IFT proposes that information-seeking behavior develops to maximize the rate of information gained per unit of time or effort invested.

It assumes that human long-term memory evolved to help survival by anticipating organismically important events. It is evolutionarily important to remember things that are important for survival. Therefore, the expected value of remembering a thing in the future should affect the probability of recalling it.

According to rational analysis, behavior is sensitive to the statistical distribution of rewards in the environment that a user has experienced. Users learn the way rewards are distributed through continued exposure to an environment and adapt their behavior accordingly. A user's behavior is rational because it is tuned to the distribution of rewards in the environment—the ecology.

The theory assumes that users are 'computationally rational': When picking an action—or deciding how to get from the present state to a state with positive rewards—users are as rational as their cognition allows. Users act based on their often inaccurate and partial beliefs, which they have formed via experience.

Computational rationality is a theory and a modeling approach rooted in bounded rationality and bounded optimality. Recent applications include typing (Figure 21.7), pointing, driving, multitasking, menu selection, and visual search. Its core assumption is that users act in accordance with what they believe is best for them.

Rational analysis is a theory of rational behavior proposed by Anderson and Schooler [21]. It examines the distribution of rewards in the environment to explain how users adapt their behavior. According to rational analysis, behavior is sensitive to the statistical distribution of rewards in the environment that a user has experienced.

bounded rationality states that we are only rational to the extent allowed by the involved constraints, or bounds.