Skip to main content
SpringerLink
Log in
Book cover

Cognitive Penetrability and the Epistemic Role of Perception pp 159–221Cite as

Palgrave Macmillan

Early Vision and Cognitive Penetrability

  • Chapter
  • First Online:

  • 219 Accesses

Part of the book series: Palgrave Innovations in Philosophy ((PIIP))

Abstract

In this chapter, I defend the thesis that early vision is Cognitively Impenetrable (CI) against very recent criticisms, some of them aimed specifically at my arguments, which state that neurophysiological evidence shows that early vision is affected in a top-down manner by cognitive states. This criticism comes from (a) studies on fast object recognition; (b) pre-cueing studies; and (c) imaging studies that examine the recurrent processes in the brain during visual perception. I argue that upon closer examination, all this evidence supports rather than defeats the thesis that early vision is CI, because it shows that (a) the information used in early vision to recognize objects very fast is not cognitive information; (b) the processes of early vision do not use the cognitive information that issues cognitive demands guiding attention or expectation in pre-cueing studies; and (c) the recurrent processes in early vision are purely stimulus-driven and do not involve any cognitive signals.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   89.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Shorter latencies of signal transmission have been reported by Innui and Kakigi (2006) who applied flash stimuli to the right eye and examined activations in eight cortical areas and found out that the cortico-cortical connection time of visual processing at the early stage was 4–6 ms. Even with this temporal profile of activation, there is still time for recurrent signals from hMT to affect V1 because they reenter V1 in about 20 ms, which is well within the time window of the first 100 ms after the initial activation of the V1 neurons.

References

  • Arratha, M., & Moore, C. M. (2014). Orientation summary statistics are limited in processing capacity. Visual Cognition, 22(8), 1018–1022.

    Article  Google Scholar 

  • Arratha, M., & Moore, C. M. (2015a). The perceptual processing capacity of summary statistics between and within feature dimensions. Journal of Vision, 15(4), 1–17.

    Article  Google Scholar 

  • Arratha, M., & Moore, C. M. (2015b). The capacity limitations of orientation summary statistics. Attention, Perception, Psychophysics, 77, 116–1131.

    Google Scholar 

  • Balas, B., Nakano, L., & Rosenholtz, R. (2009). A summary-statistic representation in peripheral vision explains visual crowding. Journal of Vision, 9(12), 13.

    Article  Google Scholar 

  • Beck, D. M., & Kastner, S. (2009). Top-down and bottom-up mechanisms in biasing competition in the human brain. Vision Research, 49, 1154–1165.

    Article  Google Scholar 

  • Biederman, I. (1987). Recognition by components: A theory of human image understanding. Psychological Review, 94, 115–147.

    Article  Google Scholar 

  • Block, N. (2007). Two neural correlates of consciousness. In N. Block (Ed.), Collected Papers, vol. 1 (pp. 342–362). Cambridge: MIT Press.

    Google Scholar 

  • Block, N. (2014). Seeing-as in the light of vision science. Philosophy and Phenomenological Research, 89(3), 560–572.

    Article  Google Scholar 

  • Brogaard, B., & Gatzia, D. (2017). Color and cognitive penetrability. Topics in Cognitive Science, 9(1), 193–214.

    Article  Google Scholar 

  • Bronfman, Z. Z., Brezis, N., Jacobson, H., & Usher, M. (2014). We see more that we can report: ‘Cost free’ color phenomenality outside focal attention. Psychological Science, 25(7), 1394–1403.

    Article  Google Scholar 

  • Bullier, J. (2001). Integrated model of visual processing. Brain Research Reviews, 36, 96–107.

    Article  Google Scholar 

  • Burge, T. (2010). Origins of Objectivity. Oxford: Clarendon Press.

    Book  Google Scholar 

  • Campbell, J. (2006). Does visual attention depend on sortal classification? Reply to Clark. Philosophical Studies, 127, 221–237.

