ISSN:0737-0024    

DOI:10.1207/s15327051hci1204_1

出版商:Lawrence Erlbaum Associates, Inc.    

年代:1997

数据来源: Taylor

摘要:

What is the role of a cognitive architecture in shaping a model built within it? Compared with a model written in a programming language, the cognitive architecture offers theoretical constraints. These constraints can be "soft," in that some ways of constructing a model are facilitated and others made more difficult, or they can be "hard," in that certain aspects of a model are enforced and others ruled out. We illustrate a variety of these possibilities. In the case of Soar, its learning mechanism is sufficiently constraining that it imposes hard constraints on models constructed within it. We describe how one of these hard constraints deriving from Soar's learning mechanism ensures that models constructed within Soar must learn a display-based skill and, other things being equal, must find display-based devices easier to learn than keyboard-based devices. We discuss the relation between architecture and model in terms of the degree to which a model is "compliant" with the constraints set by the architecture. Although doubts are sometimes expressed as to whether cognitive architectures have any empirical consequences for user modeling, our analysis shows that they do. Architectures play their part by imposing theoretical constraints on the models constructed within them, and the extent to which the influence of the architecture shows through in the model's behavior depends on the compliancy of the model.

ISSN:0737-0024    

DOI:10.1207/s15327051hci1204_2

出版商:Lawrence Erlbaum Associates, Inc.    

年代:1997

数据来源: Taylor

摘要:

The linked model of comprehension-based action planning and instruction taking (LICAI) simulates performing by exploration tasks using applications hosted on systems with graphical user interfaces. The tasks are given to the user as written exercises containing no information about the correct action sequences. LICAI's comprehension and action-planning processes are based on Kintsch's construction-integration (C-I) theory for text comprehension. The model assumes that comprehending instructions is a strategic process; instruction texts must be elaborated using specialized strategres that guide goal generation. LICAI comprehends the instructions and generates goals that are then stored in memory. The action-planning processes are controlled by goals retrieved from memory. Representations of goals that can guide exploration are restricted by the C-I architecture. The model predicts that successful exploration requires linking of the goal representation with the label on the correct object. The model is evaluated by comparing its predictions with results from an experimental study of learning by exploration by Franzke (1994, 1995). We discuss the implications of LICAI for designing instruction materials and interfaces that facilitate exploration.

ISSN:0737-0024    

DOI:10.1207/s15327051hci1204_3

出版商:Lawrence Erlbaum Associates, Inc.    

年代:1997

数据来源: Taylor

摘要:

EPIC (Executive Process-Interactive Control) is a cognitive architecture especially suited for modeling human multimodal and multiple-task performance. The EPIC architecture includes peripheral sensory-motor processors surrounding a production-rule cognitive processor and is being used to construct precise computational models for a variety of human-computer interaction situations. We briefly describe some of these models to demonstrate how EPIC clarifies basic properties of human performance and provides usefully precise accounts of performance speed.

ISSN:0737-0024    

DOI:10.1207/s15327051hci1204_4

出版商:Lawrence Erlbaum Associates, Inc.    

年代:1997

数据来源: Taylor

摘要:

The ACT-R system is a general system for modeling a wide range of higher level cognitive processes. Recently, it has been embellished with a theory of how its higher level processes interact with a visual interface. This includes a theory of how visual attention can move across the screen, encoding information into a form that can be processed by ACT-R. This system is applied to modeling several classic phenomena in the literature that depend on the speed and selectivity with which visual attention can move across a visual display. ACT-R is capable of interacting with the same computer screens that subjects do and, as such, is well suited to provide a model for tasks involving human-computer interaction. In this article, we discuss a demonstration of ACT-R's application to menu selection and show that the ACT-R theory makes unique predictions, without estimating any parameters, about the time to search a menu. These predictions are confirmed.

ISSN:0737-0024    

DOI:10.1207/s15327051hci1204_5

出版商:Lawrence Erlbaum Associates, Inc.    

年代:1997

数据来源: Taylor