Age-Related Differences in the Statistical Learning of Target Selection and Distractor Suppression

Abstract

In recent years, the use of implicit mechanisms based on statistical learning (SL) has emerged as a strong factor in biasing visuospatial attention, so that target selection is improved at frequently attended locations and distractor filtering is facilitated at frequently suppressed locations. Although these mechanisms have been consistently described in younger adults, similar evidence in healthy aging is scarce. Therefore, we studied the learning and persistence of SL of target selection and distractor suppression in younger and older adults in visual search tasks where the frequency of target (Experiment 1) or distractor (Experiment 2) was biased across spatial locations. The results show that SL of target selection was preserved in the older adults so, similar to their younger counterparts, they showed a strong and persistent advantage in target selection at locations more frequently attended. However, unlike young adults, they did not benefit from implicit SL of distractor suppression, so that distractor interference was maintained throughout the experiment independently of the contingencies associated with distractor locations. Taken together, these results provide novel evidence of distinct developmental patterns for SL of task-relevant and task-irrelevant visual information, likely reflecting differences in the implementation of proactive suppression attentional mechanisms between younger and older adults.

Full text

Psychology and Aging
Age-Related Differences in the Statistical Learning of Target Selection and
Distractor Suppression
Carlotta Lega, Valeria Di Caro, Veronica Strina, and Roberta Daini
Online First Publication, March 9, 2023. https://dx.doi.org/10.1037/pag0000735
CITATION
Lega, C., Di Caro, V., Strina, V., & Daini, R. (2023, March 9). Age-Related Differences in the Statistical Learning of Target
Selection and Distractor Suppression. Psychology and Aging. Advance online publication.
https://dx.doi.org/10.1037/pag0000735

Age-Related Differences in the Statistical Learning
of Target Selection and Distractor Suppression
Carlotta Lega
1, 2
, Valeria Di Caro
3
, Veronica Strina
1
, and Roberta Daini
1
1
Department of Psychology, University of Milano-Bicocca
2
Department of Brain and Behavioral Sciences, University of Pavia
3
Department of Neurosciences Biomedicine and Movement Sciences, University of Verona
In recent years, the use of implicit mechanisms based on statistical learning (SL) has emerged as a strong
factor in biasing visuospatial attention, so that target selection is improved at frequently attended locations
and distractor filtering is facilitated at frequently suppressed locations. Although these mechanisms have
been consistently described in younger adults, similar evidence in healthy aging is scarce. Therefore, we
studied the learning and persistence of SL of target selection and distractor suppression in younger and older
adults in visual search tasks where the frequency of target (Experiment 1) or distractor (Experiment 2) was
biased across spatial locations. The results show that SL of target selection was preserved in the older adults
so, similar to their younger counterparts, they showed a strong and persistent advantage in target selection at
locations more frequently attended. However, unlike young adults, they did not benefit from implicit SL of
distractor suppression, so that distractor interference was maintained throughout the experiment indepen-
dently of the contingencies associated with distractor locations. Taken together, these results provide novel
evidence of distinct developmental patterns for SL of task-relevant and task-irrelevant visual information,
likely reflecting differences in the implementation of proactive suppression attentional mechanisms between
younger and older adults.
Public Significance Statement
Humans can use regularities in visual scenes to improve performance in finding relevant objects (targets)
and inhibiting responses to irrelevant objects (distractors). The present study demonstrates that older
adults can learn and exploit target-related but not distractor-related spatial regularities. This finding
sheds further light on how the ability to use prior experience to guide attention may change in aging, with
potential consequences for daily activities characterized by irrelevant distractors (e.g., driving).
Keywords: statistical learning, aging, target selection, distractor suppression, visual search
Supplemental materials: https://doi.org/10.1037/pag0000735.supp
Learning environmental regularities constitutes a precious source of
information to create predictions that allow us to efficiently guide
perception and behavior (Bertels et al., 2012;Kim et al., 2009). In the
context of cognitive control, in the past few decades, it has become
clear that the allocation of visual attention in space is guided not only
by stimulus saliency and current goals but also by observers’past
experience, therefore introducing the concept of selection history or
experience-driven attention as a third attentional control mechanism
(for a review, see Anderson et al., 2021;Awh et al., 2012;Jiang, 2018).
These terms capture an overarching theoretical construct, and different
components of prior experience can exert direct influence in the
deployment of attentional priority (Anderson et al., 2021), such as
reward/punishment history (Anderson, 2016;Anderson et al., 2011;
Chelazzi et al., 2013;Della Libera & Chelazzi, 2006;Schmidtetal.,
2015) or history as a sought target (Kyllingsbæk et al., 2001,2014;Qu
et al., 2017). Among these components, statistical regularities in visual
objects locations have been demonstrated to affect spatial attention
orientation. A well-known example of such attentional guidance is
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Carlotta Lega https://orcid.org/0000-0001-7030-7639
Valeria Di Caro https://orcid.org/0000-0001-7278-245X
Carlotta Lega and Valeria Di Caro contributed equally to this work.
This article has not been submitted for consideration to any other journal, and
the article has never been posted in public websites or repositories. The main
results presented here were discussed during a recent conference (30th Congress
of the Italian Association of Psychology, September 27–30, 2022, Padua).
The authors report how the sample size was determined; describe all data
exclusions, manipulations, and all measures in the study; and follow the
Journal Article Reporting Standards (Appelbaum et al., 2018). All data,
analysis code, and research materials are available. In particular, the de-
identified data on which the study conclusions are based, the analytic code
needed to reproduce analyses, and the experimental materials are stored on
the Open Science Framework data-sharing platform at https://osf.io/ua96b/?
view_only=90dced114b1449f1ba4f2f95b432687c. This study’s design and
its analysis were not preregistered.
Correspondence concerning this article should be addressed to Carlotta
Lega, Department of Brain and Behavioral Sciences, University of Pavia,
Piazza Botta 6, 27100 Pavia, Italy. Email: carlotta.lega@unipv.it
Psychology and Aging
© 2023 American Psychological Association
ISSN: 0882-7974 https://doi.org/10.1037/pag0000735
1

