dynamic decision making laboratory carnegie mellon university 1 social and decision sciences...
TRANSCRIPT
Slide 1ACT-R models of training
www.cmu.edu/ddmlab
Agenda
Brief intro to ACT-R and the goals of MURI project
Communication with experiments
Modeling work: data fitting
Healy, Kole, Buck-Gengler and Bourne, 2004: Prolonged work will result in distinctive effects on RT and performance (speed-accuracy tradeoff).
Buck-Gengler & Healy, 2001
ACT-R models of training: Goals
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
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
The 2x2 levels of ACT-R
http://act.psy.cmu.edu
Example of declarative knowledge is “three plus four is seven”
Sheet1
SubSymbolic
Conflict Resolution (likelihood of use)
Sheet2
Sheet3
Space processes
Representation and Equations
and start manual mouse movement
Size Fuel Turb Dec
Chunk Activation
=
+
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
Matching Penalty: relevance to the specific match required
Noise: stochastic is useful to avoid getting stuck in local minima
similarity
value
mismatch
penalty
Data collection and modeling
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).
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
Speedup: Production compilation + faster access to key loc
Accuracy ↓: ↑ Noise in retrieval mechanism
Encode next number
Retrieve key location
Type next number
Extra/Missed digits:
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
Production compilation
Basic idea:
Productions are combined to form a macro production faster execution
Rule learning:
Practically: declarative procedural transition
To: Visual Motor
Model fit
Proportion correct
Total RT
Page &P
accuracy
RTs
4
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30.288
*Note: (6) First trial in block 6 is relatively high (total time)
* Note: (22) This subject had one stimulus with none correct (4895) - rows 281-285
2.717625
2.69925
2.687984375
2.65734375
2.63484375
2.643625
2.606671875
2.5431875
2.572703125
2.537359375
22.954125
* Note: (28) none were correct for 3875 - rows 206-210
* Note: (34) total times of first two trials (block 6) are relatively high compared to others
0.9204101563
0.89453125
0.892578125
0.8803710938
0.8779296875
0.8813476563
0.8715820313
0.865234375
0.857421875
0.857421875
1
2
3
4
5
0.86
0.87
0.88
0.89
0.90
0.91
2.58
2.60
2.62
2.64
2.66
2.68
2.70
Experiment 1
Chart1
2.717625
2.6969812
2.69925
2.6968212
2.687984375
2.696454
2.65734375
2.6515176
2.63484375
2.629279
2.643625
2.611095
2.606671875
2.5892239
2.5431875
2.5711465
2.572703125
2.5585287
2.537359375
2.5575435
Oserved
Predicted
Block
Page &P
accuracy
RTs
4
mf1a
l
m
x
x
0.90625
0.875
0.90625
0.875
0.890625
0.734375
0.890625
0.78125
0.78125
0.875
2.559
2.903
2.7235
2.6545
2.665
2.484
2.564
2.3085
2.2885
2.3785
0.4080702859
0.4628470358
0.4351273796
0.4239827297
0.4256972134
0.3951515915
0.4089180208
0.3633298789
0.3595509168
0.3763031558
5.506
300.043
305.549
5.189
300.039875
305.228875
8
mf2a
l
f
x
x
0.921875
0.90625
0.90625
0.734375
0.859375
0.765625
0.78125
0.765625
0.625
0.828125
3.203
3.36
3.326
3.99
3.582
3.451
3.2795
3.019
3.2465
3.333
0.5055569387
0.5263392774
0.5219222449
0.6009728957
0.5541255815
0.5379449593
0.5158076352
0.479863113
0.5114154072
0.5228353137
5.145
300.044
305.189
12
mf3a
l
f
x
x
0.953125
0.875
0.90625
0.921875
0.890625
0.96875
0.890625
0.90625
0.953125
0.8125
2.642
2.5515
2.5
2.601
2.493
2.381
2.34
2.3865
2.35
2.322
0.4219328133
0.4067955727
0.3979400087
0.4151403522
0.3967222785
0.3767593954
0.3692158574
0.3777614387
0.3710678623
0.3658622154
5.145
300.039
305.184
16
mf4a
l
m
x
x
0.921875
0.921875
0.90625
0.890625
0.875
0.9375
0.859375
0.875
0.9375
0.90625
2.945
2.839
2.6475
2.585
2.7155
2.626
2.537
2.532
2.4435
2.5005
0.4690852991
0.4531653925
0.4228359688
0.4124605474
0.4338498071
0.4192947218
0.4043204672
0.4034637013
0.3880123434
0.3980268589
5.22
300.033
305.253
20
mf5a
l
f
x
x
0.96875
0.875
0.921875
0.859375
0.9375
0.828125
0.875
0.890625
0.890625
0.953125
2.802
2.781
2.827
2.823
2.751
2.703
2.7145
2.6
2.728
2.598
0.4474681309
0.4442009889
0.4513258085
0.4507108781
0.4394905904
0.4318460457
0.433689846
0.414973348
0.435844366
0.4146391467
5.153
300.043
305.196
24
mf6a
l
m
x
x
0.9375
0.9375
0.90625
0.984375
0.90625
0.9375
0.96875
0.953125
0.921875
0.953125
2.986
2.9065
3.199
3.021
2.96
2.94
2.9595
2.716
3.005
3
0.4750898034
0.4633703273
0.5050142401
0.4801507253
0.4712917111
0.4683473304
0.4712183443
0.4339297656
0.4778444763
0.4771212547
5.068
300.04
305.108
28
mf7a
l
m
x
x *
0.875
0.875
0.921875
0.84375
0.828125
0.796875
0.921875
0.84375
0.875
0.828125
2.1115
2.1915
2.056
2.0255
1.958
1.941
1.967
1.8835
1.8665
1.844
0.3245910858
0.340741475
0.3130231103
0.3065322475
0.2918126875
0.2880255354
0.2938043599
0.2749656245
0.2710279943
0.2657609167
5.143
300.033
305.176
32
mf8a
l
f
x
x
0.84375
0.875
0.890625
0.8125
0.8125
0.921875
0.8125
0.5625
0.8125
0.78125
2.527
2.572
2.652
2.515
2.5795
2.81
2.555
2.6645
2.739
2.273
0.4026052419
0.4102709643
0.4235735197
0.4005379894
