Validating instance-based learning mechanisms outside of ACT-R

Instance-based learning theory (IBLT) has explained human decision-making in several decision tasks. IBLT works by retrieving past experiences (i.e., instances) using a subset of cognitive mechanisms from a popular cognitive architecture, ACT-R. Until recently, most IBLT models were built within the ACT-R architecture. However, due to an integrated view of cognition and ACT-R's complexity, it is difficult to distinguish between the specific contributions of ACT-R mechanisms used in IBLT from all the other mechanisms existent in ACT-R. Also, models built within the ACT-R architecture are often difficult to explain, communicate, and reuse in other systems. This research validates the main mechanisms of IBLT when used within ACT-R and when used in isolation, outside of ACT-R. Our results show that an IBLT model performs equally well in capturing human behavior within and outside of ACT-R, demonstrating the independence of these mechanisms from any complex interaction with other mechanisms in ACT-R. We discuss the implications of our results for a modular view of cognition.     

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.     

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.     

The value of cognitive foundations for dynamic social theory

It is possible to develop models of social behavior that are predicated on detailed mechanical models of cognition. Cognitively based social models are potentially unified theoretical frameworks that can be used to explain a wide variety of social phenomena. Moreover, if a knowledge representation scheme and a knowledge acquisition scheme are specified in the underlying cognitive model then it is possible to produce a dynamic social model. The resulting social model can thus be used to predict and explain not only conditions for specific behaviors but changes in those behaviors over time.

Constructuralism is a theory of social behavior that rests on a cognitive model. The cognitive model specified has a knowledge representation scheme, knowledge acquisition procedures, and control procedures for shifting cognitive attention. The resulting social model is a dynamic model that can be used to explain both conditions for the occurrence of a behavior and social and individual changes that accrue do to a series of behaviors. The explanatory breadth of the model is illustrated by looking at predictions about a variety of social phenomena including: development of shared knowledge, identical behavior by members of the society, foreign language acquisition, clique formation, civil disobedience, and diffusion of innovative information.     

Using fuzzy cognitive mapping techniques to model situation awareness for army infantry platoon leaders

This paper describes work on the development of an actionable model of situation awareness for Army infantry platoon leaders using fuzzy cognitive mapping techniques. Developing this model based on the formal representation of the platoon leader provided by the Goal-Directed Task Analysis (GDTA) methodology advances current cognitive models because it provides valuable insight on how to effectively support human cognition within the decision-making process. We describe the modeling design approach and discuss validating the model using the VBS2 simulation environment.     

Toward scale‐free like behavior under increasing cognitive load

To understand how cognition and response selection processes might emerge from dynamic brain systems, we analyzed reaction times during the performance of both a working memory task and a choice reaction time task at different levels of “cognitive load.” Our findings suggest a continuous transition—tuned by load—from random behavior toward scale‐free like behavior as an expanding connectivity process in a network poised near a critical point. © 2012 Wiley Periodicals, Inc. Complexity, 2012     

A cognitive model of spatial path-planning

Planning a path to a destination, given a number of options and obstacles, is a common task. We suggest a two-component cognitive model that combines retrieval of knowledge about the environment with search guided by visual perception. In the first component, subsymbolic information, acquired during navigation, aids in the retrieval of declarative information representing possible paths to take. In the second component, visual information directs the search, which in turn creates knowledge for the first component. The model is implemented using the ACT-R cognitive architecture and makes realistic assumptions about memory access and shifts in visual attention. We present simulation results for memory-based high-level navigation in grid and tree structures, and visual navigation in mazes, varying relevant cognitive (retrieval noise and visual finsts) and environmental (maze and path size) parameters. The visual component is evaluated with data from a multi-robot control experiment, where subjects planned paths for robots to explore a building. We describe a method to compare trajectories without referring to aligned points in the itinerary. The evaluation shows that the model provides a good fit, but also that planning strategies may vary with task loads.     

