Cognitive Neuroscience
and Embodied Intelligence
Perception and Attention
Based on book Cognition, Brain and Consciousness ed. Bernard J. Baars
courses taught by Prof. Randall O'Reilly, University of Colorado, and
Prof. Włodzisław Duch, Uniwersytet Mikołaja Kopernika
and http://wikipedia.org/
http://grey.colorado.edu/CompCogNeuro/index.php/CECN_CU_Boulder_OReilly
http://grey.colorado.edu/CompCogNeuro/index.php/Main_Page
Janusz A. Starzyk
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Image Recognition Problem
•How do receptive fields form?
•Why does the cortex encode
oriented bars of light?
Learning through correlations
based on natural scenes
•How do we recognize objects?
In different locations, sizes,
rotations, and images on the
retina
•Why does the visual system
separate into where/what pathways?
Spatial invariance is difficult, because different signs occupy partly the same receptive
fields, and the same signs in different parts of the retina which are rotated or of a
different size don't activate the same receptive fields at all.
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Recognition
Where does invariance come
from?
A 3D image based on 2D
projections, what's remembered
is just one 3D representation
(Marr 1982).
Syntactic approach: form a
whole from pieces of a model.
Variant (Hinton 1981): look for transformations (displacement, scaling,
rotation), conform to the canonical representation in the memory.
Problem: many 2D objects can form different 3D objects; it's difficult to
match the objects because the search space to connect fragments into
a whole is too large – do we really remember 3D objects?
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Gradual transformations
In the brain, rotational invariance is
strongly limited – eg. recognizing rotated
faces.
Limited invariant object recognition can be
achieved thanks to gradual hierarchical
parallel transformations, increasing
invariance and creating increasingly
complex features of distributed
representations.
Goal: not 3D, but to retain enough details to be able to recognize objects
in an invariant manner after transformation.
•Map seeking circuits in visual cognition (D. W. Arathorn, 2002 )
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Object recognition model
Model objecrec.proj has many hypercolumns, but very simple ones.
We allow for regions and transformations between LGN, V1, V2 and
V4/IT. 20 images, but only vertical/horizontal elements.
The element combinations on the IT level should react invariably.
Output = representation on the symbolic level.
Objects to be recognized,
3 out of 6 possible
segments.
Training on 0-17,
test on 18-19.
4 sizes,
5, 7, 9 and 11 pixels.
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Object recognition model properties
Hypercolumn: the same signals, displaced and partly overlapping.
Elements inside the hypercolumn compete, kWTA, elements within the
layer also compete – inhibition on a greater area.
Complete inhibition = max (local, from the whole layer).
Hypercolumns perform feature extraction across the whole field of vision
=> each hypercolumn can share the same set of weights.
Objects are represented with the help of edges in the LGN On/Off layer,
each 16x16, wrapped edges (spherical geometry).
V1: has already-learned representations of vertical and horizontal edges,
4x4 receptive fields in the LGN, there are 8 vertical and horizontal edges
for "on" and 8 for "off", together 16 = 4x4 units.
V2: 8x8 hypercolumns, signals from ¼ of the field of vision, in a 4x4
matrix.
V4/IT: 10x10, entire visual field, for such simple objects will suffice. 6
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More properties
Simulations without shared weights for the hypercolumns give the same
results, but they are significantly more costly; the Hebbian mechanism
leads to identical weights for columns with the same (xi,yi).
Without Hebb, just error correction gives completely different
representations for the hypercolumns, because it doesn't detect input
correlations.
Lack of horizontal connections – the representation of V1 is already set, so
they're not necessary and they slow down learning; these connections are
important in completion processes, illusions, recognizing obstructed
objects.
Parameters: Hebb =0.005, but between V1/V2 there is only 0.001 because
sharing weights gives more frequent activations = hence change.
Learning: a rate of 0.01 => 0.001 after 150 epochs in order to stabilize
learning and speed up the initial learning.
Network construction: BuildNet, check connection properties, r.wt. 7
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Network exploration
StepTrain, phase – and StepTrain, phase +
The whole training requires many hours; one object can be in 4 sizes and
256 positions in a 16x16 grid, together there are 1024 images of one
object, 18 training objects, 18,432 images.
A trained network after 460 epochs x 150 objects per epoch, after 30,000
presentations reaches good results, fewer than 2 presentations/image.
net_updt => cycle_updt will show learning over the whole cycle; on a
trained network, phases – and + are the same.
How does activity of V2 and V4 correlate with LGN inputs? Receptive
fields resulting from average activation can be seen looking at the
correlation of x from LGN, with y from V2 or V4, for
each element of the 8x8 hypercolumn we represent
every ri
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Averaged activation receptive fields
Activation of 16x16 LGN-on-center for one hypercolumn V2,
8x8 elements; weight sharing => others the same.
Elements from the lower left
corner of V2, receiving from ¼
of the whole LGN field.
Bright stripes = selective unit for
the edges (different sizes) in a
specific location. V2 elements
don't react to single lines only to
their combinations.
Diffused parallel stripes –
reaction to the same
combinations in different
locations.
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V2 off-center fields
LGN-off-center activation for one V2 hypercolumn
weight sharing => others the same.
These elements react
more to the ends of shorter
lines.
Elements reacting
selectively take part in the
representation of many
images, they detect
complex features shared
among different objects.
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V2 correlations – output objects
The reaction of V2 units to detecting specific objects, or V2 correlations –
averaged output 4x5 = 20 objects.
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V4 correlations – output objects
The reaction of V4 units to detecting specific objects, or V4 correlations –
averaged output 4x5.