0.4115355322
0.4487063199
0.4073909045
0.4256157246
0.4375920323
0.3565994357
5.132
300.044
305.176
3
mf1a
m
m
x
x
0.90625
0.84375
0.9375
0.765625
0.921875
0.890625
0.734375
0.890625
0.890625
0.90625
3.429
3.227
3.171
2.994
2.902
2.812
2.976
2.776
2.814
2.734
0.5351674851
0.5087989654
0.501196242
0.476251796
0.4626974081
0.4490153163
0.4736329269
0.4434194618
0.4493240931
0.4367985102
5.178
60.047
65.225
5.155875
60.04225
65.198125
7
mf2a
m
f
x
x
0.9375
0.9375
0.84375
0.921875
0.984375
0.953125
0.9375
0.875
0.921875
0.9375
3.229
3.145
3.299
3.217
3.149
3.049
3.0185
3.074
3.077
3.0405
0.509068045
0.4976206498
0.5183823155
0.5074510609
0.4981726606
0.4841574244
0.4797911802
0.4877038632
0.4881274962
0.4829450077
5.162
60.046
65.208
11
mf3a
m
m
x
x
0.9375
0.875
0.890625
0.90625
0.90625
0.859375
0.90625
0.890625
0.875
0.875
3.07
3.0745
3.079
3.111
3.1155
3.223
3.301
3.152
2.893
3.0605
0.4871383755
0.4877744974
0.4884096889
0.4929000111
0.4935277556
0.5082603055
0.5186455243
0.4985862088
0.4613484336
0.4857923838
5.125
60.033
65.158
15
mf4a
m
m
x
x
0.890625
0.78125
0.75
0.796875
0.703125
0.828125
0.71875
0.84375
0.765625
0.828125
1.969
2.0865
2.0145
2.01
1.97
1.901
1.8225
2.0395
1.852
1.874
0.2942457161
0.319418389
0.3041672717
0.3031960574
0.2944662262
0.2789821169
0.260667537
0.3095237097
0.2676409823
0.2727695866
5.183
60.032
65.215
19
mf5a
m
f
x
x
0.875
0.90625
0.6875
0.859375
0.71875
0.8125
0.8125
0.828125
0.703125
0.734375
2.0865
1.7805
1.808
1.701
1.751
1.789
1.6885
1.676
1.603
1.693
0.319418389
0.250541978
0.2571984261
0.2307043136
0.2432861461
0.2526103406
0.227501065
0.2242740143
0.2049335224
0.2286569581
5.201
60.047
65.248
23
mf6a
m
f
x
x
0.9375
0.84375
0.921875
0.8125
0.984375
0.9375
0.90625
0.90625
0.890625
0.796875
3.044
3.012
3.081
2.991
2.825
3.017
2.926
2.9445
3.194
2.982
0.4834446481
0.4788549675
0.4886916983
0.475816413
0.4510184522
0.4795753102
0.4662743218
0.4690115587
0.5043349118
0.4745076391
5.143
60.053
65.196
27
mf7a
m
f
x
x
1
0.984375
0.984375
0.984375
0.984375
1
0.96875
0.984375
0.984375
0.984375
2.745
2.448
2.598
2.61
2.604
2.7135
2.769
2.551
2.581
2.743
0.4385423488
0.3888114135
0.4146391467
0.4166405073
0.4156409799
0.4335298259
0.4423229557
0.4067104586
0.4117880045
0.4382258076
5.167
60.033
65.2
31
mf8a
m
f
x
x
0.90625
0.859375
0.890625
0.859375
0.828125
0.875
0.875
0.890625
0.859375
0.84375
2.485
2.396
2.254
2.119
2.206
2.341
2.2115
2.107
2.123
2.11
0.3953263931
0.3794868137
0.3529539117
0.3261309567
0.3436055081
0.3694014137
0.3446869437
0.3236645356
0.3269499942
0.3242824553
5.088
60.047
65.135
6
mf2a
s
f
x *
x
0.96875
1
0.921875
0.96875
0.90625
0.921875
0.9375
0.859375
0.921875
0.84375
3.233
3.2655
3.122
3.107
3.1775
3.032
2.9775
2.9
2.916
3.15
0.5096057046
0.5139496881
0.4944328987
0.4923412533
0.5020855592
0.481729197
0.4738517702
0.4623979979
0.4647875196
0.4983105538
5.19
0.04
5.23
5.167
0.040625
5.207625
10
mf3a
s
m
x
x
0.890625
0.828125
0.765625
0.890625
0.8125
0.84375
0.828125
0.796875
0.78125
0.75
1.923
1.633
1.712
1.88
1.821
1.8595
1.749
1.684
1.694
1.595
0.2839792842
0.2129861847
0.2335037603
0.2741578493
0.2603099458
0.2693961827
0.2427898095
0.2263420872
0.228913406
0.2027606874
5.129
0.041
5.17
14
mf4a
s
f
x
x
0.953125
0.921875
0.953125
0.9375
0.9375
0.9375
0.875
0.84375
0.921875
0.90625
2.562
2.65
2.492
2.467
2.5005
2.4455
2.432
2.4305
2.443
2.3745
0.4085791254
0.4232458739
0.396548038
0.3921691495
0.3980268589
0.3883676672
0.3859635706
0.3856956254
0.387923467
0.3755721739
5.153
0.041
5.194
18
mf5a
s
f
x
x
0.9375
0.90625
0.890625
0.90625
0.828125
0.921875
0.875
0.859375
0.78125
0.8125
2.825
2.805
2.86
2.777
2.699
2.988
2.7405
2.658
2.7525
2.6355
0.4510184522
0.4479328656
0.4563660331
0.4435758798
0.4312028846
0.4753805931
0.4378298064
0.4245549766
0.4397273276
0.4208630206
5.148
0.041
5.189
22
mf6a
s
f
x
x *
0.875
0.875
0.921875
0.890625
0.9375
0.875
0.890625
0.875
0.84375
0.84375
3.017
3.4525
3.378
3.171
3.2105
3.4435
3.319
3.355
3.3855
3.156
0.4795753102
0.5381336873
0.5286596452
0.501196242
0.5065726743
0.5370000873
0.5210072524
0.5256925245
0.5296228181
0.4991369945
5.182
0.04
5.222
26
mf7a
s
f
x
x
0.984375
0.90625
0.96875
0.875
0.9375
0.890625
0.859375
0.890625
0.828125
0.875
2.904
2.7585
2.877
2.9165
2.8635
2.969
2.944
3.062
3.133
2.8605
0.462996612
0.4406729883
0.4589398619
0.4648619808
0.4568971874
0.4726101976
0.4689378057
0.4860051864
0.4959603949
0.4564419521
5.122
0.04
5.162
30
mf8a
s
m
x
x
0.96875
0.96875
0.96875
0.921875
0.953125
0.953125
0.9375
0.921875
0.921875
0.90625
2.3795
2.292
2.279
2.312
2.269
2.254
2.235
2.139
2.156
2.1125
0.3764857091
0.3602146133
0.3577443252
0.3639878297
0.3558344959
0.3529539117
0.3492775275
0.3302107846
0.3336487565
0.3247967176
5.228
0.041
5.269
33
mf1a
s
f
x
x
0.953125
0.96875
0.953125
0.96875
0.890625
0.9375
0.875
0.90625
0.9375
0.890625
2.61
2.6375
2.668
2.5715
2.449
2.51
2.498
2.483
2.601
2.571
0.4166405073
0.4211924683
0.4261858252
0.4101865287
0.3889887851
0.3996737215
0.397592434
0.3949767196
0.4151403522
0.4101020766
5.184
0.041
5.225
1
mf1a
v
f
x
x
0.890625
