Novel verbal instructions recruit abstract neural patterns of time-variable information dimensionality.

Human performance is endowed by neural representations of information that is relevant for behavior, some of which are also activated in a preparatory fashion to optimize later execution. Most studies to date have focused on highly practiced actions, leaving largely unaddressed the novel re-configuration of information to generate unique whole task-sets. Using electroencephalography (EEG), this study investigated the dynamics of the content and geometry reflected on the neural patterns of control representations during re-configuration of information. We designed a verbal instruction paradigm where each trial involved novel combinations of multi-component task information. By manipulating three task-relevant factors in a sample of 40 participants (26 females, 14 males), we observed complex coding schemes throughout the trial, during both preparation and implementation stages. The temporal profiles were consistent with a hierarchical structure: whereas task information was active in a sustained manner, the coding of more concrete stimulus features was more transient. Data showed both high dimensionality and abstraction, particularly during instruction encoding and target processing. Our results suggest that whenever task content could be recovered from neural patterns of activity, there was evidence of abstract coding, with an underlying geometry that favored generalization. During target processing, where potential interference across stimulus and response factors increased, orthogonal configurations also appeared. Overall, our findings uncover the dynamic manner with which control representations operate during novel recombination unique scenarios, with changes in dimensionality and abstraction adjusting along processing stages. The neural mechanisms that support task performance in novel contexts have been largely overlooked. Cognitive control is thought to enable complex behavior through the active maintenance of task sets, containing essential information for execution. However, how novel whole combinations of information are organized in neural patterns and their temporal dependencies remain unknown. Here, using a novel complex instruction paradigm, we observed that coding of informational content and its underlying geometry followed a dynamic temporal pattern. Our results reveal varying dimensionality and abstraction throughout the trial, with neural codes generally structured in a geometry favoring generalization of relevant information across task demands. These findings provide a first glimpse into the temporal computations engaged by the brain when encountering novel recombination settings.