    Article  Google Scholar 

  • Cant, J. S., Sun, S. Z., & Xu, Y. (2015). Distinct cognitive mechanisms involved in the processing of single objects and object ensembles. Journal of Vision, 15(4), 1–21.

    Article  Google Scholar 

  • Carrasco, M. (2011). Visual attention: The past 25 years. Vision Research, 51, 1484–1525.

    Article  Google Scholar 

  • Carrasco, M., Ling, S., & Read, S. (2004). Attention alters appearance. Nature Neuroscience, 7, 308–313.

    Article  Google Scholar 

  • Cavanagh, P. (2011). Visual cognition. Vision Research, 51, 1538–1551.

    Article  Google Scholar 

  • Cecchi, A. (2014). Cognitive penetration, perceptual learning, and neural plasticity. Dialectica, 68(1), 63–95.

    Article  Google Scholar 

  • Chaumon, M., Drouet, V., & Tallon-Baudry, C. (2008). Unconscious associative memory affects visual processing before 100 ms. Journal of Vision, 8(3), 1–10.

    Article  Google Scholar 

  • Chelazzi, L., Miller, E., Duncan, J., & Desimone, R. (1993). A neural basis for visual search in inferior temporal cortex. Nature, 363, 345–347.

    Article  Google Scholar 

  • Chong, S. C., & Treisman, A. (2003). Representation of statistical properties. Vision Research, 43(4), 393–404.

    Article  Google Scholar 

  • Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36, 181–253.

    Article  Google Scholar 

  • Crane, T. (2009). Is perception a propositional attitude. The Philosophical Quarterly, 59(236), 453–470.

    Article  Google Scholar 

  • Crouzet, S. M., Kirchner, H., & Thorpe, S. J. (2010). Fast saccades toward faces: Face detection in just 100 ms. Journal of Vision, 10(4), 1–17.

    Article  Google Scholar 

  • Davis, M. (1995). Tacit knowledge and subdoxastic states. In C. MacDonald & G. MacDonald (Eds.), Philosophy of Psychology: Debates on psychological Explanation. Oxford: Blackwell.

    Google Scholar 

  • Delorme, A., Rousselet, G. A., Mace, M. J.-M., & Fabre-Thorpe, M. (2004). Interaction of top-down and bottom up processing in the fast visual analysis of natural scenes. Brain Research, 19, 103–113.

    Google Scholar 

  • Dennett, D. C. (1983). Styles of mental representation. Proceedings of the Aristotelian Society, 83, 213–226.

    Article  Google Scholar 

  • Deroy, O. (2013). Object-sensitivity versus cognitive penetrability of perception. Philosophical Studies, 162, 87–107.

    Article  Google Scholar 

  • Drewes, J., Goren, G., Zhu, W., & Elder, J. H. (2016). Recurrent processing in the formation of percept shapes. The Journal of Neuroscience, 36(1), 185–192.

    Article  Google Scholar 

  • Eisenberg, M. L., & Zacks, J. M. (2016). Ambient and focal visual processing of naturalistic activity. Journal of Vision, 16(2), 1–12.

    Article  Google Scholar 

  • Evans, G. (1982). The Varieties of Reference. Oxford: Clarendon Press.

    Google Scholar 

  • Evans, M. A., Shedden, J. M., Hevenor, S. J., & Hahn, M. C. (2000). The effect of variability of unattended information on global and local processing: Evidence from lateralization at early stages of processing. Neurophysiologia, 38, 225–239.

    Google Scholar 

  • Firestone, C., & Scholl, B. J. (2016). Cognition does not affect perception: Evaluating the evidence for ‘top-down’ effects. Behavioral and Brain Sciences. http://dx.doi.org/10.1017/S0140525X15000965.

  • Fodor, J. (1983). The Modularity of Mind. Cambridge: MIT Press.