contextual cueing, whereby repeated exposure to a specific arrange-
ment of target and distractor items exerts an influence over the
deployment of attention, leading to progressively more efficient search
(Chun & Jiang, 1998;Jiang & Wagner, 2004). Similarly, statistical
regularities in objects locations, that is, locations that have been more
frequently attended in the past, are prioritized in attentional allocation.
In particular, when the target of visual search appears in specific spatial
locations with different frequency, its selection is facilitated when it
appears in a high-probability region compared with a low-probability
region (Di Caro & Della Libera, 2021;Ferrante et al., 2018;Geng &
Behrmann, 2005;Jiang et al., 2015;Schapiro & Turk-Browne, 2015;
Wang & Theeuwes, 2018a,2018b). These types of experience-driven
effects on attention emerge very rapidly from the onset of stimulus
probability imbalances (Di Caro & Della Libera, 2021;Ferrante et al.,
2018;Huang et al., 2021;Jiang, Swallow, et al., 2013) and persist well
after the task contingencies have been eliminated (Di Caro & Della
Libera, 2021;Ferrante et al., 2018;Lee et al., 2020). This is true not
only immediately after the experimental session (Di Caro & Della
Libera, 2021;Ferrante et al., 2018;Giménez-Fernández et al., 2022;
Jiang, Swallow, et al., 2013) but also for at least 1 week after the
acquisition (Jiang et al., 2014), suggesting a long-lasting change in
attentional priority that is not a mere temporary consequence of current
cumulative expectations. Furthermore, they are largely implicit in
nature, emerging when participants are unaware of the underlying
spatial contingency (Di Caro & Della Libera, 2021;Ferrante et al.,
2018;Jiang, Swallow, et al., 2013;Jiang & Swallow, 2013), thus
indicating that they do not follow strategic goal-directed mechanisms.
Although the contextual cueing and target location probability
effects prioritize locations for attentional selection of task-relevant
stimuli, consistent evidence demonstrates that specific locations in the
visual field that are more often occupied by irrelevant-distracting
information are deprioritized and more easily ignored, the so-called
suppression history effect (Di Caro et al., 2019;Ferrante et al., 2018;
Goschy et al., 2014;Leber et al., 2016;Sauter et al., 2018,2019;Wang
& Theeuwes, 2018a,2018b). The typical finding in the latter studies is
that, over time, search becomes less affected by salient distractors that
appear in high-frequency (HF) locations than in low-frequency (LF)
locations. To explain these results, it has been proposed that environ-
mental regularities regarding the target and regularities regarding the
distractor both induce plasticity within the spatial priority map,
ultimately leading to the observable selection biases (Ferrante et al.,
2018;Gaspelin & Luck, 2018;Wang & Theeuwes, 2018a). Recent
findings have suggested that both the suppression of frequent distractor
locations and the enhancement of frequent target locations are im-
plemented via a proactive adjustment of the weight within the priority
map (Huang et al., 2022;Theeuwes et al., 2022;Won et al., 2019;
Wöstmann et al., 2022). In particular, behavioral measures of proactive
suppression are evident in decreased oculomotor capture of frequently
suppressed locations (Di Caro & Della Libera, 2021;Di Caro et al.,
2019;Sauter et al., 2021). Neural evidence of proactive suppression is
evident in the anticipation of the suppression-related electrophysiolog-
ical component (distractor positivity −component; Wang et al., 2019)
and in the reduced neural excitability in the early visual cortex
contralateral to locations where distractors were more likely to appear
before the onset of stimuli presentation (Ferrante et al., 2022). Together
these findings demonstrate that experience-driven effects characterize
both aspects of attentional processing, namely the selection of the
relevant information and the inhibition of the irrelevant information,
mediated by proactive enhancement and suppression mechanisms,
respectively.
It is generally accepted that normal aging leads to impairments in
attentional processing and particular in inhibition mechanisms
(Ashinoff, Geng, et al., 2019;Bauer et al., 2012;Gazzaley et al.,
2005;Madden et al., 2014;Mevorach et al., 2016;Potter et al., 2012;
Quigley & Müller, 2014;Tsvetanov et al., 2013). This age-related
decline in attention is associated with anatomical alteration in white
matter integrity (Bennett et al., 2012;Bennett & Madden, 2014;
Lockhart et al., 2015) and increased compensatory activity in the
frontoparietal attention network (Reuter-Lorenz & Park, 2010).
Importantly, rather than a general decline in inhibitory ability, evi-
dence seems to indicate that a prominent aspect of cognitive aging is a
decline in the ability toproactively ignore and suppress the distracting
information (Ashinoff, Tsal, et al., 2019;Braver, 2012;Kramer et al.,
1994;Lustig & Jantz, 2015). This is supported by neuroimaging
findings, showing that while younger participants engaged the proac-
tive control network when asked to filter out task-irrelevant distractors
(inhibiting distractors before they have appeared), older adults re-
cruited a reactive control mechanism for distractor inhibition (inhibit-
ing distractors only after they have appeared; Ashinoff et al., 2020;
Braver, 2012;Paxton et al., 2008;Vadaga et al., 2016). Novel
theorizing suggests that the reduced attentional control associated
with normal aging can be beneficial in a range of cognitive tasks that
rely less on top-down mechanisms and more on automatic implicit-
based learning (for a recent review, see Amer et al., 2016). This idea
stems from the findings that attentional selection history effects are
preservedin older adults. Consistent studies using contextual-cueing–
like tasks, indeed, demonstrated that healthy older adults, similar to
their younger counterpart, are faster in finding a target among
distractors on repeated displays than on new ones, highlighting an
intact ability to learn the target–context association (Howard et al.,
2004;Lyon et al., 2014;Smyth & Shanks, 2011). In a similar way,
older adults exhibit facilitated attentional processing for the visual
field quadrant in which the target has been more frequently presented
(Jiang, 2018;Twedell et al., 2017). Despite overall slower response
times compared with young adults, older adults showed a robust
ability to use location probability learning to facilitate their visual
search. Furthermore, as previously demonstrated with young adults,
this effect is highly persistent, lasting also during the extinction phase,
when the target probability imbalances were removed (Jiang et al.,
2016). These findings seem to suggest that experience-dependent
effects for attentional selection of task-relevant stimuli are preserved
in normal aging, therefore suggesting spared proactive enhancement
mechanisms in older population.
However, the evidence relative to whether and how older adults
show statistical learning (SL) for information that should be sup-
pressed is scarce (but see proportion congruence tasks; Bugg, 2015;
Bugg et al., 2011;Bugg & Crump, 2012;Campbell et al., 2012). To
the best of our knowledge, no study has investigated whether older
adults are able to use statistical regularities of distractor probability
locations (as they do for target probability locations) to guide the
deployment of spatial attention. This may help to clarify whether target
selection and distractor suppression are two outcomes of a unique
selection mechanism or instead they are independent mechanisms
(Chelazzi et al., 2019). In fact, if distractor suppression is a dedicated
and active mechanism, rather than a side effect of target enhancement,
and therefore automatic (Chelazzi et al., 2019;Schneider et al., 2022;
Wöstmann et al., 2019), we may hypothesize the two mechanisms
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2LEGA, DI CARO, STRINA, AND DAINI

