Alonso H. Vera

Rational adaptation under task and processing constraints: Implications for testing theories of cognition and action

The authors assume that individuals adapt rationally to a utility function given constraints imposed by their cognitive architecture and the local task environment. This assumption underlies a new approach to modeling and understanding cognition-cognitively bounded rational analysis-that sharpens the predictive acuity of general, integrated theories of cognition and action. Such theories provide the necessary computational means to explain the flexible nature of human behavior but in doing so introduce extreme degrees of freedom in accounting for data. The new approach narrows the space of predicted behaviors through analysis of the payoff achieved by alternative strategies, rather than through fitting strategies and theoretical parameters to data. It extends and complements established approaches, including computational cognitive architectures, rational analysis, optimal motor control, bounded rationality, and signal detection theory. The authors illustrate the approach with a reanalysis of an existing account of psychological refractory period (PRP) dual-task performance and the development and analysis of a new theory of ordered dual-task responses. These analyses yield several novel results, including a new understanding of the role of strategic variation in existing accounts of PRP and the first predictive, quantitative account showing how the details of ordered dual-task phenomena emerge from the rational control of a cognitive system subject to the combined constraints of internal variance, motor interference, and a response selection bottleneck.

Automating CPM-GOMS

CPM-GOMS is a modeling method that combines the task decomposition of a GOMS analysis with a model of human resource usage at the level of cognitive, perceptual, and motor operations. CPM-GOMS models have made accurate predictions about skilled user behavior in routine tasks, but developing such models is tedious and error-prone. We describe a process for automatically generating CPM-GOMS models from a hierarchical task decomposition expressed in a cognitive modeling tool called Apex. Resource scheduling in Apex automates the difficult task of interleaving the cognitive, perceptual, and motor resources underlying common task operators (e.g. mouse move-and-click). Apexs UI automatically generates PERT charts, which allow modelers to visualize a models complex parallel behavior. Because interleaving and visualization is now automated, it is feasible to construct arbitrarily long sequences of behavior. To demonstrate the process, we present a model of automated teller interactions in Apex and discuss implications for user modeling

A GOMS analysis of a graphic machine-paced, highly interactive task

A GOMS analysis was used to predict the behavior of an expert in a graphic, machine-paced, highly interactive task. The analysis was implemented in a computational model using the Soar cognitive architecture. Using only the information available in an instruction booklet and some simple heuristics for selecting between operators, the functional-level behavior of the expert proved to be virtually dictated by the objects visible on the display. At the keystroke-level, the analysis predicted about 60% of the behavior, in keeping with similar results in previous GOMS research. We conclude that GOMS is capable of predicting expert behavior in a broader range of tasks than previously demonstrated.

Towards real-time GOMS: a model of expert behaviour in a highly interactive task

Abstract We present an analysis of an expert performing a highly interactive computer task. The analysis uses GOMS models, specifying the Goals, Operators, Methods, and Selection rules used by the expert. Two models are presented, one with function-level operators which perform high-level functions in the domain, and one with keystroke-level operators which describe hand movements. For a segment of behaviour in which the expert accomplished about 30 functions in about 30 s, the function-level model predicted the observed behaviour well, while the keystroke-level model predicted only about half of the observed hand movements. These results, including the discrepancy between the models, are discussed.

Supporting efficient development of cognitive models at multiple skill levels: exploring recent advances in constraint-based modeling

This paper presents X-PRT, a new cognitive modeling tool supporting activities ranging from interface design to basic cognitive research. X-PRT provides a graphical model development environment for the CORE constraint-based cognitive modeling engine [7,13,21]. X-PRT comprises a novel feature set: (a) it supports the automatic generation of predictive models at multiple skill levels from a single task-specification, (b) it supports a comprehensive set of modeling activities, and (c) it supports compositional reuse of existing cognitive/perceptual/motor skills by transforming high-level, hierarchical task descriptions into detailed performance predictions. Task hierarchies play a central role in X-PRT, serving as the organizing construct for task knowledge, the locus for compositionality, and the cognitive structures over which the learning theory is predicated. Empirical evidence supports the role of task hierarchies in routine skill acquisition.

Automating human-performance modeling at the millisecond level

A priori prediction of skilled human performance has the potential to be of great practical value but is difficult to carry out. This article reports on an approach that facilitates modeling of human behavior at the level of cognitive, perceptual, and motor operations, following the CPM-GOMS method (John, 1990). CPM-GOMS is a powerful modeling method that has remained underused because of the expertise and labor required. We describe a process for automatically generating CPM-GOMS models from a hierarchical task decomposition expressed in a computational modeling tool, taking advantage of reusable behavior templates and their efficacy for generating zero-parameter a priori predictions of complex human behavior. To demonstrate the process, we present a model of automated teller machine interaction. The model shows that it is possible to string together existing behavioral templates that compose basic HCI tasks, (e.g., mousing to a button and clicking on it) to generate powerful human performance predictions. Because interleaving of templates is now automated, it becomes possible to construct arbitrarily long sequences of behavior. In addition, the manipulation and adaptation of complete models has the potential of becoming dramatically easier. Thus, the tool described here provides an engine for CPM-GOMS that may facilitate computational modeling of human performance at the millisecond level.

A constraint satisfaction approach to predicting skilled interactive cognition

In this paper we report a new approach to generating predictions about skilled interactive cognition. The approach, which we call Cognitive Constraint Modeling, takes as input a description of the constraints on a task environment, on user strategies, and on the human cognitive architecture and generates as output a prediction of the time course of interaction. In the Cognitive Constraint Models that we have built this is achieved by encoding the assumptions inherent in CPM-GOMS as a set of constraints and reasoning about them using finite domain constraint satisfaction.

Collaborative knowledge management supporting mars mission scientists

This paper describes the design and deployment of a collaborative software tool, designed for and presently in use on the Mars Exploration Rovers (MER) 2003 mission. Two central questions are addressed. Does collaborative content like that created on easels and whiteboards have persistent value? Can groups of people jointly manage collaboratively created content? Based on substantial quantitative and qualitative data collected during mission operations, it remains difficult to conclusively answer the first question while there is some positive support for the second question. The MER mission provides a uniquely rich data set on the use of collaborative tools.

Generating automated predictions of behavior strategically adapted to specific performance objectives

It has been well established in Cognitive Psychology that humans are able to strategically adapt performance, even highly skilled performance, to meet explicit task goals such as being accurate (rather than fast). This paper describes a new capability for generating multiple human performance predictions from a single task specification as a function of different performance objective functions. As a demonstration of this capability, the Cognitive Constraint Modeling approach was used to develop models for several tasks across two interfaces from the aviation domain. Performance objectives are explicitly declared as part of the model, and the CORE (Constraint-based Optimal Reasoning Engine) architecture itself formally derives the detailed strategies that are maximally adapted to these objectives. The models are analyzed for emergent strategic variation, comparing those optimized for task time with those optimized for working memory load. The approach has potential application in user interface and procedure design.

Leveraging open-source software in the design and development process

This paper presents a case study of the NASA Ames Research Center HCI Groups design and development of a problem reporting system for NASAs next generation vehicle (to replace the shuttle) based on the adaptation of an open source software application. We focus on the criteria used for selecting a specific system (Bugzilla) and discuss the outcomes of our project including eventual extensibility and maintainability. Finally, we address whether our experience may generalize considering where Bugzilla lies in the larger quantitative picture of current open source software projects.