Recently light field saliency object detection (LFSOD) has attracted increasing attention due to the significant improvements in challenging scenes using rich light field cues. While many works have significantly progressed in this field, a deeper insight into its focal nature should be developed. In this work, we propose Focal Perception Transformer (FPT), which efficiently encodes the context within the focal stack and all-focal image. Specifically, we introduce focal-related tokens to summarize image-specific characteristics and propose a token communication module (TCM) to convey information and facilitate spatial contextual modeling. The features of each image are enriched and correlated with other images through the exchange of information between the precisely encoded focal-related tokens. We also propose a focal perception enhancement (FPE) strategy to help suppress noisy background information. Extensive experiments on four widely-used benchmark datasets demonstrate that the proposed model outperforms the state-of-the-art methods. The source code will be publicly available at https://github.com/combofish/FPTNet .