Vol.: (0123456789)
1 3
GeroScience
https://doi.org/10.1007/s11357-023-00953-9
SHORT COMMUNICATION
Practice makes imperfect: stronger implicit interference
with practice in individuals at high risk of developing
Alzheimer’s disease
Shao‑Min Hung
· Sara W. Adams
· Cathleen Molloy · Daw‑An Wu
·
Shinsuke Shimojo
· Xianghong Arakaki
Received: 17 May 2023 / Accepted: 19 September 2023
© The Author(s) 2023
Abstract
Early screening to determine patient risk
of developing Alzheimer’s will allow better interven-
tions and planning but necessitates accessible meth-
ods such as behavioral biomarkers. Previously, we
showed that cognitively healthy older individuals
whose cerebrospinal fluid amyloid/tau ratio indicates
high risk of cognitive decline experienced implicit
interference during a high-effort task, signaling early
changes in attention. To further investigate atten-
tion’s effect on implicit interference, we analyzed
two experiments completed sequentially by the same
high- and low-risk individuals. We hypothesized that
if attention modulates interference, practice would
affect the influence of implicit distractors. Indeed,
while both groups experienced a strong practice
effect, the association between practice and interfer
-
ence effects diverged between groups: stronger prac-
tice effects correlated with more implicit interfer
-
ence in high-risk participants, but less interference in
low-risk individuals. Furthermore, low-risk individu-
als showed a positive correlation between implicit
interference and EEG low-range alpha event-related
desynchronization when switching from high- to
low-load tasks. This suggests that lower attention on
the task was correlated with stronger interference, a
typical phenomenon in the younger population. These
results demonstrate how attention impacts implicit
interference and highlight early differences in percep-
tion between high- and low-risk individuals.
Keywords
Pre-symptomatic Alzheimer’s disease ·
Implicit processing · Practice · Aging
Shao-Min Hung and Sara W. Adams contributed equally.
Supplementary Information
The online version
contains supplementary material available at
https:// doi.
org/ 10. 1007/ s11357- 023- 00953-9
.
S.-M. Hung (
*
)
Waseda Institute for Advanced Study, Waseda University,
Tokyo, Japan
e-mail: smhung@aoni.waseda.jp
S.-M. Hung
Faculty of Science and Engineering, Waseda University,
Tokyo, Japan
S.-M. Hung · S. W. Adams · D.-A. Wu · S. Shimojo (
*
)
Biology and Biological Engineering, California Institute
of Technology, Pasadena, CA, USA
e-mail: sshimojo@caltech.edu
C. Molloy · X. Arakaki (
*
)
Cognition and Brain Integration Laboratory, Department
of Neurosciences, Huntington Medical Research Institutes,
Pasadena, CA, USA
e-mail: xianghong.arakaki@hmri.org
S. Shimojo
Computation and Neural Systems, California Institute
of Technology, Pasadena, CA, USA
GeroScience
1 3
Vol:. (1234567890)
Introduction
Because current options for addressing Alzheimer’s dis-
ease are limited to preventative measures or palliative
treatments, diagnostic methods that can screen for indi-
viduals at high risk of cognitive decline are critical for
minimizing disease progression and allowing patients
to make decisions about their future care while they are
still cognitively healthy. Current methods of identify
-
ing such individuals are primarily limited to examining
latent pathological changes such as levels of cerebrospi-
nal fluid (CSF) biomarkers tau and amyloid beta (A
β
42
).
Although these biomarkers are well-established in the
literature and may precede the onset of symptoms by
over a decade [
1
], their potential use for widespread
screening is limited by the expense and invasiveness of
the lumbar puncture required for measurement. Behav
-
ioral biomarkers could bypass this problem but intro-
duce a new issue: given that most high-risk individuals
are cognitively healthy prior to the onset of symptoms,
it is extremely difficult to detect disease-dependent cog-
nitive decline at the preclinical stage.
