All three subprojects are progressed as planned. Throughout this project, we published 18 journal articles. We have organized a symposium and a training course at international scientific conferences to disseminate our findings and approaches to bridge computational models and brain imaging.
In subproject 1, the research team has developed a set of behavioural paradigms for intentional decision, in which human participants choose between choices with no explicit reward, with probabilistic reward, with hedonic reward or with continuous motor movements. The inclusion of ecological validity and motor complexity in decision-making paradigms enabled a comprehensive research program. We performed rigorous pilots to establish the validity and consistency of the experimental paradigms, and we successfully collected functional MRI and magnetoencephalography (MEG) data while participants were performing the tasks. On computational modelling, the research team has successfully established a platform for whole-brain large-scale simulations for intentional decisions, using biologically realistic neural mass models for individual brain regions.
Subproject 2 considers internal and external factors that may change intentional decisions. In a model-based EEG study, we confirmed that higher reward certainty leads to faster intentional choices between two equally valued options, and humans did not behave randomly in equal choices but established a consistent preference bias towards one option. Using a cognitive model, we demonstrated that the certainty and preference effects related to the speed of evidence accumulation during decision processes. Using multivariate pattern classification, we localized representations of reward certainty and preference choices in electrophysiological recordings. Furthermore, we found specific electrophysiological signatures that tracked the change in accumulation speed predicted by the cognitive model. These results highlighted the neurocognitive signatures of various behavioural effects jointly shaping intentional decisions. In a series of experiments, we have observed that learning shapes the speed and patterns of intentional decisions over consecutive sessions.
Subproject 3 examines individual differences in intentional decisions. We have successfully piloted our behavioural paradigm on an online platform and data collection is now ongoing. On brain imaging, we combined a cognitive model and a biophysical compartment model of diffusion-weighted MRI (DWI) to characterize the neuroanatomical origins of inter-individual variability in the reaction time of simple actions. We have found that motor latency during intentional actions is associated with the DWI measure of neurite density in the bilateral corticospinal tract, indicating a link between inter-individual differences in sensorimotor speed and selective microstructural properties in white matter tracts.
