Adaptive Control of Gaze and Attention

Mary HayhoeUniversity of Texas at Austin

Jelena JovancevicUniversity of Rochester

Brian Sullivan

University of Texas at Austin

Selecting information from visual scenes

What controls the selection process?

Fundamental Constraints Acuity is limited.High acuity only in central retina.

Attention is limited. Not all information in the image can be processed. Visual Working Memory is limited. Only a limited amount of information can be retained across gaze positions.

target selection

signals to muscles

inhibits SC

saccade decision

saccade command

planning movements

Neural Circuitry for Saccades

Image properties eg contrast, edges, chromatic saliency can account for some fixations when viewing images of scenes (eg Itti & Koch, 2001; Parkhurst & Neibur, 2003; Mannan et al, 1997).

Saliency and Attentional Capture

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

Saliency is computed from the image using feature maps (color, intensity,orientation) at different spatial scales, filtered with a center-surround mechanism, and then summed. Gaze goes to the peak.From Itti & Koch (2000).

Certain stimuli thought to capture attention or gaze in a bottom-up manner, by interrupting ongoing visual tasks. (eg sudden onsets, moving stimuli, etc Theeuwes et al, 2001 etc )

This is conceptually similar to the idea of salience.

Attentional Capture

Limitations of Saliency Models

Important information may not be salient eg an irregularity in the sidewalk.

Salient information may not be important - eg retinal image transients from eye/body movements.

Doesn’t account for many observed fixations, especially in natural behavior - previous lecture.(Direct comparisons: Rothkopf et al 2007, Stirk & Underwood, 2007)

Will this work in natural vision?

Foot placement

Obstacle avoidance

Heading

Viewing pictures of scenes is different from acting within scenes.

Need to Study Natural Behavior

Dynamic Environments

QuickTime™ and aYUV420 codec decompressor

are needed to see this picture.

The Problem

Any selective perceptual system must choose what to select, and when to select it.

How is this done given that the natural world is unpredictable? (The “initial access” problem, Ullman, 1984)

Answer - it’s not all that unpredictable and we’re really good at learning it.

Is bottom up capture effective in natural environments?

Looming stimuli seem like good candidates for bottom-upattentional capture (Regan & Gray, 200; Franceroni & Simons,2003).

Human Gaze Distribution when Walking

• Experimental Question: How sensitive are subjects to unexpected salient events?

• General Design: Subjects walked along a

footpath in a virtual environment while avoiding pedestrians.

Do subjects detect

unexpected potential collisions?

Virtual Walking Environment

Virtual Research V8 Head Mounted Display with 3rd Tech HiBall Wide

Area motion tracker

V8 optics with ASL501 Video Based Eye Tracker (Left) and ASL 210

Limbus Tracker (Right)

D&c emily

Video Based Tracker

Limbus Tracker

Virtual Environment

Bird’s Eye view of the virtual walking environment.

Monument

• 1 - Normal Walking: “Avoid the pedestrians while walking at a normal pace and staying on the sidewalk.”

• 2 - Added Task: Identical to condition 1. Additional instruction:” Follow the yellow pedestrian.”

Normal walking

Follow leader

Experimental Protocol

Distribution of Fixations on Pedestrians Over Time

-Pedestrians fixated most when they first appear

-Fewer fixations on pedestrians in the leader trials

0

0.2

0.4

0.6

0.8

1

Time since the appearance onscreen (sec)

Prob

abili

ty o

f fix

atio

n

0-1 1-2 2-3 3-4 4-5

Normal Walking

Follow Leader

Pedestrians’ paths

Colliding pedestrian path

What Happens to Gaze in Response to an Unexpected Salient Event?

•The Unexpected Event: Pedestrians veered onto a collision course for 1 second (10% frequency). Change occurs during a saccade.

Does a potential collision evoke a fixation?

Fixation on Collider

QuickTime™ and aYUV420 codec decompressor

are needed to see this picture.

No Fixation During Collider Period

QuickTime™ and aYUV420 codec decompressor

are needed to see this picture.

