Full text of DOI:10.1093/geronb/55.3.P187

Journal of Gerontology: PSYCHOLOGICAL SCIENCES
Copyright 2000 by The Gerontological Society of America
2000, Vol. 55B, No. 3, P187–P190
Does Age Change the Distribution of Visual
Attention? A Comment on McCalley,
Bouwhuis, and Juola (1995)
Frans W. Cornelissen and Aart C. Kooijman
Laboratory for Experimental Ophthalmology, School for Behavioral and Cognitive Neurosciences,
University of Groningen, The Netherlands.
A paper by McCalley, Bouwhuis, and Juola (1995) suggested differences between younger and older adults in the
use of visual cues. Furthermore, they reported these differences could largely be attributed to diminished (pe-
ripheral) visual processing capacities of elderly adults. Here, we reanalyze the data of McCalley and colleagues
emphasizing relative rather than absolute differences. We find that when doing so, the data do not reveal differ-
ences in the way older and younger adults transiently allocate attention during visual search. Contrary to the
conclusions of McCalley and colleagues, the similarity between the younger and older observers is therefore inde-
pendent of the characteristics of the visual information. Furthermore, in our view the data suggest that older
adults have foveal rather than peripheral visual processing difficulties. The results reemphasize the importance
of the analytical approach taken in aging research. We discuss the difficulties and relevance of controlling and
separating visual and attentional factors in age-related studies.
cCALLEY, Bouwhuis, and Juola (1995) examined
pointing out the extent to which an older person’s perfor-
mance may deviate from that of a younger person’s in such
circumstances.
whether younger and older adults differentially allo-
M
cated selective attention in a visual search task. In two ex-
periments, participants had to search for a target (a C)
among 23 distractors (Os). Reaction times and errors for
gap direction discrimination were recorded for a group of
healthy young and old participants. The experiments com-
pared the effects on target discrimination of cues presented
at various locations and at various stimulus onset asyn-
chronies (SOAs). In particular, a four-way interaction be-
tween age, target location, cue location, and SOA led to the
conclusion that older and younger adults distribute atten-
tion differently, apparent from differential costs and bene-
fits for valid and invalid cues. In a second experiment, in
which target and distractor size were scaled with eccentric-
ity, these age-related interaction effects disappeared. Mc-
Calley and colleagues therefore drew the conclusion that
the differences between older and younger participants
were due to differences in (peripheral) visual processing
capacities.
However, when the goal is to examine possible age-related
differences in the mechanisms underlying performance,
analysis of absolute reaction-time data may not suffice. On
nearly all tasks, older adults tend to be slower than younger
adults are. As has been pointed out before (e.g., Cerella,
1985; Myerson, Hale, Wagstaff, Poon, & Smith, 1990; Salt-
house, 1985), the principal effects of age on reaction time in
sensory-motor tasks and mental functioning can be well un-
derstood on the basis of a simple, multiplicative “slowing”
model. (Myerson and colleagues, 1990, proposed a nonlin-
ear model, based on the assumption of information loss over
multiple discrete processing steps, to also account for the
positively accelerated relation between latencies for older
and younger participants.) Because of the multiplicative na-
ture of the slowing phenomenon, task manipulations that re-
sult in changes in performance in younger adults, such as
cueing, can be expected to result in proportionally larger ef-
fects in older adults. Hence, before postulating specific defi-
cits based on age by task interactions, general slowing should
be ruled out as an explanation of the results. Salthouse (1988)
presented an analogous line of reasoning with respect to the
age by complexity phenomenon.
