iterated learning and the cultural ratchet - CogSci 2009

8/6/2019 iterated learning and the cultural ratchet - CogSci 2009

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The Cultural Ratchet

Iterated Learing and variations

Results

What does this mean?

Iterated Learning and the Cultural Ratchet

Aaron Beppu Tom Griffiths

Department of PsychologyUniversity of California Berkeley

Cognitive Science, 2009

Aaron Beppu, Tom Griffiths Iterated Learning and the Cultural Ratchet
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Results


Outline

1 The Cultural Ratchet

2 Iterated Learing and variations

3 Results

4 What does this mean?

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What is the Cultural Ratchet?

The process of cumulative cultural evolution requires

not only creative invention but also . . . faithful social

transmission that can work as a ratchet to prevent

slippage backward so that the newly invented artifactor practice preserves its new and improved form at

least somewhat faithfully until a further modification or

improvement comes along.

Michael Tomasello, The Cultural Origins of Human Cognitionwe do this

other animals dont

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What are some existing explanations?

We can try to explain this by focusing on individual teachers

and learners :

Were sophisticated learners (e.g. Tomasello 2001)

We learn strategies and goals from each otherWe can follow each others attentionWe can faithfully reproduce behaviors we learn from others

Were also good teachers (e.g. Gergeley & Csibra 2005)

We understand which things are hard to learnTeachers call learners attention to the most relevant partsHuman learners benefit from an enriched learningenvironment

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Iterated Learning

Modeling Cultural Evolution

hypothesis

data data data

hypothesis

...Learners are ordered into a sequence, and each agent learns

from the output of the previous agent

Can be used to study change in culture, (e.g. evolution oflanguage Kirby 2001), or to examine peoples biases (e.g.

Griffiths, Christian & Kalish, 2006)


Th C l l R h
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Iterated Learning

Analyzing Iterated Learning

Specifics:

Agents are Bayesian, and all have the same prior. Bayes Rule :

p(h|d) = p(h)p(d|h)h p(h

)p(d|h)

The data that a given agent passes on is sample according to

his posterior beliefs. p(di|di1) =

h

p(di|h)p(h|di1)

The pure iterated learning model converges to the prior beliefs

of its agents.


Th C lt l R t h t
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Iterated Learning

Cumulative Cultural Evolution

Cumulative cultural evolution will require constantly bringing in

new data from the world, in addition to some information

passed from one generation to the next. We vary what kind of

information is passed between agents.

...hypothesishypothesis

??? ??? ???

new data new data


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Iterated Learning

Observational Learning

...data datahypothesishypothesis

data...

new data new data

In the Mixed Data case, each agent receives new data from

the world, as well as data points sampled from the posterior

distribution of the previous agent. This is similar to

observational learning, achieved by observing the behavior of

another.

In this case, convergence is to

d p(h|d)p(d).


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Iterated Learning

Communicating Theories

...theory theoryhypothesishypothesis

theory

new data new data

In the Posterior Passing case, each agent receives new data

from the world. In addition, rather than receiving samples from

the previous persons posterior distribution, each agent nowreceives the whole posterior distribution. Communicating

theories might approximate how humans teach/learn.

This chain converges to the truth.


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Iterated Learning

Function learning is hard

Why function learning?

Strong bias towards positive linear functions; non-monotonic

functions are hard (Busemeyer, Byun, DeLosh & McDaniel,

1997)

The pure iterated learning case converges to a linear function

(Kalish, Griffiths, Lewandowsky, 2007)

We can select a function that is too difficult for a single person

to learn on their own, but which could plausibly be learned via

cumulative cultural evolution.

By using a difficult to learn parabola, we can separate out

groups of people converging to the posterior from people

converging to the prior.


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Apparatus


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Examples from our results

Training Testing

Pure iterated learning


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Training Testing

Mixed data



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Training Testing

Posterior passing

Mixed data



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0 2 4 6 80

0.1

0.2

0.3

0.4

0.5

0.6

Generation

RMSE

0 2 4 6 80

0.1

0.2

0.3

0.4

0.5

0.6

Generation

RMSE

pure iterated learning

mixed data

posterior passing

Left : Cumulative average error (RMSE) for each chain for each

generation. Right : Same as left, but averaged by condition.


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What do we walk away with?

Merely observing behavior of those around us isnt enough to

have cumulative cultural evolution

We need to communicate theories and beliefs about the world.

Language is one way we can do this.

This gives us an idea about the uniquely human characteristics

that facilitate cultural evolution.

What next?

Can we adequately recover the posterior distributions from ourteachers merely from observing their actions?

What can we do with richer teacher/learner interactions?

How can we reconcile this inference based perspective with

other pressures on cultural evolution?Aaron Beppu, Tom Griffiths Iterated Learning and the Cultural Ratchet
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iterated learning and the cultural ratchet - CogSci 2009

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