James J. Staszewski

The relationship between age and major league baseball performance: implications for development.

Lifetime performance data of 388 baseball players active in 1965 were analyzed to determine the age of peak performance for skills required to play baseball, to derive age-performance curves for athletic productivity, and to assess the magnitude of individual differences among elite and less able players. Cross-sectional and longitudinal analyses show that athletic performance on key indicators rises relatively quickly from age 19 to a peak age of 27 and then declines. The primary difference between elite and less able players is that performance of the elite players remains high for a longer period of time and decays more gradually. The performance of the most elite players is superior to that of less able players even at very early ages. These results parallel findings reported for other achievement domains and can be explained in terms of basic developmental processes involving the interaction of experience, physiological capacity, and motivation.

A functional model of sensemaking in a neurocognitive architecture

Sensemaking is the active process of constructing a meaningful representation (i.e., making sense) of some complex aspect of the world. In relation to intelligence analysis, sensemaking is the act of finding and interpreting relevant facts amongst the sea of incoming reports, images, and intelligence. We present a cognitive model of core information-foraging and hypothesis-updating sensemaking processes applied to complex spatial probability estimation and decision-making tasks. While the model was developed in a hybrid symbolic-statistical cognitive architecture, its correspondence to neural frameworks in terms of both structure and mechanisms provided a direct bridge between rational and neural levels of description. Compared against data from two participant groups, the model correctly predicted both the presence and degree of four biases: confirmation, anchoring and adjustment, representativeness, and probability matching. It also favorably predicted human performance in generating probability distributions across categories, assigning resources based on these distributions, and selecting relevant features given a prior probability distribution. This model provides a constrained theoretical framework describing cognitive biases as arising from three interacting factors: the structure of the task environment, the mechanisms and limitations of the cognitive architecture, and the use of strategies to adapt to the dual constraints of cognition and the environment.

Expert Decision Making in Landmine Detection

Previous study has shown that an understanding of expert performance expressed as a cognitive model represents a valuable training asset, with uses ranging from improving training design to informing feedback in intelligent tutoring systems. This paper presents performance data from an expert landmine detection operator, and a general cognitive model of frequency-based decision-making applied to the specific task of making mine-vs-clutter decisions based on sequences of discrete stimuli. The functional capabilities of the model are studied in a general testbed, then its performance is directly compared to that of the expert. Further extensions and applications of the cognitive model are discussed.

Expert detection of improvised explosive device emplacement behavior

The objective of this study was to uncover the cognitive underpinnings of Improvised Explosive Device (IED) emplacement detection expertise possessed by United States Army Unmanned Aerial System (UAS) Mission Payload Operators (MPOs) who have a proven history of success at this task. Specific issues of interest include identifying strategies used to detect IED emplacement threats, as well as identifying indicators and cues associated with IED emplacement to provide the basis for future training. We reviewed existing training programs and interviewed MPOs with varying levels of in-theater experience. Initial data gathered was verified by presenting video recordings from UAS sensors depicting possible IED emplacement activity to an additional group of experienced MPOs. These videos were used to elicit cues and strategies used to identify potential threats. Results of this study highlight tactics, techniques, and procedures (TTPs) employed by experienced MPOs. The results also emphasize the need for training on IED emplacement detection and support the presentation of feedback from tactical ground units to reinforce effective search strategies. Finally, there is support for the development of realistic IED emplacement indicators in the visual models, supporting simulation for use as an unclassified training tool for initial and reinforcement training.

Optical detection of buried explosive hazards: a longitudinal comparison of three types of imagery

Visual detection of soil disturbances is a surprisingly effective, but far from perfect way of detecting buried explosive threats such as landmines and improvised explosive devices (IEDs). This effort builds upon the few systematic studies of optical detection in this area. It investigates observer sensitivity to optical information produced by the burial of anti-tank and small anti-personnel landmines asking “How detectable are disturbed soil signatures captured in visible (VIS), shortwave infrared (SWIR), and thermal infrared (TIR), bands?” “Which band or bands are most effective for detection?” and “How well does each band support detection in the natural environment over time?” Using signal detection procedures this study presented young adults photographs showing soil disturbed by landmine burial or adjacent undisturbed surfaces with instructions to make decisions about the presence or absence of a disturbance. Stimuli spanned a six-week time period over which VIS, SWIR, and TIR imagery was collected. Results show that (a) signal strength persists surprisingly well over the observation period, (b) generally, SWIR and VIS show consistently strong performance for large anti-tank mines and SWIR shows the soil signature for the small, anti-personnel mine remarkably well. TIR lags the other two bands when using d’ to measure performance, but shows promising hit rates for anti-tank mine signatures under appropriate conditions. Generally, results show that the SWIR and VIS bands show most promise as a practical means of explosive hazards detection, although TIR can work effectively for large anti-tank mines under certain conditions. Limitations and implications for further research are discussed.

