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Adaptive automation (AA) can improve performance while addressing the problems associated with a fully automated system. The best way to invoke AA is unclear, but two ways include critical events and the operator's state. A hybrid model of AA invocation, the dynamic model of operator overload (DMOO), that takes into account critical events and the operator's state was recently shown to improve performance. The DMOO initiates AA using critical events and attention allocation, informed by eye movements. We compared the DMOO with an inaccurate automation invocation system and a system that invoked AA based only on critical events. Fewer errors were made with DMOO than with the inaccurate system. In the critical event condition, where automation was invoked at an earlier point in time, there were more memory and planning errors, while for the DMOO condition, which invocated automation at a later point in time, there were more perceptual errors. These findings provide a framework for reducing specific types of errors through different automation invocation., Presented in Human Factors in Computing Systems (CHI 2013), April 27-May 2, 2013, Paris, France.

We present a novel way of accounting for similarity judgments. Our approach posits that similarity ratings stem from three main sources: familiarity, priming, and inherent perceptual similarity. We present a process model of our approach in the cognitive architecture ACT-R, and match our model's predictions to data collected from a human subject experiment which involved simple perceptual stimuli. Familiarity accounts for rising ratings over time; priming accounts for asymmetric effects that arise when the stimuli are shown with different frequencies. Pure perceptual similarity also predicts trends in the results. Overall, our model matched the data with R2 of 0:99., in Proceedings of the 35th Annual Meeting of the Cognitive Science Society, Berlin, Germany, 31 Jul - 3 Aug 2013.

Many future decision support systems will be human-centric, i.e., require substantial human oversight and control. Because these systems often provide critical services, high assurance will be needed that they satisfy their requirements. How to develop high assurance human-centric decision systems is unknown: while significant research has been conducted in areas such as agents, cognitive science, and formal methods, how to apply and integrate the design principles and disparate models in each area is unclear. This paper proposes a novel process for developing human-centric decision systems where AI (artificial intelligence) methods namely, cognitive models to predict human behavior and agents to assist the human are used to achieve adequate system performance, and software engineering methods, namely, formal modeling and analysis, to obtain high assurance. To support this process, the paper introduces a software engineering technique formal model synthesis from scenarios and two AI techniques a model for predicting human overload and user model synthesis from participant studies data. To illustrate the process and techniques, the paper describes a decision system controlling unmanned air vehicles., Presented at the ICSE-13 Workshop on Realizing Artificial Intelligence Synergies in Software Engineering held in San Francisco, CA on 18-26 May 2013.

Person identification is a fundamental robotic capability for long-term interactions with people. It is important to know with whom the robot is interacting for social reasons, as well as to remember user preferences and interaction histories. There exist, however, a number of different features by which people can be identified. This work describes three alternative, soft biometrics (clothing, complexion, and height) that can be learned in real-time and utilized by a humanoid robot in a social setting for person identification. The use of these biometrics is then evaluated as part of a novel experiment in robotic person identification carried out at Fleet Week, New York City in May, 2012. In this experiment, Octavia employed soft biometrics to discriminate between groups of 3 people. 202 volunteers interacted with Octavia as part of the study, interacting with the robot from multiple locations in a challenging environment., Published in Proceedings of the 8th ACM/IEEE International Conference on Human-robot Interaction, 2013. Presenred at the 8th ACM/IEEE International Conference on Human-robot Interaction held in Tokyo, Japan on 3-6 Mar 2013.

A central problem in decision support tasks is operator overload in which a human operator's performance suffers because he or she is overwhelmed by the cognitive requirements of a task. To alleviate this problem, it would be useful to provide the human operator with an automated assistant to share some of the task's cognitive load. However, the development cycle for building an automated assistant is hampered by the testing phase because this involves human user studies which are costly and time-consuming to conduct. As an alternative to user studies, we propose acquiring user models which can be used as a proxy for human users during middle iterations, thereby significantly shortening the development cycle for rapid development. The primary contribution of this paper is a method for coarsely testing automated assistants by using user models acquired from traces gathered from various individual human operators. We apply this method in a case study in which we evaluate an automated assistant for users operating in a simulation of multiple unmanned aerial vehicles., To be presented at the Twenty-Sixth International Florida Artificial Intelligence Research Society Conference (FLAIRS-26) , 2013., St. Petersburg, Florida, May 22-24, 2013. The original document contains color images.

