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Splitting Attention Slows Attention: Poor Temporal Resolution in Multiple Object Tracking
- 1. Splitting Attention Slows Attention: Poor Temporal Resolution in Multiple Object Tracking Alex.Holcombe@sydney.edu.au School of Psychology Wei-Ying Chen http://www.slideshare.net/holcombea/ School of Psychology 1 @ceptional
- 2. Splitting Attention Slows Attention: Poor Temporal Resolution in Multiple Object Tracking Alex.Holcombe@sydney.edu.au School of Psychology Wei-Ying Chen http://www.slideshare.net/holcombea/ School of Psychology 1 @ceptional
- 3. Multiple object tracking • Speed limit (Holcombe & Chen, 2012) • Temporal frequency limit ??? What are the temporal limits on MOT? Are they set by availability of resource (# of targets)? 2
- 4. Tracking 2 targets with 3 objects in each ring You must fixate on the fixation point ! 3
- 5. Spatial Spatial Resolution Space Resolution Space D T D D D D T D D D D Temporal Temporal Resolution Resolution Time 4
- 6. Two-ring Experiment 3 6 9 12 Number of Objects Three-ring Experiment 3 6 9 12 Number of Objects
- 7. More objects in ring = decreased speed threshold number of objects Proportion correct Speed (rps) ! ! 6
- 8. No spatial crowding- Proportion Number of objects did not affect lapse rate correct Speed (rps) 7
- 9. Extracting the threshold speed from the data Chance: 33% 17% 11% 8% Threshold: 66% 58% 55% 54% Threshold Rate calculation: (99.5%+Guess Rate)/2 8
- 10. Threshold speed declines with # of targets and # of objects Threshold Speed (rps) 3 6 9 12 3 6 9 12 Number of objects Number of objects ! 9
- 11. Threshold temporal frequencies •relatively unaffected by 6-12 objects •decrease with # of targets Threshold Temporal Frequency (Hz) 3 6 9 12 3 6 9 12 Number of objects Number of objects ! 10
- 12. Speed limits for 2 targets so slow that would have done better by just tracking 1 ! 11
- 13. Temporal limits on tracking multiple objects # targets 1 2 3 speed limit 1.7 rps 1.3 rps ? ? temporal frequency 7 4 2.6 limit HZ HZ HZ • Set by availability of resource • So are slow perceptual limits generally? • Likely limits performance with conventional MOT displays 12
Notas do Editor
- -set phone on timer\n-get Piers’ remote\n-get green laser pointer\n-stimulus demos open\n-Wing Chen did almost all the work, just as he did in the previous talk\n
- -set phone on timer\n-get Piers’ remote\n-get green laser pointer\n-stimulus demos open\n-Wing Chen did almost all the work, just as he did in the previous talk\n
- -set phone on timer\n-get Piers’ remote\n-get green laser pointer\n-stimulus demos open\n-Wing Chen did almost all the work, just as he did in the previous talk\n
- -I’m interested in what sets the slow limits on perception and visual cognition.\n-so I’m interested in Object tracking because it’s one of our abilities that has a slow temporal resolution limit\n-we know that already for tracking ONE object thanks to a paradigm developed by VC&L\nbut they didn’t look at tracking MULTIPLE objects\n-there’s nothing in the literature about temporal frequency limits for tracking MULTIPLE objects\n-We don’t know the temporal limits on MOT and \n-we also DON’T know whether these temporal limits are set by the availability of the resource\n-that limited capacity may be set by a finite number of discrete slots, a continuous resource, or two spotlights one in each hemisphere that is timeshared among the targets\n-I’ll explain more about the temporal frequency concept in a moment, first let me explain the paradigm\n\n
- -We use multiple rings of objects. They all move at the same speed, although sometimes in opposite directions.\n-I’ll jump straight to the demo here\n-Each ring may have a target as well as distracters.\n-You may be able to do this, although it’s actually already pretty difficult even tho they’re not moving\n-DEMO: Two targets at a speed of 0.2 rps 2ring2targets9objs0.4rps.mov\nThree targets at 0.2 rps 3targets3obj0.2rps.mov\n3targets9objs0.4rpsMPG4hiquality\n
- -To explain the temporal frequency aspect, I’ve got this schematic here which rather than depicting identical red blobs uses the more interesting example of a circular sushi train- and here’s a portion of one\n-If you recognize one of these as the freshest piece of sushi in the train, you’ll want to keep your tracking focus on it\nbut your ability to do so will be affected by the spatial resolution and the temporal resolution of your tracking focus\n-In the stimulus depicted in this space-time diagram, we don’t have many blobs or pieces of sushi and although temporal resolution is coarse, it’s sufficient to exclude the distracters\n-However with the same speed if we increase the number of objects then when the target moves, its location is more rapidly occupied by a distracter, so the temporal selection window is not sufficient to keep attention on the target rather than the distracters\n-We can control the duration for which the target is available at each location by changing the speed and # of objects\n-Their product is the temporal frequency, the number of times per second that objects pass by a given location\n-----------------------------------\nHere I’m depicting a circular sushi train with the circle stretched into a line.