functional encoding in memory for goals act-r workshop august 1999 erik m. altmann ([email protected])...
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Functional encoding in memory for goals
ACT-R workshop August 1999
Erik M. Altmann ([email protected])
J. Gregory Trafton ([email protected])
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
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)
The Tower of Hanoi
4
CAB
3
2
1
4
Goal 4:CSubgoal 3:B
3
A stack model
4:C3:B4:C
2:C3:B4:C
1:B
2:C3:B4:C
2:C3:B4:C
3:B4:C
1:C3:B4:C ...
Time
Sta
ck h
eigh
t
Push 3:BRecall 3:B perfectly, despite lag
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, ...
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
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
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
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
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
Analytical framework: Activation
• What happens to a goal’s 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
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
Encoding to resist decay
Retrieval threshold
Strength test
Bas
e-le
vel a
ctiv
atio
n
Time
2:C, 1:B, 2:C
The strength test
• Cognition can anticipate retrieval context– Retrieval cue — “3” for 3:B– Retention interval — 5 to 10 seconds
• Anticipations are just knowledge– Represent as cue chunks
• Test-retrieve the goal– If test fails, encode some more
Focussed retrieval
3:B
Test retrieval
cue: 3sink: S
Retrieval
disk: 3from: Ato: Bblocked: t
Encoding context Retrieval context
disk: 3from: Ato: Bblocked: t
Main focus
cue: 3 Retrieval focus
Goal
Retrieval production
(p retrieve =focus> isa retrieval =goal>
isa goaldisk =diskto =peg
==> =focus>
disk =diskto =peg
!pop!)
Noisy retrieval without partial matching
No indexing or chaining
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
Prediction
• Encoding a goal is expensive– Not a cost-free push operation– A second or so per goal
• Prediction from serial attention model
02468101213579111315Move in solution path
Data
Large peaks = Goal encoding
Tim
e (s
ec)
Observed (AK&L 93)Simulated (MAGS), R2 = .99
Prediction
• People avoid unnecessary retrievals – Retrieval is effortful and error-prone
• Use move heuristics when they apply:Don’t-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.
Data
Valleys = Don’t-undo
02468101213579111315Move in solution path
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:?
02468101213579111315Move in solution path
Data
Small peaks = goal retrieval
Five-disk data
0246810121416135791113151719212325272931Move in solution path
Simulated (MAGS), R2 = .95
Observed (AK&L 93)
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)
– = 4.0, s = 0.3 (Altmann & Gray)
• No unconstrained parameters
Prediction
• Retrieval is error prone– E.g., might retrieve 3:C instead of 3:B
• From a previous plan or previous trial
– Incorrect retrieval starts a garden path
0102030405060
DataL
engt
h of
sol
utio
n pa
th
Observed (AK&L 93)
Five disks
Optimal
Optimal
Predicted (MAGS)
Four disks
MAGS vs. stack model (A&L 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-R’s atomic level– Generic — adapted from serial attention
(Altmann & Gray, 1999b)
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
Conclusions
• Don’t need a goal stack– Anything it can do, MAGS can do better– And without that much more analysis
• Don’t want a goal stack– Too easy and too wrong– Masks real goal-management mechanisms
Conclusions
• 40 years of research on the Tower of Hanoi
• Yet retrieve-uncovered is unpublished– Missing from Simon’s perceptual strategies– Missing from Anzai and Simon protocol
Conclusions
• Why now?– Detailed data– A precise memory theory– Throwing away the goal stack
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.
The encoding process
disk: 4from: Ato: Cblocked: t
disk: 4from: Ato: Cblocked: t
Test-passes/fails
4:C
Test-retrieval
Focussed retrieval with a “sink”
cue: 4sink: S
disk: 4from: Ato: Cblocked: t
Test-strength
Test-failsStrengthen-goal