Download - Using auto-encoders to model early infant categorization: results, predictions and insights
![Page 1: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/1.jpg)
Using auto-encoders to model early infant categorization:
results, predictions and insights
![Page 2: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/2.jpg)
Overview
• An odd categorization asymmetry was observed in 3-4 month old infants.
• We explain this asymmetry using a connectionist auto-encoder model.
• Our model made a number of predictions, which turned out to be correct.
• We used a more neurobiologically plausible encoding for the stimuli.
• The model can now show how young infants’ reduced visual acuity may actually help them do basic-level categorization.
![Page 3: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/3.jpg)
Background on infant statistical category-learning
Quinn, Eimas, & Rosenkrantz (1993) noticed a rather surprising categorization asymmetry in 3-4 month old infants:
– Infants familiarized on cats are surprised by novel dogs
– BUT infants familiarized on dogs are bored by novel cats.
![Page 4: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/4.jpg)
How their experiment worked
Familiarization phase: infants saw 6 pairs of pictures of animals, say, cats, from one category (i.e., a total of 12 different animals)
Test phase: infants saw a pair consisting of a new cat and a new dog. Their gaze time was measured for each of the two novel animals.
![Page 5: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/5.jpg)
Familiarization Trials
Infant
![Page 6: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/6.jpg)
![Page 7: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/7.jpg)
![Page 8: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/8.jpg)
![Page 9: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/9.jpg)
![Page 10: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/10.jpg)
![Page 11: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/11.jpg)
Test phase
Infant
Compare looking times
![Page 12: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/12.jpg)
Results (Quinn et al., 1993):The categorization asymmetry
– Infants familiarized on cats look significantly longer at the novel dog in the test phase than the novel cat.
– No significant difference for infants familiarized on dogs on the time they look at a novel cat compared to a novel dog.
![Page 13: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/13.jpg)
Our hypothesis
We assume that infants are hard-wired to be sensitive to novelty (i.e., they look longer at novel objects than at familiar objects).
Cats, on the whole, are less varied and thus are included in the category of Dogs.
Thus, when they have seen a number of cats, a dog is perceived as novel. But, when they have seen a number of dogs, the new cat is perceived as “just another dog.”
![Page 14: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/14.jpg)
Statistical distributions of patterns are what count
The infants are becoming sensitive to the statistical distributions of the patterns they are observing.
![Page 15: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/15.jpg)
Consider the distribution of values of a particular characteristic for Cats and Dogs
0.2 0.4 0.6 0.8 1
cats
dogs
Note that the distribution for Cats is - narrower than that of Dogs- included in that of Dogs.
![Page 16: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/16.jpg)
Suppose an infant has become familiarized with the distribution for cats
0.2 0.4 0.6 0.8 1
cats
dogs
And then sees a dog
Chances are the new stimulus will fall outside of the familiarized range of values
![Page 17: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/17.jpg)
On the other hand, if an infant has become familiarized with
the distribution for Dogs
0.2 0.4 0.6 0.8 1
cats
dogs
And then sees a cat
Chances are the new stimulus will be inside the familiarized range of values
![Page 18: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/18.jpg)
How could we model this asymmetry?
We based our connectionist model on a model of infant categorization proposed by Sokolov (1963).
![Page 19: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/19.jpg)
Sokolov’s (1963) model
Stimulus in the environment
Encode
![Page 20: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/20.jpg)
Stimulus in the environment
Encode
Decode and Compare
equal?
![Page 21: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/21.jpg)
Stimulus in the environment
Encode
Decode and Compare
Adjust
![Page 22: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/22.jpg)
Stimulus in the environment
Encode
Decode and Compare
Adjust
![Page 23: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/23.jpg)
Stimulus in the environment
Encode
Decode and Compare
Adjust
equal?
