1. Three challenges for computational models of cognition
Charles Kemp
CMU

2. Humans vs machines Performance Outstanding Not so good Machine Human
First order of business is to close this gap:
Nagging worry for non-Bayesian models: maybe the model does badly because the learning component isn’t optimal.
We have a good theoretical understanding of inductive inference. Bayesian approach puts you in a good position to explore what’s left.

3. ✓ Three challenges ✓ Composition Generativity Putting it all together
Structured
Models
Neural network/
continuous space
models ✓ ✓
Composition
Generativity
Putting it all together ✓ ✓ ✓ ✓

4. Composition: sentences
Given a database of geography facts, answer questions like:
“how many rivers run through the states bordering Colorado?”
“how many states border the state that borders the most states?”
(Mooney, 1997)

5. Liang et al, Learning dependency based compositional semantics
“A major focus of this work is
our semantic representation, DCS,
which offers a new perspective on
compositional semantics.”

6. Socher et al, Semantic compositionality through recursive matrix vector spaces

7. Opportunities/Challenges
Compositional systems that work with fuzzy concepts.

8. Generativity
“Mr. and Mrs. Dursley, of number four Privet Drive, were proud to say that they were perfectly normal, thank you very much.”

9. Computational models
(Hofstadter et al, Letter Spirit)
(Cohen, AARON)

10. Hinton et al, A fast learning algorithm for deep belief nets

11. Jern & Kemp, A probabilistic account of exemplar and category generation
Training: X X D Z N Q J Q J … M M B
Test: Generate another Z N D B
Human
Model

12. Fleuret et al, Synthetic Visual Reasoning Test
Category 1
Category 2

13. Opportunities/Challenges
Compositional systems that work with fuzzy concepts.
Avoid “cargo cult” science via benchmark engineering.

14. One problem, many settings
(Salakhutdinov, Tenenbaum, Torralba)
Psychological data:
categorization (Canini et al)
causal learning (Kemp et al)

15. One setting, many problems
Generalization, Categorization, Identification, Recognition …
(Shepard; Nosofsky; Ashby; Kemp & Jern…)

16. Many settings, many problems
Cognitive architectures (ACT-R, SOAR)
Artificial general intelligence

17. Opportunities/Challenges
Compositional systems that work with fuzzy concepts.
Avoid “cargo cult” science via benchmark engineering
Systems that solve many different problems in many different settings

18. ✓ Three challenges ✓ Composition Generativity Putting it all together
Structured
Models
Neural network/
continuous space
models ✓ ✓
Composition
Generativity
Putting it all together ✓ ✓ ✓ ✓