Optimisation of functional network resources when learning behavioural strategies for performing complex tasks

We developed two novel self-ordered switching (SOS) fMRI paradigms to investigate how human behaviour and underlying network resources are optimised when learning to perform complex tasks with multiple goals. SOS was performed with detailed feedback and minimal pretraining (study 1) or with minimal feedback and substantial pretraining (study 2). In study 1, multiple-demand (MD) system activation became less responsive to routine trial demands but more responsive to the executive switching events with practice. Default Mode Network (DMN) activation showed the opposite relationship. Concomitantly, reaction time learning curves correlated with increased connectivity between functional brain networks and subcortical regions. This ‘fine-tuning’ of network resources correlated with progressively more routine and lower complexity behavioural structure. Furthermore, overall task performance was superior for people who applied structured behavioural routines with low algorithmic complexity. These behavioural and network signatures of learning were less evident in study 2, where task structure was established prior to entering the scanner. Together, these studies demonstrate how detailed feedback monitoring enables network resources to be progressively redeployed in order to efficiently manage concurrent demands. Highlights We examine the optimisation of behaviour and brain-network resources during a novel “self-ordered switching” (SOS) paradigm. Task performance depended on generating behavioural routines with low algorithmic complexity (i.e., structured behaviours). Behaviour became more structured and reaction time decreased as SOS was practised. As behaviour became more structured, activation in multiple-demand regions decreased for simple trial events but increased for executive switching events Increases in between-network functional connectivity correlate with reaction time decreases.