![Page 24: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/24.jpg)
Stimulus in the environment
Encode
Decode and Compare
Adjust
![Page 25: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/25.jpg)
Stimulus in the environment
Encode
Decode and Compare
Adjust
![Page 26: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/26.jpg)
Stimulus in the environment
Encode
Decode and Compare
Adjust
equal?
![Page 27: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/27.jpg)
Stimulus in the environment
Encode
Decode and Compare
Adjust
![Page 28: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/28.jpg)
Stimulus in the environment
Encode
Decode and Compare
Adjust
![Page 29: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/29.jpg)
Continue looping…
…until the internal representation corresponds to the external stimulus
![Page 30: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/30.jpg)
Using an autoassociator to simulate the Sokolov model
Stimulus from the environment
![Page 31: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/31.jpg)
Stimulus from the environment
encode
![Page 32: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/32.jpg)
Stimulus from the environment
decode
encode
![Page 33: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/33.jpg)
Stimulus from the environment
decode
compare
encode
![Page 34: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/34.jpg)
Stimulus from the environment
decodeadjustweights
encode
![Page 35: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/35.jpg)
Stimulus from the environment
decode
encode
![Page 36: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/36.jpg)
Stimulus from the environment
decode
encode
![Page 37: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/37.jpg)
Stimulus from the environment
decode
encode
![Page 38: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/38.jpg)
Stimulus from the environment
decode
compare
encode
![Page 39: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/39.jpg)
Stimulus from the environment
decodeadjustweights
encode
![Page 40: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/40.jpg)
Stimulus from the environment
decode
encode
![Page 41: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/41.jpg)
Stimulus from the environment
decode
encode
![Page 42: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/42.jpg)
Stimulus from the environment
decode
encode
![Page 43: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/43.jpg)
Stimulus from the environment
decode
compare
encode
![Page 44: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/44.jpg)
Stimulus from the environment
decodeadjustweights
encode
![Page 45: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/45.jpg)
Continue looping…
…until the internal representation corresponds to the external stimulus
![Page 46: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/46.jpg)
Infant looking time network error
In the Sokolov model, an infant continues to look at the image until the discrepancy between the image and the internal representation of the image drops below a certain threshold.
In the auto-encoder model, the network continues to process the input until the discrepancy between the input and the (decoded) internal representation of the input drops below a certain (error) threshold.
![Page 47: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/47.jpg)
Input to our modelWe used a three-layer, 10-8-10, non-linear auto-encoder (i.e., a network that tries to reproduce on output what it sees on input) to model the data.
The inputs were ten feature values, normalized between 0 and 1.0 across all of the images, taken from the original stimuli used by Quinn et al. (1993). They were head length, head width, eye separation, ear separation, ear length, nose length, nose width, leg length vertical extent, and horizontal extent.
The distributions – and, especially, the amount of inclusion – of these features in shown in the following graphs.
![Page 48: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/48.jpg)
-0.2 0.2 0.4 0.6 0.8 1
1
2
3
4
0.2 0.4 0.6 0.8 1
0.5 1
1.5 2
2.5
0.2 0.4 0.6 0.8 1 1.2
0.5
1
1.5
2
2.5
-0.4 -0.2 0.2 0.4 0.6 0.8 1
0.5
1
1.5
2
-0.25 0.25 0.5 0.75 1 1.25 1.5
0.5
1
1.5
2
2.5
0.2 0.4 0.6 0.8 1
0.5
1
1.5
2
2.5
3
ear separation ear length vertical extent
head length head width eye separation
Dogs
Cats
Comparing the distributions of the input features
![Page 49: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/49.jpg)
Results of Our Simulation
0.2
0.3
0.4
0.5
"cats"learned
first
"dogs"learned
first
condition
error
novel cat
novel dog
![Page 50: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/50.jpg)
0.2
0.3
0.4
0.5
"cats"learned
first
"dogs"learned
first
condition
error
novel cat
novel dog
1 2
![Page 51: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/51.jpg)
A Prediction of the auto-encoder model
• If we were to reverse the inclusion relationship between Dogs and Cats, we should be able to reverse the asymmetry.
