Neurocomputational model of impaired arbitration between model-based and model-free learning in OCD (Kim, T. et al. In press, Brain)
refer to Lee, S.W. et al. 2014, Neuron: Computations underlying arbitration between MB and MF learning
- debugging the backward update algorithm
- reliability -> uncertainty
- tuning the boundaries of model parameters
- run simulation of model fitting using the codes below
- includes pretraining and main training sections
- interation for each trial/stage
- run model fitting using a maximum likelihood estimation method
- return fitted model parameters
- multiple seeds testing recommend for optimization
- for model validation, necessary to run parameter recovery along with action generation using the optimized model
- prepared for parallel computing
- load behavioral data for model fitting
- training the MB (fwd + bwd)/MF (sarsa) RL agents.
- imitation learning (decision_behavior_data_save) for model fitting.
- estimation of 1) the prediction uncertainty of each learning (m?_inv_Fano) and 2) the dynamic weight between the two strategies (m1_prob).
- finally, integrating the action values for the arbitration system using the weight variable.
- set the two-step decsion task structure (myMap).
- contrsuct the data structures for state (state_history), action (action_history), learning information (SPE_history, T, etc.).
- construct the data structures for uncertainty-based arbitration (Bayesian_Arb)
- transitioning subjects/agents to a next state according to state-transition probabilities.
- opt.use_data=1 for model fitting.
- Clear environment and action data after a trial (S1->S2->S3).