Probability of Fixation During Collision Period

Pedestrians’ paths

Colliding pedestrian path

More fixations on colliders in normal walking.

0

0.2

0.4

0.6

0.8

1

No Leader

Leader

Probability of fixation

Controls Colliders

Normal Walking

Small increase in probability of fixating the collider could be caused

either by a weak effect of attentional capture or by active, top-down search of the

peripheral visual field.

Why are colliders fixated?

Probability of Fixation During Collision Period

Pedestrians’ paths

Colliding pedestrian path

More fixations on colliders in normal walking.

No effect in Leader condition

0

0.2

0.4

0.6

0.8

1

No Leader

Leader

Probability of fixation

Controls Colliders

Normal Walking

Follow Leader

Small increase in probability of fixating the collider could be caused

either by a weak effect of attentional capture or by active, top-down search of the

peripheral visual field.

Failure of collider to attract attention with an added task (following) suggests that detections result from active search.

Why are colliders fixated?

Prior Fixation of Pedestrians Affects Probability of Collider Fixation

• Fixated pedestrians may be monitored in periphery, following the first fixation

• This may increase the probability of fixation of colliders

Conditional probabilitiesConditional probabilities

0

0.1

0.2

0.3

Not fixated FixatedChange in the dist. to the Leader (m) 0

0.1

0.2

0.3

No prior fixations With prior fixationsChange in the dist. to the Leader (m)

Other evidence for detection of colliders?

Do subjects slow down during collider period?

Subjects slow down, but only when they fixate collider. Implies fixation measures “detection”.

Slowing is greater if not previously fixated. Consistent with peripheral monitoring of previously fixated pedestrians.

Sum of Pedestrian Fixations Following a

Detection of a Collider

0

0.2

0.4

0.6

0.8

1

Not Fixated Fixated

Fixation durations (s)

No Leader

Leader

Detecting a Collider Changes Fixation Strategy

Longer fixation on pedestrians following a detection of a collider

“Miss” “Hit”

Time fixating normal pedestrians following detection of a collider

Normal Walking

Follow Leader

0

0.2

0.4

0.6

0.8

1

Constant Increased

Colliders Speed

Probability of fixation

Colliders

Controls

Colliders are fixated with equal probability whether or not they increase speed (25%) when they initiate the collision path.

No Leader

Effect of collider speed

0

0.2

0.4

0.6

0.8

1

1-2 2-3 3-4

Number of pedestrians

Probability of fixation

No Leader

Leader

0

0.2

0.4

0.6

0.8

1

0-5 5-10 10-15 15-20 20-25

Degrees of rotation

Probability of fixation

No Leader

Leader

0

0.2

0.4

0.6

0.8

1

3.5-4 4-4.5 4.5-5

Distance to the observer (m)

Probability of fixation

No Leader

Leader

0

0.2

0.4

0.6

0.8

1

Purple Red Green Pink

Pedestrian color

Probability of fixation

No Leader

Leader

No systematic effects of stimulus properties on fixation.

Summary

• Subjects fixate pedestrians more when they first appear in the field of view, perhaps to predict future path.

• A potential collision can evoke a fixation but the increase is modest.

• Potential collisions do not evoke fixations in the leader condition.

• Collider detection increases fixations on normal pedestrians.

To make a top-down system work, Subjects need to learn statistics of environmental events and distribute gaze/attention based on these expectations.

Subjects rely on active search to detect potentially hazardous events like collisions, rather than reacting to bottom-up, looming signals (attentional capture).

Possible reservation…

Perhaps looming robots not similar enough to real pedestrians to evoke a bottom-up response.

Walking -Real World

• Experimental question: Do subjects learn to deploy gaze

in response to the statistics of environmental events?

Experimental Setup

System components: Head mounted optics (76g), Color scene camera, Modified DVCR recorder, Eye Vision Software, PC Pentium 4, 2.8GHz processor

A subject wearing the ASL Mobile Eye

• Occasionally some pedestrians veered on a collision course with the subject (for approx. 1 sec)

• 3 types of pedestrians:

Trial 1: Rogue pedestrian - always collides Safe pedestrian - never collides Unpredictable pedestrian - collides 50% of time

Trial 2: Rogue Safe Safe Rogue Unpredictable - remains same

Experimental Design (ctd)

QuickTime™ and aYUV420 codec decompressor

are needed to see this picture.