In a recent study, we investigated a possible solu-
tion by measuring how cognitively healthy high-risk
and low-risk (defined by CSF A
β
42
/total tau protein
ratio) older individuals responded to implicit dis-
tracting visual stimuli [
2
]. Motivated by studies that
demonstrate immense interaction between attention
and implicit processing in healthy young individuals
[
3
,
4
], we hypothesized that early attentional changes
could be revealed by evaluating how implicit infor
-
mation was processed in different individuals.
Indeed, we found that cognitively healthy older
individuals at high risk of cognitive decline experi-
enced interference from an implicit (unseen) stimu-
lus when the task difficulty was high. That is, par
-
ticipants’ reaction time slowed after an incongruent
distractor was presented, even when they were not
consciously aware of its existence. However, low-
risk cognitively healthy older individuals were not
susceptible to this implicit distraction. Furthermore,
explicit distracting information (e.g., Stroop effect)
interfered with the performance of both groups at a
similar level. Taken together, these results suggest
that early changes in implicit cognition are associ-
ated with the risk of cognitive decline, likely linked
to Alzheimer’s. Specifically, the inability to suppress
implicit distractions may be able to differentiate
high-risk individuals from those at low risk of cog-
nitive decline. Furthermore, successful distractor
suppression in high-risk individuals depends on
how attention is used in a task. Implicit distracting
information only interferes with performance when
the task load is high, suggesting that the explicit
task and implicit sensory information compete for
attentional resources. However, a competing theory
is that high-risk individuals actively process implicit
information. That is, during high-load situations,
high-risk individuals direct additional attention to
the explicit task, as reflected by a stronger change
in alpha frequency in previous studies [
5
,
6
]. Fur
-
thermore, such excessive attention spills over to the
implicit stimulus, causing active processing.
Since attention deployment is at the core of implicit
processing, here we further investigated whether a
reduction of task load due to practice interacted with
implicit interference. In a related study with healthy
young participants, it was shown that short-term
practice modulated the strength of implicit interfer
-
ence [
4
]. These findings provide a solid foundation
for the current study, in which we examined existing
data from two similar task-switching experiments [
2
,
5
] completed sequentially by the same cognitively
healthy older individuals. The participants’ risk status
for cognitive decline associated with Alzheimer’s dis-
ease had been previously determined for a longitudi-
nal study and defined as cognitively healthy with nor
-
mal A
β
42
/total tau protein ratio (CH-NATs, low-risk)
or pathological ratio (CH-PATs, high-risk). Our prior
EEG study also showed that high-risk and low-risk
older individuals exhibited alpha event-related desyn-
chronization (ERD) differences, suggesting different
levels of attentional engagement in a working mem-
ory task [
7
], Stroop [
6
], and task switching [
5
]. If
successful distractor suppression was key to differen-
tiating high-risk and low-risk individuals, we hypoth-
esized that high-risk individuals would especially
benefit from having additional attentional resources
after practice to suppress distracting implicit infor
-
mation. Similarly, the practice would protect low-risk
older individuals from distraction. On the other hand,
if high-risk individuals were indeed actively process-
ing implicit information, we hypothesized that the
additional attentional resources released from prac-
tice would be directed toward implicit cues, which in
turn would cause stronger interference.
GeroScience
1 3
Vol.: (0123456789)
Methods
Participants
We examined data previously collected from thirty-
six cognitively healthy older (age range: 53–92)
participants with no motor or vision difficulties who
completed two prior studies [
2
,
5
] consisting of two
separate experimental sessions with identical explicit
tasks. In session one, participants performed the
behavioral task with concurrent scalp EEG recorded
[
5
]. In session two, participants performed the behav
-
ioral task with the addition of an implicit prime [
2
].
Four individuals who only participated in the second
session were excluded from the present study. Each
session lasted approximately 50 min. The experi-
menters and the participants were oblivious to the
physiological risk status of the participants (i.e., dou-
ble-blinded). The institutional review boards (IRB) of
the California Institute of Technology (IR19-0963)
and the Huntington Medical Research Institutes
(HMRI) (Quorum IRB, #27197) approved this study.