We built and evaluated a predictive model for resuming after an interruption. Two different experiments were run. The first experiment showed that people used a transactive memory process, relying on another person to keep track of where they were after being interrupted while retelling a story. A memory for goals model was built using the ACT-R/E cognitive architecture that matched the cognitive and behavioral aspects of the experiment. In a second experiment, the memory for goals model was put on an embodied robot that listened to a story being told. When the human storyteller attempted to resume the story after an interruption, the robot used the memory for goals model to determine if the person had forgotten the last thing that was said. If the model predicted that the person was having trouble remembering the last thing said, the robot offered a suggestion on where to resume. Signal detection analyses showed that the model accurately predicted when the person needed help., Published in Proceedings of the IEEE, v100 n3 p648-659, Mar 2012. Sponsored in part by MIPR-N0001411WX20474.

The mReactr system is a computational implementation of the mental model theory of reasoning (Johnson-Laird, 1983) that is embedded within the ACT-R cognitive architecture (Anderson, 1990). We show how the memory-handling mechanisms of the architecture can be leveraged to store and handle discrete representations of possibilities, i.e., mental models, efficiently. Namely, the iconic representation of a mental model can be distributed, in which each component of a model is represented by a chunk in ACT-R's declarative memory. Those chunks can be merged to create minimal mental models, i.e., reduced representations that do not contain redundant information. Minimal models can then be modified and inspected rapidly. We describe three separate versions of the mReactr software that minimize models at different stages of the system's inferential processes. Only one of the versions provides an acceptable model of data from an immediate inference task. The resulting system suggests that reasoners minimize mental models only when they initiate deliberative mental processes such as a search for alternative models., Presented at the Annual Meeting of the Cognitive Science Society (34th) held in Sapporo, Japan on 1- 4 August 2012 and published in proceedings of the same, p581-586. See also ADA569113, Building Bridges Across Cognitive Sciences Around the World, Proceedings of the Annual Meeting of the Cognitive Science Society (34th) held in Sapporo, Japan on 1- 4 August 2012. U.S. Government or Federal Purpose Rights License.

We propose a model of routine sequence actions based on the Memory for Goals model. The model presents a novel process description for both perseveration and anticipation errors, as well as matching error data from a previously collected dataset. Finally, we compare the current model to previous models of routine sequential action., Prepared in collaboration with Michigan State University, East Lansing, MI. See also ADA540044. International Conference on Cognitive Modeling (9th) (ICCM 2009) Held in Manchester, United Kingdom on July 23-26, 2009. U.S. Government or Federal Purpose Rights License

Recent research suggests that chimpanzees are capable of level 1 perspective taking (Flavell, 1992), but that its expression is limited to situations of increased competition (Brauer, Call, & Tomasello, 2007). We present a model utilizing gaze-following that learns in response to the behavior of a competitor. The model not only learns the proper application of the perspective taking strategy but also the critical spatial characteristics that influence the competitive pressure., International Conference of Cognitive Modeling (ICCM 2009), 24-26 July, Manchester, UK

We present an embodied model of gaze-following. The model learns how to follow another's gaze by using cognitively plausible mechanisms. It matches a classic gaze-following experiment (Corkum & Moore, 1998) and runs on an embodied robotic system., Presented at the International Conference on Cognitive Modeling (ICCM 2009) (9th) held in Manchester, United Kingdom on 24-26 July 2009. The original document contains color images.

In visual search, preattentive processes locate potential target regions and selective attention is directed to potential target locations. The current experiments examined the role of global visual clutter in participants' ability to deploy attention to target regions containing relatively more or less local clutter. Participants searched maps of high, medium, or low global clutter for a target in a high or low local clutter region. Global and local clutter influenced search time, with larger effects of local clutter as global clutter increased. In addition, there was no effect of set size on search time. We propose that the preattentive process of detecting regions likely to contain the target is less efficient as the amount of global clutter increases. Furthermore, in complex images and real world search tasks, global and local clutter measures can provide a good predictor of search efficiency when search set size is difficult to determine., Grant No. NRL BAA 08-09, BAA 55-07-01. LSU Proposal No. 33507. Submitted to the Journal Perception.

Electronic geospatial displays are common-from aircraft moving-maps to handheld GPS devices. As new data sources become available, users are tempted to display everything of interest: digital charts, satellite imagery, weather data, etc. The ensuing clutter can impact a user's ability to access, interpret, and effectively use the displayed information. This paper presents a model of display clutter comprised of global and local components, which we compared with subjective clutter ratings and target search performance. Our results suggest strong correlations between our global clutter metric and subjective ratings, and between our local clutter metric and search performance., Published in the NRL Review 2008, p163-165, 2008.

