A feedback model of perceptual learning and categorisation

Spratling, Michael W and Johnson, Mark H (2006) A feedback model of perceptual learning and categorisation. [Journal (Paginated)]

Full text available as:



Top-down, feedback, influences are known to have significant effects on visual information processing. Such influences are also likely to affect perceptual learning. This article employs a computational model of the cortical region interactions underlying visual perception to investigate possible influences of top-down information on learning. The results suggest that feedback could bias the way in which perceptual stimuli are categorised and could also facilitate the learning of sub-ordinate level representations suitable for object identification and perceptual expertise.

Item Type:Journal (Paginated)
Keywords:Perception; Learning; Neural Networks; Representation; vision
Subjects:Neuroscience > Neural Modelling
Computer Science > Neural Nets
Psychology > Perceptual Cognitive Psychology
ID Code:4885
Deposited By:Spratling, Dr Michael
Deposited On:25 May 2006
Last Modified:11 Mar 2011 08:56

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Arbib, M. A. and Liaw, J.-S. (1995). Sensorimotor transformations in the worlds of frogs and robots. Artificial Intelligence, 72:53–80.

Baker, C. I., Behrmann, M., and Olson, C. R. (2002). Impact of learning on representation of parts and wholes in monkey inferotemporal cortex. Nature Neuroscience, 5(11):1210–6.

Balkenius, C. (1995). Multi-modal sensing for robot control. In Niklasson, L. F. and Boden, M. B., editors, Current trends in connectionism, pages 203–16. Lawrence Erlbaum, Hillsdale, NJ.

Barlow, H. B. (1990). Conditions for versatile learning, Helmholtz’s unconscious inference, and the task of perception. Vision Research, 30:1561–71.

Barlow, H. B. (1994). What is the computational goal of the neocortex? In Koch, C. and Davis, J. L., editors, Large-Scale Neuronal Theories of the Brain, chapter 1. MIT Press, Cambridge, MA.

Barlow, H. B. (1995). The neuron doctrine in perception. In Gazzaniga, M. S., editor, The Cognitive Neurosciences, chapter 26. MIT Press, Cambridge, MA.

Becker, S. (1996). Mutual information maximization: models of cortical self-organization. Network: Computation in Neural Systems, 7:7–31.

Becker, S. and Hinton, G. E. (1992). A self-organizing neural network that discovers surfaces in random-dot stereograms. Nature, 355(6356):161–3.

Bravo, M. J. and Farid, H. (2003). Object segmentation by top-down processes. Visual Cognition, 10(4):471–91.

Bressler, S. L. (2002). Understanding cognition through large-scale cortical networks. Current Directions in Psychological Science, 11(2):58–61.

Budd, J. M. L. (1998). Extrastriate feedback to primary visual cortex in primates: a quantitative analysis of connectivity. Proceedings of the Royal Society of London. Series B, 265(1400):1037–44.

Bullier, J. and Nowak, L. G. (1995). Parallel versus serial processing: new vistas on the distributed organization of the visual system. Current Opinion in Neurobiology, 5(4):497–503.

Chun, M. M. (2002). Contextual cueing of visual attention. Trends in Cognitive Sciences, 4(5):170–8.

Corbetta, M. and Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3(3):201–15.

Crick, F. and Koch, C. (1998). Constraints on cortical and thalamic projections: the no-strong-loops hypothesis. Nature, 391:245–50.

Damper, R. I. and Harnad, S. R. (2000). Neural network models of categorical perception. Perception and Psychophysics, 62(4):843–67.

de Sa, V. R. (1994). Learning classification with unlabeled data. In Cowan, J. D., Tesauro, G., and Alspector, J., editors, Advances in Neural Information Processing Systems 6, pages 112–9, San Francisco, CA. Morgan Kaufmann.

de Sa, V. R. and Ballard, D. (1998). Category learning through multi-modality sensing. Neural Computation, 10(5):1097–117.

