Psychological Research

, Volume 77, Issue 1, pp 7–19 | Cite as

How affordances associated with a distractor object affect compatibility effects: A study with the computational model TRoPICALS

  • Daniele Caligiore
  • Anna M. Borghi
  • Domenico Parisi
  • Rob Ellis
  • Angelo Cangelosi
  • Gianluca Baldassarre
Original Article


Seeing an object activates both visual and action codes in the brain. Crucial evidence supporting this view is the observation of object to response compatibility effects: perception of an object can facilitate or interfere with the execution of an action (e.g., grasping) even when the viewer has no intention of interacting with the object. TRoPICALS is a computational model that proposes some general principles about the brain mechanisms underlying compatibility effects, in particular the idea that top-down bias from prefrontal cortex, and whether it conflicts or not with the actions afforded by an object, plays a key role in such phenomena. Experiments on compatibility effects using a target and a distractor object show the usual positive compatibility effect of the target, but also an interesting negative compatibility effect of the distractor: responding with a grip compatible with the distractor size produces slower reaction times than the incompatible case. Here, we present an enhanced version of TRoPICALS that reproduces and explains these new results. This explanation is based on the idea that the prefrontal cortex plays a double role in its top-down guidance of action selection producing: (a) a positive bias in favour of the action requested by the experimental task; (b) a negative bias directed to inhibiting the action evoked by the distractor. The model also provides testable predictions on the possible consequences of damage to volitional circuits such as in Parkinsonian patients.


  1. Arbib, M. A. (1997). From visual affordances in monkey parietal cortex to hippocampo-parietal interactions underlying rat navigation. Philosophical Transactions of The Royal Society B Biological Sciences, 352, 1429–1436.CrossRefGoogle Scholar
  2. Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59, 617–645.PubMedCrossRefGoogle Scholar
  3. Behrmann, M., Geng, J. J., & Shomstein, S. (2004). Parietal cortex and attention. Current Opinion in Neurobiology, 14, 212–217.PubMedCrossRefGoogle Scholar
  4. Berthier, N. E., Rosenstein, M. T., & Barto, A. G. (2005). Approximate optimal control as a model for motor learning. Psychological Review, 112, 329–346.PubMedCrossRefGoogle Scholar
  5. Borghi, A. M., Di Ferdinando, A., & Parisi, D. (2011). Objects, spatial compatibility, and affordances: A connectionist study. Cognitive Systems Research, 12, 33–44.CrossRefGoogle Scholar
  6. Caligiore, D., Borghi, A. M., Parisi, D., & Baldassarre, G. (2010a). TRoPICALS: A computational embodied neuroscience model of compatibility effects. Psychological Review, 117, 1188–1228.PubMedCrossRefGoogle Scholar
  7. Caligiore, D., Borghi, A.M., Parisi, D., Ellis, R., Cangelosi, A., & Baldassarre, G. (2011). Affordances of distractors and compatibility effects: A study with the computational model TRoPICALS. Available from Nature Precedings (
  8. Caligiore, D., Ferrauto, T., Parisi, D., Accornero, N., Capozza, M., & Baldassare, G. (2008). Using motor babbling and Hebb rules for modeling the development of reaching with obstacles and grasping. In R. Dillmann, C. Maloney, G. Sandini, T. Asfour, G. Cheng, G. Metta, & A. Ude (Eds.), International Conference on Cognitive Systems (pp. E1–E8). Karlsruhe: University of Karlsruhe.Google Scholar