having two different developmental trajectories. The study of similar-
ities or asymmetries in the learning and the persistence of implicit
statistical regularities of target and distractor probabilities between
young and healthy older adults could therefore provide important
information in this direction, as well as inform our understanding of
different versus same subsystems of experience-driven mechanisms
that underlying implicit learning for relevant and irrelevant visual
information. Given this, the purpose of this study was to compare the
ability of older and younger adults to benefit from implicit task-
relevant versus task-irrelevant spatial statistical regularities to effi-
ciently guide attentional priority.
To this aim, we evaluated the presence and the permanence of these
effects in a unique experimental setting, where the frequency manipu-
lation was exclusively linked with either target selection (Experiment 1)
or distractor suppression (Experiment 2). In Experiment 1, we ex-
pected to corroborate previous findings, by demonstrating an atten-
tional benefit in target selection both in younger and older adults when
the target appears in HF locations, with this benefit long lasting after
the task contingencies have been eliminated. These results would
replicate previous studies in younger adults that used similar or
identical paradigms (Di Caro & Della Libera, 2021;Ferrante et al.,
2018;Geng & Behrmann, 2005;Wang & Theeuwes, 2018a). Fur-
thermore, they would extend results in the older population, by
demonstrating a benefit in the spatial allocation of attention not
only when the frequency manipulation is applied to a larger portion
of the space (i.e., quadrant, see Jiang, 2018;Twedell et al., 2017) but
also to specific and restricted spatial positions of the search array.
Using the very same experimental context, in Experiment 2, we
expected younger participants to easily suppress specific locations
in the visual field that are more often occupied by nonrelevant, salient
distracting stimuli (Di Caro & Della Libera, 2021;Di Caro et al., 2019;
Ferrante et al., 2018;Goschy et al., 2014;Leber et al., 2016;Sauter
et al., 2018,2019;Wang & Theeuwes, 2018a,2018b). If the same
advantages associated with suppression history are observed also in
older adults, then this could indicate that mechanisms responsible for
incidental learning of task-relevant and task-irrelevant attentional
information in spatial attention are similarly preserved in aging.
However, if they are not observed, then the mechanisms underlying
incidental learning of task-relevant versus irrelevant information may
be dissociated and have different developmental trajectories in the life
span, indicating different use of proactive enhancement and proactive
suppression mechanisms in younger and older adults.
Experiment 1
Experiment 1 aimed to study possible age-related differences in
SL of target selection. All experiments in this study were performed
online and were approved by the University of Milano-Bicocca’s
institutional review board as part of the studies of SL protocol (RM-
2020-315).
Method
Transparency and Openness
We report how the sample size was determined; describe all data
exclusions, manipulations, and all measures in the study; and follow
the Journal Article Reporting Standards (Appelbaum et al., 2018).
All data, analysis code, and research materials are available. The
de-identified data on which the study conclusions are based, the
analytic code needed to reproduce analyses, and the experimental
materials are stored on the Open Science Framework data-sharing
platform (Foster & Deardorff, 2017). Data were analyzed using R,
Version 4.0.0 (R Core Team, 2021). The study design, hypotheses,
and analytic plan were not preregistered.
Participants
Sixty participants took part in this experiment, divided into two
groups: the younger adults group included 30 students (age, M=
23.49 years, SD =2.50, range =18–32; 10 males; years of education,
M=16.52 years, SD =2.9, range =8–21) and the older adults group
included 30 healthy older adults (age, M=69.59 years, SD =3.25,
range =64–75; 13 males; years of education, M=10.35 years, SD =
3.69, range =5–18). No information about race was collected for
this study. All participants had to sign an informed consent form prior
to the study (see below). All participants had normal or corrected-to-
normal vision and were healthy with no history of mental health
issues or neurological disorders. Before starting the experiment, the
older cohort was screened for decline in cognitive functions using the
Mini Mental State Examination (MMSE; Folstein et al., 1975). We
chose the MMSE because it is the only screening test for which an
equivalence has been demonstrated between administration in person
and online (e.g., Wadsworth et al., 2018). All of the older participants
scored within the normal range (M=29.13, SD =0.97). Data
collection started in 2020 and ended in 2022. Data were collected
online, and all participants recruited for the study were from Milan or
neighboring areas.
Power Analysis
We performed a power analysis for determining the sample size.
Di Caro et al. (2019) reported a main effect of frequency (HF vs. LF)
corresponding to a η2
p=.505 with a correlation among repeated
measures of .501. Our sample size (N=60) allows detecting a main
effect of frequency of similar size with a power of >.999, at the
conventional αlevel of .05. It also provides 80% power to detect an
interaction effect between frequency and age as small as η2
p=.032,
which is considered between medium and small (Perugini et al., 2018).
The power analysis was performed using Gpower 3.1 (Faul et al.,
2007). This power analysis was also applied to Experiment 2.
Task and Procedure
Due to the COVID-19 pandemic which prevented data collection
in the laboratory, especially for the old cohort, both Experiments 1
and 2 were performed online. The task was programmed on Inquisit
4.0.10.0 (Millisecond Software) and run online through the Inquisit
Studio web player. Participants were asked to download, install, and
run the application plug-in on their internet-connected computers
and to keep on the audio. Once the application started, participants
visualized the information sheet and the consent form. The study
began only after the consent to participate was provided. The first
part consisted of a brief demographic questionnaire followed by the
task instructions display. We employed the same visual search task
adopted in Di Caro et al. (2019;Di Caro & Della Libera, 2021),
which has proved to be reliable in detecting biases due to SL of both
target and distractor (see Figure 1). To account for different screen
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AGE-RELATED DIFFERENCES IN STATISTICAL LEARNING 3