In this comment, we will examine the extent to which the
results of McCalley and colleagues can be understood on
the basis of general slowing. Whether general slowing can
account for the pattern of results can easily be examined by
using log-transformed, rather than absolute reaction-time,
data (as has been suggested previously; Cerella, 1985; Salt-
house, 1988). The log transformation treats equal ratios as
equal intervals so that only influences exceeding the predic-
tion made by general slowing will show significant age-
These findings could have important implications for our
understanding of how age affects the use of attention as well
as older people’s visual task performance in general. Al-
though it was a well-designed study, we believe some of its
conclusions may depend strongly on the particular approach
taken in the data analysis. Here, we propose a different
method of analysis and reach different and sometimes oppo-
site conclusions. Our main point of critique is that McCalley
and colleagues used absolute reaction time (also referred to
as linear reaction time) to assess age differences. In many
“real-world, real-time” conditions, such as traffic or voca-
tion, absolute time differences are highly relevant. There-
fore, an analysis of absolute reaction time data, such as
carried out by McCalley and colleagues, is valuable for
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CORNELISSEN AND KOOIJMAN
related interactions. Alternatively, reaction-time data can be
normalized relative to a neutral condition.
the cue effects or shows a significant interaction with age
(age: F(1,21) ϭ 0.135, p ϭ .72; age by SOA: F(3,63) ϭ
1.94, p ϭ .13; age by target: F(2,42) ϭ 0.223, p ϭ .80; age
by target by SOA: F(6,126) ϭ 0.422, p ϭ .86). We per-
formed the same analysis for the error data. Although there is
a significant effect of target position, F(2,42) ϭ 5.695, p ϭ
.007, the results further mimic those for reaction time in that
there are no significant age-related interactions (age by
SOA: F(3,63) ϭ 2.164, p ϭ .10; age by target: F(2,42) ϭ
1.052, p ϭ .36; age by target by SOA: F(6,126) ϭ 1.165, p ϭ
.33). Figure 1 plots these cue effects for older and younger
adults for reaction time as a function of SOA (this is essen-
tially Figure 3 of McCalley et al., 1995, plotted logarithmi-
cally and split for the different target positions).
Therefore, when relative rather than absolute differences
in performance are emphasized, the reaction time results of
this experiment reject the notion that there are spatial or
temporal differences in the way older and younger adults
used the cue information (this result is confirmed by the er-
ror data). Hence, there is no evidence that older and younger
adults make different trade-offs between selection and inhi-
bition, as is clear from the comparable cue effects.
There is also a statistical reason for using log-trans-
formed rather than absolute reaction-time data. A statistical
test like analysis of variance (ANOVA) requires that the
measurement is normally distributed and that the variance
in the data is the same for all groups or treatments tested. In
the experiments of McCalley and colleagues, as in many
other age-related studies, this requirement is not met be-
cause the variances in the data become larger as the absolute
reaction time increases. A solution is to analyze mathemati-
cally transformed observations rather than the original ob-
servations. Clarke (1969) advises the use of a logarithmic
transformation if the standard deviation increases at the
same rate as the size of the response. This is clearly the case
with the absolute reaction time data of McCalley and col-
leagues (young participants: mean ϭ 619 ms, SD ϭ 310;
older participants: mean ϭ 1132 ms, SD ϭ 668; McCalley
et al., 1995).
We used the original data of McCalley and colleagues’
first experiment and analyzed it using a repeated measures
ANOVA and a 2 (age group) by 3 (target location) by 4 (cue
type) by 4 (SOA) mixed model design. Target and cue loca-
tion and SOA were treated as within-subject variables, and
age as a between-subject variable. The only difference in
approach with respect to McCalley and colleagues is that
we log transformed the reaction time data prior to the statis-
tical analysis.
As for the analysis of the absolute reaction time data by
McCalley and colleagues, our current analysis supports the
existence of a highly significant age effect, F(1,21) ϭ 56.0,
p Ͻ .0001, as well as significant target location, F(2,2) ϭ
26.2, p Ͻ .0001, and cue, F(3,63) ϭ 12.7, p Ͻ .0001, effects.
The results start to differentiate from those reported by Mc-
Calley and colleagues when we look at age-related interac-
tions. In particular, McCalley and colleagues reported the
existence of a significant interaction between all four exper-
imental variables. A four-way interaction would indicate that
the two age groups show significantly different cue effects
for the different target interactions with increasing SOA.
