1. Psychology 209 – Winter 2017 January 31, 2017
Development, Disintegration, and Neural Basis of Semantic Cognition: An Evolving Understanding in terms of Deep Neural Networks
Psychology 209 – Winter 2017 January 31, 2017

2. 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.
Experience affects our knowledge representations through a gradual connection adjustment process
language

3. A Principle of Learning and Representation in PDP Networks
Learning and representation are sensitive to coherent covariation of properties across experiences.
Later in the lecture we will formalize this idea more mathematically

4. What is Coherent Covariation?
The tendency of properties of objects to co-occur in clusters.
e.g.
Has wings
Can fly
Is light Or
Has roots
Has rigid cell walls
Can grow tall

5. 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.

6. Some Phenomena in Development
Progressive differentiation of concepts
Overgeneralization
Illusory correlations

8. The Rumelhart Model

9. The Training Data:
All propositions true of items at the bottom level of the tree, e.g.:
Robin can {grow, move, fly}

10. Target output for ‘robin can’ input

11. Forward Propagation of Activationaj ai
wij
neti=Sajwij
wki

12. Back Propagation of Error (d)aj
wij ai
di ~ Sdkwki
wki
dk ~ (tk-ak)
Error-correcting learning:
At the output layer: Dwki = edkai
At the prior layer: Dwij = edjaj …

15. Early
Later
Later Still
E x p e r i e
n c e

17. What Drives Progressive Differentiation?
Waves of differentiation reflect coherent covariation of properties across items.
Patterns of coherent covariation are reflected in the principal components of the property covariance matrix.
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

18. Overgeneralization of Frequent Names to Similar Objects
“tree”
“goat”
“dog”

20. Illusory Correlations
Rochel Gelman found that children think that all animals have feet.
Even animals that look like small furry balls and don’t 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.

21. A typical property that a particular object lacks
e.g., pine has leaves
An infrequent,
atypical property

22. Sensitivity to Coherence Requires ConvergenceA A A

23. Mathematical Analysis of a Simplified Linear Model
Linear network with two layers of weights
Example with very small semantic task

24. Dynamics of Learning in Simplified Linear Model
Learning rule spelled out in vector-matrix form:
In the limit of small learning rate:
At right, time course of of learning for a data set with first 3 dimensions s1 > s2 >s3 as in the EightThings dataset.

25. 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.

26. 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

27. Picture naming and drawing in
Sem. Demantia

29. Grounding the Model in What we Know About The Organization of Semantic Knowledge in The Brain
Evidence indicates that specialized brain areas subserve many different kinds of semantic information.
Semantic dementia results from progressive bilateral disintegration of the anterior temporal cortex.
Rapid acquisition of new knowledge depends on medial temporal lobes, leaving long-term semantic knowledge intact.
language

30. Proposed Architecture for the Organization of Semantic Memory
action
name
Medial Temporal Lobe
motion
Temporal
pole
color
valance
form

31. Rogers et al (2004, Psychological Review) model of semantic dementia
Gradually learns through exposure to input patterns derived from norming studies.
Representations in the temporal pole are acquired through the course of learning.
After learning, the network can activate each other type of information from name or visual input.
Representations undergo progressive differentiation as learning progresses.
Damage to units within the temporal pole leads to the pattern of deficits seen in semantic dementia.
name
assoc
function
temporal pole
vision

32. Errors in Naming for As a Function of Severity
Simulation Results
Patient Data
omissions
within categ.
superord.
Severity of Dementia
Fraction of Neurons Destroyed

33. Simulation of Delayed Copying
Visual input is presented, then removed.
After several time steps, pattern is compared to the pattern that was presented initially.
Omissions and intrusions are scored for typicality
name
assoc
function
temporal pole
vision

34. Simulation results IF’s ‘camel’ DC’s ‘swan’ Omissions by feature type
Intrusions by feature type

36. 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.

37. Further Investigations
Lexical and Semantic Deficits in SD
Individual Differences
Laterality Effects
Unilateral Resections and Recovery
Effects of TMS on Semantic Task Performance
Category specific deficits:
Why they may occur in some types of patients but not others.