Session-based recommendation aims to capture user intent from short-term, anonymous interaction sequences to recommend relevant items. From a cognitive science perspective, understanding user intent is closely tied to how humans process information, allocate attention, and make decisions under limited cognitive resources. While existing session-based methods mainly rely on ID-based modeling, such approaches face severe data sparsity and lack alignment with how users cognitively process information. Incorporating modality information can alleviate this issue. However, simple integration of ID and multimodal information often results in modality underfitting, limiting the effective use of multimodal features. To address these challenges, we propose SRIM(Session-based Recommendation with ID and Modality), a model that integrates ID and multimodal representations through a two-phase strategy: independent training followed by joint optimization. SRIM can better capture session-level intent by simulating users' actual perceptual contexts. Experiments on three real-world datasets demonstrate that SRIM significantly outperforms existing methods in session recommendation. The code for SRIM is available on GitHub {https://github.com/liang-tian-tian/SRIM}.