Computational modeling of first responders' willingness to accept radiation exposure during radiological or nuclear events.

Since the events of 9/11, a concerted interagency effort has been undertaken to create comprehensive emergency planning and preparedness strategies for the management of a radiological or nuclear event in the US. These planning guides include protective action guidelines, medical countermeasure recommendations, and systems for diagnosing and triaging radiation injury. Yet, key areas such as perception of risk from radiation exposure by first responders have not been addressed. In this study, we identify the need to model and develop new strategies for medical management of large-scale population exposures to radiation and examine the phenomena of radiation dread and its role in emergency response using an agent-based modeling approach. Using the computational modeling platform NetLogo, we developed a series of models examining factors affecting first responders' willingness to work (WTW) in the context of entering areas where radioactive contamination is present or triaging individuals potentially contaminated with radioactive materials. In these models, the presence of radiation subject matter experts (SMEs) was found to increase WTW. Degree of communication was found to be a dynamic variable with either positive or negative effects on WTW dependent on the initial WTW demographics of the test population. Our findings illustrate that radiation dread is a significant confounder for emergency response to radiological or nuclear events and that increasing the presence of radiation SME in the field and communication among first responders when such radiation SMEs are present will help mitigate the effect of radiation dread and improve first responder WTW during future radiological or nuclear events.

The impact of radiation dread on mass casualty medical management during a radiological or nuclear event.

Since the events of 9/11, a concerted interagency effort has been undertaken to create comprehensive emergency planning and preparedness strategies for management of a radiological or nuclear event in the US. These planning guides include protective action guidelines, medical countermeasure recommendations, and systems for diagnosing and triaging radiation injury. Yet, key areas such as perception of risk from radiation exposure by first responders have not been addressed. In this article, we identify the need to model and develop new strategies for the medical manage-ment of large-scale population exposures to radiation, examine the phenomena of radiation dread and its role in emergency response, and review recent findings on the willingness to work of first responders and other personnel involved in mass casualty medical management during a radiological or nuclear event.

Behavioral Cues of Humanness in Complex Environments: How People Engage With Human and Artificially Intelligent Agents in a Multiplayer Videogame.

The development of AI that can socially engage with humans is exciting to imagine, but such advanced algorithms might prove harmful if people are no longer able to detect when they are interacting with non-humans in online environments. Because we cannot fully predict how socially intelligent AI will be applied, it is important to conduct research into how sensitive humans are to behaviors of humans compared to those produced by AI. This paper presents results from a behavioral Turing Test, in which participants interacted with a human, or a simple or "social" AI within a complex videogame environment. Participants (66 total) played an open world, interactive videogame with one of these co-players and were instructed that they could interact non-verbally however they desired for 30 min, after which time they would indicate their beliefs about the agent, including three Likert measures of how much participants trusted and liked the co-player, the extent to which they perceived them as a "real person," and an interview about the overall perception and what cues participants used to determine humanness. T-tests, Analysis of Variance and Tukey's HSD was used to analyze quantitative data, and Cohen's Kappa and χ2 was used to analyze interview data. Our results suggest that it was difficult for participants to distinguish between humans and the social AI on the basis of behavior. An analysis of in-game behaviors, survey data and qualitative responses suggest that participants associated engagement in social interactions with humanness within the game.

Using Machine Learning to Classify Individuals With Alcohol Use Disorder Based on Treatment Seeking Status.

