Recent research has suggested that anterior white matter integrity, and the efficiency with which neural networks can communicate might underlie the relationship between perceptual speed and cognitive ability (Bucur et al., 2008 Deary et al., 2006). It has been suggested however, that psychophysical measures of perceptual speed, such as the inspection time required to discriminate between two objects (Deary, Johnson, & Starr, 2010) or pattern comparison (Salthouse, 1994) are more predictive of neural processing speed and cognitive ability, indicating that the extent of cognitive involvement in the processing speed measure is key.

Other more simple measures, such as four-choice reaction time are also predictive of cognitive ability in older adults (e.g., Brown, Brockmole, Gow, & Deary, 2012). Some, such as digit symbol coding, are cognitively complex. There are a range of measures of processing speed. For example, Salthouse ( 1991) showed that, not only does processing speed predict a moderate proportion of the variance in cognitive ability, but processing speed appears to mediate the relationship between age and higher order cognitive functioning. When information processing speed is taken into account the relationship between age and cognitive ability is substantially reduced. In the study of cognitive aging, one influential perspective has been that information processing speed underlies age-related decline (Salthouse, 1996). Although there was some interference from distracting objects and from previous responses, these age-related effects on processing rate seem to reflect an age-related difficulty in processing multiple objects, particularly when encoding them into visual working memory. Similar slowing was observed when simply presenting two stimuli and using a post-cue to indicate the target ( Experiment 3). However, adding three nontargets to the display resulted in age-related slowing of processing ( Experiment 2). A TAF analysis found little evidence for age differences in processing rate when a single stimulus was presented ( Experiment 1). In three experiments we measured the precision of younger and older adults' representations of target stimuli after different amounts of stimulus exposure. Examination of these studies suggests that the number of display stimuli might account for the different findings. In contrast, using the diffusion model to examine the dynamics of decision processes, Ratcliff and colleagues (e.g., Ratcliff, Thapar, & McKoon, 2006) found no evidence for age effects on processing rate across a range of tasks.

Using a time-accuracy function (TAF) analysis, Kliegl, Mayr, and Krampe ( 1994) showed clear evidence for age effects on processing rate. However, such modeling techniques have produced mixed evidence for age effects. The most advanced methods model the processing dynamics in a task in order to estimate processing rates independently of other factors that might be influenced by age, such as overall performance level and the time at which processing onsets. Multiple methods exist for measuring how age influences the rate of visual information processing.

Information processing model