Proposal for a computational model of incentive memory

One of the objectives of Artificial General Intelligence is to propose programs to solve tasks similarly to the way human beings do. The associative memory plays an essential role in the daily behavior of human beings: If we have a pleasant experience, we learn to associate this pleasant experience with the object or circumstances that caused it. Thus, in the future, we react based on the associated memory when we face a similar object or circumstance. In this article, we propose and show the implementation of an associative memory model. This proposed model will contribute to a cognitive architecture that can produce human-like behaviors. The proposal’s fundamentals come from knowledge from different areas such as psychology, neuroscience, and computing. This knowledge allows us to propose the model’s processes, components, and relationships among components. The resulting model can store and retrieve associations focused on visual stimuli and their incentive value. For its validation, we use a case study covering the stages of memory generation and retrieval. The case study considers noisy inputs, incomplete information, and similar inputs. In this way, part of our contribution is having an associative memory model able to generalize the response to different stimuli and consequently improve the creature’s adaptation to its environment, complementing planning and decision-making processes.