Anna Xu

To achieve a goal, people have to keep track of how much effort they are putting in (effort monitoring) and how well they are performing (performance monitoring), which can be informed by endogenous signals, or exogenous signals providing explicit feedback about whether they have met their goal. Interventions to improve performance often focus on adjusting feedback to direct the individual on how to better invest their efforts, but is it possible that this feedback itself plays a role in shaping the experience of how effortful the task feels? Here, we examine this question directly by assessing the relationship between effort monitoring and performance monitoring. Participants (N = 68) performed a task in which their goal was to squeeze a handgrip to within a target force level (not lower or higher) for a minimum duration. On most trials, they were given no feedback as to whether they met their goal, and were largely unable to detect how they had performed. On a subset of trials, however, we provided participants with (false) feedback indicating that they had either succeeded or failed at meeting their goal (positive vs. negative feedback blocks, respectively). Sporadically, participants rated their experience of effort exertion, fatigue, and confidence in having met the target grip force on that trial. Despite being non-veridical to their actual performance, we found that the type of feedback participants received influenced their experience of effort. When receiving negative (vs. positive) feedback, participants fatigued faster and adjusted their grip strength more for higher target force levels. We also found that confidence gradually increased with increasing positive feedback and decreased with increasing negative feedback, again despite feedback being uniformly uninformative. These results suggest differential influences of feedback on experiences related to effort and further shed light on the relationship between experiences related to performance monitoring and effort monitoring.

Visualisations facilitate the interpretation of geometrically structured data and results. However, heterogeneous geometries—such as volumes, surfaces, and networks—have traditionally mandated different software approaches. We introduce hyve , a Python library that uses a compositional functional framework to enable parametric implementation of custom visualisations for different brain geometries. Under this framework, users compose a reusable visualisation protocol from geometric primitives for representing data geometries, input primitives for common data formats and research objectives, and output primitives for producing interactive displays or configurable snapshots. hyve also writes documentation for user-constructed protocols, automates serial production of multiple visualisations, and includes an API for semantically organising an editable multi-panel figure. Through the seamless composition of input, output, and geometric primitives, hyve supports creating visualisations for a range of neuroimaging research objectives.