0.765625
0.796875
0.84375
0.8125
0.734375
0.796875
0.828125
0.78125
0.84375
3.553
3.494
3.409
3.188
3.2445
2.997
2.955
2.996
2.967
2.9425
0.5505952075
0.5433229006
0.5326270012
0.5035183127
0.5111477785
0.4766867429
0.4705574852
0.476541809
0.4723175463
0.4687164715
5.51
24.058
29.568
5.252625
22.954125
28.20675
9
mf3a
v
f
x
x
0.890625
0.8125
0.78125
0.75
0.75
0.734375
0.796875
0.828125
0.75
0.765625
2.507
2.52
2.475
2.4195
2.4215
2.3
2.271
2.245
2.2485
2.183
0.399154334
0.4014005408
0.3935752033
0.3837256264
0.3840844734
0.361727836
0.3562171342
0.3512163453
0.3518928919
0.3390537357
5.163
8.224
13.387
13
mf4a
v
f
x
x
0.921875
0.90625
0.90625
0.890625
0.9375
0.9375
0.890625
0.90625
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0.875
2.318
2.4295
2.4795
2.504
2.4545
2.6295
2.517
2.524
2.777
2.5635
0.3651134316
0.3855169034
0.3943641126
0.3986343245
0.3899630364
0.4198731751
0.4008832155
0.4020893506
0.4435758798
0.4088333218
5.187
11.981
17.168
17
mf5a
v
f
x
x
0.875
0.921875
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0.84375
0.828125
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0.765625
1.9475
1.867
1.796
1.94
1.878
1.8935
1.87
1.694
1.751
1.723
0.2894774663
0.2711443179
0.2543063323
0.2878017299
0.2736955879
0.2772653095
0.2718416065
0.228913406
0.2432861461
0.2362852774
5.136
19.242
24.378
25
mf7a
v
f
x
x
0.875
0.921875
0.953125
0.921875
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0.875
3.304
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3.459
3.5295
3.551
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3.298
3.3335
0.5190400386
0.533517862
0.5398285584
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0.538950562
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0.5503506723
0.5286596452
0.5182506513
0.5229004595
5.216
47.883
53.099
29
mf8a
v
f
x
x
0.875
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0.875
0.875
0.921875
0.90625
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0.78125
2.558
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2.412
2.44
0.4079005401
0.4079005401
0.4168068718
0.3969835083
0.3895204658
0.3856062736
0.3988077302
0.3781252456
0.3823773035
0.3873898263
5.135
4.04
9.175
34
mf2a
v
f
x *
x
0.953125
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0.96875
0.921875
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0.9375
0.921875
0.953125
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2.613
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2.35
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2.495
0.4171394097
0.4026052419
0.4006243217
0.3953263931
0.4010557258
0.3916407035
0.4000196351
0.3710678623
0.3751146847
0.39707055
5.485
43.106
48.591
35
mf6a
v
f
x
x
0.921875
0.875
0.921875
0.875
0.890625
0.875
0.890625
0.890625
0.890625
0.890625
2.877
2.7965
2.64
2.635
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2.6695
2.708
2.665
2.616
2.574
0.4589398619
0.4466148237
0.4216039269
0.4207806195
0.426673888
0.4264299252
0.43264866
0.4256972134
0.4176377397
0.4106085426
5.189
25.099
30.288
*Note: (6) First trial in block 6 is relatively high (total time)
* Note: (22) This subject had one stimulus with none correct (4895) - rows 281-285
2.717625
2.69925
2.687984375
2.65734375
2.63484375
2.643625
2.606671875
2.5431875
2.572703125
2.537359375
22.954125
* Note: (28) none were correct for 3875 - rows 206-210
* Note: (34) total times of first two trials (block 6) are relatively high compared to others
0.9204101563
0.89453125
0.892578125
0.8803710938
0.8779296875
0.8813476563
0.8715820313
0.865234375
0.857421875
0.857421875
1
2
3
4
5
0.86
0.87
0.88
0.89
0.90
0.91
2.58
2.60
2.62
2.64
2.66
2.68
2.70
Experiment 1
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
Conclusions from Model 1
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
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
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
136.psd
137.psd
Encode 4 digits
Type digits
Hit Enter
Cognitive or Motoric processes?
Encode next number
Retrieve key location
Type next number
Retrieval process in ACT-R
Deep processing
THREE
Semantic Priming
THREE
Chart1
2.7817734375
4.918484375
3.2947421875
4.46590625
2.9321197674
4.9623428723
3.3981150046
4.3597136298
2.704078125
4.578109375
3.24740625
4.2645
2.7122484641
4.5546763689
3.3141083182
4.2890422685
2.6824375
4.442703125
3.17303125
4.133265625
2.6656036596
4.3494155892
3.0963082978
4.2051502231
2.6810546875
4.2406640625
3.1083671875
4.0194140625
2.5981733312
4.3060400474
3.0545191353
4.0403175867
2.614171875
4.14396875
3.152984375
3.929671875
2.5786366017
4.2437815091
3.0535292831
3.8054488738
Presentation format continuity (at test)
Chart2
W-N
W-N
N-N
N-N
N-W
N-W
W-W
W-W
data
model
Conclusions from Model 2
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
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
Model 3: Suppression of vocal rehearsal
From Kole, Healy, Buck-Gengler 2005
less encoding (shallow processing)
But:
Deep processing better retention and durability of skills
173.psd
Kole et al.
