Transcript Slide 1

ACT-R models of training
Cleotilde Gonzalez and Wai-Tat Fu
Dynamic Decision Making Laboratory (DDMLab)
www.cmu.edu/ddmlab
Carnegie Mellon University
Dynamic Decision Making Laboratory
Social and Decision Sciences Department
Carnegie Mellon University
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Agenda
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Brief intro to ACT-R and the goals of MURI project
Communication with experiments
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Data collection and modeling
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Design of experiments using CMU Radar simulation
Modeling work: data fitting
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Healy, Kole, Buck-Gengler and Bourne, 2004: Prolonged work will result in distinctive effects
on RT and performance (speed-accuracy tradeoff).
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Buck-Gengler & Healy, 2001
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Kole, Healy, Buck-Gengler 2005
Modeling work: predictions
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Duration
–
Depth of processing
–
Repetition
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ACT-R models of training: Goals
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Determine the cognitive functions and mechanisms corresponding to
training principles
Create computational models that will be used as predictive tools for
the effect of training manipulations
Develop an easy-to-use graphical user interface to help:
– manipulate a set of training and task parameters
– determine speed and accuracy as a result of the parameter setting and
the training principles
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The 2x2 levels of ACT-R
http://act.psy.cmu.edu
(Anderson & Lebiere, 1998)
Declarative Memory
Procedural Memory
Chunks: declarative
Productions: If
facts
(cond) Then (action)
Symbolic
Activation of chunks
Conflict Resolution
(likelihood of
(likelihood of use)
retrieval)
SubSymbolic
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Space processes
Performance
Declarative
Symboli c
Subsymboli c
Retrieval of
Chunks
Application of
Product ion Rules
Noisy Activations
Control Speed and
Accuracy
Noisy Utili ties
Control Choice
Learning
Declarative
Symboli c
Subsymboli c
Procedural
Encoding
Environment and
Caching Goals
Bayesian
Learning
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Procedural
Product ion
Compil ation
Bayesian
Learning
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Representation and Equations
W  S
B  ln  t
Activation Ai  Bi 
Learning
ji
 A
j
d
j
i
j
Latency
j
Intentions
Ti  F  e
Goal
Retrieval
Productions
 Ai
Ui  Pi G  Ci   U
Succ i
Learning Pi 
Succ i  Faili
Memory
Visual
Manual
Utility
IF the goal is to categorize new stimulus
and visual holds stimulus info S, F, T
THEN start retrieval of chunk S, F, T
and start manual mouse movement
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Motor
Vision
World
Size Fuel Turb Dec
S 20 1
Stimulus
Bi
Chunk
SSL
L 20 3
S13
Y
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Chunk Activation
activation
(
)(
associative
source
activation* strength
base
= activation
+
+
mismatch
penalty
*
similarity
value
)
+ noise
A i  Bi   Wj  Sji   MPk  Simkl  N(0,s)
j
k
Activation makes chunks available to the degree that past experiences
indicate that they will be useful at the particular moment:
Base-level: general past usefulness
Associative Activation: relevance to the general context
Matching Penalty: relevance to the specific match required
Noise: stochastic is useful to avoid getting stuck in local minima
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Data collection and modeling
Decisions
Decisions
Act-R cognitive model
User
Task
Current Status
Human Data
Current status
Model Data
Comparison
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Model 1: cognitive model of data entry
From Healy, Kole, Buck-Gengler and Bourne, 2004:
– Prolonged work will result in distinctive effects on RT and performance
(speed-accuracy tradeoff).
Healy, Kole, Buck-Gengler, & Bourne (2004)
Experiment 1
0.91
2.70
2.68
0.90
2.66
0.89
2.64
0.88
2.62
Total Response Time (in s)
Proportion Correct
Total Response Time
Proportion Correct
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0.87
2.60
0.86
2.58
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2
3
4
5
Block
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Model 1: How does it work?
