Computational Cognitive Model Research Articles

Cognitive computing and eScience in health and life science research: artificial intelligence and obesity intervention programs.

To present research models based on artificial intelligence and discuss the concept of cognitive computing and eScience as disruptive factors in health and life science research methodologies. The paper identifies big data as a catalyst to innovation and the development of artificial intelligence, presents a framework for computer-supported human problem solving and describes a transformation of research support models. This framework includes traditional computer support; federated cognition using machine learning and cognitive agents to augment human intelligence; and a semi-autonomous/autonomous cognitive model, based on deep machine learning, which supports eScience. The paper provides a forward view of the impact of artificial intelligence on our human-computer support and research methods in health and life science research. By augmenting or amplifying human task performance with artificial intelligence, cognitive computing and eScience research models are discussed as novel and innovative systems for developing more effective adaptive obesity intervention programs.

Robust Point Correspondence with Gabor Scale-Invariant Feature Transform for Optical Satellite Image Registration

In this paper, a robust point correspondence algorithm is proposed to address the problems with SIFT-like methods for the optical satellite image registration. SIFT-like methods involve two issues that are rarely noticed: non-orientation selectivity in feature detection and feature redundancy in feature description. The novelty of the proposed approach is that the advantages of biologically motivated methods are adopted to resolve above two problems. Firstly, by using a 2D Gabor filter bank to model the visual cognitive computational model, intuitive and robust keypoints are detected. The proposed detector can capture salient visual properties such as the orientation and spatial frequency selectivity. Secondly, multi-characteristic scales of the keypoints are selected based on the Gabor kernel function, and then multi-feature descriptors with high discriminating power are defined. By using the proposed detector and descriptor, the feature redundancy can translate into benefits. Finally, a feature matching strategy for multi-feature descriptors is designed, to improve the reliability of feature matching. Evaluation criteria of 1-precision, RMSE and visual inspection of the matched pairs are used to demonstrate the superior performance of the proposed algorithm on optical satellite image registration.

A computational cognitive framework of spatial memory in brains and robots

Computational cognitive models of spatial memory often neglect difficulties posed by the real world, such as sensory noise, uncertainty, and high spatial complexity. On the other hand, robotics is unconcerned with understanding biological cognition. Here, we describe a computational framework for robotic architectures aiming to function in realistic environments, as well as to be cognitively plausible.We motivate and describe several mechanisms towards achieving this despite the sensory noise and spatial complexity inherent in the physical world. We tackle error accumulation during path integration by means of Bayesian localization, and loop closing with sequential gradient descent. Finally, we outline a method for structuring spatial representations using metric learning and clustering. Crucially, unlike the algorithms of traditional robotics, we show that these mechanisms can be implemented in neuronal or cognitive models.We briefly outline a concrete implementation of the proposed framework as part of the LIDA cognitive architecture, and argue that this kind of probabilistic framework is well-suited for use in cognitive robotic architectures aiming to combine spatial functionality and psychological plausibility.

