Federico Scholcover

Research Statement

My research explores how individual differences in time perception affect task performance, primarily when experiencing latency. These are human-in-the-loop style tasks where a human is actively operating a tool, such as a UAV or a telesurgical platform. However, in situations with latency, the tool does not immediately respond to input. It would be as if your car’s brakes responded a second after your foot pressed down on the brake pedal. If you know your car won’t brake immediately as you’re approaching a stop sign, you have to anticipate when you’ll need the car to brake and press the pedal earlier. This latency creates a new demand, where the operator needs to predict what the status of their tool will be and anticipate the appropriate response. My work addresses this concern by investigating the role that internal timing mechanisms play in performing these tasks, and how individual differences in these mechanisms may lead to performance differences (Scholcover & Gillan, 2018; Scholcover & Gillan, in prep). The long term goal of this line of research is to develop and refine these individual difference predictors of timing, thereby enhancing training and selection outcomes and improving performance in tasks that require anticipation or planning. My secondary research line supports this goal by investigating how our sense of time changes across different contexts and states (Scholcover, Cauffman, & Gillan, 2018; Scholcover & Gillan, in prep).

We’ve known for over 50 years that latency causes issues when performing tasks with a tool (Sheridan & Ferrell, 1963), such as a teleoperated robot. The task takes longer and operators make more errors. Approaches to addressing these issues have primarily focused on improved engineering, such as using predictive displays (Sheridan 1993), helping to bridge the gap between input and response. However, I believe that these are solutions to an issue that we fully don’t understand. We understand what happens with more latency, but we don’t have a full understanding of why it happens. On a broad level, performing these tasks at increasing levels of latency requires increasing levels of prediction from the operator. We also know that operators eventually reach a point where they adopt a different strategy known as the “move and wait” strategy where they no longer actively pilot, but instead “move” and then “wait” to see the results. In Scholcover & Gillan (2018), I break this type of task into three stages, from an operator’s perspective: input, feedback/monitoring, and adjustment. At low levels of latency, these three processes happen in near parallel. However, as latency increases, these three processes become further and further disjointed. The process of having to mentally extrapolate from the present, predicting where the tool will land, and adjusting to errors in that prediction is where performance deficits begin to occur. In other words, participants are making errors in temporally bringing these processes together and this leads to an increase in errors during the primary task.

Follow on research has extended this model to further elucidate the underlying phenomena (Scholcover & Gillan, in prep). I examined how the role of timing extended itself to different difficulty levels, operationalized by how much lateral clearance  participants had in a route. Think of lateral clearance as the difference between a three lane highway and a one lane country road. On a highway, if you have some unexpected lateral deviation, you’re likely to be in another lane, still going with traffic. Any lateral deviation on the one-lane road, and you’re in the other lane, driving against traffic (not good). In this study, we tested a model of over-/underestimation in inputs caused by a relative under-/overestimation of time. The results suggest a much richer phenomena than that found in Scholcover et. al, (2018). We again found individual differences in timing are a significant predictor of performance. Some behavioral variables followed the same general pattern as before. Interestingly, the results were in a different direction on the trial level. In contrast to the 2018 article where increasing sensitivity to time decreased the frequency of errors; in this follow up, increasing sensitivity increased errors. This implies something very exciting about this phenomena: similar sets of moment-to-moment behaviors lead to wholly different results at the trial level! My hypothesis is that participants engage in different strategies to account for overestimating and underestimating, depending on the context. My dissertation focuses on better defining these different strategies.

A common issue in individual difference research is that it tends to be quasi-experimental. In that context, we can theorize and imply causality, but it’s difficult to definitively establish. In contrast to more stable individual differences, such as personality, time perception is mutable. One’s physiological arousal (Gil & Droit-Volet, 2012; Scholcover, et al. 2018) or affective state (Droit-Volet & Meck, 2007) influences time perception. I am looking to leverage this mutability to further support causal claims in the relationship between timing and task performance. By inducing an affective state while participants perform a route navigation task with latency and collecting measures of how their sensitivity to time changes as a function of affect, I will be better positioned to support a causal model.

Long term, I will apply these models of timing and performance using route navigation with latency to a broader set of tasks that have an inherent element of timing and coordination. It will begin within the general domain of teleoperation, such as telesurgery and search operations, but then move towards broader tasks that require prediction, such as hitting a baseball. As part of understanding the role of timing within these contexts, I would like to develop a library of timing measures for use in future studies, based around many of the existing methodologies in psychophysical and perceptual research. In conjunction, these lines of research will ultimately carve out my research niche within the realm of time perception and performance.

References

Droit-Volet, S., & Meck, W. H. (2007). How emotions colour our perception of time. Trends in Cognitive Sciences, 11(12), 504-513.

Gil, S., & Droit-Volet, S. (2012). Emotional time distortions: the fundamental role of arousal. Cognition & Emotion, 26(5), 847-862.

Scholcover, F., Cauffman, S. J., & Gillan, D. J. (2018, September). Timing and Physiological Arousal: Implications for Human Performance. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 62(1), 1525-1529.

Scholcover, F., & Gillan, D. J. (2018). Using Temporal Sensitivity to Predict Performance Under Latency in Teleoperation. Human Factors, 60(1), 80-91.

Scholcover, F., & Gillan, D.J. (in preparation).  Temporal Sensitivity and Latency During Teleoperation: The Moderating Role of Track Clearance

Sheridan, T. B. (1993). Space teleoperation through time delay: Review and prognosis. IEEE Transactions on Robotics and Automation, 9(5), 592-606.

Sheridan, T. B., & Ferrell, W. R. (1963). Remote manipulative control with transmission delay. IEEE Transactions on Human Factors in Electronics, (1), 25-29.