All participants gave written consent prior to partici-
pation. The final analysis included 17 CH-NATs and
19 CH-PATs (see “
Physiological status classification
”
section) for behavioral analysis and 14 CH-NATs
and 16 CH-PATs for EEG data analysis (six were
excluded because of artifacts). Participants’ demo-
graphics, including their age, sex, education year,
and neuropsychological test scores, were reported
in a previous study [
2
]. In each analysis, the demo-
graphics were comparable between the two groups
(Table
1
, all
p
> .05).
Physiological status classification
A complete description of classification, including a
list of neuropsychological testing, MRI diagnosis of
small vessel disease, and the biochemical analysis of
lumbar CSF, was detailed in several previous stud-
ies [
5
,
6
,
8
]. In the structural scans, all participants
were awake and none of the participants showed
excessive head motion. The inspection for head
motion occurred during the scan by visual assess-
ment performed by the MRI technologist and also
during the analysis of the images by the radiologist
by visual inspection. In short, only participants who
were determined to be without (1) cognitive impair
-
ment (Clinical Dementia Rating) or (2) psychiatric or
neurological disorders were included in the current
study. Different cognitive domains including mem-
ory, executive function, and language were tested.
The CSF samples were run on both Innotest (Innoge-
netics, discontinued) and MSD platforms (K15121G,
Table 1
Demographics, post-study subjective effort exertion report, resting pulse pressure, and heart rate of CH-NATs (low-risk)
and CH-PATs (high-risk)
The two groups were comparable in each item with
p
> 0.05. The data is reported as mean (
SD
)
Behavioral data demographics
CH-NATs (low-risk)
CH-PATs (high-risk)
Total number
17
19
Age
77.29 (8.01)
74.11 (9.22)
Gender
12 females, 5 males
16 females, 3 males
Education year
16.18 (2.10)
16.89 (2.56)
Days between two sessions
15.29 (1.68)
15.84 (2.87)
Resting pulse pressure (systolic-diastolic)
48.38 (14.00)
50.89 (12.72)
Resting heart rate
65.31 (10.56)
66.79 (6.67)
Post-study questionnaire
0: Not at all — 3: Severe
Did you have any physical troubles during the task?
0.12 (0.33)
0.36 (0.64)
Difficulty: Word responding to word responding
1.12 (0.72)
1.14 (0.74)
Difficulty: Color responding to color responding
1.03 (0.67)
1.22 (0.71)
Difficulty: Word responding to color responding
1.29 (0.79)
1.50 (0.69)
Difficulty: Color responding to word responding
1.32 (0.64)
1.58 (0.65)
How much effort did you use for the task?
1.88 (0.76)
1.97 (0.65)
Do you feel tired after the task?
0.71 (0.85)
0.78 (0.86)
GeroScience
1 3
Vol:. (1234567890)
MSD) to determine total tau and A
β
42
. Participants
were then classified depending on individual CSF
A
β
42
/total tau ratios compared to a cutoff value
(2.7132) derived from a logistic regression model
that correctly diagnosed > 85% of clinically probable
AD participants [
8
]. Amyloid/total tau ratios below
the cutoff value classified participants as
c
ognitively
h
ealthy–
p
athological
a
myloid/
t
au, i.e., CH-PATs,
while participants whose ratios were above the cutoff
value were classified as
c
ognitively
h
ealthy–
n
ormal
a
myloid/
t
au, i.e., CH-NATs [
2
,
5
,
6
,
8
].
Experimental design
Participants completed both experiment sessions in
the same quiet room. The visual stimuli were gener
-
ated with E-Prime (Psychology Software Tools, Inc.)
on a Dell Precision T5610 with a 20
′′
screen. The par
-
ticipants’ movements were unconstrained, and they
observed the visual stimuli from approximately 50
cm away.
In each trial, participants responded to two con-
secutive Stroop stimuli, i.e., words whose colors
were incongruent with their literal meaning, such
as the word RED written in green (Fig.