Greater selectivity than
in V2, because of
greater invariance and
reaction to more
complex features.
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Receptive field tests
Observation of V2 and V4 reactions:
4 probes used in the tests, each shown
in all positions of the left LGN input
quadrant, or 8x8.
V2 columns react to ¼ of the whole field.
We calculate response on the V2/V4
level, quadrants respond to specific test
probes; eg. for probe 0, reactions to all
8x8 positions of this probe are in the
lower left quadrant for a given element,
all of its activity for 4 elements is in the
16x16 square.
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V2 tests for probes
Hypercolumn V2 has 8x8 elements, the reactions of each to 4 probes
averaged across all positions are in the small 16x16 squares.
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V4 tests for probes
V4 has 10x10 elements, the
reactions of each to 4
probes averaged across all
positions are in the small
16x16 squares.
Non-dependence on
position can be seen by all
the yellow squares.
Some react to single
features of probes, others to
the whole probe, and some
to the presence of elements
which are in each probe.
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Statistical tests
Table 8.1 summarizes the test results of presenting 20 objects in all
positions and the reaction (for probe >0.5) of V4 elements to these
presentations.
For one object in 256 possible positions and 4 sizes (1024 images) on
level V4 there is on average 10 different activations.
Detailed results are in objrec.swp_pre.err.
Two unknown objects 18, 19 give only errors.
Training with the goal of determining generalizations: presenting a new
object one out of 4 times; in 36 out of 256 possible positions, sizes of 5
or 9 pixels, so 14% of positions and 50% of sizes, 72 images (7%).
After 60 training epochs, 150 objects/epoch, learning constant 0.001,
object 18 gave 85% correct answers out of 1024 images;
object 19 gave 66% correct answers, for small sizes.
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Dorsal pathway
Recognition is a function of the ventral pathway, now let's turn to the
dorsal pathway. Functions: motion detection, localization, "where” and
how to act, but also on what to focus attention and how to shift attention
from one object to another.
Attention allows us to tie different properties of an object into one whole,
to solve the problem of cohesion of sensations in spite of distributed
processing; distributed activation => features related to each other,
referring to one object.
Mainly an attention model, an emergent process resulting from the
structure and dynamic of neural networks, mainly inhibition.
The effects of attention are universal, visible in different situations.
What to pay attention to? Is this a well posed question?
Dogs bite, but not only Spot, not only mongrels, not only black ones...
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Spatial attention model
The interaction of spatial representations with object recognition.
How does the ventral pathway interact with the dorsal pathway?
Different spatial representations in the parietal cortex, here is a simple
map of spatial relationships.
Posner task: attention is
directed to the cue, which
affects reaction times to a
simple target, depending
on whether it appears in
the same region or a
different region. Activation
in a specified location =>
speed of recognition.
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No cue
cue
18
cue
Spatial attention model
It's possible to mediate the attentional effects by V1, but then inhibition
will prevent switching attention to another object.
Original Posner model: the parietal cortex "frees” attention.
Model O’Reilly
There is direct feedback
(V4-V5?) between the
dorsal pathway and the
ventral pathway plus a
path through V1.
Spatial attention
influences recognition;
thicker lines = stronger
effect.
Forced by the
dorsal pathway (PC)
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Lesion studies
Consequences of damage to early visual areas
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Different visual deficits can result from neural damage at different
levels of the visual processing hierarchy.
Damage to the retina can result in monocular blindness
Damage to the LGN can lead to loss of vision in the contralateral
visual field
Damage to a small part of V1 can lead to a clearly defined scotoma.
Patients with damage to V1 area may still perform better than chance
forced choice discrimination of objects (blindsight), although they
claim they see nothing.
Although the pathway from retina to LGN to V1 provides most of
visual inputs to cortex, several alternative subcortical pathways project
to extrastriate areas (MT, V3, V4), bypassing V1. This may explain
forced choice results.
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Lesion studies
Extrastriate lesions – damage outside area V1
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Motion blindness caused by a lesion to area MT: the world
appears to be a series of still snapshots.
Crossing street is dangerous since the patient cannot tell how
fast the cars are approaching.
Pouring a cap of coffee becomes a challenge since she cannot
tell how fast the liquid was rising.
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Lesion studies
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Cortical color blindness may be caused by a lesion to area V4:
The world appears to be drained of color, just shades of gray.
Patients can perceive the boundaries of colors but cannot name
them.
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Lesion studies
Damage to ventral
object areas
Visual Agnosia: Patients with
visual agnosia have difficulties with
recognizing objects because of
impairments in basic perceptual
processing or higher-level
recognition processes
Three types of agnosia:
apperceptive agnosia,
associative agnosia, and
prosopagnosia
Agnosia=to lack knowledge of
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Lesion studies
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Patients with apperceptive agnosia can
detect the appearance of visually
presented items, but they have
difficulty perceiving their shape and
cannot recognize or name them.
Associative agnosia refers to the
inability to recognize objects, despite
apparently intact perception of the
object.
 Patient can copy a picture of the object but
does not recognize it.
 A patient mistook his wife for a hat.
 Associative agnosia results from damage
to ventral temporal cortex.
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Lesion studies