  9. Caligiore, D., Guglielmelli, E., Borghi, A. M., Parisi, D., & Baldassarre, G. (2010b). A Reinforcement Learning Model of Reaching Integrating Kinematic and Dynamic Control in a Simulated Arm Robot. In: IEEE International Conference on Development and Learning (ICDL2010), IEEE, Piscataway, NJ, pp 211–218.Google Scholar
  10. Cisek, P. (2007). Cortical mechanisms of action selection: The affordance competition hypothesis. Philosophical Transactions of The Royal Society B-Biological Sciences, 362, 1585–1599.CrossRefGoogle Scholar
  11. Cisek, P., & 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.PubMedCrossRefGoogle Scholar
  12. Clark, A. (1996). Being there–Putting brain, body and world together again. Cambridge: MIT Press.Google Scholar
  13. Culham, J. C., & Kanwisher, N. G. (2001). Neuroimaging of cognitive functions in human parietal cortex. Current Opinion in Neurobiology, 11, 157–163.PubMedCrossRefGoogle Scholar
  14. Dayan, P., & Abbott, L. F. (2001). Theoretical neuroscience: Computational and mathematical modeling of neural systems. Cambridge: MIT Press.Google Scholar
  15. Deco, G., & Rolls, E. T. (2003). Attention and working memory: A dynamical model of neuronal activity in the prefrontal cortex. European Journal of Neuroscience, 18, 2374–2390.PubMedCrossRefGoogle Scholar
  16. Ehrsson, H. H., Fagergren, A., Jonsson, T., Westling, G., Johansson, R. S., & Forssberg, H. (2000). Cortical activity in precision–versus power-grip tasks: An fMRI study. Journal of Neurophysiology, 83, 528–536.PubMedGoogle Scholar
  17. Ellis, R., Tucker, M., Symes, E., & Vainio, L. (2007). Does selecting one visual object from several require inhibition of the actions associated with non selected objects? Journal of Experimental Psychology: Human Perception and Performance, 33, 670–691.PubMedCrossRefGoogle Scholar
  18. Erlhagen, W., & Schöner, G. (2002). Dynamic field theory of movement preparation. Psychological Review, 109, 545–572.PubMedCrossRefGoogle Scholar
  19. Fagg, A. H., & Arbib, M. A. (1998). Modeling parietal-premotor interaction in primate control of grasping. Neural Networks, 11, 1277–1303.PubMedCrossRefGoogle Scholar
  20. Feldman, A. G. (1986). Once more on the equilibrium-point hypothesis (lambda model) for motor control. Journal of Motor Behavior, 18, 17–54.PubMedGoogle Scholar
  21. Fuster, J. M. (1997). The prefrontal cortex: Anatomy, physiology, and neuropsychology of the frontal lobe. Philadelphia: Lippincott-Raven.Google Scholar
  22. Fuster, J. M. (2001). The prefrontal cortex–an update: Time is of the essence. Neuron, 30, 319–333.PubMedCrossRefGoogle Scholar
  23. Galpin, A., Tipper, S. P., Dick, J. P., & Poliakoff, E. (2010). Object affordance and spatial-compatibility effects in Parkinson’s disease. Cortex, 47, 332–341.Google Scholar
  24. Gibson, J. J. (1979). The ecological approach to visual perception. Boston: Houghton Mifflin.Google Scholar
  25. Grèzes, J., Tucker, M., Armony, J., Ellis, R., & Passingham, R. E. (2003). Objects automatically potentiate action: An fMRI study of implicit processing. European Journal of Neuroscience, 17, 2735–2740.PubMedCrossRefGoogle Scholar
  26. Grill-Spector, K. (2008). Object perception: Physiology. In B. Goldstein (Ed.), Encyclopedia of perception (pp. 648–653). Sage Publications.Google Scholar
  27. Grill-Spector, K., & Malach, R. (2004). The human visual cortex. Annual Review of Neuroscience, 27, 649–677.PubMedCrossRefGoogle Scholar
  28. Haggard, P. (2008). Human volition: Towards a neuroscience of will. Nature Reviews Neuroscience, 9, 934–946.PubMedCrossRefGoogle Scholar
  29. Hubel, D. H. (1988). Eye, brain and vision. Scientific American Books: New York.Google Scholar