Characterizing optical properties of disturbed surface signatures

The burial of objects disturbs the ground surface in visually perceptible ways. This project investigated how such information can inform detection via imaging from visible through mid-infrared wavelengths. Images of the ground surface where objects were buried were collected at multiple visible through mid-infrared wavelengths prior to burial and afterward at intervals spanning approximately two weeks. Signs of soil disturbed by emplacement change over time and exposure in the natural environment and vary in salience across wavelengths for different time periods. Transient cues related to soil moisture or illumination angle can make signatures extraordinarily salient under certain conditions. Longpass shortwave infrared and multi-band mid-infrared imaging can enhance the signature of disturbed soils over visible imaging. These findings add knowledge and understanding of how soil disturbances phenomena can be exploited to aid detection.

Optical cues for buried landmine detection

Objects buried in unimproved surfaces can be inferred from the disturbance of the soil above them. We have found for mines emplaced according to U.S. military doctrine in clay-rich soils, that imaging at visible, shortwave infrared, and thermal infrared are effective at different times under various illumination conditions, and that these techniques can be synergistic. Complementary visible - thermal infrared laboratory spectral measurements show that grain size differences associated with disturbed soils can make them more reflective or emissive than undisturbed soils. However, the field measurements demonstrate that grain size effects are not significant under passive visible and shortwave infrared illumination. Instead, shortwave infrared (1.55 - 1.7 μm) imaging, in particular, is effective because the roughened disturbed soil casts a pattern of shadows under a wide range of illumination conditions that are also emphasized by a background of undisturbed soil possessing few contrast variations.

Optical Detection of Buried Explosive Hazards A Longitudinal Comparison of Three Types of Imagery

Visual detection of soil disturbances is an effective, but imperfect method for detecting buried explosive threats such as landmines and improvised explosive devices (IEDs). Building upon prior studies of optical detection, this study uses signal detection methods to measure observer sensitivity to images of soil disturbances asking “How detectable are disturbed soil signatures recorded in visible (VIS), short-wave infrared (SWIR), and thermal infrared (TIR) bands?” “How effective is each band for detection?” and “How is signature salience in each band effected by extended exposure in the natural environment?” Young adults viewed photos showing either soil disturbed by landmine burial or adjacent undisturbed surfaces and made yes/no decisions about the presence of a disturbance. Stimuli spanned a six-week time period over which VIS, SWIR, and TIR images were collected. Results show that (a) substantial signal strength lasts over the six-week period, (b) generally, SWIR and VIS show consistently strong performance for large anti-tank mines and (c) the soil signatures for the small, anti-personnel mine stay remarkably strong in SWIR. TIR sensitivity lags SWIR and VIS, but shows promising hit rates for anti-tank mine signatures under some conditions. Generally, results show that optical detection, particularly using the SWIR and VIS bands, shows promise for explosive hazards detection, at least under the conditions observed.

PETALS: a visual interface for landmine detection

Post-conflict landmines have serious humanitarian repercussions: landmines cost lives, limbs and land. The primary method used to locate these buried devices relies on the inherently dangerous and difficult task of a human listening to audio feedback from a metal detector. Researchers have previously hypothesized that expert operators respond to these challenges by building mental patterns with metal detectors through the identification of object-dependent spatially distributed metallic fields. This paper presents the preliminary stages of a novel interface - Pattern Enhancement Tool for Assisting Landmine Sensing (PETALS) - that aims to assist with building and visualizing these patterns, rather than relying on memory alone. Simulated demining experiments show that the experimental interface decreases classification error from 23% to 5% and reduces localization error by 54%, demonstrating the potential for PETALS to improve novice deminer safety and efficiency.

Defining the correctness of a diagnosis: differential judgments and expert knowledge

Approaches that use a simulated patient case to study and assess diagnostic reasoning usually use the correct diagnosis of the case as a measure of success and as an anchor for other measures. Commonly, the correctness of a diagnosis is determined by the judgment of one or more experts. In this study, the consistency of experts’ judgments of the correctness of a diagnosis, and the structure of knowledge supporting their judgments, were explored using a card sorting task. Seven expert pediatricians were asked to sort into piles the diagnoses proposed by 119 individuals who had worked through a simulated patient case of Haemophilus influenzae Type B (HIB) meningitis. The 119 individuals had varying experience levels. The expert pediatricians were asked to sort the proposed diagnoses by similarity of content, and then to order the piles based on correctness, relative to the known correct diagnosis (HIB meningitis). Finally, the experts were asked to judge which piles contained correct or incorrect diagnoses. We found that, contrary to previous studies, experts shared a common conceptual framework of the diagnostic domain being considered and were consistent in how they categorized the diagnoses. However, similar to previous studies, the experts differed greatly in their judgment of which diagnoses were correct. This study has important implications for understanding expert knowledge, for scoring performance on simulated or real patient cases, for providing feedback to learners in the clinical setting, and for establishing criteria that define what is correct in studies of diagnostic error and diagnostic reasoning.