    Google Scholar 

  • Fodor, J. (2007). The revenge of the given. In B. P. McLaughlin & J. Cohen (Eds.), Contemporary Debates in the Philosophy of Mind. Malden, MA: Blackwell.

    Google Scholar 

  • Fodor, J., & Pylyshyn, Z. (2015). Minds Without Meanings: An Essay on the Content of Concepts. Cambridge: MIT Press.

    Google Scholar 

  • Foxe, J. J., & Simpson, G. V. (2002). Flow of activation from V1 to frontal cortex in humans. Experimental Brain Research, 142(1), 139–150.

    Article  Google Scholar 

  • Frassle, S., Sommer, J., Jansen, A., Naber, M., & Einhauser, W. (2014). Binocular rivalry: Frontal activity relates to introspection and action but no to perception. The Journal of Neuroscience, 34(1), 1738–1747.

    Article  Google Scholar 

  • Freiwald, W. A., & Kanwisher, N. G. (2004). Visual elective attention: Evidence from brain imaging and neurophysiology. In M. Gazzaniga (Ed.), The Cognitive Neurosciences III (3rd ed.). Cambridge: MIT Press.

    Google Scholar 

  • Gatzia, D., & Brogaard, B. (2017). The real epsitemic significance of percptual learning. Inquiry, 543–558. https://doi.org/10.1080/0020174x.2017.1368172.

    Article  Google Scholar 

  • Gilbert, C. D., & Li, W. (2013). Top-down influences on visual processing. Nature Reviews Neuroscience, 14(5), 350–363.

    Article  Google Scholar 

  • Gilbert, C. D., Ito, M., Kapadia, M., & Westheimer, G. (2000). Interactions between attention, context and learning in primary visual cortex. Vision Research, 40, 1217–1226.

    Article  Google Scholar 

  • Goldstone, R. L., de Leeuw, J. R., & Landy, D. H. (2015). Fitting perception in and to cognition. Cognition, 135, 24–29.

    Article  Google Scholar 

  • Grill-Spector, K., Henson, R., & Martin, A. (2006). Repetition and the brain: Neural models of stimulus-specific effects. Trends in Cognitive Sciences, 10, 14–23.

    Article  Google Scholar 

  • Grill-Spector, K., Kushnir, T., Hendler, T., Edelman, S., Itzchak, Y., & Malach, R. (1998). A sequence of object-processing stages revealed by FMRI in the Human occipital lobe. Human Brain Mapping, 6, 316–328.

    Article  Google Scholar 

  • Haugeland, J. (1998). Having Thought. Cambridge: Harvard University Press.

    Google Scholar 

  • Hayden, B. Y., & Gallant, J. L. (2009). Combined effects of spatial and feature-based attention on responses to V4 neurons. Visio Research, 49, 1182–1187.

    Article  Google Scholar 

  • Heck, R. G., Jr. (2007). Are there different kinds of content? In J. Cohen & B. McLaughlin (Eds.), Contemporary Debates in the Philosophy of Mind. Oxford: Blackwell.

    Google Scholar 

  • Heeger, D. J., & Ress, D. (2004). Neuronal correlates of visual attention and perception. In M. Gazzaniga (Ed.), The Cognitive Neurosciences (3rd ed.). Cambridge: MIT Press.

    Google Scholar 

  • Hegde, J., & Kersten, D. (2010). A link between visual disambiguation and visual memory. The Journal of Neuroscience, 30(45), 15124–15133.

    Article  Google Scholar 

  • Heinen, K., Jolij, J., & Lamme, V. A. (2015). Figure-ground segregation requires two distinct periods of activity in V1: A transcranial magnetic study. Neuroreport, 16(13), 1483–1487.

    Google Scholar 

  • Hopfinger, J. B., Luck, S. J., & Hillyard, S. A. (2004). Selective attention. In M. S. Gazzaniga (Ed.), The Cognitive Neuroscience (3rd ed.). Cambridge: MIT Press.

