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Description: Contemporary models of categorization typically tend to sidestep the problem of how information is initially encoded during decision-making. Instead, a focus of this work has been to investigate how, through selective attention, stimulus representations are “contorted” such that behaviourally-relevant dimensions are accentuated (or “stretched”), and representations of irrelevant dimensions are ignored (or “compressed”). In high-dimensional real-world environments, it is computationally infeasible to sample all available information, and human decision-makers selectively sample information from sources expected to provide relevant information. To address these and other shortcomings, we develop an active sampling model, Sampling Emergent Attention (SEA), which sequentially and strategically samples information sources until the expected cost of information exceeds the expected benefit. The model specifies the interplay of two components, one involved in determining the expected utility of different information sources and the other in representing knowledge and beliefs about the environment. These two components interact such that knowledge of the world guides information sampling, and what is sampled updates knowledge. Like human decision-makers, the model displays strategic sampling behaviour, such as terminating information search when sufficient information has been sampled and adaptively adjusting the search path in response to previously sampled information. The model also shows human-like failure modes. For example, when information exploitation is prioritized over exploration, the bidirectional influences between information-sampling and learning can lead to the development of beliefs that systematically differ from reality.