Reaction time

Paul Verhaeghen, Syracuse University

(lemma to appear in G. L. Maddox (Ed.), The Encyclopedia of Aging, 4th edition, Springer Verlag )

Older adults take longer to process information than younger adults. It has long been known that

the increase in response time (RT) with age is monotonic. In a large meta-analysis on studies

using continuous age samples, Verhaeghen and Salthouse (1997) reported an age-speed

correlation of -.52, and Welford (1977) estimated that each additional year of adult age increases

choice reaction time by 1.5 ms. In fact the increase is more than linear, and accelerates notably

with advancing age (Cerella & Hale, 1994; Verhaeghen & Salthouse, 1997). Cerella & Hale

(1994) estimated that the average 70-year old functions at the speed of the average 8-year old – a

large effect.

Slowing has obvious consequences for real-life behavior, such as applying the brakes of a

car to avoid collision. Age differences in processing speed are also important because they have

been found to mediate age differences in more complex aspects of cognition such as episodic

memory, reasoning, spatial ability, and general intelligence (Salthouse, 1996; for a meta-

analysis, see Verhaeghen & Salthouse, 1997; for a review of longitudinal effects, see Hertzog,

2004).

One technique for examining age-related slowing is to plot average response times of

older adults from a single age group (roughly 60 to 75 years of age) as a function of average

response times of younger adults (18 to 25 years of age); this is called a ‘Brinley plot’, after

Brinley (1965). Early studies using archival data from a variety of information-processing tasks

showed that the young-old relationship is well approximated by a straight line with a negative

intercept and a slope significantly larger than one. The first published analysis was done on 99

data points from 18 studies; the resulting equation was RT(old) = 1.36 RT(young) – 70 ms, and

fit the data with an R2 of .95 (Cerella, Poon, & Williams, 1980). The extreme regularity of this

and subsequent data sets (e.g., Cerella, 1985, 1990; Myerson, Hale, Wagstaff, Poon, & Smith,

1990) gave rise to the hypothesis of ‘general slowing’, that is, the notion that all computational

processes in older adults are slowed to the same degree, indexed by the slope of the Brinley

function (a slope of 1.36 indicates 36% slowing for older adults of the indicated age).

The initial claims for general slowing were weakened as the field accumulated new data.

The figure shows a Brinley plot of all studies reporting single-task latencies in the 1997-2003

volumes of the journal Psychology and Aging (92 separate studies, 845 data points; upper left

panel of the figure). The tasks used in these studies were quite diverse, including such paradigms

as simple RT, choice RT, visual search, different attentional tasks, enumeration, location

discrimination, and lexical decision. Although a single linear function fits the data points well

(R2 = .87), the fit decreases considerably when the data are restricted to tasks that yield RTs of

1000 ms or less in younger adults (R2 = .67) (upper right panel). Note that the equation

reproduces the 1980 Cerella et al. result closely – the data show an average of 40% slowing, and

a small negative intercept of about 50 ms. Although the overall regression line has often been

used to characterize such data, it can be argued that slopes from individual studies provide a

better metric (Cerella, 1985; Sliwinski & Hall, 1998). Within-study Brinley slopes conform

extremely well (R2 larger than .99) to estimates derived from careful analysis of age differences

measured directly from information-processing rates in iterative tasks (Myerson, Adams, Hale, &

Jenkins, 2003). The lower left panel shows the regression lines from each study, and the lower

right panel collects the slopes of these lines in a frequency histogram. The picture that emerges

does not point to general slowing, but rather to considerable diversity in slopes. Additionally, the

figure shows that the regression lines fan out from a point situated around the (400 ms, 450 ms)

point. 400 ms is arguably close to the value of simple manual RT for young adult subjects

(Teichner & Krebs, 1974). Translating the Brinley plot to this origin eliminates the intercepts and

reduces the age relationship to the slopes alone.

What accounts for the heterogeneity in slopes? Clearly we must allow for between-study

differences – sampling variance, methods variance, and the like. But part of the heterogeneity

appears to be tied to differences in tasks and/or processes. Research points to four task

differences that lead to replicable changes in Brinley slopes. First, slopes are smaller for

sensorimotor tasks than for tasks requiring more central processing (Cerella, Poon, & Williams,

1980, estimate these slopes as 1.1 and 1.6, resp.). Second, within central processing, slopes for

tasks requiring lexical processing are smaller than slopes for tasks requiring visuospatial

processing (Lima, Hale, & Myerson, 1991, estimate these slopes to be 1.5 and 2.0, resp.; see also

Sliwinski & Hall, 1998). Third, within (mostly) visuospatial processing, tasks requiring storage

of intermediate calculations in working memory yield larger age deficits than tasks that do not

(Mayr, Kliegl, & Krampe, 1996, estimate a slope of about 2.0 for tasks without working memory

involvement and 4.0 for tasks with). Fourth, attentional processes modulate age differences in

RT, but only for tasks that divide attention (dual tasking and global task switching), and not for

tasks that require selective attention (negative priming, Stroop, and local task switching)

(Verhaeghen & Cerella, 2002, provide a series of meta-analyses supporting this fourth claim).

The origins of age-related slowing are still being debated. Some researchers point to

strategic differences, specifically increased cautiousness which drives older adults to sacrifice

speed for accuracy (e.g., Ratcliff, Thapar, & McKoon, 2001). Cautiousness or the speed-

accuracy set-point can, however, be only part of the explanation. Studies that have measured

complete time-accuracy functions (e.g., Kliegl et al., 1996; Verhaeghen, 2001) have found that

older adults are bound by less efficient functions than younger adults. Brain mechanisms that

have been advanced to account for slowing include increased neural noise (e.g., Welford, 1965;

Li & Sikström, 2002), depletion of dopamine receptors (e.g., Bäckman et al., 2000), frontal lobe

dysfunction (West, 1996), and white matter abnormalities (e.g., Deary, Leaper, Murray, Staff, &

Whalley, 2003; Gunning-Dixon & Raz, 2000).

Recently, several researchers have reported age differences in the shape of the RT

distribution, rather than just its mean. Compared to RT distributions of younger adults, those of

older adults appear to be more skewed (e.g., Spieler, Balota, & Faust, 1996) and/or more

variable, even after controlling for differences in the mean (for an overview, see Hultsch &

MacDonald, 2004). Importantly, age-related increases in RT variability were found to predict

longitudinal declines in cognition above and beyond projections based on the mean RT

(MacDonald, Hultsch, & Dixon, 2003), probably because variability is a good indicator of

attentional lapses or erratic processing tied to the integrity of the neurological substrate.

WORD COUNT 1,126

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Figure. A set of analyses on 92 studies, yielding 845 data points, from the 1997-2003 volumes of

Psychology and Aging. The two top panels show Brinley plots, one on the full data set, the other

on the data truncated at 1000 ms for younger adults. The third panel shows the regression lines

for individual studies; the fourth panel shows the frequency histogram of the slopes of these

regression lines

Brinley plot, truncated data set

RT(old) = 1.40 RT(young) - 49 msR2 = 0.67

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RT(old) = 1.44 RT(young) - 81 ms

R2 = 0.87

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