\nOne piece of sushi is the target, it and the other pieces start moving here.\nIf the number of objects is low, a lot of time elapses before the next object \nTherefore the only way you can track the \nIn the display depicted here, the train is at first stationary- four pieces here, not changing location over time and subsequently\nthey all begin to move at the same speed around the track.\nAlthough all the pieces are identical, you noticed that the chef made this piece last, making it about 20 s fresher than the rest.\nWant to know which one is the freshest one\n So you want to track it until it comes around to you.\nThe process that you use to track it, which some people identify as selective attention, will have a spatial resolution and a temporal resolution. \nThe spatial resolution is the smallest area of space that your tracking process can pick out. The temporal resolution is the smallest interval of time that your tracking process can pick out.\nIn the scenario on the left, temporal resolution is sufficient. In the scenario on the right, the train is moving at the same speed but we’ve added more sushi to the track. The consequence is that this same temporal interval of selection is no longer sufficient.\nSoon after one object leaves an area, another quickly takes its place. This distracter shares the selection window, meaning that selection cannot isolate the target. You won’t know which is the target anymore because you have both targets and distracters occupying your undifferentiated selection window.\n
- We’re going to do it in 2 experiments,one with 2 rings and one with 3 rings.\nIn the two ring experiment we’ll designate one or two targets, one in each ring\n-These displays weren’t as small as they may appear here, they nearly filled the screen\n-In the 3-ring experiment we’ll designate 2 or 3 targets, one in each ring\n-Within each ring, we also varied the number of objects to control the temporal frequency\n---------------------------------------\nEccentricity of rings: 2.5, 5.5 deg in 2-ring\n1.5, 4.5, 12 deg in 3-ring and \n\n
- -The signature of a temporal frequency limit is that when there are more objects in the ring, the speed limit decreases in in verse proportion.\n-To test whether that occurred here, we used your usual method of constant stimuli design.\n-A randomly-chosen speed was chosen on each trial, and all the rings moved at the same speed\n-One, two, or three objects were designated as targets and the participants tried to track them as the rings rotated, each occasionally reversing independently\n-The top graph shows percent correct picking the target with only 3 objects in each ring, the bottom graph shows percent correct picking the target with only 12 objects in the ring\n-Performance declined with speed of course, we’re going to pick a certain level of performance as the threshold beyond which tracking performance is poor.\n-Importantly, the threshold speed decreased dramatically with number of objects in the ring, which we’ll attribute to a temporal frequency limit. But before I get too far into that I want to get an alternative hypothesis out of the way-\n
- -That increasing the number of objects in the ring reduced performance not because it increased the temporal frequency but rather because it caused spatial crowding\n-Crowding should occur at all speeds, even when the objects are stationary, \n-So it should manifest as a reduction in the maximum performance level. In psychophysics we call this maximum level corresponds to 1 minus the fitted lapse rate, \nso spatial crowding would manifest as an increase in the lapse rate\n-However we saw no significant effect of number of objects on the lapse rates\n-As expected because the spacing of these objects was large enough to safely be outside the previously measured crowding zones\n-------------------------------------------------\n-The reason for manipulating the number of objects was to check whether there is a temporal frequency limit on performance or instead simply a speed limit or something else. Temporal interference\n-One possibility is that increasing the number of objects around the ring from 3 to 12 will cause spatial crowding to occur spatial interference.\n-Spatial interference by definition is spatial so it should not depend on speed. \n
