Topic: drift diffusion to bound model

When making decisions, combining multiple pieces of information can increase the probability of making the right choice. A decision maker's estimate of this probability, referred to as decision confidence (Hangya et al., 2016), is useful for guiding subsequent actions and learning improvements in the decision policy (Drugowitsch et al., 2019). But studies of the neural computations that give rise to confidence have been limited by the difficulty of measuring confidence in animal subjects. One promising approach uses willingness to wait for reward after choice as a proxy for confidence (Lak, 2