How do humans learn about the reliability of automation?

In a range of settings, human operators make decisions with the assistance of automation, the reliability of which can vary depending upon context. Currently, the processes by which humans track the level of reliability of automation are unclear. In the current study, we test cognitive models of learning that could potentially explain how humans track automation reliability. We fitted several alternative cognitive models to a series of participants' judgements of automation reliability observed in a maritime classification task in which participants were provided with automated advice. We examined three experiments including eight between-subjects conditions and 240 participants in total. Our results favoured a two-kernel delta-rule model of learning, which specifies that humans learn by prediction error, and respond according to a learning rate that is sensitive to environmental volatility. However, we found substantial heterogeneity in learning processes across participants. These outcomes speak to the learning processes underlying how humans estimate automation reliability and thus have implications for practice.

The Perception of Automation Reliability and Acceptance of Automated Advice.

OBJECTIVE: Examine (1) the extent to which humans can accurately estimate automation reliability and calibrate to changes in reliability, and how this is impacted by the recent accuracy of automation; and (2) factors that impact the acceptance of automated advice, including true automation reliability, reliability perception, and the difference between an operator's perception of automation reliability and perception of their own reliability. BACKGROUND: Existing evidence suggests humans can adapt to changes in automation reliability but generally underestimate reliability. Cognitive science indicates that humans heavily weight evidence from more recent experiences. METHOD: Participants monitored the behavior of maritime vessels (contacts) in order to classify them, and then received advice from automation regarding classification. Participants were assigned to either an initially high (90%) or low (60%) automation reliability condition. After some time, reliability switched to 75% in both conditions. RESULTS: Participants initially underestimated automation reliability. After the change in true reliability, estimates in both conditions moved towards the common true reliability, but did not reach it. There were recency effects, with lower future reliability estimates immediately following incorrect automation advice. With lower initial reliability, automation acceptance rates tracked true reliability more closely than perceived reliability. A positive difference between participant assessments of the reliability of automation and their own reliability predicted greater automation acceptance. CONCLUSION: Humans underestimate the reliability of automation, and we have demonstrated several critical factors that impact the perception of automation reliability and automation use. APPLICATION: The findings have potential implications for training and adaptive human-automation teaming.

Human behavioral response to fluctuating automation reliability.

Human perception of automation reliability and automation acceptance behaviours are key to effective human-automation teaming. This study examined factors that impact perceptions of automation reliability over time and the acceptance of automated advice. Participants completed a maritime vessel classification task in which they classified vessels (contacts) with the assistance of automation. In Experiment 1 automation reliability successively switched from high to low (or vice versa). In Experiment 2 automation reliability decreased by varying magnitudes before returning to high. Participants did not initially calibrate to true reliability and experiencing low automation reliability reduced future reliability estimates when experiencing subsequent high reliability. Automation acceptance was predicted by positive differences between participant perception of automation reliability and confidence in their own manual classification reliability. Experiencing low automation reliability caused perceptions of reliability and automation acceptance rates to diverge. These findings have important implications for training and adaptive human-automation teaming in complex work environments.

Molecular Basis of Selectivity and Activity for the Antimicrobial Peptide Lynronne-1 Informs Rational Design of Peptide with Improved Activity.

Antibiotic resistance is a significant threat to human health, with natural products remaining the best source for new antimicrobial compounds. Antimicrobial peptides (AMPs) are natural products with great potential for clinical use as they are small, amenable to customization, and show broad-spectrum activities. Lynronne-1 is a promising AMP identified in the rumen microbiome that shows broad-spectrum activity against pathogens such as methicillin-resistant Staphylococcus aureus and Acinetobacter baumannii. Here we investigated the structure of Lynronne-1 using solution NMR spectroscopy and identified a 13-residue amphipathic helix containing all six cationic residues. We used biophysical approaches to observe folding, membrane partitioning and membrane lysis selective to the presence of anionic lipids. We translated our understanding of Lynronne-1 structure to design peptides which varied in the size of their hydrophobic helical face. These peptides displayed the predicted continuum of membrane-lysis activities in vitro and in vivo, and yielded a new AMP with 4-fold improved activity against A. baumannii and 32-fold improved activity against S. aureus.

Clinical Potential of Statins in Prostate Cancer Radiation Therapy.

BACKGROUND/AIM: Statins are cholesterol- lowering drugs that have been shown to possess anti-tumour properties. Observational studies have shown that 3-hydroxy-3-methlyglutaryl coenzyme A reductase inhibitor (statin) use may be associated with reduced prostate cancer risk. Preclinical studies suggest that statins possess anticancer and radiosensitising properties. This review aims to determine the impact of statin use in the efficacy of radiation therapy and the therapeutic window in prostate cancer. MATERIALS AND METHODS: The scientific databases PubMed, Science Direct, EMBASE, Cochrane Collaboration, and Google Scholar were searched for articles identifying statin use in histologically confirmed prostate cancer treated with external beam radiation therapy. RESULTS: Improvement was observed in freedom from biochemical failure (91% vs. 79%), relapse free survival (72% vs. 69%), distant metastasis free survival (96% vs. 94%), and prostate-specific antigen (PSA) relapse free survival (89% vs. 83%) with statin use, however this did not translate into an overall survival benefit for patients. Conflicting data concerning clinical outcomes reduce the integrity of these findings. The literature supports the radiosensitising properties of statins and their potential antitumor effects in prostate cancer. CONCLUSION: Statin use in prostate cancer presents many obstacles yet to be overcome, which warrant attention prior to the routine implementation of statins in treatment regimes. However, there is evidence to support their beneficial use.