1
). Partici-
pants were asked to name the color when the word
was underlined (color-naming) and to name the word
when the word was not underlined (word-naming).
Only word-color incongruent stimuli were used.
When the two stimuli prompted different tasks (e.g.,
word-naming for the first, and color-naming for the
second, or vice versa), the trial was considered task-
switching. Otherwise, if the tasks were the same, it
was a non-task-switching trial. Both the Stroop and
the task-switching components were introduced to
create task load differences.
Participants were instructed to respond as accu-
rately as possible and were given ample time for each
response (up to 5 s). Although participants needed
to respond to both stimuli, only the second stimulus
in a pair was considered the “target” for the purpose
of our analysis because the task-switching effect and
implicit interference only applied to this stimulus.
Both reaction time and accuracy were measured for
the target response. In the second experimental ses-
sion, an additional gray word stimulus (17 ms) was
added between the existing word stimuli with forward
and backward masks (50 ms each). The masked word
was either congruent or incongruent with the second
word (target); congruent stimuli prompted the same
response. However, no task was given to the masked
word. Therefore, the explicit aspects of the tasks in
Fig. 1
An example trial sequence (modified from [
2
] Fig.
1
B).
This is a task-switching trial (from word-naming to color-nam-
ing) with a congruent masked distractor. Each trial began with
a 500-ms fixation, followed by the appearance of the first word
stimulus for 5000 ms or until response. In the second experi-
mental session only, a masked distractor (shown in the dashed
box) was presented before the second word stimulus (i.e., tar
-
get). The masked word was presented for 17 ms either above
or below the fixation cross, sandwiched between two 50-ms
masks consisting of random letters. No task was given to the
masked distractor. After the first stimulus (experiment one) or
distractor (experiment two), the screen was blank for 500 ms
before the target was displayed for another 5000 ms or until
response. The only difference between the two experiments
was the masked distractor presentation. SOA: stimulus onset
asynchrony
GeroScience
1 3
Vol.: (0123456789)
each session were largely identical. More details of
experimental design, rationale, and analysis are pro-
vided in the original study [
2
].
At the start of each session, participants underwent
a short period of practice until they felt comfortable
proceeding to the main experiment. The experiments
consisted of three blocks of 64 trials. In the second
session, a surprise post-experiment awareness test
was performed to assess the invisibility of the masked
words. During this test, participants were asked to
judge the location (top/bottom) of the masked stimu-
lus in a two-alternative forced-choice manner. Chance
performance on this test indicated a lack of conscious
awareness of the distractor.
Electroencephalogram (EEG) recordings
The majority of participants had EEG data collected
during the first experiment [
5
]. The data analysis is
detailed in the original study, so here only critical
information is provided. The signals were acquired
with a 21-head-sensor dry electrode system [
5
–
7
].
Sensors were placed in five main locations, including
the frontal, temporal, central, parietal, and occipital
regions. The signals were sampled at 300 Hz and later
bandpass filtered to exclude frequencies above 150 Hz
and below 0.003 Hz. In the current study, we focused
on alpha event-related desynchronization (ERD)
because several of our previous studies showed that
alpha ERD differentiated between CH-PATs and CH-
NATs. Specifically, we previously identified stronger
(more negative) alpha ERD in CH-PATs, as com-
pared to CH-NATS, during working memory tasks
[
7
] and Stroop tasks [
5
,
6
], indicating hyperactivity
during challenging tasks. We hypothesized that if
alpha ERD reflects an attentional component during
active engagement of a task, its value would correlate
with implicit interference.