Cognitive resource allocation for neural activity underlying mathematical cognition: a multi-method study

Mathematical cognition requires the allocation of computation resources, where math-specific computations are assumed to take place in the parietal cortex and math-supportive computations in the frontal cortex. Because the pupil dilation has a higher temporal resolution than functional MRI (fMRI), the study investigated to which extent the pupil dilation can help to identify cognitive resource allocation for neural activity underlying math-specific and math-supportive cognition. Combining pupillometry and event-related fMRI, we administered a multiplication verification paradigm to 15 healthy participants asking them to solve easy, moderate, and difficult multiplication tasks. The results revealed that (1) behavioral and pupil dilation data increased parametrically with task difficulty; (2) mental multiplication with increasing difficulty recruited a fronto-parietal circuit comprising left pre-supplementary motor area, left precentral gyrus, right dorsolateral prefrontal cortex, and bilateral intraparietal sulcus (IPS); and (3) pupil dilation was sensitive to cognitive resource allocation for neural activity underlying math-specific cognition in the bilateral IPS, implicating a strong reliance on numerical quantity processing during multiplication. In conclusion, the pupil dilation could be used in mathematics education as an easily acquired peripheral physiological indicator (without relying on fMRI) that might lead to a better understanding of dynamical changes in learning arithmetic abilities as a function of training, experience, and development. On a broader level, its application allows to obtain useful insights into learning disabilities such as dyscalculia, and further improve rehabilitation programs with appropriate intervention structures.     

Modeling the neurodynamic complexity of submarine navigation teams

Our objective was to apply ideas from complexity theory to derive neurophysiologic models of Submarine Piloting and Navigation showing how teams cognitively organize around changes in the task and how this organization is altered with experience. The cognitive metric highlighted was an electroencephalography (EEG)-derived measure of engagement (termed NS_E) which was modeled into a collective team variable showing the engagement of each of 6 team members as well as the engagement of the team as a whole. We show that during a navigation task the NS_E data stream contains historical information about the cognitive organization of the team and that this organization can be quantified by fluctuations in the Shannon entropy of the data stream. The fluctuations in the NS_E entropy were complex, showing both rapid changes over a period of seconds and longer fluctuations that occurred over periods of minutes. The periods of low NS_E entropy represented moments when the team’s cognition had undergone significant re-organization, i.e. when fewer NS_E symbols were being expressed. Decreases in NS_E entropy were associated with periods of poorer team performance as indicated by delays/omissions in the regular determination of the submarine’s position; parallel communication data suggested that these were also periods of increased stress. Experienced submarine navigation teams performed better than Junior Officer teams, had higher overall levels of NS_E entropy and appeared more cognitively flexible as indicated by the use of a larger repertoire of available NS_E patterns. The quantitative information in the NS_E entropy may provide a framework for designing future adaptive team training systems as it can be modeled and reported in near real time.     

Fractal response of physiological signals to stress conditions,environmental changes,and neurodegenerative diseases

In the past two decades the biomedical community has witnessed several applications of nonlinear system theory to the analysis of biomedical time series and the development of nonlinear dynamic models. The development of this area of medicine can best be described as nonlinear and fractal physiology. These studies have been intended to develop more reliable methodologies for understanding how biological systems respond to peculiar altered conditions induced by internal stress, environment stress, and/or disease. Herein, we summarize the theory and some of our results showing the fractal dependency on different conditions of physiological signals such as inter‐breath intervals, heart inter‐beat intervals, and human stride intervals. © 2007 Wiley Periodicals, Inc. Complexity 12: 12–17, 2007     