OBJECTIVE: The authors used a decision tree classifier to reduce neuropsychological, behavioral and laboratory measures to a subset of measures that best predicted whether an individual with alcohol use disorder (AUD) seeks treatment. METHOD: Clinical measures (N = 178) from 778 individuals with AUD were used to construct an alternating decision tree (ADT) with 10 measures that best classified individuals as treatment or not treatment-seeking for AUD. ADT's were validated by two methods: using cross-validation and an independent dataset (N = 236). For comparison, two other machine learning techniques were used as well as two linear models. RESULTS: The 10 measures in the ADT classifier were drinking behavior, depression and drinking-related psychological problems, as well as substance dependence. With cross-validation, the ADT classified 86% of individuals correctly. The ADT classified 78% of the independent dataset correctly. Only the simple logistic model was similar in accuracy; however, this model needed more than twice as many measures as ADT to classify at comparable accuracy. INTERPRETATION: While there has been emphasis on understanding differences between those with AUD and controls, it is also important to understand, within those with AUD, the features associated with clinically important outcomes. Since the majority of individuals with AUD do not receive treatment, it is important to understand the clinical features associated with treatment utilization; the ADT reported here correctly classified the majority of individuals with AUD with 10 clinically relevant measures, misclassifying < 7% of treatment seekers, while misclassifying 38% of non-treatment seekers. These individual clinically relevant measures can serve, potentially, as separate targets for treatment. FUNDING: Funding for this work was provided by the Intramural Research Programs of the National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institute on Drug Abuse (NIDA) and the Center for Information Technology (CIT). RESEARCH IN CONTEXT: Evidence Before This Study: Less than 10% of persons who meet lifetime criteria for Alcohol Use Disorder (AUD) receive treatment. As the etiology of AUD represents a complex interaction between neurobiological, social, environmental and psychological factors, low treatment utilization likely stems from barriers on multiple levels. Given this issue, it is important from both a research and clinical standpoint to determine what characteristics are associated with treatment utilization in addition to merely asking individuals if they wish to enter treatment. At the level of clinical research, if there are phenotypic differences between treatment and nontreatment-seekers that directly influence outcomes of early-phase studies, these phenotypic differences are a potential confound in assessing the utility of an experimental treatment for AUD. At the level of clinical practice, distinguishing between treatment- and nontreatment-seekers may help facilitate a targeted treatment approach. Previous efforts to understand the differences between these populations of individuals with AUD leveraged the multidimensional data collected in clinical research settings for AUD that are not well suited to traditional regression methods.Added Value of This Study: Alternating decision trees are well suited to deep-phenotyping data collected in clinical research settings as this approach handles nonparametric, skewed, and missing data whose relationships are nonlinear. This approach has proved to be superior in some cases to conventional clinical methods to solve diagnostic problems in medicine. We used a decision tree classifier to understand treatment- and non-treatment seeking group differences. The decision tree classifier approach chose a subset of factors arranged in an alternating decision tree that best predicts a given outcome. Assuming that the input measures are clinically relevant, the alternating decision tree that is generated has clinical value. Unlike other machine learning approaches, in addition to its predictive value, the nodes in the tree and their arrangement in a hierarchy have clinical utility. With the "if-then" logic of the tree, the clinician can learn what features become important and which recede in importance as the logic of the tree is followed. The decision tree classifier approach reduced 178 characterization measures (both categorical and continuous) in multiple domains to a decision tree comprised of 10 measures that together best classified subjects by treatment seeking status (yes/no).Implications After All the Available Evidence: We leveraged a large data set comprised of 178 clinical measures and using the decision tree approach, we have reduced these to a subset of 10 measures that accurately classified individuals with alcohol dependence by treatment utilization. From this analysis, drinking behavior variables and depression measures are strong treatment seeking predictors. Having identified a cluster of factors that predicts treatment seeking, we can assess the influence of these factors directly on the clinical study outcome measures themselves. In clinical practice these factors can be separate targets for treatment. In clinical research, the group differences my directly influence research outcomes for treatment of AUD.

Editors' Introduction: Cognitive Modeling at ICCM: Advancing the State of the Art.

Cognitive modeling is the effort to understand the mind by implementing theories of the mind in computer code, producing measures comparable to human behavior and mental activity. The community of cognitive modelers has traditionally met twice every 3 years at the International Conference on Cognitive Modeling (ICCM). In this special issue of topiCS, we present the best papers from the ICCM meeting. (The full proceedings are available on the ICCM website.) These best papers represent advances in the state of the art in cognitive modeling. Since ICCM was for the first time also held jointly with the Society for Mathematical Psychology, we use this preface to also reflect on the similarities and differences between mathematical psychology and cognitive modeling.

Validating Safecast data by comparisons to a U. S. Department of Energy Fukushima Prefecture aerial survey.

Safecast is a volunteered geographic information (VGI) project where the lay public uses hand-held sensors to collect radiation measurements that are then made freely available under the Creative Commons CC0 license. However, Safecast data fidelity is uncertain given the sensor kits are hand assembled with various levels of technical proficiency, and the sensors may not be properly deployed. Our objective was to validate Safecast data by comparing Safecast data with authoritative data collected by the U. S. Department of Energy (DOE) and the U. S. National Nuclear Security Administration (NNSA) gathered in the Fukushima Prefecture shortly after the Daiichi nuclear power plant catastrophe. We found that the two data sets were highly correlated, though the DOE/NNSA observations were generally higher than the Safecast measurements. We concluded that this high correlation alone makes Safecast a viable data source for detecting and monitoring radiation.