Model predictions: three factors
Depth
How different depth of processing (training) may affect the retention of skills
Repetition
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
Prediction 1 - Delay
3.792
3.554
3.467
3.405
3.841
4
0.238
0.287
0.325
0.374
0.387
0.436
repetition
3.955
3.797
3.715
3.662
3.994
7
0.158
0.197
0.24
0.279
0.293
0.332
3.982
3.891
3.81
3.758
4.01
10
0.091
0.119
0.172
0.2
0.224
0.252
3.995
3.952
3.88
3.825
4.01
13
0.043
0.058
0.115
0.13
0.17
0.185
4.005
3.995
3.913
3.863
4.01
16
0.01
0.015
0.092
0.097
0.142
0.147
3 factors: duration -- number of days between end of training and beginning of testing. The original experiment was 7 days, the figure on the left show the prediction of the model when the duration goes from 1 to 16. depth of processing: increasing the encoding should lead to longer retention. repetition: when subjects are retrained after a particular period, retention may last longer. Last 2 are work-in-progress.
Depth
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3.792
3.554
3.352
3.28
3.841
4
0.238
0.287
0.44
0.489
0.512
0.561
repetition
3.955
3.797
3.6
3.539
3.994
7
0.158
0.197
0.355
0.394
0.416
0.455
3.982
3.891
3.704
3.651
4.01
10
0.091
0.119
0.278
0.306
0.331
0.359
3.995
3.952
3.777
3.745
4.01
13
0.043
0.058
0.218
0.233
0.25
0.265
4.005
3.995
3.828
3.785
4.01
16
0.01
0.015
0.177
0.182
0.22
0.225
word2: immediate rehearsal of word twice during training word3: rehearse 3 times during training depth 2, depth 3, RP2, RP3 are corresponding depth and RP effects
repetition
depth 1
RP 1
depth 2
RP 2
depth 3
RP 3
word2: retrained once in between word3: retrained twice in between depth 2, depth 3, RP2, RP3 are corresponding depth and RP effects
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
Prediction 2 – depth of processing
Chart2
1
1
1
1
1
1
4
4
4
4
4
4
7
7
7
7
7
7
10
10
10
10
10
10
13
13
13
13
13
13
16
16
16
16
16
16
3.792
3.554
3.467
3.405
3.841
4
0.238
0.287
0.325
0.374
0.387
0.436
repetition
3.955
3.797
3.715
3.662
3.994
7
0.158
0.197
0.24
0.279
0.293
0.332
3.982
3.891
3.81
3.758
4.01
10
0.091
0.119
0.172
0.2
0.224
0.252
3.995
3.952
3.88
3.825
4.01
13
0.043
0.058
0.115
0.13
0.17
0.185
4.005
3.995
3.913
3.863
4.01
16
0.01
0.015
0.092
0.097
0.142
0.147
3 factors: duration -- number of days between end of training and beginning of testing. The original experiment was 7 days, the figure on the left show the prediction of the model when the duration goes from 1 to 16. depth of processing: increasing the encoding should lead to longer retention. repetition: when subjects are retrained after a particular period, retention may last longer. Last 2 are work-in-progress.
Depth
3.792
3.554
3.352
3.28
3.841
4
0.238
0.287
0.44
0.489
0.512
0.561
repetition
3.955
3.797
3.6
3.539
3.994
7
0.158
0.197
0.355
0.394
0.416
0.455
3.982
3.891
3.704
3.651
4.01
10
0.091
0.119
0.278
0.306
0.331
0.359
3.995
3.952
3.777
3.745
4.01
13
0.043
0.058
0.218
0.233
0.25
0.265
4.005
3.995
3.828
3.785
4.01
16
0.01
0.015
0.177
0.182
0.22
0.225
word2: immediate rehearsal of word twice during training word3: rehearse 3 times during training depth 2, depth 3, RP2, RP3 are corresponding depth and RP effects
repetition
depth 1
RP 1
depth 2
RP 2
depth 3
RP 3
word2: retrained once in between word3: retrained twice in between depth 2, depth 3, RP2, RP3 are corresponding depth and RP effects
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
Prediction 3 - Repetition
3.792
3.554
3.467
3.405
3.841
4
0.238
0.287
0.325
0.374
0.387
0.436
repetition
3.955
3.797
3.715
3.662
3.994
7
0.158
0.197
0.24
0.279
0.293
0.332
3.982
3.891
3.81
3.758
4.01
10
0.091
0.119
0.172
0.2
0.224
0.252
3.995
3.952
3.88
3.825
4.01
13
0.043
0.058
0.115
0.13
0.17
0.185
4.005
3.995
3.913
3.863
4.01
16
0.01
0.015
0.092
0.097
0.142
0.147
3 factors: duration -- number of days between end of training and beginning of testing. The original experiment was 7 days, the figure on the left show the prediction of the model when the duration goes from 1 to 16. depth of processing: increasing the encoding should lead to longer retention. repetition: when subjects are retrained after a particular period, retention may last longer. Last 2 are work-in-progress.
Depth
3.792
3.554
3.352
3.28
3.841
4
0.238
0.287
0.44
0.489
0.512
0.561
repetition
3.955
3.797
3.6
3.539
3.994
7
0.158
0.197
0.355
0.394
0.416
0.455
3.982
3.891
3.704
3.651
4.01
10
0.091
0.119
0.278
0.306
0.331
0.359
3.995
3.952
3.777
3.745
4.01
13
0.043
0.058
0.218
0.233
0.25
0.265
4.005
3.995
3.828
3.785
4.01
16
0.01
0.015
0.177
0.182
0.22
0.225
word2: immediate rehearsal of word twice during training word3: rehearse 3 times during training depth 2, depth 3, RP2, RP3 are corresponding depth and RP effects
repetition
depth 1
RP 1
depth 2
RP 2
depth 3
RP 3
word2: retrained once in between word3: retrained twice in between depth 2, depth 3, RP2, RP3 are corresponding depth and RP effects
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
Conclusions of predictions
More extensive training leads to better retention of skills
Re-training may be more efficient that extensive initial training for retention of skills
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
Next steps
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
www.cmu.edu/ddmlab
Agenda
Brief intro to ACT-R and the goals of MURI project
Communication with experiments
Modeling work: data fitting
Healy, Kole, Buck-Gengler and Bourne, 2004: Prolonged work will result in distinctive effects on RT and performance (speed-accuracy tradeoff).
Buck-Gengler & Healy, 2001
ACT-R models of training: Goals
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
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
The 2x2 levels of ACT-R
http://act.psy.cmu.edu
Example of declarative knowledge is “three plus four is seven”
Sheet1
SubSymbolic
Conflict Resolution (likelihood of use)
Sheet2
Sheet3
Space processes
Representation and Equations
and start manual mouse movement
Size Fuel Turb Dec
Chunk Activation
=
+
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
Matching Penalty: relevance to the specific match required
Noise: stochastic is useful to avoid getting stuck in local minima
similarity
value
mismatch
penalty
Data collection and modeling
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).