A cognitively based simulation of academic science

The models used in social simulation to date have mostly been very simplistic cognitively, with little attention paid to the details of individual cognition. This work proposes a more cognitively realistic approach to social simulation. It begins with a model created by Gilbert (1997) for capturing the growth of academic science. Gilbert’s model, which was equation-based, is replaced here by an agent-based model, with the cognitive architecture CLARION providing greater cognitive realism. Using this cognitive agent model, results comparable to previous simulations and to human data are obtained. It is found that while different cognitive settings may affect the aggregate number of scientific articles produced, they do not generally lead to different distributions of number of articles per author. The paper concludes with a discussion of the correspondence between the model and the constructivist view of academic science. It is argued that using more cognitively realistic models in simulations may lead to novel insights. Isaac Naveh obtained a master’s degree in computer science at the University of Missouri. His research interests include hybrid cognitive models and multi-agent learning. Ron Sun is Professor of Cognitive Science at Rensselaer Polytechnic Institute, and formerly the James C. Dowell Professor of Engineering and Professor of Computer Science at University of Missouri-Columbia. He received his Ph.D in 1992 from Brandeis University. His research interest centers around studies of cognition, especially in the areas of cognitive architectures, human reasoning and learning, cognitive social simulation, and hybrid connectionist models. For his paper on integrating rule-based and connectionist models for accounting for human everyday reasoning, he received the 1991 David Marr Award from Cognitive Science Society. He is the founding co-editor-in-chief of the journal Cognitive Systems Research, and also serves on the editorial boards of many other journals. He is the general chair and program chair for CogSci 2006, and a member of the Governing Board of International Neural Networks Society. His URL is: http://www.cogsci.rpi.edu/~rsun     

A novel cognitive transformation algorithm based on Gaussian cloud model and its application in image segmentation

The representation and processing of uncertain concepts are key issue for both the study of artificial intelligence with uncertainty and human knowledge processing. The intension and extension of a concept can be transformed automatically in the human cognition process, while it is difficult for computers. A Gaussian cloud model (GCM) is used to realize the cognitive transformation between intension and extension of a concept through computer algorithms, including forward Gaussian cloud transformation (FGCT) algorithms and backward Gaussian cloud transformation (BGCT) algorithms. A FGCT algorithm can transform a concept’s intension into extension, and a BGCT algorithm can implement the cognitive transformation from a concept’s extension to intension. In this paper, the authors perform a thorough analysis on the existing BGCT algorithms firstly, and find that these BGCT algorithms have some drawbacks. They cannot obtain the stable intension of a concept sometimes. For this reason, a new backward Gaussian cloud cognitive transformation algorithm based on sample division is proposed. The effectiveness and convergence of the proposed method is analyzed in detail, and some comparison experiments on obtaining the concept’s intension and applications to image segmentation are conducted to evaluate this method. The results show the stability and performance of our method.     

A Coupled Model for the Left Ventricle Including Regional Differences in Structure

Modeling cardiac function is an important task to increase the understanding of the physiological response of the heart and to determine how complex structural heart components influence the biomechanical behavior of the heart. In this communication a coupled model of orthotropic ventricular myocardium is presented using fiber and sheet orientations that is matching regionally measured experimental data. This approach generates a more realistic and homogenized stress distribution when compared to a model with a generic fiber and sheet orientation. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Recursive estimation: A unified approach to the identification estimation,and forecasting of hydrological systems

Mathematical models of hydrological and water-resource systems have been formulated in many different ways and with various levels of complexity. There are advantages to be gained, therefore, by trying to unify some of the more common models within a statistical framework which will allow for more objective methods of model calibration. In this paper, we consider the general class of linear, dynamic models, as applied to the characterisation of flow and dispersion behavior in rivers, and show how these can be unified within the context of recursive time-series analysis and estimation. This allows not only for more objective, data-based approaches to stochastic model structure identification, but also for improved statistical estimation and the development of both constant parameter and self-adaptive, Kalman-filter-based forecasting procedures. The unified approach presented in the paper is being applied successfully in other environmental areas, such as soil science, climatic data analysis, meterological forecasting, and plant physiology.     

Synthesis and analysis of temporal fault trees with PANDORA: The time of Priority AND gates

Classical Fault Tree Analysis (FTA) can determine the effects of combinations of failure events on a system but cannot capture the significance of the temporal order of events, which may be critical. In this paper, we propose an extension based on formal definition and use of Priority AND gates that enables representation of event sequences and analysis of temporal relationships in FTA. In addition, we show how this type of temporal analysis can be used in conjunction with a recently proposed method for automated fault tree synthesis to allow accurate failure analyses of system models to be carried out efficiently. The approach is demonstrated on a generic system with a shared backup component. The paper tentatively concludes that this type of temporal FTA can provide a more precise and ultimately more correct insight into the failure behaviour of a system.     