Patients with optic ataxia can perceive visual
orientation and recognize objects but cannot
perform visually guided actions.
 Optic ataxia results from damage to parietal lobe in
dorsal pathway.

Patients with prosopagnosia are still able to
recognize objects well, but have great difficulty
recognizing faces.
 All faces look the same
 Patients can recognize animals but not people

Brodman area no. 37 is responsible for
face recognition
 over 90% of cells in area 37 responds to
faces only.
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Lesion studies
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fMRI analysis of the face recognition process.
Visible is activity in right hemisphere in lower temporal area
Face recognition is important from evolutionary perspective.
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Lesion studies

Patients with achromatopsia are unable to recognize
colors.
 This is often a result of damage to area V4 or thalamus.
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Lesion studies

Daltonism refers to dichromacy characterized by a
lowered sensitivity to green light resulting in an
inability to distinguish green and purplish-red.
 It is an inherited defect in perception of red and green, or in
other words, red-green colorblindness.
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Dorsal pathway lesions
Lesions in the parietal cortex strongly affect mechanisms of attention
and spatial orientation, extensive lesions in one hemisphere lead to
hemispatial neglect, the inability to focus attention to the half of the
visual space which is opposite the lesion.
For small unilateral lesions, we can see a noticeable slowing of attention
switching to the damaged side. For more severe cases, switching
attention is not possible.
Bilateral lesions lead to Balint's syndrome, difficulties with the
coordination of hand and eye movement, simultanagnosia; differences in
attention switching times in the Posner task are small.
Posner contended that this is a result of attention binding, the inability to
disengage, but he didn't give the disengagement mechanism; it follows
after focusing attention elsewhere – a better model assumes normal
competition.
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Lesion studies
Self-portrait
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
Damage to the posterior parietal
lobe can lead to a unilateral
neglect, in which a patient
completely ignores or does not
respond to objects in the
contralateral hemifield.

