Publications

Abstract not provided.

This paper highlights key topic areas to be discussed the authors in a panel format during the Augmented Cognition thematic area paper session: 'Augmented Cognition Lessons Learned and Future Directions for Enabling 'Anyone, Anytime, Anywhere' Applications'. The term 'killer app' has been part of the vernacular in the commercial computer software and electronic devices industry to refer to breakthrough technologies [2]. A 'killer app' generally emerges with the development of related technologies that extends over some time and involves numerous variations on a basic concept. Hypotheses may be offered with respect to the conditions that will be needed to enable a similar situation with augmented cognition technologies. This paper and resulting panel session will address the numerous concepts that have emerged from the augmented cognition field to date and postulate how and when this field's first 'killer app' may emerge (e.g., 5, 10, 15, or more years from now).

Abstract not provided.

Abstract not provided.

This paper highlights key topic areas to be discussed the authors in a panel format during the Augmented Cognition thematic area paper session: 'Augmented Cognition Lessons Learned and Future Directions for Enabling 'Anyone, Anytime, Anywhere' Applications'. The term 'killer app' has been part of the vernacular in the commercial computer software and electronic devices industry to refer to breakthrough technologies [2]. A 'killer app' generally emerges with the development of related technologies that extends over some time and involves numerous variations on a basic concept. Hypotheses may be offered with respect to the conditions that will be needed to enable a similar situation with augmented cognition technologies. This paper and resulting panel session will address the numerous concepts that have emerged from the augmented cognition field to date and postulate how and when this field's first 'killer app' may emerge (e.g., 5, 10, 15, or more years from now).

Abstract not provided.

An ongoing program of research and development is utilizing nanomaterials as a basis for observing and measuring neurophysiological processes. Work commencing in fiscal year 2007 will focus on expanding current capabilities to create nanoelectrode arrays that will allow nanoscale measurement of the activity of 10's to 100's of neurons. This development is a vital step in gaining scientific insights concerning network properties associated with neural representations and processes. Specifically, attention will be focused the representation of memory in the hippocampus, for which extensive research has been conducted using laboratory rats. This report summarizes background research providing a foundation for work planned for fiscal year 2007 and beyond. In particular, the neuroanatomy and neurophysiology of the hippocampus is described. Additionally, several programs of research are described that have addressed the relationship between neurophysiological processes and behavioral measures of memory performance. These studies provide insight into methodological and analytic approaches for studying the representation of memory processes in the hippocampus. The objective of this report is to document relevant literature in a reference document that will support future research in this area.

Abstract not provided.

Variables influencing inferences about a stranger's goal during an unsolicited social interaction were explored. Experiment 1 developed a procedure for identifying cues. Experiments 2 and 3 assessed the relative importance of various cues (space, time, characteristics of oneself, characteristics of the stranger, and the stranger's behavior) for goal judgments. Results indicated that situational context cues informed goal judgments in ways that were consistent with diagnosticity ratings and typicality ratings of those cues. Stranger characteristics and stranger behaviors affected goal judgments more than would be expected from these quantitative measures of their informativeness. Nonetheless, the results are consistent with a mental model view that assumes perceivers monitor situational cues present during interactions and that goal inferences are guided by the informativeness of these cues.

A classification system is developed to identify driving situations from labeled examples of previous occurrences. The purpose of the classifier is to provide physical context to a separate system that mitigates unnecessary distractions, allowing the driver to maintain focus during periods of high difficulty. While watching videos of driving, we asked different users to indicate their perceptions of the current situation. We then trained a classifier to emulate the human recognition of driving situations using the Sandia Cognitive Framework. In unstructured conditions, such as driving in urban areas and the German autobahn, the classifier was able to correctly predict human perceptions of driving situations over 95% of the time. This paper focuses on the learning algorithms used to train the driving-situation classifier. Future work will reduce the human efforts needed to train the system. © 2005 IEEE.

