In this model a chunk is retrieved over and over. Each time a chunk is successfully retrieved in ACT-R it is referred to as a 'harvest'. In ACT-R theory, when a chunk is harvested it receives a boost in activation. In Python ACT-R this is not automatic (as it is in LISP ACT-R), so you need to add a .add function. The model illustrates this. With this in place the activation of the chunk gets stronger and stronger and the time to retrieve it decreases.