Comparative and animal cognition literature describes many models of associative learning and a multitude of experimental protocols for exploring learning phenomena. These methodologies can serve as inspiration for reinforcement learning (RL) algorithms and tasks (Shanahan et al., 2020). However, there is a considerable gap between animal cognition and RL research, both conceptually and in the assumptions made about the learning process. Associative learning models assume the presence of a “stimulus” guiding a behavioural response, which in the field of RL usually translates loosely into a state-action pair. Our research attempts to investigate and bridge this gap by implementing the A-Learning model (Ghirlanda et al., 2020) into an embodied AI system, using the purpose-built Animal-AI environment (Beyret et al., 2019). Here we present early findings of our research.