sizes, the display dimensions of the task stimuli were equalized and
automatically adjusted by the software according to the participants’
computer screen size.
All trials began with a central fixation point, a white dot shown on a
uniform dark gray background (RGB: 30, 30, 30). Following 500 ms,
a premask display was shown consisting of six gray circles (RGB: 95,
95, 95) with a gray asterisk located inside, all simultaneously presented
and equally spaced at the 1, 3, 5, 7, 9, and 11 o’clock positions of an
imaginary circle. After a random variable interval between 500 and
800 ms, the fixation dot was removed and all the circles changed color,
becoming green (RGB: 30, 120, 50), except for the singleton stimulus
acting as the target, which was the only one remaining gray. At the
same time, all asterisks disappeared unveiling one tilted gray line.
Participants were asked to discriminate the orientation of the tilted line
located inside the target, by pressing the “N”or “M”key on the
QWERTY keyboard of their PC to indicate the left or the right
inclination, respectively. The search display was shown until response.
Task instructions emphasized both speed and accuracy. If the response
was incorrect an error display appeared, accompanied by an 800 Hz
tone. In 50% of the trials, an additional red circle appeared abruptly in
the display filling one of the empty locations between other circles.
Due to its features (i.e., different color) and novelty (i.e., it is a new
item shown in the array), it acted as a highly interfering distractor.
Experiment 1 consisted of two main phases. After a brief baseline
block of 20 trials, the training phase (3 blocks of 192 trials each; 576
trials overall) started, followed by the test phase (2 blocks of 72 trials
each; 144 trials overall). During the training phase, the target appear-
ance across locations was biased, so that it could appear with HF at two
specific locations, one in each hemifield (see Figure 2). Specifically,
each HF location was occupied by the target on 38% of trials (leading
to an overall probability of 76%), whereas each LF location contained
a target with a probability of 6% (overall 24%). No bias was applied to
distractor probability, so that, when present, it could occupy one of the
six possible empty spaces with the same probability.
The test phase, where target frequency became equal across
locations, took place immediately after the training phase, within a
continuous flow of trials, so that participants did not experience
discontinuance in between. Overall, the experiment lasted ∼90 min,
with a pause after every 25 trials. At the end of the experiment,
participants were asked to complete the final survey, which evaluated
whether they were aware of spatial biases applied. Specifically, they
were first asked to report whether they had noticed some imbalances
in target spatial distribution and, second, indicate two locations in
which they thought it appeared more often.
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Figure 1
Experimental Task and Stimuli
Note. Each trial started with a central fixation dot, after 500 ms the stimulus layout appeared, which was
replaced after a variable time by the search array. The target stimulus in the search array was defined by a
circle which remained gray, as opposed to the others which turned green. In a proportion of trials, a salient
distractor appeared, which was an additional stimulus (i.e., a vertical rod in a red circle) occupying a
previously empty location. See the online article for the color version of this figure.
Figure 2
Illustration of Target Frequency Manipulations Adopted in the
Training Phase of Experiment 1
Note. Target locations associated with high frequency are indicated by the
percentages shown in bold font. The two possible assignments illustrated
were counterbalanced across participants.
4LEGA, DI CARO, STRINA, AND DAINI

Data Analysis
Statistical analyses were performed on mean reaction times (RTs)
of correct responses. Trials with RTs that did not fall within 3 SD from
the mean for each condition in each participant, separately for each
group, were excluded (∼1.4% of trials for the younger group and
∼1.3% of trials for the older group were excluded following this
criterion). We did not include task accuracy in the main analyses
because it was very high for both groups (younger adults’accuracy:
M=97.26%, SD =16.32; older adults’accuracy: M=98.09%, SD =
13.69). Furthermore, we checked for speed–accuracy trade-offs, and
results indicated no significant correlation between mean RTs and
mean accuracy for both younger (r=0.079, p=.678) and older adults
(r=0.051, p=.788). Because younger adults showed overall faster
mean RTs (M=741.58 ms, SD =199.96) than older adults (M=
1124.02 ms, SD =475.65; raw RTs are shown in Figure 3), to account
for the speed of processing in aging populations, we converted trial
RTs into z-scored RTs (z-RTs) according to the method designed by
Faust et al. (1999) and performed analyses on z-RTs. In detail, zscores
were computed for each participant as z-RT =(RT −individual mean
RT/individual standard deviation). This procedure has been used
previously in studies investigating age-related differences. Indeed, it
allows one to address group differences preserving the possible
confounding of effects due to the baseline latency difference
(Tsvetanov et al., 2013;Twedell et al., 2017). Bonferroni–Holm
correction was systematically applied to all multiple ttests, and the
pvalues reported are adjusted accordingly. All analyses were con-
ducted separately for each crucial phase of the experiment (training
and test), whereas performance during the initial trials (baseline) was
shown for visualization purposes only, to clarify the absence of any
biases when the target spatial distribution was even. To assess the
extent to which the data support the presence and, respectively, the
absence of the effects, we also performed a Bayesian analysis using
JASP software (Version 0.14.1; https://jasp-stats.org). Specifically,
Bayesian analyses of variance (ANOVAs) with the analysis of the
effects were performed. Such an analysis estimates the inclusion
Bayes factor (BF
incl
), which can be interpreted as evidence in the
data for including a predictor, either a main effect or an interaction.
BFs
incl
between 1 and 3, 3 and 10, and larger than 10 are considered,
respectively, as anecdotal, moderate, and strong evidence for includ-
ing a predictor; conversely, BFs
incl
between 1 and 1/3, 1/3 and 1/10,
and smaller than 1/10 indicate, respectively, anecdotal, moderate, or
strong evidence for excluding a predictor. A BF
incl
=1 indicates no
evidence in favor of including or excluding a predictor (van Doorn et
al., 2021). For completeness, analyses on raw RTs were conducted
separately for age groups and reported in Supplemental Materials 1.
Results
Training Phase
To investigate the effect of selection history during the training
phase—that is, while the frequency imbalances were in action—we
performed a repeated-measures ANOVA on mean z-RTs including
target frequency (HF vs. LF), distractor presence (absent vs. pres-
ent), and block (1–3) as within-subject factors and group (younger
vs. older adults) as between-subject factor. The analysis revealed a
significant main effect of target frequency, F(1, 58) =699.346,
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Figure 3
Results of Younger and Older Groups’Performance in Experiment 1
Note. Mean reaction time as a function of target frequency (high frequency or low frequency) and distractor presence, for the training and the
test phases. Error bars depict the 95% within-subject confidence intervals. (a) Younger adults: mean RTs across phases; (b) older adults: mean
RTs across phases. RTs =reaction times; HF =high frequency; LF =low frequency.
AGE-RELATED DIFFERENCES IN STATISTICAL LEARNING 5

