When faced with large amounts of data, human brains are able to swiftly react to stimuli and assert relevance of discovered information, even under uncertainty and partial knowledge. These efficient decision-making abilities rely on so-called cognitive heuristics, which are rapid, adaptive, light-weight yet very effective schemes used by the brain to solve complex problems. In a content-centric future Internet where users generate and disseminate large amounts of content through opportunistic networking techniques, individual nodes should exhibit those properties to support a scalable content dissemination system. We therefore study whether such cognitive heuristics can also be used in such a networking environment. To this end, in this paper we develop an analytical model that describes a content dissemination mechanism for opportunistic networks based on one such heuristics, known as the recognition heuristic. Our model takes into account the different popularities of content types, and highlights the impact of the shared memory contributed by individual nodes to make the dissemination process more efficient. Furthermore, our model allows us to investigate the performance of the dissemination process for very large number of nodes, which might be very difficult to carry out through a simulation-based study.