Spatiotemporal mechanisms for detecting and identifying image features in human vision


Our visual system constantly selects salient features in the environment, so that only those features are attended and targeted by further processing efforts to identify them. Models of feature detection hypothesize that salient features are localized based on contrast energy (local variance in intensity) in the visual stimulus. This hypothesis, however, has not been tested directly. We used psychophysical reverse correlation to study how humans detect and identify basic image features (bars and short line segments). Subjects detected a briefly flashed 'target bar' that was embedded in 'noise bars' that randomly changed in intensity over space and time. By studying how the intensity of the noise bars affected performance, we were able to dissociate two processing stages: an early 'detection' stage, whereby only locations of high-contrast energy in the image are selected, followed (after 100 ms) by an 'identification' stage, whereby image intensity at selected locations is used to determine the identity (whether bright or dark) of the target.

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Figure 1: Stimuli and reverse-correlation technique.
Figure 2: Mean and variance kernels for two subjects.
Figure 3: Experiment 2.
Figure 4: Mean and variance images for false alarm trials only, from experiment 1, bright bar target.


  1. 1

    James, W. Principles of Psychology (Henry Holt, New York, 1890).

  2. 2

    Kastner, S. & Ungerleider, L.G. Mechanisms of visual attention in the human cortex. Annu. Rev. Neurosci. 23, 315–341 (2000).

  3. 3

    Marr, D. Vision (Freeman, New York, 1982).

  4. 4

    Ahumada, A.J. Jr. Perceptual classification images from Vernier acuity masked by noise. Perception 26, 18 (1996).

  5. 5

    Ringach, D.L. Tuning of orientation detectors in human vision. Vision Res. 38, 963–972 (1998).

  6. 6

    Eckstein, M.P. & Ahumada, A.J. Jr. Classification images: a tool to analyze visual strategies. J. Vision 2, 1 (2002).

  7. 7

    Sagi, D. & Julesz, B. “Where” and “what” in vision. Science 228, 1217–1219 (1985).

  8. 8

    Tolhurst, D.J. & Dealy, R.S. The detection and identification of lines and edges. Vision Res. 15, 1367–1372 (1975).

  9. 9

    Thomas, J.P., Gille, J. & Barker, R.A. Simultaneous detection and identification: theory and data. J. Opt. Soc. Am. A 73, 751–758 (1982).

  10. 10

    Posner, M.I. & Cohen, Y. in Attention and Performance X: Control of Language Processes (eds. Bouma, H. & Bowhuis, D.G.) 531–556 (Erlbaum, Hillsdale, NJ, 1984).

  11. 11

    Nakayama, K. & Mackeben, M. Sustained and transient components of focal visual attention. Vision Res. 29, 1631–1647 (1989).

  12. 12

    Ziebell, O. & Nothdurft, H.-C. Cueing and pop-out. Vision Res. 39, 2113–2125 (1999).

  13. 13

    Nothdurft, H.-C. Attention shifts to salient targets. Vision Res. (in press).

  14. 14

    Morrone, M.C. & Burr, D.C. Feature detection in human vision: a phase-dependent energy model. Proc. R. Soc. Lond. B Biol. Sci. 235, 221–245 (1988).

  15. 15

    Itti, L. & Koch, C. A saliency-based mechanism for overt and covert shifts of visual attention. Vision Res. 40, 1489–1506 (2000).

  16. 16

    Itti, L. & Koch, C. Computational modeling of visual attention. Nat. Rev. Neurosci. 2, 194–203 (2001).

  17. 17

    Li, Z. A saliency map in primary visual cortex. Trends Cogn. Sci. 6, 9–16 (2002).

  18. 18

    Constantinidis, C. & Steinmetz, M.A. Neuronal responses in area 7a to multiple-stimulus displays: I. Neurons encode the location of the salient stimulus. Cereb. Cortex 11, 581–591 (2001).

  19. 19

    Supèr, H., Spekreijse, H. & Lamme, A.V. Two distinct modes of sensory processing observed in monkey primary visual cortex (V1). Nat. Neurosci. 4, 304–310 (2001).

  20. 20

    Spitzer, H., Desimone, R. & Moran, J. Increased attention enhances both behavioral and neuronal performance. Science 240, 338–340 (1988).

  21. 21

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

  22. 22

    Martínez, A. et al. Putting spatial attention on the map: timing and localization of stimulus selection processes in striate and extrastriate visual areas. Vision Res. 41, 1437–1457 (2001).

  23. 23

    Gottlieb, J.P., Kusunoki, M. & Goldberg, M.E. The representation of visual salience in monkey parietal cortex. Nature 391, 481–484 (1998).

  24. 24

    Piotrowski, L.N. & Campbell, F.W. A demonstration of the visual importance and flexibility of spatial-frequency amplitude and phase. Perception 11, 337–346 (1982).

  25. 25

    Kustov, A.A. & Robinson, D.L. Shared neural control of attentional shifts and eye movements. Nature 384, 74–77 (1996).

  26. 26

    Corbetta, M. et al. A common network of functional areas for attention and eye movements. Neuron 21, 761–773 (1998).

  27. 27

    Parkhurst, D., Law, K. & Niebur, E. Modeling the role of salience in the allocation of overt visual attention. Vision Res. 42, 107–123 (2002).

  28. 28

    Green, D.M. & Swets, J.A. Signal Detection Theory and Psychophysics (Wiley, New York, 1966).

  29. 29

    Abbey, C.K. & Eckstein, M.P. Classification image analysis: estimation and statistical inference for two-alternative forced-choice experiments. J. Vision 2, 66–78 (2002).

  30. 30

    Efron, B. & Tibshirani, R. An Introduction to the Bootstrap (Chapman & Hall, New York, 1993).

  31. 31

    Adelson, E.H. & Bergen, J.R. . Spatiotemporal energy models for the perception of motion. J. Opt. Soc. Am. A 2, 284–299 (1985).

  32. 32

    Pollen, D.A. & Ronner, S.F. Visual cortical neurons as localized spatial frequency filters. IEEE Trans. Sys. Cybern. 13, 907–916 (1983).

  33. 33

    Pelli, D.G. Uncertainty explains many aspects of visual contrast detection and discrimination. J. Opt. Soc. Am. A 2, 1508–1530 (1985).

  34. 34

    Ahumada, A.J. Jr. Detection of tones masked by noise: a comparison of human subjects with digital-computer-simulated energy detectors of varying bandwidths. Thesis (Technical Report No. 29), UCLA (1967).

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We thank H. Barlow, C. Blakemore, B. Cumming, A. Parker, D. Ringach and B. Zenger-Landolt for useful discussions. P.N. was supported by the McDonnell-Pew Program in Cognitive Neuroscience (while at Oxford) and the Wellcome Trust (while at Stanford). D.J.H. was supported by a National Eye Institute grant.

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Correspondence to Peter Neri.

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Neri, P., Heeger, D. Spatiotemporal mechanisms for detecting and identifying image features in human vision. Nat Neurosci 5, 812–816 (2002).

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