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
Speedup: Production compilation + faster access to key loc
Accuracy ↓: ↑ Noise in retrieval mechanism
Encode next number
Retrieve key location
Type next number
Extra/Missed digits:
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
Production compilation
Basic idea:
Productions are combined to form a macro production faster execution
Rule learning:
Practically: declarative procedural transition
To: Visual Motor
Model fit
Proportion correct
Total RT
Page &P
accuracy
RTs
4
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0.859375
0.921875
0.84375
3.233
3.2655
3.122
3.107
3.1775
3.032
2.9775
2.9
2.916
3.15
0.5096057046
0.5139496881
0.4944328987
0.4923412533
0.5020855592
0.481729197
0.4738517702
0.4623979979
0.4647875196
0.4983105538
5.19
0.04
5.23
5.167
0.040625
5.207625
10
mf3a
s
m
x
x
0.890625
0.828125
0.765625
0.890625
0.8125
0.84375
0.828125
0.796875
0.78125
0.75
1.923
1.633
1.712
1.88
1.821
1.8595
1.749
1.684
1.694
1.595
0.2839792842
0.2129861847
0.2335037603
0.2741578493
0.2603099458
0.2693961827
0.2427898095
0.2263420872
0.228913406
0.2027606874
5.129
0.041
5.17
14
mf4a
s
f
x
x
0.953125
0.921875
0.953125
0.9375
0.9375
0.9375
0.875
0.84375
0.921875
0.90625
2.562
2.65
2.492
2.467
2.5005
2.4455
2.432
2.4305
2.443
2.3745
0.4085791254
0.4232458739
0.396548038
0.3921691495
0.3980268589
0.3883676672
0.3859635706
0.3856956254
0.387923467
0.3755721739
5.153
0.041
5.194
18
mf5a
s
f
x
x
0.9375
0.90625
0.890625
0.90625
0.828125
0.921875
0.875
0.859375
0.78125
0.8125
2.825
2.805
2.86
2.777
2.699
2.988
2.7405
2.658
2.7525
2.6355
0.4510184522
0.4479328656
0.4563660331
0.4435758798
0.4312028846
0.4753805931
0.4378298064
0.4245549766
0.4397273276
0.4208630206
5.148
0.041
5.189
22
mf6a
s
f
x
x *
0.875
0.875
0.921875
0.890625
0.9375
0.875
0.890625
0.875
0.84375
0.84375
3.017
3.4525
3.378
3.171
3.2105
3.4435
3.319
3.355
3.3855
3.156
0.4795753102
0.5381336873
0.5286596452
0.501196242
0.5065726743
0.5370000873
0.5210072524
0.5256925245
0.5296228181
0.4991369945
5.182
0.04
5.222
26
mf7a
s
f
x
x
0.984375
0.90625
0.96875
0.875
0.9375
0.890625
0.859375
0.890625
0.828125
0.875
2.904
2.7585
2.877
2.9165
2.8635
2.969
2.944
3.062
3.133
2.8605
0.462996612
0.4406729883
0.4589398619
0.4648619808
0.4568971874
0.4726101976
0.4689378057
0.4860051864
0.4959603949
0.4564419521
5.122
0.04
5.162
30
mf8a
s
m
x
x
0.96875
0.96875
0.96875
0.921875
0.953125
0.953125
0.9375
0.921875
0.921875
0.90625
2.3795
2.292
2.279
2.312
2.269
2.254
2.235
2.139
2.156
2.1125
0.3764857091
0.3602146133
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0.3639878297
0.3558344959
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0.3492775275
0.3302107846
0.3336487565
0.3247967176
5.228
0.041
5.269
33
mf1a
s
f
x
x
0.953125
0.96875
0.953125
0.96875
0.890625
0.9375
0.875
0.90625
0.9375
0.890625
2.61
2.6375
2.668
2.5715
2.449
2.51
2.498
2.483
2.601
2.571
0.4166405073
0.4211924683
0.4261858252
0.4101865287
0.3889887851
0.3996737215
0.397592434
0.3949767196
0.4151403522
0.4101020766
5.184
0.041
5.225
1
mf1a
v
f
x
x
0.890625
0.765625
0.796875
0.84375
0.8125
0.734375
0.796875
0.828125
0.78125
0.84375
3.553
3.494
3.409
3.188
3.2445
2.997
2.955
2.996
2.967
2.9425
0.5505952075
0.5433229006
0.5326270012
0.5035183127
0.5111477785
0.4766867429
0.4705574852
0.476541809
0.4723175463
0.4687164715
5.51
24.058
29.568
5.252625
22.954125
28.20675
9
mf3a
v
f
x
x
0.890625
0.8125
0.78125
0.75
0.75
0.734375
0.796875
0.828125
0.75
0.765625
2.507
2.52
2.475
2.4195
2.4215
2.3
2.271
2.245
2.2485
2.183
0.399154334
0.4014005408
0.3935752033
0.3837256264
0.3840844734
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0.3562171342
0.3512163453
0.3518928919
0.3390537357
5.163
8.224
13.387
13
mf4a
v
f
x
x
0.921875
0.90625
0.90625
0.890625
0.9375
0.9375
0.890625
0.90625
0.859375
0.875
2.318
2.4295
2.4795
2.504
2.4545
2.6295
2.517
2.524
2.777
2.5635
0.3651134316
0.3855169034
0.3943641126
0.3986343245
0.3899630364
0.4198731751
0.4008832155
0.4020893506
0.4435758798
0.4088333218
5.187
11.981
17.168
17
mf5a
v
f
x
x
0.875
0.921875
0.828125
0.859375
0.734375
0.84375
0.828125
0.875
0.828125
0.765625
1.9475
1.867
1.796
1.94
1.878
1.8935
1.87
1.694
1.751
1.723
0.2894774663
0.2711443179
0.2543063323
0.2878017299
0.2736955879
0.2772653095
0.2718416065
0.228913406
0.2432861461
0.2362852774
5.136
19.242
24.378
25
mf7a
v
f
x
x
0.875
0.921875
0.953125
0.921875
0.9375
0.9375
0.90625
0.890625
0.890625
0.875
3.304
3.416
3.466
3.199
3.459
3.5295
3.551
3.378
3.298
3.3335
0.5190400386
0.533517862
0.5398285584
0.5050142401
0.538950562
0.5477131862
0.5503506723
0.5286596452
0.5182506513
0.5229004595
5.216
47.883
53.099
29
mf8a
v
f
x
x
0.875
0.890625
0.890625
0.875
0.875
0.921875
0.90625
0.90625
0.859375
0.78125
2.558
2.558
2.611
2.4945
2.452
2.43
2.505
2.3885
2.412
2.44
0.4079005401
0.4079005401
0.4168068718
0.3969835083
0.3895204658
0.3856062736
0.3988077302
0.3781252456
0.3823773035
0.3873898263
5.135
4.04
9.175
34
mf2a
v
f
x *
x
0.953125
0.984375
0.96875
0.96875
0.921875
0.890625
0.9375
0.921875
0.953125
0.96875
2.613
2.527
2.5155
2.485
2.518
2.464
2.512
2.35
2.372
2.495
0.4171394097
0.4026052419
0.4006243217
0.3953263931
0.4010557258
0.3916407035
0.4000196351
0.3710678623
0.3751146847
0.39707055
5.485
43.106
48.591
35
mf6a
v
f
x
x
0.921875
0.875
0.921875
0.875
0.890625
0.875
0.890625
0.890625
0.890625
0.890625
2.877
2.7965
2.64
2.635
2.671
2.6695
2.708
2.665
2.616
2.574
0.4589398619
0.4466148237
0.4216039269
0.4207806195
