KG

Feature Integration Theory Notes

Feature Integration Theory and Guided Search

Observations about Visual Search

  • Observation #1:
    • Visual search is easy if the target differs from non-targets in a simple feature.
    • Visual search is difficult if the target differs from non-targets in a conjunction of features.
  • Observation #2:
    • At attended locations, features are correctly bound together.
    • At unattended locations, features are often incorrectly bound together.
  • Observation #3:
    • Arrangements of colored shapes into textures are much easier if different parts of a scene differ in simple features compared to a conjunction of features.

Feature Integration Theory (FIT)

  • A cognitive model explaining that we perceive coherent objects by binding simple features together at attended locations.

Visual Input/Stimulus/Scene Processing Stages

  • Pre-attentive Stage:
    • Simple features (e.g., color, orientation, edges, movement) are processed independently and in parallel across the visual field.
    • Feature maps are created for each dimension, coding which features are at which locations.
    • Examples include pre-attentive representations of:
      • Blue
      • Red
      • Vertical orientations
      • Horizontal orientations
      • Edges
  • Master Map of Locations:
    • Codes where visual information is located but not what it is.
  • Attentional Stage:
    • Attention is directed, like a spotlight, randomly around the master map of locations.
    • When a location is attended, features from different dimensions are bound together into an object representation (called an object file).
  • Post-attentive Stage:
    • Object representations (object files) are created one-by-one as attention is directed to locations in the master map.

FIT Explanation of Visual Search

  • Feature Search:
    • Without attention, simple features are extracted simultaneously across the entire visual field.
    • By monitoring for the presence of activity on the known target feature, the target can be found instantly (or rejected as being absent instantly).
  • Conjunction Search:
    • Attention is required to bind features together.
    • Each location needs to be selected (in random order) to bind features together from different feature dimensions until the proper binding is found.

Feature Binding and Attention

  • Binding features together requires attention.
  • No attention = features remain unbound to an object.
  • If asked about unattended locations, one must guess their combination.

Texture Perception

  • Binding features into an object representation requires attention.
  • Without directed attention toward individual items, a texture display just looks like a bunch of simple features (e.g., circles, diamonds, reds, greens).
  • Only after directing attention toward several individual items can one notice differences between different parts of the scene.

Clicker Question: Role of Attention in FIT

  • The correct answer is:
    • C. Attention binds features together into coherent object representations by selecting a location on the master map.

Visual Search Difficulty Prediction

  • Visual Scene A, B, C.
  • B is harder than C which is harder than A.
  • This is impossible in FIT, since search is either feature or conjunction.