Team Coordination Dynamics in Human-Autonomy Teaming

Project overview. The current study focuses on the nature of team coordination dynamics within all-human teams and Human-Autonomy Teams (HAT) in the context of the development of a fully-fledged synthetic agent that is a computational cognitive model for a three-agent Unmanned Aircraft System (UAS) ground crew. In this study, the relationship between team coordination dynamics and team performance within the HAT and all-human teams is considered. To serve as a teammate, the synthetic agent must be able to communicate and coordinate with its human teammates in a constructive and timely manner (Demir, McNeese, & Cooke, 2016). In this current research, there were three heterogeneous team members who communicated via a text-based communication system to photograph target waypoints. Each team member had a different role: (1) navigator – provides information regarding a flight plan with speed and altitude restrictions of each waypoint; (2) pilot – controls the UAS by adjusting its altitude and airspeed by negotiating with the photographer to take a good photo for the target waypoints; and (3) photographer – screens camera settings, and sends feedback to the other team members regarding the status of target’s photograph. At each target waypoint, this coordination sequence among the team members, called Information-Negotiation-Feedback (INF), is captured by a Kappa Score (Gorman, Amazeen, & Cooke, 2010) that describes the sequence and timing of the information coordination. Three conditions were created that manipulated the pilot role: (1) Synthetic – pilot was the synthetic agent, (2) Control – pilot was a randomly assigned participant, and (3) Experimenter – pilot was an experimenter who was highly experienced with the task and focused on pushing and pulling the information in a timely manner. Method. In this experiment, there were 30 teams (ten teams for each condition): control teams were composed of three participants randomly assigned to each role; synthetic and experimenter teams were composed of only two participants randomly assigned to the navigator and photographer roles. The experiment consisted of five missions (each 40 minutes) in which teams needed to take as many “good” photos as possible of ground targets while avoiding alarms and rule violations. Several measures were obtained from this research, including team performance scores (mission and target level), team process measures (situation awareness, process ratings, communication and coordination), and other measures (teamwork knowledge, workload, and demographics). The research reported here identifies how differences in team coordination, captured by Kappa, relate to performance of all human teams and HAT teams. In this paper, we focus on: (1) target level team performance scores calculated based on the time spent inside a target waypoint to get a good photo; and (2) two team coordination dynamics measures: stability and team communication determinism. Stability was inversely related to the largest Lyapunov Exponent which was estimated by Kappa, that is, the INF coordination sequence. Team communication determinism was estimated from communication data using Joint Recurrence Quantification Analysis (Marwan, Carmen, Thiel, & Kurths, 2007) and served as an index of flexible behavior. Results and discussion. In general, findings indicate that (1) synthetic teams were most stable, followed by experimenter teams, who were moderately stable, and control teams, who were least stable; and (2) extreme stability and instability corresponded to lower levels of performance; experimenter teams performed best, followed by control teams and, then synthetic teams. Thus, synthetic agents could be made more effective if interventions were developed to enhance the flexibility and adaptive nature of HATs (Demir, 2017).

DGCC: data-driven granular cognitive computing

2016 is the 60 anniversary of the birth of artificial intelligence (AI). In the past 60 years, many AI theoretical models have been proposed and great achievements have been accomplished. Most intelligent computing models are inspired by various human/ natural/social intelligence mechanisms. Three main schools of artificial intelligence are formed, that is, symbolism, connectionism and behaviorism. Artificial intelligence is intelligence exhibited by machines. It is applied when a machine mimics cognitive functions that humans associate with other human minds. Achievements of cognitive science could give much inspiration to AI. Cognitive computing is one of the core fields of artificial intelligence. It aims to develop a coherent, unified, universal mechanism inspired by human mind’s capabilities. It is one of the most critical tasks for artificial intelligence researchers to develop advanced cognitive computing models. Cognitive computing is the third and most transformational phase in computing’s evolution, after the two distinct eras of computing—the tabulating era and the programming era. Inspired by human’s granularity thinking, problem solving mechanism and the cognition law of “global precedence”, a new powerful cognitive computing model, data-driven granular cognitive computing (DGCC), is proposed in this paper. It takes data as a special kind of knowledge expressed in the lowest granularity level of a multiple granularity space. It integrates two contradictory mechanisms, namely, the human’s cognition mechanism of “global precedence” which is a cognition process of “from coarser to finer” and the information processing mechanism of machine learning systems which is “from finer to coarser”, in a multiple granularity space. It is also based on the idea of data-driven. The research issues of DGCC to be further addressed are discussed. Based on DGCC, deep learning is neither classified into symbolism, nor connectionism. It is taken as a combination of symbolism and connectionism, and named hierarchical structuralism in this paper. The HD $$^3$$ characteristics (hierarchical, distributed, data-driven, and dynamical) of the hierarchical structuralism are analyzed. DGCC provides a granular cognitive computing framework for efficient knowledge discovery from big data.