Patients with damaged spatialtemporal recognition forget
about half the space even
though they see it

Patients with right parietal damage
may ignore the left half of the
visual field, eat half of the food
from the plate, or apply make-up to
half of the face.
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Unilateral Neglect
Horizontal line bisection task
Copying drawings
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Lesion studies
Bilateral lesions to parietal areas can lead to a
much more profound deficit called Balint’s
syndrome, which is primarily a disruption of
spatial attention.
 It can be characterized by three main deficits:

 Optic ataxia – inability to point into a target
 Ocular apraxia – inability to shift the gaze
 Simultanagnosia – inability to perceive more than one
object in the visual field

People with Balint’s syndrome appear blind since
they only focus on one object and cannot shift
attention to anything else.
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Linking brain activity and visual experience
Imagine you are sitting in a dark room and looking at a jacket on a chair.
 Since you cannot see well, your perception is driven by your imagination
– you may perceive a strange animal, a person, or a statue sitting there.
 When vision is ambiguous, perception falters or alternates between
different things. This is known as multistable perception.
 There are many examples of multistable patterns or ambiguous figures
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that
scientists
use
to
investigate
these
neural
correlates
of
consciousness.
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Linking brain activity and visual experience
You can cause
binocular rivalry here
using a pair of redgreen glasses
Binocular rivalry: what you see is what you get activated

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When two very different pattern are shown, one to each eye, the brain cannot
fuse them together like it would normally do.
What happens is striking: awareness of one pattern last few seconds, then the
other pattern appears
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Linking brain activity and visual experience
What happens in the brain during binocular rivalry?
 Tong et al. tackled this problem by focusing on two category-selective
areas in the ventral temporal lobes (FFA and PPA). They used the redgreen filter glasses to present a face to one eye and house to the other
eye. Depending on which image was perceived, they observed
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activities
either
in
FFA
(face)
or
PPA
(house).
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Linking brain activity and visual experience
Strength of activation of FFA and
PPA was the same in the
rivalry experiment as in the
case of stimulus alternation.
Another approach is to train
monkey to report which of two
patterns is dominant during
binocular rivalry and measure
activity of a single neurons in
different parts of the brain.
This experiment supports
interactive model of visual
perception where feedback
projection modulates lower
levels.
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Linking brain activity and visual experience
Another way to separate physical stimulation and perceptual
awareness is a visual detection task.
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A subject has to detect a particular pattern.
The researcher makes the pattern harder and harder to see.
Sometimes there is no pattern at all in the picture.
Because this task gets difficult, people will get it wrong sometimes.
What is interesting, that when there is ‘false positive’ (people see pattern
even when it is not there), there is strong activity in areas V1, V2, and V3.
 When the faint stimulus is not detected activities in these areas are much
weaker.
 So, it does not matter what was presented, but what does matter is what is
happening in the brain.
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Linking brain activity and visual experience
(a)
(b)
(c)
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Close your left eye, look directly at the cross with your right eye
and move the page up close to your nose, then move it slowly
away from your face, while keeping your eye fixed on the
cross. At the right distance, which should be around 12 inches
(30 cm) away from the page you should notice the red dot
vanish.
Likewise, notice how the black stripes now fill-in; they become
joined and the red dot vanishes.
Brain fills-in perception of the blind spot using visual
information from around the blind spot – constructive
perception or perceptual filling-in.
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Linking brain activity and visual experience
Adelson's motion without movement
Optical illusions are a result of
our mind filling-in patterns
based on experience
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Linking brain activity and visual experience
Two color spirals
Zoom in on the color spiral – two colors are the same shade of green.
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Linking brain activity and visual experience

