Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-19T07:30:49.651Z Has data issue: false hasContentIssue false
  • English
  • Français

Three comments on Teller’s “bridge locus”

Published online by Cambridge University Press:  28 November 2013

J. ANTHONY MOVSHON
J. ANTHONY MOVSHON*
Affiliation:
Center for Neural Science, New York University, New York, New York
*
*Address correspondence to: J. Anthony Movshon, Center for Neural Science, New York University, 4 Washington Place, Room 809, New York, NY 10003. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The notion of a set of neurons that form a “bridge locus” serving as the immediate substrate of visual perception is examined in the light of evidence on the architecture of the visual pathway, of current thinking about perceptual representations, and of the basis of perceptual awareness. The bridge locus is likely to be part of a tangled web of representations, and this complexity raises the question of whether another scheme that relies less on geography might offer a better framework. The bridge locus bears a close relationship to the neural correlate of consciousness (NCC), and like the NCC may be a concept which is no longer precise enough to provide a useful basis for reasoning about the relationship between brain activity and perceptual experience.

Keywords

PerceptionLinking hypothesesBridge locus
Type
Retrospective and prospective analyses of linking propositions
Information
Visual Neuroscience , Volume 30 , Special Issue 5-6: Linking Hypotheses in Visual Neuroscience: A Tribute to Davida Teller , November 2013 , pp. 219 - 222
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Averbeck, B.B., Latham, P.E. & Pouget, A. (2006). Neural correlations, population coding and computation. Nature Reviews. Neuroscience 7, 358–366.CrossRefGoogle ScholarPubMed
Beck, J.M., Ma, W.J., Kiani, R., Hanks, T., Churchland, A.K., Roitman, J., Shadlen, M.N., Latham, P.E. & Pouget, A. (2008). Probabilistic population codes for Bayesian decision making. Neuron 60, 1142–1152.CrossRefGoogle ScholarPubMed
Block, N. (2005). Two neural correlates of consciousness. Trends in Cognitive Science 9, 46–52.CrossRefGoogle ScholarPubMed
Brindley, G.S. (1960). Physiology of the Retina and Visual Pathway. London: Edward Arnold.Google Scholar
Brindley, G.S. (1970). Physiology of the Retina and Visual Pathway (2nd ed.). London: Edward Arnold.Google Scholar
Crick, F. & Koch, C. (2003). A framework for consciousness. Nature Neuroscience 6, 119–126.CrossRefGoogle ScholarPubMed
Ernst, M.O. & Banks, M.S. (2002). Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 429–433.CrossRefGoogle Scholar
Felleman, D.J. & Van Essen, D.C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex 1, 1–47.CrossRefGoogle ScholarPubMed
Ganguli, D. & Simoncelli, E.P. (2011). Implicit encoding of prior probabilities in optimal neural populations. In Advances in Neural Information Processing Systems (NIPS*10), ed. Lafferty, J., Williams, C., Zemel, R., Shawe-Taylor, J. & Culotta, A., pp. 658–666. Cambridge, MA: MIT Press.Google Scholar
Girshick, A.R., Landy, M.S. & Simoncelli, E.P. (2011). Cardinal rules: Visual orientation perception reflects knowledge of environmental statistics. Nature Neuroscience 14, 926–932.CrossRefGoogle ScholarPubMed
Gregory, R.L. (1966). Eye and Brain: The Psychology of Seeing. London: Weidenfeld & Nicolson.Google Scholar
Gu, Y., Angelaki, D.E. & Deangelis, G.C. (2008). Neural correlates of multisensory cue integration in macaque MSTd. Nature Neuroscience 11, 1201–1210.CrossRefGoogle ScholarPubMed
Guillery, R.W. & Sherman, S.M. (2002). Thalamic relay functions and their role in corticocortical communication: Generalizations from the visual system. Neuron 33, 1–20.CrossRefGoogle ScholarPubMed