Cognitive modeling of age‐related differences in information search behavior

In this study, we evaluated the ability of computational cognitive models of web‐navigation like CoLiDeS and CoLiDeS+ to model i) user interactions with search engines and ii) individual differences in search behavior due to variations in cognitive factors such as aging. CoLiDeS and CoLiDeS+ were extended to predict user clicks on search engine result pages. Their performance was evaluated using actual behavioral data from an experiment in which 2 types of information search tasks (simple vs. difficult), were presented to younger and older participants. The results showed that the model predictions matched significantly better with the actual user behavior on difficult tasks compared to simple tasks and with younger participants compared to older participants, especially for difficult tasks. Also, the matches were significantly better with CoLiDeS+ compared to CoLiDeS, especially for difficult tasks. We conclude that the advanced capabilities of CoLiDeS+, such as incorporating contextual information and implementing backtracking strategies enable it to predict user behavior significantly better than CoLiDeS, especially on difficult tasks. The usefulness of these modeling outcomes for the design of support systems for older adults is discussed.

The role of domain knowledge in cognitive modeling of information search

Computational cognitive models developed so far do not incorporate individual differences in domain knowledge in predicting user clicks on search result pages. We address this problem using a cognitive model of information search which enables us to use two semantic spaces having a low (non-expert semantic space) and a high (expert semantic space) amount of medical and health related information to represent respectively low and high knowledge of users in this domain. We also investigated two different processes along which one can gain a larger amount of knowledge in a domain: an evolutionary and a common core process. Simulations of model click behavior on difficult information search tasks and subsequent matching with actual behavioral data from users (divided into low and high domain knowledge groups based on a domain knowledge test) were conducted. Results showed that the efficacy of modeling for high domain knowledge participants (in terms of the number of matches between the model predictions and the actual user clicks on search result pages) was higher with the expert semantic space compared to the non-expert semantic space while for low domain knowledge participants it was the other way around. When the process of knowledge acquisition was taken into account, the effect of using a semantic space based on high domain knowledge was significant only for high domain knowledge participants, irrespective of the knowledge acquisition process. The implications of these outcomes for support tools that can be built based on these models are discussed.

Studying strategies and types of players: experiments, logics and cognitive models

How do people reason about their opponent in turn-taking games? Often, people do not make the decisions that game theory would prescribe. We present a logic that can play a key role in understanding how people make their decisions, by delineating all plausible reasoning strategies in a systematic manner. This in turn makes it possible to construct a corresponding set of computational models in a cognitive architecture. These models can be run and fitted to the participants’ data in terms of decisions, response times, and answers to questions. We validate these claims on the basis of an earlier game-theoretic experiment about the turn-taking game “Marble Drop with Surprising Opponent”, in which the opponent often starts with a seemingly irrational move. We explore two ways of segregating the participants into reasonable “player types”. The first way is based on latent class analysis, which divides the players into three classes according to their first decisions in the game: Random players, Learners, and Expected players, who make decisions consistent with forward induction. The second way is based on participants’ answers to a question about their opponent, classified according to levels of theory of mind: zero-order, first-order and second-order. It turns out that increasing levels of decisions and theory of mind both correspond to increasing success as measured by monetary awards and increasing decision times. Next, we use the logical language to express different kinds of strategies that people apply when reasoning about their opponent and making decisions in turn-taking games, as well as the ‘reasoning types’ reflected in their behavior. Then, we translate the logical formulas into computational cognitive models in the PRIMs architecture. Finally, we run two of the resulting models, corresponding to the strategy of only being interested in one’s own payoff and to the myopic strategy, in which one can only look ahead to a limited number of nodes. It turns out that the participant data fit to the own-payoff strategy, not the myopic one. The article closes the circle from experiments via logic and cognitive modelling back to predictions about new experiments.

The Cognitive Science of Sketch Worksheets.