0.426673888
0.4264299252
0.43264866
0.4256972134
0.4176377397
0.4106085426
5.189
25.099
30.288
*Note: (6) First trial in block 6 is relatively high (total time)
* Note: (22) This subject had one stimulus with none correct (4895) - rows 281-285
2.717625
2.69925
2.687984375
2.65734375
2.63484375
2.643625
2.606671875
2.5431875
2.572703125
2.537359375
22.954125
* Note: (28) none were correct for 3875 - rows 206-210
* Note: (34) total times of first two trials (block 6) are relatively high compared to others
0.9204101563
0.89453125
0.892578125
0.8803710938
0.8779296875
0.8813476563
0.8715820313
0.865234375
0.857421875
0.857421875
1
2
3
4
5
0.86
0.87
0.88
0.89
0.90
0.91
2.58
2.60
2.62
2.64
2.66
2.68
2.70
Experiment 1
Chart1
2.717625
2.6969812
2.69925
2.6968212
2.687984375
2.696454
2.65734375
2.6515176
2.63484375
2.629279
2.643625
2.611095
2.606671875
2.5892239
2.5431875
2.5711465
2.572703125
2.5585287
2.537359375
2.5575435
Oserved
Predicted
Block
Page &P
accuracy
RTs
4
mf1a
l
m
x
x
0.90625
0.875
0.90625
0.875
0.890625
0.734375
0.890625
0.78125
0.78125
0.875
2.559
2.903
2.7235
2.6545
2.665
2.484
2.564
2.3085
2.2885
2.3785
0.4080702859
0.4628470358
0.4351273796
0.4239827297
0.4256972134
0.3951515915
0.4089180208
0.3633298789
0.3595509168
0.3763031558
5.506
300.043
305.549
5.189
300.039875
305.228875
8
mf2a
l
f
x
x
0.921875
0.90625
0.90625
0.734375
0.859375
0.765625
0.78125
0.765625
0.625
0.828125
3.203
3.36
3.326
3.99
3.582
3.451
3.2795
3.019
3.2465
3.333
0.5055569387
0.5263392774
0.5219222449
0.6009728957
0.5541255815
0.5379449593
0.5158076352
0.479863113
0.5114154072
0.5228353137
5.145
300.044
305.189
12
mf3a
l
f
x
x
0.953125
0.875
0.90625
0.921875
0.890625
0.96875
0.890625
0.90625
0.953125
0.8125
2.642
2.5515
2.5
2.601
2.493
2.381
2.34
2.3865
2.35
2.322
0.4219328133
0.4067955727
0.3979400087
0.4151403522
0.3967222785
0.3767593954
0.3692158574
0.3777614387
0.3710678623
0.3658622154
5.145
300.039
305.184
16
mf4a
l
m
x
x
0.921875
0.921875
0.90625
0.890625
0.875
0.9375
0.859375
0.875
0.9375
0.90625
2.945
2.839
2.6475
2.585
2.7155
2.626
2.537
2.532
2.4435
2.5005
0.4690852991
0.4531653925
0.4228359688
0.4124605474
0.4338498071
0.4192947218
0.4043204672
0.4034637013
0.3880123434
0.3980268589
5.22
300.033
305.253
20
mf5a
l
f
x
x
0.96875
0.875
0.921875
0.859375
0.9375
0.828125
0.875
0.890625
0.890625
0.953125
2.802
2.781
2.827
2.823
2.751
2.703
2.7145
2.6
2.728
2.598
0.4474681309
0.4442009889
0.4513258085
0.4507108781
0.4394905904
0.4318460457
0.433689846
0.414973348
0.435844366
0.4146391467
5.153
300.043
305.196
24
mf6a
l
m
x
x
0.9375
0.9375
0.90625
0.984375
0.90625
0.9375
0.96875
0.953125
0.921875
0.953125
2.986
2.9065
3.199
3.021
2.96
2.94
2.9595
2.716
3.005
3
0.4750898034
0.4633703273
0.5050142401
0.4801507253
0.4712917111
0.4683473304
0.4712183443
0.4339297656
0.4778444763
0.4771212547
5.068
300.04
305.108
28
mf7a
l
m
x
x *
0.875
0.875
0.921875
0.84375
0.828125
0.796875
0.921875
0.84375
0.875
0.828125
2.1115
2.1915
2.056
2.0255
1.958
1.941
1.967
1.8835
1.8665
1.844
0.3245910858
0.340741475
0.3130231103
0.3065322475
0.2918126875
0.2880255354
0.2938043599
0.2749656245
0.2710279943
0.2657609167
5.143
300.033
305.176
32
mf8a
l
f
x
x
0.84375
0.875
0.890625
0.8125
0.8125
0.921875
0.8125
0.5625
0.8125
0.78125
2.527
2.572
2.652
2.515
2.5795
2.81
2.555
2.6645
2.739
2.273
0.4026052419
0.4102709643
0.4235735197
0.4005379894
0.4115355322
0.4487063199
0.4073909045
0.4256157246
0.4375920323
0.3565994357
5.132
300.044
305.176
3
mf1a
m
m
x
x
0.90625
0.84375
0.9375
0.765625
0.921875
0.890625
0.734375
0.890625
0.890625
0.90625
3.429
3.227
3.171
2.994
2.902
2.812
2.976
2.776
2.814
2.734
0.5351674851
0.5087989654
0.501196242
0.476251796
0.4626974081
0.4490153163
0.4736329269
0.4434194618
0.4493240931
0.4367985102
5.178
60.047
65.225
5.155875
60.04225
65.198125
7
mf2a
m
f
x
x
0.9375
0.9375
0.84375
0.921875
0.984375
0.953125
0.9375
0.875
0.921875
0.9375
3.229
3.145
3.299
3.217
3.149
3.049
3.0185
3.074
3.077
3.0405
0.509068045
0.4976206498
0.5183823155
0.5074510609
0.4981726606
0.4841574244
0.4797911802
0.4877038632
0.4881274962
0.4829450077
5.162
60.046
65.208
11
mf3a
m
m
x
x
0.9375
0.875
0.890625
0.90625
0.90625
0.859375
0.90625
0.890625
0.875
0.875
3.07
3.0745
3.079
3.111
3.1155
3.223
3.301
3.152
2.893
3.0605
0.4871383755
0.4877744974
0.4884096889
0.4929000111
0.4935277556
0.5082603055
0.5186455243
0.4985862088
0.4613484336
0.4857923838
5.125
60.033
65.158
15
mf4a
m
m
x
x
0.890625
0.78125
0.75
0.796875
0.703125
0.828125
0.71875
0.84375
0.765625
0.828125
1.969
2.0865
2.0145
2.01
1.97
1.901
1.8225
2.0395
1.852
1.874
0.2942457161
0.319418389
0.3041672717
0.3031960574
0.2944662262
0.2789821169
0.260667537
0.3095237097
0.2676409823
0.2727695866
5.183
60.032
65.215
19
mf5a
m
f
x
x
0.875
0.90625
0.6875
0.859375
0.71875
0.8125
0.8125
0.828125
0.703125
0.734375
2.0865
1.7805
1.808
1.701
1.751
1.789
1.6885
1.676
1.603
1.693
0.319418389
0.250541978
0.2571984261
0.2307043136
0.2432861461
0.2526103406
0.227501065
0.2242740143
0.2049335224
0.2286569581
5.201
60.047
65.248
23
mf6a
m
f
x
x
0.9375
0.84375
0.921875
0.8125
0.984375
0.9375
0.90625
0.90625
0.890625
0.796875
3.044
3.012
3.081
2.991
2.825
3.017
2.926
2.9445
3.194
2.982
0.4834446481
0.4788549675
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0.4662743218
0.4690115587
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0.4745076391
5.143
60.053
65.196
27
mf7a
m
f
x
x
1
0.984375
0.984375
0.984375
0.984375
1
0.96875
0.984375
0.984375
0.984375
2.745
2.448
