Publications

Abstract not provided.

Performance at Transportation Security Administration (TSA) airport checkpoints must be consistently high to skillfully mitigate national security threats and incidents. To accomplish this, Transportation Security Officers (TSOs) must exceptionally perform in threat detection, interaction with passengers, and efficiency. It is difficult to measure the human attributes that contribute to high performing TSOs because cognitive ability such as memory, personality, and competence are inherently latent variables. Cognitive scientists at Sandia National Laboratories have developed a methodology that links TSOs’ cognitive ability to their performance. This paper discusses how the methodology was developed using a strict quantitative process, the strengths and weaknesses, as well as how this could be generalized to other non-TSA contexts. The scope of this project is to identify attributes that distinguished high and low TSO performance for the duties at the checkpoint that involved direct interaction with people going through the checkpoint.

Visual search has been an active area of research – empirically and theoretically – for a number of decades, however much of that work is based on novice searchers performing basic tasks in a laboratory. This paper summarizes some of the issues associated with quantifying expert, domain-specific visual search behavior in operationally realistic environments.

Visual search data describe people’s performance on the common perceptual problem of identifying target objects in a complex scene. Technological advances in areas such as eye tracking now provide researchers with a wealth of data not previously available. The goal of this work is to support researchers in analyzing this complex and multimodal data and in developing new insights into visual search techniques. We discuss several methods drawn from the statistics and machine learning literature for integrating visual search data derived from multiple sources and performing exploratory data analysis. We ground our discussion in a specific task performed by officers at the Transportation Security Administration and consider the applicability, likely issues, and possible adaptations of several candidate analysis methods.

Numerous domains, ranging from medical diagnostics to intelligence analysis, involve visual search tasks in which people must find and identify specific items within large sets of imagery. These tasks rely heavily on human judgment, making fully automated systems infeasible in many cases. Researchers have investigated methods for combining human judgment with computational processing to increase the speed at which humans can triage large image sets. One such method is rapid serial visual presentation (RSVP), in which images are presented in rapid succession to a human viewer. While viewing the images and looking for targets of interest, the participant’s brain activity is recorded using electroencephalography (EEG). The EEG signals can be time-locked to the presentation of each image, producing event-related potentials (ERPs) that provide information about the brain’s response to those stimuli. The participants’ judgments about whether or not each set of images contained a target and the ERPs elicited by target and non-target images are used to identify subsets of images that merit close expert scrutiny [1]. Although the RSVP/EEG paradigm holds promise for helping professional visual searchers to triage imagery rapidly, it may be limited by the nature of the target items. Targets that do not vary a great deal in appearance are likely to elicit useable ERPs, but more variable targets may not. In the present study, we sought to extend the RSVP/EEG paradigm to the domain of aviation security screening, and in doing so to explore the limitations of the technique for different types of targets. Professional Transportation Security Officers (TSOs) viewed bag X-rays that were presented using an RSVP paradigm. The TSOs viewed bursts of images containing 50 segments of bag X-rays that were presented for 100 ms each. Following each burst of images, the TSOs indicated whether or not they thought there was a threat item in any of the images in that set. EEG was recorded during each burst of images and ERPs were calculated by time-locking the EEG signal to the presentation of images containing threats and matched images that were identical except for the presence of the threat item. Half of the threat items had a prototypical appearance and half did not. We found that the bag images containing threat items with a prototypical appearance reliably elicited a P300 ERP component, while those without a prototypical appearance did not. These findings have implications for the application of the RSVP/EEG technique to real-world visual search domains.

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A key challenge in developing complete human equivalence is how to ground a synoptic theory of cognition in neural reality. Both cognitive architectures and neural models provide insight into how biological brains work, but from opposite directions. Here the authors report on initial work aimed at interpreting connectomic data in terms of algorithms. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000. © 2011 The authors and IOS Press. All rights reserved.

In this work, we developed a self-organizing map (SOM) technique for using web-based text analysis to forecast when a group is undergoing a phase change. By 'phase change', we mean that an organization has fundamentally shifted attitudes or behaviors. For instance, when ice melts into water, the characteristics of the substance change. A formerly peaceful group may suddenly adopt violence, or a violent organization may unexpectedly agree to a ceasefire. SOM techniques were used to analyze text obtained from organization postings on the world-wide web. Results suggest it may be possible to forecast phase changes, and determine if an example of writing can be attributed to a group of interest.

In the preceding discussion paper, I proposed a theory of prefrontal cortical organization that was fundamentally intended to address the question: How does prefrontal cortex (PFC) support the various functions for which it seems to be selectively recruited? In so doing, I chose to focus on a particular function, analogy, that seems to have been largely ignored in the theoretical treatments of PFC, but that does underlie many other cognitive functions (Hofstadter, 2001; Holyoak & Thagard, 1997). At its core, this paper was intended to use analogy as a foundation for exploring one possibility for prefrontal function in general, although it is easy to see how the analogy-specific interpretation arises (as in the comment by Ibáñez). In an attempt to address this more foundational question, this response will step away from analogy as a focus, and will address first the various comments from the perspective of the initial motivation for developing this theory, and then specific issues raised by the commentators. © 2010 Psychology Press.

In this work, we developed a self-organizing map (SOM) technique for using web-based text analysis to forecast when a group is undergoing a phase change. By 'phase change', we mean that an organization has fundamentally shifted attitudes or behaviors. For instance, when ice melts into water, the characteristics of the substance change. A formerly peaceful group may suddenly adopt violence, or a violent organization may unexpectedly agree to a ceasefire. SOM techniques were used to analyze text obtained from organization postings on the world-wide web. Results suggest it may be possible to forecast phase changes, and determine if an example of writing can be attributed to a group of interest.

