1. Psychology 209 – Winter 2017 Feb 28, 2017
The CLS theory: Basic features, role of replay and responses to recent challenges
Psychology 209 – Winter 2017
Feb 28, 2017

4. The Rumelhart Model

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

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

7. Emergence of Meaning in Learned Distributed Representations
Distributed representations that capture aspects of meaning emerge through a gradual learning process
The progression of learning and the representations formed capture many aspects of cognitive development
Differentiation of concepts
Generalization, illusory correlations and overgeneralization
Domain-specific variation in importance of feature dimensions
Reorganization of conceptual knowledge

8. What happens in this system if we try to learn something new?
Such as a Penguin

9. Learning Something New
Used network already trained with eight items and their properties.
Added one new input unit fully connected to the representation layer
Trained the network with the following pairs of items:
penguin-isa living thing-animal-bird
penguin-can grow-move-swim

10. Rapid Learning Leads to Catastrophic Interference

11. Effect of a Hippocampal Lesions
Intact performance on tests of intelligence, general knowledge, language, other acquired skills
Dramatic deficits in formation of some types of new memories:
Explicit memories for episodes and events
Paired associate learning
Arbitrary new factual information
Temporally graded retrograde amnesia:
lesion impairs recent memories leaving remote memories intact.
Note: HM’s lesion was bilateral

12. A Complementary Learning System in the Medial Temporal Lobes
action
name
Medial Temporal Lobe
motion
Temporal
pole
color
valance
form

13. Avoiding Catastrophic Interference with Interleaved Learning

14. Initial Storage in the Hippocampus Followed by Repeated Replay Leads to the Consolidation of New Learning in Neocortex, Avoiding Catastrophic Interference
action
name
Medial Temporal Lobe
motion
Temporal
pole
color
valance
form

15. Inside the MTL… Pattern separation: Sparse random conjunctive coding
Floating threshold idea
How learning can increase pattern separation
Cheating during ‘retrieval’ by bypassing the dentate

16. In more detail… Input from neocortex comes into EC
Drastic pattern separation occurs in DG
Downsampling in CA3
Moderate invertable sparsified representation in CA1
One- or fewish- shot learning in DG, CA3, CA3-CA1 allows reconstruction of ERC pattern from partial input.

17. Challenges to the theory
Inference and generalization can depend on the MTL
Better learning of ‘premises’ leads to better ability to make inferences
Sometimes new information can be integrated into neocortical learning systems quickly

18. How might hippocampus support inference and generalization?
Finding missing links in the transitive inference task
‘Similarity based generalization’
Relying on partial activation of multiple memories to decide if a stimulus is familiar or unfamiliar

19. The Second Challenge to the Theory
Richard Morris
The Second Challenge to the Theory
Rapid Consolidation of Schema Consistent Information

20. Tse et al (Science, 2007, 2011) During training, 2 wells
uncovered on each trial

21. Lesion Control Day 2 Day 9 Day 16 After New
Initial learning of flavor-place associations is gradual.
After initial learning, one new pair of flavor-place associations learned in one trial.
Performance is unaffected by HPC lesion 48 hrs after learning new associations.
Not only was the new material learned quickly, it appears to have been rapidly integrated into the neocortex
Lesion Control

22. Rapid Gene Induction for New Schema Consistent Information
Old paired associates (OPA)
New paired associates (NPA)
New map (NM)
Caged Control (CC)

24. Implications for Theory
“These findings indicate that the rate at which systems consolidation occurs in the neocortex can be influenced by what is already known. In contrast, in the complementary learning systems approach, the hippocampus is said to be `specialized for rapidly memorizing specific events’ and the neocortex for ‘slowly learning the statistical regularities of the environment.’”

25. Or did I simply fail to convey the full schema underlying the theory?
Are These Findings Really Inconsistent with Complementary Learning Systems Theory?
Or did I simply fail to convey the full schema underlying the theory?
Why, after all, did I choose the penguin to demonstrate the importance of the MTL in new learning?

26. Schemata and Schema Consistent Information
What is a ‘schema’?
An organized knowledge structure into which existing knowledge is organized.
What is schema consistent information?
Information that can be added to a schema without disturbing it.
What about a penguin?
Partially consistent
Partially inconsistent
In contrast, consider
a trout
a cardinal

27. New Simulations
Initial training with eight items and their properties as before.
Added one new input unit fully connected to the representation layer also as before
Trained the network on one of the following pairs of items:
penguin-isa & penguin-can
trout-isa & trout-can
cardinal-isa & cardinal-can

28. New Learning of Consistent and Partially Inconsistent Information
INTERFERENCE

29. Connection Weight Changes after Simulated NPA, OPA and NM Analogs
Tse Et al 2011

30. How Does It Work?

31. How Does It Work?

32. Take home messages The brain clearly contains many learning systems
Hippocampus
Neocortex
Basal Ganglia
Amygdala
Generally speaking learning is likely to depend on many systems working together at the same time.
These systems can be parameterized very differently
Sparse vs Dense
Different learning rates
Reward- vs. prediction error driven
We can also make explicit conscious inferences sometimes
This ability likely depends on many systems working together, especially if the information needed to link inferences is stored in memory