Der, R. and Smyth, D. (1998). Local online learning of coherent information. Neural Networks, 11(5):909–25.

Desimone, R. (1996). Neural mechanisms for visual memory and their role in attention. Proceedings of the National Academy of Sciences USA, 93:13494–9.

Desimone, R. and Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuro- science, 18:193–222.

Driver, J., Davis, G., Russell, C., Turatto, M., and Freeman, E. (2001). Segmentation, attention and phenomenal visual objects. Cognition, 80(1-2):61–95.

Durbin, R. and Rumelhart, D. E. (1989). Product units: a computationally powerful and biologically plausible extension to backpropagation networks. Neural Computation, 1:133–42.

Ebdon, M. (1992). The uniformity of cerebral neocortex and its implications for cognitive science. Technical Report CSRP-228, School of Cognitive and Computing Sciences, University of Sussex.

Fabre-Thorpe, M., Delorme, A., Marlot, C., and Thorpe, S. (2001). A limit to the speed of processing in ultra-rapid visual categorization of novel natural scenes. Jouranl of Cognitive Neuroscience, 13:171–80.

Felleman, D. J. and Van Essen, D. C. (1991). Distributed hierarchical processing in primate cerebral cortex. Cerebral Cortex, 1:1–47.

Foldiak, P. (1990). Forming sparse representations by local anti-Hebbian learning. Biological Cybernetics, 64:165–70.

Friston, K. J. and B _uchel, C. (2000). Attentional modulations of effective connectivity from V2 to V5/MT in humans. Proceedings of the National Academy of Sciences USA, 97(13):7591–6.

Friston, K. J. and Price, C. J. (2001). Dynamic representations and generative models of brain function. Brain Research Bulletin, 54(3):275–85.

Fukushima, K. (1980). Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4):193–202.

Fukushima, K. (1988). Neocognitron: a hierarchical neural network capable of visual pattern recognition. Neural Networks, 1(2):119–30.

Gauthier, I., Skudlarski, P., Gore, J. C., and Anderson, A. W. (2000). Expertise for cars and birds recruits brain areas involved in face recognition. Nature Neuroscience, 3(2):191–7.

Gilbert, C. D. (1996). Plasticity in visual perception and physiology. Current Opinion in Neurobiology, 6(2):269–

Goldstone, R. L. (1998). Perceptual learning. Annual Review of Psychology, 49:585–612.

Goldstone, R. L. and Barsalou, L. (1998). Reuniting perception and conception. Cognition, 65:231–62.

Goldstone, R. L., Lippa, Y., and Shiffrin, R. M. (2001). Altering object representations through category learning. Cognition, 78:27–43.

Goldstone, R. L., Steyvers, M., Spencer-Smith, J., and Kersten, A. (2000). Interactions between perceptual and conceptual learning. In Diettrich, E. and Markman, A. B., editors, Cognitive Dynamics: Conceptual Change in Humans and Machines, pages 191–228. Lawrence Erlbaum Associates, Mahwah, NJ.

Grossberg, S. (1986). The adaptive self-organisation of serial order in behaviour: speech, language, and motor control. In Schwab, E. C. and Nusbaum, H. C., editors, Pattern Recognition by Humans and Machines, volume 1: Speech Recognition, London, UK. Academic Press.

Grossberg, S. (1987). Competitive learning: from interactive activation to adaptive resonance. Cognitive Science, 11:23–63.

Grossberg, S. and Raizada, R. (2000). Contrast-sensitive perceptual grouping and object-based attention in the laminar circuits of primary visual cortex. Vision Research, 40(10-12):1413–32.

Hancock, P. J. B., Bruce, V., and Burton, A. M. (2000). Recognition of unfamiliar faces. Trends in Cognitive Sciences, 4(9):330–7.

Harnad, S. (1987). Psychophysical and cognitive aspects of categorical perception: a critical overview. In Harnad, S., editor, Categorical Perception; The Groundwork of Cognition, chapter 1. Cambridge University Press, Cambridge, UK.

