Christopher W. Myers

Interactive team cognition.

Cognition in work teams has been predominantly understood and explained in terms of shared cognition with a focus on the similarity of static knowledge structures across individual team members. Inspired by the current zeitgeist in cognitive science, as well as by empirical data and pragmatic concerns, we offer an alternative theory of team cognition. Interactive Team Cognition (ITC) theory posits that (1) team cognition is an activity, not a property or a product; (2) team cognition should be measured and studied at the team level; and (3) team cognition is inextricably tied to context. There are implications of ITC for theory building, modeling, measurement, and applications that make teams more effective performers.

The synthetic teammate project

The main objective of the Synthetic Teammate project is to develop language and task enabled synthetic agents capable of being integrated into team training simulations. To achieve this goal, the agents must be able to closely match human behavior. The initial application for the synthetic teammate research is creation of an agent able to perform the functions of a pilot for an Unmanned Aerial Vehicle (UAV) simulation as part of a three-person team. The agent, or synthetic teammate, is being developed in the ACT-R cognitive architecture. The major components include: language comprehension and generation, dialog management, agent-environment interaction, and situation assessment. Initial empirical results suggest that the agent-environment interaction is a good approximation to human behavior in the UAV environment, and we are planning further empirical tests of the synthetic teammate operating with human teammates. This paper covers the project’s modeling approach, challenges faced, progress made toward an integrated synthetic teammate, and lessons learned during development.

ProtoMatch: a tool for analyzing high-density, sequential eye gaze and cursor protocols.

ProtoMatch is a software tool for integrating and analyzing fixed-location and movement eye gaze and cursor data. It provides a comprehensive collection of protocol analysis tools that support sequential data analyses for eye fixations and scanpaths as well as for cursor “fixations” (dwells at one location) and “cursorpaths” (movements between locations). ProtoMatch is modularized software that integrates both eye gaze and cursor protocols into a unified stream of data and provides an assortment of filters and analyses. ProtoMatch subsumes basic analyses (i.e., fixation duration, number of fixations, etc.) and introduces a method of objectively computing the similarity between scanpaths or cursorpaths using sequence alignment. The combination of filters, basic analyses, and sequence alignment in ProtoMatch provides researchers with a versatile system for performing both confirmatory and exploratory sequential data analyses (Sanderson & Fisher, 1994).

Visual scan adaptation during repeated visual search

There is no consensus as to how to characterize eye fixations during visual search. On the one hand, J. M. Wolfe, G. A. Alvarez, and T. S. Horowitz (2000) have described them as a haphazard sequence of fixations. On the other hand is research that shows systematic repetition of visual patterns when freely viewing a scene (T. Foulsham & G. Underwood, 2008; D. Noton & L. W. Stark, 1971a). Two experiments are reported that demonstrate the repetition and adaptation of visual scans during visual search, supporting an adaptive scanning hypothesis. When trials were repeated in a simple search task, visual scan similarity and search efficiency increased. These increments in similarity and efficiency demonstrate the systematic and adaptive nature of visual scans to the characteristics of the visual environment during search.

Toward a situation model in a cognitive architecture

The ability to coherently represent information that is situationally relevant is vitally important to perform any complex task, especially when that task involves coordinating with team members. This paper introduces an approach to dynamically represent situation information within the ACT-R cognitive architecture in the context of a synthetic teammate project. The situation model represents the synthetic teammate’s mental model of the objects, events, actions, and relationships encountered in a complex task simulation. The situation model grounds textual information from the language analysis component into knowledge usable by the agent-environment interaction component. The situation model is a key component of the synthetic teammate as it provides the primary interface between arguably distinct cognitive processes modeled within the synthetic teammate (e.g., language processing and interactions with the task environment). This work has provided some evidence that reasoning about complex situations requires more than simple mental representations and requires mental processes involving multiple steps. Additionally, the work has revealed an initial method for reasoning across the various dimensions of situations. One purpose of the research is to demonstrate that this approach to implementing a situation model provides a robust capability to handle tasks in which an agent must construct a mental model from textual information, reason about complex relationships between objects, events, and actions in its environment, and appropriately communicate with task participants using natural language. In this paper we describe an approach for modeling situationally relevant information, provide a detailed example, discuss challenges faced, and present research plans for the situation model.