    Google Scholar 

  • Im, H. Y., & Halberda, J. (2013). The effects of sampling and internal noise of the representation of ensemble average size. Attention, Perception, Psychophysics, 75, 278–286.

    Article  Google Scholar 

  • Innui, K., & Kakigi, R. (2006). Temporal analysis of the flow from V1 to extrastriate cortex. Journal of Neurophysiology, 96, 775–784.

    Article  Google Scholar 

  • Itti, L., & Koch, C. (2001). Computational modelling of visual attention. Nature Reviews Neuroscience, 2, 194–204.

    Article  Google Scholar 

  • Johnson, J. S., & Olshausen, B. A. (2005). The earliest EEG signatures of object recognition in a cued target task are postsensory. Journal of Vision, 5, 299–312.

    Google Scholar 

  • Johnston, M. (2006). Better than mere knowledge: The function of sensory awareness. In T. S. Gendler & J. Hawthorne (Eds.), Perceptual Experience. Oxford: Clarendon Press.

    Google Scholar 

  • Joulesz, B. (1981). Textons, the elements of texture perception, and their interactions. Nature, 90(12), 91–97.

    Article  Google Scholar 

  • Kastner, S., & Ungerleider, L. G. (2000). Mechanisms of visual attention in the human cortex. Annual Review of Neuroscience, 23, 315–341.

    Article  Google Scholar 

  • Kastner, S., Pinsk, M. A., De Weerd, P., Desimone, R., & Ungerleider, L. (1999). Increased activity in human visual cortex during directed attention in the absence of visual stimulation. Neuron, 22, 751–761.

    Article  Google Scholar 

  • Kelly, S. D. (2001). Demonstrative concepts and experience. The Philosophical Review, 110(3), 397–420.

    Article  Google Scholar 

  • Kerkoerle, T. van., M. W., Dagnino, B., Gariel-Mathis M.-A., Poort, J., Togy, C. van der., & Roelfsema, P. R. (2014). Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex. Proceedings of the National Academy of Science, USA (PNAS), 114(40), 14332–14341.

    Google Scholar 

  • Keysers, C., Xiao, D. K., Fldiak, P., & Perrett, D. (2014). The speed of sight. Journal of Cognitive Neuroscience, 13, 90–101.

    Article  Google Scholar 

  • Kirchner, H., & Thorpe, S. J. (2006). Ultra-rapid object detection with saccadic movements: Visual processing speed revisited. Vision Research, 46, 1762–1776.

    Article  Google Scholar 

  • Kok, P., Brouwer, G., van Gerven, M., & de Lange, F. (2013). Prior expectations bias sensory representations in visual cortex. Journal of Neuroscience, 33, 16275–16284.

    Article  Google Scholar 

  • Kok, P., Failing, M., & de Lange, F. (2014). Prior expectations evoke stimulus templates in the primary visual cortex. Journal of Cognitive Neuroscience, 26, 1546–1554.

    Article  Google Scholar 

  • Lamme, V. A. F. (2003). Why visual attention and awareness are different. Trends in Cognitive Sciences, 7(1), 12–18.

    Article  Google Scholar 

  • Lamme, V. A. F. (2005). Independent neural definitions of visual awareness and attention. In A. Raftopoulos (Ed.), The Cognitive Penetrability of Perception: An Interdisciplinary Approach. Hauppauge, NJ: NovaScience Books.

    Google Scholar 

  • Lamme, V. A. F., & Roelfsema, P. R. (2000). The distinct modes of vision offered by feedforward and recurrent processing. Trends in Neuroscience, 23, 571–579.

    Article  Google Scholar 

  • Lawrence, B. M., White, R. L., & Snyder, L. H. (2005). Delay-period activity in visual, visuomovement, and movement neurons in the front eye field. Journal of Neurophysiology, 94(2), 1498–1508.

    Article  Google Scholar 

  • Lee, J., & John, H. R., & Maunsell, J. H. R. (2009). A normalization model of attentional modulation of single responses. PLoS One, IV(2), e4651.