Hart, W.D. (1996). Dualism. In A Companion to the Philosophy of Mind, ed. Guttenplan, S., pp. 265–267. Oxford: Blackwell.Google Scholar
Hedges, J.H., Gartshteyn, Y., Kohn, A., Rust, N.C., Shadlen, M.N., Newsome, W.T. & Movshon, J.A. (2011). Dissociation of neuronal and psychophysical responses to local and global motion. Current Biology: CB 21, 2023–2028.CrossRefGoogle ScholarPubMed
Helmholtz, H.V. (1924). Treatise on Physiological Optics (trans. Southall, J.P.). Rochester, NY: Optical Society of America.Google Scholar
Hilgetag, C.-C., O’Neill, M.A. & Young, M.P. (1996). Indeterminate organization of the visual system. Science 271, 776–777.CrossRefGoogle ScholarPubMed
Hubel, D.H. & Wiesel, T.N. (2004). Brain and Visual Perception. New York: Oxford University Press.CrossRefGoogle Scholar
Jazayeri, M. & Movshon, J.A. (2006). Optimal representation of sensory information by neural populations. Nature Neuroscience 9, 690–696.CrossRefGoogle ScholarPubMed
Jazayeri, M. & Movshon, J.A. (2007). A new perceptual illusion reveals mechanisms of sensory decoding. Nature 446, 912–915.CrossRefGoogle ScholarPubMed
Lettvin, J.Y., Maturana, H.R., Mcculloch, W.S. & Pitts, W.H. (1959). What the frog’s eye tells the frogs brain. Proceedings of the Institute of Radio Engineers 47, 1940–1951.Google Scholar
Lieberman, M.D. & Cunningham, W.A. (2009). Type I and Type II error concerns in fMRI research: Re-balancing the scale. Social Cognitive and Affective Neuroscience 4, 423–428.CrossRefGoogle ScholarPubMed
Markov, N.T., Ercsey-Ravasz, M.M., Ribeiro Gomes, A.R., Lamy, C., Magrou, L., Vezoli, J., Misery, P., Falchier, A., Quilodran, R., Gariel, M.A., Sallet, J., Gamanut, R., Huissoud, C., Clavagnier, S., Giroud, P., Sappey-Marinier, D., Barone, P., Dehay, C., Toroczkai, Z., Knoblauch, K., Van Essen, D.C. & Kennedy, H. (2012). A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cerebral Cortex, in press [Epub ahead of print]. doi:10.1093/cercor/bhs270.Google ScholarPubMed
Rees, G., Kreiman, G. & Koch, C. (2002). Neural correlates of consciousness in humans. Nature Reviews. Neuroscience 3, 261–270.CrossRefGoogle ScholarPubMed
Shadlen, M.N. & Movshon, J.A. (1999). Synchrony unbound: A critical evaluation of the temporal binding hypothesis. Neuron 24, 67–77.CrossRefGoogle ScholarPubMed
Shadlen, M.N. & Newsome, W.T. (1998). The variable discharge of cortical neurons: Implications for connectivity, computation, and information coding. The Journal of Neuroscience 18, 3870–3896.CrossRefGoogle ScholarPubMed
Teller, D.Y. (1980). Locus questions in visual science. In Visual Coding and Adaptability, ed. Harris, C.S., pp. 151–176. New York: Lawrence Erlbaum Associates.Google Scholar
Teller, D.Y. (1984). Linking propositions in visual science. Vision Research 10, 1233–1246.CrossRefGoogle Scholar
Teller, D.Y. & Pugh, E.N. Jr. (1983). Linking propositions in color vision. In Colour Vision: Physiology and Psychophysics, ed. Mollon, J.D. & Sharpe, L.T., pp. 577–589. London: Academic Press.Google Scholar
Vezoli, J., Falchier, A., Jouve, B., Knoblauch, K., Young, M. & Kennedy, H. (2004). Quantitative analysis of connectivity in the visual cortex: Extracting function from structure. Neuroscientist 10, 476–482.CrossRefGoogle ScholarPubMed
von der Malsburg, C. (1981). The correlation theory of brain function. Max Planck Institute for Biophysical Chemistry, Internal Report 81–2. Reprinted inModels of Neural Networks II, ed. Domany, E., van Hemmen, J.L. & Schulten, K.Berlin, Germany: Springer.Google Scholar
Weiss, Y., Simoncelli, E.P. & Adelson, E.H. (2002). Motion illusions as optimal percepts. Nature Neuroscience 5, 598–604.CrossRefGoogle ScholarPubMed
Yuille, A.L. & Bülthoff, H.H. (1996). Bayesian decision theory and psychophysics. In Perception and Bayesian Inference, ed. Knill, D.C. & Richards, W., pp. 123–161. Cambridge: Cambridge University Press.CrossRefGoogle Scholar