Functional encoding in memory for goals

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Functional encoding in memory for goals

• ACT-R workshop August 1999
• Erik M. Altmann (altmann_at_gmu.edu)
• J. Gregory Trafton (trafton_at_itd.nrl.navy.mil)

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Means-ends tasks

• Means-ends behavior
• Suspend a goal
• Work on subgoals
• Resume the goal at an appropriate time
• Examples
• Monkey and bananas
• Giving a talk
• Making photocopies

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The Tower of Hanoi

• The foundational means-ends task
• In cognitive science
• Understood in terms of the goal stack
• Completely understood
• Or is it?
• Good data (Anderson, Kushmerick, Lebiere, 1993)

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The Tower of Hanoi
1
2
3
4
C
A
B
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A stack model
4C
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The stack as representation

• The typical assumption in task analysis
• Implicit in problem behavior graph
• Explicit in GPS, GOMS, ...
• The standard theory of goal management
• In cognitive architectures
• ACT-R, Soar
• In cognitive models generally
• E.g., ACT-PRO, 3CAPS Better Raven, ...

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The stack as representation

• The appeal
• Robust and general
• Applies to a wide variety of tasks
• Supported by empirical data
• At some level of abstraction
• The problem
• At best, a high-level simplification
• At worst, wrong

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Goal-selection order

• LIFO order not used when not needed
• Selection order in arithmetic (VanLehn)
• Order depends on context
• Display-based problem-solving, situated action,
  distributed representation
• Capture error

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Pending goals

• Displaced by memory load (Just Carpenter)
• Decay when not rehearsed (Byrne Bovair)
• Intrude when rehearsed (Altmann Trafton,
  1999b)
• Affected by goal content
• Intention superiority (Goschke Kuhl)
• Suggesting that activation affects availability

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Research approach

• Model Tower of Hanoi data without a stack
• For goals
• Ask how to make up the lost functionality
• Domain knowledge
• External cues
• Existing memory theory
• If it suffices, the theory is strengthened
• If it fails, then at least we know why

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Memory as goal store (MAGS)

• Memory encoding retention retrieval
• Assume passive retention
• Assume strategic encoding
• Using knowledge of retrieval context
• Assume strategic retrieval
• Using knowledge to select retrieval cues

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Analytical framework Activation

• What happens to a goals activation over time?
• Two kinds of activation (in ACT-R)
• Base-level activation from use
• Priming from context
• Total activation predicts current need
• So memory returns the most active element

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Encoding to resist decay

• Strengthen base-level activation
• Strength test to say how much is enough
• Cognition asking itself, Got it?
• If yes, stop strengthening and move on
• If no, strengthen some more
• Test interleaved with strengthening
• Strengthen enough but not too much

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Encoding to resist decay
2C, 1B, 2C
Base-level activation
Time
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The strength test

• Cognition can anticipate retrieval context
• Retrieval cue 3 for 3B
• Retention interval 5 to 10 seconds
• Anticipations are just knowledge
• Represent as cue chunks
• Test-retrieve the goal
• If test fails, encode some more

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Focussed retrieval
Goal
3B
cue 3 sink S
Retrieval context
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Retrieval production
(p retrieve focusgt isa retrieval
goalgt isa goal disk disk to peg gt
focusgt disk disk to peg !pop!)
No indexing or chaining
Noisy retrieval without partial matching
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Empirical test

• Anderson, Kushmerick, Lebiere (1993)
• Subjects instructed in goal-recursion strategy
• Response-time data are from perfect trials
• Cognition on those trials most stack-like
• Strongest test of the MAGS model

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Prediction

• Encoding a goal is expensive
• Not a cost-free push operation
• A second or so per goal
• Prediction from serial attention model

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Data

• Large peaks Goal encoding

Time (sec)
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Prediction

• People avoid unnecessary retrievals
• Retrieval is effortful and error-prone
• Use move heuristics when they apply

Dont-undo IF the just-moved disk was 1, and X
is the smaller of the two other top disks,
and Y is the larger of the two other top
disks, THEN move X on top of Y.
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Data

• Valleys Dont-undo

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Prediction

• Prefer goal retrieval to re-planning
• Depends on selecting the right retrieval cue
• No perfect pop operation
• Cue selection heuristic

Retrieve-uncovered IF the uncovered disk is
X, THEN try to retrieve X?
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Data

• Small peaks goal retrieval

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Five-disk data
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Parameters

• ACT-R defaults
• W 1.0, F 1.0, d 0.5
• Adopted from other models
• Perceptual encoding time 185 msec (Anderson,
  Matessa, Lebiere, 1997)
• t 4.0, s 0.3 (Altmann Gray)
• No unconstrained parameters

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Prediction

• Retrieval is error prone
• E.g., might retrieve 3C instead of 3B
• From a previous plan or previous trial
• Incorrect retrieval starts a garden path

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Data
Length of solution path
Optimal
Optimal
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MAGS vs. stack model (AL 98)

• Based on declarative memory
• Not on a privileged stack
• Broader empirical coverage
• Detailed account of RT and error
• Only ToH model to address both (before today)
• Functional encoding and retrieval processes
• Specified at ACT-Rs atomic level
• Generic adapted from serial attention(Altmann
  Gray, 1999b)

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Implications

• Need a two-high architectural stack
• A main focus for problem state
• A retrieval focus for concentrating
• Main and retrieval focuses are mutually exclusive
  (Altmann Trafton, 1999b)
• One is reliable
• One is predictive

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Conclusions

• Dont need a goal stack
• Anything it can do, MAGS can do better
• And without that much more analysis
• Dont want a goal stack
• Too easy and too wrong
• Masks real goal-management mechanisms

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Conclusions

• 40 years of research on the Tower of Hanoi
• Yet retrieve-uncovered is unpublished
• Missing from Simons perceptual strategies
• Missing from Anzai and Simon protocol

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Conclusions

• Why now?
• Detailed data
• A precise memory theory
• Throwing away the goal stack

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References

• Model code hfac.gmu.edu/people/altmann/toh
• Altmann Trafton (1999a). Memory for goals An
  architectural perspective. Proc. Cog. Sci. 21.
• Altmann Trafton (1999b). Memory for goals in
  means-ends behavior. Manuscript submitted for
  publication.

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The encoding process
disk 4 from A to C blocked t