Results
Original key findings replicated: stronger implicit
interference in CH-PATs
The analyses below are reported in the format of
the mean (standard error of the mean [SEM]) unless
otherwise specified. The implicit interference was
deemed the target reaction time slowing caused by
an incongruent implicit distractor, as compared to a
congruent implicit prime. Because only thirty-six out
of forty participants from the previous study were
considered here, we repeated the same analyses to
ensure that the results were similar. Indeed, the mean
accuracy of the awareness test was 44.58% (2.32%)
and slightly below chance (compared to 50%,
t
(29)
= −2.33,
p
= 0.03), confirming the implicit nature of
the distractor. No difference in distractor awareness
was found between CH-NATs (44.26 (3.8) %) and
CH-PATs (45.00 (2.21) %) (two-sample
t
-test,
t
(28)
= 0.1543,
p
= 0.88). Similarly, we reproduced the
key finding that during the high-load, color-naming
condition, CH-PATs had more interference from an
implicit response-incongruent distractor than CH-
NATs (CH-PATs: 4.21 (1.51) % change in reaction
time,
t
(18) = 2.79,
p
= 0.01; CH-NATs: −0.31 (1.1)
% change in reaction time,
t
(16) = −0.28,
p
= 0.78).
A direct comparison between the two yielded
t
(34) =
2.37,
p
= 0.02, Cohen’s
d
= 0.81.
Practice has distinct effects on CH-PATs and
CH-NATs
Both groups exhibited a practice effect in the second
session. Overall, between the two sessions, the aver
-
age reaction time decreased by 6.19% (3.04%), while
the average accuracy increased by 6.09% (1.40%).
The time interval between the two sessions was
comparable across the two groups (CH-PATs: 15.84
(2.87) days; CH-NATs: 15.29 (1.68) days,
t
(34) =
0.16,
p
= 0.87).
We investigated whether practice over time differ
-
entially affects performance of the low-risk and high-
risk participants. To this end, we first performed two
separate mixed-effect analyses of variance (ANOVAs)
on the
mean
accuracy and on the reaction time with
one between-subject factor (participant status: CH-
NATs/CH-PATs) and one within-subject factor (ses-
sion number: one/two). The following data are col-
lapsed across all three factors in the original design
(task switching vs. non-switching, word-naming vs.
color-naming, and incongruent vs. congruent distrac-
tors). Analysis of the accuracy yielded a main effect
of session number,
F
(1, 34) = 21.66,
p
< 0.0001 ,
η
p
2
= 0.39, and an interaction between session and partic-
ipants’ status,
F
(1, 34) = 9.03,
p
= 0.005 ,
η
p
2
= 0.21.
The analysis of reaction time yielded a main effect
of session,
F
(1, 34) = 4.20,
p
= 0.048 ,
η
p
2
= 0.11.
GeroScience
1 3
Vol:. (1234567890)
Post hoc analyses revealed that CH-PATs exhibited
a stronger practice effect in terms of accuracy (CH-
NATs: 2.08% (1.36%) vs. CH-PATs: 9.67% (2.05 %),
t
(34) = −3.01,
p
= 0.005) but not reaction time (CH-
NATs: 4.06% (4.44%) vs. CH-PATs: 8.10% (4.24%),
t
(34) = -0.66,
p
= 0.52).
To address the impact of practice on implicit
interference, we ran a correlation analysis (Pearson’s
correlation) between the implicit distractor interfer
-
ence effect and the practice effect in CH-NATs and
CH-PATs, respectively. For the purpose of this analy
-
sis, the practice effect was defined as the percentage
decrease of reaction time in the second session com-
pared to the first because the interference effect was
only observed in the domain of reaction time. Please
note that the implicit distractor interference was cal-
culated entirely based on the results of the second ses-
sion. By converting reaction times into percentages
for both interference and practice effects, baseline
performance differences and the long tails in typical
reaction time distributions were eliminated. Analysis
of the CH-NATs yielded a marginal, negative corre-
lation between practice and implicit interference (
r
= −0.46,
p
= 0.06). However, the same analysis on
CH-PATs yielded a positive correlation between the
two (
r
= 0.50,
p
= 0.03). We calculated
z
-scores for
the
r
values and directly compared the two groups’
correlations with a two-tailed test, which resulted in a
p
-value of 0.004. These results showed that the inter
-
action between practice and the implicit interference
was distinct between CH-NATs and CH-PATs: bet-
ter performance in the second session was correlated
with less implicit distraction in CH-NATs, while for
CH-PATs, better performance in the second session
led to stronger distraction (Fig.