p<.001, η2
p=.923, BF
incl
=∞, showing that participants were
faster when the target appeared at HF locations compared with the
LF conditions. The interaction between target frequency and group
was not significant, F(1, 58) =2.332, p=.132, and η2
p=.039.
Importantly, the Bayes factor for this crucial interaction is BF
incl
=
0.111, which indicates moderate to strong evidence for excluding
the interaction effect, confirming that the effect due to selection
history was comparable between younger and older adults (see
Figure 3, for raw RTs and Figure 4, for mean z-RT). To appreciate
the magnitude of the selection history effect separately for each
group, the mean differences and the inferential statistics are pro-
vided in Table 1.
The main effect of block was significant, F(2, 116) =59.195, p<
.001, η2
p=.505, BF
incl
=∞, and so was its interaction with target
frequency, F(2, 116) =12.135, p<.001, η2
p=.173, BF
incl
=137.83,
thus suggesting that from the beginning of the training phase to the
end, the effect due to selection history tended to increase (for post
hoc comparisons, see Table 1).
This improvement over time was comparable between younger
and older adults as shown by the lack of interaction between target
frequency, block, and group, F(2, 116) =0.603, p=.549, η2
p=.010,
BF
incl
=0.015 (Figure 4). The main effect of distractor presence was
also significant, F(1, 58) =129.155, p<.001, η2
p=.690, BF
incl
=∞,
and so was its interaction with block, F(2, 116) =5.766, p=.004,
η2
p=.090, BF
incl
=0.949, indicating that distractor presence
impacts on the task performance, and that the inference decreased
over time. The interaction between distractor presence and target
frequency was not significant, F(1, 58) =0.893, p=.349, η2
p=
.015, BF
incl
=0.111, highlighting that distractor cost was not
modulated by the effects due to selection history. All of these
effects were comparable between younger and older adults, as
shown by the lack of interactions between group and all the other
factors (all ps<.1).
Test Phase
To explore whether the effects due to selection history were
maintained over time thus surviving even in the extinction
regimen—that is, when the frequency imbalances were no longer
in place—we conducted an ANOVA on mean z-RTs, which
included target frequency, distractor presence, and block (4–5)
as within-subject factors and group (younger vs. older adults) as
between-subject factor. This analysis showed that the acquired bias
was maintained over time as indicated by the significant main
effect of target frequency, F(1, 58) =99.235, p<.001, η2
p=.631,
BF
incl
=∞. The interaction between target frequency and group
was not significant, F(1, 58) =0.422, p=.518, η2
p=.007, and
the Bayes factor BF
incl
=0.034 indicates a strong evidence for
excluding the interaction effect, thus confirmed that the residual
effects of selection history were comparable between younger
and older adults (see Figure 3, for raw RTs and Figure 4,formean
z-RT). The mean differences and the statistical results of ttests
comparisons between HF and LF conditions within each group are
provided in Table 1. The main effect of distractor presence was
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Figure 4
Results of Younger and Older Groups’Performance Across Blocks in Experiment 1
Note. Mean standardized RT in zscore as a function of target frequency (high frequency or low frequency) and distractor presence across
blocks. Error bars depict the 95% within-subject confidence intervals. The baseline phase refers to initial balanced trials not included in the main
analysis. RT =reaction time; HF =high frequency; LF =low frequency.
6LEGA, DI CARO, STRINA, AND DAINI

significant, F(1, 58) =55.063, p<.001, η2
p=.487, BF
incl
=1.238 ×
10
8
, and the magnitude of distractor cost was comparable between
the two groups as showed by the nonsignificant interaction between
distractor presence and group, F(1, 58) =0.843, p=.362, η2
p=
.014, BF
incl
=0.035.
The interaction between target frequency and distractor presence,
F(1, 58) =3.847, p=.055, η2
p=.062, BF
incl
=0.519, was not
significant, whereas the interaction between those factors and group,
F(1, 58) =4.069, p=.048, η2
p=.066, BF
incl
=0.010, was
significant, thus suggesting that the residual effects of the previous
selection history could be biased by distractor presence differently
for older and younger adults. Post hoc comparisons revealed that
younger adults maintained the effect of selection history indepen-
dently of the distractor presence (selection history effect in distractor
present vs. distractor absent trials): t(29) =0.04, p=.965, d=.007;
post hoc comparisons between HF vs. LF separated for distractor
present and distractor absent trials are provided in Table 1. Instead,
in the older adults’group, those residual effects tended to be better
evident when the distractor was present than in distractor absent
trials (selection history effect in distractor present vs. distractor
absent trials): t(29) =2.604, p=.014, d=.475; see Table 1. Finally,
the interaction between target frequency and block was significant,
F(1, 58) =4.388, p=.041, η2
p=.070, BF
incl
=0.281, whereas the
three-way interaction target frequency by block by group was not
significant, F(1,58) =1.114, p=.296, η2
p=.019, BF
incl
=0.003,
thus suggesting that in both groups, the residual effects tended to
become weaker as the session proceeded (Figure 4). None of the
other effects reached the significance (all ps>.2).
Awareness of Target Frequency Bias
Forty-one out of the sixty participants involved in the study (18
older and 23 younger adults) reported in the yes/no questionnaire
having no impression when the target appeared more often at the two
locations associated with the HF bias. Therefore, to exclude that
explicit awareness could have a role in determining the effects of
selection history, we replicated statistical analysis on the main
effects involving the variable target frequency by excluding the
aware participants from the experimental sample. The obtained
results were consistent with those reported for the original analysis
(see Supplemental Materials 2).
Experiment 2
Experiment 2 aimed to study possible age-related differences in
SL of distractor suppression.
Method
Participants
A new cohort of 60 participants took part in this experiment,
divided into two groups: the younger adults group included
30 students (age, M=24.33 years, SD =2.30, age range =19–32;
12 males; years of education, M=16.3 years, SD =2.69, range =8–18)
and the older adults group included 30 healthy older adults (age,
M=68.96 years, SD =3.28, range =65–75; 19 males; years of
education, M=13.2 years, SD =4.28, range =5–21). Race/
ethnicity of participants was not assessed. None of the participants
in Experiment 2 had previously participated in Experiment 1. All
participants had normal or corrected-to-normal vision and were
healthy with no history of mental health issues or neurological
disorders. The older participants were screened for decline in
cognitive functions using the MMSE (Folstein et al., 1975). All
of the older participants scored within the normal range (M=27.9,
SD =1.18). Data collection started in 2020 and ended in 2022. Data
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Table 1
Experiment 1: Summary of Mean Differences in z-RT and Inferential Statistics of Post Hoc Comparisons Separated for Younger and Older
Group
Age group Comparison Mean difference SE t(29) pvalue Cohen’sd
Younger adults HF training –LF training −1.538 0.074 20.770 <.001*3.792
SL Block 1 –SL Block 2 −0.202 0.129 1.562 .129 0.285
SL Block 2 –SL Block 3 −0.207 0.101 2.041 .101 0.373
SL Block 1 –SL Block 3 −0.408 0.121 3.143 .012*0.574
DP training –DA training −0.495 0.055 8.986 <.001*1.641
HF test –LF test −0.692 0.103 6.712 <.001*1.225
DP test –DA test −0.379 0.070 5.425 <.001*0.990
HF test DA –LF test DA −0.695 0.108 6.443 <.001*1.176
HF test DP –LF test DP −0.689 0.137 5.023 <.001*0.917
Older adults HF training –LF training −1.370 0.081 16.853 <.001*3.077
SL Block 1 –SL Block 2 −0.375 0.122 3.065 .009*0.560
SL Block 2 –SL Block 3 −0.052 0.130 0.397 .694 0.073
SL Block 1 –SL Block 3 −0.427 0.121 3.519 .004*0.642
DP training –DA training −0.479 0.066 7.296 <.001*1.332
HF test –LF test −0.789 0.107 7.366 <.001*1.345
DP test –DA test −0.486 0.093 5.208 <.001*0.951
HF test DA –LF test DA −0.580 0.130 4.457 <.001*0.814
HF test DP –LF test DP −0.998 0.137 7.262 <.001*1.326
Note.Meandifferencesinz-RT and statistics (tvalues, pvalues, Cohen’sd) for post hoc comparisons within each age group. RT =reaction time; HF =
high-frequency target; LF =low-frequency target; SL =statistical learning effect (i.e., LF −HF); DA =distractor absent; DP =distractor present; SE =
standard error.
*Indicates significant pvalues.
AGE-RELATED DIFFERENCES IN STATISTICAL LEARNING 7

