Lomonosov Moscow State University Cognitive Seminar 6102004 Dynamic
- Slides: 92
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 lab photo Jennifer Di. Mase George Alvarez David Fencsik Randy Birnkrant Jeremy Wolfe Helga Arsenio Linda Tran (not pictured)
Multi-element visual tracking task (MVT) • Devised by Pylyshyn & Storm (1988) • Method for studying attention to dynamic objects
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
Demo demo mvt 4
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
Accounts of MVT performance • • FINSTs (Pylyshyn, 1989) Virtual polygons (Yantis, 1992) Object files (Kahneman & Treisman, 1984) “Object-based attention”
These are all (partially) wrong • • FINSTs (Pylyshyn, 1989) Virtual polygons (Yantis, 1992) Object files (Kahneman & Treisman, 1984) “Object-based attention”
Common assumptions • Low level (1 st order) motion system updates higher-level representation – FINST – Object file – Virtual polygon • Continuous computation in the present
Overview • MVT and attention • Tracking across the gap • Tracking trajectories
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 Di. Mase, Jeremy Wolfe
MVT and attention • Clearly a limited-capacity resource • Attentional priority to tracked items (Sears & Pylyshyn) • Hypothesis: MVT is mutually exclusive with visual search
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)
AOC Theory
General methods - normalization • Single task = 100 • Chance = 0 • Dual task performance scaled to distance between single task performance and chance
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
General methods - tracking • 10 disks • 5 disks cued • Speed = 9°/s
AOC Theory
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 attentiondemanding tasks which do not share visual attention resources?
Gold standard: tracking vs. tone detection
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
Gold standard AOC
Tracking + search method • Tracking – Duration = 5 s • Search N=9 – 2 AFC “E” vs. “N” – Distractors = rest of alphabet – Set size = 5 – Duration staircased (mean = 156 ms) – Onset = 2 s
Tracking + search method
Tracking + search AOC
Tracking + search AOC
Does tracked status matter? L T L L
method • Tracking – Duration = 3 s • Search N=9 – 2 AFC left- or right-pointing T – Distractors = rotated Ls – Set size = 5 – Duration staircased (mean = 218 ms) – Onset = 1 s
search inside tracked set T T L L L
search outside tracked set L L L T T L L
mixed search inside tracked set search outside tracked set blocked
inside vs. outside AOC
Does spatial separation matter? P E H F V
method • Tracking – Duration = 5 s • Search – 2 AFC “E” vs. “N” – Distractors = rest of alphabet – Set size = 5 – Duration = 200 ms – Onset = 2 s N=9
spatial separation AOC
search v track summary
MVT and search • • Clearly not mutually exclusive Not pure independence Close to gold standard MVT and search use independent resources?
Two explanations • Separate attention mechanisms • Time sharing
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
Track across the gap method
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
track across the gap asymptotes
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
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 – 4 AFC: Report orientation of T • Duration of search task staircased (326 ms)
search during gap AOC
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
Summary • MVT and visual search can be performed independently in the same trial • May support independent “visual attention” mechanisms • May support time-sharing
Summary • Tracking across the gap data support time sharing • Tracking across the gap data raise new questions
What is the mechanism? • Not a continuous computation in the present • Not first order motion mechanisms • Not apparent motion Randall Birnkrant, Jennifer Di. Mase, Sarah Klieger, Linda Tran, Jeremy Wolfe
None of these theories fit • FINSTs (Pylyshyn, 1989) • Virtual polygons (Yantis, 1992) • Object files (Kahneman & Treisman, 1984)
What is the mechanism? • Some sort of amodal perception? (e. g. tracking behind occluders, Scholl & Pylyshyn, 1999) • … but there are no occlusion cues!
Scholl & Pylyshyn, 1999
Maybe the gap is just an impoverished occlusion stimulus • No occlusion/disocclusion cues • Synchronous disappearance
Predictions of impoverished occlusion hypothesis • Occlusion cues will improve performance • Asynchronous disappearance will improve performance
Method • • Track for 5 s Speed = 12°/s Track 4 of 10 disks Independent variables (blocked) – Gap duration: 107 ms, 307 ms, 507 ms – Occlusion cues absent, present – Disappearances synchronous, asynchronous • N = 15
synchronous disappearance items invisible but continue to move all items reappear simultaneously
synchronous disappearance + occlusion disocclusion begins
Occlusion/Disocclusion
asynchronous disappearance item reappears one item at a time disappears but continues to move
asynchronous disappearance + occlusion one item at a time begins to be occluded. . . moves while invisible. . . then disoccludes
comparing cue types
Occlusion hypothesis fails • Occlusion cues don’t help • Asynchronous disappearance doesn’t help
Method • • Track for 5 s Speed = 12°/s Synchronous condition only Independent variables (blocked) – Gap duration: 107 ms, 307 ms, 507 ms – Occlusion cues absent, present – Track 4, 5, or 6 of 10 disks • N = 11
comparing cue types
Occlusion hypothesis fails • Occlusion cues don’t help • Occlusion cues can actually harm performance • Asynchronous disappearance doesn’t help
What is the mechanism? • • Not a continuous computation in the present Not first order motion mechanisms Not apparent motion Not amodal perception (occlusion)
How do we reacquire targets? • remember last location (backward) • store trajectory (forward) David Fencsik, Sarah Klieger, Jeremy Wolfe
location-matching account Memorized pre-gap target location. Nearest to memorized location: identified as target. First Post-Gap Frame
trajectory-matching account Memorized pre-gap target trajectory. On target trajectory: identified as target. First Post-Gap Frame
Shifting post-gap location Last visible pre-gap location Expected post-gap location +1 0 -1 opposite of expected location = Stimulus trajectory
shifting post-gap location predictions
Shifting post-gap location methods • • • track for 5 s speed = 8°/s track 5 of 10 disks gap duration = 300 ms post-gap location condition blocked stimuli continue to move after gap
shifting post-gap location
Location vs. trajectory-matching • support for location-matching – see also Keane & Pylyshyn 2003; 2004 • but advantage for -1 is suspicious
Location vs. trajectory-matching + time +1. 0 + time +1. 5 time +2. 0 +
shift & stop methods • • • track for 4 -6 s speed = 9°/s track 2 or 5 of 10 disks gap duration = 300 ms post-gap location condition blocked stimuli stop after gap
moving vs. static after gap
moving vs. static after gap
2 vs. 5 targets
Location vs. trajectory-matching • support for location-matching • However. . . – conditions are blocked – observers might see their task not as tracking across the gap, but learning which condition they’re in – might not tell us about normal target recovery
Location vs. trajectory-matching • can subjects use trajectory information? • always have items move during gap • vary whether trajectory information is available or not
moving condition invisible motion
static condition invisible motion
manipulate pre-gap information methods • • track for 4 s speed = 9°/s track 1 to 4 of 10 disks gap duration = 300 ms
manipulate pre-gap information
manipulate pre-gap information
Location vs. trajectory-matching • observers can use trajectory information • unlimited (or at least > 4) capacity for locations • smaller (1 or 2) capacity for trajectories
Conclusions • Flexible attention system allows rapid switching between MVT and other attention -demanding tasks • Some representation allows recovery of tracked targets after 300 -400 ms gaps • This representation includes location and trajectory information
Speculation • MVT reveals two mechanisms, rather than just one • Frequently (but perhaps not continuously) updated location store • Attention to trajectories
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