This work studies publications in the field of cognitive science and utilizes natural language processing (NLP) and graph theoretical techniques to connect the analysis of the papers' content (abstracts) to the context (citation, journals). We apply hierarchical topic modeling on the abstracts and community detection algorithms on the citation network, and measure content-context discrepancy to find academic fields that study similar topics but do not cite each other or publish in the same venues. These results show a promising, systemic framework to identify opportunities for scientific collaboration in highly interdisciplinary fields such as cognitive science and machine learning.