This report documents work undertaken to endow the cognitive framework currently under development at Sandia National Laboratories with a human-like memory for specific life episodes. Capabilities have been demonstrated within the context of three separate problem areas. The first year of the project developed a capability whereby simulated robots were able to utilize a record of shared experience to perform surveillance of a building to detect a source of smoke. The second year focused on simulations of social interactions providing a queriable record of interactions such that a time series of events could be constructed and reconstructed. The third year addressed tools to promote desktop productivity, creating a capability to query episodic logs in real time allowing the model of a user to build on itself based on observations of the user's behavior.

This project is being conducted by Sandia National Laboratories in support of the DARPA Augmented Cognition program. Work commenced in April of 2002. The objective for the DARPA program is to 'extend, by an order of magnitude or more, the information management capacity of the human-computer warfighter.' Initially, emphasis has been placed on detection of an operator's cognitive state so that systems may adapt accordingly (e.g., adjust information throughput to the operator in response to workload). Work conducted by Sandia focuses on development of technologies to infer an operator's ongoing cognitive processes, with specific emphasis on detecting discrepancies between machine state and an operator's ongoing interpretation of events.

This report documents an exploratory FY 00 LDRD project that sought to demonstrate the first steps toward a realistic computational representation of the variability encountered in individual human behavior. Realism, as conceptualized in this project, required that the human representation address the underlying psychological, cultural, physiological, and environmental stressors. The present report outlines the researchers' approach to representing cognitive, cultural, and physiological variability of an individual in an ambiguous situation while faced with a high-consequence decision that would greatly impact subsequent events. The present project was framed around a sensor-shooter scenario as a soldier interacts with an unexpected target (two young Iraqi girls). A software model of the ''Sensor Shooter'' scenario from Desert Storm was developed in which the framework consisted of a computational instantiation of Recognition Primed Decision Making in the context of a Naturalistic Decision Making model [1]. Recognition Primed Decision Making was augmented with an underlying foundation based on our current understanding of human neurophysiology and its relationship to human cognitive processes. While the Gulf War scenario that constitutes the framework for the Sensor Shooter prototype is highly specific, the human decision architecture and the subsequent simulation are applicable to other problems similar in concept, intensity, and degree of uncertainty. The goal was to provide initial steps toward a computational representation of human variability in cultural, cognitive, and physiological state in order to attain a better understanding of the full depth of human decision-making processes in the context of ambiguity, novelty, and heightened arousal.

The authors describe a naturalistic behavioral model for the simulation of small unit combat. This model, Klein's recognition-primed decision making (RPD) model, is driven by situational awareness rather than a rational process of selecting from a set of action options. They argue that simulated combatants modeled with RPD will have more flexible and realistic responses to a broad range of small-scale combat scenarios. Furthermore, they note that the predictability of a simulation using an RPD framework can be easily controlled to provide multiple evaluations of a given combat scenario. Finally, they discuss computational issues for building an RPD-based behavior engine for fully automated combatants in small conflict scenarios, which are being investigated within Sandia's Next Generation Site Security project.

Despite extensive safety analysis and application of safety measures, there is a frequent lament, "Why do we continue to have accidents?" Two breakdowns are prevalent in risk management and prevention. First, accidents result from human actions that engineers, analysts and management never envisioned and second, controls, intended to preclude/mitigate accident sequences, prove inadequate. This paper addresses the first breakdown, the inability to anticipate scenarios involving human action/inaction. The failure of controls has been addressed in a previous publication (Forsythe and Grose, 1998). Specifically, this paper presents an approach referred to as "surety." The objective of this approach is to provide high levels of assurance in situations where potential system failure paths cannot be fully characterized. With regard to human elements of complex systems, traditional approaches to human reliability are not sufficient to attain surety. Consequently, an Organic Model has been developed to account for the organic properties exhibited by engineered systems that result from human involvement in those systems.