Harnad, S. (2003). Categorical perception. In Encyclopedia of Cognitive Science. Macmillan: Nature Publishing Group.

Hasegawa, I. and Miyashita, Y. (2002). Categorizing the world: expert neurons look into key features. Nature Neuroscience, 5(2):90–1.

Hauser, M. and Mel, B. (2003). Dendrites: bug or feature. Current Opinion in Neurobiology, 13(3):372–83.

Hochstein, S. and Ahissar, M. (2002). View from the top: hierarchies and reverse hierarchies in the visual system. Neuron, 36(5):791–804.

Hubel, D. H. and Wiesel, T. N. (1977). Functional architecture of macaque monkey visual cortex. Proceedings of the Royal Society of London. Series B, 198:1–59.

Hummel, J. E. (2001). Complementary solution to the binding problems in vision: implications for shape perception and object recognition. Visual Cognition, 8:489–517.

Humphreys, G. W. and Forde, E. M. E. (2001). Hierarchies, similarity, and interactivity in object recognition: ‘category-specific’ neuropsychological deficits. Behavioral and Brain Sciences, 24(3):453–76.

Huppe, J. M., James, A. C., Payne, B. R., Lomber, S. G., Girard, P., and Bullier, J. (1998). Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons. Nature, 394(6695):784–7.

Johansen, M. K. and Palmeri, T. J. (2002). Are there representational shifts during category learning? Cognitive Psychology, 45:482–553.

Kanwisher, N. and Wojciulik, E. (2000). Visual attention: insights from brain imaging. Nature Reviews Neuroscience, 1(2):91–100.

Karni, A. (1996). The acquisition of perceptual and motor skills: a memory system in the adult human cortex. Cognitive Brain Research, 5:39–48.

Karni, A. and Bertini, G. (1997). Learning perceptual skills: behavioral probes into adult cortical plasticity. Current Opinion in Neurobiology, 7(4):530–5.

Kastner, S. and Ungerleider, L. G. (2000). Mechanisms of visual attention in the human cortex. Annual Review of Neuroscience, 23:315–41.

Kay, J. and Phillips, W. A. (1997). Activation functions, computational goals and learning rules for local processors with contextual guidance. Neural Computation, 9(4):895–910.

Keysers, C. and Perrett, D. I. (2002). Visual masking and RSVP reveal neural competition. Trends in Cognitive Sciences, 6(3):120–5.

Keysers, C., Xiao, D. K., F _oldi _ak, P., and Perrett, D. I. (2001). The speed of sight. Journal of Cognitive Neuro- science, 13(1):90–101.

Kobatake, E., Wang, G., and Tanaka, K. (1998). Effects of shape-discrimination training on the selectivity of inferotemporal cells in adult monkeys. Journal of Neurophysiology, 80(1):324–30.

Koerner, E., Tsujino, H., and Masutani, T. (1997). A cortical-type modular neural network for hypothetical reasoning. Neural Networks, 10(5):791–814.

Kohonen, T. (1997). Self-Organizing Maps. Springer-Verlag, Berlin.

Kording, K. P. and Konig, P. (2000). Learning with two sites of synaptic integration. Network: Computation in Neural Systems, 11(1):25–39.

Kording, K. P. and Konig, P. (2001). A spike based learning rule for generation of invariant representations. Journal of Physiology (Paris), 94(5–6):539–48.

Lamme, V. A. F. (1995). The neurophysiology of figure-ground segregation in primary visual cortex. Journal of Neuroscience, 15(2):1605–15.

Lamme, V. A. F. and Roelfsema, P. R. (2000). The distinct modes of vision offered by feedforward and recurrent processing. Trends in Neurosciences, 23(11):571–9.

Lamme, V. A. F., Sup `er, H., and Spekreijse, H. (1998). Feedforward, horizontal, and feedback processing in the visual cortex. Current Opinion in Neurobiology, 8(4):529–35.