    Article  Google Scholar 

  • Ling, S., Liu, T., & Carrasco, M. (2009). How spatial and feature-based attention affect the gain and tuning of population responses. Vision Research, 49, 1194–1204.

    Article  Google Scholar 

  • Liu, T., Stevens, S. T., & Carrasco, M. (2007). Comparing the time course and efficacy of spatial and feature-based attention. Vision Research, 47, 108–113.

    Article  Google Scholar 

  • Liu, H., Agam, Y., Madsen, J., & Krelman, G. (2009). Timing, timing, timing: Fast decoding of object information from intracranial field potentials in human visual cortex. Neuron, 62, 281–290.

    Article  Google Scholar 

  • Luck, S. J. 1995. Multiple mechanisms of visual-spatial attention: Recent evidence from human electrophysiology. Behavioral and Brain Research, 71, 113–123.

    Article  Google Scholar 

  • Lupyan, G. (2015). Object knowledge changes visual appearance: Semantic effects on color afterimages. Acta Psychologica, 161, 117–130.

    Article  Google Scholar 

  • Marr, D. (1982). Vision: A Computational Investigation into Human Representation and Processing of Visual Information. San Francisco, CA: Freeman.

    Google Scholar 

  • Montemayor, C., & Haladjian, H. (2015). Consciousness, Attention, and Conscious Attention. Cambridge: MIT Press.

    Book  Google Scholar 

  • Morishima, Y., Akaishi, R., Yamada, Y., Okuda, J., Toma, K., & Sakai, K. (2008). Task-specific signal transmission from prefrontal cortex in visual selective attention. Nature Neuroscience, 12(1), 85–90.

    Article  Google Scholar 

  • Murray, S. O., Schrater, P., & Kersten, D. (2004). Perceptual grouping and the interactions between visual cortical areas. Neural Networks, 17, 695–705.

    Article  Google Scholar 

  • Newen, A., & Vetter, P. (2016). Why cognitive penetration of our perceptual experience is still the most plausible account. Consciousness and Cognition. http://dx.doi.org/10.1016/j.concog.2016.09.005.

  • Nobre, A. C., Rohenkhol, G., & Stokes, M. G. (2012). Nervous anticipation: Top-down biasing across space and time. In M. Posner (Ed.), Cognitive Neuroscience of Attention (2nd ed.). New York, NY: The Guilford Press.

    Google Scholar 

  • Ogilivie, R., & Carruthers, P. (2015). Opening up vision: The case against encapsulation. Review of Philosophy and Psychology. https://doi.org/10.1007/s13164-015-0294.

  • O’Shea, J., Muggleton, N. G., Cowey, A., & Walsh, V. (2004). Timing of target discrimination in human front eye fields. Journal of Cognitive Neuroscience, 16(6), 1060–1067.

    Article  Google Scholar 

  • Peacocke, C. (1998). Nonconceptual content defended. Philosophy and Phenomenological Research, 58(2), 381–388.

    Article  Google Scholar 

  • Peacocke, C. (2001). Does perception have a nonconceptual content? The Journal of Philosophy, XCVIII(5), 239–269.

    Article  Google Scholar 

  • Peterson, M. A., & Enns, J. (2005). The edge complex: Implicit memory for figure assignment in shape perception. Perception and Psychophysics, 67(4), 727–740.

    Article  Google Scholar 

  • Phillips, B. (2017). The shifting border between perception and cognition. Nous, 1–31. https://doi.org/10.1111/nous.12218.

  • Plomp, G., Hervais-Adelma, A., Astofli, L., & Michel, C. M. (2015). Early recurrence and ongoing parietal driving during elementary visual processing. Nature, Scientific Reports, 5, 18733. https://doi.org/10.1038/srep18733.