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In this work, we developed a self-organizing map (SOM) technique for using web-based text analysis to forecast when a group is undergoing a phase change. By 'phase change', we mean that an organization has fundamentally shifted attitudes or behaviors. For instance, when ice melts into water, the characteristics of the substance change. A formerly peaceful group may suddenly adopt violence, or a violent organization may unexpectedly agree to a ceasefire. SOM techniques were used to analyze text obtained from organization postings on the world-wide web. Results suggest it may be possible to forecast phase changes, and determine if an example of writing can be attributed to a group of interest.

Abstract not provided.

Attitudes play a significant role in determining how individuals process information and behave. In this paper we have developed a new computational model of population wide attitude change that captures the social level: how individuals interact and communicate information, and the cognitive level: how attitudes and concept interact with each other. The model captures the cognitive aspect by representing each individuals as a parallel constraint satisfaction network. The dynamics of this model are explored through a simple attitude change experiment where we vary the social network and distribution of attitudes in a population. Copyright © 2010, Association for the Advancement of Artificial Intelligence. All rights reserved.

Functional brain imaging is of great interest for understanding correlations between specific cognitive processes and underlying neural activity. This understanding can provide the foundation for developing enhanced human-machine interfaces, decision aides, and enhanced cognition at the physiological level. The functional near infrared spectroscopy (fNIRS) based event-related optical signal (EROS) technique can provide direct, high-fidelity measures of temporal and spatial characteristics of neural networks underlying cognitive behavior. However, current EROS systems are hampered by poor signal-to-noise-ratio (SNR) and depth of measure, limiting areas of the brain and associated cognitive processes that can be investigated. We propose to investigate a flexible, tunable, multi-spectral fNIRS EROS system which will provide up to 10x greater SNR as well as improved spatial and temporal resolution through significant improvements in electronics, optoelectronics and optics, as well as contribute to the physiological foundation of higher-order cognitive processes and provide the technical foundation for miniaturized portable neuroimaging systems.

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The purpose of this work was to help develop a research roadmap and small proof ofconcept for addressing key problems and gaps from the perspective of using text analysis methods as a primary tool for detecting when a group is undergoing a phase change. Self- rganizing map (SOM) techniques were used to analyze text data obtained from the tworld-wide web. Statistical studies indicate that it may be possible to predict phase changes, as well as detect whether or not an example of writing can be attributed to a group of interest.

The target article by Leech et al. presents a compelling computational theory of analogy-making. However, there is a key difficulty that persists in theoretical treatments of analogy-making, computational and otherwise: namely, the lack of a detailed account of the neurophysiological mechanisms that give rise to analogy behavior. My commentary explores this issue. © 2008 Cambridge University Press.

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Human behavior is a function of an iterative interaction between the stimulus environment and past experience. It is not simply a matter of the current stimulus environment activating the appropriate experience or rule from memory (e.g., if it is dark and I hear a strange noise outside, then I turn on the outside lights and investigate). Rather, it is a dynamic process that takes into account not only things one would generally do in a given situation, but things that have recently become known (e.g., there have recently been coyotes seen in the area and one is known to be rabid), as well as other immediate environmental characteristics (e.g., it is snowing outside, I know my dog is outside, I know the police are already outside, etc.). All of these factors combine to inform me of the most appropriate behavior for the situation. If it were the case that humans had a rule for every possible contingency, the amount of storage that would be required to enable us to fluidly deal with most situations we encounter would rapidly become biologically untenable. We can all deal with contingencies like the one above with fairly little effort, but if it isn't based on rules, what is it based on? The assertion of the Cognitive Systems program at Sandia for the past 5 years is that at the heart of this ability to effectively navigate the world is an ability to discriminate between different contexts (i.e., Dynamic Context Discrimination, or DCD). While this assertion in and of itself might not seem earthshaking, it is compelling that this ability and its components show up in a wide variety of paradigms across different subdisciplines in psychology. We begin by outlining, at a high functional level, the basic ideas of DCD. We then provide evidence from several different literatures and paradigms that support our assertion that DCD is a core aspect of cognitive functioning. Finally, we discuss DCD and the computational model that we have developed as an instantiation of DCD in more detail. Before commencing with our overview of DCD, we should note that DCD is not necessarily a theory in the classic sense. Rather, it is a description of cognitive functioning that seeks to unify highly similar findings across a wide variety of literatures. Further, we believe that such convergence warrants a central place in efforts to computationally emulate human cognition. That is, DCD is a general principle of cognition. It is also important to note that while we are drawing parallels across many literatures, these are functional parallels and are not necessarily structural ones. That is, we are not saying that the same neural pathways are involved in these phenomena. We are only saying that the different neural pathways that are responsible for the appearance of these various phenomena follow the same functional rules - the mechanisms are the same even if the physical parts are distinct. Furthermore, DCD is not a causal mechanism - it is an emergent property of the way the brain is constructed. DCD is the result of neurophysiology (cf. John, 2002, 2003). Finally, it is important to note that we are not proposing a generic learning mechanism such that one biological algorithm can account for all situation interpretation. Rather, we are pointing out that there are strikingly similar empirical results across a wide variety of disciplines that can be understood, in part, by similar cognitive processes. It is entirely possible, even assumed in some cases (i.e., primary language acquisition) that these more generic cognitive processes are complemented and constrained by various limits which may or may not be biological in nature (cf. Bates & Elman, 1996; Elman, in press).

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.