Magnetic Resonance Imaging (MRI) is a powerful medical imaging modality that offers diagnostic information without harmful ionizing radiation.

Neural datasets often contain measurements of neural activity across multiple trials of a repeated stimulus or behavior. An important problem in the analysis ofsuch datasets istocharacterizesystematic aspects ofneural activity that carry information about the repeated stimulus or behavior of interest, which can be considered "signal", and to separate them from the trial-to-trial fluctuations in activity that are not time-locked to the stimulus, which for purposes of such analyses can be considered "noise". Gaussian Process factor models provide a powerful tool for identifying shared structure in high-dimensional neural data.

Individuals often make different decisions when faced with the same context, due to personal preferences and background. For instance, judges may vary in their leniency towards certain drug-related offenses, and doctors may vary in their preference for how to start treatment for certain types of patients.

The performance of participatory systems will depend on individual reporting decisions. Thus, flat and sequential systems will perform better than a minimal system. The best-case performance of any participatory system will exceed the performance of any of its components. Given a participatory system, we can conduct this evaluation by simulating the parameters in the individual disclosure model shown above. The sequential system outperforms static personalized systems when all group attributes are reported.