Linear Sequential Unmasking–Expanded (LSU-E): A general approach for improving decision making as well as minimizing noise and bias

All decision making, and particularly expert decision making, requires the examination, evaluation, and integration of information. Research has demonstrated that the order in which information is presented plays a critical role in decision making processes and outcomes. Different decisions can be reached when the same information is presented in a different order [1,2]. Because information must always be considered in some order, optimizing this sequence is important for optimizing decisions. Since adopting one sequence or another is inevitable —some sequence must be used— and since the sequence has important cognitive implications, it follows that considering how to best sequence information is paramount. In the forensic sciences, existing approaches to optimize the order of information processing (sequential unmasking [3] and Linear Sequential Unmasking [4]) are limited in terms of their narrow applicability to only certain types of decisions, and they focus only on minimizing bias rather than optimizing forensic decision making in general. Here, we introduce Linear Sequential Unmasking–Expanded (LSU-E), an approach that is applicable to all forensic decisions rather than being limited to a particular type of decision, and it also reduces noise and improves forensic decision making in general rather than solely by minimizing bias.