A NATURALISTIC COMPUTATIONAL MODEL OF HUMAN BEHAVIOR IN NAVIGATION AND SEARCH TASKS

Planning, navigation, and search are fundamental human cognitive abilities central to spatial problem solving in search and rescue, law enforcement, and military operations. Despite a wealth of literature concerning naturalistic spatial problem solving in animals, literature on naturalistic spatial problem solving in humans is comparatively lacking and generally conducted by separate camps among which there is little crosstalk. Addressing this deficiency will allow us to predict spatial decision making in operational environments, and understand the factors leading to those decisions. The present dissertation is comprised of two related efforts, (1) a set of empirical research studies intended to identify characteristics of planning, execution, and memory in naturalistic spatial problem solving tasks, and (2) a computational modeling effort to develop a model of naturalistic spatial problem solving. The results of the behavioral studies indicate that problem space hierarchical representations are linear in shape, and that human solutions are produced according to multiple optimization criteria. The Mixed Criteria Model presented in this dissertation accounts for global and local human performance in a traditional and naturalistic Traveling Salesman Problem. The results of the empirical and modeling efforts hold implications for basic and applied science in domains such as problem solving, operations research, human-computer interaction, and artificial intelligence

Visual Search and Target Selection Using a Bounded Optimal Model of State Estimation & Control

Visual attention and motor control are tightly coupled in domains requiring a human operator to interact with a visual interface. Here, we integrate a boundedly optimal visual attention model with two separate motor control models and compare the predictions made by these models against perceptual and motor data collected from human subjects engaged in a parafoveal detection task. The results indicate that humans use an optimal motor control policy limited by precision constraints – humans executed ballistic movements using near-optimal velocity (i.e., bang-bang control), but imprecision in those movements often caused participants to overshoot their targets, necessitating corrective action. Motor movements did not reflect response hedging, but rather a perceptual-motor policy permitting ballistic movements to a target only after localization confidence exceeded a threshold. We conclude that a boundedly-optimal perceptual-motor model can predict aspects of human performance visual search tasks requiring motor response

Development of the PEBL Traveling Salesman Problem Computerized Testbed

The traveling salesman problem (TSP) is a combinatorial optimization problem that requires finding the shortest path through a set of points (“cities”) that returns to the starting point. Because humans provide heuristic near-optimal solutions to Euclidean versions of the problem, it has sometimes been used to investigate human visual problem solving ability. The TSP is also similar to a number of tasks commonly used for neuropsychological assessment (such as the trail-making test), and so its utility in assessing reliable individual differences in problem solving has sometimes been examined. Nevertheless, the task has seen little widespread use in clinical and assessment domains, in part because no standard software implementation or item set is widely available with known psychometric properties. In this paper, we describe a computerized version of TSP running in the free and open source Psychology Experiment Building Language (PEBL). The PEBL TSP task is designed to be suitable for use within a larger battery of tests, and to examine both standard and custom TSP node configurations (i.e., problems). We report the results of a series of experiments that help establish the test’s reliability and validity. The first experiment examines test-retest reliability, establishes that the quality of solutions in the TSP are not impacted by mild physiological strain, and demonstrates how solution quality obtained by individuals in a physical version is highly correlated with solution quality obtained in the PEBL version. The second experiment evaluates a larger set of problems, and uses the data to identify a small subset of tests that have maximal coherence. A third experiment examines test-retest reliability of this smaller set that can be administered in about five minutes, and establishes that these problems produce composite scores with moderately high (R = .75) test-retest reliability, making it suitable for use in many assessment situations, including evaluations of individual differences, personality, and intelligence testing

Is Malaysia’s banded langur, Presbytis femoralis femoralis, actually Presbytis neglectus neglectus? Taxonomic revision with new insights on the radiation history of the Presbytis species group in Southeast Asia

