Visual　context　plays　an　important　role　in　humans＇　top-down　gaze　movement　control　for　target　searching.　Exploring　the　mental　development　mechanism　in　terms　of　incremental　visual　context　encoding　by　population　cells　is　an　interesting　issue.　This　paper　presents　a　biologically　inspired　computational　model.　The　visual　contextual　cues　were　used　in　this　model　for　top-down　eye-motion　control　on　searching　targets　in　images.　We　proposed　a　population　cell　coding　mechanism　for　visual　context　encoding　and　decoding.　The　model　was　implemented　in　a　neural　network　system.　A　developmental　learning　mechanism　was　simulated　in　this　system　by　dynamically　generating　new　coding　neurons　to　incrementally　encode　visual　context　during　training.　The　encoded　context　was　decoded　with　population　neurons　in　a　top-down　mode.　This　allowed　the　model　to　control　the　gaze　motion　to　the　centers　of　the　targets.　The　model　was　developed　with　pursuing　low　encoding　quantity　and　high　target　locating　accuracy.　Its　performance　has　been　evaluated　by　a　set　of　experiments　to　search　different　facial　objects　in　a　human　face　image　set.　Theoretical　analysis　and　experimental　results　show　that　the　proposed　visual　context　encoding　algorithm　without　weight　updating　is　fast,　efficient　and　stable,　and　the　population-cell　coding　generally　performs　better　than　single-cell　coding　and　k-nearest-neighbor　(k-NN)-based　coding.