• We selected the new stimuli from dog- and cat-breeder books (and very slightly morphed some of these stimuli).
• We created a set of Cats and Dogs, such that Cats now included Dogs – i.e., the Cat category was the broad category and the Dog category was the narrow category.
![Page 52: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/52.jpg)
Reversing the Inclusion Relationship
Eye separation
Ear length
“Reversed” distributions:Cats include Dogs
Old distributions:Dogs include Cats
-0.2 0.2 0.4 0.6 0.8 1
1
2
3
4
-0.25 0.25 0.5 0.75 1 1.25 1.5
0.5
1
1.5
2
2.5
Dogs
Cats
Cats
Dogs
0 1 2 3 4 5 6 7 8 9
10 11
0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0 1,1
0 1 2 3 4 5 6 7 8 9
10 11 12 13 14
0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0 1,1
Dogs
Dogs
Cats
Cats
![Page 53: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/53.jpg)
Results
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Cats Dogs
Familiarization stimuli
Netw
ork
err
or
new cat
new dog
20
30
40
50
60
70
80
Cats Dogs
Familiarization stimuliA
tten
tio
n
New cat
New dog
Prediction by the model 3-4 month infant data
![Page 54: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/54.jpg)
Removing the inclusion relationship:Another prediction from the model
Our model also predicts that, regardless of the variance of each category, if we remove the inclusion relationship, we should eliminate the categorization asymmetry.
![Page 55: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/55.jpg)
A new set of cat/dog stimuli was created in which there is no inclusion relationship
Cats
Dogs
![Page 56: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/56.jpg)
Prediction and Empirical Results: The categorization asymmetry disappears.
0
0.1
0.2
0.3
0.4
0.5
Dogs Cats
Familiarization stimuli
Ave
rag
e er
ror
novel dogs
novel cats
0
10
20
30
40
50
60
70
Dogs Cats
Familiarization stimuli
Att
entio
n %
novel dogs
novel cats
Prediction of the auto-encoder Infant data
![Page 57: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/57.jpg)
A critique of our methodology: The use of explicit features
• We used explicit features (head length, leg length, ear separation, nose length, etc.) to characterize the animals (we hand-measured the values using the photos shown to the infants).
• We decided instead to use simply Gabor-filtered spatial-frequency information to characterize the pictures.
![Page 58: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/58.jpg)
The Forest and the Trees:What are “spatial frequencies”?
The Forest from 10 miles away
Very low spatial frequencies
![Page 59: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/59.jpg)
The Forest and the Trees:What are “spatial frequencies”?
Low spatial frequencies
The Forest from 5 miles away
The Forest from 5 miles away
![Page 60: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/60.jpg)
The Forest and the Trees:What are “spatial frequencies”?
Medium spatial frequencies
The Forest from 5 miles away
The Forest from 5 miles away
The Forest from 1 mile away
![Page 61: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/61.jpg)
The Forest and the Trees:What are “spatial frequencies”?
Medium-high spatial frequenciess
The Forest from 5 miles away
The Forest from 5 miles away
The Forest from 05 miles away; outline of some Trees
The Forest from 1/2 mile away; outline of some Trees
![Page 62: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/62.jpg)
The Forest and the Trees:What are “spatial frequencies”?
High spatial frequenciess
The Forest from 5 miles away
The Forest from 5 miles away
The Forest from 05 miles away; outline of some Trees
The Forest from 05 miles away; outline of some Trees
The Forest from 200 m. away; Trees visible, but no branches or leaves
![Page 63: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/63.jpg)
The Forest and the Trees:What are “spatial frequencies”?
Very high spatial frequenciess
The Forest from 5 miles away
The Forest from 5 miles away
The Forest from 05 miles away; outline of some Trees
The Forest from 05 miles away; outline of some Trees
The Forest from 200 yards away; Trees visible, but no branches or leaves
50 m. away; Forest no longer visible. Trees with branches visible but no leaves
![Page 64: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/64.jpg)
The Forest and the Trees:What are “spatial frequencies”?
