A Cognitive Model of Cued Trials Task-Switching Performance

Task-switching paradigms have been used to investigate cognitive control processes, emulating everyday tasks which require regulating cognitive resources. Cognitive modelling approaches, in the context of task-switching have sought to identify constituent processes contributing to task-switching performance. However, these approaches have typically used diffusion decision models (DDM) that are not designed to capture task-switching behaviour. Here, we extend a recent model of task-switching (Steyvers et al., 2019) to capture the complex dynamics of task activation in a cued-trials task-switching paradigm in a sample of community-dwelling older adults (n=177, 60-70 years). The model is based on an evidence-accumulation architecture extended to incorporate cognitive processes associated with task-set maintenance and updating that occur in the cue-target interval, namely task-set activation and deactivation. Results show that, with the additional processes associated with task activation, the model captures qualitative and quantitative trends in behavioural data that are consistent with task-switching literature (e.g., switch cost, mixing cost, and congruency costs) and provides a strong, quantitative basis for some of the theoretically proposed processes underlying task-switching performance (e.g., task activation). On examination of the parameter values of the weakest and strongest performers on five different task-switching metrics, the model provided unique insights into individual differences on the cognitive processes driving performance in cued trials task-switching behaviour. By disentangling key cognitive processes thought to underlie task switching performance (e.g., task set reconfiguration and task set inertia), our model provides greater precision with which to describe the underlying processes that have been observed beyond the standard mean RT measures.