This article presents a theory in which automatization is construed as the acquisition of a domain-specific knowledge base, formed of separate representations, instances, of each exposure to the task. Processing is considered automatic if it relies on retrieval of stored instances, which will occur only after practice in a consistent environment. Practice is important because it increases the amount retrieved and the speed of retrieval; consistency is important because it ensures that the retrieved instances will be useful. The theory accounts quantitatively for the power-function speed-up and predicts a power-function reduction in the standard deviation that is constrained to have the same exponent as the power function for the speed-up. The theory accounts for qualitative properties as well, explaining how some may disappear and others appear with practice. More generally, it provides an alternative to the modal view of automaticity, arguing that novice performance is limited by a lack of knowledge rather than a scarcity of resources. The focus on learning avoids many problems with the modal view that stem from its focus on resource limitations. Automaticity is an important phenomenon in everyday men-tal life. Most of us recognize that we perform routine activities quickly and effortlessly, with little thought and conscious aware-ness--in short, automatically (James, 1890). As a result, we of-ten perform those activities on "automatic pilot " and turn our minds to other things. For example, we can drive to dinner while conversing in depth with a visiting scholar, or we can make coffee while planning dessert. However, these benefits may be offset by costs. The automatic pilot can lead us astray, caus-ing errors and sometimes catastrophes (Reason & Myceilska, 1982). If the conversation is deep enough, we may find ourselves and the scholar arriving at the office rather than the restaurant, or we may discover that we aren't sure whether we put two or three scoops of coffee into the pot. Automaticity is also an important phenomenon in skill acqui-sition (e.g., Bryan & Harter, 1899). Skills are thought to consist largely of collections of automatic processes and procedures

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When on the move, cognitive resources are reserved partly for passively monitoring and reacting to contexts and events, and partly for actively constructing them. The Resource Competition Framework (RCF), building on the Multiple Resources Theory, explains how psychosocial tasks typical of mobile situations compete for cognitive resources and then suggests that this leads to the depletion of resources for task interaction and eventually results in the breakdown of fluent interaction. RCF predictions were tested in a semi-naturalistic field study measuring attention during the performance of assigned Web search tasks on mobile phone while moving through nine varied but typical urban situations. Notably, we discovered up to eight-fold differentials between micro-level measurements of attentional resource fragmentation, for example from spans of over 16 seconds in a laboratory condition dropping to bursts of just a few seconds in difficult mobile situations. By calibrating perceptual sampling, reducing resource usage for tasks of secondary importance, and resisting the impulse to switch tasks before finalization, participants compensated for the resource depletion. The findings are compared to previous studies in office contexts. The work is valuable in many areas of HCI dealing with mobility. ACM Classification Keywords: H5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous

The authors propose the idea of threaded cognition, an integrated theory of concurrent multitasking—that is, performing 2 or more tasks at once. Threaded cognition posits that streams of thought can be represented as threads of processing coordinated by a serial procedural resource and executed across other available resources (e.g., perceptual and motor resources). The theory specifies a parsimonious mechanism that allows for concurrent execution, resource acquisition, and resolution of resource conflicts, without the need for specialized executive processes. By instantiating this mechanism as a computational model, threaded cognition provides explicit predictions of how multitasking behavior can result in interference, or lack thereof, for a given set of tasks. The authors illustrate the theory in model simulations of several representative domains ranging from simple laboratory tasks such as dual-choice tasks to complex real-world domains such as driving and driver distraction.

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Interruptions can have lower cost if delivered at moments of lower mental workload during task execution, and cognitive theorists have speculated that these moments occur at subtask boundaries. In this article, we empirically test this speculation by examining how workload changes during task execution, focusing on subtask boundaries. In a carefully controlled experiment, users performed several interactive tasks while their pupil dilation, a reliable measure of workload, was continuously measured. The workload data was precisely aligned to the corresponding models of task execution and analyzed. Our principal results include (i) workload changes throughout the execution of a goal-directed task; (ii) workload exhibits momentary decreases at subtask boundaries compared to the preceding subtasks; (iii) the amount of decrease is larger at boundaries higher in the task model; and (iv) different types of subtasks induce different amounts of workload. We situate these findings within resource theories of attention and discuss important implications for interruption management systems.

Abstract — Why is it that people cannot keep their hands still when they talk? One reason may be that gesturing actually lightens cognitive load while a person is thinking of what to say. We asked adults and children to remember a list of letters or words while explaining how they solved a math problem. Both groups remembered significantly more items when they gestured during their math explanations than when they did not gesture. Gesturing appeared to save the speakers’ cognitive resources on the explanation task, permitting the speakers to allocate more resources to the memory task. It is widely accepted that gesturing reflects a speaker’s cognitive state, but our observations suggest that, by reducing cognitive load, gesturing may also play a role in shaping that state. Gesturing occurs across ages, tasks, and cultures (Feyereisen & de Lannoy, 1991). Although in theory gesture could be nothing more than meaningless hand waving, recent research has found that gesturing conveys meaningful information (Clark, 1996; Goldin-Meadow, Mc-Neill, & Singleton, 1996; Kendon, 1980; McNeill, 1992), information that is not always found in the speech it accompanies (Goldin-Meadow, Alibali, & Church, 1993). For example, a speaker might say, “I ran all the way upstairs ” while moving her index finger upward in a spiral. It is through the speaker’s gestures, and only her gestures, that the listener knows the staircase is a spiral. Moreover, gesture is noticed. The information that gesture conveys frequently has an impact on the message listeners take from the communication (Alibali, Flevares,

this paper, we explore the human factors issues involved in using virtual environments for the analysis of scientific data and phenomena. We first discuss the features of virtual reality and the nature of scientific data exploration (Section 2). In Section 3, we outline possible ways to visually represent scientific data, and we describe the perceptual and cognitive considerations that can be significant in architecting a virtual world that will effectively depict the researchers' data. In many ways, the promise of virtual reality lies in the level and the novel forms of interactivity that are possible in a virtual environment. We discuss the human factors issues related to interacting with a virtual world in Section 4, examining the issues related to navigation and travel within a virtual world as well as those related to searching for, grasping, and manipulating objects in the world. 2 Virtual Environments for Scientific Data Analysis Visual analysis of scientific data sets is often best supported by an interactive environment in which the researcher is able to view the entire data set, move forward and backward through time, search for specific values or features, obtain quantitative information about particular data locations, and manipulate objects in the data space. Virtual reality provides an especially interactive environment, and should be well suited to supporting the interactive exploration of scientific data sets [13]. Humans have considerable experience in interacting with a 3-dimensional world, moving about, and interacting with and using tools. In virtual environments, head-tracking or hand-tracking is often used to establish position -- the scientist physically moves or makes gestures in 3-space. This makes navigation within the 3-dimensional model world...