Reaction Times to Predictable Visual Patterns Reflect Neural Responses in Early Visual Cortex
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
It has long been speculated that the visual system should use a coding strategy that takes advantage of statistical redundancies in images. But how such a coding strategy should manifest in neural responses has been less clear. Low-level image structure related to the power spectrum of natural images appears to be captured by a hard-wired efficient code in the retina of the fly and precortical structures like the LGN of cats that maximizes information content through the limited capacity channel of the optic nerve. But visual images are typically filled with higher-order structure beyond that captured by the power spectrum and visual cortex is not constrained by the same capacity limits as the optic nerve. Whether and how visual cortex can flexibly code for higher order redundancies is unknown. Here we show using psychophysical techniques that the neural response in early human visual cortex may be modulated by orientation redundancies in images such that a visual feature that is contained within a predictive pattern results in slower reaction times than a feature that deviates from a pattern, suggesting lower neural responses to predictable stimuli in the visual cortex. Our results point to a neural response in early visual cortex that is sensitive to global patterns and redundancies in visual images and is in marked contrast to standard models of cortical visual processing.
Abstract
It has long been speculated that the visual system should use a coding strategy that takes advantage of statistical redundancies in images. But how such a coding strategy should manifest in neural responses has been less clear. Low-level image structure related to the power spectrum of natural images appears to be captured by a hard-wired efficient code in the retina of the fly and precortical structures like the LGN of cats that maximizes information content through the limited capacity channel of the optic nerve. But visual images are typically filled with higher-order structure beyond that captured by the power spectrum and visual cortex is not constrained by the same capacity limits as the optic nerve. Whether and how visual cortex can flexibly code for higher order redundancies is unknown. Here we show using psychophysical techniques that the neural response in early human visual cortex may be modulated by orientation redundancies in images such that a visual feature that is contained within a predictive pattern results in slower reaction times than a feature that deviates from a pattern, suggesting lower neural responses to predictable stimuli in the visual cortex. Our results point to a neural response in early visual cortex that is sensitive to global patterns and redundancies in visual images and is in marked contrast to standard models of cortical visual processing.
참고문헌 (41)
- Edward H. Adelson, "Spatiotemporal energy models for the perception of motion", Journal of the Optical Society of America A, 2(2), pp.284~299 (1985), The Optical Society[DOI]
- Thomas D. Albright, "Contextual Influences on Visual Processing", Annual Review of Neuroscience, 25(1), pp.339~379 (2002), Annual Reviews[DOI]
- Allman, J., "Stimulus specific responses from beyond the classical receptive field: Neurophysiological mechanisms for local-global comparisons in visual neurons", Annual Reviews of Neuroscience, 8, pp.407~430 (1985)
- Angelucci, A., "Contribution of feedforward, lateral and feedback connections to the classical receptive field center and extra-classical receptive field surround of primate V1 neurons", Progress in Brain Research, 154, pp.93~120 (2006)[DOI]
- Fred Attneave, "Some informational aspects of visual perception.", Psychological Review, 61(3), pp.183~193 (1954), American Psychological Association (APA)[DOI]
- Wyeth Bair, "Time Course and Time-Distance Relationships for Surround Suppression in Macaque V1 Neurons", The Journal of Neuroscience, 23(20), pp.7690~7701 (2018), Society for Neuroscience[DOI]
- Barlow, H. B., "Sensory Communication", pp.217~234 (1961), MIT Press
- Brainard, D. H., "The psychophysics toolbox", Spatial Vision, 10, pp.433~436 (1997)
- M. Carandini, "Do We Know What the Early Visual System Does?", Journal of Neuroscience, 25(46), pp.10577~10597 (2005), Society for Neuroscience[DOI]
- Matteo Carandini, "Linearity and Normalization in Simple Cells of the Macaque Primary Visual Cortex", The Journal of Neuroscience, 17(21), pp.8621~8644 (1997), Society for Neuroscience[DOI]
- James R. Cavanaugh, "Nature and Interaction of Signals From the Receptive Field Center and Surround in Macaque V1 Neurons", Journal of Neurophysiology, 88(5), pp.2530~2546 (2002), American Physiological Society[DOI]
- James R. Cavanaugh, "Selectivity and Spatial Distribution of Signals From the Receptive Field Surround in Macaque V1 Neurons", Journal of Neurophysiology, 88(5), pp.2547~2556 (2002), American Physiological Society[DOI]