How can we facilitate human-robot teamwork? The teamwork literature has identified the need to know the capabilities of teammates. How can we integrate the knowledge of another capabilities for a justifiably intelligent teammate? This paper describes extensions to the cognitive architecture, ACT - R, and the use of artificial intelligence (AI) and cognitive science approaches to produce a more cognitively-plausible, autonomous robotic system that "mentally" simulates the decision-making of its teammate. The extensions to ACT-R added capabilities to interact with the real world through the robot's sensors and effectors and simulate the decision-making of its teammate. The AI applications provided visual sensor capabilities by methods clearly different than those used by humans. The integration of these approaches into intelligent team-based behavior is demonstrated on a mobile robot. Our "TeamBot" matches the descriptive work and theories on human teamwork. We illustrate our approach in a spatial, team-oriented task of a guard force responding appropriately to an alarm condition that requires the human and robot team to "man" two guard stations as soon as possible after the alarm., For presentation at the Association for the Advancement of Artificial Intelligence (AAAI) Conference on Artificial Intelligence (23rd) AAAI 2008, in Chicago, IL on 13-17 Jul 2008. The original document contains color images.

In visual search, preattentive processes locate potential target regions and selective attention is directed to potential target locations. The current experiments examined the role of global visual clutter in participants' ability to deploy attention to target regions containing relatively more or less local clutter. Participants searched maps of high, medium, or low global clutter for a target in a high or low local clutter region. Global and local clutter influenced search time, with larger effects of local clutter as global clutter increased. in addition, there was no effect of set size on search time. We propose that the preattentive precess of detecting regions likely to contain the target is less efficient as the amount of global clutter increases. Furthermore, in complex images and real world search tasks, global and local clutter measures can provide a good predictor of search efficiency when search set size is difficult to determine., Submitted to the journal "Perception." Additional NRL Grant No. 55-07-01.

Previous research examining how people resume a task following an interruption has focused primarily on pure memory processes. In this paper, we focus on the perceptual processes underlying task resumption and show that spatial memory guides task resumption. In Experiment 1, fixation patterns suggest participants were able to resume remarkably close to where they were in the task prior to interruption. In Experiment 2, a spatial interruption disrupted resumption performance more than a non-spatial interrupting task. Together, these results implicate spatial memory as a mechanism for resumption., Prepared in collaboration with George Mason University, Fairfax, VA.

Most theories of graph comprehension posit the existence of a graph schema to account for people's prior knowledge of how to understand different graph types. The graph schema is, however, a purely theoretical construct: there are no empirical studies that have explicitly examined the nature of the graph schema. We sought to determine whether graph schemas are based on perceptual features or on a common invariant structure shared between certain graphs. The process of activating the graph schema was isolated as participants responded to graphs presented in pure and mixed blocks. Any differences in reaction time between the blocks could be attributed to loading the appropriate schema. Results from a series of experiments using five types of graphs suggest graph schemas are based on the graphical framework, a common invariant structure among certain types of graphs. These results provide insight into the comprehension of novel graphs., Prepared in collaboration with George Mason University, Fairfax, VA. ONR grant no. N00014-03-WX3-0001.

We describe a computational cognitive architecture for robots which we call ACT-R/E (ACT-R/Embodied). ACT-R/E is based on ACT-R, but uses different visual, auditory and movement modules. We describe a model that uses ACT-R/E to integrate visual and auditory information to perform conversation tracking in a dynamic environment. We also performed an empirical evaluation study which shows that people see our conversational tracking system as extremely natural., Presented at HRI'08, held in Amsterdam, The Netherlands, on 12-15 Mar 2008. The original document contains color images.

Task analytic theories of graph comprehension account for the perceptual and conceptual processes required to extract specific information from graphs. Comparatively, the processes underlying information integration have received less attention. We propose a new framework for information integration that highlights visual integration and cognitive integration. During visual integration, pattern recognition processes are used to form visual clusters of information; these visual clusters are then used to reason about the graph during cognitive integration. In three experiments the processes required to extract specific information and to integrate information were examined by collecting verbal protocol and eye movement data. Results supported the task analytic theories for specific information extraction and the processes of visual and cognitive integration for integrative questions. Further, the integrative processes scaled up as graph complexity increased, highlighting the importance of these processes for integration in more complex graphs. Finally, based on this framework, design principles to improve both visual and cognitive integration are described., Prepared in collaboration with George Mason University, Fairfax, VA.