2.598
2.61
2.604
2.7135
2.769
2.551
2.581
2.743
0.4385423488
0.3888114135
0.4146391467
0.4166405073
0.4156409799
0.4335298259
0.4423229557
0.4067104586
0.4117880045
0.4382258076
5.167
60.033
65.2
31
mf8a
m
f
x
x
0.90625
0.859375
0.890625
0.859375
0.828125
0.875
0.875
0.890625
0.859375
0.84375
2.485
2.396
2.254
2.119
2.206
2.341
2.2115
2.107
2.123
2.11
0.3953263931
0.3794868137
0.3529539117
0.3261309567
0.3436055081
0.3694014137
0.3446869437
0.3236645356
0.3269499942
0.3242824553
5.088
60.047
65.135
6
mf2a
s
f
x *
x
0.96875
1
0.921875
0.96875
0.90625
0.921875
0.9375
0.859375
0.921875
0.84375
3.233
3.2655
3.122
3.107
3.1775
3.032
2.9775
2.9
2.916
3.15
0.5096057046
0.5139496881
0.4944328987
0.4923412533
0.5020855592
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x *
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mf7a
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0.984375
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mf8a
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0.96875
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0.953125
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1
mf1a
v
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x
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0.890625
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3.553
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28.20675
9
mf3a
v
f
x
x
0.890625
0.8125
0.78125
0.75
0.75
0.734375
0.796875
0.828125
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2.507
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2.3
2.271
2.245
2.2485
2.183
0.399154334
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0.3837256264
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13
mf4a
v
f
x
x
0.921875
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mf5a
v
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0.875
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25
mf7a
v
f
x
x
0.875
0.921875
0.953125
0.921875
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0.9375
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53.099
29
mf8a
v
f
x
x
0.875
0.890625
0.890625
0.875
0.875
0.921875
0.90625
0.90625
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0.78125
2.558
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2.611
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0.4079005401
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0.3781252456
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5.135
4.04
9.175
34
mf2a
v
f
x *
x
0.953125
0.984375
0.96875
0.96875
0.921875
0.890625
0.9375
0.921875
0.953125
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2.613
2.527
2.5155
2.485
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2.464
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2.35
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2.495
0.4171394097
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0.3953263931
0.4010557258
0.3916407035
0.4000196351
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0.3751146847
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5.485
43.106
48.591
35
mf6a
v
f
x
x
0.921875
0.875
0.921875
0.875
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0.875
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0.890625
0.890625
2.877
2.7965
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2.635
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2.6695
2.708
2.665
2.616
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30.288
*Note: (6) First trial in block 6 is relatively high (total time)
* Note: (22) This subject had one stimulus with none correct (4895) - rows 281-285
2.717625
2.69925
2.687984375
2.65734375
2.63484375
2.643625
2.606671875
2.5431875
2.572703125
2.537359375
22.954125
* Note: (28) none were correct for 3875 - rows 206-210
* Note: (34) total times of first two trials (block 6) are relatively high compared to others
0.9204101563
0.89453125
0.892578125
0.8803710938
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1
2
3
4
5
0.86
0.87
0.88
0.89
0.90
0.91
2.58
2.60
2.62
2.64
2.66
2.68
2.70
Experiment 1
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
Conclusions from Model 1
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
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
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
136.psd
137.psd
Encode 4 digits
Type digits
Hit Enter
Cognitive or Motoric processes?
Encode next number
Retrieve key location
Type next number
Retrieval process in ACT-R
Deep processing
THREE
Semantic Priming
THREE
Chart1
2.7817734375
4.918484375
3.2947421875
4.46590625
2.9321197674
4.9623428723
3.3981150046
4.3597136298
2.704078125
4.578109375
3.24740625
4.2645
2.7122484641
4.5546763689
3.3141083182
4.2890422685
2.6824375
4.442703125
3.17303125
4.133265625
2.6656036596
4.3494155892
3.0963082978
4.2051502231
2.6810546875
4.2406640625
3.1083671875
4.0194140625
2.5981733312
4.3060400474
3.0545191353
4.0403175867
2.614171875
4.14396875
3.152984375
3.929671875
2.5786366017
4.2437815091
3.0535292831
3.8054488738
Presentation format continuity (at test)
Chart2
W-N
W-N
N-N
N-N
N-W
N-W
W-W
W-W
data
model
Conclusions from Model 2
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
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
Model 3: Suppression of vocal rehearsal
From Kole, Healy, Buck-Gengler 2005
less encoding (shallow processing)
But:
Deep processing better retention and durability of skills
173.psd
Kole et al.