Extremely high spatial frequencies
The Forest from 5 miles away
The Forest from 5 miles away
The Forest from 05 miles away; outline of some Trees
The Forest from 05 miles away; outline of some Trees
The Forest from 200 yards away; Trees visible, but no branches or leaves
50 yards away; Forest no longer visible. Trees with branches visible but no leaves
10 m. away; Forest no longer visible. Trees with branches and individual leaves visible
![Page 65: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/65.jpg)
The Forest and the Trees:Combining spatial frequencies to obtain the full image
The Forest from 5 miles away
The Forest from 1 mile away
The Forest from 1/2 mile away; outline of some Trees
The Forest from 400 m. away; outline of some Trees
The Forest from 200 m. away; Trees visible, but no branches or leaves
50 m. away; Forest no longer visible. Trees with branches visible but no leaves
10 m. away; Forest no longer visible. Trees with branches and individual leaves visible
Full image
![Page 66: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/66.jpg)
Cats: infant-to-adult visual acuity
Very low spatial frequencies
Two-month old vision
3-4 month old vision
(almost) adult vision
![Page 67: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/67.jpg)
Cats: infant-to-adult visual acuity
![Page 68: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/68.jpg)
![Page 69: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/69.jpg)
![Page 70: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/70.jpg)
![Page 71: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/71.jpg)
![Page 72: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/72.jpg)
![Page 73: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/73.jpg)
Adult Vision with full range of spatial frequencies
![Page 74: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/74.jpg)
Spatial frequency maps of images with Gabor filtering
This allows us to characterize each dog/cat image with a 26-unit vector.
We “cover” this map with spatial-frequency ovals along various orientations of the image. (Each oval is normalized to have approximately the same energy.)
low freq. high
freq.
spatial-frequency map
![Page 75: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/75.jpg)
This is an experiment.
Consider the following image.
![Page 76: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/76.jpg)
![Page 77: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/77.jpg)
![Page 78: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/78.jpg)
![Page 79: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/79.jpg)
![Page 80: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/80.jpg)
Moral of the story:
Sometimes too much detail hinders categorization (even for adults!)
![Page 81: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/81.jpg)
The same is true for infants: Reducing high-frequency information improves category discrimination for distinct categories
Reducing the range of the spatial frequencies from the retinal map to V1 decreases within-category variance.
This decreases the difference between two exemplars of the same category, but increases the difference between exemplars from two different categories.
This will make learning “distant” basic-level or super-ordinate category distinctions easier (but subordinate-level category distinctions will be more difficult).
![Page 82: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/82.jpg)
In other words, reduced visual acuity might actually be good for infant categorization.
• Visual acuity in infants is not the same as that of adults. They do not perceive high-spatial frequencies (i.e., fine details), or perceive them only poorly.
• This reduced visual acuity may actually improve perceptual efficiency by eliminating the “information overload” caused by too many extraneous fine details likely to overwhelm their cognitive system.
• Thus, distant basic-level category and super-ordinate level category learning may actually be facilitated by reduced visual acuity.
![Page 83: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/83.jpg)
Reducing visual acuity in our model to simulate young-infant vision by removing high spatial frequencies
High spatial frequencies
![Page 84: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/84.jpg)
Reducing visual acuity in our model to simulate young-infant vision by removing high spatial frequencies
High spatial frequencies
![Page 85: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/85.jpg)
Reducing visual acuity in our model to simulate young-infant vision by removing high spatial frequencies
High spatial frequencies
![Page 86: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/86.jpg)
Reducing visual acuity in our model to simulate young-infant vision by removing high spatial frequencies
The high spatial frequencies have been removed. The autoencoder will work with input from these images, thereby simulating early infant vision.
