The Selective Tuning Model of Visual Attention

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Studying Visual Attention with theVisual Search
Paradigm Marc Pomplun Department of Computer
Science University of Massachusetts at
Boston E-mail marc_at_cs.umb.edu Homepage
http//www.cs.umb.edu/marc/
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Studying Visual Attention with theVisual Search
Paradigm

• Overview
• The Feature Integration Theory
• Visual Search
• The Guided Search Theory
• The Area Activation Model

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The Binding Problem

• Different features of the visual scene are coded
  by separate systems
• e.g., direction of motion, location, color and
  orientation
• How do we know this?
• Anatomical neurophysiological evidence
• Brain Imaging (fMRI PET)
• So how do we experience a coherent world?

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Feature Integration Theory (Treisman et al)

• Attention is used to bind features together
• Code one object at a time on the basis of its
  location
• Bind together whatever features are attended at
  that location

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Feature Integration Theory

• Sensory features (color, size, orientation,
  etc) are coded in parallel by specialized modules
• Modules form two kinds of maps
• Feature maps (e.g., color maps, orientation maps
  etc.)
• A master map of locations

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Feature Integration Theory

• Feature maps contain two kinds of information
• - presence of a feature anywhere in the field
  (theres something red out there)
• - implicit spatial information about the feature
• Activity in the feature maps can tell us which
  features are contained in the visual scene.
• It cannot tell us which other features the green
  blob has.
• The master map codes the location of features.

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Feature Integration Theory

• The basic idea of the FIT is that visual
  attention is used for
• Locating features
• Binding appropriate features together
• There are two stages of object perception
• Preattentive stage Individual features are
  extracted in parallel across the whole visual
  scene.
• Attentive stage When attention is directed to
  a location, the local features are combined
  to form a whole.

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Feature Integration Theory

• Attention moves within the location map
• Focus of attention selects whatever features are
  linked to that location
• Features of other objects are excluded
• Attended features are then entered into the
  current temporary object representation

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Feature Integration Theory

• Empirical evidence for the FIT has been obtained
  through
• Visual search tasks
• Illusory conjunctions
• We will focus on the paradigm of visual search.

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Visual Search
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Feature Search

• Is there a red T in the display?

T
T
T
Target defined by a single feature
T
T
T
T
T
T
T
T
According to FIT, this should not demand
attention
Target should pop out
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Conjunction Search

• Is there a red T in the display?

T
X
X
T
Target is now defined by its shape and color
T
T
X
T
T
T
T
T
X
This involves binding features and so should
demand attention
X
Need to attend to each item until target is
found
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Feature Search

• Changing the number of distractors

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Conjunction Search

• Changing the number of distractors

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Visual Search Experiments

• Record time taken to determine whether target is
  present or not
• Vary the number of distractors
• Search for features should be independent of the
  number of distractors
• Conjunction search should get slower with more
  distractors

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Visual Search
Feature targets pop out ? flat display size
function
Conjunction targets demand serial search ?
significant slope
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Problem with FIT Pop-Out of Conjunction Targets

• A moving X pops out of a display of moving Os
  and static Xs

Target is defined by a conjunction of movement
and form
At least some conjunctions do not require
focal attention
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Guided Search Theory

• The Guided Search Theory (GST) is similar to the
  FIT in that it also assumes two subsequent stages
  of visual search performance
• a preattentive, parallel stage
• an attentive, serial stage
• However, the main difference to FIT is that GST
  assumes the preattentive stage to obtain spatial
  saliency information that is used to guide
  attention in the serial stage.

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Guided Search Theory

• According to GST, saliency is encoded in an
  additional map, called the saliency map.
• The saliency map is created during the
  preattentive stage and can combine multiple
  features if necessary.
• In the subsequent serial search process,
  attention is first directed to the highest peak
  in the saliency map, then to the second-highest,
  and so on.
• This visual guidance allows efficient search even
  for some conjunction targets.

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Guided Search Theory

• Support for the GST comes from eye-movement
  research.
• Eye-movement recording allows researchers to
  determine the items that a subject looks at
  during visual search.

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Guided Search Theory
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Guided Search Theory

• In the previous example,
• 80 of fixations were closest to an item
  sharing color with the target,
• 20 of fixations were closest to an item
  sharing orientation with the target.
• It seems that the color dimension is guiding the
  subjects visual search process.
• Of course, due to imprecision of eye movements
  and their measurement, better statistics are
  necessary to determine the guiding dimension.
  

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Guided Search Theory

• In visual search tasks, subjects are usually
  guided by one target feature or a combination of
  target features.
• This supports the idea of GST that preattentively
  derived information from multiple dimensions
  guides and thereby facilitates the subsequent
  serial search process.

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Guided Search Theory

• There are two problems with GST
• According to GST, grouping the guiding
  distractors should result in reduced guidance
  (less bottom-up activation). However, the
  opposite happens.
• There is no quantitative implementation of a
  Guided Search model that could predict
  guidance, i.e., saccadic selectivity for a given
  search task.
• To overcome these problems, we proposed the Area
  Activation Model of saccadic selectivity in
  visual search tasks.

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Area Activation

• Assumptions
• Processing resources during a fixation are
  distributed like a two-dimensional Gaussian
  function centered at fixation.
• Fixation positions are chosen to allow a maximum
  of information processing according to the
  assumed processing resources.
• Scan paths are chosen in such a way that they
  connect the optimal fixation positions with
  minimal eye-movement cost (path length).

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Area Activation - Strong Guidance
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Area Activation - Strong Guidance
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Area Activation - Weak Guidance
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Area Activation - Weak Guidance
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Area Activation - Empirical Results
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Area Activation

• Problems with the Area Activation Model
• Empirical number of fixations per trial needs
  to be known in advance.
• Only very basic factors influencing visual
  search have been implemented so far.
• Nevertheless, Area Activation can be considered a
  very first step towards a quantitative model of
  visual search.

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Conclusions
We have discussed how the visual search paradigm
can be employed to investigate the mechanisms of
visual attention. Various models of attention
have been developed and evaluated with visual
search tasks in more recent studies, this was
done based on eye-movement data. In the next
lecture, we will look at slightly different
paradigms, which are aimed at identifying factors
that determine visual scan paths. See you then!