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The drift-diffusion model (DDM) implements an optimal decision procedure for stationary, 2-alternative forced-choice tasks. The height of a decision threshold applied to accumulating information on each trial determines a speed-accuracy tradeoff (SAT) for the DDM, thereby accounting for a ubiquitous feature of human performance in speeded response tasks. However, little is known about how participants settle on particular tradeoffs. One possibility is that they select SATs that maximize a subjective rate of reward earned for performance. For the DDM, there exist unique, reward-rate-maximizing values for its threshold and starting point parameters in free-response tasks that reward correct responses (R. Bogacz, E. Brown, J. Moehlis, P. Holmes, & J. D. Cohen, 2006). These optimal values vary as a function of response-stimulus interval, prior stimulus probability, and relative reward magnitude for correct responses. We tested the resulting quantitative predictions regarding response time, accuracy, and response bias under these task manipulations and found that grouped data conformed well to the predictions of an optimally parameterized DDM. (Contains 13 footnotes, 4 tables, and 21 figures.)

INTRODUCTION: Helicopter air ambulance (HAA) operations are associated with unique risks and human factors considerations due to the on-call nature of the work, low altitude operations, and routes to novel locations. Assessing events voluntarily reported to the Aviation Safety Reporting System (ASRS) database provides important information about precursors to risk in these operations. METHODS: The National Aeronautics and Space Administration's (NASA) ASRS database was queried for reports that related to 14 C.F.R. Part 135 and HAA operations between 2013 and 2023. Reports were then filtered to include only those referencing rotorcraft. Narratives, synopses, and event data were evaluated for overall trends across reports. Trends were identified with a coding scheme that iteratively examined organizational factors, event and personnel data, errors, and unsafe conditions. RESULTS: A total of 97 ASRS reports were analyzed. Most reports were flightcrew related (n = 84; 87%), while others documented issues with maintenance (n = 17) or Air Traffic Control (ATC; n = 12). The most frequently reported issue was lack of situation awareness or the ability to perceive, understand, and respond to events (n = 57), followed by pilot/ATC communication challenges (n = 35), judgment/decision-making errors (n = 24), and errors adhering to procedure (n = 21). Organizational factors related to policy/procedural issues (n = 15), commercial performance pressures (n = 13), and safety culture issues (n = 6) were also identified. The most frequently reported consequence was on aircraft systems or components (n = 24), followed by near mid-air collisions (n = 14), loss of control (n = 3), or impacts to patient outcome (n = 7). DISCUSSION: Analyses of ASRS reports related to HAA operations identified a range of risk factors and safety critical areas to address in Part 135 and HAA operations. Findings suggest that training and interventions related to situation awareness, communication between pilots and ATC, and maintenance operations would be useful in mitigating future accidents or incidents. Other organizational factors identified included policy issues, procedural issues, and commercial pressures, which suggest room for growth in HAA operations to improve safety outcomes. Learning Objectives 1. Understand current safety risks in helicopter air ambulance operations using voluntary safety reporting data. 2. The audience will learn about current human factors considerations in helicopter air ambulance operations. [ABSTRACT FROM AUTHOR]

Mycobacterium lepromatosis is a newly discovered leprosy-causing organism. Preliminary phylogenetic analysis of its 16S rRNA gene and a few other gene segments revealed significant divergence from Mycobacterium leprae, a well-known cause of leprosy, that justifies the status of M. lepromatosis as a new species. In this study we analyzed the sequences of 20 genes and pseudogenes (22,814 nucleotides). Overall, the level of matching of these sequences with M. leprae sequences was 90.9%, which substantiated the species-level difference; the levels of matching for the 16S rRNA genes and 14 protein-encoding genes were 98.0% and 93.1%, respectively, but the level of matching for five pseudogenes was only 79.1%. Five conserved protein-encoding genes were selected to construct phylogenetic trees and to calculate the numbers of synonymous substitutions (dS values) and nonsynonymous substitutions (dN values) in the two species. Robust phylogenetic trees constructed using concatenated alignment of these genes placed M. lepromatosis and M. leprae in a tight cluster with long terminal branches, implying that the divergence occurred long ago. The dS and dN values were also much higher than those for other closest pairs of mycobacteria. The dS values were 14 to 28% of the dS values for M. leprae and Mycobacterium tuberculosis, a more divergent pair of species. These results thus indicate that M. lepromatosis and M. leprae diverged ~10 million years ago. The M. lepromatosis pseudogenes analyzed that were also pseudogenes in M. leprae showed nearly neutral evolution, and their relative ages were similar to those of M. leprae pseudogenes, suggesting that they were pseudogenes before divergence. Taken together, the results described above indicate that M. lepromatosis and M. leprae diverged from a common ancestor after the massive gene inactivation event described previously for M. leprae. doi: 10.1128/JB.00762-09