This chapter surveys recent advances in our understanding of perceptual learning and reasoning processes that are crucial in image analysis, updating the presentation in the first volume on the human factors of remote sensing (Hoffman and Markman, 2001).

Regarding expert knowledge, methods of cognitive task analysis, knowledge elicitation, and knowledge representation are now well understood, and this chapter illustrates these methods in a system that represents the knowledge of expert terrain analysts. Regarding expert reasoning, studies in many domains, such as firefighting, power plant operation, jurisprudence, and design engineering, have shown that experts often make decisions through rapid recognition of causal factors and goals, rather than through any explicit process of generating and evaluating solutions (Klein and Hoffman, 1992). Research also led to a general model of deliberative decision making, the Data/Frame Model of Sensemaking (Klein, et al., 2003, 2006a,b). This chapter discusses how Recognition-priming and Sensemaking apply to remote sensing image interpretation. We now know a great deal about expertise (for reviews, see Ericsson, et al., 2018; Hoffman, et al., 2017) and indeed, Expertise Studies has become a field in its own right. An exciting challenge is to better understand how experts can interpret and integrate multiple data types, including data that are dynamic. We anticipate that models of cognition that have been inspired by the Expertise Studies and the NDM paradigm will continue to inform research and development in the field of remote sensing, with applications ranging all the way from training to the design of software decision-support systems and visualization systems. At the same time, research on the cognition of expert remote sensing image interpreters will continue to enrich our models of expert cognition and perception.