Abstract not found

We summarize selected recent developments and promising directions for improving the quality of models of human performance in synthetic environments. The potential uses and goals for behavioral models in synthetic environments are first summarized. Within that context, we examine relevant, current work related to modeling more complete performance, for example, on cognitive modeling of emotion, advanced techniques for testing and building models of behavior, new cognitive architectures, and agent and Belief, Desires and Intentions (BDI) technology. The report also considers the usability of these systems as an important but neglected aspect of their performance. A list of projects with high payoff for modeling human performance in synthetic environments is noted.

This article investigates how and why people remember the existence of hidden information. To obtain data on this kind of memory phenomenon, we observed an experienced programmer doing her own work at her own computer. The programmer's interaction with the computer generates much more information than fits on the screen at once. Most of this information is hidden, scrolled out of the way by the programming environment to make Direct all correspondence to: Erik M. Altmann, George Mason University, Human Factors & Applied Cognition, Mailstop 2E5, Fairfax, VA 22030 USA; E-Mail: altmann@gmu.edu

This paper describes LICAI, a model that simulates peforming tasks by exploration where the tasks are given to the user in the form of written exercises that contain no information about the correct action sequences. LICAI's comprehension processes and the action planning processes are based on Kintsch.'s .constmc. tion-in,tegrafio n theo for text comprehension. The model comprehends me mstmcuons ano generates goats which are then stored in memory. The action planning process is eontroled by goals retrieved from memory cued by displays generated by the application.

Introduction Integrating theory, data, and knowledge about cognitive psychology and human performance in a way that is useful for guiding design in HCI is still not a simple matter. However, there have been significant advances since Card, Moran, and Newell wrote the above passage. One of the key advances is the development of cognitive architecture, the subject of this chapter. The chapter will first consider the what it is to be cognitive architecture and why cognitive architecture is relevant for HCI. In order to detail the present state of cognitive architectures in HCI, it is important to consider some of the past use of cognitive architectures in HCI research. Then, four architectures actively in use in the research community (LICAI/CoLiDeS, Soar, EPIC, and ACT-R/ PM) and their application to HCI will be examined. The chapter will conclude with a discussion of the future of cognitive architectures in HCI. 1.1 What Are Cognitive Architectures? Most any di

The notion that memory for goals is organized as a stack is central in cognitive theory in that stacks are core constructs leading cognitive architectures. However, the stack over-predicts the strength of goal memory and the precision of goal selection order, while under-predicting the maintenance cost of both. A better way to study memory for goals is to treat them like any other kind of memory element. This approach makes accurate and wellconstrained predictions and reveals the nature of goal encoding and retrieval processes. The approach is demonstrated in an ACT-R model of human performance on a canonical goal-based task, the Tower of Hanoi. The model and other considerations suggest that cognitive architectures should enforce a two-element limit on the depth of the stack to deter its use for storing task goals while preserving its use for attention and learning.

Unified theories regularly appear in psychology. They also regularly fail to fulfil all of their goals. Newell (1990) called for their revival, using computer modelling as a way to avoid the pitfalls of previous attempts. His call, embodied in the Soar project has so far, however, failed to produce the breakthrough it promised. One of the reasons for the lack of success of Newell's approach is that the methodology commonly used in psychology, based on controlling potentially confounding variables by using group data, is not the best way forward for developing unified theories of cognition. Instead, we propose an approach where (a) the problems related to group averages are alleviated by analysing subjects individually; (b) there is a close interaction between theory building and experimentation; and (c) computer technology is used to routinely test versions of the theory on a wide range of data. The advantages of this approach heavily outweigh the disadvantages.

interaction, artificial intelligence. People make use of hidden external information, first recalling that it exists and then finding it. This dissertation investigates the memory phenomena involved in recalling that external information exists. We present data in which a programmer navigates to hidden features in a real-world task environment. We then present a model that accounts for this navigation by encoding and using simple episodic memories for having seen a feature. The model inherits constraints from its underlying cognitive architecture, which specify that learning is passive and pervasive, and that it creates simple memories that depend on the feature itself being present as a cue. The nature of these memories requires the model to recall features to its mind’s eye as cues in order to retrieve them. This retrieval process requires domain knowledge: familiarity with features in order to imagine them, and an idea of when it would be useful to recall having seen them. Recalling that a hidden feature exists prompts the model to scroll to that feature. Thus the model’s access to external information is a function of passively-encoded episodic memories, and retrieval of these memories using knowledge. As a claim applied to people, this appears to overlap with a recently-

(job-shop-scheduling problems) zu lösen. Mit Hilfe des Chunking-Mechanismus von Soar wird episodisches Wissen über die Belegungsreihenfolge von Aufträgen auf Maschinen memoriert. Bei der Entwicklung des Modells wurden zahlreiche qualitative (z. B. Transfereffekte) und quantitative Befunde (z. B. Bearbeitungszeiten) aus einer früheren empirischen Untersuchung berücksichtigt. In einer Validierungsstudie wurden dieselben Aufgaben von 14 Probanden und dem Modell bearbeitet. Die Passung von Simulationsdaten und empirischen Ergebnissen fiel insgesamt gut aus. Allerdings löst das Modell die Aufgaben schneller und zeigt auch einen etwas besseren Lernverlauf als die Probanden. Das Modell liefert eine Erklärung für das Rauschen, das typischerweise bei Bearbeitungszeiten zu beobachten ist: es handelt sich um erworbenes Wissen, das mehr oder weniger gut und auch unterschiedlich häufig auf neue Situationen übertragen wird. Der Lernverlauf der Probanden entspricht nur für aggregierte Daten einer Potenzfunktion

The success of simulation environments will depend partly on how realistically the models mimic human behavior. While human behavior is affected by various moderators (see Pew and Mavor [28], for an initial list), cognitive models typically do not take into account the effects of many of these moderators. We propose that cognitive models can be augmented to account for such effects by modifying either their knowledge or the parameters of the architecture that they are built with. To provide an example of the two ways in which cognitive models can be modified to capture the effects of behavior moderators, we present an ACT-R model that performs a cognitive task while being affected by the moderators of as anxiety and pre-task appraisal. These changes are validated in a preliminary way by comparison with human data, which shows us where these models can be improved and provides lessons for further work. Most importantly, we argue that more realistic models of human behavior reflecting these moderators and individual differences can be achieved by implementing similar modifications within other cognitive models and by reusing these modifications for an existing architecture as an overlay.