Abstract and Figures

We tested younger and older observers’ attention and long-term memory functions in a “hybrid search” task, in which observers look through visual displays for instances of any of several types of targets held in memory. Apart from a general slowing, search efficiency did not change with age. In both age groups, reaction times increased linearly with the visual set size and logarithmically with the memory set size, with similar relative costs of increasing load (Experiment 1). We replicated the finding and further showed that performance remained comparable between age groups when familiarity cues were made irrelevant (Experiment 2) and target-context associations were to be retrieved (Experiment 3). Our findings are at variance with theories of cognitive aging that propose age-specific deficits in attention and memory. As hybrid search resembles many real-world searches, our results might be relevant to improve the ecological validity of assessing age-related cognitive decline.
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Aging, Neuropsychology, and Cognition
A Journal on Normal and Dysfunctional Development
ISSN: 1382-5585 (Print) 1744-4128 (Online) Journal homepage: https://www.tandfonline.com/loi/nanc20
Age doesn’t matter much: hybrid visual and
memory search is preserved in older adults
Iris Wiegand & Jeremy M. Wolfe
To cite this article: Iris Wiegand & Jeremy M. Wolfe (2019): Age doesn’t matter much: hybrid
visual and memory search is preserved in older adults, Aging, Neuropsychology, and Cognition,
DOI: 10.1080/13825585.2019.1604941
To link to this article: https://doi.org/10.1080/13825585.2019.1604941
© 2019 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group.
Published online: 03 May 2019.
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Age doesnt matter much: hybrid visual and memory search
is preserved in older adults
Iris Wiegand
a,b,c
and Jeremy M. Wolfe
a,d
a
Visual Attention Lab, Brigham & Womens Hospital, Cambridge, MA, USA;
b
Max Planck UCL Centre for
Computational Psychiatry and Ageing Research, Berlin, Germany;
c
Center for Lifespan Psychology, Max
Planck Institute for Human Development, Berlin, Germany;
d
Departments of Ophthalmology & Radiology,
Harvard Medical School, Boston, MA, USA
ABSTRACT
We tested younger and older observersattention and long-term
memory functions in a hybrid searchtask, in which observers
look through visual displays for instances of any of several types of
targets held in memory. Apart from a general slowing, search
eciency did not change with age. In both age groups, reaction
times increased linearly with the visual set size and logarithmically
with the memory set size, with similar relative costs of increasing
load (Experiment 1). We replicated the nding and further showed
that performance remained comparable between age groups
when familiarity cues were made irrelevant (Experiment 2) and
target-context associations were to be retrieved (Experiment
3). Our ndings are at variance with theories of cognitive aging
that propose age-specicdecits in attention and memory. As
hybrid search resembles many real-world searches, our results
might be relevant to improve the ecological validity of assessing
age-related cognitive decline.
ARTICLE HISTORY
Received 5 October 2018
Accepted 1 April 2019
KEYWORDS
Cognitive aging; attention;
episodic memory; visual
search; memory search
Introduction
Decline in attention and episodic long-term memory (LTM) are considered two hallmarks
of cognitive aging (Craik & Salthouse, 2011; Hoyer & Verhaeghen, 2006; Wang, Daselaar,
& Cabeza, 2017). Attentional functions are often measured using visual search tasks. In
typical visual search tasks, an observer looks for a specic target item among several
non-target items in a display (Wolfe, 1994). Visual search eciency can be quantied by
the slopes of the function relating the reaction times (RT) for detecting the target to the
number of display items, the visual set size (Wolfe & Horowitz, 2004). It has been shown
that older adults produce steeper search slopes than younger adults under conditions
where visual selection is hard because targets are dicult to distinguish from the
surrounding non-target distractors (Madden & Whiting, 2004). By contrast, no age
dierences were observed when selection is less eortful and targets pop-outdue to
salient dierences between the targets and distractorsfeatures. Though overall RT are
somewhat slower, older adults show at search slopes, just as younger adults (Plude &
Doussard-Roosevelt, 1989; Whiting, Madden, Pierce, & Allen, 2005). This pattern has been
CONTACT Iris Wiegand wiegand@mpib-berlin.mpg.de
AGING, NEUROPSYCHOLOGY, AND COGNITION
https://doi.org/10.1080/13825585.2019.1604941
© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License
(http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any
medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
taken as evidence that visual selection involving top-down attentional control can be
impaired in older age, while selection based on bottom-up guidance is largely spared
(Madden, 2007). However, some forms of top-down attentional guidance, such as cueing
and prior knowledge of a target-relevant feature, as well as priming, can be unaected
or even enhanced in older age (Madden, Whiting, Cabeza, & Huettel, 2004; Madden,
Whiting, Spaniol, & Bucur, 2005).
Episodic LTM is often assessed by testing individualsability to recognize previously
memorized items. Dual-process theories make a distinction between recollection- and
familiarity-based recognition, which are dierentially aected by aging (Jacoby, 1991;
Yonelinas, 2002). Recollection refers to the retrieval of details of the experienced event. It
is considered a relatively slow, controlled process that is assumed to decline in older
age. In contrast, familiarity, a feelingof having had a prior encounter without con-
scious recollection of details, is considered to be a faster and rather automatic process
that is less aected by aging (Koen & Yonelinas, 2014). Accordingly, age-related perfor-
mance decline is typically more pronounced when a recognition tasks requires the
recollection of elements of the study episode, such as associations between objects,
or objects and a context, whereas performance is not aected or is much less aected
by aging when old and new objects can be distinguished based on familiarity or novelty
alone (Koen & Yonelinas, 2016; Old & Naveh-Benjamin, 2008; Wolk, Mancuso, Kliot,
Arnold, & Dickerson, 2013).
Although visual attention and LTM have long been proposed to be intertwined
(Bundesen, 1990; Cabeza, Ciaramelli, Olson, & Moscovitch, 2008; Desimone & Duncan,
1995), the two functions, as well as their age-related decline, are usually assessed in
separate experimental tasks or by distinct neuropsychological test instruments.
However, in many real-world tasks, like searching for a memorized list of items in
a grocery store, the processes act together: Unless you are having a very simple meal,
in order to collect what you need for dinner from the market, you have to remember the
list of items in the recipe. Wolfe (2012), borrowing from Schneider and Shirin (1977),
introduced the hybrid search task, which combines selective attention and LTM into
one single laboratory task. First, observers memorized between 1 and 100 target objects.
These were photographs of real-world objects, making them easy to commit to LTM
(Konkle, Brady, Alvarez, & Oliva, 2010; Standing, 1973). Then, observers looked for an
instance of any of those memorized target objects within displays that contained
a varying number of distractor objects. Using younger adults (YA) as observers, Wolfe
found that reaction times in hybrid search increased linearly with the number of
distractors in the visual display, but increased logarithmically with the number of target
objects held in LTM. This implies that the time costs for adding the second, third, fourth,
and each further item to the visual set size are constant. By contrast, in search through
memory, adding the second item to a memory set costs relatively more time than
adding the fourth, the fourth will be more costly than the 8th and so on. Thus, YA
seem to be able to activate an impressively large number of search templatesfrom
LTM. Indeed, the logarithmic RT × memory set size function is robust in hybrid search for
memory set sizes of 100 items (Drew & Wolfe, 2014; Wolfe, 2012) and beyond
(Cunningham & Wolfe, 2014). Furthermore, this basic pattern of results has been
replicated under a range of conditions. If observers are asked to search for any member
of dierent categories (e.g., any animal, plant, ag, or car), rather than searching for
2I. WIEGAND AND J. M. WOLFE
specic object images, again, RT rise logarithmically with the number of categories in
memory (Cunningham & Wolfe, 2014). Moreover, the result is not restricted to object
pictures. A similar log function is also observed if the targets are words (Boettcher &
Wolfe, 2015).
Like the linear RT × visual set size function is used to quantify visual search eciency,
the logarithmic RT × memory set size function can serve as a useful tool to test
observerscontrol over memory search processes under varying conditions (Boettcher,
Drew, & Wolfe, 2018). To date, hybrid search has only been applied to study YA, whose
selective attention and LTM functions are assumed to function quite well. However,
hybrid search tasks also are a promising tool to better understand how age-related
changes in visual attention and LTM interact to aect search tasks that we often perform
in the real world.
In three experiments, we investigated age dierences in several variants of hybrid
search. In Experiment 1, we compared RT × visual set size functions and RT × memory
set size functions of YA and older adults (OA) to quantify age-related decline in attention
and memory, respectively. Unexpectedly, we did not nd evidence for qualitative age
dierences apart from general RT slowing. In Experiment 2, we tested whether OA
would show a decit when targets could not be dierentiated from distractors based
on item familiarity. However, OAs, as YAs, search performance was not aected when
familiarity cues were removed. In Experiment 3, we challenged recollection further and
included retrieval of target-context association and interference by lures (memorized
targets associated with another context) in the task. While RT and errors indicated some
interference by lures, also here, the eects were comparable between age groups.
Experiment 1
In the rst experiment, we examined whether the linear RT × visual set size function
and logarithmic RT × memory set size functions shown in YA many times could be
replicated in OA. Secondly, the design allowed us to test for age dierences in visual
selection and LTM separately, by looking at RT × visual set size function and RT ×
memory set size functions, respectively. We expected to nd evidence for age-
specic decline in both of these functions, as would be indicated by steeper slopes
in OA as compared to YA.
Participants
In all experiments, we compared newly assessed data of OA to samples of YA reported in
Wolfe and colleaguesprevious studies. We aimed for similar sample sizes in groups of
YA and OA. The YAs average age was around 30 in all experiments. When recruiting the
OA, we aimed for an average age around 70 in these groups. We further controlled that
the sample groupsgender distributions were balanced and comparable across age
groups and experiments.
In Experiment 1, we used the data from 10 YA (30.5 years, 4 male, 6 female)
previously reported by Wolfe (2012) and collected new data from 12 OA at the Max
Planck Institute for Human Development, Berlin, Germany. Two OA were excluded from
the analyses because they did not nish the whole experiment, leaving 10 OA
AGING, NEUROPSYCHOLOGY, AND COGNITION 3
(70.7 years, 4 male, 6 female) in the nal sample. All participantsvision was 20/25 or
better, assessed by the ETDRS Near Vision Chart (YA) and Snellen chart (OA). OA were
further screened with the Mini-mental State Examination (MMSE, Folstein, Folstein, &
McHugh, 1975), in which they all scored 26 or higher out of a maximum score of 30,
indicating no symptoms of beginning dementia. We further assessed cognitive reserve
(Cognitive Reserve Index (CRIq); Nucci, Mapelli, & Mondini, 2012) and perceived cogni-
tive failures (Cognitive Failures Questionnaire (CFQ), Broadbent, Cooper, FitzGerald, &
Parkes, 1982) to obtain descriptives of the samples cognitive health (Table 1). All
observers gave informed consent and were paid; YA received $10 per hour and OA
received 8 Euro per hour for their time. Data sets were collected in accordance with the
Declaration of Helsinki on ethical principles. The experiments were approved by the
Partners Healthcare Corporation Institutional Review Board (YA) and the ethics commit-
tee of the Max Planck Institute for Human Development (OA).
Procedure
The procedures were the same as those described by Wolfe (2012), Experiment 1.
Observers searched visual displays of 1, 2, 4, 8, or 16 photographs of unique objects
for any of 1, 2, 4, 8, or 16 items held in memory. Stimuli were presented and responses
collected on computers running MATLAB and the Psychophysics Toolbox (Brainard,
1997). Every observer completed ve blocks, one for each memory set size. In each
block, the observers rst memorized a set of 1 to 16 items (Figure 1, left) and were then
given a recognition test before proceeding to the visual search for the memorized
targets. Each item from the memory set was presented in isolation at the center of
the display, for 3 seconds for YA and for 5 seconds for OA. Observers were instructed to
memorize the objects by watching them and maybe say the name of the object to
themselves.
Each memory test consisted of 2 × N items where N is the memory set size, and
50% of the items were old (targets) and 50% were new. Observers had to score
above 80% correct on two successive recognition memory tests to proceed to the
visual search trials. Thus, the minimum number of memory tests for a given memory
set was two. For most of the participants, two memory tests were sucient to
achieve the score. On average, YA needed 2.33.5 memory tests. The maximum
number of tests required by one young participant was 7. OA needed 2.03.3 tests
Table 1. Mean and standard deviation (in parentheses) of the older adultsscores in
the cognitive failures questionnaire (CFQ) and cognitive reserve questionnaire (CRIq).
Note that the questionnaires were only completed by 9 out of the 10 observers.
Cognitive Failures (CFQ) Cognitive Reserve (CRIq)
Forgetfullness 9.11 (2.67) Education 118.89 (9.75)
Distractability 5.00 (3.35) Work 114.00 (15.31)
FalseTriggering 2.33 (1.23) Leisure 135.11 (9.45)
Total 17.56 (6.54) Total 131.22 (5.67)
4I. WIEGAND AND J. M. WOLFE
on average and the maximum number of tests required by one older participant was
7. In both age groups, the average performance on the last memory test was
98100%.
After the memory set was encoded, observers performed 16 practice trials and YA
performed 500 experimental trials and OA performed 400 experimental trials
1
of visual
search through random arrays of objects (Figure 1, right). One of the to-be-remembered
targets was present on 50% of the trials and no targets were present on the other half.
Target present and target absent trials varied randomly and trials were randomly divided
among the ve visual set sizes (1, 2, 4, 8, and 16) within a block. Stimuli were visible until
observers responded with a target-present or target-absent key press under instructions
to be as quick and accurate as possible. RT and accuracy were recorded. The same
process was repeated for each of the ve memory set sizes. The order of blocks was
counterbalanced across observers.
