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Table 3.
Operations in five modules for simple 4*4 Sudoku (for task (a) of Fig. 2)
Scan Time Visual
Goal
Retrieval Imaginal Production
1 .0
Encode ?
?
Encode
0.5
Encode row Visualizing
Focus row
Encode 4
Encoding
4
Encode
1.0
Encode 2, 1
2, 1
Encode
Integrating
4, 2, 1
Solve
Retrieving 4, 2, 1, (3)
3
Retrieve fact
1.5
Pressing
(3)
Press key
Table 4.
Operations in five modules for complex 4*4 Sudoku (for task (b) of Fig. 2)
Scan Time Visual
Goal
Retrieval Imaginal Production
1 .0
0.5
Encode ?
Visualizing
?
Encode
Focus Row
Encode row
Evaluate
1.0
Encode 3
Encoding
3
Focus Col
Encode col
Encode
Encode4
3,4,(1,2)
4
1.5
Encode 1
1
Evaluate
2
2.0
Integrating
3,4,1
Solve
2.5
Retrieving 3, 4, 1, (2)
2
Retrieve fact
3.0
Pressing
(2)
Press key
complexity of problem. In modeling 4*4 Sudoku, complexity of heuristics, such as
dimension number and valid information distribution decides the procedural sys-
tem and the status of goal. With goal status controlled and oriented, procedural
module organizes the schedule, including selected attention, problem representa-
tion, information communication and integration and state transformation, and
so on.
3.4
Fitting an ACT-R to the BOLD Response
In this study, the regions of interest include five predefined regions related to
five ACT-R modules [6, 10]. Solid lines in parts (a) to (e) of Figure 3 display
BOLD response predictions for these regions. The horizontal coordinates in the
figures are scan numbers. And the base line scans to be contrasted is two former
scans before the stimulus onset. The mean correlation of ACT-R prediction and
fMRI data is 0.87 and the mean difference is 0.057 which shows the prediction
is acceptable. Part (a) of Figure 3 shows the results for the left fusiform region
corresponding to the visual module. The predictions from the visual module
show a good match in two type tasks. The correlation with the left fusiform is
0.93. The prediction of BOLD response of visual region in complex tasks shows
