dynamics of learning: a case study jay mcclelland stanford university

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Dynamics of learning: A case study Jay McClelland Stanford University

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Page 1: Dynamics of learning: A case study Jay McClelland Stanford University

Dynamics of learning: A case study

Jay McClellandStanford University

Page 2: Dynamics of learning: A case study Jay McClelland Stanford University

Piaget’s theory of stages and development

• Our knowledge takes qualitatively different forms at different stages

• This occurs because our knowledge schemas adapt with experience– ‘Equilibration: Assimilation & Accommodation’

• Questions about the theory:– What do these words mean?– How does it work?– How can incremental adjustments lead to qualitative

change?

Page 3: Dynamics of learning: A case study Jay McClelland Stanford University

An example domain: The balance scale

Will the scale tip left, tip right, or balance?

Page 4: Dynamics of learning: A case study Jay McClelland Stanford University

Siegler’s First Two Rules

Rule assessment procedure: 24 problems of 6 typesChildren are characterized as ‘using a rule’ if 20/24 responses are consistent with it

Page 5: Dynamics of learning: A case study Jay McClelland Stanford University

Findings

• Children 5-7 generally behave in accordance with Rule 1

• Then, most children’s behavior is briefly consistent with Rule 2, before they progress to more complex patterns of responding

• Around the time of these transitions, children exhibit increasing, graded sensitivity to the distance cue– Rule 1 kids will take distance into account if the

distance difference is large enough

Page 6: Dynamics of learning: A case study Jay McClelland Stanford University

Siegler’s Intervention Experiment

• Two groups of ‘Rule 1’ children– Young (about 5 yrs old)– Older (7-8 yrs old)

• All children see 16 ‘conflict’ problems

• After the experience:– Older children progress to Rule 2– Younger children show no change or start

choosing randomly

Page 7: Dynamics of learning: A case study Jay McClelland Stanford University

An Alternative to Rules

• Knowledge is implicit– In the connections in a neural

network• Experience with problems

shapes the connections• The network learns to

become sensitive to weight and distance gradually through the accumulation of small changes

• Like children, it shows graded sensitivity to distance

Page 8: Dynamics of learning: A case study Jay McClelland Stanford University

Graded Differences in Connections Lead to Different Outcomes of Learning at Different Points in Development

• Training with conflict problems at epoch 20 leads to degradation of performance.

• Training at 40 epochs leads to progress.

Page 9: Dynamics of learning: A case study Jay McClelland Stanford University

Is the Model too Continuous?

• ‘Rules’ might be seen as similar to phases of matter (solid, liquid, gas)

• This view predicts that children’s performance should exhibit ‘catastrophe flags’ that signify such transitions

Page 10: Dynamics of learning: A case study Jay McClelland Stanford University

Another Interesting Question

Can the model teach itself?

Page 11: Dynamics of learning: A case study Jay McClelland Stanford University

Van der Maas et al Experiment

• Pre-test• A series of distance problems• Post-test• Over 300 children tested!

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Van der Maas et al Catastrophe Flags

• Pre and post-test– ‘Inaccessible region’– Jump across gap

• Distance series– ‘Maxwell’– ‘Hysteresis’– ‘Sudden jump’

Page 22: Dynamics of learning: A case study Jay McClelland Stanford University

Findings

Rule 1Rule 2

Hysteris

is

Max

wellJu

mp

Unscorab

le05

101520253035

Distance Series: Proportion of ChildrenShowing Each Type of

Pattern

Proportion of Children

Pretest Scores

Posttest Scores

Page 23: Dynamics of learning: A case study Jay McClelland Stanford University

Simulation with Model that Teaches Itself

Rule 1Rule 2

Hysteris

is

Max

wellJu

mp

Unscorab

le0

10

20

30

40

50

60ChildrenSimulation

Model Pretest Scores

Model Posttest Scores

Distance Series: Children vs. Model

Page 24: Dynamics of learning: A case study Jay McClelland Stanford University

Conclusion

• We are far from a perfect understanding of transitions in learning

• But we have come a long way• Simple rule-based models miss a lot• Models with learning dynamics capture quite

a lot more:– They show gradual knowledge accumulation and

yet capture patterns of change similar to what we see in children