A postcompletion error is a distinct type of procedural error where one fails to complete the final step of a task. While redesigning interfaces and providing explicit cues have been shown to be effective in reducing the postcompletion error rate, these methods are not always feasible or well liked. This paper demonstrates how specific eye movement measures can be used to predict when a user will make a postcompletion error. We describe a real-time eye gaze system that provides cues to the user if and only if there is a high probability of the user making a postcompletion error., Presented at CHI 2008 held in Florence, Italy on 5-10 April 2008. Published in the Proceedings of CHI 2008. The original document contains color images.

We conducted an experiment to investigate whether spatial processing is used in graph comprehension tasks. Using an interference paradigm, we demonstrate that a graph task interfered more with performance on a spatial memory task than on a visual "non-spatial" memory task. Reaction times showed there was no speed-accuracy tradeoff. We conclude that it was the spatial nature of the graph task that caused the additional interference in the spatial memory task. We propose that current theories of graph comprehension should be expanded to include a spatial processing component., Presented at the Annual Conference of the Cognitive Science Society (29th), CogSci 2007, Nashville, TN on 1-4 Aug 2007 and published in proceedings of the same, p1563-1568.; ISBN 978-0-9768318-3-9.

Imagine a meteorologist preparing a weather forecast. In addition to years of experience and a vast store of domain knowledge, the forecaster has access to satellite images, to computer generated weather models and programs to display them in a variety of ways, and to an assortment of special-purpose tools that provide additional task-relevant data. There is no shortage of data, yet despite this array of resources, the task remains very challenging. One source of complexity is the uncertainty inherent in these data. To complicate matters further, the uncertainty in the data is not explicitly represented; rather, the visualizations indicate that the data are exactly as they appear. The visualizations thus invite the forecaster to map uncertain data to certain values, yet to do so would most likely lead to erroneous predictions. This example illustrates the basic question we investigate in this paper: how do people, especially experts, deal with uncertainty in highly spatial domains, when the data are inherently uncertain but the tools actually display very little uncertainty? We first examine how uncertainty affects operations in three representative domains, submarine operations (military), meteorology (geoscience), and fMRI research (scientific visualization), in which dealing with uncertainty is a critical component of the task. We then investigate how experts in two of these domains, meteorology and fMRI, manage uncertainty as they perform problem-solving activities and make decisions as part of their regular task performance., Prepared in collaboration with the University of Pittsburgh. Published in Thinking with Data, edited by Lovett and Shah, Psychology Press, 2007. ISBN 978-0-8058-5422-0.

Theories accounting for the process of primary task resumption following an interruption have focused on the suspension and retrieval of a specific goal. The ability to recall the spatial location of where in the task one was prior to being interrupted may also be important. We show that being able to maintain a spatial representation of the primary task facilitates task resumption. Participants were interrupted by an instant message window that either partially or fully occluded the primary task interface. Reaction time measures show that participants were faster at resuming in the partial occlusion condition. In addition, eye track data suggest that participants were more accurate at returning to where they left off, suggesting that they were able to maintain a spatial representation of the task and use this information to resume more quickly., Presented at the Annual Meeting of the Human Factors and Ergonomics Society (51st), held in Baltimore, MD, on 1-5 Oct 2007. Prepared in collaboration with George Mason University, Fairfax, VA. The original document contains color images.

Research has shown that different types of interruptions can affect their disruptiveness. However, it is unclear how different features of the interrupting task determine its disruptive effects. Specifically, some theories predict that the difficulty of an interruption does not contribute to the disruptive effects of that interruption alone. Disruptive effects can be mediated by the extent to which the interrupting task interferes with the ability to rehearse during the interruption. In this experiment participants performed a single primary task with three interruptions of different difficulty. We found that interruptions were more disruptive when the task minimized the participant's ability to rehearse (as measured by the number of mental operators required to perform the task) and not just when they were more difficult. These results suggest that the ability to rehearse during an interruption is critical in facilitating resumption of a primary task., Presented at the Annual Meeting of the Human Factors and Ergonomics Society (51st), held in Baltimore, MD, on 1-5 Oct 2007. Prepared in collaboration with George Mason University.