- David J. Field, "Contour integration by the human visual system: Evidence for a local “association field”", Vision Research, 33(2), pp.173~193 (1993), Elsevier BV[DOI]
- David Fitzpatrick, "Seeing beyond the receptive field in primary visual cortex", Current Opinion in Neurobiology, 10(4), pp.438~443 (2000), Elsevier BV[DOI]
- David J. Heeger, "Normalization of cell responses in cat striate cortex", Visual Neuroscience, 9(2), pp.181~197 (1992), Cambridge University Press (CUP)[DOI]
- J. P. Jones, "An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex", Journal of Neurophysiology, 58(6), pp.1233~1258 (1987), American Physiological Society[DOI]
- Sung Jun Joo, "Long-Range, Pattern-Dependent Contextual Effects in Early Human Visual Cortex", Current Biology, 22(9), pp.781~786 (2012), Elsevier BV[DOI]
- Sung Jun Joo, "Contextual effects in human visual cortex depend on surface structure", Journal of Neurophysiology, 111(9), pp.1783~1791 (2014), American Physiological Society[DOI]
- Mitesh K. Kapadia, "Spatial Distribution of Contextual Interactions in Primary Visual Cortex and in Visual Perception", Journal of Neurophysiology, 84(4), pp.2048~2062 (2000), American Physiological Society[DOI]
- SABINE KASTNER, "Neuronal Correlates of Pop-out in Cat Striate Cortex", Vision Research, 37(4), pp.371~376 (1997), Elsevier BV[DOI]
- 김태훈, "저수준 시각적 특질이 위협 탐지에 미치는 효과: 뱀 탐지 이론의 검증", 감성과학, 23(3), pp.47~62 (2020), 한국감성과학회[DOI]
- J. J. Knierim, "Neuronal responses to static texture patterns in area V1 of the alert macaque monkey", Journal of Neurophysiology, 67(4), pp.961~980 (1992), American Physiological Society[DOI]
- Philip A. Kragel, "Emotion schemas are embedded in the human visual system", Science Advances, 5(7) (2019), American Association for the Advancement of Science (AAAS)[DOI]
- VA Lamme, "The neurophysiology of figure-ground segregation in primary visual cortex", The Journal of Neuroscience, 15(2), pp.1605~1615 (1995), Society for Neuroscience[DOI]
- Tai Sing Lee, "Hierarchical Bayesian inference in the visual cortex", Journal of the Optical Society of America A, 20(7), pp.1434~ (2003), The Optical Society[DOI]
- Peter Lennie, "Coding of color and form in the geniculostriate visual pathway (invited review)", Journal of the Optical Society of America A, 22(10), pp.2013~2033 (2005), The Optical Society[DOI]
- Xiangrui Li, "Generating high gray-level resolution monochrome displays with conventional computer graphics cards and color monitors", Journal of Neuroscience Methods, 130(1), pp.9~18 (2003), Elsevier BV[DOI]
- J A Movshon, "Spatial and temporal contrast sensitivity of neurones in areas 17 and 18 of the cat's visual cortex.", The Journal of Physiology, 283(1), pp.101~120 (1978), Wiley[DOI]
- HANS-CHRISTOPH NOTHDURFT, "Response modulation by texture surround in primate area V1: Correlates of “popout” under anesthesia", Visual Neuroscience, 16(1), pp.15~34 (1999), Cambridge University Press (CUP)[DOI]
- Bruno A. Olshausen, "How Close Are We to Understanding V1?", Neural Computation, 17(8), pp.1665~1699 (2005), MIT Press - Journals[DOI]
- Pelli, D. G., "The VideoToolbox software for visual psychophysics : Transforming numbers into movies", Spatial Vision, 10, pp.437~442 (1997)
- Uri Polat, "Collinear stimuli regulate visual responses depending on cell's contrast threshold", Nature, 391(6667), pp.580~584 (1998), Springer Science and Business Media LLC[DOI]
- Posner, M. I., "Attention and the detection of signals", Journal of Experimental Psychology : General, 109, pp.160~174 (1980)
- Rajesh P. N. Rao, "Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects", Nature Neuroscience, 2(1), pp.79~87 (1999), Springer Science and Business Media LLC[DOI]
- Dario L. Ringach, "Spatial Structure and Symmetry of Simple-Cell Receptive Fields in Macaque Primary Visual Cortex", Journal of Neurophysiology, 88(1), pp.455~463 (2002), American Physiological Society[DOI]
- Jeffrey N. Rouder, "Bayesian t tests for accepting and rejecting the null hypothesis", Psychonomic Bulletin & Review, 16(2), pp.225~237 (2009), Springer Science and Business Media LLC[DOI]
- Nicole C Rust, "In praise of artifice", Nature Neuroscience, 8(12), pp.1647~1650 (2005), Springer Science and Business Media LLC[DOI]
- Robert Shapley, "A new view of the primary visual cortex", Neural Networks, 17(5-6), pp.615~623 (2004), Elsevier BV[DOI]
- Adam M. Slllito, "Visual cortical mechanisms detecting focal orientation discontinuities", Nature, 378(6556), pp.492~496 (1995), Springer Science and Business Media LLC[DOI]
- Odelia Schwartz, "Natural signal statistics and sensory gain control", Nature Neuroscience, 4(8), pp.819~825 (2001), Springer Science and Business Media LLC[DOI]
- Karl Zipser, "Contextual Modulation in Primary Visual Cortex", The Journal of Neuroscience, 16(22), pp.7376~7389 (1996), Society for Neuroscience[DOI]
