Altering the Number of Targets During Multiple Object Tracking

Altering the Number of Targets During Multiple Object Tracking Justin Ericson and James Christensen th Air Force Research Laboratory, 711 HPW, RHCP • Pylyshn and Annan (2006) found that participants had

difficulty inhibiting automatically selected targets from a pre-cued set of tracked objects • Wolfe, Place and Horowitz (2007) used a paradigm described as Multiple Object Juggling (MOJ) where objects in the tracked set were added or removed throughout the duration of a trial, rather than manipulating precues • Over the course of the entire trial they found little to no performance cost associated with the MOJ

Design

Model Analysis

Offload

Cue (5s) Motion (10s) Tone

Upload

Current Research

• 10 naïve participants drawn from personnel currently employed at Wright-Patterson Air Force Base • Task was implemented in Matlab utilizing the Psychophysics Toolbox (Brainard, 1997; Pelli, 1997) • Participants received instruction in basic task performance as well as offload and upload conditions • 16 trials per participant of 6 trial types, randomized: o Long (20s) 3 and 4 dots o Short (10s) 3 and 4 dots o 3 with upload to 4 (3+), 4 with offload to 3 (4-) • The display size subtended 20.6 visual angle, with each dot 1.2 visual angle • Dots moved in a linear trajectory at approximately 10 per second, occluding each other when passing and repulsing off the white frame border • Photometrically equiluminant red and green color cues paired with a tone were used to direct adding or subtracting a dot

Cue (5s) Motion (10s)

0

Tone

Results

Discussion

Motion (10s)

Offloading to 3 dots vs. constant 3 dots Response

4

Number of dots tracked

Method

Response

3.5 3 2.5 2 1.5 3 dots

1

4-

0.5

Model 4-

0 0s

10s

20s

Trial length

Uploading a 4th dot vs. constant 4 dots 4

Number of dots tracked

• Does modifying the tracked set during tracking result in performance differences, specifically a deficit when inhibiting a previously selected target? • Separate trials where objects are added and removed in order to look for differences • Control for sequential effects in the analysis by comparing actual performance to predictions based on the assumption of no switch cost

Motion (10s)

• Assumptions: performance is a decreasing function of time , number of dots tracked, and individual differences. • Use short (10s) trials and long (20s) unmodified trials to predict performance on upload and offload trials, via slope estimates for each half of long trials • Model each individual based on their data from the other conditions; also include sequential effects (i.e. probability of offloading a dot that had already been lost vs. still being tracked). 4.5 • Model predicts 3+ quite well, with 4 43+ slope and intercept not significantly y = 1.1223x - 0.5493 different from 1 and 0; lower load in 3.5 R² = 0.759 first half benefits performance 3 • Excellent R2 for 4-, but model 2.5 overpredicts performance : there is 2 a penalty for switching in 4y = 0.4028x + 1.1431 1.5 R² = 0.9114 • Model is not just scaling individual 1 differences: R2 drops by .2-.4 when 0.5 correlating long 3 dots with 4- and long 4 dots with 3+ 0 Actual Data

Background

3.5 3 2.5 2 1.5 4 dots

1

3+

0.5

Model 3+

0 0s

10s

Trial length

20s

• Offloading and uploading result in opposite effects when compared to unmodified tracking. • This may explain the null result found with MOJ • Use of a simple model predicts performance well for upload but not offload.

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Model Prediction

• Our results showed that there is a drop in performance when removing a target from the tracked set • Over the long term, the effects of adding and dropping dots may cancel out leading to results such as Wolfe et al (2007) • Similar to Pylyshn and Annan (2006), we demonstrate that inhibiting an attended item (cued by popout features) is more effortful than an adding an object to our tracked set • Future directions include testing variable length trials to fill out performance curves, and using eyetracking to investigate disruptions of scan strategy due to the add/drop cues

References Brainerd, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10¸433-436. Pelli, D. G. (1997). The videotoolbox software for visual psychophysics: Transforming numbers into movies. Spatial Vision, 10, 437-442. Pylyshn, Z. W., & Annan, V. (2006). Dynamics of target selection in multiple object tracking. Spatial Vision, 19, 485-504 Wolfe, J. M., Place, S. S., & Horowitz, T. S. (2007). Multiple object juggling: Changing what is tracked during extended multiple object tracking. Psychonomic Bulletin & Review, 14, 344-349

Acknowledgements Flight Psychophysiology Laboratory Research Team – Justin Estepp, Iris Davis, Jason Monnin, Bill Miller, and Glenn Wilson Funding provided by: Consortium Research Fellows Program – 4214 King Street, Alexandria VA 22302 Air Force Research Laboratory – 711th HPW, Human Effectiveness Directorate