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

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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?


What is “ processing? Give examples of both.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.


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.


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



Neural Circuitry for Saccades reward. (

planning movements

target selection

saccade decision

saccade command

inhibits SC

Substantia nigra pc

signals to muscles

Substantia nigra pc modulates caudate


Neurons at all levels of saccadic eye movement circuitry reward. (

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


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.


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)


Give some examples that eye movements are patterns in natural behavior, and for modeling these processes using Reinforcement Learning. learned.

Jovancevic & Hayhoe 2009 Real Walking


Experimental Design (ctd) patterns in natural behavior, and for modeling these processes using Reinforcement Learning.

  • 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


Learning to adjust gaze
Learning to Adjust Gaze patterns in natural behavior, and for modeling these processes using Reinforcement Learning.

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


Detection of signs at intersection results from frequent looks

Top Down strategies: Learn where to look patterns in natural behavior, and for modeling these processes using Reinforcement Learning.

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


Give some examples that reveal patterns in natural behavior, and for modeling these processes using Reinforcement Learning. 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.


Briefly patterns in natural behavior, and for modeling these processes using Reinforcement Learning. 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?


What happens to gaze in response to an unexpected salient event
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?


Probability of fixation during collision period
Probability of Fixation During Collision Period Event?

Pedestrians’ paths

Colliding pedestrian path

More fixations on colliders in normal walking.

No effect in Leader condition

Normal Walking

Follow Leader

Controls Colliders


Why are colliders fixated? Event?

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.


Detecting a collider changes fixation strategy
Detecting a Collider Changes Fixation Strategy Event?

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


Subjects rely on active search to detect potentially Event?

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.


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