And then God created reptiles
- but where to put them?
Stephanie Duhoux
Miriam Gade
Ingrid Nieuwenhuis
Visual learning: identification and categorization
Representations:
Variability and specificity
Generalization across variations:
Different levels of specificty:
Individual level:
my cat
Categorical level:
Basic level:
a cat
Superordinate
level:
a mammal
Where in the brain?
• Monkeys:
Inferior temporal cortex: critical role in visual object
recognition (Logothetis et al., 1995; Sigala et al., 2001;
Tanaka, 1996)
• Human:
Temporal cortex, sensitive to the categorization level of
the stimuli, depend on the level of expertise of the
observer (Gauthier et al., 1997; Gauthier et al., 2000).
Debate on the processing of the faces and other categories:
domain-specificity view, Fusiform Face Area (Kanwisher et al., 1997)
expertise view (Gauthier et al., 2003)
Other view: the representations of faces and objects in ventral temporal cortex are
widely distributed and overlapping.
Palmeri, Wong & Gauthier, TICS 2004
Still to be known
• How are categories formed and represented in the
brain?
• Which object features are represented?
• How are these representations affected by
categorization training?
• How does a new category integrate a network of
existing category representations?
Animal categorization modelling
etc
Self organizing map
(already explained)
Inputs
red
blue
yellow
walk
fly
swim
eggs
milk
feathers
scales
hair
0 legs
2 legs
4 legs
6 legs
8 legs
harmful
carnivore
herbivore
omnivore
warm blood
red
red
blue
blue
yellow
yellow
walk
walk
fly
fly
swim
swim
eggs
eggs
milk
milk
feathers
feathers
scales
scales
hair
hair
0 legs
0 legs
2 legs
2 legs
4 legs
4 legs
6 legs
6 legs
8 legs
8 legs
harmfull
harmfull
carni
carni
herbi
herbi
omni
omni
warm
blood
warm
blood
20 mammals, 20 insects, 20 birds, 20 fish
color
walks
Hair
Warm
blooded
Parameters
• Default training: starting with large radius
and learning rate which decreases over
time
red
0.55
blue
0.6
yellow
0.55
walk
0.85
fly
0.05
swim
0.2
eggs
0.05
milk
1.0
feathers
0
scales
0
hair
0.95
0 legs
0.05
2 legs
0.15
4 legs
0.8
6 legs
0
8 legs
0
harmful
0.2
carni
0.2
herbi
0.5
omni
0.3
warm
blood
1.0
How to plot?
You can calculate the “average”
mammal, and look where that is
represented on the map
And then God
created
reptiles…..
The brain must be flexible and
always able to store more;
How do we manage to store new
items, even whole new
categories without messing up
what we already know?
Catastrophic interference
If you present only the new items to
the network, the structure of the
information that was already stored
becomes catastrophically
disturbed…..
Model
• Categorization:
– Default training: starting with large radius and
learning rate which decreases over time
• More biological plausible “brainy”:
– Fixed learning rate and radius
– Low learning rate (otherwise no stability) and
thus very many learning epochs
Parameters used
• Train model on 4 categories first
– Alpha 0.1, 100 trials, radius 5 (cheating)
– Alpha 0.05, 10000 trials, radius 4
• Train model on reptiles (and amphibians)
– Alpha 0.05, radius 4
• 5, 10, 20, 50 trials
Four categories after initial training
5 cycles of only reptile learning
10 cycles of only reptile learning
20 cycles of only reptile learning
start
5
10
20
50
start
5
10
20
50
start
5
10
20
50
start
5
10
20
50
start
5
10
20
50
So how does the brain solve this?!
• Interleaved learning
– The new information is presented to the
cortex but the information in the cortex is also
activated in between to prevent over-writing of
old information
Cortex
Categorization
REM
sleep
information
Slow
Wave
Sleep
Hippocampus
Parameters used
• Train model on 4 categories first
– Alpha 0.1, 100 trials, radius 5 (cheating)
– Alpha 0.05, 10000 trials, radius 4
• Train model on all the categories including
the reptiles and amphibians
– Alpha 0.05, radius 4
• 5, 100, 1000, 10000 trials
start
5
100
1000
10000
start
5
100
1000
10000
start
5
100
1000
10000
start
5
100
1000
10000
start
5
100
1000
10000
Thanks for your attention