Three challenges for computational models of

cognition

Charles Kemp

CMU

Humans vs machines

Outstanding

Not so good

Machine Human

Performance

Three challenges

1. Composition

2. Generativity

3. Putting it all together

StructuredModels

Neural network/continuous space

models

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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)

Liang et al, Learning dependency based compositional semantics

“A major focus of this work isour semantic representation, DCS,which offers a new perspective oncompositional semantics.”

Socher et al, Semantic compositionality through recursive matrix vector spaces

Opportunities/Challenges

1. Compositional systems that work with fuzzy concepts.

Generativity

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

Computational models

(Hofstadter et al, Letter Spirit)

(Cohen, AARON)

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

Training:

…Z NX

MQ JX

MQ JD

B

Test: Generate another

Z ND

B

HumanModel

Jern & Kemp, A probabilistic account of exemplar and category generation

Fleuret et al, Synthetic Visual Reasoning Test

Category 1

Category 2

Opportunities/Challenges

1. Compositional systems that work with fuzzy concepts.

2. Avoid “cargo cult” science via benchmark engineering.

One problem, many settings

(Salakhutdinov, Tenenbaum, Torralba)

Psychological data: categorization (Canini et al) causal learning (Kemp et al)

One setting, many problems

Generalization, Categorization, Identification, Recognition …

(Shepard; Nosofsky; Ashby; Kemp & Jern…)

Many settings, many problems

• Cognitive architectures (ACT-R, SOAR)

• Artificial general intelligence

Opportunities/Challenges

1. Compositional systems that work with fuzzy concepts.

2. Avoid “cargo cult” science via benchmark engineering

3. Systems that solve many different problems in many different settings

Three challenges

1. Composition

2. Generativity

3. Putting it all together

StructuredModels

Neural network/continuous space

models

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