Spatial filtering during visual search: Evidence from human electrophysiology.

Luck, Steven J., Hillyard, Steven A.·1994·Journal of Experimental Psychology: Human Perception and Performance

Abstract

The identification of targets in visual search arrays may be improved by suppressing competing information from the surrounding distractor items. The present study provided evidence that this hypothetical filtering process has a neural correlate, the "N2pc" component of the event-related potential waveform. The N2pc was observed when a target item was surrounded by competing distractor items but was absent when the array could be rejected as a nontarget on the basis of simple feature information. In addition, the N2pc was eliminated when filtering was discouraged by removing the distractor items, making the distractors relevant, or making all items within an array identical. Combined with previous topographic analyses, these results suggest that attentional filtering occurs in occipital cortex under the control of feedback from higher cortical regions after a preliminary feature-based analysis of the stimulus array.

Cites (65)

  1. A feature-integration theory of attention.
    Treisman, Anne M., Gelade, Garry · 1980 · Cognitive Psychology
  2. The Cognitive neurosciences
    2005 · Choice Reviews Online
  3. Two cortical visual systems
    Leslie G. Ungerleider · 1982 · Medical Entomology and Zoology
  4. Forest before trees: The precedence of global features in visual perception
    Navon, David · 1977 · Cognitive Psychology
  5. Visual search and stimulus similarity.
    Duncan, John, Humphreys, Glyn W. · 1989 · Psychological Review
  6. Spherical splines for scalp potential and current density mapping
    Perrin, F., Pernier, J., Bertrand, O., Echallier, J.F. · 1989 · Electroencephalography and Clinical Neurophysiology
  7. Feature analysis in early vision: Evidence from search asymmetries.
    Anne Treisman, Stephen Gormican · 1988 · Psychological Review
  8. Guided search: An alternative to the feature integration model for visual search.
    Jeremy M. Wolfe, Kyle R. Cave, Susan L. Franzel · 1989 · Journal of Experimental Psychology Human Perception & Performance
  9. Selective Attention Gates Visual Processing in the Extrastriate Cortex
    Moran, Jeffrey, Desimone, Robert · 2003 · Essential Sources in the Scientific Study of Consciousness
  10. Attention and Brain Function
    Risto, Näätänen · 2018 · Routledge eBooks

Show all 65 references

Cited by (25)

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    Folstein, Jonathan R., Van Petten, Cyma · 2008 · Psychophysiology
  2. Electrophysiological correlates of feature analysis during visual search
    LUCK, STEVEN J., HILLYARD, STEVEN A. · 1994 · Psychophysiology
  3. The N2pc component as an indicator of attentional selectivity
    Eimer, Martin · 1996 · Electroencephalography and Clinical Neurophysiology
  4. Electrophysiological Indices of Target and Distractor Processing in Visual Search
    Hickey, Clayton, Di Lollo, Vincent, McDonald, John J. · 2009 · Journal of Cognitive Neuroscience
  5. Reward Changes Salience in Human Vision via the Anterior Cingulate
    Hickey, Clayton, Chelazzi, Leonardo, Theeuwes, Jan · 2010 · The Journal of Neuroscience

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CoChat. (2026). Citation check: Spatial filtering during visual search: Evidence from human electrophysiology. https://app.cochat.ai/papers/10.1037//0096-1523.20.5.1000
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Luck, S. J., & Hillyard, S. A. (1994). Spatial filtering during visual search: Evidence from human electrophysiology. Journal of Experimental Psychology: Human Perception and Performance. https://doi.org/10.1037//0096-1523.20.5.1000

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