SAwSu: An Integrated Model of Associative and Reinforcement Learning

Successfully explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here, we introduce a computational model which integrates associative learning (AL) and reinforcement learning (RL). We contrast the integrated model with standalone AL and RL models in three simulation studies. First, a synthetic grid-navigation task is employed to highlight performance advantages for the integrated model in an environment where the reward structure is both diverse and dynamic. The second and third simulations contrast the performances of the three models in behavioral experiments, demonstrating advantages for the integrated model in accounting for behavioral data.

Computational Cognitive Models Iso Ecologically Optimal Strategies

Our work with the Argus Prime (Schoelles & Gray, 2001) simulated task environment has uncovered a variety of strategies that subjects use, at least sometimes, during target acquisition. However, it is difficult to determine how well subjects implement these strategies and, if implemented, how much these strategies contribute to overall performance. Recently, we have adopted Byrne and Kirliks (2002) cognitive-ecological approach to determine what strategies work best in different task environments. In the work reported here, we took one computational cognitive model and, holding all else constant, swapped in and out alternative strategies for target acquisition. We then ran each of these simulated human users ten times through each of four interface conditions.

Synthetic Teammate Communication and Coordination with Humans

A synthetic teammate based on ACT-R cognitive architecture has been developed to function as an Air Vehicle Operator in the context of a three-agent Unmanned Aerial Vehicle (UAV) ground control team taking part in studies in a Synthetic Task Environment (STE). In order for the synthetic teammate to function as team player with human teammates, it needs to skillfully handle the subtleties of team communication and coordination. Data from early synthetic teammate interactions with two human teammates are presented here to illustrate team communication and coordination challenges for the synthetic teammate. In turn, the synthetic teammate limitations have highlighted the intricacies involved in effective teamwork. Communication, though a terrifically challenging problem in itself, is only a foundation for coordinated teamwork or interacting as a team player.

Validating Computational Cognitive Process Models across Multiple Timescales

Validating Computational Cognitive Process Models across Multiple Timescales Model comparison is vital to evaluating progress in the fields of artificial general intelligence (AGI) and cognitive architecture. As they mature, AGI and cognitive architectures will become increasingly capable of providing a single model that completes a multitude of tasks, some of which the model was not specifically engineered to perform. These models will be expected to operate for extended periods of time and serve functional roles in real-world contexts. Questions arise regarding how to evaluate such models appropriately, including issues pertaining to model comparison and validation. In this paper, we specifically address model validation across multiple levels of abstraction, using an existing computational process model of unmanned aerial vehicle basic maneuvering to illustrate the relationship between validity and timescales of analysis.

Memory models of visual search - searching in-the-head vs. in-the-world?

Visual search takes place whenever we are looking for something. But when a stimulus has been visually encoded on a previous occasion, memory processes can supplement or compete with eye movements during search. While previous research has mostly focused on the perceptual features that allow us to identify a target among distractors in single shot searches (Wolfe, 1998, Psych. Science), recent findings have highlighted the contributions of visual short-term memory (VSTM) to search processes (Alvarez & Cavanagh, 2004, Psych. Science). We present a paradigm of repeated serial search that attempts to illuminate the potential roles of working memory (Anderson & Matessa, 1997, Psych. Review) and VSTM in visual search. A series of simple process models exemplifies various ways in which memory for items and/or locations can facilitate or obliterate search. Within a cognitive engineering approach, we developed multiple computational models that allowed us to explore and explicate the consequences of assumptions about VSTM capacity and organization, and the interaction between long-term memory and VSTM. Each model yielded distinct performance profiles based on the sequential order of target stimuli. We investigated our model predictions through an experiment that employed a serial search paradigm. Each of 10 targets (showing alphanumeric captions) had to be found on average twice per trial. As some items could be mere distractors and next targets were presented (auditorily) whenever the current target was found, participants could not anticipate the target sequence. Detailed comparisons between search performance, eye data and our computational models show clear evidence for memory processes for both target and distractor information, both within a single search and across multiple searches. Also, a between-subjects manipulation of target visibility shows that the use of knowledge-in-the-head (or memory) increases as the perceptual-motor costs of visual access are increased. The work reported was supported by a grant from the Air Force Office of Scientific Research AFOSR #F49620-031-0143.