Larkum, M. E., Senn, W., and L _uscher, H.-R. (2004). Top-down dendritic input increases the gain of layer 5 pyramidal neurons. Cerebral Cortex, 14(10):1059–70.

Larkum, M. E., Zhu, J. J., and Sakmann, B. (1999). A new cellular mechanism for coupling inputs arriving at different cortical layers. Nature, 398(6725):338–41.

Lee, T. S., Mumford, D., Romero, R., and Lamme, V. A. F. (1998). The role of primary visual cortex in higher level vision. Vision Research, 38(15-16):2429–54.

Lee, T. S., Yang, C. F., Romero, R. D., and Mumford, D. (2002). Neural activity in early visual cortex reflects behavioral experience and higher-order perceptual saliency. Nature Neuroscience, 5(6):589–97.

Logothetis, N. (1998). Object vision and visual awareness. Current Opinion in Neurobiology, 8(4):536–44.

Luck, S. J., Chelazzi, L., Hillyard, S. A., and Desimone, R. (1997). Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex. Journal of Neurophysiology, 77:24–42.

Marr, D. (1982). Vision. Freeman, San Francisco, CA.

Marshall, J. A. (1995). Adaptive perceptual pattern recognition by self-organizing neural networks: context, uncertainty, multiplicity, and scale. Neural Networks, 8(3):335–62.

McAdams, C. J. and Maunsell, J. H. R. (2000). Attention to both space and feature modulates neuronal responses in macaque area V4. Journal of Neurophysiology, 83(3):1751–5.

McClelland, J. L. and Rumelhart, D. E. (1981). An interactive activation model of context effects in letter percep- tion, part 1: an account of basic findings. Psychological Review, 88:375–407.

Mehta, A. D., Ulbert, I., and Schroeder, C. E. (2000). Intermodal selective attention in monkeys. II: physiological mechanisms of modulation. Cerebral Cortex, 10:359–70.

Mel, B. W. and Koch, C. (1990). Sigma-pi learning: on radial basis functions and cortical associative learning. In Touretzsky, D. S., editor, Advances in Neural Information Processing Systems 2, pages 474–81, San Francisco, CA. Morgan Kaufmann.

Mountcastle, V. B. (1998). Perceptual Neuroscience: The Cerebral Cortex. Harvard University Press, Cambridge, MA.

Mumford, D. (1994). Neuronal architectures for pattern-theoretic problems. In Koch, C. and Davis, J. L., editors, Large-Scale Neuronal Theories of the Brain, pages 125–52. MIT Press, Cambridge, MA.

Needham, A. (2001). Object recognition and object segregation in 4.5-month-old infants. Journal of Experimental Child Psychology, 78(1):3–22.

Oja, E. (1989). Neural networks, principle components, and subspaces. International Journal of Neural Systems, 1:61–8.

Olson, C. R. (2001). Object-based vision and attention in primates. Current Opinion in Neurobiology, 11(2):171– 9.

Olson, I. R., Chun, M. M., and Allison, T. (2001). The contextual guidance of attention: human intracranial event-related potential evidence for feedback modulation in anatomically early temporally late stages of visual processing. Brain, 124(7):1417–25.

O’Reilly, R. C. (1998). Six principles for biologically based computational models of cortical cognition. Trends in Cognitive Sciences, 2(11):455–62.

O’Reilly, R. C. and Farah, M. J. (1999). Simulation and explanation in neurospychology and beyond. Cognitive Neuropsychology, 16:49–72.

O’Reilly, R. C. and Munakata, Y. (2000). Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain. MIT Press, Cambridge, MA.

Palmeri, T. J. and Gauthier, I. (2004). Visual object understanding. Nature Reviews Neuroscience, 5(4):291–303.

Pare, D., Lang, E. J., and Destexhe, A. (1998). Inhibitory control of somatodendritic interactions underlying action potentials in neocortical pyramidal neurons in vivo: an intracellular and computational study. Neuroscience, 84(2):377–402.

