Overconfidence in Projecting Uncertain Spatial Trajectories

• Published in: Human factors Vol. 58; no. 6; pp. 899 - 914
• Main Authors: Herdener, Nathan, Wickens, Christopher D., Clegg, Benjamin A., Smith, C. A. P.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.09.2016; Human Factors and Ergonomics Society
• Subjects: Accuracy; Adult; Bias; Calibration; Experiments; Human factors; Human factors research; Humans; Hurricanes; Mathematical models; Metacognition - physiology; Position (location); Predictions; Ships; Space life sciences; Space Perception - physiology; Thinking; Trajectories; Uncertainty; Variance; navigation; cognition; metacognition; decision making; knowledge
• ISSN: 0018-7208; 1547-8181
• DOI: 10.1177/0018720816645259

Objective The aim of this study was to understand factors that influence the prediction of uncertain spatial trajectories (e.g., the future path of a hurricane or ship) and the role of human overconfidence in such prediction. Background Research has indicated that human prediction of uncertain trajectories is difficult and may well be subject to overconfidence in the accuracy of forecasts as is found in event prediction, a finding that indicates that humans insufficiently appreciate the contributions of variance in nature to their predictions. Method In two experiments, our paradigm required participants to observe a starting point, a position at time T, and then make a prediction of the location of the trajectory at time NT. They experienced several trajectories from the same underlying model but perturbed by random variance in heading and speed. Results In Experiment 1A, people predicted linear paths well and were better in heading predictions than in speed predictions. However, participants greatly underestimated the variance in predicted location, indicating overconfidence. In Experiment 1B, the effect was replicated with frequencies rather than probabilities used in variance estimates. In Experiment 2, people predicted nonlinear trajectories poorly, and overconfidence was again observed. Overconfidence was reduced on the more difficult predictions. In both main experiments, those better at predicting the mean were not better at predicting the variance. Conclusions Predicting the level of uncertainty in spatial trajectories is not well done and may involve qualitatively different abilities than prediction of the mean. Application Improving real-world performance at prediction demands developing better understanding of variability, not just the average case. Biases in prediction of uncertainty may be addressed through debiasing training and/or visualization tools that could assist in more calibrated action planning.