What is meant by “top-down” and “bottom-up” processing? Give examples of both.

1. What is meant by “top-down” and “bottom-up” processing? Give examples of both. Bottom up processes are evoked by the visual stimulus. Top down processes are operations that reflect the subject’s current cognitive goals. In the case of eye movements, fixations that are for the purpose of getting specific information to accomplish a task are said to reflect top down control. Fixations that are evoked automatically by the occurrence of a stimulus are said to be under bottom up control. Examples?

2. What is “Neuroeconomics”? Explain how the saccadic eye movement circuitry is influenced by reward. Humans/primates exhibit behaviors that lead to expected reward. Reward is provided by the release of dopamine.

3. Neurons at all levels of saccadic eye movement circuitry are sensitive to reward. Neurons in substantia nigra pc in basal ganglia release dopamine. These neurons signal expected reward. This provides the neural substrate for learning gaze patterns in natural behavior, and for modeling these processes using Reinforcement Learning.

4. Dopaminergic neurons in basal ganglia signal expected reward. (Schultz, 2000) SNpc Expected reward is absent. Response to unexpected reward Increased firing for earlier or later reward

5. Conditioned stimulus predicts reward

6. Neural Circuitry for Saccades planning movements target selection saccade decision saccade command inhibits SC Substantia nigra pc signals to muscles Substantia nigra pc modulates caudate

7. Neurons at all levels of saccadic eye movement circuitry are sensitive to reward. LIP: lateral intra-parietal cortex. Neurons involved in initiating a saccade to a particular location have a bigger response if reward is bigger or more likely SEF: supplementary eye fields FEF: frontal eye fields Caudate nucleus in basal ganglia

8. Cells in caudate signal both saccade direction and expected reward. Hikosaka et al, 2000 Monkey makes a saccade to a stimulus - some directions are rewarded.

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

10. Give some examples that eye movements are learned. Jovancevic & Hayhoe 2009 Real Walking

11. Experimental Design (ctd) • 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 Trail 2: Rogue Safe Safe Rogue Unpredictable - remains same

12. Learning to Adjust Gaze • Changes in fixation behavior fairly fast, happen over 4-5 encounters (Fixations on Rogue get longer, on Safe shorter)

13. Top Down strategies: Learn where to look Detection of signs at intersection results from frequent looks. Shinoda et al. (2001) “Follow the car.” or “Follow the car and obey traffic rules.” Time fixating Intersection. Road Car Roadside Intersection

14. Give some examples that reveal attentional limitations in visual processing Difficult to detect color change in one of 8 colored squares. Invisible gorilla Color-changing card trick What are these examples called? What conclusions has been drawn from these experiments.

15. Briefly summarize the experiment by Jovancevic, Hayhoe, & Sullivan. What did they find? • Experimental Question: How sensitive are subjects to unexpected salient events (looming)? • General Design: Subjects walked along a footpath in a virtual environment while avoiding pedestrians. Do subjects detect unexpected potential collisions?

16. What Happens to Gaze in Response to an Unexpected Salient Event? Pedestrians’ paths Colliding pedestrian path • TheUnexpected Event: Pedestrians on a non-colliding path changed onto a collision course for 1 second (10% frequency). Change occurs during a saccade. Does a potential collision (looming) attract gaze?

17. Probability of Fixation During Collision Period Pedestrians’ paths Colliding pedestrian path More fixations on colliders in normal walking. No effect in Leader condition Normal Walking Follow Leader Controls Colliders

18. Why are colliders fixated? Small increase in probability of fixating the collider. Failure of collider to attract attention with an added task (following) suggests that detections result from top-down monitoring.

19. Detecting a Collider Changes Fixation Strategy Time fixating normal pedestrians following detection of a collider Normal Walking Follow Leader “Miss” “Hit” Longer fixation on pedestrians following a detection of a collider

20. Subjects rely on active search to detect potentially hazardous events like collisions, rather than reacting to bottom-up, looming signals. To make a top-down system work, Subjects need to learn statistics of environmental events and distribute gaze/attention based on these expectations.