Attention, Perception, & Psychophysics

, Volume 77, Issue 1, pp 258–271 | Cite as

A decisional account of subjective inflation of visual perception at the periphery

  • Guillermo Solovey
  • Guy Gerard Graney
  • Hakwan Lau
Article

Abstract

Human peripheral vision appears vivid compared to foveal vision; the subjectively perceived level of detail does not seem to drop abruptly with eccentricity. This compelling impression contrasts with the fact that spatial resolution is substantially lower at the periphery. A similar phenomenon occurs in visual attention, in which subjects usually overestimate their perceptual capacity in the unattended periphery. We have previously shown that at identical eccentricity, low spatial attention is associated with liberal detection biases, which we argue may reflect inflated subjective perceptual qualities. Our computational model suggests that this subjective inflation occurs because under the lack of attention, the trial-by-trial variability of the internal neural response is increased, resulting in more frequent surpassing of a detection criterion. In the current work, we hypothesized that the same mechanism may be at work in peripheral vision. We investigated this possibility in psychophysical experiments in which participants performed a simultaneous detection task at the center and at the periphery. Confirming our hypothesis, we found that participants adopted a conservative criterion at the center and liberal criterion at the periphery. Furthermore, an extension of our model predicts that detection bias will be similar at the center and at the periphery if the periphery stimuli are magnified. A second experiment successfully confirmed this prediction. These results suggest that, although other factors contribute to subjective inflation of visual perception in the periphery, such as top-down filling-in of information, the decision mechanism may be relevant too.

Keywords

Peripheral vision Subjective perception Perceptual decision making Psychophysics Signal detection theory 

