Michael Lewis Bernard

Mindfulness is associated with fewer PTSD symptoms, depressive symptoms, physical symptoms, and alcohol problems in urban firefighters.

OBJECTIVE This study investigated the association between mindfulness, other resilience resources, and several measures of health in 124 urban firefighters. METHOD Participants completed health measures of posttraumatic stress disorder (PTSD) symptoms, depressive symptoms, physical symptoms, and alcohol problems and measures of resilience resources including mindfulness, optimism, personal mastery, and social support. The Mindful Awareness and Attention Scale (MAAS; Brown & Ryan, 2003) was used to assess mindfulness. Participants also completed measures of firefighter stress, number of calls, and years as a firefighter as control variables. Hierarchical multiple regressions were conducted with the health measures as the dependent variables with 3 levels of independent variables: (a) demographic characteristics, (b) firefighter variables, and (c) resilience resources. RESULTS The results showed that mindfulness was associated with fewer PTSD symptoms, depressive symptoms, physical symptoms, and alcohol problems when controlling for the other study variables. Personal mastery and social support were also related to fewer depressive symptoms, firefighter stress was related to more PTSD symptoms and alcohol problems, and years as a firefighter were related to fewer alcohol problems. CONCLUSIONS Mindfulness may be important to consider and include in models of stress, coping, and resilience in firefighters. Future studies should examine the prospective relationship between mindfulness and health in firefighters and others in high-stress occupations.

Model-Free Learning and Control in a Mobile Robot

A model-free, biologically-motivated learning and control algorithm called S-learning is described as implemented in an Surveyor SRV-1 mobile robot. S-learning demonstrated learning of robotic and environmental structure sufficient to allow it to achieve its goals (finding high- or low-contrast views in its environment). No modeling information about the task or calibration information about the robot’s actuators and sensors were used in S-learning’s planning. The ability of S-learning to make movement plans was completely dependent on experience it gained as it explored. Initially it had no experience and was forced to wander randomly. With increasing exposure to the task, S-learning achieved its goals with more nearly optimal paths. The fact that this approach is model-free implies that it may be applied to many other systems, perhaps even to systems of much greater complexity.

Predicting the anticipated emotional and behavioral responses to an avian flu outbreak

Background The purpose of this study was to develop a model to predict the emotional and behavioral responses to an avian flu outbreak. Methods The participants were 289 university students ranging in age, income, and ethnic backgrounds. They were presented with scenarios describing avian flu outbreaks affecting their community. They reported their anticipated emotional responses (positive emotion, negative emotion) and behavioral responses (helping, avoidance, sacrifice, illegal behavior) as if the scenarios were actually occurring. They also were assessed on individual differences expected to predict their responses. Results Participants were only modestly familiar with the avian flu and anticipated strong emotional and behavioral responses to an outbreak. Path analyses were conducted to test a model for predicting responses. The model showed that age, sex, income, spirituality, resilience, and neuroticism were related to responses. Spirituality, resilience, and income predicted better emotional responses, and neuroticism and female sex predicted worse emotional responses. Age, sex, income, and spirituality had direct effects on behavior. The emotional responses were directly related to each behavior and mediated the effects of individual differences. Conclusion Emotional responses may be important in predicting behavior after an outbreak of avian flu, and personal characteristics may predict both emotional and behavioral responses.

Preparing for the aftermath: Using emotional agents in game-based training for disaster response

Ground truth, a training game developed by Sandia National Laboratories in partnership with the University of Southern California GamePipe Lab, puts a player in the role of an incident commander working with teammate agents to respond to urban threats. These agents simulate certain emotions that a responder may feel during this high-stress situation. We construct psychology-plausible models compliant with the Sandia Human Embodiment and Representation Cognitive Architecture (SHERCA) that are run on the sandia cognitive runtime engine with active memory (SCREAM) software. SCREAMs computational representations for modeling human decision-making combine aspects of ANNs and fuzzy logic networks. This paper gives an overview of ground truth and discusses the adaptation of the SHERCA and SCREAM into the game. We include a semiformal description of SCREAM.

Foundations to the unified psycho-cognitive engine.

This document outlines the key features of the SNL psychological engine. The engine is designed to be a generic presentation of cognitive entities interacting among themselves and with the external world. The engine combines the most accepted theories of behavioral psychology with those of behavioral economics to produce a unified simulation of human response from stimuli through executed behavior. The engine explicitly recognizes emotive and reasoned contributions to behavior and simulates the dynamics associated with cue processing, learning, and choice selection. Most importantly, the model parameterization can come from available media or survey information, as well subject-matter-expert information. The framework design allows the use of uncertainty quantification and sensitivity analysis to manage confidence in using the analysis results for intervention decisions.

Temporal semantics: An Adaptive Resonance Theory approach

Encoding sensor observations across time is a critical component in the ability to model cognitive processes. All biological cognitive systems receive sensory stimuli as continuous streams of observed data over time. Therefore, the perceptual grounding of all biological cognitive processing is in temporal semantic encodings, where the particular grounding semantics are sensor modalities. We introduce a technique that encodes temporal semantic data as temporally integrated patterns stored in Adaptive Resonance Theory (ART) modules.