Computational modeling of sketch understanding is interesting both scientifically and for creating systems that interact with people more naturally. Scientifically, understanding sketches requires modeling aspects of visual processing, spatial representations, and conceptual knowledge in an integrated way. Software that can understand sketches is starting to be used in classrooms, and it could have a potentially revolutionary impact as the models and technologies become more advanced. This paper looks at one such effort, Sketch Worksheets, which have been used in multiple classroom experiments already, with students ranging from elementary school to college. Sketch Worksheets are a software equivalent of pencil and paper worksheets commonly found in classrooms, but they provide on-the-spot feedback based on what students draw. They are built on the CogSketch platform, which provides qualitative visual and spatial representations and analogical processing based on computational models of human cognition. This paper explores three issues. First, we examine how research from cognitive science and artificial intelligence, combined with the constraints of creating new kinds of educational software, led to the representations and processing in CogSketch. Second, we examine how these capabilities have been used in Sketch Worksheets, drawing upon experiments with fifth-grade students in biology and college students in engineering design and in geoscience. Finally, we examine some open issues in sketch understanding that need to be addressed to better model high-level aspects of vision, and for sketch understanding systems to reach their full potential for supporting education.

Integrating a cognitive computational model of planning and decision-making considering affective information

Planning and decision-making are two of the cognitive functions involved in the solution of problems. These functions, among others, have been studied from the point of view of a new field known as cognitive informatics focused on the development of cognitive architectures, autonomous agents, and human robots that are capable of showing human-like behavior. We present an exhaustive study of current biological and computational models proposed in the fields of neuroscience, psychology, and cognitive informatics. Also, we present a deep review of the brain areas involved in planning, decision-making, and affection. However, the majority of the proposed computational models are seeking to mimic human external behavior. This paper aims to contribute to the cognitive informatics field with an innovative cognitive computational model of planning and decision-making. The two main differences of our model with respect to the current models in the literature are: (i) our model considers affective and motivational information as a basic and essential trigger in planning and decision-making processes; (ii) our model attempts to mimic both the internal human brain as well as the external human behavior. We developed a computational model capable of offering a direct mapping from human brain areas to computational modules of our model. Thus, in this paper we present our model from a conceptual, formal, and computational approach in order to show how our proposal must be implemented. Finally, a set of tests were conducted in order to validate our proposal. These tests show an interesting comparison between the behavior of our prototype and the behavior exhibited by some people involved in a case study.

Functional Equivalence of Sleep Loss and Time on Task Effects in Sustained Attention

Research on sleep loss and vigilance both focus on declines in cognitive performance, but theoretical accounts have developed largely in parallel in these two areas. In addition, computational instantiations of theoretical accounts are rare. The current work uses computational modeling to explore whether the same mechanisms can account for the effects of both sleep loss and time on task on performance. A classic task used in the sleep deprivation literature, the Psychomotor Vigilance Test (PVT), was extended from the typical 10-min duration to 35min, to make the task similar in duration to traditional vigilance tasks. A computational cognitive model demonstrated that the effects of time on task in the PVT were equivalent to those observed with sleep loss. Subsequently, the same mechanisms were applied to a more traditional vigilance task-the Mackworth Clock Task-providing a good fit to existing data. This supports the hypothesis that these different types of fatigue may produce functionally equivalent declines in performance.

Cognitive control explains the mutual transfer between dimensional change card sorting and first-order false belief understanding: A computational modeling study on transfer of skills

Abstract While most 3-year-olds fail both in the false belief task of theory of mind and Dimensional Change Card Sorting task of cognitive control, most 4-year-olds are able to pass these tasks. Different theories have been constructed to explain this co-development. To investigate the direction of the developmental relationship between false belief reasoning and cognitive control, Kloo and Perner (2003) trained 3-year-olds on the false belief task in one condition and on the Dimensional Change Card Sorting task in another condition. They found that there is a mutual transfer between the two tasks, meaning that training children with the Dimensional Change Card Sorting task with feedback significantly improved children’s performance on the false belief task and vice versa. In this study, we aim to provide an explanation for the underlying mechanisms of this mutual transfer by constructing computational cognitive models. In contrast to the previous theories, our models show that the common element in the two tasks is two competing strategies, only one of which leads to a correct answer. Providing children with explicit feedback trains them to use a strategy of control instead of using a simpler reactive strategy. Therefore, we propose that children start to pass the false belief and cognitive control tasks once they learn to be flexible in their behavior depending on the current goal.

