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.
2 - Simple activation
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|2k||v. 1||Mar 6, 2012, 10:39 AM||ccm lab|