This review considers data obtained during the entire research period of state-dependent learning. Understanding of this phenomenon has significantly evolved during the past decades, as a result of the increasing amount of facts revealed while studying state-dependent learning. Consequently, a situation has arisen where different papers may describe same phenomena using different terms. This does not promote understanding of the described phenomena. Therefore a need for a paper emerged, that would analyze the evolution of state-dependent learning and would offer terminology corresponding to all the data collected on the subject.
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Article Synopsis
- Understanding complex animal behavior is essential for connecting brain functions to actions, and recent research is exploring dynamic modeling approaches for long-term behaviors like navigation.* -
- The introduction of Discrete Dynamical Inverse Reinforcement Learning (dDIRL) allows for modeling animal behavior over extended periods by focusing on internal state-specific rewards and Markovian transitions between these states.* -
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