Research has shown that riding a motorcycle can potentially be much more dangerous than operating a conventional vehicle. There are factors inherent in driving or riding a small two wheeled vehicle, such as a motorcycle, moped or even bicycle that can potentially decrease their ability to be seen or noticed by other drivers. This disadvantage is reflected in the disproportionate over-representation of injuries and/or fatalities incurred by this particular driving group. This creates a significant problem which deserves dedicated evaluation as to causative factors and/or influential variables. The following research was conducted with intentions to investigate the topic of motorcycle conspicuity so as to further explain the variables which positively contribute to a motorcycle being seen and to supplement the body of knowledge that currently exists on this topic. This study specifically evaluated the influence of sex, age, motorcycle lighting conditions, and vehicular daytime running lights upon one's ability to effectively detect a motorcycle within a "high fidelity" simulated environment. This research additionally sought to examine the feasibility and validity of using a novel fixed base "high fidelity" simulator for the evaluation of motorcycle conspicuity. The results from this research clearly indicate a link between vehicular DRLs and the effective detection of motorcycles and also support previous research as to the effectiveness of motorcycle DRLs. Additionally, these results suggest that as one ages, certain degradations in vision, cognition, and physiology occur which decrease one's performance in detecting and responding to a motorcycle. These findings additionally provide support for the use of a "high definition" fixed base simulator as a valid technology for the evaluation of motorcycle conspicuity.

Error related negativity (ERN) is a pronounced negative evoked response potential (ERP) that follows a known error. This neural pattern has the potential to communicate user awareness of incorrect actions within milliseconds. While the implications for human-machine interface and augmented cognition are exciting, the ERN has historically been evoked only in the laboratory using complex equipment while presenting simple visual stimuli such as letters and symbols. To effectively harness the applied potential of the ERN, detection must be accomplished in complex environments using simple, preferably single-electrode, EEG systems feasible for integration into field and workplace-ready equipment. The present project attempted to use static photographs to evoke and successfully detect the ERN in a complex visual search task: motorcycle conspicuity. Drivers regularly fail to see motorcycles, with tragic results. To reproduce the issue in the lab, static pictures of traffic were presented, either including or not including motorcycles. A standard flanker letter task replicated from a classic ERN study (Gehring et al., 1993) was run alongside, with both studies requiring a binary response. Results showed that the ERN could be clearly detected in both tasks, even when limiting data to a single electrode in the absence of artifact correction. These results support the feasibility of applied ERN detection in complex visual search in static images. Implications and opportunities will be discussed, limitations of the study explained, and future directions explored.