Brandon S PerelmanArmy Research Laboratory | ALC · Human Research and Engineering Directorate (HRED)
Brandon S Perelman
PhD
About
44
Publications
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Introduction
I am a Research Psychologist with CCDC U.S. Army Research Laboratory at the Aberdeen Proving Ground in MD. I am a graduate of Michigan Technological University's Applied Cognitive Science and Human Factors doctoral program, under Shane T. Mueller (obereed.net/). I obtained my M.S. in Experimental Psychology from Saint Joseph's University, and completed my undergraduate education in biology at Sarah Lawrence College.
Additional affiliations
May 2015 - present
September 2014 - December 2014
May 2014 - August 2014
Education
September 2012 - May 2015
September 2010 - May 2012
September 2003 - May 2007
Publications
Publications (44)
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 f...
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-bas...
Current operational human-agent teaming paradigms place the full burden of danger on non-human agents. Shifting this burden entirely to the robot is currently possible due to the nature of the limited situations in which teleoperated robots are currently employed in military contexts. However, as the roles of non-human agents grow, robots are expec...
Route planning is a critical behavior for human-intelligent agent (H-IA) team mobility. The scientific community has made major advances in improving route planner optimality and speed. However, human factors, such as the ability to predict and understand teammates’ actions and goals, are necessary for trust development in H-IA teams. Trust is espe...
The modern world is evolving rapidly, especially with respect to the development and proliferation of increasingly intelligent, artificial intelligence (AI) and AI-related technologies. Nevertheless, in many ways, what this class of technologies has offered as return on investment remains less impressive than what has been promised. In the present...
The U.S. Army is currently working to integrate artificial intelligence, or AI-enabled systems, into military working teams in the form of both embodied (i.e., robotic) and embedded (i.e., computer or software) intelligent agents with the express purpose of improving performance during all phases of the mission. However, this is largely uncharted t...
While trust has a long and rich history, there are multiple principles, theories, models, and study topics that need both refinement and revision to develop appropriate team trust metrics for effective human-autonomy teaming. This chapter builds on current theory and research to develop an effective roadmap forward to developing these metrics. The...
In multi-agent systems, agents' abilities are often used to classify a system as either homogeneous or heterogeneous. In the context of multi-agent reinforcement learning (MARL) systems, the agents can also be homogeneous or heterogeneous in their strategies. In this work, we explore instances where agents with homogeneous capabilities must collabo...
This technical note, the third in its series, describes the use of multimodal cueing concepts to improve mission execution. This work is a subcomponent of the US Army Combat Capabilities Development Command (CCDC) Army Research Laboratory’s (ARL’s) second project in the Human-Autonomy Teaming Essential Research Program, transparent multimodal crew...
This technical note, the first in its series, describes a general overview of the US Army Combat Capabilities Development Command Army Research Laboratory’s (ARL’s) second project in the Human-Autonomy Teaming Essential Research Program, transparent multimodal crew interface designs, in support of the US Army modernization priority Next Generation...
This technical note, the second in its series, describes the integration of interface concepts for improving mobility planning. This work is a subcomponent of the US Army Combat Capabilities Development Command Army Research Laboratory’s (ARL’s) second project in the Human-Autonomy Teaming Essential Research Program, transparent multimodal crew int...
Trust is a critical factor in the development and maintenance of effective human-autonomy teams. As such, new processes are needed to classify affective state change that could be related to either an accurate or a misaligned change in trust that occurs during collaboration. The task for the current study was a leader-follower, simulated driving ta...
Backward Recognition Masking (BRM) of sound occurs when two sounds are presented close in time to one another and the second sound hinders the recognition of the first sound. Previous studies on BRM used either white noise or sine tones. Here, we present backward recognition thresholds using environmental sounds. 34 young normal hearing individuals...
Chen et al. (2014) proposed the situation awareness-based agent transparency (SAT) model, which is a framework for improving human situation awareness and understanding of autonomous agents’ actions, intentions, goals and reasoning. Research using the SAT model as a framework has traditionally focused on displaying transparency concept information...
Artificial intelligence (AI) has enormous potential for military applications. Fully realizing the conceived benefits of AI requires effective interactions among Soldiers and computational agents in highly uncertain and unconstrained operational environments. Because AI can be complex and unpredictable, computational agents should support their hum...
Understanding complex environmental sound perception is critical for understanding human behavior in real-world settings. Whereas simple stimuli can be processed on the basis of physical characteristics alone, environmental sounds contain semantic and contextual information, which can lead to asymmetrical similarity ratings. The goals of this study...
