A method for behavior analysis performed by an apparatus comprising a signal acquisition module, a state-transition feature extraction module, a first-level behavior-model fitting module, a task-volatility estimation module, a prediction-error estimation module, and a model-selection module, the method comprising:
- acquiring behavior-related signals of a subject including at least one of:
- a choice sequence, a reward history, a task-response record, or observation-derived state information;
- extracting, from the behavior-related signals, state-transition features representing changes in latent behavioral states of the subject;
- learning, by the first-level behavior-model fitting module, a first behavioral model from the state-transition features;
- computing an uncertainty metric comprising at least one of:
- a state-transition uncertainty value, a state-space complexity value, or a novelty metric based on the state-transition features and the first behavioral model;
- computing a prediction error based on a difference between an expected behavioral outcome and an observed behavioral outcome;
- learning a second behavioral model based on output of the first behavioral model and further based on the uncertainty metric;
- generating a behavioral parameter of the second behavioral model by integrating the uncertainty metric and the prediction error;
- comparing the behavioral parameter with a plurality of stored behavior-model profiles associated with candidate models;
- and
- selecting and outputting a model corresponding to a behavior-model profile that best matches the behavioral parameter.