Five-Year-Olds' Systematic Errors in Second-Order False Belief Tasks Are Due to First-Order Theory of Mind Strategy Selection: A Computational Modeling Study.

The focus of studies on second-order false belief reasoning generally was on investigating the roles of executive functions and language with correlational studies. Different from those studies, we focus on the question how 5-year-olds select and revise reasoning strategies in second-order false belief tasks by constructing two computational cognitive models of this process: an instance-based learning model and a reinforcement learning model. Unlike the reinforcement learning model, the instance-based learning model predicted that children who fail second-order false belief tasks would give answers based on first-order theory of mind (ToM) reasoning as opposed to zero-order reasoning. This prediction was confirmed with an empirical study that we conducted with 72 5- to 6-year-old children. The results showed that 17% of the answers were correct and 83% of the answers were wrong. In line with our prediction, 65% of the wrong answers were based on a first-order ToM strategy, while only 29% of them were based on a zero-order strategy (the remaining 6% of subjects did not provide any answer). Based on our instance-based learning model, we propose that when children get feedback “Wrong,” they explicitly revise their strategy to a higher level instead of implicitly selecting one of the available ToM strategies. Moreover, we predict that children’s failures are due to lack of experience and that with exposure to second-order false belief reasoning, children can revise their wrong first-order reasoning strategy to a correct second-order reasoning strategy.

Formalizing Cognitive Acceptance of Arguments: Durum Wheat Selection Interdisciplinary Study

In this paper we present an interdisciplinary approach that concerns the problem of argument acceptance in an agronomy setting. We propose a computational cognitive model for argument acceptance based on the dual model system in cognitive psychology. We apply it in an agronomy setting within a French national project on durum wheat.

Task inhibition, conflict, and the n-2 repetition cost: A combined computational and empirical approach

Task inhibition (also known as backward inhibition) is an hypothesised form of cognitive inhibition evident in multi-task situations, with the role of facilitating switching between multiple, competing tasks. This article presents a novel cognitive computational model of a backward inhibition mechanism. By combining aspects of previous cognitive models in task switching and conflict monitoring, the model instantiates the theoretical proposal that backward inhibition is the direct result of conflict between multiple task representations. In a first simulation, we demonstrate that the model produces two effects widely observed in the empirical literature, specifically, reaction time costs for both (n-1) task switches and n-2 task repeats. Through a systematic search of parameter space, we demonstrate that these effects are a general property of the model’s theoretical content, and not specific parameter settings. We further demonstrate that the model captures previously reported empirical effects of inter-trial interval on n-2 switch costs. A final simulation extends the paradigm of switching between tasks of asymmetric difficulty to three tasks, and generates novel predictions for n-2 repetition costs. Specifically, the model predicts that n-2 repetition costs associated with hard-easy-hard alternations are greater than for easy-hard-easy alternations. Finally, we report two behavioural experiments testing this hypothesis, with results consistent with the model predictions.

Computational cognitive modeling of the temporal dynamics of fatigue from sleep loss.