References

  1. Alvarez, G. A. (2011). Representing multiple objects as an ensemble enhances visual cognition. Trends in Cognitive Sciences, 15(3), 122–131. doi:10.1016/j.tics.2011.01.003 PubMedCrossRefGoogle Scholar
  2. Anstis, S. (1998). Picturing peripheral acuity. Perception, 27(7), 817–825.PubMedCrossRefGoogle Scholar
  3. Azzopardi, P., & Cowey, A. (1993). Preferential representation of the fovea in the primary visual cortex. Nature, 361(6414), 719–721. doi:10.1038/361719a0 PubMedCrossRefGoogle Scholar
  4. Balas, B., Nakano, L., & Rosenholtz, R. (2009). A summary-statistic representation in peripheral vision explains visual crowding. Journal of Vision, 9(12), 13.1–18. doi:10.1167/9.12.13 CrossRefGoogle Scholar
  5. Banks, M. S., Sekuler, A. B., & Anderson, S. J. (1991). Peripheral spatial vision: limits imposed by optics, photoreceptors, and receptor pooling. Journal of the Optical Society of America A, Optics and Image Science, 8(11), 1775–1787.PubMedCrossRefGoogle Scholar
  6. Bisley, J. W. (2011). The neural basis of visual attention. The Journal of Physiology, 589(Pt 1), 49–57. doi:10.1113/jphysiol.2010.192666 PubMedCentralPubMedCrossRefGoogle Scholar
  7. Block, N. (2011). Perceptual consciousness overflows cognitive access. Trends in Cognitive Sciences, 15(12), 567–575. doi:10.1016/j.tics.2011.11.001 PubMedCrossRefGoogle Scholar
  8. Bosman, C. A., Schoffelen, J.-M., Brunet, N., Oostenveld, R., Bastos, A. M., Womelsdorf, T., … Fries, P. (2012). Attentional stimulus selection through selective synchronization between monkey visual areas. Neuron, 75(5), 875–88. doi:10.1016/j.neuron.2012.06.037
  9. Boucart, M., Moroni, C., Thibaut, M., Szaffarczyk, S., & Greene, M. (2013). Scene categorization at large visual eccentricities. Vision Research, 86, 35–42. doi:10.1016/j.visres.2013.04.006 PubMedCrossRefGoogle Scholar
  10. Brainard, D. H. (1997). The Psychophysics Toolbox. Spatial Vision, 10(4), 433–436.PubMedCrossRefGoogle Scholar
  11. Bressler, D. W., & Silver, M. A. (2010). Spatial attention improves reliability of fMRI retinotopic mapping signals in occipital and parietal cortex. NeuroImage, 53(2), 526–533. doi:10.1016/j.neuroimage.2010.06.063 PubMedCentralPubMedCrossRefGoogle Scholar
  12. Burnham, K. P., & Anderson, D. R. (2002). Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach (p. 488). Springer.Google Scholar
  13. Cafaro, J., & Rieke, F. (2010). Noise correlations improve response fidelity and stimulus encoding. Nature, 468(7326), 964–967. doi:10.1038/nature09570 PubMedCentralPubMedCrossRefGoogle Scholar
  14. Carrasco, M. (2011). Visual attention: The past 25 years. Vision Research. doi:10.1016/j.visres.2011.04.012 Google Scholar
  15. Carrasco, M., & Frieder, K. S. (1997). Cortical magnification neutralizes the eccentricity effect in visual search. Vision Research, 37(1), 63–82.PubMedCrossRefGoogle Scholar
  16. Cohen, M. A., & Dennett, D. C. (2011). Consciousness cannot be separated from function. Trends in Cognitive Sciences. doi:10.1016/j.tics.2011.06.008 Google Scholar
  17. Curcio, C. A., Sloan, K. R., Kalina, R. E., & Hendrickson, A. E. (1990). Human photoreceptor topography. The Journal of Comparative Neurology, 292(4), 497–523. doi:10.1002/cne.902920402 PubMedCrossRefGoogle Scholar
  18. Daniel, P. M., & Whitteridge, D. (1961). The representation of the visual field on the cerebral cortex in monkeys. The Journal of Physiology, 159, 203–221.PubMedCentralPubMedCrossRefGoogle Scholar
  19. Dehaene, S., & Changeux, J.-P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70(2), 200–227. doi:10.1016/j.neuron.2011.03.018 PubMedCrossRefGoogle Scholar
  20. DeValois, R. L., & DeValois, K. K. (1988). Spatial Vision (p. 400). Oxford University Press.Google Scholar
  21. Dorfman, D. D., & Alf, E. (1969). Maximum-likelihood estimation of parameters of signal-detection theory and determination of confidence intervals—Rating-method data. Journal of Mathematical Psychology, 6(3), 487–496.CrossRefGoogle Scholar
  22. Eckstein, M. P., Peterson, M. F., Pham, B. T., & Droll, J. A. (2009). Statistical decision theory to relate neurons to behavior in the study of covert visual attention. Vision Research, 49(10), 1097–1128. doi:10.1016/j.visres.2008.12.008 PubMedCrossRefGoogle Scholar
  23. Efron, B., & Tibshirani, R. J. (1994). An Introduction to the Bootstrap (p. 456). CRC Press.Google Scholar
