD, Hanes DP, Thompson KG, King DJ. Saccade target selection in frontal eye field of macaque. I Visual and premovement activation. J Neurosci. 1995;15:6905–18.

    CAS  PubMed  Google Scholar 

  61. Schultz W. Predictive reward signal of dopamine neurons. J Neurophys. 1998; 80:1–27.

    CAS  Google Scholar 

  62. Serre T, Kouh M, Cadieu C, Knoblich U, Kreiman G, Poggio T. A theory of object recognition: computations and circuits in the feedforward path of the ventral stream in primate visual cortex. CBCL Memo 259, MIT (2005).

  63. Sharma G. Digital color imaging handbook. New York: CRC Press; 2003.

    Google Scholar 

  64. Taira M, Mine S, Georgopoulos AP, Murata A, Sakata H. Parietal cortex neurons of the monkey related to the visual guidance of hand movement. Exp Brain Res. 1990;83:29–36.

    Article  CAS  PubMed  Google Scholar 

  65. Taylor JG, Hartley M, Taylor N, Panchev C, Kasderidis S. A hierarchical attention-based neural network architecture, based on human brain guidance, for perception, conceptualisation, action and reasoning. Image Vis Comput. 2009;27:1641–57.

    Article  Google Scholar 

  66. Thompson KG, Bichot NP. A visual saliency map in the primate frontal eye field. Prog Brain Res. 2005;147:251–62.

    PubMed  Google Scholar 

  67. Ulloa A, Bullock D. A neural network simulating human reach-grasp coordination by continuous updating of vector positioning commands. Neural Netw. 2003;16(8):1141–60.

    Article  PubMed  Google Scholar 

  68. Ungerleider LG, Haxby JV. ‘What’ and ‘where’ in the human brain. Curr Opin Neurobiol. 1994;4:157–65.

    Article  CAS  PubMed  Google Scholar 

  69. Wiersing H, Koerner E. Learning optimized features for hierarchical models of invariant object recognition. Neural Comput. 2003;15(7):1559–88.

    Article  Google Scholar 

  70. Williams SM, Goldman-Rakic PS. Widespread origin of the primate mesofrontal dopamine system. Cereb Cortex. 1998;8:321–45.

    Article  CAS  PubMed  Google Scholar 

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Cutsuridis, V., Taylor, J.G. A Cognitive Control Architecture for the Perception–Action Cycle in Robots and Agents. Cogn Comput 5, 383–395 (2013). https://doi.org/10.1007/s12559-013-9218-z

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