Nudelman interpolation and the band method

In previous work the authors developed a new addition of the band method based on a Grassmannian approach for solving a completion/extension problem in a general, abstract framework. This addition allows one to obtain a linear fractional representation of all solutions of the abstract completion problem from special extensions which are not necessarily band extensions (for the positive case) or triangular extensions (for the contractive case). In this work we extend this framework to a somewhat more general setting and show how one can obtain formulas for the required special extensions from solutions of a system of linear equations. As an application we show how the formalism can be applied to the bitangential Nevanlinna-Pick interpolation problem, a case which, up to now, was not amenable to the band method.The first author was partially supported by National Science Foundation grant DMS-9500912.     

Cognitive modeling of socially transmitted affordances: a computational model of behavioral adoption tested against archival data from the Stanford Prison Experiment

Social learning and adoption of new affordances govern the rise of new a variety of behaviors, from actions as mundane as dance steps to those as dangerous as new ways to make improvised explosive device (IED) detonators. Traditional diffusion models and social network structures fail to adequately explain who would be likely to imitate new behavior and why some agents adopt the behavior while others do not. To address this gap, a cognitive model was designed that represents well-known socio-cognitive factors of attention, social influence, and motivation that influence learning and adoption of new behavior. This model was implemented in the Performance Moderator Function Server (PMFServ) agent-based cognitive architecture, enabling the creation of simulations where affordances spread memetically through cognitive mechanisms. This approach models facets of behavioral adoption that have not been explored by existing architectures: unintentional learning, multi-layered social and environmental attention cues, and contextual adoption. To examine the effectiveness of this model, its performance was tested against data from the Stanford Prison Experiment collected from the Archives of the History of American Psychology.     

Learning of shared attention in sociable robotics

Sociable robots are embodied agents that are part of a heterogeneous society of robots and humans. They should be able to recognize human beings and each other, and to engage in social interactions. The use of a robotic architecture may strongly reduce the time and effort required to construct a sociable robot. Such architecture must have structures and mechanisms to allow social interaction, behavior control and learning from environment. Learning processes described on Science of Behavior Analysis may lead to the development of promising methods and structures for constructing robots able to behave socially and learn through interactions from the environment by a process of contingency learning. In this paper, we present a robotic architecture inspired from Behavior Analysis. Methods and structures of the proposed architecture, including a hybrid knowledge representation, are presented and discussed. The architecture has been evaluated in the context of a nontrivial real problem: the learning of the shared attention, employing an interactive robotic head. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human and the environment. The obtained results show that the robotic architecture is able to produce appropriate behavior and to learn from social interaction.     

How Students “Unpack” the Structure of a Word Problem: Graphic Representations and Problem Solving

This research investigated how fourth and fifth grade students spontaneously ‘unpacked’ a word problem when generating a graphic representation to aid in problem solution. Relationships among the type of graphic representation produced, spatial visualization, drawing ability, gender, and problem solving also were examined and described. Instrumentation developed for the study included several math challenge tasks, a spatial visualization task, and a drawing task. For one of the math challenge tasks, students were instructed to draw a picture to assist them with problem solution. These graphic representations generated by students were rated as pictorial or as displaying some level of schematic representation. Schematic representations included germane information from the problem supportive of problem solution. Pictorial representations included expressive and extraneous elements not necessary for problem solution, with no schematic elements. Findings indicated that the majority of students rendered schematic representations, with girls more likely than boys to use schematic representations at a statistically significant level. Students who used schematic visual representations were more successful problem solvers than those pictorially representing problem elements. The more “schematic‐like” the visual representation, the more successful students were at problem solution. Drawing a pictorial representation in the math challenge task also was negatively correlated to drawing skill.     

Individual behavior and macro social properties. An agent-based model

The paper aims at presenting an agent-based modeling exercise to illustrate how small differences in the cognitive properties of agents can generate very different macro social properties. We argue that it is not necessary to assume highly complicated cognitive architectures to introduce cognitive properties that matter for computational social science purposes. Our model is based on different simulation settings characterized by a gradual sophistication of behavior of agents, from simple heuristics to macro-micro feedback and other second-order properties. Agents are localized in a spatial interaction context. They have an individual task but are influenced by a collective coordination problem. The simulation results show that agents can generate efficiency at a macro level particularly when socio-cognitive sophistication of their behavior increases.