Does the Brain Use Symbols or Distributed Representations?

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Does the Brain Use Symbols or Distributed
Representations?

• James L. McClelland
• Department of Psychology andCenter for Mind,
  Brain, and ComputationStanford University

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Parallel Distributed Processing Approach to
Semantic Cognition

• Representation is a pattern of activation
  distributed over neurons within and across brain
  areas.
• Bidirectional propagation of activation underlies
  the ability to bring these representations to
  mind from given inputs.
• The knowledge underlying propagation of
  activation is in the connections.

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Development and Degeneration

• Learned distributed representations in an
  appropriately structured distributed
  connectionist system underlies the development of
  conceptual knowledge.
• Gradual degradation of the representations
  constructed through this developmental process
  underlies the pattern of semantic disintegration
  seen in semantic dementia.

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Differentiation, Illusory Correlations, and
Overextension of Frequent Names in Development
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The Rumelhart Model
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The Training Data
All propositions true of items at the bottom
levelof the tree, e.g. Robin can grow, move,
fly
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Target output for robin can input
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Forward Propagation of Activation
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Back Propagation of Error (d)
aj
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Error-correcting learning At the output
layer Dwki edkai At the prior layer Dwij
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Early Later LaterStill
Experie nce
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Why Does the Model Show Progressive
Differentiation?

• Learning in the model is sensitive to patterns of
  coherent covariation of properties
• Coherent Covariation
• The tendency for properties of objects to co-vary
  in clusters

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Patterns of Coherent Covariation in the Training
Set

• Patterns of coherent covariation are reflected in
  the principal components of the property
  covariance matrix of the training patterns.
• Figure shows attribute loadings on the first
  three principal components
• 1. Plants vs. animals
• 2. Birds vs. fish
• 3. Trees vs. flowers
• Same color features covary in
  component
• Diff color anti-covarying
  features

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Illusory Correlations

• Rochel Gelman found that children think that all
  animals have feet.
• Even animals that look like small furry balls and
  dont seem to have any feet at all.
• A tendency to over-generalize properties typical
  of a superordinate category at an intermediate
  point in development is characteristic of the PDP
  network.

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A typical property thata particular object
lacks e.g., pine has leaves
An infrequent, atypical property
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A One-Class and a Two-Class Naïve Bayes
Classifier Model
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Accounting for the networks representations with
classes at different levels of granularity
Regression Beta Weight
Epochs of Training
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Overgeneralization of Frequent Names to Similar
Objects
goat
tree
dog
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Why Does Overgeneralization of Frequent Names
Increase and then decrease?

• In the simulation shown, dogs are experienced 10
  times as much as any other animal, and there are
  4 other mammals, 8 other animals, and ten plants.
• In a one-class model, goat is a living thing
• P(name is Dogliving thing) 10/32 .3
• In a two-class model, goat is an animal
• P(name is Doganimal) 10/22 .5
• In a five class model, goat is a mammal
• P(name is Dogmammal) 10/15 .67
• In a 23 class model, goat is in a category by
  itself
• P(name is Doggoat) 0

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Sensitivity to Coherence Requires Convergence
A
A
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Inference and Generalizationin the PDP Model

• A semantic representation for a new item can be
  derived by error propagation from given
  information, using knowledge already stored in
  the weights.
• Crucially
• The similarity structure, and hence the pattern
  of generalization depends on the knowledge
  already stored in the weights.

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Start with a neutral representation on the
representation units. Use backprop to adjust the
representation to minimize the error.
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The result is a representation similar to that of
the average bird
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Use the representation to infer what this new
thing can do.
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Differential Importance (Marcario, 1991)

• 3-4 yr old children see a puppet and are told he
  likes to eat, or play with, a certain object
  (e.g., top object at right)
• Children then must choose another one that will
  be the same kind of thing to eat or that will
  be the same kind of thing to play with.
• In the first case they tend to choose the object
  with the same color.
• In the second case they will tend to choose the
  object with the same shape.

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Adjustments to Training Environment

• Among the plants
• All trees are large
• All flowers are small
• Either can be bright or dull
• Among the animals
• All birds are bright
• All fish are dull
• Either can be small or large
• In other words
• Size covaries with properties that differentiate
  different types of plants
• Brightness covaries with properties that
  differentiate different types of animals

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Similarities of Obtained Representations
Brightness is relevant for Animals
Size is relevant for Plants
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Development and Degeneration

• Sensitivity to coherent covariation in an
  appropriately structured Parallel Distributed
  Processing system underlies the development of
  conceptual knowledge.
• Gradual degradation of the representations
  constructed through this developmental process
  underlies the pattern of semantic disintegration
  seen in semantic dementia.

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Disintegration of Conceptual Knowledge in
Semantic Dementia

• Progressive loss of specific knowledge of
  concepts, including their names, with
  preservation of general information
• Overgeneralization of frequent names
• Illusory correlations

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Picture namingand drawing in Sem. Demantia
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Proposed Architecture for the Organization of
Semantic Memory
name
action
motion
Temporal pole
color
form
valance
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Rogers et al (2005) model of semantic dementia
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Errors in Naming for As a Function of Severity
Simulation Results
Patient Data
Severity of Dementia
Fraction of Neurons Destroyed
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Simulation of Delayed Copying

• Visual input is presented, then removed.
• After several time steps, pattern is compared to
  the pattern that was presented initially.

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IFs camel
DCs swan
Simulation results
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Conclusion

• Distributed representations gradually
  differentiate in ways that allow them to capture
  many phenomena in conceptual development.
• Their behavior is approximated by a blend of
  Naïve Bayes classifiers across several levels of
  granularity, with the blending weights shifting
  toward finer grain categories as learning
  progresses.
• Effects of damage are approximated by a reversal
  of this tendency degraded representations
  retain the coarse-grained level knowledge but
  loose the finer-grained information.
• We are currently extending the models to address
  the sharing of knowledge across structurally
  related domains, Ill be glad to discuss this
  idea in response to questions.