  30. Iberall, T., & Arbib, M. A. (1990). Schemas for the control of hand movements: An essay on cortical localization. In M. A. Goodale (Ed.), Vision and action: The control of grasping (pp. 163–180). Norwood: Ablex Publishing.Google Scholar
  31. Jahanshahi, M., Jenkins, H., Brown, R. G., Marsden, C. D., Passingham, R. E., & Brooks, D. J. (1995). Self-initiated versus externally triggered movements. I. An investigation using measurement of regional cerebral blood flow with pet and movement-related potentials in normal and Parkinson’s disease subjects. Brain, 118, 913–933.PubMedCrossRefGoogle Scholar
  32. Jeannerod, M. (1994). The representing brain: Neural correlates of motor intention and imagery. Behavioral and Brain Sciences, 17, 187–246.CrossRefGoogle Scholar
  33. Kandel, E. R., Schwartz, J. H., & Jessel, T. M. (2000). Principles of Neural Science. New York: McGraw-Hill.Google Scholar
  34. Knight, R. T., Staines, W. R., Swickc, D., & Chaoc, L. L. (1999). Prefrontal cortex regulates inhibition and excitation in distributed neural networks. Acta Psychologica, 101, 159–178.PubMedCrossRefGoogle Scholar
  35. Kohonen, T. (1997). Self-Organizing Maps (Second Edition ed.). Berlin: Springer-Verlag.CrossRefGoogle Scholar
  36. Lang, A., & Lozano, A. (1998). Parkinson’s disease. New England Journal of Medicine, 339, 1044–1053.PubMedCrossRefGoogle Scholar
  37. Lisman, J. (1989). A mechanism for the Hebb and the anti-Hebb processes underlying learning and memory. Proceedings of the National Academy of Sciences of the United States of America, 86(23), 9574–9578.PubMedCrossRefGoogle Scholar
  38. Logothetis, N. K., Pauls, J., & Poggio, T. (1995). Shape representation in the inferior temporal cortex of monkeys. Current Biology, 5, 552–563.PubMedCrossRefGoogle Scholar
  39. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of premotor cortex function. Annual Review of Neuroscience, 24, 167–202.PubMedCrossRefGoogle Scholar
  40. Milner, D. A., & Goodale, M. A. (1995). The Visual Brain in Action. Oxford: Oxford University Press.Google Scholar
  41. Murata, A., Gallese, V., Luppino, G., Kaseda, M., & Sakata, H. (2000). Selectivity for the shape, size, and orientation of objects for grasping in neurons of monkey parietal area AIP. Journal of Neurophysiology, 83, 2580–2601.PubMedGoogle Scholar
  42. Nachev, P., Kennard, C., & Husain, M. (2008). Functional role of the supplementary and pre-supplementary motor areas. Nature Reviews Neuroscience, 9, 856–869.PubMedCrossRefGoogle Scholar
  43. Noë, A. (2004). Action in perception. In H. Putnam & N. Block (Eds.), Perception (Vol. 37). MIT Press.Google Scholar
  44. Nolfi, S. (2009). Behavior and cognition as a complex adaptive system: Insights from robotic experiments. In C. Hooker (Ed.), Handbook of the Philosophy of Science. Volume 10: Philosophy of Complex Systems. General editors: Dov M. Gabbay, Paul Thagard and John Woods. Elsevier.Google Scholar
  45. Oguro, H., Ward, R., Bracewel, M., Hindle, J., & Rafal, R. (2009). Automatic activation of motor programs by object affordances in patients with Parkinson’s disease. Neuroscience Letters, 9, 856–869.Google Scholar
  46. Oztop, E., Bradley, N. S., & Arbib, M. A. (2004). Infant grasp learning: A computational model. Experimental Brain Research, 158, 480–503.CrossRefGoogle Scholar
  47. Parisi, D., Ceccon, F., & Nolfi, S. (1990). Econets: Neural networks that learn in an environment. Network, 1, 149–168.CrossRefGoogle Scholar
  48. Plunkett, K., & Elman, J. L. (1997). Exercises in rethinking innateness: A handbook for connectionist simulations. Cambridge: The MIT Press.Google Scholar