Perrett, D. I., Oram, M. W., and Ashbridge, E. (1998). Evidence accumulation in cell populations responsive to faces: an account of generalisation of recognition without mental transformations. Cognition, 67:111–45.

Peterson, M. A. and Gibson, B. S. (1993). Shape recognition inputs to figure-ground organisation in three- dimensional displays. Cognitive Psychology, 25:383–429.

Pevtzow, R. and Goldstone, R. L. (1994). Categorization and the parsing of objects. In Proceedings of the 16th Annual Conference of the Cognitive Science Society, pages 717–22, Hillsdale, NJ. Lawrence Erlbaum Associates.

Phillips, W. A., Kay, J., and Smyth, D. (1995). The discovery of structure by multi-stream networks of local processors with contextual guidance. Network: Computation in Neural Systems, 6(2):225–46.

Phillips, W. A. and Singer, W. (1997). In search of common foundations for cortical computation. Behavioural and Brain Sciences, 20(4):657–722.

Posner, M. I., DiGirolamo, G. J., and Fernandez-Duque, D. (1997). Brain mechanisms of cognitive skills. Consciousness and Cognition, 6:267–90.

Quinn, P. C. and Johnson, M. H. (2000). Global-before-basic object categorization in connectionist networks and 2-month-old infants. Infancy, 1(1):32–46.

Rao, R. P. N. (1999). An optimal estimation approach to visual perception and learning. Vision Research, 39(11):1963–89.

Rao, R. P. N. and Ballard, D. H. (1997). Dynamical model of visual recognition predicts neural response properties in the visual cortex. Neural Computation, 9(4):721–63.

Rao, R. P. N. and Ballard, D. H. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1):79–87.

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

Reynolds, J. H., Pasternak, T., and Desimone, R. (2000). Attention increases sensitivity of V4 neurons. Neuron, 26:703–14.

Riesenhuber, M. and Poggio, T. (1999a). Are cortical models really bound by the ”binding problem”? Neuron, 24(1):87–93.

Riesenhuber, M. and Poggio, T. (1999b). Hierarchical models of object recognition in cortex. Nature Neuro- science, 2(11):1019–25.

Riesenhuber, M. and Poggio, T. (2000). Models of object recognition. Nature Neuroscience, 3(supplement):1199– 1204.

Rockland, K. S. (1998). Complex microstructures of sensory cortical connections. Current Opinion in Neurobiology, 8:545–51.

Roskies, A. L. (1999). The binding problem. Neuron, 24(1):7–9. Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). Learning internal representations by error propagation. In Rumelhart, D. E., McClelland, J. L., and The PDP Research Group, editors, Parallel Distributed Processing: Explorations in the Microstructures of Cognition. Volume 1: Foundations, pages 318–62. MIT Press, Cambridge, MA.

Rumelhart, D. E. and Zipser, D. (1985). Feature discovery by competitive learning. Cognitive Science, 9:75–112.

Sagi, D. and Tanne, D. (1994). Perceptual learning: learning to see. Current Opinion in Neurobiology, 4:195–9.

Salinas, E. and Abbott, L. F. (1996). A model of multiplicative neural responses in parietal cortex. Proceedings of the National Academy of Sciences USA, 93:11956–61.

Salinas, E. and Sejnowski, T. J. (2001). Gain modulation in the central nervous system: where behavior, neurophysiology and computation meet. The Neuroscientist, 7(5):430–40.

Salinas, E. and Thier, P. (2000). Gain modulation: a major computational principle of the central nervous system. Neuron, 27:15–21.

Schroeder, C. E., Mehta, A. D., and Foxe, J. J. (2001). Determinants of attentional control in cortical processing: evidence from human and monkey electrophysiologic investigations. Frontiers in Bioscience, 6:d672–84.

