A Spectrum of Diffusion Models for Hierarchical Control of Attention: From Sequential to Parallel Processing

Sequential Sampling Models (SSMs) are ubiquitously applied to empirical data of two or more alternative choice tasks, subsuming a large variety of task paradigms. Nevertheless the space of models typically considered is often limited to those that are analytically tractable for inference. More recently the field of simulation based inference has enabled the development and evaluation of a much broader class of models. Here we leverage developments in likelihood free inference using artificial neural networks in order to evaluate a range of models applied to a hierarchical decision making task. Participants were presented with stimuli, in the form of lines that varied across three dimensions: movement direction, line orientation and color. These three features imply three potential decisions (dominant motion direction etc.) on a given trial. One feature was considered the ‘high-dimension’, and determined which of the remaining two ‘low-dimensional’ features were relevant for a given choice scenario. The task is therefore hierarchical, in that the high dimensional features acts as a filter on which one of two remaining tasks a subject needs to solve. To investigate the corresponding cognitive strategies used by participants to solve these tasks, we developed a range of diffusion model variants to assess whether participants accumulate evidence strictly hierarchically and therefore sequentially, in parallel, or via a hybrid resource rational approach. We will assess model fits and posterior predictive simulations to arbitrate between these accounts and to link them to trial-by-trial neural dynamics (via EEG) associated with encoding of higher and lower dimensional features.