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