Backward Recognition Masking (BRM) of sound occurs when two sounds are presented close in time to one another and the second sound hinders the recognition of the first sound. Previous studies on BRM used either white noise or sine tones. Here, we present backward recognition thresholds using environmental sounds. 34 young normal hearing individuals...
As technologies become more complex, the use of heterogeneous man-machine teams will become more prevalent. The promise of such heterogeneous teams is that team members with diverse backgrounds bring with them expertise in different areas. However, differences in training, priorities, and professional culture have the potential to influence decisio...
There are a number of reasons to use computer-based simulation in human-robot interaction research. Most predominant is the assessment of humanin-the-loop interactions for robotic technologies that do not yet exist, are in prototype development, or are in early test and evaluation stages of development. In these cases, simulation can provide insigh...
Robust mapping capabilities are a critical technology for intelligent robotic systems. They can (1) provide valuable information to human and robot teammates without requiring prior knowledge or experience and (2) enable other, higher-level behaviors, such as autonomous navigation and exploration. To maximize interpretability, a map must be coheren...
A goal for future robotic technologies is to advance autonomy capabilities for independent and collaborative decision-making with human team members during complex operations. However, if human behavior does not match the robots' models or expectations, there can be a degradation in trust that can impede team performance and may only be mitigated t...
A goal for future robotic technologies is to advance autonomy capabilities for independent and collaborative decision-making with human team members during complex operations. However, if human behavior does not match the robots’ models or expectations, there can be a degradation in trust that can impede team performance and may only be mitigated t...
Recent studies suggest that perceptual similarity among sounds can be used to predict accuracy in perceptual tasks (Dickerson & Gaston, 2014; Gaston, et al., 2017). We operationalize this similarity as distance in a 2D similarity space generated using multidimensional scaling (MDS) on pair-wise similarity stimulus ratings. We tested listeners’ abil...
A major barrier to effective spatial decision-making in human-agent teams is that humans and algorithms use different mechanisms to solve spatial problems, frequently leading them to produce different solutions. Incongruity between algorithm-generated solutions and human spatial mental models results in higher workload in mixed-initiative systems,...
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 s...
Sound events in the real-world rarely occur in isolation and when competing sounds are present, the effect on perception can be significant (e.g., various forms of masking). The present study examines auditory localization for impulse sounds modeled after the real-world sound event of small-arms fire. Small-arms fire consists of two distinct sounds...
Real-world sounds are perceived as more than just a collection of acoustic attributes. They contain both acoustic and semantic attributes, which together influence perception. Acoustic measurements are usually well-defined and broadly agreed upon, but semantic information can be difficult to operationalize. Semantic attributes contain information r...
Everyday listening involves identifying an ever-changing milieu of sound sources in the environment. Recent studies have demonstrated that change perception during complex listening tasks is highly error prone; errors can exceed 30% for sounds that are clearly detectable and identifiable in isolation. This change deafness has been generally attribu...
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 solvi...
In many real-world route planning and search tasks, humans must solve a combinatorial optimization problem that holds many similarities to the Euclidean Traveling Salesman Problem (TSP). The problem spaces used in real-world tasks differ most starkly from traditional TSP in terms of optimization criteria - Whereas the traditional TSP asks participa...
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...
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 ment...
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 engage...
Does visual fidelity and cognitive fidelity affect learning in a video game? In this paper we present data collected from 65 participants who played one of four different versions of a 3D video game, Heurística, designed to train decision making. We analyzed learning using a 2 cognitive fidelity × 2 visual fidelity between subjects analysis of vari...
To assess the efficacy of using eyeblink frequency modulation to detect deception about a third party, 32 participants were sent on a mission to deliver a package to an interviewer. 17 of the participants lied to the interviewer about the details of their mock mission and 15 responded truthfully. During the interview, eyeblink frequency data were c...
In this paper, we examine the effects of three video game variables: camera perspective (1st person versus 3rd person), session duration, and repeated play on training participants to mitigate three cognitive biases. We developed a 70 minute, 3D immersive video game for use as an experimentation test bed. One-hundred and sixty three participants ei...
In the present study, we used a synthetic task environment representing an aerial wilderness search and rescue task to test and validate neurocomputational models of the human hippocampus. Participants completed two tasks: a search task, in which they searched a wooded area for multiple targets, and a route choice task, in which they searched high...
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 searched for targets using a more detailed track up satellite image r...