    Article  Google Scholar 

  • Potter, M. C., Wyble, B., Hagmann, C. E., & McCourt, E. S. (2014). Detecting meaning in RSVP at 13 ms per picture. Attention, Perception, Psychophysics, 76, 270–279.

    Article  Google Scholar 

  • Pylyshyn, Z. (1999). Is vision continuous with cognition? Behavioral and Brain Sciences, 22, 341–365.

    Google Scholar 

  • Pylyshyn, Z. (2003). Seeing and Visualizing: It’s Not What You Think. Cambridge: MIT Press.

    Book  Google Scholar 

  • Pylyshyn, Z. (2007). Things and Places: How the Mind Connects with the World. Cambridge: MIT Press.

    Book  Google Scholar 

  • Raftopoulos, A. (2001a). Is perception informationally encapsulated? The issue of the theory-ladenness of perception. Cognitive Science, 25, 423–451.

    Article  Google Scholar 

  • Raftopoulos, A. (2001b). Reentrant pathways and the theory-ladenness of observation. Philosophy of Science, 68, 187–200.

    Article  Google Scholar 

  • Raftopoulos, A. (2009). Cognition and Perception: How Do Psychology and Neural Science Inform Philosophy? Cambridge: MIT Press.

    Book  Google Scholar 

  • Raftopoulos, A. (2014). The cognitive impenetrability of the content of early vision is a necessary and sufficient condition for purely nonconceptual content. Philosophical Psychology, 27(5), 601–620.

    Article  Google Scholar 

  • Raftopoulos, A. (2015). Cognitive penetrability and consciousness. In J. S. Zeimbekis & A. Raftopoulos (Eds.), Cognitive Effects on Perception: New Philosophical Perspectives (pp. 268–298). Oxford: Oxford University Press.

    Chapter  Google Scholar 

  • Raftopoulos, A., & Zeimbekis, J. (2015). The cognitive penetrability of perception: An overview. In J. Zeimbekis & A. Raftopoulos (Eds.), The Cognitive Penetrability of Perception: New Perspectives. Oxford: Oxford University Press.

    Chapter  Google Scholar 

  • Raftopoulos, A. (2017). Pre-cueing, the epistemic role of early vision and the cognitive impenetrability of early vision. Frontiers in Science, Psychology, 8, 1156. https://doi.org/10.3389/fpsyg.2017.01156.

    Article  Google Scholar 

  • Reynolds, J. H., & Chelazzi, L. (2004). Attentional modulation of visual processing. Annual Review of Neuroscience, 27, 611–647.

    Article  Google Scholar 

  • Rock, I. (1983). The Logic of Perception. Cambridge: MIT Press.

    Google Scholar 

  • Roe, A. W., Chelazzi, L., Connor, C. E., Conway, B. R., Fujita, I., Gallant, J. L., et al. (2012). Toward a unified theory of visual areas V4. Neuron, 74, 12–29.

    Article  Google Scholar 

  • Roelfsema, P. R., Lamme, V. A. F., & Spekreijse, H. (1998). Object-based attention in the primary visual cortex of the macaque monkey. Nature, 395, 376–381.

    Article  Google Scholar 

  • Rosenholtz, R., Huang, J., Raj, A., Balas, B. J., & Ilie, L. (2012). A summary statistic representation in peripheral vision explains visual search. Journal of Vision, 12(4), 14.

    Article  Google Scholar 

  • Schall, J. D., & Bichot, N. P. (1998). Neural correlates of visual and motor decision processes. Current Opinions in Neurobiology, 76, 2841–2852.

    Google Scholar 

  • Searle, J. R. (1995). Consciousness, explanatory inversion and cognitive science. In C. MacDonald & G. Macdonald (Eds.), Philosophy of Psychology: Debates on Psychological Explanation. Oxford: Blackwell.

    Google Scholar 

  • Shibata, K., Yamagishi, N., Naokazu, G., Yoshioka, T., Yamashita, O., Sato, M., et al. (2008). The effects of feature attention on prestimulus cortical activity in the human visual system. Cerebral Cortex, 18, 1644–1675.