2
, left).
Greater low
-range alpha ERD difference between
task
-switching and non-switching trials on EEG
in session one is correlated with interference in
CH-NATs
In the first session, EEG data was analyzed for the
majority of participants (14 of 17 CH-NATS and 16
of 19 CH-PATs), which allowed us to determine the
correlation between prior EEG data and later behav
-
ioral performance in these two groups. To minimize
multiple comparisons and to focus on an attention
component, we examined the most distinctive alpha
event-related desynchronization (ERD) difference
between the CH-NATs and CH-PATs. In the origi-
nal study, we calculated the difference in the central
area (C3, Cz, C4) alpha ERD between high-load
task-switching trials and low-load non-switching tri-
als (alpha ERD (switching)–alpha ERD (non-switch-
ing)). Note that ERD is a reduction of power, and a
stronger (more negative) ERD is therefore a stronger
Fig.
2 Left
Distinct correlations between practice effect
(percentage decrease of reaction time in the second session;
positive values indicate a stronger practice effect, i.e., more
decrease in reaction time,
x
-axis) and implicit interference
(percentage decrease of reaction time between incongruent dis-
tractor and congruent prime; positive values indicate stronger
interference, i.e., more increase in reaction time, y-axis) in
CH-NATs (low-risk) and CH-PATs (high-risk).
Right
Positive
correlation between lower alpha (8-11 Hz) ERD (event-related
desynchronization) difference (
x
-axis) and the implicit interfer
-
ence (
y
-axis) was observed only in CH-NATs. Each dot repre-
sents a participant. The dotted lines were linearly fitted to the
two correlations
GeroScience
1 3
Vol.: (0123456789)
reduction. For CH-PATs, the alpha ERD difference
was negative, indicating that these participants had
stronger low alpha ERDs during switching trials than
non-switching trials. For CH-NATs, the opposite was
observed: their alpha ERD difference was positive,
showing stronger low alpha ERDs during non-switch-
ing trials. Together, these results suggest that CH-
PATs have stronger attentional engagement during
task switching than on non-switching tasks, whereas
CH-NATs have more engagement when not changing
tasks. These results were also found with the reduced
cohort in our study (low alpha ERD mean difference
(
SD
), CH-PATs: −0.26 (0.63) vs. CH-NATs: 0.37
(0.67),
t
(28) = −2.68,
p
= 0.01, Cohen’s
d
= 1.00)
(Fig.
3
).
We ran a correlation analysis between the afore-
mentioned low alpha ERD difference in the first ses-
sion and the implicit interference in the second session
in both groups. CH-PATs had no correlation between
their low alpha ERD difference and implicit interfer
-
ence (
r
= −0.16,
p
= 0.54). In contrast, there was a
positive correlation between low alpha ERD differ
-
ence and implicit interference in CH-NATs (
r
= 0.56,
p
= 0.04) (Fig.
2
, right). A further
z
-score compari-
son between the two correlations revealed a
p
-value
of 0.05. These results showed that the CH-NATs who
exhibited a higher low-range alpha ERD difference in
the first session also experienced stronger distraction
in the second session. This finding suggests that in
low-risk participants, having lower attentional engage-
ment in switching trials was associated with stronger
implicit interference. A framework for how CH-PATs
and CH-NATs utilized attentional resources differ
-
ently is provided in "
Discussion
" section.
Discussion
In our two original studies, we examined the elec-
trophysiological signatures [
5
] during a cognitively
demanding task as well as the behavioral response
to implicit distracting information [
2
] in cognitively
healthy older individuals with a high risk (CH-PATs)
or low risk (CH-NATs) of cognitive decline. The
largely identical experimental designs and partici-
pants of these studies created a unique opportunity
for the current study to investigate if practice or the
EEG signatures found in the first session were asso-
ciated with implicit interference. Overall, we found
that practice had distinct effects on high-risk and
low-risk individuals. While the practice effect was
negatively correlated with interference in the low-risk
participants, a positive correlation was found in the
high-risk participants. Furthermore, in low-risk par
-
ticipants, stronger interference was associated with
a greater low-range alpha ERD difference between
task-switching and non-switching trials, suggesting
that lower attentional engagement in switching tri-
als is associated with susceptibility to interference.
