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The brain’s transmission rate and speed of information processing influence the quantity of information that can be evaluated and consequently constrain attentional capacity. Processing speed, as measured by reaction time, was studied intermittently as an indicator of individual differences in mental function in the late nineteenth century, but this line of investigation was essentially abandoned until the 1970s [1].

It was studied as to whether sensorimotor performance relevant to car-driving is altered by administration of the antidepressant pirlindole alone and under the additional influence of ethanol. In a first study the effects of placebo, imipramine and pirlindole were compared. In a second experiment, the effects of pirlindole were investigated under the additional influence of ethanol.

Measurements of various parameters derived from different reaction time (RT) paradigms are found to be correlated with psychometric measurements of general mental ability. Such RT-derived measurements, when combined in a multiple regression equation, predict some 50 percent or more of the variance in IQ or This relationship of IQ or g to RT parameters indicates that our standard IQ tests tap fundamental processes involved in individual differences in specific knowledge, acquired skills, or cultural background.

The general purpose of this experimgnt is to evaluate the specificity of electrophysiological correlates of decision making in the brain; in particular, its main goal is to investigate possible relationships between right and left hemijsphere scalp recorded electrical activity during a choice reaction time task and either the stimulated or the responding side of the body.

It is well known that various nitrogenous metabolites accumulate in the blood of uremic patients1,2. These substances, which occur in uremic blood in elevated concentrations, are considered to be toxic and to give rise to many uremic symptoms.

As was discussed in Chapter 1, the first step that must be taken in the creation of a general implementation of the chunking theory of learning, is the generalization of the model of task performance. In this chapter, we do exactly this, through the analysis and modeling of performance in a set of related stimulus-response compatibility tasks. This excursion into compatibility phenomena is a digression from the primary focus on practice, but it is a necessary step in the development of the chunking model. We will return to the discussion of practice in Chapter 4.

A very large, though fragmentary and disconnected, literature shows that as people grow old their cognitive efficiency declines. Gerontologists try to specify the precise details of this decline, to calibrate its relative extent in various components of cognition function, and to understand the nature of the changes which bring it about. To do this they have traditionally depended on empirical techniques and functional models of cognitive performance developed by human experimental psychologists working with normal young adults. There are signs that this dependence has now become so complete that it stifles original work. This has not occurred because of the lethargy or dullness of gerontologists but rather because the models currently used by human experimental psychologists have a particular crucial, and very general, limitation. They are all models for steady-state systems working under optimal conditions in the able young. They do not allow us to describe the nature of changes in human performance. Gerontologists are primarily concerned with descriptions and explanations of change.

At the meeting on Monitoring Behaviour and Supervisory Control held in Berchtesgarten in 1976, someone made the point that we possess a very large body of knowledge about human performance and how to measure it, and yet we are still unable to put it to efficient use in designing man-machine systems. Surely, he said, it should be possible to find a unifying theory which would tie it all together in a useful way.

A role for the lateral cerebellar system (hemisphere and dentate nucleus) in the planning, initiation and execution of movement is suggested by its connectivity: it receives input from the associative regions of cerebral cortex and projects to the precentral motor cortical areas (Allen and Tsukahara, 1974). In a recent study, we found that the discharge of dentate neurons, occurring well in advance of movement, does not specify the subsequent direction of movement in a simple reaction time (RT) task (Chapman et al., 1986). This is in marked contrast to, for example, motor cortex where the large majority of neurons, in this same task, are directional (Lamarre et al., 1980, 1981, 1983). It was, furthermore, difficult to reconcile these observations with those of other investigators who have reported a large proportion of direction-sensitive responses in dentate (Thach, 1970; Harvey et al., 1979; Stein, 1978; cf. Thach et al., 1982). It was suggested that the relative absence of direction-dependent discharge in this previous study might be explained by nature of the task used — a simple RT situation, with no preparatory period, in which the GO signal did not specify the desired direction of movement.

‘Traditional’ information theory still has a good deal to offer us in our consideration of mental workload. It is clear even from early work on perceptual-motor skill that subjective expectations, coding strategies, learned S-R compatibilities and the like will remain the private province of the subject. In consequence the Holy Grail of an absolute measurement of task load remains unattainable. However, I believe that in the short term the rule does hold that mental workload is some function of the uncertainty the subject must resolve, and the rate at which he attempts to resolve it in pursuit of his goal.

Earlier chapters in this book by Drs. Miller and Moody have already provided an excellent historical background to studies of reaction time using animal subjects. Those discussions have admirably illustrated the advantages of this behavioral technique, particularly as it has been applied to an analysis of sensory processes. While my interest in the study of reaction time in monkeys also derives from my association with Dr. Stebbins during a postdoctoral year in the Department of Physiology and Biophysics at the University of Washington, I have been concerned with the use of reaction time in a more traditional sense, i.e., in the study of information processing.