Results
To compare performance of YA and OA in hybrid search, we performed mixed ANOVAs
with the within-subject factors Visual Set Size (1, 2, 4, 8, 16) and Memory Set Size (1, 2, 4,
8, 16) and the between-subject factor Age (YA, OA) on raw RT, z-transformed RT (zRT),
and error rates in target present trials (misses). Signicant interactions involving the
factor Age were followed-up with independent T-tests, using the bonferroni correction
for multiple comparisons. Note that we did not analyze error rates in target absent trials
(false alarms) due to a bug in the experiment that the group of OA completed (but see
Experiment 2 for analyses of false alarms, where this problem was eliminated). Very few
trials coded as target absent trials in the experiment erroneously contained a target item
and thus, were recorded as false alarms, if the participants selected the target. Note that
this problem does not corrupt the correct target absent RT.
Contrary to our initial hypotheses, analyses on zRT and accuracy data often did not
reveal main eects of age and/or interactions between age and set size variations. Given
Figure 1. Procedure of Experiment 1. Observers are exposed to 116 items that will serve as the
memory set for a block of trials (example memory set of 4 items) and are tested to conrm that the
set is memorized. Observers then perform target-present/target-absent visual search trials in displays
with 116 objects (example display size of 4 and 8 items).
AGING, NEUROPSYCHOLOGY, AND COGNITION 5
the usual diculties with null eects, we were concerned that we may have been
underpowered to detect signicant evidence for eects of age. Therefore, we also ran
Bayesian ANOVAs using JASP (http://www.jasp-stats.org). The Bayesian analysis grades
the evidence for two competing statistical models based on the data. In contrast to
classic hypothesis testing based on p-values and eect sizes, the sample size is less
critical to interpret the evidence for or against a given hypothesis based on a Bayes
factor. Secondly, dierent from classical hypothesis testing, the Bayesian analysis pro-
vides an estimate of how strongly the data support not only the presence of
a hypothesized eect, but also how strongly a null eect is supported. As the number
of possible models in mixed designs such as ours is huge, we preselected models to
specically test main eects of age and interactions involving age, according to the
recommendations of Rouder, Morey, Speckman, and Province (2012),
Rouder, Morey, Verhagen, Swagman, and Wagenmakers (2017), and Wagenmakers
et al. (2018). BF01was computed as evidence for H0/H1 and BF10as evidence for
H1/H0. Thus, BF01 > 1 indicates support for H0 (simpler model) and BF10 > 1 indicates
support for the H1. The H1 assumed an eect of age. We interpret Bayes factors
according to Kass and Raftery (1995). Bayes factors of 13 indicate only scarce support
for a hypothesis. Bayes factors of 320 indicate considerable evidence. Bayes factors
between 20150 indicate strong evidence, and Bayes factors >150 indicated very strong
evidence for a hypothesis. In Experiment 1, we compared simpler models that included
only the main eects of visual set size and memory set size and their interaction to
models also including the main eect of age and interactions between age and visual
set size and memory set size.
Raw RT
For RT analyses, we excluded trials on which observers made an incorrect response and
trials on which the RT was excessively low (< 250 ms) or high (> 7000 ms for YA and
>10,000 ms for OA). These were < 2% of all trials in each age group. Figure 2 shows mean
RT on target present and target absent trials for YA and OA. OA are substantially slower
than YA. However, it is clear that, in both age groups, RT × visual set size functions were
linear and RT × memory set size functions were logarithmic. The ANOVA on raw RT in target
absent and target present trials revealed signicant main eects of Visual Set Size, Memory
Set Size, and Age [all F > 45.0, all p < .001, all η
2
> .766 (90% CI 0.717; 0.783)]. In addition, all
2-way and 3-way interactions between Visual Set Size, Memory Set Size, and Age were
signicant [all F > 2.0, all p < .015, all η
2
> .100 (90% CI 0.1020.108)] except one marginally
signicant interaction between Memory Set Size and Age [F(4,72) = 2.671, p = .058,
η
2
> .118 (90% CI 0.004; 0.214]. The interactions reect that the RT increase with increasing
visual and memory set sizes was larger in OA than YA. However, this age eect could be
explained by a simple slowing of OAs RT. The interaction does not necessarily imply that
OA show relatively higher RT costs by increasing set sizes, which would be indicative of
qualitative age dierences in hybrid search.
Z-transformed RT
Z-transforming the RT is one way to ascertain if the dierences between YA and OA
reect a qualitative dierence or simply a quantitative, age-related slowing. The
6I. WIEGAND AND J. M. WOLFE
z-transformation controls for individual dierences in baseline RT (Faust, Balota, Spieler,
& Ferraro, 1999). In this analysis, within each individual, the overall mean was subtracted
from each conditions mean, and divided by the standard deviation of the condition
means. In Experiment 1, 5 × 5 conditions resulted from the Visual Set Size and Memory
Set Size manipulations. Each individuals condition z-scores greater than zero represent
slower responses, whereas z-scores lower than zero represent faster responses, relative
to this individuals mean. The resulting standardized values allow comparing the relative
condition dierences between individuals independent of individual dierences in mean
raw RT, including overall age-related slowing. It preserves other changes in the data.
Thus, for example, if increasing memory load had a disproportionate eect on OA, those
changes in the RT × memory set size functions would be preserved. In each experiment,
the outlier-corrected RT were z-transformed.
Figure 3 shows mean zRT on target present and target absent trials for YA and OA.
After eliminating individual dierences in baseline RT, the zRT × set size functions looked
very similar for YA and OA. The ANOVAs on zRT revealed signicant main eects of
Visual Set Size and Memory Set Size and the interaction between the two factors in both
target present and target absent trials [all F > 85.0, all p < .001, all η
2
> .828 (90% CI .790;
.840)]. However, the main eects of Age were not signicant [both F(1,18)<0.10, both
p > .75, both η
2
< .005 (90% CI .000; .094)]. For target present trials, the 2-way
interactions between Visual Set size and Age [F(4,72) = 4.73, p = .002, η
2
= .208 (90%
CI .053; .303)] and Memory Set Size and Age [F(4,72) = 2.96, p = .025, η
2
= .140 (90% CI
.010-.228)] were signicant. Post-hoc independent T-tests comparing YA and OA showed
that OA were relatively slower than YA at visual set size 2 [T(18) = 3.64, p = .002,
d = 1.628 (95% CI 0.5882.635)], and OA were relatively faster than YA at memory set
size 2 [T(18) = 3.22, p = .005, d = 1.440 (95% CI 0.431; 2.418)]. For target absent trials,
Figure 2. Raw Reaction Times (RT). RT (in milliseconds, ms) are plotted as a function of visual set size
(VSS) and of memory set size (MSS) for younger adults (blue, solid lines) and older adults (red,
dashed lines). RT are shown for target present and target absent trials.
AGING, NEUROPSYCHOLOGY, AND COGNITION 7
the 3-way interaction between Visual Set Size, Memory Set Size, and Age was signicant
[F = 2.398, p = .002, η
2
> .118 (90% CI .024; .129)]. OA were relatively slower than YA at
visual set size 16 [T(18) = 2.17, p = .04, d = 0.971 (95% CI 0.028; 1.890)] and memory set
size 2 [T(18) = 2.40, p = .03, d = 1.073 (95% CI 0.117; 2.003)], while OA were relatively
faster than YA at memory set size 8 [T(18) = 2.90, p = .01, d = 1.297 (95% CI 0.310;
2.255)]. However, note that none of the post-hoc independent T-tests survived the
bonferroni correction. Clearly, our results do not indicate that OA show consistently
higher RT costs than YA with increasing visual or memory set size in hybrid search.
The Bayesian ANOVA on zRT data revealed that the simpler models were more than 5
times more likely than the models that also included the main eect of Age or the Age
x Memory Set Size interaction or both (all BF01 > 5.64). However, Bayes factors indicated
more evidence for the models that also included the Age x Visual Set Size interaction vs.
the simpler models (all BF10 > 1799.60).
Errors
Figure 4 shows the error rates of YA and OA in target present trials. Miss rates rose up to >15%
in conditions with larger set sizes, however, not more so in OA than YA. In fact, numerically,
OAsmissrateswerelowerthanYAs. Statistical analyses were performed on arcSin trans-
formed error data (Hogg & Craig, 1995). The ANOVA on error rates revealed main eects of
Visual Set Size and Memory Set Size and a signicant interaction between the two factors [all
F > 4.34, all p < .001, all η
2
> .190 (90% CI .089; .218)]. There was no signicant main eect of Age
[F(1,18) = 2.40, p = .138, η
2
= .118 (90% CI .000; .343)]. The interactions between Visual Set Size
and Age and between Memory Set Size and Age were signicant [both F(4,72)>5.84, both
p < .001, both η
2
> .245 (90% CI .082; .342)]. OA missed less targets than YA at highest set sizes
Figure 3. z-Transformed Reaction Times (zRT). zRT are plotted as a function of visual set size (VSS)
and of memory set size (MSS) for younger adults (blue, solid lines) and older adults (red, dashed
lines). zRT are shown for target present and target absent trials.
8I. WIEGAND AND J. M. WOLFE
(Visual Setsize 16/Memory Set Size 16: T(18)>2.28, p < .022, d = 1.020 (95% CI 0.071; 1.944); all
other p > .05). The 3-way interaction between Visual Set Size, Memory Set Size, and Age was
not signicant [F(16,288) = 1.35, p = .165, η
2
= .070 (90% CI .000; .343)].
The same pattern was supported by the Bayesian ANOVA. Bayes factors produced
strong evidence for Age x Visual Set Size and Age x Memory Set Size interactions (all
BF10 > 529.44). Evidence for the main eect of Age was equivocal (BF10 = 1.68).
Discussion
First, we replicated the pattern of linear RT × visual set size and logarithmic RT ×
memory set size functions in a sample of OA. Second, we did not nd evidence for
qualitative age dierence in hybrid search. OA were considerably slower than YA,
however, the relative dierences between visual and memory set size conditions were
comparable across age groups. This speaks against age-specic decline in the attention
and memory processes that are involved in this form of hybrid search.
One possible explanation for OAs preserved performance in Experiment 1 could be
that they recognized targets based on item familiarity. Within a block of the hybrid
search task, the targets appeared repeatedly while distractors were always new; thus,
targets became much more familiar over trials compared to their surrounding distrac-
tors. Consider, for example, a block with 400 trials and a memory set of 4 objects. In 200
target-present trials, each of the 4 targets appeared 50 times. Thus, targets appeared 50
times more often than distractors, which were always new. We know from previous
aging studies on LTM processes that familiarity-based recognition can be preserved in
older age, while recollection-based recognition is more impaired (Koen & Yonelinas,
2016). In Experiment 2, we therefore tested whether age dierences in hybrid search
would occur if relying on familiarity-by-frequency is not sucient to distinguish targets
from distractors.
Experiment 2
In Experiment 2, we used another variant of the original hybrid search experiment, introduced
by Wolfe, Boettcher, Josephs, Cunningham, and Drew (2015). In this task, distractor items and
target items appeared with the same frequency over trials in a block, thus, distractors were as
familiar as targets. Notably, Wolfe et al. (2015)demonstratedthatYAdidnotrelyonfamiliarity-
Figure 4. Errors. Proportion of misses are plotted as a function of visual set size (VSS) and of memory
set size (MSS) for younger adults (blue, solid lines) and older adults (red, dashed lines).
AGING, NEUROPSYCHOLOGY, AND COGNITION 9
by-frequency in hybrid search; their search performance did not dier between conditions
with familiar distractors and new distractors. However, because of the assumed age-specic
decit in recollection, OA may rely on familiarity to a stronger degree. Evidence from neuroi-
maging studies suggested that OA compensate for decits in recollection by relying more on
familiarity (Cabeza et al., 2004; Daselaar, Fleck, Dobbins, Madden, & Cabeza, 2006). If this is
indeed the case, OA should show a performance decline relative to YA, if distractors appear
equally often as targets in the search display and,thus,targetscannotberecognizedbasedon
item familiarity alone. We tested this assumption by comparing a sample of OA to the YA
tested by Wolfe et al. (2015).
Participants
We used the data of 12 YA (29.3 years, 5 male, 7 female) previously reported by Wolfe et al.
(2015) and collected new data of 12 OA (73.2 years, 6 male, 6 female) at the Visual Attention
Lab, Brigham & Womens Hospital, Cambridge, MA, US. Participants were rst screened to
determine their eligibility for the study. All participantsvision was 20/25 or better (ETDRS Near
Vision Chart) and none was colorblind (Ishihara Test). All OA reported having no history of any
neurological, psychiatric, or chronic somatic disease. None showed signs of beginning demen-
tia as assessed by the MMSE (Folstein et al., 1975), in which all OA scored >26. None showed
signs of mild-severe depression as assessed by the Center for Epidemiologic Studies
Depression Scale (CES-D, Radlo,1977), in which all scored <16. OA further lled out
a demographic health survey and we measured their verbal IQ (North American Adult
Reading Test (NAART; Uttl, 2002), visuo-motor speed (Digit Symbol Substitution Test (DSST,
Wechsler, 1958), cognitive reserve (CRIq, Nucci et al., 2012), and perceived cognitive failures
(CFQ,Broadbentetal.,1982) to obtain descriptive statistics about the sample (Table 2). All
observers gave informed consent and were paid; YA received $10 per hour and OA received
$11 per hour for their time. Data sets were collected in accordance with the Declaration of
Helsinki on ethical principles. The experiments were approved by the Partners Healthcare
Corporation Institutional Review Board.