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These pictures illustrate
another type of filling-in
known as neon color
spreading (a) and visual
phantoms (b).
Neon color spreading were
found in V1 area.
In a similar way apparent
motion that we see in a
movie theater is another
type of filling-in by neural
activities in V1 area.
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Linking brain activity and visual experience
Neural correlates of object recognition
In binocular rivalry, activity in the fusiform face area and parahippocampal
place area is closely linked to the observer’s awareness of faces and houses.
 Other studies deals with visually masked objects which can just barely be
recognized.
 Mooney face shown in figure can be recognized at right orientation, while it is
hard to recognized at different orientations.
 If the objects are recognized activity in ventral temporal region is greater, while
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activity
in
V1
region
shows
no
difference..
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Manipulations of visual awareness
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To find out causal relations
between activities in various brain
regions it is useful to directly
stimulate the selected brain area
with electrical impulses.
One way is to use implants for
instance in V1 area
Another way is to use transcranial
magnetic stimulation (TMS)
TMS involves rapidly generating a
magnetic field outside of the head to
induce electrical activity on the
cortical surface.
Patients report various experiences
including ‘out of body experience’ –
seeing its own body from above.
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Manipulations of visual awareness
Unconscious perception
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We use the term unconscious perception when subjects
report not seeing a stimulus, but their behavior or brain
activity suggests that specific information about the
unperceived stimulus was indeed processed by the brain.
When two different stimuli are flashed in quick
succession, the visual system can no longer separate the
two stimuli.
Instead, what people perceive is a mix, or a fused blend
of the two images.
They may respond to individual images in various brain
areas without being aware of seeing them
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Manipulations of visual awareness
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For instance, a quick presentation of a red square followed by a green
square can be perceived as a yellow one.
Presentation of the images of the house or face in complementary colors
to different eyes has the same effect of not seeing one.
However, the brain still responds to these unseen patterns – fusiform face
area (FFA) to face and parahippocampal place area (PPA) to house. 45
Summary
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Vision is our most important sensory modality.
We discussed the functional properties of neurons as visual signals
travel up from the retina to the primary visual cortex and onward to
higher areas in the dorsal and ventral visual pathways.
Progressing up the visual pathway, receptive fields gradually
become larger and respond to more complex stimuli, following the
hierarchical organization of the visual system.
V1 supports conscious vision, provides visual features like
orientation, motion and binocular disparity.
V4 is important for color perception.
MT is important for motion perception.
Damage to dorsal pathway leads to optic ataxia (neglect).
Damage to ventral temporal cortex leads to impairments in object or
face recognition.
In ventral temporal cortex some regions like LOC have general role
in object recognition, while others like FFA and PPA are more
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specialized
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Attention model
Model attn_simple.proj from page
http://grey.colorado.edu/CompCogNeuro/index.php/CECN1_AttnSimple
Stimuli: single activations in one of
7 places, for two objects (cue,
target).
3 layers, invariance increases, each
element of the higher layer
combines 3 lower ones, from this
V1 is 2x7, Spat1, Obj1 2x5,
Spat2, Obj2 is 2x3, output 2x1.
Reaction time: time needed for the
activity of the target output
connected with Obj2 to reach 0.6
Spat2 reacts only to location.
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Exploring the model
r.wt will show connections.
The control panel has several scaling parameters:
spat_obj = 2, weight scaling spat=>obj, obj_spat =0.5 (not shown)
v1_spat = 2, stronger than v1_obj, light noise noise_var = 0.0005
cue_dur = 200 number of cycles during the time when the cue is
presented, which is followed by the target.
3 situations for Multi_objs: a) two different objects, b) two identical
objects, c) two different objects in the same place.
act, step through all events several times
View Graph_log and Run –recognition of overlapping elements is
generally slower; view text_log; view batch_text_log, run batch. 48
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Posner Task
env_type std_Posner
view events: 0 only target,
1 cue on the left, target on the left,
2 cue on the left, target on the right.
Activation is not zeroed after presentation of the first stimulus, only after
the whole group.
Display on, clear graph log, step.
Batch will repeat 10x, graph =>
How does the network shorten time
on the same side?
How does it lengthen time on the
opposite side?
Test spat_obj=1 and v1_spat=1.5, 1
Change to even_type Close_Posner and check the effects.
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Simple model of the Posner task
Object recognition times: normalization scales the
results to the average adult time.
Cue
Adult
Valid
Invalid
D
350
msec
390
msec
40
msec
Elderly
540
600
60
Patients
640
760
120
Elderly
normalized
0.65
350
390
40
Patients
normalized
0.55
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350
418
68
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Lesion effects
Patients with lesions even after normalization have significantly longer
times on the Posner task, while the elderly after normalization have
differences just like normal adults.
Lesion in a model: env_type Std_Posner,