The disjunct distribution of Presbytis femoralis subspecies across Sumatra (P. f. percura), southern (P. f. femoralis) and northern (P. f. robinsoni) Peninsular Malaysia marks the unique vicariance events in the Sunda Shelf. However, the taxonomic positions and evolutionary history of P. f. femoralis are unresolved after decades of research. To elucidate this evolutionary history, we analyzed 501 base pairs of the mitochondrial HVSI gene from 25 individuals representing Malaysia’s banded langur, with the addition of 29 sequences of Asian Presbytis from Genbank. Our results revealed closer affinity of P. f. femoralis to P. m. mitrata and P. m. sumatrana while maintaining the monophyletic state of P. f. femoralis as compared to P. f. robinsoni. Two central theses were inferred from the results; (1) P. f. femoralis does not belong in the same species classification as P. f. robinsoni, and (2) P. f. femoralis is the basal lineage of the Presbytis in Peninsular Malaysia. Proving the first hypothesis through genetic analysis, we reassigned P. f. femoralis of Malaysia to Presbytis neglectus (Schlegel’s banded langur) (Schlegel in Revue Methodique, Museum d’Histoire Naturelle des Pays-Bas 7:1, 1876) following the International Code of Zoological Nomenclature (article 23.3). The ancestors of P. neglectus are hypothesized to have reached southern Peninsular Malaysia during the Pleistocene and survived in refugium along the western coast. Consequently, they radiated upward, forming P. f. robinsoni and P. siamensis resulting in the highly allopatric distribution in Peninsular Malaysia. This study has successfully resolved the taxonomic position of P. neglectus in Peninsular Malaysia while providing an alternative biogeographic theory for the Asian Presbytis

Examining Memory for Search Using a Simulated Aerial Search and Rescue Task

In this paper, we report on the development of a synthetic task environment (STE) representing wilderness search and rescue using unmanned aerial vehicles (UAVs) for investigating human unmanned aerial search behavior. Participants navigated using a north up topographical map and detected targets using a more detailed track up satellite image representing the view through the UAV’s camera. Participants then completed (1) a path reconstruction task and (2) a memory test in which they indicated locations where they found targets. These tasks aim to address two information types that map onto distinct visual processing pathways afferent to the hippocampus. We discuss example applications using this paradigm, including several methods for scoring memory and navigation performance. Finally, we discuss how the STE enables assessment of the effects of combining or separating pilot and sensor operator roles, search behaviors and strategies, and other human factors limitations faced by operators in aerial search tasks

Identifying Mental Models of Search in a Simulated Flight Task Using a Pathmapping Approach

Aerial assets are often used for missions such as intelligence, surveillance, target acquisition and reconnaissance. The pilot’s search decisions reflect a mental model for the search space, including characteristics such as target prioritization, distance-reward evaluations, and path optimization cri-teria. To investigate differences in these mental models, we examined 23 participants’ paths flown in a synthetic task environment in which they piloted a simulated aircraft to search for targets rep-resenting missing persons. Determining similarity among flight paths is a challenge. To accom-plish this, we used a new tool (Pathmapping, a package in the R statistical computing language; Mueller, Perelman, & Veinott, 2015) to determine area-based path similarities among the test sub-jects’ flight paths, and mixture modeling to analyze those similarities. The results indicate that an area-based measure of path similarity can be used to infer mental models from flight paths pro-duced during a simulated search task

An optimization approach for mapping and measuring the divergence and correspondence between paths

Many domains of empirical research produce or analyze spatial paths as a measure of behavior. Previously, approaches for measuring the similarity or deviation between two paths have either required timing information or have used ad hoc or manual coding schemes. In this paper, we describe an optimization approach for robustly measuring the area-based deviation between two paths we call ALCAMP (Algorithm for finding the Least-Cost Areal Mapping between Paths). ALCAMP measures the deviation between two paths and produces a mapping between corresponding points on the two paths. The method is robust to a number of aspects in real path data, such as crossovers, self-intersections, differences in path segmentation, and partial or incomplete paths. Unlike similar algorithms that produce distance metrics between trajectories (i.e., paths that include timing information), this algorithm uses only the order of observed path segments to determine the mapping. We describe the algorithm and show its results on a number of sample problems and data sets, and demonstrate its effectiveness for assessing human memory for paths. We also describe available software code written in the R statistical computing language that implements the algorithm to enable data analysis

