Imaginary ELFs and other things you've never seen before: A comparative analysis of computational memory models on the fan and extra-list feature effects

How do humans judge that a stimulus is novel? Novelty judgement is a fundamental property of human memory and an important problem for artificial intelligence. While computational memory models can predict speed and accuracy of recall and recognition, many models fail to predict response time and accuracy on rejected foil items in experimental tasks. We present a formal analysis of computational models of human memory, including MINERVA (Hintzman, 1986), IRM (Mewhort & Johns, 2005), ACT-R DM (Anderson, 2009), and HDM (Kelly, Arora, West, & Reitter, 2020). We test the models on two tasks: the fan effect (Anderson, 1974) and the extra-list feature (ELF; Johns & Mewhort, 2003) effect. The models are able to perform the fan effect on target items when using a multiple recall strategy, but not when using a recognition judgement or single recall. To account for the ELF effect, we propose a new model that uses complex-valued vectors. We compare and contrast our model to existing models and discuss the implications of our theoretical findings for memory modelling and deep learning.