Procedure
The procedures were the same described by Wolfe et al. (2015), Experiment 1. This
variant of the hybrid search task included one condition with balanced target and
distractor familiarity (i.e., frequency) and one condition in which distractors were always
new (see Figure 5). The latter was similar to the version we used in Experiment 1 in the
present study, except that set size 1 was not included. In the condition with new
distractors, as also in Experiment 1, targets repeated over trials while distractors did
not, so that targets appeared many times more often than distractors; for example 50
times more often in a block with 400 trials and memory set size of 4. In the condition
with familiar distractors, distractors appeared with the same frequency as targets.
Observers memorized 2, 4, 8, or 16 targets before searching for those targets among 2, 4, 8,
or 16 distractors. Stimuli were presented and responses collected on computers running
MATLAB and the Psychophysics Toolbox (Brainard, 1997). In the condition with new distrac-
tors, distractor items were sampled from a large set of items, so that no distractor was ever seen
more than once in the experiment. In the condition with familiar distractors, distractor items
10 I. WIEGAND AND J. M. WOLFE
were sampled from a subset designed so that the average number of appearances of each
distractor was the same as the average number of appearances of each target. With visual set
sizesof2,4,8,and16,therewere2,800slotsfordistractoritems.Thus,forexample,intheblock
with memory set size 4, we used 56 distinct distractors (2,800/50).
Observers completed eight blocks in total, one for each of the four memory set sizes
for each of the two distractor conditions (familiar, new). Each item from the memory set
was presented in isolation at the center of the display, for 3 seconds for YA and for
5 seconds for OA. After viewing targets of the memory set, observers had to pass two
old/new recognition tests with at least 75% correct responses. Each memory test
consisted of 2 × N items where N is the memory set size, and 50% of the items were
old (targets) and 50% were new. In the condition with familiar distractors, the new half
was drawn from the set of items that were subsequently used as distractors in the visual
search task. For most of the participants, two memory tests were sucient to achieve
the score. On average, YA needed 2.13.1 memory tests. The maximum number of tests
required by two younger participants was 5. OA needed 2.02.9 tests on average and
the maximum number of tests required by two older participants was 4. The average
performance on the last memory test was 92100% in YA and 96100% in OA.
Figure 5. Search Displays in Experiment 2. After memorizing the targets, observers perform the
target-present/target-absent visual search trials. Example search trials on the left show displays from
the condition with new distractors, where distractors are only presented once in a block while
targets repeat over trials. Example trials on the right show displays from the condition with familiar
distractors, where distractors appear just as often as the targets. The target is highlighted by the red
dashed-lined square for illustration purposes.
Table 2. Mean and standard deviation (in parentheses) of the older adultsscores in the North
American Adult Reading Test (NAART) including estimates of verbal (FIQ), performance (PIQ), full-
scale (FIQ) intelligence quotients, and total scores, the cognitive failures questionnaire (CFQ)
including subscales and total score, and the cognitive reserve questionnaire (CRIq) including
subscales and total score, and the time to complete the Digit Symbol Substitution Test (DSST) (in
seconds).
Visuo-motor Speed (DSST) Verbal Abilities (NAART) Cognitive Failures (CFQ) Cognitive Reserve (CRIq)
44.08 (9.22) VIQ 118.30 (8.12) Forgetfulness 13.50 (9.33) Education 129.50 (10.77)
PIQ 114.5 (3.83) Distractibility 8.17 (4.91) Work 124.42 (15.07)
FIQ 118.7 (7.11) False Triggering 6.33 (4.08) Leisure 119.58 (20.15)
Total score 11.67 (9.11) Total score 21.50 (13.37) Total score 132.50 (13.57)
AGING, NEUROPSYCHOLOGY, AND COGNITION 11
Having passed the memory test, observers performed 16 practice and 400 experi-
mental trials of visual search in each of the four memory set size blocks in both
conditions (8 blocks in total). Targets were present on 50% of the trials. Target present
and target absent trials varied randomly and trials were randomly divided among the
four visual set sizes (2, 4, 8, and 16) within a block. Stimuli were visible until observers
responded with a target-present or target-absent key press and RT and accuracy were
recorded under instructions to be as quick and accurate as possible. The same process
was repeated for each of the four memory set sizes in both distractor conditions. The
order of blocks and conditions was counterbalanced across observers. For the OA, the
experiment was split into multiple, at least two, sessions over several days to prevent the
inuence of fatigue and sustained attention problems.
Results
To compare performance of YA and OA in hybrid search with new vs. familiar distractors,
we performed mixed ANOVAs with the within-subject factors Condition (New
Distractors, Familiar Distractors), Visual Set Size (2, 4, 8, 16), and Memory Set Size (2, 4,
8, 16) and the between-subject factor Age (YA, OA) on raw RT, zRT, and error rates, in
target present and target absent trials. Signicant main eects and interactions invol-
ving the factors Condition and/or Age were followed-up with paired T-tests (New
Distractors vs. Familiar Distractors) within each age group, using the Bonferroni correc-
tion for multiple comparisons.
Again, we also ran Bayesian ANOVAs on zRT and accuracy data. First, as in Experiment 1, we
compared simpler models that included only the main eectsofVisualSetSizeandMemory
Set Size and their interaction with models also including a main eect of Age and/or interac-
tions between Age and Visual Set Size and Memory Set Size. Second, we compared models
including main eects and interactions of the factors Age and Condition to the simplermodels.
Raw RT
For RT analyses, we excluded trials on which observers made an incorrect response and trials
on which the RT was excessively low (< 200 ms) or high (> 5000 ms for YA and > 10,000 ms for
OA). These were < 2% of all trials in both age groups. Figure 6 shows mean RT on target present
and target absent trials for YA and OA. Apart from the overall group dierence in RT
demonstrating slower responses in OA than YA, again, the data show a linear RT × visual set
size function and logarithmic RT × memory set size in both age groups. Interestingly,
distractor-familiarity did not alter the RT × set size functions in either age group; thus, there
was no indication of an age-specicdecit when targets could not be recognized based on
item-familiarity alone. In the ANOVAs, the main eects of Age, Visual Set Size, Memory Set Size
and also all 2-way interactions between those three factors were signicant [all F(3,66)>8.73,
p < .002, all η
2
> .284 (90% CI .116; .393)]. Of note, there were no signicant main eects of
Condition [both F(1,22)<1.10, both p > .30, both η
2
< .050 (90% CI .000; .234)] and no
interactions of Condition and Age [both F(1,22)<0.50, both p > .68, both η
2
< .022 (90% CI
.000; .182)]. The 3-way interaction between Visual Set Size, Memory Set Size, and Age was
signicant in target absent trials [F(3,66) = 2.89, p = .042, η
2
= .116 (90% CI .002; .213)] and the
4-way interaction between Condition, Visual Set Size, Memory Set Size, and Age was signicant
in target present trials [F(9,198) = 3.48, p = .001, η
2
= .137 (90% CI .037; .175)]. However, the
12 I. WIEGAND AND J. M. WOLFE
analyses on zRT (see next paragraph) demonstrated that these interactions were largely
explained by generalized slowing.
Figure 6. Reaction Times (RT). RT (in milliseconds, ms) are plotted as a function of visual set size
(VSS) and of memory set size (MSS) for younger adults (blue) and older adults (red), for the
conditions with new distractors (solid lines) and with familiar distractors (dotted lines). RT are
shown for target present and target absent trials.
AGING, NEUROPSYCHOLOGY, AND COGNITION 13
Z-transformed RT
As in Experiment 1, to test for qualitative age dierences beyond general slowing, we
performed the same analyses on zRT. Figure 7 shows mean zRT on target present and
Figure 7. z-Transformed Reaction Times (zRT). zRT are plotted as a function of visual set size (VSS)
and of memory set size (MSS) for younger adults (blue) and older adults (red), for the conditions
with new distractors (solid lines) and with familiar distractors (dotted lines). RT are shown for target
present and target absent trials.
14 I. WIEGAND AND J. M. WOLFE
target absent trials for YA and OA. The ANOVA revealed signicant main eects of Visual
Set Size and Memory Set Size and interactions between the two factors [all F > 60.00, all
p > .001, all η
2
> .736 (90% CI .675-.763)]. The main eects of Age were not signicant
[both F(1,22)<0.50, both p > .50, both η
2
< .021 (90% CI .000-.182)]. Again, there were no
main eects of Condition or interactions between Condition and Age [all F < 1.20, all
p > .25, all η
2
< .054 (90% CI .000; .241)]. Also on zRT, there was a signicant 4-way
interaction between Condition, Visual Set size, Memory Set size, and Age [F
(9,198) = 3.26, p = .001, η
2
> .129 (90% CI .033; .167)] in target present trials and
a signicant interaction between Condition and Visual Set size in target absent trials
[F(3,66) = 4.47, p = .006, η
2
= .169 (90% CI .031; .275)]. In target present trials, YA
responded slower in the condition with familiar distractors than new distractors at visual
set size 16/memory set size 16 [T(11) = 3.04, p = .01, d = 0.877]. OA responded slower in
the condition with familiar distractors than new distractors at visual set size 2/memory
set size 8 [T(11) = 2.41, p = .03, d = 0.522] and at visual set size 8/memory set size 4 [T
(11) = 2.34, p = .04, d = 0.608]. However, none of the post-hoc paired T-tests comparing
conditions with familiar vs. new distractors survived the bonferroni correction. It is clear
from the data (Figure 7) that there was no consistent eect of Condition and/or Age
across the visual and memory set size conditions.
The Bayesian ANOVA also supported the conclusion that the eects of set size did not
vary with age in the zRT. Bayes factors supported evidence against main eects of Age
(both BF01 > 7.04) and against the Age × Visual Set Size and Age × Memory Set Size
interactions (all BF01 > 76.92). Moreover, distractor familiarity did not aect the zRT in
either age group. The Bayes factors further indicated somewhat more evidence for the
absence of eects of Condition than for the presence of such eects (both BF10 > 2.45).
Similarly, Bayes factors argued against the signicance of interactions between Age and
Condition (all BF01 > 8.72).
Errors
Figure 8 shows mean error rates on target present (misses) and target absent (false
alarms) trials for YA and OA. The primary message of this analysis is that familiar
distractors do not cause higher error rates, neither in YA nor OA. As in Experiment 1,
error rates were relatively high (>15%) in conditions with larger set sizes. Importantly,
neither misses nor false alarms increased with age; in fact, OA were more accurate than
YA. Furthermore, distractor-familiarity neither increased misses nor false alarms in either
age group; rather, observers made more errors in the condition with new distractors.
The ANOVAs on arcSin transformed false alarms revealed signicant main eects of
Visual Set Size and Memory Set Size and interactions between the two factors [all
F > 7.40, all p > .001, all η
2
> .250 (90% CI .139; .302)]. The main eect of Age was
also signicant [F(1,22) = 10.91, p = .003, η
2
= .332 (90% CI .039; .469)], indicating fewer
false alarms in OA than YA. A signicant main eect of Condition [F(1,22) = 9.12,
p = .006, η
2
= .293 (90% CI .055; .489)] reected that observers made more false alarms
in the condition with new distractors than in the condition with familiar distractors.
Notably, this speaks against increased interference by familiar distractors. Finally, the
ANOVA on false alarms revealed signicant interactions between Age and Condition [F
(1,22) = 4.77, p = .040, η
2
= .178 (90% CI .005; .387)] and Age and Visual Set Size [F
(3,66) = 8.32, p < .001, η
2
= .274 (90% CI .108; .384)]. Only YA made fewer false alarms in
AGING, NEUROPSYCHOLOGY, AND COGNITION 15
the condition with new distractors compared to the condition with familiar distractors.
YA made more false alarms than OA in the condition with new distractors, and age
group dierences increased with display size.
Figure 8. Errors. Proportion of misses and false alarms are plotted as a function of visual set size
(VSS) and memory set size (MSS) for younger adults (blue) and older adults (red), for the conditions
with new distractors (solid lines) and familiar distractors (dotted lines).
16 I. WIEGAND AND J. M. WOLFE
The ANOVAs on arcSin transformed misses revealed signicant main eects of Visual
Set Size and Memory Set Size and interactions between the two factors [all F > 2.04, all
p > .04, all η
2
> .085 (90% CI .003; .111)]. A signicant main eect of Age [F(1,22) = 7.85,
p = .01, η
2
= .263 (90% CI .039; .469)] reected that OA missed less targets than YA. There
was a trend signicant main eect of Condition [F(1,22) = 3.63, p = .070, η
2
= .142 (90%
CI .000; .350)].
The Bayesian ANOVA on error rates also revealed evidence for a main eect of Age for
both misses and false alarms (BF10 > 3.95). For misses, the Bayes factor indicated
evidence for the Age × Memory Set Size interaction (BF10 = 7.58), while the factor
was equivocal for the Age × Visual Set Size interaction (BF01 = 0.80). There was further
modest evidence for the absence of the Age × Condition interaction (BF01 = 2.30). For
false alarms, the Bayes factor supported the presence of the Age × Visual Set Size
interaction (BF10 = 233.79), while the factor was equivocal for the Age × Memory Set
Size interaction (BF01 = 1.22). There was evidence for the main eect of Condition
(BF10 = 29.18) and somewhat for the Age × Condition interaction (BF10 = 2.21).
Discussion
In Experiment 2, we replicated the ndings of Experiment 1, demonstrating no evidence
for qualitative age-related changes in hybrid search over and above an overall slowing
with age. Importantly, we further showed that OA, similar to YA (Wolfe et al., 2015), did
not rely on item familiarity alone in hybrid search. The relative familiarity of targets and
distractors, here operationalized as the frequency of item occurrence in the task, did
neither slow RT nor increased error rates in any of the age groups.
