We propose a class of dynamic models that capture subjective (and hence unobservable) constraints on the amount of information a decision maker can acquire, pay attention to, or absorb, via an Information Choice Process (icp). An icp specifies the information that can be acquired about the payoff-relevant state in the current period, and how this choice affects what can be learned in the future. In spite of their generality, wherein icps can accommodate any dependence of the information constraint on the history of information choices and state realizations, we show that the constraints imposed by them are identified up to a dynamic extension of Blackwell dominance. All the other parameters of the model are also uniquely identified. Behaviorally, the model is characterized by a novel recursive application of static properties.