This　paper　focuses　on　heuristics　retrieval　in　human　problem　solving　by　combining　computational　cognitive　modeling　and　neuroimaging.　An　event-related　fMRI　(functional　Magnetic　Resonance　Imaging)　experiment　was　conducted　on　a　simplified　Sudoku　puzzle　problem　solving　and　an　ACT-R　(Adaptive　Control　of　Thought-Rational)　cognitive　model　was　developed　to　simulate　the　information　processing　processes　of　heuristics　retrieval　to　solve　the　problems.　We　assume　that　when　participants　retrieve　heuristics,　they　may　conform　to　a　principle　of　maximizing　the　information　effectiveness　and　minimizing　the　cognitive　cost.　Based　on　the　assumption,　the　model　was　built　to　predict　both　behavioral　performance　and　neurophysiologic　activities.　Compared　ACT-R　predictions　with　fMRI　results,　the　difference　on　response　time　is　0.287　and　the　average　correlation　of　BOLD　(Blood　Oxygenation　Level-Dependent)　response　between　them　is　about　0.95.　The　high　fitness　supports　our　assumption.　This　study　shows　that　heuristics　retrieving　in　human　problem　solving　is　an　optimizing　process　in　which　appropriate　information　is　selected　actively　through　visual　selective　attention　based　on　goal-oriented　to　minimize　the　cost　of　time　and　energy.　It　may　shed　light　on　developing　a　new　heuristic　search　model　based　on　cognition　for　Web　intelligence.　©　2010　IEEE.