This nding appears surprising in light of dual-process episodic LTM models of aging,
which state that familiarity-based recognition can be preserved, while recollection-based
recognition is impaired in older age (Koen & Yonelinas, 2016; Yonelinas, 2002). Notably,
in many empirical studies supporting this dichotomy, the contributions of familiarity and
recollection were estimated using a remember/knowprocedure (Gardiner, 1988;
Tulving, 1985), or receiver-operating characteristic (ROC) curve based on condence
ratings (Yonelinas, 1999). Performance measures in the present search task, by contrast,
were not dependent on participantssubjective judgment about the quality of their
memory, which can potentially inuence the magnitude of age eects (Duarte, Henson,
& Graham, 2007; Koen & Yonelinas, 2014).
Also according to the inhibitory decit hypothesis (Hasher & Zacks, 1988), one would
have expected that OA are more susceptible to distractor interference, especially when
those are as familiar as targets. Our results do not support this. In fact, counterintuitively,
across conditions, OA missed fewer targets and made fewer false alarms than YA and
this age dierence increased with display size. This pattern of results may reect that (in
addition to not showing a memory decit) OA are more careful searchers than YA and
try to avoid erroneous responses. Possibly, this rather strategic dierence between age
groups partly accounts for the RT slowing in OA that is observed across conditions. Note
that a similar trend, with fewer misses in OA than YA, was also observed in Experiment 1.
In order to understand the (non-existent) age eects in hybrid search, another
process dissociation might be more helpful. In their traditional work on hybrid search,
Schneider and Shirin (1977) introduced the distinction between consistent mapping
AGING, NEUROPSYCHOLOGY, AND COGNITION 17
(CM) and variable mapping(VM) conditions in memory search. In CM tasks, the same
set of stimuli is used as target items across trials, so that whenever one of these stimuli
appears, it requires the same response. In VM tasks, stimuli appear as both target and
distractor items across trials, so that the response to the same item can vary from trial to
trial. The hybrid search tasks of Experiments 1 and 2 can be classied as two forms of CM
task. In Experiment 1 and the New Distractorcondition of Experiment 2, only the target
set remained constant within a block of trials while distractors were always new. In the
Familiar Distractorscondition of Experiment 2, both the target and the distractor set
were constant over a block of trials. Schneider and Shirin (1977) proposed that
under CM conditions, a stimulus-response association is learned over trials, which
enables the observers to recognize targets by some form of automatic process. By
contrast, exibly shifting between stimulus-response associations under VM conditions
requires more controlled processes. Automatic processing modes are typically not
aected or, if they are aected, they are less aected by aging than those that rely on
deliberate cognitive control (Fisk & Rogers, 1991a; Jennings & Jacoby, 1993; Wiegand,
Finke, Müller, & Töllner, 2013). This might explain why we did not nd age dierences
under CM conditions in hybrid search in Experiment 1 and 2. Indeed, previous studies
using a hybrid search task similar to Schneider and Shirins original work showed that
age eects were more pronounced under VM than CM conditions (Fisk & Rogers, 1991b).
Similarly, age-related decline was evident in a Sternberg memory search task (Anders,
Fozard, & Lillyquist, 1972) and visual search task (Plude, Hoyer, & Lazar, 1982) when the
target set varied from trial to trial, but eliminated when the set was constant. We tested
age dierences in a variant of hybrid search including VM conditions in Experiment 3.
Experiment 3
In Experiment 3, we included a condition in hybrid search, in which a target-context
association needed to be retrieved. The associative decit hypothesispostulates that
age dierences in recognition memory reect a diculty in binding components of
a memory episode into units and retrieving those bound units (Naveh-Benjamin, 2000).
Ample empirical data has been mustered to support this hypothesis. It has been shown
that age-related decline is more pronounced when item-item or item-context associa-
tions have to be retrieved than when single items are retrieved. This is true under a wide
variety of experimental manipulations (Old & Naveh-Benjamin, 2008), including associa-
tions between pictures (Naveh-Benjamin, Hussain, Guez, & Bar-On, 2003). Perhaps, we
would nd age dierences beyond slowing, if our hybrid search task involved associative
recollection.
The hybrid search task of Experiment 3 was introduced by Boettcher et al. (2018). In
one of three blocks, participants memorized two groups of eight objects each. The eight
items of one group were associated with one background and the eight items of the
other group were associated with another background. Items that were targets in one
context could appear as luredistractors in another context, in which a dierent set of
targets was relevant. Note that this is a type of VM condition (Schneider & Shirin, 1977).
In two further blocks, participants memorized a single group of eight or sixteen objects
presented on the same background. These blocks could be considered as CM conditions,
since the target set was constant over trials and targets never appeared as distractors.
18 I. WIEGAND AND J. M. WOLFE
We presumed that, due to a decit in retrieving the item-context associations, OA,
compared to YA, would show a proportionally larger RT increase in VM compared
to CM conditions and show stronger interference by lures under VM conditions.
Participants
We used the data of 18 YA (33.3 years, 8 male, 10 female) previously reported by
Boettcher et al (2018, Experiment 1a), and collected new data of 20 OA at the Visual
Attention Lab, Brigham & Womens Hospital, Cambridge, MA, US. Two OA were excluded
because they did not complete all experimental blocks, resulting in a nal sample of 18
OA (71.6 years, 10 male, 8 female). All participantsvision was 20/25 or better (ETDRS
Near Vision Chart) and none was colorblind (Ishihara Test). OA further reported to not
have any history of neurological, psychiatric, or chronic somatic disorders. None showed
signs of beginning dementia (all except one participant scored > 26 in the MMSE
2
)or
mild to severe depression (all scored <16 in the CES-D). OA further lled out
a demographic health survey and we measured verbal IQ (NAART, Uttl, 2002), visuo-
motor speed (DSST, Wechsler, 1958), cognitive reserve (CRIq, Nucci et al., 2012), and
perceived cognitive failures (CFQ, Broadbent et al., 1982) to obtain descriptives about
the samples cognitive health (Table 3). All observers gave informed consent and were
paid; YA received $10 per hour and OA received $11 per hour for their time. Data sets
were collected in accordance with the Declaration of Helsinki on ethical principles. The
experiments were approved by the Partners Healthcare Corporation Institutional Review
Board.
Procedures
Experiment 3 was a variant of hybrid search that was introduced by Boettcher et al.
(2018, Experiment 1a). In this hybrid search task, targets are presented on a background
scene chosen randomly from a set of six possibilities (beach, city, classroom, desert,
forest, or mountain). Stimuli were presented and responses collected on computers
running MATLAB and the Psychophysics Toolbox (Brainard, 1997). The experiment had
three blocks and in each block just one or two backgrounds were relevant. Backgrounds
were not repeated between blocks. During the memorization phase, each item from the
Table 3. Mean and standard deviation (in parentheses) of the older adultsscores in the North
American Adult Reading Test (NAART) including estimates of verbal (FIQ), performance (PIQ), full-
scale (FIQ) intelligence quotients, and total scores, the cognitive failures questionnaire (CFQ)
including subscales and total score, and the cognitive reserve questionnaire (CRIq) including
subscales and total score, and the time to complete the Digit Symbol Substitution Test (DSST) (in
seconds).
Visuo-motor Speed (DSST) Verbal Abilities (NAART) Cognitive Failures (CFQ) Cognitive Reserve (CRIq)*
47.83 (9.30) VIQ 119.31 (5.01) Forgetfulness 12.78 (8.52) Education 127.82 (8.92)
PIQ 114.97 (2.37) Distractibility 8.00 (3.94) Work 128.71 (23.66)
FIQ 119.57 (4.40) False Triggering 6.28 (3.56) Leisure 136.35 (20.80)
Total score 10.56 (5.02) Total score 22.17 (11.08) Total score 141.00 (15.17)
*one participant did not complete the CRIq
AGING, NEUROPSYCHOLOGY, AND COGNITION 19
memory set was presented in isolation at the center of the display on its assigned
background, for 3 seconds for YA and for 5 seconds for OA. In the Single Memory Set
Size 8condition, observers memorize a single group of eight objects presented on the
same background. In the Single Memory Set Size 16condition, observers memorized
a single group of 16 objects presented on the same background. In the Partitioned
Memory Set Size 8condition, observers memorized two groups of eight objects each.
The eight items of one group were associated with one background, and eight items of
the other group were associated with another context (see Figure 9).
Following the encoding, observers went on to the old/new recognition test. Each
memory test consisted of 2 × N items where N is the memory set size, and 50% of the
items were old (targets) and 50% were new. In the condition with partitioned
memory sets, observers had to recognize each target and, in addition, they had to
remember the background associated with each target. For this test, observers
needed to indicate if the object is from the rst group, the second group, or not
a target (new) by button press on the keyboard (1,2,or3). Observers had to pass
two recognition tests with at least 90% accuracy. If the criterion was not reached, the
memory set items were presented again and another recognition test was run. For
most of the participants, two memory tests were sucient to achieve the score. On
average, YA needed 2.12.5 memory tests across set size conditions. The maximum
number of tests required by two younger participants was 4. OA needed 2.22.6 tests
on average and the maximum number of tests required by one older participant was
5.Theaverageperformanceonthelastmemorytestwas99100% in YA and
98100% in OA. When the recognition test was passed, observers moved on to the
search phase.
In the search phase, observers searched through visual displays of either six or 12
items. Of these, 50% contained one target. Having passed the memory test, observers
Figure 9. Procedure of the partition block in Experiment 3. Observers are exposed to 8 items
presented on one background image (List A) and then 8 items presented on another background
image (List B). The observers are tested to conrm that the item and the associated background
image are memorized. During the search trials, the background image indicates the relevant target
set to the observer. Targets from the irrelevant set could appear as lures. In the other two blocks, 8
or 16 items were presented on the same background image during learning and in the search task.
20 I. WIEGAND AND J. M. WOLFE
performed 16 practice trials and 224 experimental trials in each block. Observers were
instructed to click on the target as quickly as possible. If they believed no target was
present, they clicked on a no targetbox positioned on the left side of the screen.
The background image specied which set of targets was relevant for the current
trial. For the blocks with single memory sets, this was always the same background.
For the block with partitioned memory sets, the background could be either of the
two scenes presented during the memorization phase, randomly varying over trials.
Critically, luresappeared in the condition with partitioned memory sets: A lure was
dened as an item that was a target in one background, but was presented on the
other background (e.g., a sausage on the beach. You might be looking for a sausage,
but in the forest, see Figure 9). A target was correct only if it was presented on the
relevant background, and not otherwise. Thus, a lure was a distractor item and
clicking on a lure constituted a false alarm error. Lures appeared on 50% of the
search trials, independently of target presence/absence. Participants received feed-
back about their responses. The order of blocks was randomized between partici-
pants. The targets for each condition were chosen randomly from the stimulus set
and could not repeat between conditions. Items in the memory sets were not
semantically related to each other or to the background, on which they were
presented.
Results
Single versus partitioned memory sets
First, we compared YAs and OAs performance between the blocks with a single
memory set of 8 targets, a single memory set of 16 targets, and two partitioned memory
sets of 8 targets each. For the block with partitioned target sets, we excluded the trials
that contained lures. The impact of lures was analyzed separately (see below). For RT
analyses, we excluded trials on which observers made an incorrect response and trials
on which the RT was excessively low (<200 ms) or high (>10000 ms for YA and
>20000 ms for OA). This was less than 1% of the data in each age group. We ran
mixed ANOVAs with the within-subject factors Block (Single Memory Set Size 8, Single
Memory Set Size 16, Partitioned Memory Set Size 8), Visual Set Size (6, 12), and Age (YA,
OA), on raw RT, zRT, and error rates, in target present and target absent trials. Signicant
interactions involving the factor Age were followed-up with ANOVAs within the age
groups.
Again, we also ran Bayesian ANOVAs on zRT and accuracy data. We compared simpler
models that included only the main eect of Visual Set Size to models that also included
main eects and interactions of the factors Age and Block.
Raw RT
Figure 10, upper panel, shows the RT in target present and target absent trials for
both age groups. The ANOVA on RT revealed a signicant main eect of Block [both F
(2,68)>5.10, both p < .01, both η
2
> .130 (90% CI .020; .242)], reecting that RT were
longest in the block with two partitioned targets sets of 8, and longer in the block
with a single target set of 16 than with a single target set of 8. There were further
signicant main eects of Visual Set Size and Age, as well as signicant interactions
AGING, NEUROPSYCHOLOGY, AND COGNITION 21
between the two factors [all F(1,34)>13.37, all p .001, all η
2
> .281 (90% CI .085;
.450)]. OA responded more slowly than YA and the RT increase with display size was
larger for OA. Furthermore, there were signicant interactions between Block and
Visual Set Size [both F(1,34)>4.02, both p < .025, both η
2
> .105 (90% CI .009; .213)].
RT increased more with display size in the block with a single target set of 16 items
compared to the blocks with a single target set of 8 and the block with partitioned
target sets of 8 items. The interactions of Age and Block and of Age, Block, and Visual
Set Size were not signicant [all F(2,68)<1.42, all p > .24, all η
2
< .041 (90% CI
.000; .121)].
Z-transformed RT
Figure 10, middle panels, shows the zRT in target present and target absent trials for
both age groups. As in Experiments 1 and 2, once the RT were z-transformed, the eects
of the Age factor disappeared. Neither the main eects of Age nor the interactions
involving the Age factor were signicant [all F(2,68)<2.30.62, all p > .10, all η
2
< .060
(90% CI .000; .156)]. For target present and target absent trials, the expected main eects
Figure 10. Reaction Times (RT), z-transformed RT (zRT), and Error Rates. RT (in milliseconds, ms), zRT,
and error rates in target present and target absent trials are plotted for the single memory set size
(MSS) 8, single MSS 16, and partitioned MSS 8, for younger adults and older adults.
