This article is concerned with systems that accommodate and exploit the properties of the graphical user interface as an environment. Many of these systems are interface agents, in the sense defined by Maes (Shneiderman and Maes 1997, p. 53): "An [interface] agent basically interacts with the application just like you interact with the application. " Although nowadays the term "interface agent" encompasses all types of agents that interact with users, even those that depend not at all on the characteristics of the interface medium, we will focus on a more specialized interpretation of the term, to mean agents that interact with the objects in a direct manipulation graphical user interface, or, in other words, softbots whose sensors and effectors are the input and output capabilities of the interface (Lieberman 1998). Other efforts we describe, not necessarily related to agents, operate within the same constraints, treating the user interface as an environment rather than only a communication medium.

In this paper a tool will be presented that simulates human perception and motor behavior in interaction with graphical user interfaces in the Microsoft Windows environment. The simulated hand and eye tool can be used in combination with any cognitive architecture. In order to validate the simulated hand and eye an experiment has been conducted in which human subjects showed simple, low-level interaction behavior. Eye movements and finger presses were measured and recorded. This allowed for basic validation of the simulated hand and eye. Furthermore, based on the collected data, an adjustment of the EMMA theory is suggested. To demonstrate the full usage if the tool, ACT-R has been used to model basic skill acquisition in instructed interaction behavior.

Simulating the effects of behavior moderators within a cognitive architecture is essential for building cognitive models that can realistically capture the full range of human performance. We demonstrate that some of these effects can be modeled by varying parameters of the cognitive architecture and some by modifying the knowledge that is built into the models. As an example of implementing the two approaches, we present an ACT-R model that performs serial subtraction under varying levels of task-appraisal and with and without anxiety realized as worry.

Abstract not found