Although the high-risk participants exhibited similar
implicit inteference to what has been observed in a
healthy young population [
4
], our study provides evi-
dence that an increase in implicit interference with
decreased task load is not a part of normal aging.
Instead, these changes provide a potential indicator
for risk of cognitive decline.
Our original study showed that, albeit being cogni-
tively healthy, high-risk individuals were distracted to a
higher extent by a subliminal cue [
2
]. One likely expla-
nation of this phenomenon is that directing additional
attention toward the sensory environment could help
maintain task performance and keep high-risk indi-
viduals overtly cognitively healthy, but this strategy
comes at the expense of processing irrelevant sensory
information when subliminal stimuli are incongruent.
This interpretation suggests an active processing of
subliminal information in high-risk individuals, which
CH-NATs
CH-PAT
s
-2
-1
0
1
2
alpha ERD (dB power)
EEG in first session
Fig. 3
Alpha ERD difference between task-switching and
non-switching trials in session one in CH-NATs and CH-PATs.
On average, CH-PATs exhibited a stronger ERD (reduction in
power) during task-switching than non-switching trials, while
CH-NATs showed the opposite
GeroScience
1 3
Vol:. (1234567890)
is further supported in the current study. The opposite
correlations between practice and implicit interference
in low-risk and high-risk individuals indicate distinct
underlying cognitive infrastructures in these two popu-
lations. If practice reduces cognitive load, individu-
als should have more cognitive resources available in
the second session. Based on our results, low-risk and
high-risk individuals utilized these additional cogni-
tive resources differently. Low-risk individuals learned
to better inhibit distracting implicit information, while
more practice in high-risk individuals led to stronger
interference, possibly due to additional processing of
distracting implicit information. The higher attentional
commitment in high-risk individuals is demonstrated
by a stronger alpha ERD compared to low-risk individ-
uals during various challenging tasks [
5
–
7
], including
the first session of our study. In the second session, we
speculate that high-risk individuals used the cognitive
resources that were freed by practice to take in addi-
tional sensory information such as subliminal cues.
Future research is needed to unravel the neural machin-
ery of the attention network in high-risk individuals.
We believe that the opposite effects of practice on
implicit interference in high-risk and low-risk popu-
lations provide valuable data for working toward
possible future interventions. Theoretically, practice
leads to an increase in available attentional resources
due to decreased task load. The key finding of the
current study is that the use of this additional atten-
tion differs in high- and low-risk populations, which
could signal different cognitive or perceptual strate-
gies. Recently, a number of studies have conducted
cognitive training as well as repetitive transcranial
magnetic stimulus (rTMS) as interventions to com-
bat the cognitive decline in Alzheimer’s disease
[
9
–
11
]. Specifically, most of these studies focused
on improving higher-level cognitive abilities, such
as memory or language, that are directly relevant to
the clinically observable cognitive decline in Alzhei-
mer’s. However, our findings suggest that cognitive
decline at earlier stages could be perceptual; that is,
the decline could influence how one’s sensory system
selects and suppresses information in the environ-
ment. Therefore, can we minimize the effects of this
decline by training perceptual pathways in high-risk
individuals? Visual perceptual learning, defined as
long-term improvement resulting from visual experi-
ence, has shed light on this question. In the domain
of visual perceptual learning, behavioral and neural
improvement have been shown toward impercepti-
ble visual stimuli after a prolonged training period.
Chang et al. [
12
] showed post-training improve-
ment in a coherent motion discrimination task in
both younger and older participants. Importantly,
both groups showed improvement when the motion
was sub-threshold (i.e., not consciously perceived).
