The brain continuously samples its environment, yet not all sensory experiences are equally informative. How does the brain estimate the information content of ongoing sensory input, and how does this computation shape behavior? In this talk, I will present a series of studies combining information theory, behavior, computational modeling and systems neuroscience in humans and mice to investigate the neural basis of boredom. We propose that boredom emerges as an aversive signal when sensory experience conveys only little information, and functions together with curiosity as a complementary motivational system regulating adaptive exploration: Boredom acts as an undirected push factor driving individuals away from low information states, whereas curiosity acts as a directed pull factor promoting the search for specific sources of information. Across species, humans and mice reliably avoid sensory environments with low information content, and subjective boredom scales with the empirical entropy of sensory stimulation. At the neural level, information content is represented in both the magnitude and the population patterns of cortical activity, providing a translational neural signature of experienced information. Together, these findings suggest that the brain continuously monitors sensory information to regulate exploratory behavior and maintain an optimal balance between environmental stability and novelty.