Fading memory, waning attention: Modeling output interference with a dynamic diffusion model
A ubiquitous finding in memory research is that over the course of a recall or recognition test, memory performance declines. This phenomenon is referred to as output interference, reflecting the notion that it results from the interference of information recalled or encountered during test with subsequent retrieval. Indeed, there is a large body of experimental evidence indicating that the decline in memory performance is not simply due to a longer study-test gap or increasing fatigue. However, a limitation of previous studies is that the influence of interference versus attentional processes is typically inferred from the experimental context rather than measured directly. Moreover, performance is usually assessed across blocks of trials rather than single trials. Thus, the relative contribution and the exact trajectories of memory processes and attention in output interference remain unclear. We propose to address this open question with a dynamic diffusion model: The diffusion model is a popular cognitive model for the analysis of reaction times in binary decision tasks. In the context of recognition memory, it allows researchers to disentangle retrieval processes – such as the speed of information uptake as measured by the drift rate – from attention-related processes – such as the response criterion as measured by the boundary-separation parameter. By implementing the diffusion model in a recently proposed deep-learning based superstatistics framework, we can assess the dynamics of these parameters over the course of the memory test and, thus, directly measure the relative contribution of the associated processes to output interference. Applying this dynamic approach to empirical data, we show that both drift rate and boundary separation decline over the course of the test. Thus, the finding emphasizes the role of both interference and attention in the emergence of output interference in recognition memory. Moreover, it highlights the usefulness of the neural superstatistics framework for dynamic cognitive models.