Running memory span: A comparison of behavioral capacity limits with those of an attractor neural network

We studied a computational model of short term memory capacity that performs a simulated running memory span task using Hebbian learning and rapid decay of connection strengths to keep recent items active for later recall. This model demonstrates recall performance similar to humans performing the same task, with a capacity limit of approximately three items and a prominent recency effect. The model also shows that this capacity depends on decay to release the model from accumulating interference. Model findings are compared with data from two behavioral experiments that used varying task demands to tax memory capacity limits. Following additional theoretical predictions from the computational model, behavioral data support that when task demands require attention to be spread too thin to keep items available for later recall, capacity limits suffer. These findings are important both for understanding the mechanisms underlying short term memory capacity, and also to memory researchers interested in the role of attention in capacity limitations.