22 I. WIEGAND AND J. M. WOLFE
of Block and Visual Set Size were signicant [all F(2,68)>6.17, all p < .005, all η
2
> .153
(90% CI .033; .268)]. The interaction between Block and Visual Set Size did not quite
reach the p < 0.05 level for target present trials [F(2,68) = 2.865, p = .064, η
2
= .078 (90%
CI .000; .177)], but did for target absent trials [F(2,68) = 3.713, p = .029, η
2
> .098 (90% CI
.005; .203)].
The Bayesian ANOVA on zRT supported the conclusion that neither the eects of
set size nor of block varied with age. For target absent and target present trials, Bayes
factors indicated the absence of main eects of Age (BF01 > 5.38) and Age × Visual
Set size interactions (BF01 > 22.22). The Bayes factors supported evidence for main
eects of Block (BF10 > 763.42); however, not for the Age × Block interactions
(BF01 > 2.08)
Errors
Figure 10, lower panels, shows error rates in target present (misses) and target absent
(false alarms) trials for both age groups. Miss rates were moderate (up to 15%), while
false alarm rates were very low (<3%). Again, ANOVAs were run on arcSin transformed
error rates. As can be seen in the target present data, if anything, OA missed fewer
targets than YA, though this was only trend signicant [F(1,34) = 3.16, p = .08, η
2
= .085
(90% CI .000; .248)]. The main eects of Block were signicant [both F(2,68)>5.80, both
p < .01, both η
2
> .145 (90% CI .029; .260)]. Observers missed fewer targets, but made
more false alarms in the block with a single memory set of 8 compared to the other two
blocks. For misses only, there was a main eect of Visual Set Size [F(1,34) = 83.11,
p < .001, η
2
= .710 (90% CI .549; .787)], reecting that observers missed more targets
when the display size was larger. There was further a trend signicant main eect of Age
[F(1,34) = 2.84, p = .10, η
2
= .085 (90% CI .000; .248)], as OA missed slightly fewer targets
than YA.
Bayes factors for the eect of Age were equivocal for misses (BF01 = 1.06) and
indicated absence of the eect for false alarms (BF01 = 4.17). The Bayes factors indicated
evidence for the main eects of Block (BF10 > 33.28) and evidence against the Age ×
Block interactions (BF01 > 4.93)
Interference by lures
Next, we examined the eect of lure presence on YAs and OAs performance in the
block with two partitioned memory sets of 8 targets. Again, for RT analyses, we excluded
trials on which observers made an incorrect response and trials on which the RT was
excessively low (<200 ms) or high (>10000 ms for YA and >20000 ms for OA). This was
less than 1% of the data. We ran mixed ANOVAs with the within-subject factors Lure
(Present, Absent), Visual Set Size (6, 12), and Age (YA, OA), on raw RT, zRT, and error
rates, in target present and target absent trials. Signicant interactions involving the
factor Age were followed-up with ANOVAs within the age groups.
In the Bayesian ANOVAs on zRT and accuracy data, we compared simpler models that
included only the main eects of Visual Set Size to models that also included main
eects and interactions of the factors Age and Lure.
AGING, NEUROPSYCHOLOGY, AND COGNITION 23
Raw RT
Figure 11, upper panels, shows the RT for target present and target absent trials as
a function of visual set size and lure presence. It should be clear from the gure
that OAs responses are not markedly more disrupted by lures than YAs. The
ANOVA revealed signicant main eects of Lure for target present and target
absent trials [both F(1,34)>9.48, p < .005, both η
2
> .218 (90% CI .045; .392)],
reecting that observers responded more slowly whenever a lure was present in
the display. In addition, the main eects of Visual Set Size and Age, and the
interaction between both factors were signicant [all F(1,34)>12.71, all p .001,
all η
2
> .272 (90% CI .072; .441)]. OAsRTwerelongerthanYAs, and RT increased
with the display size more in OA than YA. For target present trials, there was further
atrendsignicant interaction of Lure and Age [F(1,34) = 3.284, p = .079, η
2
= .088
(90% CI .000; .252)].
Figure 11. Reaction Times (RT), z-transformed RT (zRT), and Error Rates. RT (in ms), zRT, and error
rates are plotted for trials with lure and target present and absent, for younger adults and older
adults, in the condition with partitioned memory sets.
24 I. WIEGAND AND J. M. WOLFE
Z-transformed RT
Figure 11, middle panels, shows the zRT for target present and target absent trials as
a function of visual set size and lure presence. The ANOVA also revealed the main eects
of Lure and Visual Set Size [all F(1,34)>16.31, all p < .001, all η
2
> .323 (90% CI .017; .332)].
The main eects of Age and the interactions between Age and Visual Set Size, however,
were not signicant [all F(1,34)<0.93, all p > .34, all η
2
< .028 (90% CI .000; .160)]. The
interaction of Lure and Age was signicant in target present trials [F(1,34) = 6.38,
p = .016, η
2
= .158 (90% CI .017; .332)]. This was because lure presence slowed hit RT
signicantly in YA [F(1,17) = 18.30, p = .001, η
2
= .518 (90% CI .202; .673)], but not in OA
[F(1,17) = 1.25, p = .25, η
2
= .076 (90% CI .000; .298)].
The Bayes factors also indicated the absence of main eects of Age (BF01 > 4.69) and
of Age × Visual Set size interactions (BF01 > 20.00). Main eects of Lure were supported
for both target present and absent trials (BF10 > 905.13). For target absent trials, the
Bayes factor supported the absence of an Age × Lure interaction (BF01 = 21.15), while
the eect was equivocal for target present trials (BF01 = 0.66).
Errors
Figure 11, lower panels, shows mean error rates on target present (misses) and target absent
(false alarms) trials for YA and OA. The ANOVA on misses revealed a signicant main eect of
Lure [F(1,34) = 5.20, p = .029, η
2
= .133 (90% CI .007; .304)] and a trend signicant interaction of
Lure and Age [F(1,34) = 3.47, p = .07, η
2
= .092 (90% CI .000; .258)]. OA missed more targets
when a lure was present [F(1,17) = 5.99, p = .026, η
2
= .261 (90% CI .019; .481)] while lure
presence did not aect the miss rates in YA [F(1,17) = 0.15, p = .700, η
2
= .009 (90% CI .000;
.166)]. In addition, observers missed more targets when more items were in the display,
indicated by a signicant main eect of Visual Set Size [F(1,34) = 22.50, p < .001, η
2
= .398
(90% CI .181; .548)]. The main eect of Age was not signicant [F(1,34) = 0.10, p = .753, η
2
=.003
(90% CI .000; .084)], and no other interactions were signicant [all F < 1.86, all p > .18, all
η
2
< .052 (90% CI .000; .202)].
The ANOVA on false alarms revealed a signicant main eect of Lure [F
(1,34) = 26.84, p < .001, η
2
= .441 (90% CI .222; .583)]. Observers made more false
alarms when a lure appeared in the display (quite possibly responding to the lure as
though it were a target). There was a numerical dierence between YAsandOAs
false alarm rates, but the main eect of Age was not signicant [F(1,34) = 0.68,
p = .415, η
2
= .020 (90% CI .000; .144)]. The 2- and 3-way interactions between
Visual Set Size and Age and between Lure, Visual Set Size, and Age were trend
signicant [both F(1,34)>3.00, both p < .10, both η
2
> .081 (90% CI .000; .244)].
Follow-up ANOVAs showed that the main eect of Lure was signicant in both age
groups [both F(1,17)>9.80, both p < .01, both η
2
> .365 (90% CI .074; .564)]. However,
only in OA, also the main eect of Visual Set Size was signicant [F(1,17) = 6.53,
p=.021,η
2
= .277 (90% CI .026; .495)]. OAs false alarm rates increased with display
size, while YAs false alarm rates did not vary with display size [F(1,17) = 0.93,
p = .764, η
2
= .052 (90% CI .000; .265)].
Bayes factors supported the absence of main eects of Age (BF01 > 3.21) and Age × Visual
Set size interactions (BF01 > 9.35) both for misses and false alarms. Main eects of Lure were
somewhat supported for misses (BF10 = 2.65) and strongly supported for false alarms
AGING, NEUROPSYCHOLOGY, AND COGNITION 25
(BF10 = 1.125e-9). There was evidence for the Age × Lure interaction for false alarms
(BF10 = 14.13), while the eect was equivocal for misses (BF10 = 1.42).
Discussion
While there are eects of age in Experiment 3, those eects do not suggest a large age-
related decit in associative memory in hybrid search. Older observers were somewhat
(and not signicantly) more likely to be confused by the presence of a lure, but the
relatively small magnitude of this eect makes it clear that they were successfully
maintaining the associations of one half of the targets with one background and of
the other half with another background.
Holding the target-background associations in mind had a cost for all observers.
Observers responded more slowly on those blocks in which two partitioned memory
sets were associated with dierent background contexts, compared to blocks in which
a single memory set was presented in a single context.
3
The analyses on zRT showed
that this eect was of similar relative magnitude in YA and OA. This result diers from
earlier studies showing that the RT dierence between CM and VM conditions increased
with age (Madden, 1982; Rogers & Fisk, 1991b). The RT dierence between CM and VM
conditions was interpreted to reect that observers adopt a faster, more automatic
processing mode under CM conditions and a slower, more controlled processing
mode under VM conditions (Schneider & Shirin, 1977). Possibly, the design in the
earlier studies reinforced the formation of an automatic processing mode in the CM
condition more than in the present hybrid search task. First, the memory set sizes were
smaller with a maximum of only 4, which may have allowed the observers to build-up
a unitized representation of the entire memory set. Furthermore, observers were trained
over several test sessions, for up to 2,500 trials. Thus, in the present task, observers may
have persisted in a more controlled processing mode even in the blocks with single
memory sets. This may have reduced the classic dierences between CM and VM
conditions (see also Boettcher et al., 2018), as well as potential age dierences therein.
When we tested associative memory performance more directly by examining lure
interference in the block with partitioned memory sets, our results showed that both YA
and OA were moderately aected by lures. The eects diered as a function of age, but
importantly, both groups were generally able to maintain the correct target/background
association. Otherwise, if the target/background associations were lost, observers should
have always chosen the lure items when the target was absent, and in 50% on target-
present trials. Clearly, the error rates were not that high (YA < 9%, OA < 14%). In target
absent trials, the eect of lure presence on RT and accuracy was similar across age
groups. When a lure was present on a target absent trial, observers made more false
alarms and showed moderate RT costs (on correct trials). By contrast, age did interact
with lure presence in target present trials. OA missed more targets when a lure was
present, while lure presence did not aect YAs accuracy. On the other hand, lure
presence slowed YAs, but not OAs, RT. This pattern could be explained as a form of
speed-accuracy trade-o. Presumably, when YA landed on the lure, they took the time
to access its associated context before moving on to search for the correct target. OA, by
contrast, may have more often mistaken the lure for a target and, thus, have quit search
before the correct target was found. This asymmetric change in the decision criterion
26 I. WIEGAND AND J. M. WOLFE
with age in target present trials is potentially interesting and worth future exploration.
However, together with the age-invariant performance pattern in target absent trials,
the results do not provide evidence that a general associative memory decit in older
age (Naveh-Benjamin, 2000) severely aected hybrid search performance in OA.
General discussion
This project set out to quantify the eects of age-related decline in visual attention and
LTM on hybrid (visual and memory) search performance. The results of our experiments
are surprisingly good news: Apart from a generalized age-related slowing of RT, we
found very little evidence for qualitative age dierences in hybrid search.
Our ndings are at odds with a number of cognitive aging theories postulating
impaired attention and LTM in older age (Balota, Dolan, & Duschek, 2000; Craik, Byrd,
& Swanson, 1987; McDowd & Shaw, 2000; West, 1996). More specically in earlier visual
search experiments, an age-specicdecit in attentional control was inferred from the
relatively larger age eects in (inecient) conjunction compared to (ecient) feature
search tasks (Madden, 2007). Preserved top-down guidance in older age was reported
under conditions of predictive cues or prior knowledge that allowed observers to pre-
activate target-relevant features (Madden et al., 2005). Clearly, in the hybrid search tasks
used here, observers had to look for conjunctions of features and RT × set size functions
were not at. Which item of the target set would occur was unpredictable, thus,
observers could not activate one constant target template in a preparatory manner.
Nevertheless, we did not observe less ecient visual search in OA than YA once age-
related general slowing was factored out. With respect to age-related decline in LTM,
previous evidence found spared performance for single-item familiarity-based recogni-
tion (Koen & Yonelinas, 2014,2016). However, older adults typically showed perfor-
mance decrements when the recollection of details and associations was required (Old &
Naveh-Benjamin, 2008; Wolk et al., 2013). We ruled out familiarity-based recognition of
the target items as the basis of preserved performance in our hybrid search task in
Experiment 2 and we showed largely undisturbed associative LTM in Experiment 3.
Is there something special about hybrid search that diminishes or even eliminates the
eects of aging on performance? We used meaningful pictures (distinct photo objects
images) as stimulus material in the present tasks, for which discriminability and memory
capacity is astonishingly good (Brady, Konkle, Alvarez, & Oliva, 2008; Standing, 1973).