Fixation on Collider

Effect of Collision Probability

Probability of fixating increased with higher collision probability.

(Probability is computed during period in the field of view, not just collision interval.)

Detecting Collisions: proactive or reactive?

• Probability of fixating risky pedestrian similar, whether or not he/she actually collides on that trial.

Almost all of the fixations on the Rogue were made before the collision path onset (92%).

Thus gaze, and attention are anticipatory.

Effect of Experience

Pedestrian fixations after conflicting experience (Trial 2)

0

0.2

0.4

0.6

0.8

1

Safe (previously Rogue) Rogue (previously Safe)

Probability of fixation

Safe and Rogue pedestrians interchange roles.

Pedestrian fixations with no prior experience (Trial 1)

0

0.2

0.4

0.6

0.8

1

Safe Rogue

Probability of fixation

Learning to Adjust Gaze

• Changes in fixation behavior fairly fast, happen over 4-5 encounters (Fixations on Rogue get longer, on Safe shorter)

N=5

Shorter Latencies for Rogue Fixations

• Rogues are fixated earlier after they appear in the field of view. This change is also rapid.

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

Effect of Behavioral Relevance

Fixations on all pedestrians go down when pedestrians STOP instead of COLLIDING.STOPPING and COLLIDING should have comparable salience.

Note the the Safe pedestrians behave identically in both conditions - only the Rogue changes behavior.

• Fixation probability increases with probability of a collision path.

• Fixation probability similar whether or not the pedestrian collides on that encounter.

• Fixations are anticipatory.• Changes in fixation behavior fairly rapid

(fixations on Rogue get longer, and earlier, and on Safe shorter, and later)

Summary

Neural Substrate for Learning Gaze Patterns

Dopaminergic neurons in basal ganglia signal expected reward.

Neurons at all levels of saccadic eye movement circuitry are sensitive to reward. (eg Hikosaka et al, 2000; 2007; Platt & Glimcher, 1999; Sugrue et al, 2004; Stuphorn et al, 2000 etc)

This provides the neural substrate for learning gaze patterns in natural behavior, and for modelling these processes using Reinforcement Learning. (eg Sprague, Ballard, Robinson, 2007)

target selection

signals to muscles

inhibits SC

saccade decision

saccade command

planning movements

Neural Circuitry for Saccades

Virtual Humanoid has a small library of simple visual behaviors:– Sidewalk Following– Picking Up Blocks– Avoiding Obstacles

Each behavior uses a limited, task-relevant selection of visual information from scene.

Walter the Virtual Humanoid

Sprague, Ballard, & Robinson TAP (2007)

QuickTime™ and aYUV420 codec decompressor

are needed to see this picture.

R L Modeling of Gaze Control

Walter learns where/when to direct gaze using reinforcementlearning algorithm.

Walter’s sequence of fixations

obstacles

sidewalk

litter

Subjects must learn the statistical structure of theworld and allocate attention and gaze accordingly.

Control of gaze, and attention, is proactive, not reactive, and thus is model based.

Anticipatory use of gaze is probably necessary for much visually guided behavior, because of visuo-motor delays.

Subjects behave very similarly despite unconstrained environment and absence of instructions.

Need reinforcement learning models to account forcontrol of attention and gaze in natural world.

Conclusions

• Task-based models can do a good job by learning scene statistics (Real walking: Jovancevic & Hayhoe, 2007)

• Another solution: attention may be attracted to deviations from expectations based on memory representation of scene.

How do subjects perceive unexpected events?ctd

• Hollingworth & Henderson (2002) argue that elaborate representations of scenes are built up in long-term memory.

• To detect a change, subjects may compare the current image with the learnt representation.

• If so, such representations might serve as a basis for attracting attention to changed regions of scenes (eg Brockmole & Henderson, 2005).

