Study Practice Test Fruit- Pear

We have recently developed a neural network learning algorithm that accounts for how we strengthen weak memories and punish competing memories. The algorithm accomplishes these goals by raising and lowering inhibition, and learning based on the resulting changes in activation. Here, we use the model to address several puzzles from the domain of retrieval-induced forgetting (Anderson, 2003), including: How does the strength of the competing memories affect the amount of competitor punishment? How does the strength of the target (to-be-retrieved) memory affect competitor punishment? Why does retrieval practice lead to more competitor punishment than simply presenting the target item? We also use the model to address non-monotonic effects of retrieval practice, whereby repeated practice first strengthens, then weakens competing memories. We show that the model can account for the main features of the retrievalinduced forgetting data space. More importantly, the model establishes boundary conditions on these effects, which may be useful in explaining why some findings are not always obtained. Background: Competitors are Punished Our original motivation for this research was to model data on competitive dynamics and memory. Across several domains, researchers have found that competitors are punished during memory retrieval. More specifically: When a representation is activated by a retrieval cue, but that representation loses the competition to be retrieved, it suffers a lasting decrease in accessibility (on the order of hours and possibly longer). This principle is illustrated very nicely by Michael Anderson's work on retrievalinduced forgetting, illustrated below (see Levy & Anderson, 2002, for a review)