Grouping in object recognition:  The role of a Gestalt law in letter identification   Pelli, Denis G., Majaj, Najib J., Ra...
Gestalt Law Grouping (proximity) Most are binary discrimination tasks in the past
Binary discrimination task <ul><li>Differs from ordinary object recognition task </li></ul><ul><li>Quick  </li></ul><ul><l...
Tasks in psychology experiment <ul><li>Detecting </li></ul><ul><li>Discriminating </li></ul><ul><li>Identifying </li></ul>
Detecting <ul><li>Special case of discriminating </li></ul><ul><li>( Exist   or  Do not exist ) </li></ul>
Discriminating and Identifying <ul><li>Identification   =  identification of  n  object </li></ul><ul><li>Discrimination  ...
A well-known example from speech perception <ul><li>Hard task     ABX   ( ABA  or  ABB ) </li></ul><ul><li>Simple task  ...
<ul><li>Simple  X ( A  or  B ) task   light memory load  </li></ul><ul><li>Hard  ABX  task    higher memory load. </li><...
<ul><li>Grouping  </li></ul><ul><li>effects  </li></ul><ul><li>categorized object recognition </li></ul>
To study object recognization Study letter recognition Identify snake letter
<ul><li>Independent Variables : wiggle </li></ul><ul><li>Dependent Variables : Efficiency </li></ul>based on  the snake on...
Wiggle    The angle of sinusoid with the axis Rotating  Offsetting  Phase shifting
<ul><li>To measure the relatively efficiency of recognition </li></ul><ul><li>Use computer program to set an ideal observe...
<ul><li>Measure the threshold contrast for 82% correct identification, both Human and Ideal observer. </li></ul><ul><li>Co...
Human observer <ul><li>Two undergraduate observer </li></ul><ul><li>More data each observer </li></ul>Use the concept akin...
Efficiency   To neglect Zero-noise threshold E 0  Apply high background noise to elevate threshold    E>>E 0   E 0  beco...
 
Zero wiggle    Efficiency = 8% At wiggle higher than 15 o Efficiency
Conclusion <ul><li>Wiggle raises human threshold, not ideal observer </li></ul><ul><li>Gestalt laws play an important role...
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Grouping In Object Recognition

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Grouping In Object Recognition

  1. 1. Grouping in object recognition: The role of a Gestalt law in letter identification Pelli, Denis G., Majaj, Najib J., Raizman, Noah, Christian, Christopher J., Kim, Edward & Palomares, Melanie C. (2009). Psychology and Neural Science, New York University, New York, NY, USA. Cognitive Neuropsychology, 26 (1), 36-49.
  2. 2. Gestalt Law Grouping (proximity) Most are binary discrimination tasks in the past
  3. 3. Binary discrimination task <ul><li>Differs from ordinary object recognition task </li></ul><ul><li>Quick </li></ul><ul><li>Familiar </li></ul><ul><li>Meaningful </li></ul><ul><li>Named </li></ul>
  4. 4. Tasks in psychology experiment <ul><li>Detecting </li></ul><ul><li>Discriminating </li></ul><ul><li>Identifying </li></ul>
  5. 5. Detecting <ul><li>Special case of discriminating </li></ul><ul><li>( Exist or Do not exist ) </li></ul>
  6. 6. Discriminating and Identifying <ul><li>Identification = identification of n object </li></ul><ul><li>Discrimination = identification of 2 object </li></ul><ul><li>But different mechanisms </li></ul>
  7. 7. A well-known example from speech perception <ul><li>Hard task  ABX ( ABA or ABB ) </li></ul><ul><li>Simple task  X ( A or B ) </li></ul><ul><li>The Difference on voice onset time in ABX task is much more noticeable </li></ul>
  8. 8. <ul><li>Simple X ( A or B ) task  light memory load </li></ul><ul><li>Hard ABX task  higher memory load. </li></ul><ul><li>The simple one  Less noticeable </li></ul><ul><li>The hard one  more noticeable </li></ul>Difference for voice onset time
  9. 9. <ul><li>Grouping </li></ul><ul><li>effects </li></ul><ul><li>categorized object recognition </li></ul>
  10. 10. To study object recognization Study letter recognition Identify snake letter
  11. 11. <ul><li>Independent Variables : wiggle </li></ul><ul><li>Dependent Variables : Efficiency </li></ul>based on the snake on the grass
  12. 12. Wiggle  The angle of sinusoid with the axis Rotating Offsetting Phase shifting
  13. 13. <ul><li>To measure the relatively efficiency of recognition </li></ul><ul><li>Use computer program to set an ideal observer ,as a reference for human performance on a absolute scale. </li></ul><ul><li>Geisler, 1989 </li></ul>
  14. 14. <ul><li>Measure the threshold contrast for 82% correct identification, both Human and Ideal observer. </li></ul><ul><li>Compute contrast energy at threshold, integrated square of the contrast function </li></ul>so the efficiency and energy are proportional to squared contrast First proposed by Watson & Pelli, 1983
  15. 15. Human observer <ul><li>Two undergraduate observer </li></ul><ul><li>More data each observer </li></ul>Use the concept akin to the method of psychophysics Draw conclusion from individual, not averages
  16. 16. Efficiency  To neglect Zero-noise threshold E 0 Apply high background noise to elevate threshold  E>>E 0 E 0 become relatively insignificant Tanner, Birdsall (1958) Pelli, Farell (1999)
  17. 18. Zero wiggle  Efficiency = 8% At wiggle higher than 15 o Efficiency
  18. 19. Conclusion <ul><li>Wiggle raises human threshold, not ideal observer </li></ul><ul><li>Gestalt laws play an important role in letter identification, and may be an evidence of its importance at ordinary object recognition. </li></ul>-END

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