were collected online, and all participants recruited for the study
were from Milan or neighboring areas.
Task and Procedure
The task and the procedure were the same as in Experiment 1
except for the crucial difference concerning the stimuli and locations
associated with SL. In this experiment, the target appeared with the
same probability across locations. Overall, the distractor was present
in 64% of trials and appeared more often at two specific locations,
one for each hemifield and counterbalanced across participants. The
degree of the imbalance between HF and LF locations was similar
to that used in Experiment 1 (see Figures 5 and 6; HF: overall 76%
of the distractor present trials, 38% for each location; LF: overall
24% of the distractor present trials, 6% for each location).
Participants performed a brief baseline block (20 trials), followed
by a training phase (3 blocks of 200 trials each one; 600 trials
overall) and a test phase (2 blocks of 72 trials each one; 144 trials
overall). Overall, the experiment lasted ∼90 min, with a pause after
every 25 trials.
Data Analysis
Statistical analyses were performed by adopting the same approach
described for Experiment 1. The filtering criteria led to discarding
∼1.6% of the trials for the younger group and ∼3.3% of the trials for
the older adults’group.
As shown in Experiment 1, we did not include task accuracy in
the analyses because it was very high for both groups (younger
adults’accuracy: M=97.35%, SD =16.07; older adults’accuracy:
M=98.82%, SD =10.79). Furthermore, we checked for speed–
accuracy trade-offs, and results indicated no significant correlation
between mean RTs and mean accuracy for both younger (r=0.190,
p=.316) and older adults (r=−140, p=.460). Because younger
adults showed overall faster mean RTs (M=745.32 ms, SD =
216.28) than older adults (M=1411.73 ms, SD =475.47), we
performed analyses on standardized RTs (z-RTs). Bayesian ANOVAs
were performed to further interpret the results.
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Figure 5
Illustration of Distractor Frequency Manipulations Adopted in the
Training Phase of Experiment 2
Note. Distractor locations associated with high frequency are indicated by
the percentages shown in bold font. The two possible assignments illustrated
were counterbalanced across participants.
Figure 6
Results of Younger Group Performance in Experiment 2
Note. Mean reaction time as a function of distractor frequency (present with high frequency, or HF, present with low frequency, or LF, and
absent), for the training and the test phases. Error bars depict the 95% within-subject confidence intervals. (a) Younger adults: mean RTs across
phases and (b) Older adults: mean RTs across phases. RTs =reaction times; HF =high frequency; LF =low frequency.
8LEGA, DI CARO, STRINA, AND DAINI

Results
Training Phase
To explore the effect of suppression history during the training
phase, we conducted a repeated-measures ANOVA on mean z-RTs
with distractor frequency (present with HF, present with LF, and
absent) and block (1–3) as within-subject factors and group (youn-
ger vs. older adults) as between-subject factor.
Results showed a significant main effect of distractor fre-
quency, F(2, 116) =81.733, p<.001, η2
p=.585, BF
incl
=∞,
and, crucially, a significant interaction between distractor fre-
quency and group, F(2, 116) =3.868, p=.024, η2
p=.063 (see
Figure 6, for raw RTs and Figure 7, for mean z-RT). The Bayesian
analysis indicated that data provided anecdotal evidence for
excluding the interaction effect (BF
incl
=0.258).
Post hoc comparisons showed that while the presence of a salient
distractor reduced the z-RTs in general and this cost was comparable
between groups, t(58) =0.374, p=.710, d=.097, younger and older
adults performances were differently affected by suppression history
(suppression history effect in younger vs. older adults): t(58) =3.144,
p=.003, d=.812; the mean differences and the statistical results of
ttests comparisons among HF, LF, and distractor absent conditions
within each group are presented in Table 2. Together, these statistics
indicated that suppression history, but not distractor presence, mainly
explains the observed data on group differences.
The main effect of block, F(2, 116) =116.769, p<.001, η2
p=
.668, BF
incl
=∞, and the interaction between distractor frequency
and block, F(4, 232) =5.901, p<.001, η2
p=.092, BF
incl
=6.790,
were also significant.
Further post hoc comparisons revealed a general reduction of
distractor cost as the training proceeded for both groups, whereas the
effect of suppression history did not significantly change across
blocks (Figure 7; mean differences and post hoc comparisons across
blocks are provided in Table 3). None of the interaction of group
with the other factors was significant (all ps>.4).
Test Phase
To explore whether the effects due to suppression history were
maintained over time, we conducted a repeated-measures ANOVA
on mean z-RTs during the test phase, with distractor frequency
(absent, present with HF, and present with LF) and block (4–5) as
within-subject factors and group (younger vs. older adults) as
between-subject factor. Results have shown a significant main effect
of distractor frequency, F(2, 116) =26.557, p<.001, η2
p=.314,
BF
incl
=3.005 ×10
7
, whereas the interaction between distractor
frequency and group was not significant, F(2, 116) =1.503, p=
.227, η2
p=.025, BF
incl
=0.061. Post hoc comparisons indicated that
distractor presence continued to interfere on the performances of
both younger and older adults (distractor cost younger vs. older
adults): t(58) =1.632, p=.108, d=.421; see Figure 6, for raw RTs
and Figure 7, for mean z-RT. However, no significant effect was
found between distractors appearing at previous HF locations, thus
showing that neither younger nor older adults maintained residual
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Figure 7
Results of Younger and Older Groups’Performance Across Blocks in Experiment 2
Note. Mean standardized RT in zscore as a function of distractor frequency (present with high frequency, or HF, present with low frequency, or
LF, and absent), across blocks. Error bars depict the 95% within-subject confidence intervals. The baseline phase refers to initial balanced trials
not included in the main analysis. RT =reaction time; HF =high frequency; LF =low frequency.
AGE-RELATED DIFFERENCES IN STATISTICAL LEARNING 9

