Recent research has demonstrated that heightened motivational levels promote enhanced attention capabilities. However, the relation between attentional systems and the trait-based ability to sustain a motivational state long-term is less understood. Grit refers to one’s ability and willingness to pursue long-term goals despite setbacks. This report presents the results of two studies conducted to examine the relation between facets of Grit–Consistency and Perseverance and attention networks, assessed using the Attention Network Test (ANT). Across both studies Grit–Perseverance was related to performance on the ANT. In Study 1, Grit–Perseverance was negatively related to alerting indicating that individuals who were high on Perseverance were more likely to show a smaller alerting effect. In particular, Grit–Perseverance was negatively correlated with reaction times in the no cue trials. In Study 2, we assessed ERP components associated with attention networks. Individuals with higher scores on Grit–Perseverance were more likely to demonstrate smaller mean difference in N1 amplitudes for double cue relative to no cue trials, suggesting an attenuated alerting effect. Our findings indicate that individuals high on Grit–Perseverance may have enhanced sustained attention. Specifically individuals with high Grit–Perseverance appear to exhibit a more efficient alerting system in the no cue trials. Implications of high levels of Grit on cognitive performance are discussed.

Objective This article presents two studies (one simulation and one pilot) that assess a custom computer algorithm designed to predict motion sickness in real-time. Background Virtual reality has a wide range of applications; however, many users experience visually induced motion sickness. Previous research has demonstrated that changes in kinematic (behavioral) parameters are predictive of motion sickness. However, there has not been research demonstrating that these measures can be utilized in real-time applications. Method Two studies were performed to assess an algorithm designed to predict motion sickness in real-time. Study 1 was a simulation study that used data from Smart et al. (2014). Study 2 employed the algorithm on 28 new participants’ motion while exposed to virtual motion. Results Study 1 revealed that the algorithm was able to classify motion sick participants with 100% accuracy. Study 2 revealed that the algorithm could predict if a participant would become motion sick with 57% accuracy. Conclusion The results of the present study suggest that the motion sickness prediction algorithm can predict if an individual will experience motion sickness but needs further refinement to improve performance. Application The algorithm could be used for a wide array of VR devices to predict likelihood of motion sickness with enough time to intervene.

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