Computational models have become common tools in psychology. They provide quantitative instantiations of theories that seek to explain the functioning of the human mind. In this paper, we focus on identifying deep theoretical similarities between two very different models. Both models are concerned with how fatigue from sleep loss impacts cognitive processing. The first is based on the diffusion model and posits that fatigue decreases the drift rate of the diffusion process. The second is based on the Adaptive Control of Thought - Rational (ACT-R) cognitive architecture and posits that fatigue decreases the utility of candidate actions leading to microlapses in cognitive processing. A biomathematical model of fatigue is used to control drift rate in the first account and utility in the second. We investigated the predicted response time distributions of these two integrated computational cognitive models for performance on a psychomotor vigilance test under conditions of total sleep deprivation, simulated shift work, and sustained sleep restriction. The models generated equivalent predictions of response time distributions with excellent goodness-of-fit to the human data. More importantly, although the accounts involve different modeling approaches and levels of abstraction, they represent the effects of fatigue in a functionally equivalent way: in both, fatigue decreases the signal-to-noise ratio in decision processes and decreases response inhibition. This convergence suggests that sleep loss impairs psychomotor vigilance performance through degradation of the quality of cognitive processing, which provides a foundation for systematic investigation of the effects of sleep loss on other aspects of cognition. Our findings illustrate the value of treating different modeling formalisms as vehicles for discovery.

Mind the Noise When Identifying Computational Models of Cognition from Brain Activity.

The aim of this study was to analyze how measurement error affects the validity of modeling studies in computational neuroscience. A synthetic validity test was created using simulated P300 event-related potentials as an example. The model space comprised four computational models of single-trial P300 amplitude fluctuations which differed in terms of complexity and dependency. The single-trial fluctuation of simulated P300 amplitudes was computed on the basis of one of the models, at various levels of measurement error and at various numbers of data points. Bayesian model selection was performed based on exceedance probabilities. At very low numbers of data points, the least complex model generally outperformed the data-generating model. Invalid model identification also occurred at low levels of data quality and under low numbers of data points if the winning model's predictors were closely correlated with the predictors from the data-generating model. Given sufficient data quality and numbers of data points, the data-generating model could be correctly identified, even against models which were very similar to the data-generating model. Thus, a number of variables affects the validity of computational modeling studies, and data quality and numbers of data points are among the main factors relevant to the issue. Further, the nature of the model space (i.e., model complexity, model dependency) should not be neglected. This study provided quantitative results which show the importance of ensuring the validity of computational modeling via adequately prepared studies. The accomplishment of synthetic validity tests is recommended for future applications. Beyond that, we propose to render the demonstration of sufficient validity via adequate simulations mandatory to computational modeling studies.

A computational cognitive modeling approach to understand and design mobile crowdsourcing for campus safety reporting

The under-reporting of public safety incidents is a long-standing issue. In this paper, we propose a computational cognitive modeling approach to understand and design a mobile crowdsourcing system for improving campus safety reporting. In particular, we adopt drift-diffusion models (DDMs) from cognitive psychology to investigate the effect of various factors on users reporting tendency for public safety. Our lab experiment and online study show consistent results on how location context impacts people's reporting decisions. This finding informs the design of a novel location-based nudge mechanism, which is tested in another lab experiment with 84 participants and proved to be effective in changing users reporting decisions. Our follow-up interview study further suggests that the influence of people's mobility patterns (e.g., expected walking distance) could explain why the nudge design is effective. Our work not only informs the design of mobile crowdsourcing for public safety reporting but also demonstrates the value of applying a computational cognitive modeling approach to address HCI research questions more broadly. A computational cognitive modeling approach is used to study crowdsourcing decisions.Location context and crime severity impact people's safety reporting decisions.A novel location-based nudge mechanism is created and proved to be effective.

Determination of the Proper Rest Time for a Cyclic Mental Task Using ACT-R Architecture.