    Article  Google Scholar 

  • Silvanto, J., Lavie, N., & Walsh, V. (2006). Stimulation of the human frontal eye fields modulates sensitivity of extrastriate visual cortex. Journal of Neurophysiology, 96(2), 941–945.

    Article  Google Scholar 

  • Silvanto, J., Cowey, A., Lavie, N., & Walsh, V. (2005). Striate cortex (V1) activity gates awareness of motion. Nature Neuroscience, 8(2), 143–144.

    Article  Google Scholar 

  • Spelke, E. S. (1988). Object perception. In A. I. Goldman (Ed.), Readings in Philosophy and Cognitive Science (pp. 447–461). Cambridge: MIT Press.

    Google Scholar 

  • Stich, S. (1978). Beliefs and subdoxastic states. Philosophy of Science, 45, 499–518.

    Article  Google Scholar 

  • Taylor, P. C. J., & Nobre, A. (2007). FEF TMS affects visual cortical activity. Cerebral Cortex, 17, 391–399.

    Article  Google Scholar 

  • Thompson, K. G., & Schall, J. D. (2000). Antecedents and correlates of visual detection and awareness in macaque prefrontal cortex. Vision Research, 40, 1523–1538.

    Article  Google Scholar 

  • Torralba, A., & Oliva, A. (2003). Statistics of natural image categories. Network, 14, 391–412.

    Article  Google Scholar 

  • Ullman, S., Vidal-Naquet, M., & Sali, E. (2002). Visual features of intermediate complexity and their use in classification. Nature Neuroscience, 5(7), 682–687.

    Article  Google Scholar 

  • Utochkin, I. S. (2015). Ensemble summary statistics as a basis for rapid visual categorization. Journal of Vision, 15(4), 1–14.

    Article  Google Scholar 

  • Vandenbroucke, A. R. F., Fahrenfort, J. J., Sligte, I. G., & Lamme, V. A. F. (2014). Seeing without knowing: Neural signatures of perceptual inference in the absence of report. Journal of Cognitive Neuroscience, 26(5), 955–969.

    Article  Google Scholar 

  • Vetter, P., & Newen, A. (2014). Varieties of cognitive penetration in visual perception. Consciousness and Cognition, 27, 62–75.

    Article  Google Scholar 

  • Wang, P., & Cottrell, G. W. (2017). Central and peripheral vision for scene recognition: A neurocomputational modeling exploration. Journal of Vision, 17(4), 1–22.

    Article  Google Scholar 

  • Wilson, H. R., & Wilkinson, F. (2015). From orientations to objects: Configural processing in the ventral system. Journal of Vision, 15(7:4), 1–10.

    Article  Google Scholar 

  • Wokke, M., Sligte, I. G., Scholte, H. S., & Lamme, V. A. F. (2012). Two critical periods in early visual cortex during figure-ground segregation. Brain and Behavior, 2(6), 763–777.

    Article  Google Scholar 

  • Wyart, V., Nobre, A., & Summerfield, C. (2012). Dissociable prior influences of signal probability and relevance on visual contrast sensitivity. Proceedings of the National Academy of Sciences, 109, 3593–3598.

    Article  Google Scholar 

  • Zhou, H. H., & Thompson, K. G. (2009). Cognitively directed spatial attention in the frontal eye field in anticipation of visual stimuli to be discriminated. Vision Research, 49, 1205–1215.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Psychology, University of Cyprus, Nicosia, Cyprus

    Athanassios Raftopoulos

Authors
  1. Athanassios Raftopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Athanassios Raftopoulos .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 The Author(s)

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Raftopoulos, A. (2019). Early Vision and Cognitive Penetrability. In: Cognitive Penetrability and the Epistemic Role of Perception. Palgrave Innovations in Philosophy. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-10445-0_3

Download citation

Publish with us

Policies and ethics

Search

Navigation