An interference model of visual working memory: serial-position effects

We will present work that starts to build a bridge between formal models of visual working memory for simultaneously presented arrays on the one hand, and models for explaining recall of sequentially presented lists on the other. We start from our Interference Model (Oberauer & Lin, 2017) and extend it to the recall of sequentially presented lists of visual objects. The model builds on the assumption that access access to items in working memory relies on cue-based retrieval, which engenders interference as a main source of performance limitation. To account for serial-position effects, we add two new assumptions: With each new item working memory is updated by automatically down-grading previous contents. To counteract this process, encoding of early list items receives an attentional boost, which declines for successive items, creating a primacy gradient. We present a Bayesian hierarchical version of the model, and fits to data of a continuous-reproduction visual-working memory experiment in which up to six items were presented sequentially, and tested in a random order. The model accounts for the distributions of errors, the set-size effect, and serial-position effects over input and output positions.