Objective Analysis of the effect of mental fatigue on a cognitive task and determination of the right start time for rest breaks in work environments. Background Mental fatigue has been recognized as one of the most important factors influencing individual performance. Subjective and physiological measures are popular methods for analyzing fatigue, but they are restricted to physical experiments. Computational cognitive models are useful for predicting operator performance and can be used for analyzing fatigue in the design phase, particularly in industrial operations and inspections where cognitive tasks are frequent and the effects of mental fatigue are crucial. Method A cyclic mental task is modeled by the ACT-R architecture, and the effect of mental fatigue on response time and error rate is studied. The task includes visual inspections in a production line or control workstation where an operator has to check products' conformity to specifications. Initially, simulated and experimental results are compared using correlation coefficients and paired t test statistics. After validation of the model, the effects are studied by human and simulated results, which are obtained by running 50-minute tests. Results It is revealed that during the last 20 minutes of the tests, the response time increased by 20%, and during the last 12.5 minutes, the error rate increased by 7% on average. Conclusion The proper start time for the rest period can be identified by setting a limit on the error rate or response time. Application The proposed model can be applied early in production planning to decrease the negative effects of mental fatigue by predicting the operator performance. It can also be used for determining the rest breaks in the design phase without an operator in the loop.

The Synthetic Teammate as a Team Player in Command-and-Control Teams

Project overview. The current project is part of a larger effort that focuses on Human-Automation Teaming (HAT) interaction in the context of the development, integration, and validation of a computational cognitive model that acts as a full-fledged synthetic teammate for a three-agent Unmanned Aircraft System (UAS) ground control crew. Our most recent effort looked at team process and team performance within the HAT. In order to be considered a team player, the synthetic teammate must be able to communicate and coordinate with its human teammates and do so in a subtle manner (Demir et al., 2016). In this task, there were three different and interdependent team members: 1) Air Vehicle Operator (AVO) – controls the UAS’s heading, altitude, and airspeed; 2) Data Exploitation, Mission Planning, and Communications (DEMPC) – provides a dynamic flight plan as well as speed and altitude restrictions; and 3) Payload Operator (PLO) – monitors sensor equipment, negotiates with the AVO, and takes photographs of target waypoints. The communication within a three-agent UAS team occurred over a text-based communications system. In this research, there were three conditions which are differentiated by the AVO role: 1) the Synthetic - the synthetic teammate was assigned the AVO role; 2) the Control - the AVO was an inexperienced human participant; 3) the Experimenter - the AVO was one of the experimenters who was experienced with the task. The experimenter AVO asked questions of other team members to ensure timely and adaptive passing of information at target waypoints. In this current study, the coordination among the team members occurs at each target waypoint and requires a specific sequence of information passing for an optimum team performance (Cooke, Gorman, Duran, & Taylor, 2007): the information is provided by the DEMPC about the upcoming target waypoint to the AVO. After that, the PLO and the AVO negotiate regarding an appropriate altitude and airspeed for the target waypoints about required camera settings. Finally, the PLO sends feedback to other team members about the status of the target photo. Method. Activities during this period included conducting an experiment to: 1) evaluate the synthetic teammate’s performance, and the HAT team performance in comparison to all human teams, 2) understand how team process differs between all human and human-synthetic teams and how this impacts performance, and 3) compare the human-synthetic teams and all human control teams to a team with a pilot that is experienced in pushing and pulling information across the team. For this experiment, participants were randomly assigned for the duration of the experiment. Within each of the five missions, teams were told to obtain as many “good” photos as possible while avoiding alarms and rule violations in less than 40 minutes. The overall focus of this paper is: team process that is comprised of eight verbal behaviors associated with team effectiveness; team performance that is a combination of mission variables, including the rate of successful target photographs, time spent in alarm and warning states (for each individual), and the critical waypoint acquisition rate; and target processing efficiency took into account the time spent inside a target waypoint to get a good photo. Results and discussion. In general, findings indicate that synthetic AVOs perform more poorly than control AVOs in terms of team performance. Synthetic teams perform as well at the mission level as control teams. However, in terms of target processing efficiency, synthetic teams perform poorer than control teams. In terms of team process, synthetic teams demonstrate interaction patterns corresponding to more pulling of information than pushing with little change over time. In summary, these results indicate that there is a strong potential for using synthetic team member as a teammate in real world tasks and for training.