Effects of Sleep Deprivation on Performance during a Change Signal Task with Adaptive Dynamics

Augmented cognition, which refers to real-time modifications to a human-system interface to improve performance and includes dynamic task environments with automated adaptations, can serve to protect against performance impairment under challenging work conditions. However, the effectiveness of augmented cognition as a countermeasure for performance impairment due to sleep loss is unknown. Here, in a controlled laboratory study, an adaptive version of a Change Signal task was administered repeatedly to healthy adults randomized to 62 h of total sleep deprivation (TSD) or a rested control condition. In the computerized task, a left- or right-facing arrow was presented to start each trial. In a subset of trials, a second arrow facing the opposite direction was presented after a delay. Subjects were to respond within 1000 ms of the trial start by pressing the arrow key corresponding to the single arrow (Go trials) or to the second arrow when present (Change trials). The Change Signal Delay (CSD)-i.e., the delay between the appearance of the first and second arrows-was shortened following incorrect responses and lengthened following correct responses so that subsequent Change trials became easier or harder, respectively. The task featured two distinct CSD dynamics, which produced relatively stable low and high error rates when subjects were rested (Low and High Error Likelihood trials, respectively). During TSD, the High Error Likelihood trials produced the same, relatively high error rate, but the Low Error Likelihood trials produced a higher error rate than in the rested condition. Thus, sleep loss altered the effectiveness of the adaptive dynamics in the Change Signal task. A principal component analysis revealed that while subjects varied in their performance of the task along a single dominant dimension when rested, a second inter-individual differences dimension emerged during TSD. These findings suggest a need for further investigation of the interaction between augmented cognition approaches and sleep deprivation in order to determine whether and how augmented cognition can be relied upon as a countermeasure to performance impairment in operational settings with sleep loss.

Harder Than You Think: How Outside Assistance Leads to Overconfidence

Cognitive ability consists not only of one's internal competence but also of the augmentation offered by the outside world. How much of our cognitive success is due to our own abilities, and how much is due to external support? Can we accurately draw that distinction? Here, we explored when and why people are unaware of their reliance on outside assistance. Across eight experiments (N = 2,440 participants recruited from Amazon Mechanical Turk), people showed improved metacognitive calibration when assistance occurred after a delay or required active choice. Furthermore, these findings apply across a wide range of cognitive tasks, including semantic memory (Experiments 1a and 1b), episodic memory (Experiments 2a and 2b), and problem solving (Experiments 3a-3d). These experiments offer important insights into how we understand our own abilities when we rely on outside help.

A Motivational Model of BCI-Controlled Heuristic Search

Several researchers have proposed a new application for human augmentation, which is to provide human supervision to autonomous artificial intelligence (AI) systems. In this paper, we introduce a framework to implement this proposal, which consists of using Brain⁻Computer Interfaces (BCI) to influence AI computation via some of their core algorithmic components, such as heuristic search. Our framework is based on a joint analysis of philosophical proposals characterising the behaviour of autonomous AI systems and recent research in cognitive neuroscience that support the design of appropriate BCI. Our framework is defined as a motivational approach, which, on the AI side, influences the shape of the solution produced by heuristic search using a BCI motivational signal reflecting the user's disposition towards the anticipated result. The actual mapping is based on a measure of prefrontal asymmetry, which is translated into a non-admissible variant of the heuristic function. Finally, we discuss results from a proof-of-concept experiment using functional near-infrared spectroscopy (fNIRS) to capture prefrontal asymmetry and control the progression of AI computation of traditional heuristic search problems.

Toward Improving Surgical Outcomes by Incorporating Cognitive Load Measurement into Process-Driven Guidance

This paper summarizes the accomplishments and recent directions of our medical safety project. Our process-based approach uses a detailed, rigorously-defined, and carefully validated process model to provide a dynamically updated, context-aware and thus, "Smart" Checklist to help process performers understand and manage their pending tasks [7]. This paper focuses on support for teams of performers, working independently as well as in close collaboration, in stressful situations that are life critical. Our recent work has three main thrusts: provide effective real-time guidance for closely collaborating teams; develop and evaluate techniques for measuring cognitive load based on biometric observations and human surveys; and, using these measurements plus analysis and discrete event process simulation, predict cognitive load throughout the process model and propose process modifications to help performers better manage high cognitive load situations. This project is a collaboration among software engineers, surgical team members, human factors researchers, and medical equipment instrumentation experts. Experimental prototype capabilities are being built and evaluated based upon process models of two cardiovascular surgery processes, Aortic Valve Replacement (AVR) and Coronary Artery Bypass Grafting (CABG). In this paper we describe our approach for each of the three research thrusts by illustrating our work for heparinization, a common subprocess of both AVR and CABG. Heparinization is a high-risk error-prone procedure that involves complex team interactions and thus highlights the importance of this work for improving patient outcomes.