![Page 87: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/87.jpg)
Two simulations with Gabor-filtered input
• Reproducing previous results: Using vectors of the 26 weighted spatial-frequency values, instead of explicit feature values, produces autencoder network results similar to those produced by infants tested on the same images
• Reduced visual acuity: This is produced by largely eliminating high-spatial frequency information from the input (i.e., “blurry” vision) actually significantly improves the network’s ability to categorize the images presented to it.
![Page 88: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/88.jpg)
Reproducing previous results (Cats are the more variable category)
Network generalization errors with Gabor-filtered spatial-frequency information
Results for 3-4 month old infants
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Cats Dogs
Familiarization
Netw
ork
err
or
new cat
new dog
Results with explicit feature values (French et al., 2001)
0.24
0.25
0.26
0.27
cats dogs
novel cat
novel dog
Large jump in error
Very little jump in error
![Page 89: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/89.jpg)
Conclusion about the use of Gabor-filtered input instead of explicit
feature measurements
• Spatial frequency data in the model produces a reasonable fit to empirical data.
• We avoid the thorny issue of using a particular set of “high-level” feature measurements (ear length, eye separation, etc.) to characterize the images used in the simulations.
![Page 90: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/90.jpg)
Reduced visual acuity
Reduced perceptual acuity in 3-4 month old infants produces an advantage for differentiating perceptually distant basic-level categories and super-ordinate categories.
![Page 91: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/91.jpg)
Simulation 2: The advantage in 3-4 month old infants of reduced visual acuity
• Above 3-4 cycles/degree: very little contribution
• Above 7.1 cycles/degree: no contribution
The frequencies removed or reduced were:
Network used:
26-16-26 feedforward BP autoencoder network (learning rate: 0.1, momentum: 0.9)
![Page 92: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/92.jpg)
Close categories vs. Very dissimilar categories
When a network is familiarized on one category (say, Cat), reduced visual acuity decreases errors (i.e., improves generalization) for novel exemplars in the same category or very similar categories (like Dog).
But it should help in discriminating dissimilar categories. So, for example, reduced visual acuity should produce a greater jump in error for network (or increased attention for an infant) familiarized on Cats when exposed to Cars.
![Page 93: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/93.jpg)
When trained on one category (Cats), errors on dissimilar categories (Cars) are increased by reduced visual acuity (i.e., better category discrimination).
Larger the error = better discrimination.
Jump in error
0
0.02
0.04
0.06
0.08
0.1
Adult vision Infant vision
![Page 94: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/94.jpg)
A Prediction of the ModelConsider Quinn et al. (1993)
Familiarized on Cats
Jump in interest
No jump in interest.
Cat
Familiarized on Dogs
Dog
But what if we took this test Cat and, by adding only high spatial-frequency information, transformed it into this Dog?
![Page 95: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/95.jpg)
Familiarized on Cats
Prediction: No jump in interest
No jump in interest.
Cat
Familiarized on Dogs
Cat
Presumably what the 3-month old infant would see is this:
The asymmetry would disappear, even though adults would perceive a series of cats followed by a dog and would expect a jump in infants’ interest, as there usually is for a novel dog following familiarization on cats.
![Page 96: Using auto-encoders to model early infant categorization: results, predictions and insights](https://reader036.vdocuments.mx/reader036/viewer/2022081603/56649eff5503460f94c14ff6/html5/thumbnails/96.jpg)
Modeling Dogs and Cats: Conclusions
A simple connectionist auto-encoder does a good job of reproducing certain surprising infant categorization data.
This model makes testable predictions…
Gabor-filtered spatial-frequency input is neurobiologically plausible and produces a good approximation to infant categorization data.
A counter-intuitive learning advantage for categorizing distant basic-level categories and super-ordinate categories arises from reduced acuity input.
…that have subsequently been confirmed in infants.
This supports a statistical, perceptually based, on-line categorization mechanism in young infants