- -A complication with changing the number of objects in the ring is that it changes the chance rate\n-We corrected for that by changing the performance level considered the threshold accordingly, basically by picking the speed corresponding to halfway down the psychometric function\n-Although we get the same pattern of results when we analyze the data in other ways\n-Logistic regression adjusted by an assumed 1% lapse rate\n-We also tried it with 75% thresholds, but it didn’t change the pattern of speed limits I’m about to show you\n
- -Here I’m plotting the threshold speed as a function of the number of objects in the ring\n-It declines steadily with the number of objects in the ring, both for the 2-ring experiment and for the 3-ring experiment\n-What we also see is a reduction in the speed limit when there are more targets to track\n-From 1 to 2 targets the speed limit decreases , that’s the 2-ring experiment and comparing 2 targets to 3 targets \n-If what’s causing this decline is actually a temporal frequency limit, then when we convert these numbers to temporal frequency, we should get a flat line\n-We do that conversion by multiplying each speed by the number of objects to get the temporal frequency and when we plot the data\n
- -This is what we get- we see good evidence that temporal frequency is limiting performance for these conditions\n-With 1 target, a limit of 7 Hz, with an enormous drop to about 4 Hz for 2 targets. \n-When there are only 3 objects in the ring however, we get a different result- tracking is not possible above about 5.2 Hz. \n-This suggests that there is also a speed limit operating here, of about 1.7 rps \n-When there’s only 3 objects in the ring, you hit that speed well before you get to the 7 Hz limit. VC&L found the same thing, so I’m pretty confident of that\n-What’s completely new here is the decrease in this temporal frequency limit with number of targets\n-It falls to about 4 Hz with 2 targets. And we replicated that with the 3-ring experiment, again with 2 targets finding about 4 Hz\n-For 3 targets the temporal frequency limit fell still further, to about 2.6 Hz\n-As far as the speed limit, it’s hard to know what’s happening because the temporal frequency limit has fallen so far with additional targets that you may be hitting that first, obscuring the speed limit\n
- -To give you a sense of how big this effect is,\n-We calculated how big the speed limit reduction would be if, when you’re told to track 2 targets, you only track 1 and then guess on the other\n-Those threshold speeds are shown here with dotted lines.\n-The amazing thing is that in every condition the speed threshold for two targets was actually worse than the speed threshold subjects would have gotten if they had only tracked 1\n-THIS MEANS THAT\n-At high speeds it requires so much resource to be successful that if you give half to one target and half to the other, you fail at both\nwhereas if you’d given all your juice to one, you woulda been all right\n----------------------------------------\nYour performance in the 2-target condition was so bad that you woulda done better by completely ignoring the second target and just tracking the one\n\n
- -This could lead to a new understanding of slow limits on perception & visual cognition\n-In the conventional MOT display with balls bouncing around randomly or in the real world with soccer players bouncing off each other randomly,\n-There certainly are going to be other stimulus factors affecting performance, such as spatial interactions or crowding. We know crowding exists so that when two objects get really close to each other, that’s going to hurt performance\n-What hasn’t been discussed is temporal interference- when a target leaves a particular location and a distracter replaces it within a couple hundred milliseconds, tracking will fail. I haven’t found any tracking theory that describes that\n-But a successful tracking theory will have to explain this near halving of temporal resolution\n-Interestingly, although nobody’s written about this, it sits best with serial spotlight theory\n-With a single spotlight, when you have two targets it’ll can only visit each half as often, which predicts a halving of the temporal frequency limit, which is approximately what we found. \n-One spotlight in each hemisphere might mimic this data very well\n----------------------------\n-The narrowest temporal interval that tracking can access is first of all coarse even with just 1 target.\n-This is consistent with it being a high-level limitation rather than being limited by motion perception itself\n-Second we found an enormous effect of number of targets\n-This supports the idea that a limited-capacity resource determines the narrowest interval you can select\n-An exciting prospect is that this is a general attentional limit that will generalize to many other tasks, but we’ll have to do more experiments to find out\n