Figure 3.

The complexity and generality of the decision-making models. All models are capable of capturing basic behavioral statistics such as the RT and the response accuracy. The simple accumulator models and the sequential sampling models are suitable to describe the congregate activity of large neural populations (e.g., fMRI or EEG/MEG signals). The most complex model (i.e., the spiking neural network) can be used to account for dynamics of neural circuits.