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Top1. Introduction
This paper proposes a new design methodology for a man-machine system by applying qualitative reasoning to the mental load in an interactive environment. We identify the cognitive state that determines whether the driver will be ready to operate a device in car navigation. From this cognitive-state identification, we develop a minimum mental load methodology to achieve comfortable machine operation. In addition, we empirically measure and verify the heavy loads experienced by a series of car drivers.
Although the research into qualitative reasoning has been vigorously developed, including various naïve physical understandings (De Kleer & Brown, 1984), the modeling of electronic circuits (Dague, Raiman, & Devès, 1987), the diagnosis of an airplane engine (Abbot, 1988) medical diagnosis (Ohwada & Mizoguchi, 1988), application of environmental learning (Forbus & Whalley, 1994), modeling of car parts (Šuc, Vladusic, & Bratko, 2004), and dynamics systems, no previous qualitative-reasoning studies considered the cognitive state and mental load. Of course, in order to identify the cognitive state, it is necessary to measure data relating to this state. In this paper, we will measure the driver eye movements while driving.
We would like to observe that, although there are many studies about the relationship between eye movement and cognitive processes, no research has measured eye data in a real man-machine environment in car navigation. We define “eye movement data measurement” as measuring the eye movement of automobile drivers on a public road. We can acquire data for the various actions of a car driver, in particular braking, acceleration, and steering angles, from our experiment car. Since we can naturally recognize the relationship between the driver’s cognition status and the associated mental load and eye movements from our acquired data, it is the most realistic approach that data measurement using this method can apply to qualitative reasoning.
As background for these studies, we should point out several things. First, next-generation systems not only raise the issue of upgrading the functions but must also respond to just-in-time function choices and service provision. Second, next-generation systems will recognize the user’s status, such as the heavy load of aging drivers, and then will make it possible for users to operate the machines. In addition, a machine response that modifies the user’s status is also necessary when the system judges the user’s mental status. These background studies are thus based on the fact that providing information to the driver when he has a low cognitive load is preferable to providing excess information to a driver when he is overly stressed.
To achieve these next-generation services, we use bio-information such as heart rate and blood pressure, and then infer the status of the user. Although there are some mathematical analyses in the fields of biomedical engineering and brain science, including engineering control models that are applied to bio-information, they still remain at the model analysis stage. Still, there are very important insights in these developed models (Findlay & Gilchrist, 2003). An objective of these studies is to understand human reactions, so we introduce these important insights as the basis of our model development. In particular, our modeling in this paper is based on the “Data Limited and Resource Limited” model from Norman and Bobrow (1975) (hereafter referred to as the N&B model) since the N&B model is able to give us a very effective observation of human and resource allocations.
Although there are both individual differences in the cognition process and fuzziness in unconscious reflection, there are common characteristics of cognition in the nature of eye movement, specifically in the generation of saccade and the relationship between the cognition of close observation and heavy loads. The eye movement data differs from other physiological data with regard to the possibility of observing the cognition status.