Efforts have always been deployed to surpass limitations in human cognitive abilities to enhance aspects such as task accuracy, work effectiveness and error management. Cognitive enhancement is a field aiming at improving human cognition in order to overcome those limitations. It bears important interest from the human factors community given its potential for reducing errors in complex operational environments. Yet, cognitive enhancement strategies are rarely used outside the lab and practical applications are scarce. The current paper presents a brief summary of the literature on human cognitive enhancement and discusses key operational applications of the main methods reported in this field. Using a human factors perspective, the paper also outlines how such techniques could be integrated into decision-support tools to support operators facing cognitive challenges in complex operational domains, including those experiencing functional limitations preventing them to contribute to the workforce.

Although simulations can be useful tools to train dynamic decision making (DDM) skills, studies show that mere practice with simulated environments leads to limited improvements in performance. Simulated environments often show little or no transparency about the underlying structure. Making information about the system and the consequences of decisions available to users has been found to enhance learning. We tested a glass-box approach using highly interactive feedback tools to support implicit learning in a 3-hour DDM training session. Ninety participants were assigned to either the control (no training) or implicit learning condition. While performance on the training scenario improved over time, learning took place mostly in the beginning of the training session, and final performance remained far from optimal. Performance in the training scenario was positively correlated to performance in a test scenario. However, implicit learning did not improve performance on the test scenario compared to the control group.

Policy capturing is a decision analysis technique normally using linear statistical models to infer the basis of expert judgments. The purpose of the present work was to test if the C4.5 decision tree (DT) algorithm (a non-linear machine learning method) is more effective at capturing individual’s decision policies than the standard linear technique. Human classification behavior was measured in a simulated naval air-defence task to compare decision tree models and linear logistic regression models in terms of their descriptive and predictive accuracy. Results show that C4.5 was superior in terms of goodness-of-fit and cross-validation performance. Decision tree complexity was significantly correlated to individuals’ response times. The classification rules derived from each individual were actually more reliable than their human counterparts – replicating a classic finding in policy capturing. We conclude that C4.5 is a useful policy capturing tool in the context of a complex dynamic task environment.