1. Psychology 209 – Winter 2018 March 8, 2018
Successes, Limitations, and Future Directions for Neural Network Models of Cognition
Psychology 209 – Winter March 8, 2018

2. Topics and students who raised them
Life-long learning
Michael M, Yochai, Zack
Purposivity
Yochai, Zack
One shot/transfer learning
Kelsey
Built in/explicit vs learned/emergent
Jeff H
Renata
Exploiting physiology: time
Alex
Michael Ko
Learning to function as a group
Ari
Disorders
Autism: Michael M
Alzheimers: Greg
Affective disorders: Neil
Alternative hardware
Catherine
Jeff B
Hybrid Architectures
Ian

3. Lake et al Pattern recognition vs model building:
Cognition is about using these models to understand the world, to explain what we see, to imagine what could have happened that didn’t, or what could be true that isn’t, and then planning actions to make it so.

4. Start up software Intuitive physics Intuitive psychology
Infants have primitive object concepts that allow them to track objects over time and allow them to discount physically implausible trajectories – e.g. they know that objects will persist over time and that they are solid and coherent.
Intuitive psychology
Infants understand that other people have mental states like goals and beliefs, and this understanding strongly constrains their learning and predictions.

5. Learning as model building
Explaining observed data through the construction of causal models of the world.
‘Early present capacities for intuitive physics and psychology are also causal models of the world’.
A primary job of learning is to extend and enrich these models and build analogous causally-structured theories of other domains.
Human learning is richer and more efficient than state-of-the-art algorithms in machine learning
Compositionality and learning to learn are ingredients that make this type of rapid model learning possible

6. Model Based and Model Free Methods
Using a model is cumbersome and slow; model free reinforcement learning can allow real-time ‘control’.
Humans combine MB and MF competitively and cooperatively

7. Two Challenges
Characters
Frostbite

8. One example: Omniglot Classification of new examples
Generation of new examples
Parsing an object into its parts
Generation of new concepts from related examples

9. Lake et al’s solution

10. How might you address this challenge using neural networks?

11. DQN learns Frostbyte slowly – people can do well from brief instruction or from watching a good player
Construct an igloo
Jump on white ice flows
Gather fish
Don’t fall in the water
Avoid geese & polar bears

12. How might you address this challenge using neural networks?

13. Emergent intelligence vs built-in intelligence
May be easier to create
Since it is designed, it is likely to be easier to understand
You need to have just the right stuff to get the stuff you want to learn to fit within it
May not deal with quasiregularity
Emergent
Not as easy to create
Not as easy to understand
Deals with quairegularity
Involves less prior commitment to structure

14. What other challenges can you envision?