Using Psychologically Plausible Operator Cognitive Models to Enhance Operator Performance

Research by Sandia National Laboratories (SNL) is currently being conducted that seeks to embody human-like cognitive capacities in machines by transforming the human-machine interaction so that it more closely resembles a human-to-human interaction. This document reports on the initial phase of research and development by SNL in creating a capability whereby a machine-based cognitive model provides a real-time awareness of the cognitive state of an operator. In the capability referred to as “Discrepancy Detection,” the machine uses an operators cognitive model to monitor its own state and when there is evidence of a discrepancy between the actual state of the machine and the operators perceptions concerning the state of the machine, a discrepancy may be signaled. The current project offers successful evidence that a machine may accurately infer an operators interpretation of situations based on an individualized cognitive model of the operator.

Modeling Populations of Interest in Order to Simulate Cultural Response to Influence Activities

This paper describes an effort by Sandia National Laboratories to model and simulate populations of specific countries of interest as well as the population’s primary influencers, such as government and military leaders. To accomplish this, high definition cognition models are being coupled with an aggregate model of a population to produce a prototype, dynamic cultural representation of a specific country of interest. The objective is to develop a systems-level, intrinsic security capability that will allow analysts to better assess the potential actions, counteractions, and influence of powerful individuals within a country of interest before, during, and after an US initiated event. 1 Societal Assessment Capability The United States is finding itself increasingly engaged in the development of unconventional partnerships that require a variety of non-traditional activities to better support political and economic stability in regions of interest. Unfortunately, there is no effective means to adequately forecast and assess how both individual leaders, and the people they influence, will behave with regard to possible US policies and actions. It is asserted here that an accurate characterization of a society must represent this interaction between people under control, those influencing power, and external variables, such as US actions or oil revenue variation (in counties dependent on oil). While assessment tools have modeled and simulated societies, they have, thus far, been limited to gross behavioral models. Furthermore, no existing macroeconomic or societal model addresses security dynamics or coordinated multiple kinetic and non-kinetic interventions. We believe that the phenomena that maintain or transition dictatorship and democracy have recently become understandable enough to pose testable hypotheses amenable to simulation. As such, the ability to address intervention dynamics and unintended, higher order consequences is a key goal of this work. In pursuit of this goal, Sandia National Laboratories (Sandia) has developed a prototype societal assessment capability that assists in the behavioral influence analysis of foreign targets of interest. The objective of the described work is to develop a systems-level capability that will allow analysts to better assess potential actions and counter-actions of individuals interacting within a foreign country of interest before, during, and after an US initiated event. The assessment is designed to address the dynamics that drive stability and instability. Specifically, it is designed to: (1) assess adversarial choice options that allow analysts to pose “what-if” queries concerning hypothetical policy and/or military initiatives to help determine how and why a population may react to a specific event, leader, or operation across time, (2) assess potential blind spots by providing analysts with the ability to better understand higher order interaction effects between leaders and local societies and how allegiances are formed and changed over time, (3) perform risk analysis by determining the limiting assumptions and unknowns for the successful outcome, and (4) perform risk management by establishing whether there are delayed consequences that will require mitigation or adjustments to planning. Collectively, this type of simulation is designed to permit assessment of shaping activities and US tactics in an operational environment by creating a system that can help an analyst better understand the interaction between leaders and local societies and how allegiances are formed and changed over time. To accomplish this Sandia is utilizing its extensive technical expertise in Modeling & Simulation (M&S) to create a social simulation platform that couples HighDefinition Cognitive Models (HDCM) with a cultural, economic, and policy-based simulation. The HDCMs are purposely designed to computationally represent the mindset of specific individuals, including their cognitive perceptions, goals, emotion states, and action intentions. The actions of one HDCM can affect the mindset and actions of others, as well as the general mindset of the society in which they are situated. The society, computationally represented in this initial effort by Sandia’s Systems Dynamics-based Aggregate Societal Model (SDASM) can, in turn, affect the actions of the HDCMs (see Figure 1). The HDCM is focused on individual or smallgroup level of analysis, whereas the SDASM is focused at an aggregate level social, economic, and cultural level of analysis. These models are joined to provide a highfidelity, scaleable assessment tool of individuals, small groups, and society to produce outcome distributions investigating attitudinal and behavioral reactions to US policies for a given country, group, or ethnic region. Figure 1. A conceptual view of Sandia’s High Definition Aggregate Societal Model-

Using Computational Modeling to Examine Shifts Towards Extremist Behaviors in European Diaspora Communities

We created a simulation model to investigate potential links between the actions of violent extremist organizations (VEOs), people in the VEO’s home country, and diaspora communities from that country living in the West. We created this model using the DYMATICA framework, which uses a hybrid cognitive-system dynamics modeling strategy to simulate behaviors based on psycho-social theory. Initial results of the model are given, focusing on increases to VEO funding and recruiting resulting from an invasion of the VEO’s home country. Western intervention, prejudice, and economic drivers are also considered.

Adult Neurogenesis: Implications on Human And Computational Decision Making

Adult neurogenesis is the incorporation of new neurons into established, functioning neural circuits. Current theoretical work in the neurogenesis field has suggested that new neurons are of greatest importance in the encoding of new memories, particularly in the ability to fully capture features which are entirely novel or being experienced in a unique way. We present two models of neurogenesis (a spiking, biologically realistic model as well as a basic growing feedforward model) to investigate possible functional implications. We use an information theoretic computational complexity measure to quantitatively analyze the information content encoded with and without neurogenesis in our spiking model. And neural encoding capacity (as a function of neuron maturation) is examined in our simple feedforward network. Finally, we discuss potential functional implications for neurogenesis in high risk environments.