1

Spatial Analysis of Eye Movements!

Spatial Analysis

n Heat Maps (Attention Maps)!n Area of Interest (AOI) Analysis!

2

Heat Maps!

Image

4

Fixations Overlaid

5

Gridded Heat Maps

n Gridded Heat Map!¨ Divide image into matrix of cells. Brightness of each

cell proportional to:!n  Number of fixations!n  Total fixation duration!

6

2

Gridded Heat Map

7

Smoothed Heat Maps

n Convolve fixation map with a Gaussian, i.e. for each fixation add a 2D-Gaussian centered at that location!

n Width of Gaussian is arbitrary. We can use functional field of view (e.g. σ=2°), but there are no good guidelines.!

n Amplitude of Gaussian: 1 or proportional to fixation duration !

8

Smooth Heat Map

9

Heat Map

10

Effect of Gaussian Width σ

11

σ=20 σ=5

Heat Map

12

Using Woodford’s SC package

3

3D Heat Map

13

Limitations of Heat Maps

n Heat maps represent distribution of fixations, but not more!

14

Central Bias n  Participants have a tendency to fixate the center

screen.!n  Compute global fixation distribution over all

experimental conditions.!

15

Statistical Analysis!

16

Statistical Analysis

n By smoothing fixations, heat map values at nearby locations are highly correlated. Statistical problem similar to that of fMRI!

n Using Random Field Theory!n Using Bootstrapping!

17

Bootstrapping n  Method!

¨  Create random samples (with replacement) from a distribution and compare to empirical fixation distribution (e.g. in a particular experimental condition)!

¨  Sampling from Uniform distribution, Gaussian distribution, empirical global fixation distribution!

4

iMap3

19 Gaussian-smoothed heat maps

Caldara, R., & Miellet, S. (2011). iMap: A Novel Method for Statistical Fixation Mapping of Eye Movement data. Behavior Research Methods, 43(3), 864-78

iMap3 n  The t-test maps are enhanced

using threshold-free cluster enhancement (FCE), which takes amplitude and extent of signal into account!

n  To compensate for multiple comparisons, test using bootstrap!

20

Saliency Maps!

21

Saliency Analysis Saliency analyses processes information in a number of channels that are sensitive to changes in different image characteristics (intensity, color, orientation).!!Information in these channels are combined into a single saliency map, which indicates “interesting” points in any of these component maps.!!To what extent can saliency explain fixation distributions? !!

Saliency Toolbox

23

http://www.saliencytoolbox.net/

Characteristics of Fixated Locations

n  Tatler, Baddeley & Gilchrist (2005)!

n  Characterize lumincance, chromaticity, contrast, and edge content of images.!

n  Compare fixated with non-fixated areas.!

n  Fixated areas tend to be more distinctive in high spatial frequencies!

5

Heat Maps and Saliency

n Correlation-based Measures!¨ Compute correlation between smoothed heat maps

and saliency map!

n Kullback-Leibler Divergence!¨ Measure of overall dissimilarity of two probability

distributions!

n ROC Analysis!

25

KL(H,S) = sk loghijsiji, j

∑

Heat Maps and Saliency n  Le Meur & Baccino (2012) Methods for comparing scanpaths and

saliency maps, Behavior Research Methods, 45, 251-266.!

26

Fixation Analysis Software (for Windows)

Area of Interest Analysis!

Fixations Overlaid

28

Region Map

29

AOI Area Normalization

n With uniform distribution of fixation across image, number of fixations in an AOI is proportional to AOI area!

n Solution: Divide number of fixations by AOI area to obtain number of fixations per unit area in each AOI!

30

6

Central Bias Effect

n Possible Solution!¨ Compute overall fixation map averaged over all

experimental conditions!¨  Interpret deviations from the overall fixation map !

31

Content Bias

n Two AOIs with the same area may have different or more complex content, such that dwell time is longer for one than the other.!

32

Creating your own AOIs

n Many eye tracking systems (e.g. Eyelink, ASL, Dikablis) come with software for defining areas of interest!

n Alternatively, use a Paint program!

33

Dynamic AOI Analysis!

Dynamic AOIs

n Egomotion: Head and body movements!¨ Many modern eye trackers deal with egomotion within

limits!

n Problems with!¨ Large scale egomotion!¨ Object motion!

35

Dynamic AOIs

36

7

Keyframing

n Frame-by-Frame Coding is extremely time-consuming!

n Use of keyframing!

37

keyframing in 3ds Max for size and path interpolation

Motion & Eye Tracking

n Combine eye tracking with tracking of head, body and objects.!

38

Demo and Exercises!