Laura A. Zimmerman

PC_Eyewitness: A computerized framework for the administration and practical application of research in eyewitness psychology

Eyewitness identification evidence is an important aspect of our legal system. Society relies on witnesses to identify suspects whom they have observed during the commission of a crime. Because a witness has only a mental representation of the individual he or she observed, law enforcement must rely on verbal descriptions and identification procedures to document eyewitness evidence. The present article introduces and details a computer program, referred to asPC_Eyewitness (PCE), which can be used in laboratories to conduct research on eyewitness memory. PCE is a modular program written in Visual Basic 6.0 that allows a researcher to present stimuli to a participant, to conduct distractor tasks, to elicit verbal descriptors regarding a target individual, and to present a lineup for the participant to provide an identification response. To illustrate the versatility of the program, several classic studies in the eyewitness literature are recreated in the context of PC E. The program is also shown to have applications in the conduct of field research and for use by law enforcement to administer lineups in everyday practice. PCE is distributed at no cost.

The calibration of trust in an automated system: A sensemaking process

Today operators in complex task domains such as aviation, process control, medicine and the military must work closely with automation to achieve levels of performance that would be impossible to reach independently. To maximize the full benefit of this man-machine relationship operator trust must be appropriately calibrated with the trustworthiness of the system. Misalignment of trust with the automations capabilities and limitations may lead to error, inefficiency and possible rejection of the technology. The purpose of this paper is to propose sensemaking as a theoretical framework for describing trust calibration. This framework can serve as a guide for developers of intelligent software tools to improve trust calibration training. The authors discuss the Experiential User Guide (EUG) concept as one example of how to train trust calibration by improving operator sensemaking.