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Dynamic attention and predictive tracking

Lomonosov Moscow State University Cognitive Seminar, 6/10/2004. Dynamic attention and predictive tracking. Todd S. Horowitz Visual Attention Laboratory Brigham & Women’s Hospital Harvard Medical School. Sarah Klieger. Jennifer DiMase. George Alvarez. Helga Arsenio. lab photo.

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Dynamic attention and predictive tracking

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  1. Lomonosov Moscow State University Cognitive Seminar, 6/10/2004 Dynamic attention and predictive tracking Todd S. Horowitz Visual Attention Laboratory Brigham & Women’s Hospital Harvard Medical School

  2. Sarah Klieger Jennifer DiMase George Alvarez Helga Arsenio lab photo David Fencsik Randy Birnkrant Jeremy Wolfe Linda Tran (not pictured)

  3. Multi-element visual tracking task (MVT) • Devised by Pylyshyn & Storm (1988) • Method for studying attention to dynamic objects

  4. Multi-element visual tracking task (MVT) • Present several (8-10) identical objects • Cue a subset (4-5) as targets • All objects move independently for several seconds • Observers asked to indicate which objects were cued

  5. Demo demo mvt4

  6. Interesting facts about MVT • Can track 4-5 objects (Pylyshyn & Storm, 1988) • Tracking survives occlusion (Scholl & Pylyshyn, 1999) • Involves parietal cortex (Culham, et al, 1998) • “Clues to objecthood” - Scholl

  7. Accounts of MVT performance • FINSTs (Pylyshyn, 1989) • Virtual polygons (Yantis, 1992) • Object files (Kahneman & Treisman, 1984) • “Object-based attention”

  8. These are all (partially) wrong • FINSTs (Pylyshyn, 1989) • Virtual polygons (Yantis, 1992) • Object files (Kahneman & Treisman, 1984) • “Object-based attention”

  9. Common assumptions • Low level (1st order) motion system updates higher-level representation • FINST • Object file • Virtual polygon • Continuous computation in the present

  10. Overview • MVT and attention • Tracking across the gap • Tracking trajectories

  11. MVT and attention • Clearly a limited-capacity resource • Attentional priority to tracked items (Sears & Pylyshyn) • Hypothesis: MVT is mutually exclusive with other attentional tasks George Alvarez, Helga Arsenio, Jennifer DiMase, Jeremy Wolfe

  12. MVT and attention • Clearly a limited-capacity resource • Attentional priority to tracked items (Sears & Pylyshyn) • Hypothesis: MVT is mutually exclusive with visual search

  13. MVT and attention • Clearly a limited-capacity resource • Attentional priority to tracked items (Sears & Pylyshyn) • Hypothesis: MVT is mutually exclusive with visual search • Method: Attentional Operating Characteristic (AOC)

  14. AOC Theory

  15. General methods - normalization • Single task = 100 • Chance = 0 • Dual task performance scaled to distance between single task performance and chance

  16. General methods - staircases • Up step (following error) = 2 x down step • Asymptote = 66.7% accuracy • Staircase runs until 20 reversals • Asymptote computed on last 10 reversals

  17. General methods - tracking • 10 disks • 5 disks cued • Speed = 9°/s

  18. AOC Theory

  19. AOC reality • Tasks can interfere at multiple levels • Interference can occur even when resource of interest (here visual attention) is not shared • How “independent” are two attention-demanding tasks which do not share visual attention resources?

  20. Gold standard: tracking vs. tone detection

  21. Gold standard method • Tracking • Duration = 6 s • Tone duration • 10 600 Hz tones • Onset t = 1 s • ITI = 400 ms • Distractor duration = 200 ms • Task: target tone longer or shorter? • Target duration staircased (31 ms) • Dual task priority varied N = 10

  22. Gold standard AOC

  23. Tracking + search method • Tracking • Duration = 5 s • Search • 2AFC “E” vs. “N” • Distractors = rest of alphabet • Set size = 5 • Duration staircased (mean = 156 ms) • Onset = 2 s N = 9

  24. Tracking + search method

  25. Tracking + search AOC

  26. Tracking + search AOC

  27. T L T Does tracked status matter? L L L

  28. method • Tracking • Duration = 3 s • Search • 2AFC left- or right-pointing T • Distractors = rotated Ls • Set size = 5 • Duration staircased (mean = 218 ms) • Onset = 1 s N = 9

  29. T L search inside tracked set L L T L L

  30. T search outside tracked set L L L T L L

  31. inside vs. outside AOC

  32. Does spatial separation matter? P E H F V

  33. method • Tracking • Duration = 5 s • Search • 2AFC “E” vs. “N” • Distractors = rest of alphabet • Set size = 5 • Duration = 200 ms • Onset = 2 s N = 9

  34. spatial separation AOC

  35. search v track summary

  36. MVT and search • Clearly not mutually exclusive • Not pure independence • Close to gold standard • MVT and search use independent resources?

  37. Two explanations • Separate attention mechanisms • Time sharing

  38. Predictions of time sharing hypothesis • Should be able to leave tracking task for significant periods with no loss of performance • Should be able to do something in that interval

  39. Track across the gap method

  40. Track across the gap method • Track 4 of 8 disks • Speed = 6°/s • Blank interval onset = 1, 2, or 3 s • Trajectory variability: 0°, 15°, 30°, or 45° every 20 ms • Blank interval duration staircased (dv) • N = 11

  41. track across the gap asymptotes

  42. Predictions of time sharing hypothesis • Should be able to leave tracking task for significant periods with no loss of performance (see also Yin & Thornton, 1999) - confirmed • Should be able to do something (e.g. search) in that interval

  43. search during gap method • AOC method • Tracking task same as before • Search task in blank interval • Target = rotated T • Distractors = rotated Ls • Set size = 8 • 4AFC: Report orientation of T • Duration of search task staircased (326 ms)

  44. search during gap AOC

  45. Predictions of time sharing hypothesis • Should be able to leave tracking task for significant periods of time with no loss of performance (see also Yin & Thornton, 1999) - confirmed • Should be able to do something (e.g. search) in that interval - confirmed

  46. Summary • MVT and visual search can be performed independently in the same trial • May support independent “visual attention” mechanisms • May support time-sharing

  47. Summary • Tracking across the gap data support time sharing • Tracking across the gap data raise new questions

  48. What is the mechanism? • Not a continuous computation in the present • Not first order motion mechanisms • Not apparent motion Randall Birnkrant, Jennifer DiMase, Sarah Klieger, Linda Tran, Jeremy Wolfe

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