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Low-level visual saliency does not predict change detection in natural scenes Stirk, A.,& Underwood, G.(2007). Journal of ...
<ul><li>change blindness </li></ul><ul><ul><li>Maintenance failed (Rensink,2002; Simons & Levin, 1997) </li></ul></ul><ul>...
<ul><li>T op–down and/or  B ottom–up  </li></ul><ul><li>-> allocation of attention </li></ul><ul><li>Wright (2005) </li></...
<ul><li>I nfluenced by top–down processes. </li></ul><ul><li>When semantic information is low, bottom–up processes may hav...
Methods <ul><li>2x2 design </li></ul><ul><li>Salience (high level vs. low level) </li></ul><ul><li>Scene-schema(consistent...
Methods
 
Methods
<ul><li>24 participants </li></ul><ul><li>10scenes (19.7 °   ×  13.9  ° ) </li></ul><ul><ul><li>4 changed images </li></ul...
Procedure <ul><li>按鍵回答 “ SAME” or “DIFFERENT” </li></ul><ul><li>重複” Flicker” ,直到受試者做出反應 </li></ul><ul><li>練習: 8  trials ( ...
 
Results <ul><li>Consistency  RT : </li></ul><ul><ul><li>F(1, 23) = 5.38,  p = .03 </li></ul></ul><ul><ul><li>IC 2341.7 < C...
 
<ul><li>Consistency  ACC : </li></ul><ul><ul><li>F(1, 23) = 15.55, p = .001 </li></ul></ul><ul><ul><li>IC  87.3% >  C  78....
 
Discussion <ul><li>I nconsistent-object detection advantage </li></ul><ul><li>C ategories of objects guide visual attentio...
<ul><li>c hange detection based solely on the visual properties of a scene and finds that semantic salience. </li></ul>
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Low Level Visual Saliency Does Not Predict Change

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Low Level Visual Saliency Does Not Predict Change

  1. 1. Low-level visual saliency does not predict change detection in natural scenes Stirk, A.,& Underwood, G.(2007). Journal of Vision , 7(10), 1-10.
  2. 2. <ul><li>change blindness </li></ul><ul><ul><li>Maintenance failed (Rensink,2002; Simons & Levin, 1997) </li></ul></ul><ul><li>Coherence field dissolves </li></ul><ul><ul><li>Rensink (2000) </li></ul></ul><ul><li>flicker </li></ul><ul><ul><li>(Rensink ,1997) </li></ul></ul><ul><ul><li>A–blank–A’–blank-A </li></ul></ul>
  3. 3. <ul><li>T op–down and/or B ottom–up </li></ul><ul><li>-> allocation of attention </li></ul><ul><li>Wright (2005) </li></ul><ul><ul><li>change detection in natural scenes could be predicted </li></ul></ul><ul><ul><li>subjective measures </li></ul></ul>
  4. 4. <ul><li>I nfluenced by top–down processes. </li></ul><ul><li>When semantic information is low, bottom–up processes may have a greater influence on the allocation of attention </li></ul>
  5. 5. Methods <ul><li>2x2 design </li></ul><ul><li>Salience (high level vs. low level) </li></ul><ul><li>Scene-schema(consistent vs. inconsistent) </li></ul>
  6. 6. Methods
  7. 8. Methods
  8. 9. <ul><li>24 participants </li></ul><ul><li>10scenes (19.7 ° × 13.9 ° ) </li></ul><ul><ul><li>4 changed images </li></ul></ul><ul><ul><li>1 original image </li></ul></ul><ul><ul><li>80 trials : </li></ul></ul><ul><ul><li>4*10 change pairs 、 4*10 no-change pairs </li></ul></ul>
  9. 10. Procedure <ul><li>按鍵回答 “ SAME” or “DIFFERENT” </li></ul><ul><li>重複” Flicker” ,直到受試者做出反應 </li></ul><ul><li>練習: 8 trials ( 有 Feedback ) </li></ul><ul><li>正式: 80 trials ( 沒有 Feedback ) </li></ul>
  10. 12. Results <ul><li>Consistency RT : </li></ul><ul><ul><li>F(1, 23) = 5.38, p = .03 </li></ul></ul><ul><ul><li>IC 2341.7 < C 2549.2 </li></ul></ul><ul><li>Visual Saliency RT : </li></ul><ul><ul><li>F(1, 23) = 1.78, p = .20, </li></ul></ul><ul><ul><li>No main effect </li></ul></ul>
  11. 14. <ul><li>Consistency ACC : </li></ul><ul><ul><li>F(1, 23) = 15.55, p = .001 </li></ul></ul><ul><ul><li>IC 87.3% > C 78.3%, </li></ul></ul><ul><li>Visual Saliency ACC : </li></ul><ul><ul><li>F(1, 23) = 0.26, p = .62, </li></ul></ul><ul><ul><li>No main effect </li></ul></ul>
  12. 16. Discussion <ul><li>I nconsistent-object detection advantage </li></ul><ul><li>C ategories of objects guide visual attention </li></ul><ul><li>Violations to the scene-schema </li></ul><ul><li>-> stronger perceptual representation </li></ul>
  13. 17. <ul><li>c hange detection based solely on the visual properties of a scene and finds that semantic salience. </li></ul>

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