Evaluating path planning in human-robot teams: Quantifying path agreement and mental model congruency

The integration of robotic systems into daily life is increasing, as technological advancements facilitate independent and interdependent decision-making by autonomous agents. Highly collaborative human-robot teams promise to maximize the capabilities of humans and machines. While a great deal of progress has been made toward developing efficient spatial path planning algorithms for robots, comparatively less attention has been paid to developing reliable means by which to assess the similarities and differences in path planning decisions and associated behaviors of humans and robots in these teams. This paper discusses a tool, the Algorithm for finding the Least Cost Areal Mapping between Paths (ALCAMP), which can be used to compare paths planned by humans and algorithms in order to quantify the differences between them, and understand the user\u27s mental models underlying those decisions. In addition, this paper discusses prior and proposed future research related to human-robot collaborative teams. Prior studies using ALCAMP have measured path divergence in order to quantify error, infer decision-making processes, assess path memory, and assess team communication performance. Future research related to human-robot teaming includes measuring formation and path adherence, testing the repeatability of navigation algorithms and the clarity of communicated navigation instructions, inferring shared mental models for navigation among members of a group, and detecting anomalous movement

Pathfinding in the cognitive map: Network models of mechanisms for search and planning

The hippocampus has long been thought to be critical in learning and representing the cognitive map, and thus support functions such as search, pathfinding and route planning. This work aims to demonstrate the utility of hippocampus-based neural networks in modeling human search task behavior. Human solutions to pathfinding problems are generally fast but approximate, in contrast to traditional AI approaches. In this paper, we report data on a human search task, and then examine a set of models, based upon the structure of the hippocampus, which use a goal scent mechanism similar to the optimal pathfinding algorithms used in artificial intelligence systems. We compare five distinct search models, and conclude that a goal scent model driven by multiple goals spread throughout the search space provides the best and most accurate account of the human data. This research suggests a convergence in traditional AI and biologically- inspired approaches to pathfinding that may be mutually beneficial

Development of the PEBL Traveling Salesman Problem Computerized Testbed

The traveling salesman problem (TSP) is a combinatorial optimization problem that requires finding the shortest path through a set of points (“cities”) that returns to the starting point. Because humans provide heuristic near-optimal solutions to Euclidean versions of the problem, it has sometimes been used to investigate human visual problem solving ability. The TSP is also similar to a number of tasks commonly used for neuropsychological assessment (such as the trail-making test), and so its utility in assessing reliable individual differences in problem solving has sometimes been examined. Nevertheless, the task has seen little widespread use in clinical and assessment domains, in part because no standard software implementation or item set is widely available with known psychometric properties. In this paper, we describe a computerized version of TSP running in the free and open source Psychology Experiment Building Language (PEBL). The PEBL TSP task is designed to be suitable for use within a larger battery of tests, and to examine both standard and custom TSP node configurations (i.e., problems). We report the results of a series of experiments that help establish the test’s reliability and validity. The first experiment examines test-retest reliability, establishes that the quality of solutions in the TSP are not impacted by mild physiological strain, and demonstrates how solution quality obtained by individuals in a physical version is highly correlated with solution quality obtained in the PEBL version. The second experiment evaluates a larger set of problems, and uses the data to identify a small subset of tests that have maximal coherence. A third experiment examines test-retest reliability of this smaller set that can be administered in about five minutes, and establishes that these problems produce composite scores with moderately high (R = .75) test-retest reliability, making it suitable for use in many assessment situations, including evaluations of individual differences, personality, and intelligence testing