  24. Gorea, A., & Sagi, D. (2000). Failure to handle more than one internal representation in visual detection tasks. Proceedings of the National Academy of Sciences of the United States of America, 97(22), 12380–12384. doi:10.1073/pnas.97.22.12380 PubMedCentralPubMedCrossRefGoogle Scholar
  25. Green, D. M., & Swets, J. A. (1989). Signal Detection Theory and Psychophysics (p. 521). Peninsula Pub.Google Scholar
  26. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science (New York, N.Y.), 220(4598), 671–80. doi:10.1126/science.220.4598.671
  27. Kouider, S., de Gardelle, V., Sackur, J., & Dupoux, E. (2010). How rich is consciousness? The partial awareness hypothesis. Trends in Cognitive Sciences, 14(7), 301–307. doi:10.1016/j.tics.2010.04.006 PubMedCrossRefGoogle Scholar
  28. Lamme, V. A. F. (2010). How neuroscience will change our view on consciousness. Cognitive Neuroscience, 1(3), 204–220. doi:10.1080/17588921003731586 PubMedCrossRefGoogle Scholar
  29. Lau, H. (2008). A higher order Bayesian decision theory of consciousness. Progress in Brain Research, 168, 35–48.PubMedCrossRefGoogle Scholar
  30. Lau, H., & Rahnev, D. A. (2011). The paradoxical negative relationship between attention-related spontaneous neural activity and perceptual decisions. Journal of Vision, 11(11), 20–20. doi:10.1167/11.11.20 CrossRefGoogle Scholar
  31. Lau, H., & Rosenthal, D. (2011). Empirical support for higher-order theories of conscious awareness. Trends in Cognitive Sciences, 15, 365–373. doi:10.1016/j.tics.2011.05.009 PubMedCrossRefGoogle Scholar
  32. Levi, D. M. (2008). Crowding–an essential bottleneck for object recognition: a mini-review. Vision Research, 48(5), 635–654. doi:10.1016/j.visres.2007.12.009 PubMedCentralPubMedCrossRefGoogle Scholar
  33. Lima, B., Singer, W., Chen, N.-H., & Neuenschwander, S. (2010). Synchronization dynamics in response to plaid stimuli in monkey V1. Cerebral Cortex (New York, N.Y.: 1991), 20(7), 1556–73. doi:10.1093/cercor/bhp218
  34. Ma, W. J. (2010). Signal detection theory, uncertainty, and Poisson-like population codes. Vision Research, 50(22), 2308–2319. doi:10.1016/j.visres.2010.08.035 PubMedCrossRefGoogle Scholar
  35. Macmillan, N. A., & Creelman, C. D. (2004). Detection Theory: A User’s Guide (p. 512). Psychology Press.Google Scholar
  36. McDonnell, M. D., & Abbott, D. (2009). What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology. PLoS Computational Biology, 5(5), e1000348. doi:10.1371/journal.pcbi.1000348 PubMedCentralPubMedCrossRefGoogle Scholar
  37. Morales, J., Solovey, G., Maniscalco, B., Rahnev, D., De Lange, F. P., & Lau, H. (2014). Low Attention Impairs Optimal Incorporation of Prior Knowledge in Perceptual Decisions. Manuscript submitted for publication.Google Scholar
  38. Mullen, K. T. (1991). Colour vision as a post-receptoral specialization of the central visual field. Vision Research, 31(1), 119–130.PubMedCrossRefGoogle Scholar
  39. Noorlander, C., Koenderink, J. J., Den Olden, R. J., & Edens, B. W. (1983). Sensitivity to spatiotemporal colour contrast in the peripheral visual field. Vision Research, 23(1), 1–11.PubMedCrossRefGoogle Scholar
  40. O’Regan, J. K. (1992). Solving the “real” mysteries of visual perception: the world as an outside memory. Canadian Journal of Psychology, 46, 461–488. doi:10.1037/h0084327 PubMedCrossRefGoogle Scholar
  41. Oliva, A. (2005). Gist of the Scene. In L. Itti, G. Rees, & J. Tsotsos (Eds.), Neurobiology of Attention (pp. 251–256). San Diego: Elsevier.CrossRefGoogle Scholar
  42. Parkes, L., Lund, J., Angelucci, A., Solomon, J. A., & Morgan, M. (2001). Compulsory averaging of crowded orientation signals in human vision. Nature Neuroscience, 4(7), 739–744. doi:10.1038/89532 PubMedCrossRefGoogle Scholar
  43. Pelli, D. G. (1997). The VideoToolbox software for visual psychophysics: transforming numbers into movies. Spatial Vision, 10(4), 437–442.PubMedCrossRefGoogle Scholar
  44. Pelli, D. G., & Tillman, K. A. (2008). The uncrowded window of object recognition. Nature Neuroscience, 11(10), 1129–1135.PubMedCentralPubMedCrossRefGoogle Scholar
  45. Pestilli, F., Carrasco, M., Heeger, D. J., & Gardner, J. L. (2011). Attentional enhancement via selection and pooling of early sensory responses in human visual cortex. Neuron, 72(5), 832–846. doi:10.1016/j.neuron.2011.09.025 PubMedCentralPubMedCrossRefGoogle Scholar