Furthermore, associative recognition for pictures is superior relative to words (e.g.,
Hockley, 2008). Age group dierences might be more prominent when more abstract
material is to be processed (Park et al., 1996). Indeed, previous studies that reported age
dierences in search tasks used more confusable stimuli, such as letters, digits, and simple
shapes (Madden, 1982;2004). Earlier memory studies have shown that memory for pictures
is largely equivalent across YA and OA (Park, Puglisi, & Sovacool, 1983,Parketal.,1996;
Smith, Park, Cherry, & Berkovsky, 1990). Nevertheless, the age decit in associative recogni-
tion was still evident when pictures were used as memoranda (Naveh-Benjamin et al., 2003;
Ratcli&M
cGoon,2015). It has been argued that if an eective strategy is accessible to both
YA and OA, either by spontaneous/incidental usage or by training, age dierences can be
reduced, even in associative memory tasks (Dennis & McCormick-Huhn, 2018;Naveh-
Benjamin, Brav, & Levy, 2007). Perhaps, within the search task context, the picture material
AGING, NEUROPSYCHOLOGY, AND COGNITION 27
promoted the incidental build-up of perceptually and semantically relatively rich target
representations. Those would facilitate memory retrieval, on the one hand, and ecient top-
down guided search in YA and OA, on the other (Madden & Plude, 1993; Plude & Hoyer,
1986). How could the rich target representations have possibly facilitated the hybrid search?
Previous research has shown that semantic information improves memory for targets, but
not distractors, in visual search with realistic photo images (Williams & Henderson, 2005).
The target-specic semantic memory benet is less pronounced in OA than YA, but still
evident (Williams, Zacks, & Henderson, 2009). It has further been found that YA and OA can
use preexisting knowledge of semantic and syntactic relations between real-world objects
and context to guide search in scenes (e.g., Neider & Kramer, 2011; Neider & Zelinsky, 2006;
Vo & Henderson, 2009; Wolfe, Vo, Evans, & Greene, 2011). In the present experiments, the
objects in the memory sets were not semantically related to each other or, in Experiment 3,
to the context, in which they appeared. Also the spatial positions of the targets were
arbitrary and varied over trials. However, we cannot exclude that observers applied
a strategy to create semantic associations between the semantically meaningful targets,
which helped them to activate and nd them. A target-specic semantic encoding benet
(Thomas & Williams, 2014) might also explain why increasing distractor-familiarity by
repetition in Experiment 2 did not cause stronger distractor interference. In Experiment 3,
the meaningfulness of the context may have further enforced observers to use it as cues, as
a form of environmental support, on which OA rely more heavily than YA (Lindenberger &
Mayr, 2014). It would be an interesting question for future experiments to examine age
dierences in hybrid search experiments, in which perceptual and conceptual features of
targets and distractors are experimentally manipulated and controlled. Furthermore, future
studies should test the generalizability of age-related strategy dierences in dierent search
task and its relation to real-world performance.
Of course, we did observe substantial age dierences in RT overall. The general
slowing of processing speed is a ubiquitous nding across many tasks and incorporated
in models since early cognitive aging research (Birren, 1965; Cerella, 1985; Salthouse,
1996). Task-specic age-eects can be detected when superimposed on general (i.e.
task-unspecic) slowing by rescaling the data (e.g., Faust et al., 1999). We used the
z-transformation of RT in an eort to separate qualitative dierences between age
groups from purely quantitative eects of reduced speed. Across these data, z-transfor-
mation eliminated age eects. Of note, many previous studies did not distinguish
between absolute and relative age eects on RT (Fisk & Rogers, 1991b; Madden &
Whiting, 2004). Thus, it remains unclear whether those really reect function-specic
age-related impairments beyond generalized slowing (Park & Festini, 2017; Rabbitt,
2017). Our data suggest that generalized slowing might account for the bulk of the
eects. Notably, it is still a contentious point in aging research whether age-related
slowing reects widespread, unspecicinuences or is specic to particular processes
(e.g., Salthouse, 2000; Wen et al., 2011). Sensory-perceptual, cognitive, and motor-
processes could contribute to this age-related slowing. We can therefore not completely
rule out that age-related decits in attention and memory slowed RT in the present task;
they may simply have not aected the RT × set size functions in hybrid search.
Finally, it is important to note that the sample groups tested in the present study were
healthy, high-performing individuals. OAs educational level and cognitive reserve scores
28 I. WIEGAND AND J. M. WOLFE
were high according to standardized norm groups representative for their age group in the
population (Nucci et al., 2012). Thus, presumably, our OAs brains either largely maintained
the network structures underlying attention and memory (Nyberg, Lövdén, Riklund,
Lindenberger, & Bäckman, 2012) or had above-average resources to counteract severe
performance decline in the face of age-related neuronal changes (Reuter-Lorenz & Cappell,
2008;Stern,2009). Future studies that combine behavioral with neuronal data (e.g. EEG) in
an age comparison (Dockree, Brennan, Osullivan, Robertson, & Oconnell, 2015; Wiegand
et al., 2014) could more directly address the contribution of brain maintenance and
compensation to preserved hybrid search performance in OA.
Whatever the reason(s) for preserved hybrid search performance in older age, our
nding has an important implication. The correlation between standard cognitive tests
and diculties experienced in daily life has been criticized as being only weak to
moderate (Burgess et al., 2006; Chaytor & Schmitter-Edgecombe, 2003). Dierent from
most laboratory tasks, hybrid search tasks share more features with complex real-world
search tasks and allow us to analyze performance beyond simplied trial structures.
Thus, adopting the structure of hybrid search tasks in cognitive test instruments could
be a rst step to achieve a better correspondence between standardized cognitive
assessment and real-world performance.
Notes
1. OA performed fewer trials than YA, because we were concerned that higher fatigue and
sustained attention decit in older age could confound the age eects Experiment 1 lasting
>2h (Staub, Doignon-Camus, Després, & Bonnefond, 2013). In Experiment 2, we split the
experiment into at least two sessions that were performed on two dierent days to prevent
strong eects of fatigue on search performance.
2. One participant had a borderline score of 25 in the MMSE. We repeated all analyses
excluding this participant, which revealed the same pattern of results. As this participant
scored normal in all other screening tests and showed good performance in the memory
test, we decided to include him in the nal sample.
3. Remember that we excluded the trials including lures from the comparison between the
blocks with partitioned vs. single memory sets to assess dierences between processing
modes under VM and CM conditions independently of actual lure interference. However,
note that taking out the lures in the partitioned block (making this a CM condition, too) did
not speed average RT much in YA (Boettcher et al., 2018).
Acknowledgments
The authors are grateful to Samira Epp, Makaela Nartker, Hayden Schill, Neslihan Sener, Caroline
Seidel, and Erica Westenberg for their assistance during data collection and analysis.
Disclosure statement
No potential conict of interest was reported by the authors.
AGING, NEUROPSYCHOLOGY, AND COGNITION 29
Funding
This work was supported by the European Unions Horizon 2020 research and innovation pro-
gramme, Marie Sklodowska-Curie Actions, under grant 702483 (IW); and the National Insititutes of
Health under grant NIH EY017001 (JMW); and the Army Research Oce (JMW).
ORCID
Iris Wiegand http://orcid.org/0000-0003-2160-7939
Science Framework platform
Experiment 1: DOI 10.17605/OSF.IO/NVUHY
Experiment 2: DOI 10.17605/OSF.IO/8HGEU
Experiment 3: DOI 10.17605/OSF.IO/ATWKJ
References
Anders, T. R., Fozard, J. L., & Lillyquist, T. D. (1972). Eects of age upon retrieval from short-term
memory. Developmental Psychology,6(2), 214.
Balota, D. A., Dolan, P. O., & Duchek, J. M. (2000). Memory changes in healthy older adults. In E.
Tulving, & F. M. I Craik (Eds.), The oxford handbook of memory (pp. 395409). New York, NY:
Oxford University Press.
Birren, J. E. (1965). Age changes in speed of behavior: Its central nature and physiological
correlates. In A. T. Welford & J. E. Birren (Eds.), Behavior, aging, and the nervous system (pp.
191216).
Boettcher, S. E., Drew, T., & Wolfe, J. M. (2018). Lost in the supermarket: Quantifying the cost of
partitioning memory sets in hybrid search. Memory & Cognition,46(1), 4357.
Boettcher, S. E., & Wolfe, J. M. (2015). Searching for the right word: Hybrid visual and memory
search for words. Attention, Perception, & Psychophysics,77(4), 11321142.
Brady, T. F., Konkle, T., Alvarez, G. A., & Oliva, A. (2008). Visual long-term memory has a massive
storage capacity for object details. Proceedings of the National Academy of Sciences,105
(38),1432514329.
Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision,10, 433436.
Broadbent, D. E., Cooper, P. F., FitzGerald, P., & Parkes, K. R. (1982). The cognitive failures ques-
tionnaire (CFQ) and its correlates. British Journal of Clinical Psychology,21(1), 116.
Bundesen, C. (1990). A theory of visual attention. Psychological Review,97(4), 523547.
Burgess, P. W., Alderman, N., Forbes, C., Costello, A., Coates, L. M.-A., Dawson, D. R., . . . Channon, S.
(2006). The case for the development and use of ecologically validmeasures of executive
function in experimental and clinical neuropsychology. Journal of the International
Neuropsychological Society,12(2), 194209.
Cabeza, R., Ciaramelli, E., Olson, I. R., & Moscovitch, M. (2008). The parietal cortex and episodic
memory: An attentional account. Nature Reviews Neuroscience,9(8), 613.
Cabeza, R., Daselaar, S. M., Dolcos, F., Prince, S. E., Budde, M., & Nyberg, L. (2004). Task-independent
and task-specic age eects on brain activity during working memory, visual attention and
episodic retrieval. Cerebral Cortex,14(4), 364375.
Cerella, J. (1985). Information processing rates in the elderly. Psychological Bulletin,98(1), 67.
Chaytor, N., & Schmitter-Edgecombe, M. (2003). The ecological validity of neuropsychological
tests: A review of the literature on everyday cognitive skills. Neuropsychology Review,13(4),
181197.
Craik, F. I., Byrd, M., & Swanson, J. M. (1987). Patterns of memory loss in three elderly samples.
Psychology and Aging,2,7986.
30 I. WIEGAND AND J. M. WOLFE
Craik, F. I., & Salthouse, T. A. (2011). The handbook of aging and cognition (4th ed.). New York, NY:
Psychology Press.
Cunningham, C. A., & Wolfe, J. M. (2014). The role of object categories in hybrid visual and memory
search. Journal of Experimental Psychology: General,143(4), 15851599.
Daselaar, S. M., Fleck, M. S., Dobbins, I. G., Madden, D. J., & Cabeza, R. (2006). Eects of healthy
aging on hippocampal and rhinal memory functions: An event-related fMRI study. Cerebral
Cortex,16(12), 17711782.
Dennis, N. A., & McCormick-Huhn, J. M. (2018). Item and associative memory decline in healthy
aging. In J. T. Wixted (Ed.), Stevenshandbook of experimental psychology and cognitive neu-
roscience, vol 1: Learning and memory (pp. 323362). New York, NY: John Wiley & Sons, Inc.
Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review
of Neuroscience,18(1), 193222.
Dockree, P. M., Brennan, S., Osullivan, M., Robertson, I. H., & Oconnell, R. G. (2015). Characterising
neural signatures of successful aging: Electrophysiological correlates of preserved episodic
memory in older age. Brain and Cognition,97,4050.
Drew, T., & Wolfe, J. M. (2014). Hybrid search in the temporal domain: Evidence for rapid, serial
logarithmic search through memory. Attention, Perception, & Psychophysics,76(2), 296303.
Duarte, A., Henson, R. N., & Graham, K. S. (2007). The eects of aging on the neural correlates of
subjective and objective recollection. Cerebral Cortex,18(9), 21692180.
Faust, M. E., Balota, D. A., Spieler, D. H., & Ferraro, F. R. (1999). Individual dierences in
information-processing rate and amount: Implications for group dierences in response
latency. Psychological Bulletin,125(6), 777799.
Fisk, A. D., & Rogers, W. A. (1991a). Development of skilled performance: An age-related perspec-
tive. In D. Damos (Ed.), Multiple task performance (pp. 415443). London: Wiley.
Fisk, A. D., & Rogers, W. A. (1991b). Toward an understanding of age-related memory and visual
search eects. Journal of Experimental Psychology: General,120(2), 131149.
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). Mini-mental state: A practical method for
grading the cognitive state of patients for the clinician. Journal of Psychiatric Research,12(3),
189198.
Gardiner, J. M. (1988). Functional aspects of recollective experience. Memory & Cognition,16(4),
309313.
Hasher, L., & Zacks, R. T. (1988). Working memory, comprehension, and aging: A review and a new
view. Psychology of Learning and Motivation,22, 193225.
Hockley, W. E. (2008). The picture superiority eect in associative recognition. Memory & Cognition,
36(7), 13511359.
Hogg, R. V., & Craig, A. T. (1995). Introduction to mathematical statistics (5th ed.). Upper Saddle
River, NJ: Prentice Hall.
Hoyer, W. J., & Verhaeghen, P. (2006). Memory aging. In J. E. Birren & K. W. Schaie (Eds.), Handbook
of the psychology of aging (6th ed., pp. 209232). London: Elsevier.
Jacoby, L. L. (1991). A process dissociation framework: Separating automatic from intentional uses
of memory. Journal of Memory and Language,30(5), 513541.
Jennings, J. M., & Jacoby, L. L. (1993). Automatic versus intentional uses of memory: Aging,
attention, and control. Psychology and Aging,8(2), 283293.
Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association,90
(430), 773795.
Koen, J. D., & Yonelinas, A. P. (2014). The eects of healthy aging, amnestic mild cognitive
impairment, and Alzheimers disease on recollection and familiarity: A meta-analytic review.
Neuropsychology Review,24(3), 332354.
