Developing Models for Multi-Talker Listening Tasks using the EPIC Architecture: Wrong Turns and Lessons Learned

This report describes the development of a series of computational cognitive architecture models for the multi-channel listening task studied in the fields of audition and human performance. The models can account for the phenomena in which humans can respond to a designated spoken message in the context of multiple simultaneous speech messages from multiple speakers - the so-called "cocktail party effect." They are the first models of a new class that combine psychoacoustic perceptual mechanisms with production-system cognitive processing to account for the end-to-end performance in an important empirical literature.Office of Naval Research, Cognitive Science Program, under grant numbers N00014-10-1-0152 and N00014-13-1-0358, and the U. S. Air Force 711 HW Chief Scientist Seedling programhttp://deepblue.lib.umich.edu/bitstream/2027.42/108165/1/Kieras_Wakefield_TR_EPIC_17_July_2014.pdf-1Description of Kieras_Wakefield_TR_EPIC_17_July_2014.pdf : Technical report conten

The acquisition of procedures from text: A production-system analysis of transfer of training

Learning a cognitive skill from written instructions can be viewed as consisting of converting the propositional content of the written material into a representation of procedural knowledge, such as production rules. In a transfer of training experiment, subjects learned from step-by-step instructions a series of related procedures, in different training orders, for operating a simple device. The strong between-procedure transfer effects were predicted by a simple model of transfer in which individual production rules can be transferred or re-used in the representation of a new procedure if they had been used in a previously learned procedure. Apparently, this transfer mechanism acts on declarative propositional representations of the production rules, suggesting that it is more similar to comprehension processes than to conventional practice mechanisms, or to Anderson's learning principles (1982, Psychological Review, 89, 369-406; 1983, The architecture of cognition, Cambridge, MA, Harvard Univ. Press).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/26028/1/0000101.pd

Diagrammatic displays for engineered systems: effects on human performance in interacting with malfunctioning systems

Computer graphics displays make it possible to display both the topological structure of a system in the form of a schematic diagram and information about its current state using color-coding and animation. Such displays should be especially valuable as user interfaces for decision support systems and expert systems for managing complex systems. This report describes three experiments on the cognitive aspects of such displays. Two experiments involved both fault diagnosis and system operation using a very simple artificial system; one involved a complex real system in a fault diagnosis task. The major factors of interest concerned the topological content of the display--principally, the extent to which the system structural relationships were visually explicit, and the availability and visual presentation of state information. Displays containing a topologically complete diagram presenting task-relevant state information at the corresponding point on the diagram appear to be superior to displays that violate these principles. A short set of guidelines for the design of such displays is listed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30028/1/0000396.pd

Task Analysis and the Design of Functionality

this article is that the successful design of functionality requires a task analysis early enough in the system design to enable the developers to create a system that effectively supports the user's task. Thus the proper goal of the design of functionality is to choose functions that are both useful in the user's task, and which together with a good user interface, results in a system that is usable, being easy to learn and easy to use. The user's task is not just to interact with the computer, but to get a job done. Thus understanding the user's task involves understanding the user's task domain and the user's larger job goals. Many systems are designed for ordinary people, who presumably lack specialized knowledge, and so the designers might believe that they understand the user's task adequately well without any further consideration. This belief is often not correct; the tasks of even ordinary people are often poorly understood by developers. But in contrast, many economically significant systems are intended for expert users, and understanding their tasks is absolutely critical. For example, a system to assist a petroleum geologist must be based on an understanding of the knowledge and goals of the petroleum geologist. To be useful, such a system will require functions that produce information useful for the geologist; to be usable, the system will have to provide these functions in a way that the frequent and most important activities of the geologist are well supported. Thus, for success, the developer must design not just the user interface, but also the functionality behind the interface. The purpose of this article is to provide some background and beginning "how to" information on how to conduct a task analysis and how to approach the design of functionality..

Using the Keystroke-Level Model to Estimate Execution Times

Introduction The Keystroke-Level Model (KLM), proposed by Card, Moran, & Newell (1983), predicts task execution time from a specified design and specific task scenario. Basically, you list the sequence of keystroke-level actions the user must perform to accomplish a task, and then add up the times required by the actions. It is not necessary to have an implemented or mocked-up design; the KLM requires only that the user interface be specified in enough detail to dictate the sequence of actions required to perform the tasks of interest. The actions are termed keystroke level if they are at the level of actions like pressing keys, moving the mouse, pressing buttons, and so forth, as opposed to actions like "log onto system" which is much more abstract. The KLM requires that you describe how the user would do the task in terms of actions at this keystroke level. The basic actions are called operators, in the sense of operators in the Model Human Pr

SCATTERSHOT, VITIS, COLLAGE

From the Rice Thresher Archive, a collection of newspaper articles published in the student newspaper for Rice University. Genre: New

The persistent visual store as the locus of fixation memory in visual search tasks

Abstract Experiments on visual search have demonstrated the existence of a relatively large and reliable memory for which objects have been fixated; an indication of this memory is that revisits (fixations on previously fixated objects) typically comprise only about 5% of fixations. Any cognitive architecture that supports visual search must account for where such memory resides in the system and how it can be used to guide eye movements in visual search. This paper presents a simple solution for the EPIC architecture that is consistent with the overall requirements for modeling visually-intensive tasks and other visual memory phenomena