Encode next number
Wrong digits: Noise in retrieval
Initiation
time
Retrieve key location
All numbers encoded
Execution
time
Type next number
Extra/Missed digits:
Noise in stopping criteria
All numbers typed
Conclusion
time
Hit Enter
Speedup: Production compilation + faster access to key loc
Accuracy ↓: ↑ Noise in retrieval mechanism
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Production compilation
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Basic idea:
– Productions are combined to form a macro production  faster
execution
– Rule learning:
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• Retrievals may be eliminated in the process
• Practically: declarative  procedural transition
Data Entry task:
– From:
– To:
Visual  Retrieval (key loc)  Motor
Visual  Motor
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Model fit
Total RT
Proportion correct
0.94
2.75
0.92
Proportion Correct
Predicted
0.9
0.88
0.86
Total Response Time (msec)
2.7
Observed
Oserved
2.65
Predicted
2.6
2.55
0.84
0.82
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2
3
4
5
6
7
8
Block
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10
2.5
1
2
3
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6
7
8
9
10
Block
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Conclusions from Model 1
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Production compilation results in faster execution
Fatigue may correspond to the increase of activation
noise (in retrieval and stopping), producing more errors
and a decrease in the proportion of correct responses
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Model 2: Long-term repetition priming
•
From Buck-Gengler and Healy,
2001:
– Depth of processing:
• Old numbers typed faster than
new numbers
• Trained using word format 
faster at testing than those
trained using numbers
– Word format  more elaborate
processing  better retention
of skills
– Effect seems to depend on
“type of processing”, not just
amount of encoding time
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Buck-Gengler and Healy, 2001, methods
Encode 4 digits
Type digits
Hit Enter
1 week delay
Training stage
Word
Numerals
Number row
Keypad (motor process)
Response:
Numbers
Letters (cognitive)
W or w/o
Articulatory
Suppression
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Testing stage
Word
Numerals
X
Old Numbers
New Numbers
Retention of skills depends on:
1. Cognitive or Motoric processes?
2. Depth of processing?
3. Mental fatigue?
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Model 2: how does it work?
Encode next number
Initiation
time
Retrieve key location
Retrieval process in ACT-R
All numbers encoded
Execution
time
Type next number
All numbers typed
Conclusion
time
Hit Enter
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Deep processing
“THREE”
Episodic
representation
of stimuli
THREE
(Semantic Concept)
“3”
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“T”
(Key location)
Semantic processing leads
to deeper processing of stimuli
L3
(Key location)
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Semantic Priming
or
“THREE”
“3”
retrieve
L3
Sji
THREE
(Semantic Concept)
Ai  Bi  W j S ji  
j
• Semantic concept boosting retrieval of key location
• Faster retrieval when trained in word format
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RT by block, response and
presentation formats (at training)
numeral-digit (data)
numeral-letter (data)
5.00
word-digit (data)
word-letter (data)
numeral-digit (model)
4.50
numeral-letter (model)
Mean RT (s)
word-digit (model)
4.00
word-letter (model)
3.50
3.00
2.50
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2
3
4
5
Block
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Presentation format continuity (at
test)
data
3.65
model
Mean RT (s)
3.6
3.55
3.5
3.45
3.4
W-N
N-N
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N-W
W-W
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Conclusions from Model 2
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The long-term repetition priming (RP) can be explained by
semantic priming mechanism in ACT-R
The results from the word and letter conditions suggest
that it is not the amount of processing, but the “type” of
processing that leads to the RP
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Model 3: Suppression of vocal
rehearsal
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From Kole, Healy, BuckGengler 2005
– less encoding (shallow
processing)
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Old numbers still faster than
new numbers
– But:
• Word format at training  not
significantly faster
• Deep processing  better
retention and durability of
skills
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Kole et al.
data AS Digit
Training Latency
data AS Word
data SIL Digit
3.6
data SIL Word
3.4
model AS Digit
3.2
model AS Word
model SIL Digit
3
model SIL Word
2.8
2.6
2.4
2.2
2
1
2
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Model predictions: three factors
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Delay
– How performance deteriorates with time after training
Depth
– How different depth of processing (training) may affect the retention of
skills
Repetition
– How re-training may help retention of skills
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Prediction 1 - Delay
0.45
0.4
0.35
depth effect
0.3
RP effect
0.25
0.2
0.15
0.1
0.05
0
0
2
4
6
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Prediction 2 – depth of processing
0.7
0.6
depth 1
RP 1
depth 2
RP 2
depth 3
RP 3
0.5
0.4
0.3
0.2
0.1
0
0
2
4
6
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Prediction 3 - Repetition
0.8
0.7
depth 1
RP 1
depth 2
RP 2
depth 3
RP 3
0.6
0.5
0.4
0.3
0.2
0.1
0
0
2
4
6
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Conclusions of predictions
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Training effects decay approx exponentially with time
More extensive training leads to better retention of skills
Re-training may be more efficient that extensive initial
training for retention of skills
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Social and Decision Sciences Department
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Next steps
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Integrate a set of model parameters for the data entry task and
ACT-R parameters into a prediction tool
Continue participating in the development of new experiments
using the Radar task
Integrate ACT-R predictions to IMPRINT
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