Supporting resource-based analysis of task information needs

We investigate here an approach to modelling the dynamic information requirements of a user performing a number of tasks, addressing both the provision and representation of information, viewing the information as being distributed across a set of resources. From knowledge of available resources at the user interface, and task information needs we can identify whether the system provides the user with adequate support for task execution. We look at how we can use tools to help reason about these issues, and illustrate their use through an example.We also consider a full range of analyses suggested using this approach which could potentially be supported by automated reasoning systems.(undefined

Understanding multitasking through parallelized strategy exploration and individualized cognitive modeling

Human multitasking often involves complex task interactions and subtle tradeoffs which might be best understood through detailed computational cognitive modeling, yet traditional cognitive modeling approaches may not explore a sufficient range of task strategies to reveal the true complexity of multitasking behavior. This study proposes a systematic approach for exploring a large number of strategies using a computer-cluster-based parallelized modeling system. The paper demonstrates the efficacy of the approach for investigating and revealing the effects of different microstrategies on human performance, both within and across individuals, for a time-pressured multimodal dual task. The modeling results suggest that multitasking performance is not simply a matter of interleaving cognitive and sensorimotor processing but is instead heavily influenced by the selection of subtask microstrategies. Author Keywords Cognitive modeling; high performance computing; mode

Antinuclear antibodies in primary pulmonary hypertension

The association of positive antinuclear antibodies with the clinical and hemodynamic features of 43 patients with primary pulmonary hypertension and 16 patients with secondary pulmonary hypertension was investigated. Each patient had determinations of antinuclear antibodies using a KB cell substrate immunofluorescent test. Of the patients with primary pulmonary hypertension, 40% had positive antinuclear antibodies at titers of 1:80 dilutions or greater. There were no differences between patients with primary pulmonary hypertension and positive antinuclear antibodies compared with those with negative antinuclear antibodies in relation to clinical or hemodynamic status. A 6% incidence raie of antinuclear antibodies was found in patients with secondary pulmonary hypertension, similar to that in the normal population.The clinical, hemodynamic, serologic and histologic similarity between patients with primary pulmonary hypertension and those with unexplained pulmonary hypertension associated with collagen vascular disorders suggests that primary pulmonary hypertension in some patients may represent a collagen vascular disease confined to the lungs. The frequency of positive antinuclear antibody tests would place primary pulmonary hypertension between rheumatoid arthritis and scleroderma in the spectrum of collagen vascular diseases. Further studies are necessary, however, before one might expect that immunosuppressive therapy would be beneficial to these patients

Decision Process in Human-Agent Interaction: Extending Jason Reasoning Cycle

The main characteristic of an agent is acting on behalf of humans. Then, agents are employed as modeling paradigms for complex systems and their implementation. Today we are witnessing a growing increase in systems complexity, mainly when the presence of human beings and their interactions with the system introduces a dynamic variable not easily manageable during design phases. Design and implementation of this type of systems highlight the problem of making the system able to decide in autonomy. In this work we propose an implementation, based on Jason, of a cognitive architecture whose modules allow structuring the decision-making process by the internal states of the agents, thus combining aspects of self-modeling and theory of the min

Implementing Rules with Aritificial Neurons

Rule based systems are an important class of computer languages. The brain, and more recently neuromorphic systems, is based on neurons. This paper describes a mechanism that converts a rule based system, specified by a user, to spiking neurons. The system can then be run in simulated neurons, producing the same output. The conversion is done making use of binary cell assemblies, and finite state automata. The binary cell assemblies, eventually implemented in neurons, implement the states. The rules are converted to a dictionary of facts, and simple finite state automata. This is then cached out to neurons. The neurons can be simulated on standard simulators, like NEST, or on neuromorphic hardware. Parallelism is a benefit of neural system, and rule based systems can take advantage of this parallelism. It is hoped that this work will support further exploration of parallel neural and rule based systems, and su

Observations of Toroidal Coupling for Low-N Alfven Modes in the Tca Tokamak

The antenna structure in the TCA tokamak is phased to excite preferentially Alfven waves with known toroidal and poloidal wave numbers. Surprisingly, the loading spectrum includes both discrete and continuum modes with poloidal wave numbers incompatible with the antenna phasing. These additional modes, which are important for our heating experiments, can be attributed to linear mode coupling induced by the toroidicity of the plasma column, when we take into account ion-cyclotron effects

A computational model of perception and action for cognitive robotics

Robots are increasingly expected to perform tasks in complex environments. To this end, engineers provide them with processing architectures that are based on models of human information processing. In contrast to traditional models, where information processing is typically set up in stages (i.e., from perception to cognition to action), it is increasingly acknowledged by psychologists and robot engineers that perception and action are parts of an interactive and integrated process. In this paper, we present HiTEC, a novel computational (cognitive) model that allows for direct interaction between perception and action as well as for cognitive control, demonstrated by task-related attentional influences. Simulation results show that key behavioral studies can be readily replicated. Three processing aspects of HiTEC are stressed for their importance for cognitive robotics: (1) ideomotor learning of action control, (2) the influence of task context and attention on perception, action planning, and learning, and (3) the interaction between perception and action planning. Implications for the design of cognitive robotics are discussed