Thus subjects should be more sensitive to changes in familiar environments than to unfamiliar ones because the memory representation is well-defined.

Overview of the Experiment

• Question: If subjects become familiar with an environment, are changes more likely to attract attention? (cf Brockmole & Henderson, 2005).

• Design: Subjects walked along a footpath in a virtual environment including both stable & changing objects while avoiding pedestrians.

Virtual Environment

Virtual Environment

MONUMENT

Experimental Setup

Video Based Tracker

V8 optics with ASL501 Video Based Eye Tracker (Left)

Virtual Research V8 Head Mounted Display with 3rd Tech HiBall Wide Area motion tracker

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

Replaced

Disappearance New Object

Moved Object

Object Changes

Stable Objects

Procedure

• Two groups, 19 subjects/ group:– Inexperienced Group: One familiarization trial– Experienced Group: 19 familiarization laps before

the changes occurred

QuickTime™ and aCinepak decompressor

are needed to see this picture.

QuickTime™ and aCinepak decompressor

are needed to see this picture.

• Total gaze duration on changed objects were much longer after experience in the environment.

• Fixation durations on stable objects were almost the same for the two groups.

Stable Objects Changing Objects

0

5 0

1 0 0

1 5 0

2 0 0

2 5 0

3 0 0

3 5 0

4 0 0

4 5 0

5 0 0

5 5 0

6 0 0

6 5 0

7 0 0

msec

N o E x p e r i e n c e

E x p e r i e n c e d

Experienced

Inexperienced

Average gaze duration/object/lap

Effects of Different Changes

0

1 0 0

2 0 0

3 0 0

4 0 0

5 0 0

6 0 0

7 0 0

8 0 0

9 0 0

1 0 0 0

1 1 0 0

msec

I n e x p e r i e n c e d

E x p e r i e n c e d

Stable

Replaced

Disappeared Moved New

Experienced

Inexperienced

0

1 0

2 0

3 0

4 0

5 0

6 0

G r o u n d E n v i r o n m e n t P e d e s t r i a n s C h a n g i n g O b j. S t a b le O b j .

0

1 0

2 0

3 0

4 0

5 0

6 0

G r o u n d E n v i r o n m e n t P e d e s t r i a n s C h a n g i n g O b j . S t a b l e O b j.

Distribution of gaze

Object fixations account for only a small percentage of gaze allocation.

Inexperienced Experienced

Change Blindness• Probability of being aware of the changes was

correlated with gaze duration on the changing objects (rho=0.59).

• Awareness of the changes was low, suggesting that

fixations are a more sensitive indicator. • Change blindness in the natural world may be fairly uncommon, because most scenes are familiar.

• Suggests we learn the structure of natural scenes over time, and that attention is attracted by deviations from the normal state.

• These results are consistent with Brockmole &

Henderson (2005) and generalize the result to immersive environments and long time scales.

• Consistent with Predictive Coding models of cortical function.

Predictive Coding: Input is matched to stored representation.

-+

U

U Te = I - Ur

LGN CortexrI

Rao & Ballard, 1999.

Top-down signal based on memory

Bottom-up input from retina

Difference signal reveals mis-match

Unmatched residual signal prompts a re-evaluation of image data and may thereby attract attention.

• A mechanism that attracts attention and gaze based on mis-match with a model is similar to the idea of Bayesian “Surprise” (Itti & Baldi, 2005).

• One question is where the prior comes from. Itti & Baldi calculate surprise with respect to image changes over a short time scale. Here we suggest surprise is measured with respect to a memory representation.

“Surprise”

Conclusion

• Familiarity with the visual environment increases the probability that gaze will be attracted to changes in the scene.

• A mechanism whereby attention is attracted by deviations from a learnt representation may serve as a useful adjunct to task-driven fixations when unexpected events occur in natural visual environments.

Thank You

Behaviors Compete for Gaze/ Attentional Resources

The probability of fixation is lower for both Safe and Rogue pedestrians in both the Leader conditions than in the baseline condition .

Note that all pedestrians are allocated fewer fixations, even the Safe ones.