To further substantiate this, McCalley and colleagues cal-
culated cueing effects (shown in Figure 3 and tabulated in
Table 2 of McCalley and colleagues, 1995). (This approach
can enhance the statistical power in a way that is similar to
using a paired t test.) However, no statistical analysis was
performed on these data.
According to McCalley and colleagues, the (in our view
nonexistent) age-related differences in attentional process-
ing of Experiment I might have been caused by differential
visual processing capacities. They attempted to control for
such effects in their second experiment. This experiment
was nearly identical to the first one except that target and
distractor sizes were scaled with eccentricity to compensate
for the reduced resolving power of peripheral vision. In this
In our current log-based analysis, the four-way interac-
tion is not significant, F(18,378) ϭ 1.33, p ϭ .17. (Note that
after log transformation the reaction-time data now behave
similarly to the error data for which this four-way interac-
tion was not reported as significant in McCalley and col-
leagues’ original analysis [p. 320]).
Using the log-transformed reaction-time data, we further
calculated for each participant the cueing effect, that is, the
costs inflicted by invalid cues plus the benefits caused by
valid cues. We analyzed these cueing effects using a repeated
measures ANOVA with target location and SOA as within-
subject variables, and age as a between-subjects variable.
Except for an overall trend in the effect of SOA, F(3,63) ϭ
2.69, p ϭ .05, none of the parameters significantly affects
Figure 1. Mean cueing effect (difference between reaction time for
invalid and valid cues) as a function of stimulus onset asynchrony
(SOA) and target location for older (open symbols) and younger
(filled symbols) observers. Bars show standard errors. Logarithmi-
cally transformed reaction time data of Experiment I of McCalley,
Bouwhuis, and Juola (1995) were used to derive this figure.

AGE CHANGES IN VISUAL ATTENTION?
P189
experiment, the four-way interaction of age with the other
three experimental variables disappeared. McCalley and col-
leagues therefore concluded that the age-related attentional
effects found in the first experiment could be attributed to
(peripheral) visual processing difficulties. Log transforming
the reaction-time data of Experiment II does not result in a
different conclusion than that already drawn by McCalley
and colleagues. On the basis of our current analysis of Ex-
periment I, we can now draw the conclusion that the similar-
ity of cue effects in younger and older participants is
independent of the specific visual stimulus characteristics.
Scaling targets for eccentricity had a somewhat unex-
pected side effect. Whereas in Experiment I reaction times
increased with eccentricity, in Experiment II reaction times
(and error rates) decreased with eccentricity (see Table 8 of
McCalley et al., 1995). McCalley and colleagues explain
this finding as either an attentional effect, an effect of stimu-
lus degradation, or visual interference. We would like to
suggest a further possibility. Scaling factors differ widely
for different visual tasks (for an overview see, e.g., Drasdo,
1991). The scaling McCalley and colleagues used was
based on data published by Anstis (1974) for letter acuity
thresholds. Data published on the scaling of Landolt-C acu-
ity with eccentricity (data of Weymouth, 1958, and Virsu,
Näsänen, & Osmoviita, 1987, cited in Table 19.2 of Drasdo,
1991) show that the required increase in size for Landolt-
C–type stimuli is much smaller (in the order of a factor 2)
than the scaling based on Anstis’s letter acuity experiment.
Scaling the Landolt-C target and the distractors according to
Anstis’s formula therefore induces an overcompensation for
eccentricity. Consequently, reaction time and errors will de-
crease, rather than increase with eccentricity.
As indicated by McCalley and colleagues, older people
profited most from (over)scaling target size with eccentric-
ity. This can quite readily be understood on the basis of the
nonlinear relationship between performance and visual
stimulus characteristics such as contrast and size (perfor-
mance saturates at roughly 10 times the threshold value for
these factors). In the first experiment, the targets measured
0.55 deg of visual angle with 0.1 deg gaps. Older partici-
pants’ visual acuity will tend to be somewhat lower than
that of younger participants (in a group of seven young
adults, mean age 25, we found Snellen acuity to be 1.26,
whereas that of 10 healthy older adults, mean age 70, was
1.0). Gap size will therefore have been closer to older peo-
ple’s acuity limit (6ϫ threshold) than to that of the younger
people (7.6ϫ threshold). Due to the nonlinear relationship,
those with the lower acuity, that is, the older adults, will
profit most from an increase in size.
centricity is such that we would conclude that older adults
have relatively more problems identifying central and fewer
problems identifying peripheral targets. This could poten-
tially be due to their somewhat lower foveal visual acuity.