effects of previous suppression history (suppression history effect in
younger vs. older adults): t(58) =0.804, p=.424, d=.208; the mean
differences and the statistical results of ttests comparisons among
HF, LF, and distractor absent conditions within each group are
provided in Table 2. None of the other effects reached significance
(all ps>.1).
Awareness of Distractor Frequency Bias
Ten out of the 60 participants involved in the study (five older and
five younger adults) noticed that the distractor appeared more often
at the two locations associated with the HF bias. Therefore, to
exclude that explicit awareness could have a role in determining the
effects of suppression history, we replicated statistical analysis on
the main effects involving the variable distractor frequency by
excluding the aware participants from the experimental sample.
The obtained results were consistent with the ones reported in the
original analysis (see Supplemental Materials 2).
Discussion
In two experiments, we sought to ascertain the ability of younger
and older adults to implicitly learn statistical regularities of target
selection (Experiment 1) and distractor suppression (Experiment 2)
to guide attentional priority and optimize visual search. Results
show attentional benefit in target selection both in younger and older
adults when the target appears in HF locations, with the magnitude
of learning being comparable in the two groups. Furthermore, this
benefit to favor previously HF locations lasted long after the target
contingencies has been eliminated both in younger and older adults.
Using the same experimental context, results also demonstrated that
younger participants show suppression history effects, so that
specific locations in the visual field that are more often occupied
by irrelevant distracting stimuli become more easily suppressed.
However, the statistical contingencies associated with distractor
suppression were not durable and did not affect the attentional
priority maps once the distractor probability returned to be equal
across spatial locations. Crucially, we demonstrated for the first time
that older adults did not benefit from learning statistical regularities
of distractor suppression: While salient singleton distractors behav-
iorally interfered during the visual search task, this effect was
independent from distractor probabilities contingencies. Therefore,
while learning of target location probabilities and learning of
distractor location probabilities can both be considered two forms
of habit-related attentional behaviors, they are differently imple-
mented in younger and older adults. We discuss these findings as a
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Table 2
Experiment 2: Summary of Mean Differences in z-RT and Inferential Statistics of Post Hoc Comparisons Separated for Younger and Older
Group
Age group Comparison Mean difference SE t(29) pvalue Cohen’sd
Younger adults HF training –LF training −0.319 0.066 4.805 <.001*0.877
HF training –DA training 0.467 0.074 6.314 <.001*1.153
LF training –DA training 0.786 0.075 10.537 <.001*1.924
HF test –LF test −0.175 0.149 1.179 .247 0.215
HF test –DA test 0.664 0.140 4.738 <.001*0.865
LF test –DA test 0.839 0.125 6.696 <.001*1.222
Older adults HF training –LF training 0.010 0.081 0.119 .905 0.021
HF training –DA training 0.674 0.090 7.518 <.001*1.373
LF training –DA training 0.664 0.113 5.864 <.001*1.071
HF test –LF test −0.004 0.153 0.026 .979 0.004
HF test –DA test 0.491 0.144 3.403 .002*0.621
LF test –DA test 0.491 0.130 3.805 <.001*0.694
Note. Mean differences in z-RT and statistics (tvalues, pvalues, Cohen’sd) for post hoc comparisons within each age group. RT =reaction time; HF =
high-frequency distractor; LF =low-frequency distractor; DA =distractor absent; SL =statistical learning effect (i.e., LF −HF); SE =standard error.
*Indicates significant pvalues.
Table 3
Experiment 2: Post Hoc Comparisons for Distractor Frequency ×Block
Comparison Mean difference SE t(59) pvalue Cohen’sd
D.cost Block 1 –D.cost Block 2 −0.460 0.105 4.397 <.001*0.568
D.cost Block 2 –D.cost Block 3 −0.007 0.093 0.070 .944 0.009
D.cost Block 1 –D.cost Block 3 −0.467 0.098 4.743 <.001*0.614
SL Block 1 –SL Block 2 −0.001 0.143 0.009 .993 0.001
SL Block 2 –SL Block 3 −0.029 0.129 0.226 .822 0.029
SL Block 1 –SL Block 3 −0.030 0.143 0.213 .832 0.028
Note. Mean differences in z-RT and statistics (tvalues, pvalues, Cohen’sd) for post hoc comparisons. Because distractor frequency is a three-level
variable (distractor absent, distractor present at HF location, and distractor present at LF location), we compared distractor cost (D.cost =distractor
absent −distractor present) and statistical learning effect (SL =LF −HF) across blocks. HF =high-frequency distractor; LF =low-frequency distractor;
RT =reaction time; SL =statistical learning.
*Indicates significant pvalues.
10 LEGA, DI CARO, STRINA, AND DAINI