Koen, J. D., & Yonelinas, A. P. (2016). Recollection, not familiarity, decreases in healthy ageing:
Converging evidence from four estimation methods. Memory,24(1), 7588.
Konkle, T., Brady, T. F., Alvarez, G. A., & Oliva, A. (2010). Scene memory is more detailed than you
think: The role of categories in visual long-term memory. Psychological Science,21(11),
15511556.
AGING, NEUROPSYCHOLOGY, AND COGNITION 31
Lindenberger, U., & Mayr, U. (2014). Cognitive aging: Is there a dark side to environmental support?
Trends in Cognitive Sciences,18(1), 715.
Madden, D. J. (1982). Age dierences and similarities in the improvement of controlled search.
Experimental Aging Research,8(2), 9198.
Madden, D. J., & Plude, D. J. (1993). Selective preservation of selective attention. In J. Cerella,
J. Rybash, W. Hoyer, & M. L. Commons (Eds.), Adult information processing: Limits on loss (pp.
273300). San Diego, CA: Academic Press.
Madden, D. J., & Whiting, W. L. (2004). Age-related changes in visual attention. In P. T. Costa &
I. C. Siegler (Eds.), Recent advances in psychology and aging (pp. 4188). Amsterdam: Elsevier.
Madden, D. J. (2007). Aging and visual attention. Current Directions in Psychological Science,16(2),
7074.
Madden, D. J., Whiting, W. L., Cabeza, R., & Huettel, S. A. (2004). Age-related preservation of
top-down attentional guidance during visual search. Psychology and Aging,19(2), 304309.
Madden, D. J., Whiting, W. L., Spaniol, J., & Bucur, B. (2005). Adult age dierences in the implicit
and explicit components of top-down attentional guidance during visual search. Psychology and
Aging,20(2), 317329.
McDowd, J. M., & Shaw, R. J. (2000). Attention and aging: A functional perspective. In F. I. M. Craik
& T. A. Salthouse (Eds.), The handbook of aging and cognition (2nd ed., pp. 221292). Mahwah,
NJ: Erlbaum.
Naveh-Benjamin, M. (2000). Adult age dierences in memory performance: Tests of an associative
decit hypothesis. Journal of Experimental Psychology: Learning, Memory, and Cognition,26(5),
1170.
Naveh-Benjamin, M., Brav, T. K., & Levy, O. (2007). The associative memory decit of older adults:
The role of strategy utilization. Psychology and Aging,22(1), 202.
Naveh-Benjamin, M., Hussain, Z., Guez, J., & Bar-On, M. (2003). Adult age dierences in memory
performance: Further support for an associative decit hypothesis. Journal of Experimental
Psychology: Learning, Memory, and Cognition,29(5), 826837.
Neider, M. B., & Kramer, A. F. (2011). Older adults capitalize on contextual information to guide
search. Experimental Aging Research,37(5), 539571.
Neider, M. B., & Zelinsky, G. J. (2006). Scene context guides eye movements during visual search.
Vision Research,46(5), 614621.
Nucci, M., Mapelli, D., & Mondini, S. (2012). Cognitive Reserve Index questionnaire (CRIq): A new
instrument for measuring cognitive reserve. Aging Clinical and Experimental Research,24(3),
218226.
Nyberg, L., Lövdén, M., Riklund, K., Lindenberger, U., & Bäckman, L. (2012). Memory aging and
brain maintenance. Trends in Cognitive Sciences,16(5), 292305.
Old, S. R., & Naveh-Benjamin, M. (2008). Dierential eects of age on item and associative
measures of memory: A meta-analysis. Psychology and Aging,23(1), 104118.
Park, D. C., & Festini, S. B. (2017). Theories of memory and aging: A look at the past and a glimpse
of the future. The Journals of Gerontology: Series B,72(1), 8290.
Park, D. C., Puglisi, T., & Sovacool, M. (1983). Memory for pictures, words, and spatial location in
older adults: Evidence for pictorial superiority. Journal of Gerontology,38(5), 582588.
Park, D. C., Smith, A. D., Lautenschlager, G., Earles, J. L., Frieske, D., Zwahr, M., & Gaines, C. L. (1996).
Mediators of long-term memory performance across the life span. Psychology and Aging,11(4),
621.
Plude, D. J., & Doussard-Roosevelt, J. A. (1989). Aging, selective attention, and feature integration.
Psychology and Aging,4(1), 98.
Plude, D. J., & Hoyer, W. J. (1986). Age and the selectivity of visual information processing.
Psychology and Aging,1(1), 410.
Plude, D. J., Hoyer, W. J., & Lazar, J. (1982). Age, response complexity, and target consistency in
visual search. Experimental Aging Research,8(2), 99102.
Rabbitt, P. (2017). Speed of visual search in old age: 1950 to 2016. The Journals of Gerontology:
Series B,72(1), 5160.
32 I. WIEGAND AND J. M. WOLFE
Radlo,L.S.(1977). The CES-D scale: A self-report depression scale for research in the general
population. Applied Psychological Measurement,1(3), 385401.
Ratcli, R., & McKoon, G. (2015). Aging eects in item and associative recognition memory for
pictures and words. Psychology and Aging,30(3), 669674.
Reuter-Lorenz, P. A., & Cappell, K. A. (2008). Neurocognitive aging and the compensation
hypothesis. Current Directions in Psychological Science,17(3), 177182.
Rouder, J. N., Morey, R. D., Speckman, P. L., & Province, J. M. (2012). Default Bayes factors for
ANOVA designs. Journal of Mathematical Psychology,56(5), 356374.
Rouder, J. N., Morey, R. D., Verhagen, J., Swagman, A. R., & Wagenmakers, E. J. (2017). Bayesian
analysis of factorial designs. Psychological Methods,22(2), 304.
Salthouse, T. A. (1996). The processing-speed theory of adult age dierences in cognition.
Psychological Review,103(3), 403.
Salthouse, T. A. (2000). Aging and measures of processing speed. Biological Psychology,54(13),
3554.
Schneider, W., & Shirin, R. M. (1977). Controlled and automatic human information processing:
I. Detection, search, and attention. Psychological Review,84(1), 1.
Smith, A. D., Park, D. C., Cherry, K., & Berkovsky, K. (1990). Age dierences in memory for concrete
and abstract pictures. Journal of Gerontology,45(5), P205P209.
Standing, L. (1973). Learning 10000 pictures. Quarterly Journal of Experimental Psychology,25(2),
207222.
Staub, B., Doignon-Camus, N., Després, O., & Bonnefond, A. (2013). Sustained attention in the
elderly: What do we know and what does it tell us about cognitive aging? Ageing Research
Reviews,12(2), 459468.
Stern, Y. (2009). Cognitive reserve. Neuropsychologia,47(10), 20152028.
Thomas, M. D., & Williams, C. C. (2014). The target eect: Visual memory for unnamed search
targets. The Quarterly Journal of Experimental Psychology,67(11), 20902104.
Tulving, E. (1985). Memory and consciousness. Canadian Psychology/psychologie Canadienne,26(1),
112.
Uttl, B. (2002). North American adult reading test: Age norms, reliability, and validity. Journal of
Clinical and Experimental Neuropsychology,24(8), 11231137.
Võ, M. L. H., & Henderson, J. M. (2009). Does gravity matter? Eects of semantic and syntactic
inconsistencies on the allocation of attention during scene perception. Journal of Vision,9(3), 24.
Wagenmakers, E. J., Love, J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., & Morey, R. D. (2018).
Bayesian inference for psychology, part II: Example applications with JASP. Psychonomic Bulletin
and Review,25,5876.
Wang, W. C., Daselaar, S. M., & Cabeza, R. (2017). Episodic memory decline and healthy aging. In
J. P. Stein (Ed.), Reference module in neuroscience and biobehavioral psychology (2nd ed., pp.
476496). Amsterdam: Elsevier.
Wechsler, D. (1958). The measurement and appraisal of adult intelligence. Baltimore, MD: Williams &
Wilkens.
Wen, W., Zhu, W., He, Y., Kochan, N. A., Reppermund, S., Slavin, M. J., . . . Sachdev, P. (2011). Discrete
neuroanatomical networks are associated with specic cognitive abilities in old age. Journal of
Neuroscience,31(4), 12041212.
West, R. L. (1996). An application of prefrontal cortex function theory to cognitive aging.
Psychological Bulletin,120(2), 272.
Whiting, W. L., Madden, D. J., Pierce, T. W., & Allen, P. A. (2005). Searching from the top down:
Ageing and attentional guidance during singleton detection. The Quarterly Journal of
Experimental Psychology Section A,58(1), 7297.
Wiegand, I., Finke, K., Müller, H. J., & Töllner, T. (2013). Event-related potentials dissociate percep-
tual from response-related age eects in visual search. Neurobiology of Aging,34(3), 973985.
Wiegand, I., Töllner, T., Dyrholm, M., Müller, H. J., Bundesen, C., & Finke, K. (2014). Neural correlates
of age-related decline and compensation in visual attention capacity. Neurobiology of Aging,35
(9), 21612173.
AGING, NEUROPSYCHOLOGY, AND COGNITION 33
Williams, C. C., & Henderson, J. M. (2005). Incidental visual memory for targets and distractors in
visual search. Perception & Psychophysics,67(5), 816827.
Williams, C. C., Zacks, R. T., & Henderson, J. M. (2009). Age dierences in what is viewed and
remembered in complex conjunction search. The Quarterly Journal of Experimental Psychology,
62(5), 946966.
Wolfe, J. M. (1994). Guided search 2.0 a revised model of visual search. Psychonomic Bulletin &
Review,1(2), 202238.
Wolfe, J. M. (2012). Saved by a log: How do humans perform hybrid visual and memory search?
Psychological Science,23(7), 698703.
Wolfe, J. M., Boettcher, S. E., Josephs, E. L., Cunningham, C. A., & Drew, T. (2015). You look familiar,
but I dont care: Lure rejection in hybrid visual and memory search is not based on familiarity.
Journal of Experimental Psychology: Human Perception and Performance,41(6), 15761587.
Wolfe, J. M., & Horowitz, T. S. (2004). What attributes guide the deployment of visual attention and
how do they do it? Nature Reviews Neuroscience,5(6), 495.
Wolfe, J. M., Võ, M. L. H., Evans, K. K., & Greene, M. R. (2011). Visual search in scenes involves
selective and nonselective pathways. Trends in Cognitive Sciences,15(2), 7784.
Wolk, D. A., Mancuso, L., Kliot, D., Arnold, S. E., & Dickerson, B. C. (2013). Familiarity-based memory
as an early cognitive marker of preclinical and prodromal AD. Neuropsychologia,51(6),
10941102.
Yonelinas, A. P. (1999). The contribution of recollection and familiarity to recognition and
source-memory judgments: A formal dual-process model and an analysis of receiver operating
characterstics. Journal of Experimental Psychology: Learning, Memory, and Cognition,25(6), 1415.
Yonelinas, A. P. (2002). The nature of recollection and familiarity: A review of 30 years of research.
Journal of Memory and Language,46(3), 441517.
34 I. WIEGAND AND J. M. WOLFE
... One may expect that age-related decline in attention and memory results in higher RT costs for older adults as visual and memory set sizes increase. However, our previous results from single-target hybrid search did not reveal age-specific impairments beyond general age-related slowing (Wiegand & Wolfe, 2019), and our present data replicate that finding. Apart from changing RT ϫ set size functions, memory decline may cause older observers to omit certain target types entirely because they dropped from their memory. ...
... Earlier findings reliably showed that RT increase logarithmically with memory set size in simple hybrid search (Cunningham & Wolfe, 2014;Wolfe, 2012;Wolfe et al., 2015). We recently demonstrated that the log shape of this function is preserved in older age (Wiegand & Wolfe, 2019) and that RTs also rise logarithmically with memory set size in hybrid foraging in younger observers (Wolfe et al., 2016). Accordingly, we examined RTs as a linear function of the log of the memory set size. ...
... These results confirm and expand upon what we have already demonstrated in simple hybrid search, where observers look for only one single target per trial. We found that the relative increase in RT with increasing memory set size up to 16 objects in memory, was similar across age groups (Wiegand & Wolfe, 2019). Here, we further show that even under higher memory load, with a memory set size up to 64 objects, there was no evidence for an age-specific impairment in memory search. ...
Article
In hybrid foraging tasks, observers search visual displays, so called patches, for multiple instances of any of several types of targets with the goal of collecting targets as quickly as possible. Here, targets were photorealistic objects. Younger and older adults collected targets by mouse clicks. They could move to the next patch whenever they decided to do so. The number of targets held in memory varied between 8 and 64 objects, and the number of items (targets and distractors) in the patches varied between 60 and 105 objects. Older adults foraged somewhat less efficiently than younger adults due to a more exploitative search strategy. When target items became depleted in a patch and search slowed down, younger adults acted according to the optimal foraging theory and moved on to the next patch when the instantaneous rate of collection was close to their average rate of collection. Older adults, by contrast, were more likely to stay longer and spend time searching for the last few targets. Within a patch, both younger and older adults tended to collect the same type of target in "runs." This behavior is more efficient than continual switching between target types. Furthermore, after correction for general age-related slowing, RT × set size functions revealed largely preserved attention and memory functions in older age. Hybrid foraging tasks share features with important real-world search tasks. Differences between younger and older observers on this task may therefore help to explain age differences in many complex search tasks of daily life. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
... In a similar vein, Wiegand and Wolfe (2020) conducted three hybrid visual search experiments with younger and older adults. These authors replicated previous literature with younger adults that adding items to the search display (i.e., set-size) led to a linear increase in RT, whereas adding items to the memory set led to a logarithmic increase in RT. Wiegand and Wolfe (2020) observed this pattern in older adults as well. Across the three experiments, beyond a generalised slowing for older adults, there was no evidence of qualitative differences in search slopes for younger versus older adults (Wiegand & Wolfe, 2020). ...