Schyns, P. G., Goldstone, R. L., and Thibaut, J.-P. (1998). The development of features in object concepts. Behavioural and Brain Sciences, 21(1):1–54.

Sigala, N. and Logothetis, N. K. (2001). Visual categorization shapes feature selectivity in the primate temporal cortex. Nature, 415:318–20.

Sigman, M. and Gilbert, C. D. (2000). Learning to find a shape. Nature Neuroscience, 3(3):264–9.

Spratling, M. W. (2002). Cortical region interactions and the functional role of apical dendrites. Behavioral and Cognitive Neuroscience Reviews, 1(3):219–28.

Spratling, M. W. (2005). Learning viewpoint invariant perceptual representations from cluttered images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5):753–61.

Spratling, M. W. and Hayes, G. M. (2000). Learning synaptic clusters for non-linear dendritic processing. Neural Processing Letters, 11(1):17–27.

Spratling, M. W. and Johnson, M. H. (2001). Dendritic inhibition enhances neural coding properties. Cerebral Cortex, 11(12):1144–9.

Spratling, M. W. and Johnson, M. H. (2002). Pre-integration lateral inhibition enhances unsupervised learning. Neural Computation, 14(9):2157–79.

Spratling, M. W. and Johnson, M. H. (2003). Exploring the functional significance of dendritic inhibition in cortical pyramidal cells. Neurocomputing, 52-54:389–95.

Spratling, M. W. and Johnson, M. H. (2004a). A feedback model of visual attention. Journal of Cognitive Neuroscience, 16(2):219–37.

Spratling, M. W. and Johnson, M. H. (2004b). Neural coding strategies and mechanisms of competition. Cognitive Systems Research, 5(2):93–117.

Sugase, Y., Yamane, S., Ueno, S., and Kawano, K. (1999). Global and fine information coded by single neurons in the temporal visual cortex. Nature, 400:869–73.

Thornton, C. (1996). Re-presenting representation. In Peterson, D. M., editor, Forms of Representation: An Interdisciplinary Theme for Cognitive Science, pages 152–62. Intellect Books, Exeter, UK.

Thornton, C. (1997). Truth-from-trash learning and the mobot footballer. Technical Report CSRP 504, School of Cognitive and Computing Sciences, University of Sussex.

Toates, F. (1998). The interaction of cognitive and stimulus-response processes in the control of behaviour. Neuroscience and Biobehavioural Reviews, 22(1):59–83.

Treue, S. (2001). Neural correlates of attention in primate visual cortex. Trends in Neurosciences, 24(5):295–300.

von der Malsburg, C. (1973). Self-organisation of orientation sensitive cells in the striate cortex. Kybernetik, 14:85–100.

Wallis, G. and B _ulthoff, H. (1999). Learning to recognize objects. Trends in Cognitive Sciences, 3(1):22–31.

Walsh, V., Ashbridge, E., and Cowey, A. (1998). Cortical plasticity in perceptual learning demonstrated by transcranial magnetic stimulation. Neuropsychologia, 36(4):363–7.

Watanabe, T., Sr., J. E. N., Koyama, S., Mukai, I., Liederman, J., and Sasaki, Y. (2002). Greater plasticity in lower-level than higher-level visual motion processing in a passive perceptual learning task. Nature Neuroscience, 5(10):1003–9.

Yuste, R., Gutnick, M. J., Saar, D., Delaney, K. R., and Tank, D. W. (1994). Ca2+ accumulations in dendrites of neocortical pyramidal neurons: an apical band and evidence for two functional compartments. Neuron, 13(1):23–43.

Zemel, R. S., Behrmann, M., Mozer, M. C., and Bevalier, D. (2002). Experience-dependent perceptual grouping and object-based attention. Journal of Experimental Psychology: Human Perception and Performance,n28(1):202–17.

Zipser, K., Lamme, V. A. F., and Schiller, P. H. (1996). Contextual modulation in primary visual cortex. Journal


Repository Staff Only: item control page