Developmental Psychology has been used in conjunction with the algorithmic and/or dynamical methods to design AI agents, as it deals with human cognitive development from birth. When applied, the conjunction with the algorithmic method can build interpretable AI agents (have humanly comprehensible processes and characteristics) through the use of concept-representing symbols. It, however, leads to symbol-grounding and/or other problems that offset its merit on interpretability, which may facilitate the creation of human intelligence-level AGI. On the other hand, the conjunction with the dynamical method emphasises in the design of AI agents on the real-time unfolding of processes internal to, and in the interactions with the environment of, these agents. However, its related literature is dominated by work on uninterpretable AI agents. Also, none of the current literatures on the combination of Developmental Psychology with the hybrid of the two methodologies deal with the resolution of all the mentioned deficiencies. Thus, the novel AI agent 3.1M is presented in this paper, and modelled as a set of dynamical difference equations containing Developmental Psychology-based and concept-representing mathematical operators acting on information, some of which are derived from its interactions with an artificial environment. Its interpretability is facilitated by the operators, as demonstrated in the analytical investigations on its computer simulation-confirmed cognitive abilities (e.g., the generalization and fusion of the information), and on the dynamics of the information attribute inspired by the nature of human consciousness. The dynamics is one of the major 3.1M features envisioned as necessary to create the future developmental AGI version of 3M, i.e., the abstract base class of 3.1M.