... Wiegand and Wolfe (2020) observed this pattern in older adults as well. Across the three experiments, beyond a generalised slowing for older adults, there was no evidence of qualitative differences in search slopes for younger versus older adults (Wiegand & Wolfe, 2020). Again, this suggests that after discounting the general slowing effect, visual search is largely preserved across age. ...
... These results are consistent with recent studies highlighting an absence of qualitative visual search differences in older adults (Wiegand et al., 2019;Wiegand & Wolfe, 2020). Although Madden et al. (2004) similarly concluded that top-down contributions to visual search were preserved as a function of age, their results actually showed an exacerbated effect of their manipulation of the predictive value of the singleton being the target for older adults. ...
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Visual search is a psychological function integral to most people’s daily lives. The extent to which visual search efficiency, and in particular the ability to use top-down attention in visual search, changes across the lifespan has been the focus of ongoing research. Here we sought to understand how the ability to frequently and dynamically change the target in a conjunction search task was impacted by ageing. To do this, we compared visual search performance of a group of younger and older adults under conditions in which the target type was determined by a cue and could change on trial-to-trial basis (Intermixed), versus when the target type was fixed for a block of trials (Blocked). While older adults were overall slower at the conjunction visual search task, and both groups were slower in the Intermixed compared with the Blocked Condition, older adults were not disproportionately impacted by the Intermixed relative to the Blocked conditions. These results indicate that the ability to frequently change the target of visual search is preserved in older adults. This conclusion is consistent with an emerging consensus that many aspects of visual search and top-down contributions to it are preserved across the lifespan. It is also consistent with a growing body of work which challenges the neurocognitive theories of aging that predict sweeping deficits in complex top-down components of cognition.
... Finally, it is also possible that in an LTM version of our task, older adults' performance would also be preserved. Importantly, although some studies have reported comparable performance of YA and OA in LTM (e.g., error rates in a hybrid visual and LTM memory search task, see Wiegand & Wolfe, 2020), to date, no known variable was identified which would consequently lead to such results (see Fraundorf et al., 2019). Importantly, in some cases, even if the performance was found to be well preserved with age, there were still some effects of age linked with the interference from associative lures (see Wiegand & Wolfe, 2020). ...
... Importantly, although some studies have reported comparable performance of YA and OA in LTM (e.g., error rates in a hybrid visual and LTM memory search task, see Wiegand & Wolfe, 2020), to date, no known variable was identified which would consequently lead to such results (see Fraundorf et al., 2019). Importantly, in some cases, even if the performance was found to be well preserved with age, there were still some effects of age linked with the interference from associative lures (see Wiegand & Wolfe, 2020). Also, some findings suggest that OA, compared to YA, may not present increased FA rate for single abstract objects (Koutstaal et al., 2003;Pidgeon and Morcom, 2014). ...
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While the knowledge on age-related differences in susceptibility to episodic false memories is extensive, little is known about this phenomenon in visual short-term memory (STM). Our previous behavioural research indicated that older adults are more confident of their erroneous STM recognitions than young adults. However, unlike in episodic memory, we did not find support for older adults’ higher rate of false alarms. To further understand this specific age-difference, here we investigated its neural correlates. First, the pattern of behavioural results replicated the one from our previous experiment. Second, younger adults, when compared to older adults, exhibited higher false recognition-related activity of the visual cortex, the anterior cingulate cortex, the frontal operculum/insular cortex as well as regions within the anterior and dorsolateral prefrontal cortex. No age-differences were observed in hippocampal activity. Third, younger but not older adults presented higher activity in the anterior cingulate cortex and the frontal operculum/insular cortex for false recognitions when compared to highly confident correct rejections. Finally, frontal activity was influenced by both the individuals’ performance and their metacognitive abilities. The results suggest that age-related differences in confidence of STM false recognitions may arise from age-differences in performance monitoring and uncertainty processing rather than in hippocampal-mediated binding.
... Indeed, older adults are slower than young adults when they have to find a target defined by a conjunction of features such as a specific combination of a shape and colour (e.g., a red square among red circles and blue squares; Foster et al., 1995;Humphrey & Kramer, 1997;Madden et al., 1996;Whiting et al., 2005). This is interpreted as an impairment in topdown, voluntary attention, which has to be sequentially shifted from item to item to identify the target (but see Wiegand & Wolfe, 2020), with each shift increasing the total search time. However, studies have reported that older adults perform comparably with young adults when the target pops out in terms of a single feature, for instance, unique colours or orientations alone (e.g., a red circle among blue circles), which provides strong bottom-up guidance and can be detected with parallel processing (e.g., Humphrey & Kramer, 1997;Whiting et al., 2005). ...
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Full-text available
Visual search is a crucial, everyday activity that declines with aging. Here, referring to the environmental support account, we hypothesized that semantic contextual associations between the target and the neighboring objects (e.g., a teacup near a tea bag and a spoon), acting as external cues, may counteract this decline. Moreover, when searching for a target, viewers may encode information about the co-present distractor objects, by simply looking at them. In everyday life, where viewers often search for several targets within the same environment, such distractor objects may often become targets of future searches. Thus, we examined whether incidentally fixating a target during previous trials, when it was a distractor, may also modulate the impact of aging on search performance. We used everyday object arrays on tables in a real room, where healthy young and older adults had to search sequentially for multiple objects across different trials within the same array. We showed that search was quicker: (1) in young than older adults, (2) for targets surrounded by semantically associated objects than unassociated objects, but only in older adults, and (3) for incidentally fixated targets than for targets that were not fixated when they were distractors, with no differences between young and older adults. These results suggest that older viewers use both environmental support based on object semantic associations and object information incidentally encoded to enhance efficiency of real-world search, even in relatively simple environments. This reduces, but does not eliminate, search decline related to aging.
... Specifically, in previous work, difficulties in learning the association between visual stimuli and reward-values may have caused reduced reward effects in older age, rather than a reduced impact of reward on visual processing per se. The absence of age differences in the effects of target value and prevalence in the present hybrid foraging task add to our accumulating evidence of preserved attention and memory processes in perceptually and conceptually rich, engaging search tasks that resemble real-world searches (Wiegand & Wolfe, 2020;. Age-related impairments may occur also in hybrid foraging, if the search task taps into other age-sensitive cognitive processes, like incidental associative learning (Wiegand, Westenberg, & Wolfe, in press). ...
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The prevalence and reward-value of targets have an influence on visual search. The strength of the effect of an item’s reward-value on attentional selection varies substantially between individuals and is potentially sensitive to aging. We investigated individual and age differences in a hybrid foraging task, in which the prevalence and value of multiple target types was varied. Using optimal foraging theory measures, foraging was more efficient overall in younger than older observers. However, the influence of prevalence and value on target selections was similar across age groups, suggesting that the underlying cognitive mechanisms are preserved in older age. When prevalence was varied but target value was balanced, younger and older observers preferably selected the most frequent target type and were biased to select another instance of the previously selected target type. When value was varied, younger and older observers showed a tendency to select high-value targets, but preferences were more diverse between individuals. When value and prevalence were inversely related, some observers showed particularly strong preferences for high-valued target types, while others showed a preference for high-prevalent, albeit low-value, target types. In younger adults, individual differences in the selection choices correlated with a personality index, suggesting that avoiding selections of low-value targets may be related to reward-seeking behaviour.
... Despite TVA's specificity in assessing attention-related parameters, some studies have shown that this typical laboratory assessment may underestimate the cognitive capacities of, e.g., elderly persons (e.g. Wiegand and Wolfe, 2020). Using a more realistic hybrid search task, Wiegand and Wolfe found no evidence for an age-related decline, except for overall response time. ...
Article
Full-text available
As a formal theory, Bundesen’s theory of visual attention (TVA) enables the estimation of several theoretically meaningful parameters involved in attentional selection and visual encoding. As of yet, TVA has almost exclusively been used in restricted empirical scenarios such as whole and partial report and with strictly controlled stimulus material. We present a series of experiments in which we test whether the advantages of TVA can be exploited in more realistic scenarios with varying degree of stimulus control. This includes brief experimental sessions conducted on different mobile devices, computer games, and a driving simulator.Overall, six experiments demonstrate that the TVA parameters for processing capacity and attentional weight can be measured with sufficient precision in less controlled scenarios and that the results do not deviate strongly from typical laboratory results, although some systematic differences were found.
... Previous research on hybrid search showed that observers do not easily restrict their memory search in tasks using cues. In studies by Boettcher et al. (2018) and Wiegand and Wolfe (2020), younger and older adults learned explicit target-context associations before a search task. Eight target objects were associated with one context, and eight different target objects with another. ...
Article
Full-text available
Sequence learning effects in simple perceptual and motor tasks are largely unaffected by normal aging. However, less is known about sequence learning in more complex cognitive tasks that involve attention and memory processes and how this changes with age. In this study, we examined whether incidental and intentional sequence learning would facilitate hybrid visual and memory search in younger and older adults. Observers performed a hybrid search task, in which they memorized four or 16 target objects and searched for any of those target objects in displays with four or 16 objects. The memorized targets appeared either in a repeating sequential order or in random order. In the first experiment, observers were not told about the sequence before the experiment. Only a subset of younger adults and none of the older adults incidentally learned the sequence. The “learners” acquired explicit knowledge about the sequence and searched faster in the sequence compared to random condition. In the second experiment, observers were told about the sequence before the search task. Both younger and older adults searched faster in sequence blocks than random blocks. Older adults, however, showed this sequence-learning effect only in blocks with smaller target sets. Our findings indicate that explicit sequence knowledge can facilitate hybrid search, as it allows observers to predict the next target and restrict their visual and memory search. In older age, the sequence-learning effect is constrained by load, presumably due to age-related decline in executive functions.
... Older adults are often slower and less accurate than are younger adults in performing visual-search tasks, suggesting an age-related decline in attentional functioning 9,10 . The slope of RT and display-size function is typically higher for older adults than for younger adults 11,12 . When the visual target is a featural singleton, however, older adults typically exhibit independence between RT and display size, indicating a highly efficient search despite slower overall RT 13,14 . ...
Article
Full-text available
Reduced retinal illuminance affects colour perception in older adults, and studies show that they exhibit deficiencies in yellow-blue (YB) discrimination. However, the influence of colour cues on the visual attention in older individuals remains unclarified. Visual attention refers to the cognitive model by which we prioritise regions within the visual space and selectively process information. The present study aimed to explore the effect of colour on visual search performance in older observers. In our experiment, younger observers wearing glasses with a filter that simulated the spectral transmittance of the aging human lens and older observers performed two types of search tasks, feature search (FS) and conjunction search (CS), under three colour conditions: red-green, YB, and luminance. Targets and distractors were designed on the basis of the Derrington–Krauskopf–Lennie colour representation. In FS tasks, reaction times changed according to colour in all groups, especially under the YB condition, regardless of the presence or absence of distractors. In CS tasks with distractors, older participants and younger participants wearing glasses showed slower responses under chromatic conditions than under the achromatic condition. These results provide preliminary evidence that, for older observers, visual search performance may be affected by impairments in chromatic colour discrimination.
... Therefore, more adaptions of the DIIN task are required, i.e., a computerized task with real-world objects or a real task with abstract shapes. There are efforts to design laboratory visual search tasks that allow testing attention and memory performance in real-world behavior, e.g., airport security and medical screening in a controlled way (Evans et al., 2013;Wolfe et al., 2013) and recently also to test age differences therein (Wiegand and Wolfe, 2018). These approaches should be taken into account when developing comparable tasks in order to answer questions about ecological validity. ...
Article
Full-text available
Cognitive performance is often found to be lower in older adults, especially when the task requires memory, executive functions, or selective attention. But this alleged deterioration may have been overestimated in the past due to ecologically invalid testing. To verify this possible misjudgment here we compared age-related memory performance in a typical, abstract computer task to a paper-pencil test with a real-world map and to an even more realistic task that took place in a real room with everyday objects. Retention and response intervals differed between the tasks as they had to be adjusted to the different settings. Twenty-seven younger (19-29 years old) and twenty-three older participants (61-77 years old) took part in the study. As expected younger participants outperformed the older ones in the computer task. However, although older adults' performance was better in both more realistic tasks, the delta to the young remained the same as in the computer task. Hence, these results do not support the general notion that older adults would profit from more realistic test scenarios. On the other hand, performance in a clinical screening task correlated only with the performance in the real world task suggesting that this task reflected the general cognitive status of participants better than the more abstract tasks. Finally, it was observed that the presence of task-irrelevant distractor items actually helped older adults to improve their performance in the paper pencil task arguing against the assumption of a general age-related impairment of inhibition. In sum, the present results show that age-related changes in memory are neither simply explained by reduced abilities to deal with abstract computer tasks nor by disturbed inhibition processes.
Article
Two types of newly designed pharmaceutical pictograms (with and without context) were compared with an existing type of certified pictograms regarding their search efficiency. Each of the 30 participants had to search a total of 1′090 “fictitious” medical shelves for a certain box defined by the amount and type of medical instructions given (memory size) and presented among a variable number of other boxes (set size). The boxes contained the different types of pictograms mentioned above. Calculated factorial analyses on reaction time data, among others, showed that the two newly designed pictogram types make search more efficient compared to existing types of pictograms (i.e., flatter reaction time x set size slopes). Furthermore, regardless of the type of pictogram, this set size effect became more pronounced with larger memory sizes. Overall, the newly designed pictograms need fewer attentional resources and therefore might help to increase patient adherence.
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