Model predictions: three factors
Depth
How different depth of processing (training) may affect the retention of skills
Repetition
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
Prediction 1 - Delay
3.792
3.554
3.467
3.405
3.841
4
0.238
0.287
0.325
0.374
0.387
0.436
repetition
3.955
3.797
3.715
3.662
3.994
7
0.158
0.197
0.24
0.279
0.293
0.332
3.982
3.891
3.81
3.758
4.01
10
0.091
0.119
0.172
0.2
0.224
0.252
3.995
3.952
3.88
3.825
4.01
13
0.043
0.058
0.115
0.13
0.17
0.185
4.005
3.995
3.913
3.863
4.01
16
0.01
0.015
0.092
0.097
0.142
0.147
3 factors: duration -- number of days between end of training and beginning of testing. The original experiment was 7 days, the figure on the left show the prediction of the model when the duration goes from 1 to 16. depth of processing: increasing the encoding should lead to longer retention. repetition: when subjects are retrained after a particular period, retention may last longer. Last 2 are work-in-progress.
Depth
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3.792
3.554
3.352
3.28
3.841
4
0.238
0.287
0.44
0.489
0.512
0.561
repetition
3.955
3.797
3.6
3.539
3.994
7
0.158
0.197
0.355
0.394
0.416
0.455
3.982
3.891
3.704
3.651
4.01
10
0.091
0.119
0.278
0.306
0.331
0.359
3.995
3.952
3.777
3.745
4.01
13
0.043
0.058
0.218
0.233
0.25
0.265
4.005
3.995
3.828
3.785
4.01
16
0.01
0.015
0.177
0.182
0.22
0.225
word2: immediate rehearsal of word twice during training word3: rehearse 3 times during training depth 2, depth 3, RP2, RP3 are corresponding depth and RP effects
repetition
depth 1
RP 1
depth 2
RP 2
depth 3
RP 3
word2: retrained once in between word3: retrained twice in between depth 2, depth 3, RP2, RP3 are corresponding depth and RP effects
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
Prediction 2 – depth of processing
Chart2
1
1
1
1
1
1
4
4
4
4
4
4
7
7
7
7
7
7
10
10
10
10
10
10
13
13
13
13
13
13
16
16
16
16
16
16
3.792
3.554
3.467
3.405
3.841
4
0.238
0.287
0.325
0.374
0.387
0.436
repetition
3.955
3.797
3.715
3.662
3.994
7
0.158
0.197
0.24
0.279
0.293
0.332
3.982
3.891
3.81
3.758
4.01
10
0.091
0.119
0.172
0.2
0.224
0.252
3.995
3.952
3.88
3.825
4.01
13
0.043
0.058
0.115
0.13
0.17
0.185
4.005
3.995
3.913
3.863
4.01
16
0.01
0.015
0.092
0.097
0.142
0.147
3 factors: duration -- number of days between end of training and beginning of testing. The original experiment was 7 days, the figure on the left show the prediction of the model when the duration goes from 1 to 16. depth of processing: increasing the encoding should lead to longer retention. repetition: when subjects are retrained after a particular period, retention may last longer. Last 2 are work-in-progress.
Depth
3.792
3.554
3.352
3.28
3.841
4
0.238
0.287
0.44
0.489
0.512
0.561
repetition
3.955
3.797
3.6
3.539
3.994
7
0.158
0.197
0.355
0.394
0.416
0.455
3.982
3.891
3.704
3.651
4.01
10
0.091
0.119
0.278
0.306
0.331
0.359
3.995
3.952
3.777
3.745
4.01
13
0.043
0.058
0.218
0.233
0.25
0.265
4.005
3.995
3.828
3.785
4.01
16
0.01
0.015
0.177
0.182
0.22
0.225
word2: immediate rehearsal of word twice during training word3: rehearse 3 times during training depth 2, depth 3, RP2, RP3 are corresponding depth and RP effects
repetition
depth 1
RP 1
depth 2
RP 2
depth 3
RP 3
word2: retrained once in between word3: retrained twice in between depth 2, depth 3, RP2, RP3 are corresponding depth and RP effects
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
Prediction 3 - Repetition
3.792
3.554
3.467
3.405
3.841
4
0.238
0.287
0.325
0.374
0.387
0.436
repetition
3.955
3.797
3.715
3.662
3.994
7
0.158
0.197
0.24
0.279
0.293
0.332
3.982
3.891
3.81
3.758
4.01
10
0.091
0.119
0.172
0.2
0.224
0.252
3.995
3.952
3.88
3.825
4.01
13
0.043
0.058
0.115
0.13
0.17
0.185
4.005
3.995
3.913
3.863
4.01
16
0.01
0.015
0.092
0.097
0.142
0.147
3 factors: duration -- number of days between end of training and beginning of testing. The original experiment was 7 days, the figure on the left show the prediction of the model when the duration goes from 1 to 16. depth of processing: increasing the encoding should lead to longer retention. repetition: when subjects are retrained after a particular period, retention may last longer. Last 2 are work-in-progress.
Depth
3.792
3.554
3.352
3.28
3.841
4
0.238
0.287
0.44
0.489
0.512
0.561
repetition
3.955
3.797
3.6
3.539
3.994
7
0.158
0.197
0.355
0.394
0.416
0.455
3.982
3.891
3.704
3.651
4.01
10
0.091
0.119
0.278
0.306
0.331
0.359
3.995
3.952
3.777
3.745
4.01
13
0.043
0.058
0.218
0.233
0.25
0.265
4.005
3.995
3.828
3.785
4.01
16
0.01
0.015
0.177
0.182
0.22
0.225
word2: immediate rehearsal of word twice during training word3: rehearse 3 times during training depth 2, depth 3, RP2, RP3 are corresponding depth and RP effects
repetition
depth 1
RP 1
depth 2
RP 2
depth 3
RP 3
word2: retrained once in between word3: retrained twice in between depth 2, depth 3, RP2, RP3 are corresponding depth and RP effects
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
Conclusions of predictions
More extensive training leads to better retention of skills
Re-training may be more efficient that extensive initial training for retention of skills
Dynamic Decision Making Laboratory Carnegie Mellon University * Social and Decision Sciences Department
Next steps
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