  49. Pouget, A., Dayan, P., & Zemel, R. (2000). Information processing and population codes. Nature Reviews Neuroscience, 1, 125–132.PubMedCrossRefGoogle Scholar
  50. Redgrave, P., Prescott, T. J., & Gurney, K. (1999). The basal ganglia: A vertebrate solution to the selection problem? Neuroscience, 89, 1009–1023.PubMedCrossRefGoogle Scholar
  51. Redgrave, P., Rodriguez, M., Smith, Y., Rodriguez-Oroz, M. C., Lehericy, S., Bergman, H., et al. (2010). Goal-directed and habitual control in the basal ganglia: Implications for Parkinson’s disease. Nature Reviews Neuroscience, 11, 760–772.PubMedCrossRefGoogle Scholar
  52. Rizzolatti, G., & Craighero, L. (2004). The mirror neuron system. Annual Review of Neuroscience, 27, 169–192.PubMedCrossRefGoogle Scholar
  53. Rizzolatti, G., Fogassi, L., & Gallese, V. (1997). Parietal cortex: From sight to action. Current Opinion in Neurobiology, 7, 562–567.PubMedCrossRefGoogle Scholar
  54. Rizzolatti, G., Luppino, G., & Matelli, M. (1998). The organization of the cortical motor system: New concepts. Electroencephalography and Clinical Neurophysiology/Electromyography and Motor Control, 106, 283–296.Google Scholar
  55. Simon, O., Mangin, J. F., Cohen, L., Bihan, D. L., & Dehaene, S. (2002). Topographical layout of hand, eye, calculation, and language-related areas in the human parietal lobe. Neuron, 33, 475–487.PubMedCrossRefGoogle Scholar
  56. Sobel, I., & Feldman, G. (1968). A 3x3 isotropic gradient operator for image processing, Presentation for Stanford Artificial Project.Google Scholar
  57. Sternberg, S. (1969). The discovery of processing stages: Extensions of Doder’s method. In W. G. Koster (Ed.), Attention and Performance II. Amsterdam: North-Holland Publishing Company.Google Scholar
  58. Tucker, M., & Ellis, R. (2001). The potentiation of grasp types during visual object categorization. Visual Cognition, 8, 769–800.CrossRefGoogle Scholar
  59. Tucker, M., & Ellis, R. (2004). Action priming by briefly presented objects. Acta Psychologica, 116, 185–203.PubMedCrossRefGoogle Scholar
  60. Ungerleider, L. G., & Mishkin, M. (1982). Two cortical visual systems. In D. J. Ingle, M. A. Goodale, & R. J. W. Mansfield (Eds.), Analysis of visual behavior (pp. 549–586). Cambridge: MIT Press.Google Scholar
  61. Van Essen, D. C., Lewis, J. W., Drury, H. A., Hadjikhani, N., Tootell, R. B., Bakircioglu, M., et al. (2001). Mapping visual cortex in monkeys and humans using surface-based atlases. Vision Research, 41, 1359–1378.PubMedCrossRefGoogle Scholar
  62. Vinberg, J., & Grill-Spector, K. (2008). Representation of shapes, edges, and surfaces across multiple cues in the human visual cortex. Journal of Neurophysiology, 99, 1380–1393.PubMedCrossRefGoogle Scholar
  63. Weiner, K. S., Grill-Spector (2012) Neural representations of faces and limbs neighbor in human high-level visual cortex: Evidence for a new organization principle (accepted to this special issue).Google Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Daniele Caligiore
    • 1
    • 2
  • Anna M. Borghi
    • 2
    • 1
  • Domenico Parisi
    • 1
    • 2
  • Rob Ellis
    • 3
  • Angelo Cangelosi
    • 4
  • Gianluca Baldassarre
    • 1
  1. 1.Laboratory of Computational Embodied Neuroscience, Istituto di Scienze e Tecnologie della CognizioneConsiglio Nazionale delle Ricerche (LOCEN-ISTC-CNR)RomaItaly
  2. 2.Embodied Cognition Laboratory (EMCO-Lab), Dipartimento di PsicologiaUniversità di BolognaBolognaItaly
  3. 3.School of PsychologyUniversity of PlymouthPlymouthUK
  4. 4.School of Computing & MathematicsUniversity of PlymouthPlymouthUK

Personalised recommendations