Lesion, lesion_lay = Spat1_2 to handicap both levels, the number of
locations = half, number of elements = half, or 1 of 2.
number of elements = half, or 1.
Check (r.wt) that the weights
were zeroed: two elements in the
right corner of Spat_1, and one
from the upper right corner of
Spat_2
Batch to see the effect.
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Lesions reversed
If we reverse the task and switch attention from the side with the lesion
to the other side.
Set env_type to Reverse_Posner:
differences are significantly smaller
(different scale).
Why? The normal side more easily
competes with the damaged side, so the
differences decrease – in accord with the
patient observations.
Bilateral lesions: Std_Posner, Full for
location, half for a number of units, Batch
The effect is clear, but weaker than for
unilateral lesions.
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Full lesion
Unilateral neglect with extensive damage.
Simulation: Multi_obj, half for locations, full for a number of units, Run
The network has a tendency to focus
attention on the undamaged side, regardless
of the presentation, neglecting half the area.
Patients with unilateral neglect are incapable
of picturing one side of the space only when
the other side has a strong stimulus
competing for attention (phenomenon of
extinction).
Similar neglect for Std_Posner.
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Delay effects
If after the cue we make a delay of about 500 ms, there appears an
"inhibition of return" phenomenon, times partially reverse, a change in
location causes a faster reaction! This can be simulated by lengthening
the cue presentation time and allowing for neuron fatigue
(accommodation).
Defaults, No_lesion,
enc_type = Std_Posner, accommodate
Change from 75 to 200 every 25 ms
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Object-based attentional effects
Attentional effects connected with the interaction of location and object
recognition will be similar to attentional effects connected with the
recognition of competing objects (object-based attention).
Env_type Obj_attn, View Events
Events: 2 objects without cues.
Cue in the central location,
two objects in the central area, the
network should focus on the first.
Last two: cue and 2 objects
in the same place; yellow = greater activation.
Defaults, Step: the first object influences the selection even if the
second object is more active.
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Summary
Attention effects appear naturally in the model as a result of competition
between inhibition, interconnection, the necessity of compromise.
Similar effects can be seen in different cortical mechanisms.
Some psychological mechanisms (slowing attention) show themselves
to be unnecessary.
Attention effects supply specific information allowing models to be finetuned to comply with experiment results and allowing the use of these
models for other predictions; there is also a lot of neurophysiological
data concerning attention.
Limits of this model:
lack of effects connected with the thalamus (Wager, O’Reilly),
very simple representation of objects (one feature).
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Complex recognition model
Model objectrec_multiobj.proj.gz, Chapt. 8.6.1
This model has two extra
layers: Spat1 connected
with V1 and Spat2
connected with V2.
The Spat1 layer has an
excitatory selfconnection, allowing it to
focus on one object.
The Target layer shows
which image was chosen
and whether it matches
the output.
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Two objects in different places
BuildNet, r.wt to check connections, receptive fields in V1.
LoadNet, r.wt to check after training.
Spat_1 reacts to 8x8 fields in V1, wrapping the right onto the left
Spat_2 reacts to 16x16 fields in V2.
Two objects (perpendicular lines) with the same activation in different
locations.
StepTest, object # 12, presented in the lower left corner.
Initial oscillations, but gradual advantage of one of the two locations and
the object found there; influence on the lower layers, in V1 remains the
activation of only one.
View Test_log; we can see the errors in recognition, because the objects
are small, and the simultaneous activation of V1 introduces confusion –
lack of a saccade mechanism leading to the next, and not simultaneous
activation.
Reducing fm_sapt1_scale from 1 to 0.01, simultanagnosia, it's not
possible to recognize two objects, only one!
EE141
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Influence of spatial location
Spatial activation can at the most modulate the recognition process,
otherwise we'll know where, but not what.
This is ensured by inhibition and competition, recognition is a combination
of spatial activation and strengthened features in lower layers.
Switching objects: we turn on accommodation of neurons.
Accommodate, InitStep, TestStep
After fatiguing the neurons with the first object, attention moves to the
second, after layer Spat1.
Errors are often made, this is not yet a good control mechanism.
Attention connected with an object can also be seen in this model.
View, Test_Process_ctrl, environment from vis_sim_test => obj_attn_test
(at bottom of ScriptEnv). Apply, Reinit, Step.
The network recognizes object 17; Step network recognizes 12 and 17,
stays with 17
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Some answers
 Why does the primary visual cortex react to oriented edges?
Because correlational learning in a natural environment leads to this
type of detector.
 Why does the visual system separate information into the dorsal
pathway and the ventral pathway?
Because signal transformations extract qualitatively different
information, strengthening some contrasts and weakening others.
 Why does damage to the parietal cortex lead to disorders of spatial
orientation and attention (neglect)?
Because attention is an emergent property of systems with
competition.
 How do we recognize objects in different locations, orientations,
distances, with different images projected on the retina?
Thanks to transformations, which create distributed representations
based on increasingly complex and spatially invariant features.
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