  46. Posner, M. I. (1980). Orienting of attention. Quarterly Journal of Experimental Psychology. doi:10.1080/00335558008248231 PubMedGoogle Scholar
  47. Rahnev, D. A., Bahdo, L., De Lange, F. P., & Lau, H. (2012a). Pre-Stimulus hemodynamic activity in dorsal attention network is negatively associated with decision confidence in visual perception. Journal of Neurophysiology, 108(5), 1529–1536. doi:10.1152/jn.00184.2012 PubMedCrossRefGoogle Scholar
  48. Rahnev, D. A., Maniscalco, B., Graves, T., Huang, E., de Lange, F. P., & Lau, H. (2011). Attention induces conservative subjective biases in visual perception. Nature Neuroscience, 14(12), 1513–1515. doi:10.1038/nn.2948 PubMedCrossRefGoogle Scholar
  49. Rahnev, D. A., Maniscalco, B., Luber, B., Lau, H., & Lisanby, S. H. (2012b). Direct injection of noise to the visual cortex decreases accuracy but increases decision confidence. Journal of Neurophysiology, 107(6), 1556–1563. doi:10.1152/jn.00985.2011 PubMedCrossRefGoogle Scholar
  50. Rounis, E., Maniscalco, B., Rothwell, J. C., Passingham, R. E., & Lau, H. (2010). Theta-burst transcranial magnetic stimulation to the prefrontal cortex impairs metacognitive visual awareness. Cognitive Neuroscience, 1(3), 165–175. doi:10.1080/17588921003632529 PubMedCrossRefGoogle Scholar
  51. Scholl, B. J. (2001). Objects and attention: The state of the art. Cognition. doi:10.1016/S0010-0277(00)00152-9 Google Scholar
  52. Simonotto, E., Riani, M., Seife, C., Roberts, M., Twitty, J., & Moss, F. (1997). Visual Perception of Stochastic Resonance. Physical Review Letters, 78(6), 1186–1189.CrossRefGoogle Scholar
  53. Simons, D. J., & Chabris, C. F. (1999). Gorillas in our midst: sustained inattentional blindness for dynamic events. Perception, 28(9), 1059–1074.PubMedCrossRefGoogle Scholar
  54. Simons, D. J., & Levin, D. T. (1997). Change blindness. Trends in Cognitive Sciences, 1(7), 261–267.PubMedCrossRefGoogle Scholar
  55. Strasburger, H., Rentschler, I., & Jüttner, M. (2011). Peripheral vision and pattern recognition: A review. Journal of Vision, 11, 1–82. doi:10.1167/11.5.13.Contents CrossRefGoogle Scholar
  56. Summerfield, C., & Egner, T. (2009). Expectation (and attention) in visual cognition. Trends in Cognitive Sciences, 13(9), 403–409. doi:10.1016/j.tics.2009.06.003 PubMedCrossRefGoogle Scholar
  57. Van Pelt, S., & Fries, P. (2013). Visual stimulus eccentricity affects human gamma peak frequency. NeuroImage, 78, 439–447. doi:10.1016/j.neuroimage.2013.04.040 PubMedCrossRefGoogle Scholar
  58. Virsu, V., Näsänen, R., & Osmoviita, K. (1987). Cortical magnification and peripheral vision. Journal of the Optical Society of America. A, 4(8), 1568. doi:10.1364/JOSAA.4.001568 CrossRefGoogle Scholar
  59. Watson, A. B., & Pelli, D. G. (1983). QUEST: a Bayesian adaptive psychometric method. Perception & Psychophysics, 33(2), 113–120.CrossRefGoogle Scholar
  60. Wyart, V., Nobre, A. C., & Summerfield, C. (2012). Dissociable prior influences of signal probability and relevance on visual contrast sensitivity. Proceedings of the National Academy of Sciences of the United States of America, 109(9), 3593–3598. doi:10.1073/pnas.1120118109 PubMedCentralPubMedCrossRefGoogle Scholar
  61. Zak, I., Katkov, M., Gorea, A., & Sagi, D. (2012). Decision criteria in dual discrimination tasks estimated using external-noise methods. Attention, Perception, & Psychophysics, 74(5), 1042–1055. doi:10.3758/s13414-012-0269-0 CrossRefGoogle Scholar
  62. Zhang, H., Morvan, C., & Maloney, L. T. (2010). Gambling in the visual periphery: a conjoint-measurement analysis of human ability to judge visual uncertainty. PLoS Computational Biology, 6(12), e1001023. doi:10.1371/journal.pcbi.1001023 PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© The Psychonomic Society, Inc. 2014

Authors and Affiliations

  • Guillermo Solovey
    • 1
    • 2
    • 3
    • 4
  • Guy Gerard Graney
    • 1
  • Hakwan Lau
    • 1
    • 5
  1. 1.Department of PsychologyColumbia UniversityNew YorkUSA
  2. 2.Instituto de Cálculo, FCEyNUniversidad de Buenos AiresBuenos AiresArgentina
  3. 3.Laboratorio de Neurociencia IntegrativaBuenos AiresArgentina
  4. 4.CONICETBuenos AiresArgentina
  5. 5.Department of PsychologyUniversity of California Los AngelesLos AngelesUSA

Personalised recommendations