Eye-tracking Study Using Cellular Automata Patterns as Visual Stimuli

Implications for Current Models of Stimulus-Driven Selection Process

Anirudh Tiwathia, Cristian Opazo

Vassar College

Overview: Perception

Experience is enabled through perception, hence to understand experience and cognition we must examine perception.

Limiting and Selecting Information

Perception can be thought of as a mechanism for limiting and selecting information.

No agent can successfully perceive all the information available in its environment

		a) the visual system only samples three regions of the light spectrum (reg, green and blue)
		b) the best visual acuity is limited to the central 5 ° of the visual field

Evolutionary importance: survival is directly linked to the efficiency in selecting relevant information and ignoring the rest.
		e.g. the amount of information on the human optic nerve is around (10^8) bits of information per second  
			- this is too much information to be processed the brain

Significant  research into human perception has shown that we experience only a fraction of the available information

This study aims to examine the perceptual mechanisms involved in this selection process

Overview: Perception

Mechanisms of Selection

Since only a limited amount of the available information is selected, there must be efficient selection mechanisms

Two main attentional mechanisms have been identified in the selection process:

Bottom-up Selection:
	 fast , saliency-based, stimulus-driven

Top-down Selection:
	slower, goal-directed, governed by intentions or expections

The relationship between these two mechanisms is poorly understood and remains controversial.

This study more specifically examined bottom-up selection mechanisms using an eye-tracking device

Methodology

Studying Attentional Mechanisms

A significant portion of the research addressing these attentional mechanisms use some version of an eye-tracking paradigm

The main reasons for using the eye-tracking paradigm are:

	a) it is very difficult and costly to examine the neural correlates of  selective attentional mechanisms
	
	b) eye-movement and attention have been shown to be related
	 
		i)  focal attention is a necessary precursor for successful eye-movement

		ii) shifts in attention are accomponied with preparation for eye-movement
 
		iii) the two mechanisms share neurophysiology

This study used an eye-tracking device to measure eye-movements of participants viewing CA images in a "goal-free" testing condition

Examining Bottom-Up Mechanism

A total of 18 participants were tested
	9 participants were naive to NKS, CA theory, and CA images
	9 participants were experienced with NKS and CA images (NKS Summer School students)

Methods

We monitored participants' eye movements using an eye tracker as they watched  a series of CA images in a "goal-free" testing condition

Advantages of using Cellular Automata Generated Images as Stimuli

The advantages of using CA images were:
	1. Giving promenence to the bottom-up mechanism:
		i) For the naive group, the absence of familiarity would reduce previously developed top-down biases
		ii) Abstract, complex, and engaging images 

	2. Examining the impact of structure on eye-movement

	3. A New Kind of Science

Results

The results that arose from this study can be divided into three categories:

	1. Salience of Regularity and Randomness
	
	2. Economical Oculomotor Strategy in Images with Nested Structure
	
	3. Effects of familiarity

Results: Salience of Regularity and Randomness

"Pockets of Regularity" were salient in regions of randomness
		i.e. a majority of participants fixate on the regions of regularity

Results: Economical Oculomotor Strategies

CA Images with Nested Structure

Eye-movement results from CA-images with nested structure led to two main identifiable behaviors:
 
	1. Fixation on blank spaces in nested images (without explicit axis of symmetry) 
	
	2. "Side-picking" and "Affinity for Center Column" in images with explicit axis of symmetry

Results: Economical Oculomotor Strategies

Fixations on Blank Spaces

In CA Images generated from rules such as Rule 60, Rule 90 and Totalistic Rule 948, many participants showed considerable fixations on blank spaces:

Show[Import["Simple rule90 - distribution.bmp"]];

(In this image, the circles represent the amount of time across all participants, spent in that region i.e. the radius of the circle represents the duration of time spend there. We can clearly see evidence for fixations in blank spaces. This is quite unprecedented in eye-tracking studies)

--Play Data

	1. Fixation on blank spaces has not been reported in previous studies. 
	
	2. Some studies explicitly reported the absence of fixation upon blanks spaces
	
	3. There was differentiation between blank space inside and outside the CA
	
	4. Clearly, no lower level features are causing the fixations

Discussion: Economical Oculomotor Strategies

Fixation on Blank Spaces

As mentioned earlier, fixation on blank spaces has not been previously reported. How can we then interpret these results:

	1. Processing geometric figures by focusing on the center
	
	2. Economical Position: For a nested image, that fixation on blank regions could be most economical
	
		a) Conspicuity Area: max region of focal attention around a fixation point for a given stimulus condition
		
		b) Conspicuity Area is linked directly to density of stimulus around fixation
		
		c) fixation upon empty spaces could increase conspicuity area and results in more parallel processing of information

(Both these explanations are based on the idea that oculomotor strategy is guided by considerations of economical positioning of fixation location. Another reason for considering the case for economical is the phenomenon of "side-picking" and "affinity for center column")

Results: Economical Oculomotor Strategies

"Side-picking" and "Affinity for Center Column"

Both these behaviours were only noticed in nested images with an explicit axis of symmetry 
		
		-- for example, we see such images by running Rule 105 and Rule 150 with simple initial conditions

Show[Import["Simple rule105 NKS - center col.jpg"]];

Play data animations

Discussion: Economical Oculomotor Strategies

Consequences for Current Models of Stimulus-Driven Selection Mechanisms

1. Current Models of Bottom-Up mechanism CANNOT account for such behaviours

	All bottom-up models account only for saliency based on low-level stimulus features

	No bottom-up models take structure of image into account 
	
	No bottom-up models do not account for economical oculomotor strategies

2. Currently, no computational models can predict the kinds of fixation patterns observed here

In light of the behaviors observer here, it is clear that current models of Bottom-up selection need to be updated

Acknowledgements

This research opportunity was supported by the Undergraduate Research Summer Institute (URSI) at Vassar College and uniquely motivated by the New Kind of Science Summer School 2004. 

Special thanks are extended to Stephen Wolfram for his insight and encouragement.