How do problem solvers represent visual-spatial information in complex problem solving tasks? This paper explores the predictions of symbolic computation, embodied problem solving and a neurocomputational theory for what factors influence internal representation choices. Across two studies, data are collected from experts and novices in three different, complex visual-spatial problem-solving domains (weather forecasting, submarine target motion analysis, and fMRI data analysis). Internal spatial representations are coded from spontaneous gestures made during cued-recall summaries of problem solving activities. Analyses of domain differences, expertise differences, and changes over time with problem solving suggest that neurocomputational constraints play a larger role than the nature of the visual input or the nature of the underlying real world being examined through problem solving, especially for expert problem solvers. The particular neurocomptuational feature that was found to drive internal representation choice is the required spatial precision of the main goals of problem solving., Prepared in collaboration with University of Pittsburgh, grant no's. N00014-02-1-0113 and N00014-03-1-0061.

The process of resuming an interrupted task has been understood by task level goals. Recent empirical evidence has implicated spatial memory as a component of the resumption process suggesting that spatial level representations are important as well. We collected eye track data in an interruptions paradigm to examine the perceptual processes involved in resumption. Four models were created to illustrate the importance of the role of spatial representations and further, to demonstrate how the task level and spatial representations can be integrated., Presented at the International Conference on Cognitive Modeling (8th), held in Ann Arbor, MI, on 27-29 Jul 2007. Prepared in collaboration with George Mason University, Fairfax, VA. The original document contains color images.

What are the characteristics of long-term learning? We investigated the characteristics of long-term, symbolic learning using the Soar and ACT-R cognitive architectures running cognitive models of two simple tasks. Long sequences of problems were run collecting data to answer fundamental questions about long-term, symbolic learning. We examined whether symbolic learning continues indefinitely, how the learned knowledge is used and whether computational performance degrades over the long term. We report three findings. First, in both systems, symbolic learning eventually stopped. Second, learned knowledge was used differently in different stages but the resulting production knowledge was used uniformly. Finally, both Soar and ACT-R do eventually suffer from degraded computational performance with long-term continuous learning. We also discuss ACT-R implementation and theoretic causes of ACT-R's computational performance problems and settings that appear to avoid the performance problems in ACT-R.

Interruption of a complex cognitive task can entail, for the "interruptee," a sense of having to recover afterward. The authors examined this recovery process by measuring the time-course of responses following an interruption. They sampled over 13,000 interruptions to obtain stable data. Results show that response times dropped in a smooth curvilinear pattern for the first 10 responses (15 sec or so) of postinterruption performance. They explain this pattern in terms of the cognitive system retrieving a displaced mental context from memory incrementally, with each retrieved element adding to the set of primes facilitating the next retrieval. The model explains a learning effect in the data in which the time-course of recovery changes over blocks, and is generally consistent with current representational theories of expertise., Pub. in Psychonomic Bulletin and Review, v14 n6, p1079-1084, 2007. MIPR-N0001405WX20011 and MIPR-N0001405WX30020.

Our topic in this chapter is not so much what happens when experts have to work "out of context," but how cognitive engineering might help weather forecasters, in particular, remain within familiar decision-making spaces by improving on their display technology. Most weather forecasters get data, charts, and satellite images from Internet sources. In this chapter, we discuss some of what we know about how weather forecasters use information technology to display and support the interpretation of complex meteorological visualizations. Based on notions of human-centered computing (HCC), we offer some suggestions on how to improve the visualizations and tools., Prepared in collaboration with the Institute for Human and Machine Cognition, Pensacola, FL. Published in R. R. Hoffamn, Experise Out of Context, p337-358, Lawrence Erlbaum Associates, 2007.

Good team members seem to have the ability to simulate what others on the team will do in different situations. Team researchers have long studied what makes an effective team. Their methodology has been to examine how high and low performing teams accomplish team-related tasks. They have suggested that a good team-member has three knowledge components (Cannon-Bowers, Salas, & Converse, 1993): (1) Knowledge of own capabilities [meta-knowledge], (2) Knowledge of the task, and (3) Knowledge about the capabilities of their teammates. Most researchers have suggested that these three components are deeply inter-related; without any one of these, a person is not a good team member. However, without a computational theory, these claims can be difficult to examine empirically. The focus of this paper is the third component, the cognitive modeling done of a teammate's cognitive processes. This shared understanding of teammates frequently called a shared mental model (Mathieu, Heffner, Goodwin, Salas, & Cannon-Bowers, 2000). We start with the premise that humans use themselves as an initial model of their teammate, and then refine it as the team (and individuals within the team) gains experience. Our primary research goal is to create a computational theory of teamwork by modeling the individuals within the team so that we can eventually build plausible robots for teamwork and human-robot interaction., Presented at the International Conference on Cognitive Modeling (8th) held in Ann Arbor, MI on 27-29 July 2007. Published in the Proceedings of the International Conference on Cognitive Modeling (8th), p253-254, 2007.

