Camouflaged object detection (COD) is a challenging task with broad applications across various domains. While many existing methods focus on extracting discriminative features and reconstructing details of camouflaged objects, they often struggle to achieve satisfactory performance on difficult cases due to inaccurate object localization. To address this issue, we propose a novel fixation-guided and edge-enhanced network (FENet), which integrates human visual modeling through multi-task-driven joint learning. Specifically, we first employ fixation prediction to model spatial attention guided by human visual priors and embed this visual attention into multi-scale feature fusion. Then, we perform edge prediction to drive edge-guided feature extraction. Finally, we design an edge reconstruction module to integrate both fixation and edge cues, enabling more precise boundary delineation and holistic object segmentation. Extensive quantitative and qualitative experiments on four public datasets demonstrate the superior performance and strong generalization of our approach. The results also confirm the benefits of incorporating fixation prediction into COD.