We describe a novel, model-based approach to augmented cognition. We first discuss a subset of cognitive limitations that are likely to impair performance. We then note traditional approaches to the enhancement of cognitive abilities and their shortcomings, and compare the augmented approach to that of the traditional human-computer interface design. We discuss an example of our approach involv...

Learning how to allocate attention properly is essential for success at many tasks. Extant theories of categorization assume that learning to allocate attention is an error-driven process, where shifts in attention are made to reduce error. The present work introduces a new measure, error bias, which compares the amount of attentional change in response to incorrect responses versus correct res...

Research in many disciplines has used eye-tracking technology to investigate the differences in the visual attention of experts and novices. For example, it has been shown that experts in art and chess spend more time than novices looking at relevant information. Thus, it may be helpful to give novices more direct insight into the way experts allocate their visual attention, for example using a...

A fundamental question in reading research concerns whether attention is allocated strictly serially, supporting lexical processing of one word at a time, or in parallel, supporting concurrent lexical processing of two or more words (Reichle, Liversedge, Pollatsek, & Rayner, 2009). The origins of this debate are reviewed. We then report three simulations to address this question using artificia...

The communication environment within organizations provides multiple opportunities for informational retrieval throughout the day. While previous research has explored media choice from a sender’s perspective, little research has explored the choices receivers or targets make in attending to messages and information streams. This paper explores the process that targets use to evaluate messages ...

In two empirical studies of attention allocation during category learning, we investigate the idea that category learners learn to allocate attention optimally across stimulus dimensions. We argue that "optimal" patterns of attention allocation are model or process specific, that human learners do not always optimize attention, and that one reason they fail to do so is that under certain condit...