Ann Speed

Recreating Raven's: Software for systematically generating large numbers of Raven-like matrix problems with normed properties

Raven’s Progressive Matrices is a widely used test for assessing intelligence and reasoning ability (Raven, Court, & Raven, 1998). Since the test is nonverbal, it can be applied to many different populations and has been used all over the world (Court & Raven, 1995). However, relatively few matrices are in the sets developed by Raven, which limits their use in experiments requiring large numbers of stimuli. For the present study, we analyzed the types of relations that appear in Raven’s original Standard Progressive Matrices (SPMs) and created a software tool that can combine the same types of relations according to parameters chosen by the experimenter, to produce very large numbers of matrix problems with specific properties. We then conducted a norming study in which the matrices we generated were compared with the actual SPMs. This study showed that the generated matrices both covered and expanded on the range of problem difficulties provided by the SPMs.

Abstract relational categories, graded persistence, and prefrontal cortical representation

Human behavior is characterized by creativity, flexibility, and adaptability. Psychologists have argued that this is a result of analogical reasoning processes. Neuroimaging studies point to PFC as a critical component of a larger network; however, it remains unclear how the brain accomplishes analogical reasoning. This paper presents a theory of prefrontal cortical function that attempts to explain the neural mechanisms of analogical processing in the context of the broader theoretical and empirical work on PFC. Specifically, the current paper proposes that neurons in PFC are particularly sensitive to relationship information, and that they develop response preferences for relationship information that increases in abstractness and complexity along the posterior–anterior axis. Further, this theory posits that representation formation in PFC is driven by fronto-striatal circuits and that the persistence of these representations is determined by environmental consequences such that the longer the representation predicts reward or punishment, the longer the representation lasts. Finally, it is suggested that because analogy has been proposed as a core cognitive process, underlying many other interesting cognitive phenomena such as learning, creativity, and decision-making, it could serve as a useful tool for studying PFC function in general.

Determining the optimal time on x-ray analysis for Transportation Security Officers.

The Transportation Security Administration has a large workforce of Transportation Security Officers, most of whom perform interrogation of x-ray images at the passenger checkpoint. To date, TSOs on the x-ray have been limited to a 30-min session at a time, however, it is unclear where this limit originated. The current paper outlines methods for empirically determining if that 30-min duty cycle is optimal and if there are differences between individual TSOs. This work can inform scheduling TSOs at the checkpoint and can also inform whether TSOs should continue to be cross-trained (i.e., performing all 6 checkpoint duties) or whether specialization makes more sense.

Exploratory Analysis of Visual Search Data

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.

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.

The Role of Visual Inspection in the 21st Century

Visual inspection research has a long history spanning the 20th century and continuing to the present day. Current efforts in multiple venues demonstrate that visual inspection continues to have a vital role for many different types of tasks in the 21st century. The nature of this role spans the range from traditional human visual inspection to fully automated detection of defects. Consequently, today’s practitioners must not only successfully identify and apply lessons learned from the past, but also explore new areas of research in order to derive solutions for modern day issues such as those presented by introducing automation during inspection. A key lesson from past research indicates that the factors that can degrade performance will persist today, unless care is taken to design the inspection process appropriately.

Exploring Human-Technology Interaction in Layered Security Military Applications

System-of-systems modeling has traditionally focused on physical systems rather than humans, but recent events have proved the necessity of considering the human in the loop. As technology becomes more complex and layered security continues to increase in importance, capturing humans and their interactions with technologies within the system-of-systems will be increasingly necessary. After an extensive job-task analysis, a novel type of system-of-systems simulation model has been created to capture the human-technology interactions on an extra-small forward operating base to better understand performance, key security drivers, and the robustness of the base. In addition to the model, an innovative framework for using detection theory to calculate d’ for individual elements of the layered security system, and for the entire security system as a whole, is under development.

The Controlled, Large Online Social Experimentation Platform (CLOSE)

We present a new platform to do online, social influence experiments – the Controlled, Online Social Experimentation (CLOSE) system. We describe it’s development, potential uses and justification for use. The CLOSE platform can be used to do long term (weeks to months) experiments in which we can manipulate the interaction networks (within the experiment) of a diverse (drawn from Amazon Mechanical Turk (AMT)) subject pool.

A Quantitative Methodology for Identifying Attributes Which Contribute to Performance for Officers at the Transportation Security Administration

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.

Key parameters for modeling information diffusion in populations

Modeling and simulation can be an important tool in helping develop techniques to better communicate safety-critical information for disaster preparation and recovery. However, these tools are only moderately useful if they do not capture both the social component (how information diffuses in a population through communication between individuals) and the cognitive component (how individuals integrate information and change behavior). The objective of this paper is to lay the groundwork for more complex simulations by providing a summarization of some of the important phenomenon identified in the attitude change literature. We describe four processes that are important to capture: (1) the drive for consistency; (2) information distortion; (3) persuasion route; and (4) implicit/explicit attitudes. We describe the experiments that illustrated these phenomenon and the factors that influence them (cognitive load, attitude relationships, and the social network). Finally, we describe a conceptual model that captures some of these processes and can be used as a starting point.