We agree with McCalley and colleagues that the age by
target location interaction in the error data of Experiment I
might indicate that older people use attention to offset vi-
sual processing difficulties (but then only the sustained
component of attention; see Nakayama & Mackeben, 1989).
Our interpretation, however, would be that older partici-
pants were using attention to attempt to compensate for
their foveal rather than peripheral visual difficulties.
We further agree with McCalley and colleagues that such
visual factors need to be controlled before definitive conclu-
sions can be drawn on age-related effects in visual attention.
In our view, one further aspect that should be controlled
when investigating visual attention is the effort with which
people carry out the task that serves to establish baseline
performance. Ideally, effort and baseline performance
should be similar for both older and younger adults. The
large differences in reaction time and error rates in the neu-
tral conditions suggest this was not the case in the experi-
ments of McCalley and colleagues. It will be hard to obtain
comparable baseline performance for older and younger
participants using the same stimulus display (even when tar-
gets are scaled for eccentricity as the results of McCalley
and colleagues’ second experiment show). A potentially via-
ble approach might be the use of different stimuli for older
and younger adults, adapted in size and contrast to each
group’s or even individual’s visual capacities. Cue effects
could then be established relative to the baseline performance
that is established using these individually scaled stimuli.
Furthermore, large differences in error rate between
younger and older adults, as was the case in the experiments
of McCalley and colleagues (see Tables 1 and 7, in McCal-
ley et al., 1995), may indicate the use of different speed-
accuracy trade-offs (e.g., Pachella, 1974). Such differences
should be avoided because they imply younger and older
participants operated at different levels of certainty when re-
sponding, thereby hampering a proper evaluation of age-
related influences on processing (e.g., Myerson et al., 1990).
In conclusion, McCalley and colleagues’ (1995) results
on the influences of aging on the use of selective attention
in visual search can be accommodated by the notion of gen-
eral slowing. As such, our current interpretation of their re-
sults is in line with other recent findings on visual search,
attention and peripheral target localization (e.g., Hartley &
Kieley, 1995; Hartley, Kieley, & McKenzie, 1992; Kramer,
Hahn, Irwin, & Theeuwes, 1999; Scialfa, Esau, & Joffe,
1998; Seiple, Szlyck, Yang, & Holopigian, 1996).
Using relative rather than absolute comparisons may lead
to further differences in interpretation of the data. McCalley
and colleagues emphasize that older participants had more
problems identifying peripheral targets. The proportional
increase in reaction time of the older adults (relative to that
of the young) is 81% for central and 73% for outside targets,
respectively. In the second experiment, in which targets were
scaled with eccentricity to control for potential stimulus visi-
bility effects, the proportional increase for older participants
is 52% for central and 40% for outside targets, respectively.
Therefore, the age-related change in reaction time with ec-
Acknowl edgment s
We thank Dr. L. T. McCalley for making the raw data of Experiment I
available to us. We thank three anonymous referees for many useful sugges-
tions and comments. Frans W. Cornelissen is supported by a grant from
Visio, the Dutch National Foundation for Visually Impaired and Blind.
Address correspondence to Frans W. Cornelissen, Laboratory for Exper-
imental Ophthalmology, School for Behavioral and Cognitive
Neurosciences, University of Groningen, P.O. Box 30.001, 9700 RB
Groningen, The Netherlands. E-mail: f.w.cornelissen@med.rug.nl

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CORNELISSEN AND KOOIJMAN
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Received June 20, 1997
Accepted December 2, 1999
Decision Editor: Toni C. Antonucci, PhD