possible failure of proactive distractor suppression mechanisms in
older adults. Together these results expand our understanding of the
experience-driven attentional mechanisms and show two different
developmental trajectories for SL of target selection and distractor
suppression, suggesting the involvement of two, at least in part,
different mechanisms.
The results of Experiment 1 align nicely with the general idea that
visual attention in space is guided by observers’past experience (for
a review, see Anderson et al., 2021;Awh et al., 2012;Jiang, 2018).
In particular, results on younger adults are in line with relevant
literature (Di Caro & Della Libera, 2021;Ferrante et al., 2018;Geng
& Behrmann, 2005;Jiang et al., 2015;Schapiro & Turk-Browne,
2015;Wang & Theeuwes, 2018a), thus supporting the notion of
robust and long-lasting selection history effects. It is important to
note that although the experiment was performed online, the results
perfectly replicate the previous literature on younger adults. Simi-
larly, we demonstrated that older adults implicitly learn statistical
regularities of task-relevant visual information to guide and effi-
ciently allocate visuospatial attention, as demonstrated by a rapid
and persistent facilitation toward HF spatial locations. The present
findings are in line with previous literature suggesting that habitual
attention mechanisms are preserved in older healthy adults. Consis-
tent evidence demonstrated that contextual cueing, that is, repeat-
edly searching within the same display, facilitates visual search in
older adults as their younger counterparts (Howard et al., 2004;
Lyon et al., 2014;Smyth & Shanks, 2011). Subsequent studies
investigating the effect of aging on habitual attention, tried to isolate
the selective effect of spatial attention from context processing. By
manipulating the frequency with which the target appears in one of
the quadrants, studies have consistently demonstrated that older
adults implicitly acquired a spatial preference toward the high-
probability quadrant, thus indicating intact habitual spatial attention
in older adults independent of specific search display (Jiang, 2018;
Jiang et al., 2016;Twedell et al., 2017). The present findings
corroborate and extend those studies, by demonstrating a benefit
in the spatial allocation of attention not only when the frequency
manipulation is applied to a larger portion of the space (i.e.,
quadrant, see Jiang, 2018;Jiang et al., 2016;Twedell et al.,
2017) but also to restricted (and opposite in terms of visual field)
spatial positions of the search array. Taken together, these findings
support the notion that the preservation of habitual attention in older
adults may allow them to proficiently allocate visuospatial attention.
More in general, these results may suggest that automatic and
implicit-based attentional learning mechanisms may be preserved
even despite a more general deficit in attentional mechanisms and
reduced cognitive control induced by aging or development and
neurocognitive disease (Amer et al., 2016). In line with this notion,
unimpaired spatial location probability learning has been demon-
strated not only in older adults (Jiang, 2018;Jiang et al., 2016;
Twedell et al., 2017) but also in patients with Parkinson’s disease
(Sisk et al., 2018), in children (Lee et al., 2020;Yang & Song, 2021),
and in autistic spectrum disorder (Jiang, Capistrano, et al., 2013).
Experiment 2 explores the pure implicit learning of task-
irrelevant visual information, isolated from task-relevant ones.
Here, we demonstrated that younger participants benefit from
suppression history effect, so that specific locations in the visual
field that are more often occupied by nonrelevant distracting stimuli
become more easily suppressed. This result is in line with recent
findings demonstrating that, during the training phase, when the
statistical imbalances are in place, younger adults can learn not only
to prioritize spatial locations where task-relevant stimuli are more
often encountered but also to deprioritize those spatial locations
where salient, yet task-irrelevant, distractors appear more frequently
(Di Caro & Della Libera, 2021;Di Caro et al., 2019;Ferrante et al.,
2018;Goschy et al., 2014;Leber et al., 2016;Sauter et al., 2018,
2019,2021;Wang & Theeuwes, 2018a,2018b). However, we did
not find traces of suppression history during the test phase, once the
task contingencies have been eliminated. This result replicate
previous findings adopting the same experimental protocol by Di
Caro and Della Libera (2021), where the survival of suppression
history in the short term was evident just in the oculomotor behavior,
but not in the RTs of manual task responses (see Supplemental
Materials 2;Di Caro & Della Libera, 2021), thus suggesting higher
sensitivity of eye movement measures in detecting SL effects. In this
sense, it would be interesting in future studies to replicate the present
findings by comparing the younger versus older adults’oculomotor
performance.
More interestingly, we demonstrated for the first time that older
adults do not benefit from learning of statistical regularities associ-
ated with task-irrelevant visual information to guide spatial atten-
tion. It is important to note that adopting the same experimental
context allow us to make a direct comparison between the effect on
visual search of SL of task-relevant versus task-irrelevant informa-
tion, thus confirming different, age-related subsystems of implicit
experience-driven spatial attention. The asymmetry that we demon-
strated with the present findings may indicate that at least for stimuli
that can be clearly and unequivocally identified as task relevant or
task-irrelevant, those type of experience-driven attention mechan-
isms seem to follow different age-related trajectories, which may be
supported by independent neural correlates. Congruently, in two
recent electrophysiological studies, van Moorselaar et al. (2020,
2021) demonstrated that the neural effects of predictions based on
regularities in the environment depend both on the dimension (i.e.,
whether it is spatial or feature) and the task relevance (i.e., whether it
is the target or the distractor) of these regularities, thus suggesting
different neural architectures depending on the characteristics of
visual predictions. Furthermore, the asymmetry that we observed
indicated that, on the one hand, both younger and older adults are
able to implement proactive enhancement mechanisms to boost the
selection of high-frequent, task-relevant visual information. On the
other hand, they differently engage proactive suppression mechan-
isms to inhibit the location that frequently contains task-irrelevant
information, such that within the spatial priority map that location
competed less for attention. These results can be ascribed to the age-
related decline in the ability to proactively ignore distracting
information (Ashinoff et al., 2020;Ashinoff, Tsal, et al., 2019;
Braver, 2012;Lustig & Jantz, 2015). In line with this interpretation,
Ashinoff et al. (2020) recently demonstrated that when old parti-
cipants have to inhibit a salient distractor, they engaged dorsal
frontoparietal regions as the younger participants; however, the left
temporoparietal junction and the inferior frontal gyrus, which are
part of the ventral attention network, are uniquely engaged in old
participants, thus indicating that the parietal contribution to salience
suppression is modulated by age, with the ventral frontoparietal
network engaged in addition to a dorsal network. The authors
interpreted this result as a predominant recruitment of reactive
inhibitory mechanisms for distractor suppression in older adults.
Thus, we can hypothesize that favoring a reactive suppression
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AGE-RELATED DIFFERENCES IN STATISTICAL LEARNING 11

mechanism when you must deal with a salient distractor in the older
population prevents the activation of proactive inhibitory mechan-
isms crucial to highlight the differences related to distractor fre-
quency manipulation at the behavioral level.
As alternative interpretation of the present findings, we may hypoth-
esize that performing the two tasks required older and younger
participants different perceptual resources to process the relevant
information (Ball et al., 1988;Lavie et al., 2004). For the younger
group, the processing of the relevant information (i.e., target) is easier
and demands few resources, leaving enough resources to further
process the contingencies associated with distractor spatial locations.
In contrast, for the older group, the process of the relevant information
is perceptually more demanding, and there would be no resources left to
process the irrelevant information and its spatial location (Lavie, 1995;
Lavie & Tsal, 1994;Madden & Langley, 2003;Manini et al., 2021).
Importantly, the results of the second experiment may thus reflect a
failure to learn distractor statistical regularities in older adults. Alterna-
tively, older adults may indeed learn the contingencies, but fail to
implement proactive inhibition mechanisms, keeping a reactive strategy
to suppress distractors and therefore preventing seeing the effects
observed in younger adults. With the present experimental design,
we cannot disentangle between these two accounts, and it remains a
crucial aspect to investigate in future studies. Furthermore, future
research will have to elucidate how expectation of task-relevant versus
task-irrelevant visual information influences target selection and dis-
tractor inhibition at the neural level in older populations. These
observations, together with the present findings, will help clarify the
brain network responsible for adaptively and flexibly responding to
visual stimuli, providing further evidence of the neural underpinning of
experience-driven attentional mechanisms in healthy aging. Finally, a
possible limitation of the study lies in the between-subject experimental
design adopted for testing selection versus suppression history effects.
Indeed, a within-subject design would have been some advantages in
terms of possibility to directly compare experience-driven effects of
selection and inhibition. However, considering that the experiments
were conducted online and administered to an old cohort, we decided to
perform the two experiments separately. Nonetheless, it would be
interesting for future studies to have an experiment that manipulates the
key variables (selection vs. suppression) in a within-subject design.
Furthermore, the lack of additional information about race and ethnicity
of participants, as well as any information related to background
cognitive ability of the older population (e.g., vocabulary and fluid
ability) may be considered as a limitation of this study (but see Jugert
et al., 2022). It would be interesting for future studies to investigate
whether these individual factors can affect implicit attentional SL
mechanisms in both populations.
In conclusion, our data show that taking advantage of SL
regularities to guide visual spatial attention is not uniformly spared
or impaired in normal aging. Instead, it seems strictly dependent on
the specific attentional processes involved in the acquisition bias,
that is, target selection and distractor suppression. In particular, the
present finding suggests preserved proactive enhancement mechan-
isms in older adult, but not proactive inhibition mechanisms, that
ultimately allow them to increase visual selective attention. This can
have important practical implications, suggesting for instance to
favor the consistency of spatial placements of important and relevant
information and not of irrelevant information, when older adults are
faced with complex visual scenes that required to inhibit irrelevant,
yet grabbing visual distractors.
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Received May 23, 2022
Revision received February 3, 2023
Accepted February 6, 2023 ▪
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AGE-RELATED DIFFERENCES IN STATISTICAL LEARNING 15