How should a robot represent and reason about spatial information when it needs to collaborate effectively with a human? The form of spatial representation that is useful for robot navigation may not be useful in higher-level reasoning or working with humans as a team member. To explore this question, we have extended previous work on how children and robots learn to play hide and seek to a human-robot team covertly approaching a moving target. We used the cognitive modeling system, ACT-R, with an added spatial module to support the robot's spatial reasoning. The robot interacted with a team member through voice, gestures, and movement during the team's covert approach of a moving target. This paper describes the new robotic system and its integration of metric, symbolic, and cognitive layers of spatial representation and reasoning for its individual and team behavior., Published in the Twenty-Second Conference on Artificial Intelligence (AAAI-07), held in Vancouver, Canada on 22-26 July 2007.

It is easy to force a weather forecaster to work out of context -- simply move him or her to some new locale. Effects of ocean currents, seasonal variations, and effects of land masses change everything. Any knowledge of trends that the forecaster had relied on are now utterly useless. The information that the weather forecaster uses is often downloaded from external Web sites. Local weather organizations use (or build) support tools for displaying downloaded data and images and for building and displaying their own forecasts. To optimize these tools, consideration must be given to the user-tooltask triad that is central to the principles of human-centered computing (HCC). The traditional way human factors engineers approach this problem is to perform a task analyses to determine how people operate in a specific domain on a specific task. Cognitive Task Analysis (CTA) is a set of methods that takes into account the perception (i.e., vision), cognition (i.e., decision making), and motor actions (i.e., mouse movements) needed to accomplish a task. In this chapter, we build on CTA methods by suggesting that comparative cognitive task analysis (C2TA) can help solve the aforementioned problems. C2TA is based on replication studies conducted in different environments. Because it derives data from more than one environment, C2TA provides insight into interface design that single-site studies and individual CTA methods cannot., Prepared in collaboration with the Naval Research Laboratory, Washington, DC and the University of Connecticut, Storrs, CT. Published in Expertise Out Of Context: Proceedings of the Sixth International Conference on Naturalistic Decision Making, p327-336, May 2007; ISBN 978-0-8058-5509-8.

The characteristics of long-term, symbolic learning were investigated using Soar and ACT-R models of a task to rearrange blocks into specific configurations. Long sequences of problems were run collecting data to answer fundamental questions about long-term, symbolic learning. The questions were whether symbolic learning continues indefinitely, how learned knowledge is used, and whether performance degrades over the long term. It was found that in both systems symbolic learning eventually stopped, ACT-R produced three observable phases of learning, and both Soar and ACT-R suffer from the utility problem of degraded performance with continuous on-line learning.

How do children learn how to play hide and seek? At ages 3-4, children do not typically have perspective taking ability, so their hiding ability should be extremely limited. The authors show through a case study that a 3-1/2-year-old child can, in fact, play a credible game of hide and seek, even though she does not seem to have perspective taking ability. They propose that children are able to learn how to play hide and seek by learning the features and relations of objects (e.g., containment, under) and use that information to play a credible game of hide and seek. They model this hypothesis within the ACT-R cognitive architecture and put the model on a robot, which is able to mimic the child's hiding behavior. They also take the "hiding" model and use it as the basis for a "seeking" model. They suggest that using the same representations and procedures that a person uses allows better interaction between the human and robotic system.

For the last few years, our lab has been attempting to build robots that are similar to humans in a variety of ways. Our goal has been to build systems that think and act like a person rather than look like a person since the state of the art is not sufficient for a robot to look (even superficially) like a human person. We believe that there are at least two reasons to build robots that think and act like a human. First, how an artificial system acts has a profound effect on how people act toward the system. Second, how an artificial system thinks has a profound effect on how people interact with the system., Published in Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation as Chapter 10, p252-278, 2006. ISBN 0521839645 (Hardcover).