Learning plasticity and flexibility of visual process-
ing in the older individuals have also been demon-
strated through structural changes in the underlying
visual pathway. Yotsumoto et al. [
13
] showed that
after a 3-day texture discrimination training, both
older and younger participants exhibited behavio-
ral improvement. However, only older participants
exhibited white matter fiber density changes in the
early visual areas (i.e., V1-V3) as measured by frac-
tional anisotropy. Visual perceptual learning thus
provides a promising approach for training the older
individuals to use specific information, whether
supraliminal or subliminal, to support their perfor
-
mance. Critically, the finding that older individuals
can learn from irrelevant information that younger
individuals suppress [
12
] sheds light on the impor
-
tance of inhibitory control in filtering and encoding
external information in the older individuals.
In low-risk individuals, the positive correlation
between low alpha ERD difference between task-
switching and non-switching trials in the first session
and implicit interference in the second session illus-
trates the similarities between low-risk cognitively
healthy older adults and healthy young participants.
That is, when low-risk participants are more attentive
to a target, implicit interference is less likely to occur,
just as in younger individuals. Recently, with concur
-
rent tracking of target- and distractor-triggered alpha
oscillations, Gutteling and colleagues [
14
] showed
that distractor-triggered alpha oscillations increased
significantly when the target was more difficult to
process. These results from young individuals offer
another explanation regarding distractors: task load
could modulate alpha power in response to both dis-
tractors and targets. Future research on older individ-
uals is needed to disentangle the target and distrac-
tor effects and to strengthen the current results with a
larger sample size.
Taken together, however, our results provide evi-
dence that practice, although typically considered a pos-
itive effect, may uniquely influence high-risk individu-
als by increasing susceptibility to implicit interference.
GeroScience
1 3
Vol.: (0123456789)
In low-risk participants, however, practice appears to
remain positive as their behavioral and neurophysi-
ological data demonstrate high similarity with that of
younger individuals. How and when the practice effects
of the two groups bifurcate is a critical research ques-
tion for better understanding early cognitive decline in
Alzheimer’s.
Funding
We are grateful for the research funding provided
by the James Boswell Postdoctoral Fellowship, the Caltech
BBE Divisional Postdoctoral Fellowship, and the sub-award
under the Aligning Consciousness Research with US Funding
Mechanisms by Templeton World Charity Foundation (TWCF:
0495) to S-MH and LK, Whittier Foundation to HMRI, and
the National Institute of Health R56 (R56AG063857) and R01
(R01AG063857) to SS and XA.
Data availability
The current study was not preregistered.
The raw data and code are available upon reasonable request.
Declarations
Ethical approval
The institutional review boards (IRB) of the
California Institute of Technology (IR19-0963) and the Hun-
tington Medical Research Institutes (HMRI) (Quorum IRB,
#27197) approved this study.
Consent to participate
All participants gave written consent
prior to participation.
Competing interests
The authors declare no competing inter
-
ests.
Open Access
This article is licensed under a Creative Com-
mons Attribution 4.0 International License, which permits
use, sharing, adaptation, distribution and reproduction in any
medium or format, as long as you give appropriate credit to the
original author(s) and the source, provide a link to the Crea-
tive Commons licence, and indicate if changes were made. The
images or other third party material in this article are included
in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not
included in the article’s Creative Commons licence and your
intended use is not permitted by statutory regulation or exceeds
the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit
http://creativecommons.org/licenses/by/4.0/
.
References
1.
Mattsson-Carlgren N, Leuzy A, Janelidze S, Palmqvist S,
Stomrud E, Strandberg O, et al. The implications of dif-
ferent approaches to define AT(N) in Alzheimer disease.
Neurology. 2020;94(21):e2233–44.
2.
Hung S-M, Wu D-A, Shimojo S, Arakaki X. Stronger
implicit interference in cognitively healthy older partici-
pants with higher risk of Alzheimer’s disease. Alz Dem
Diag Ass Dis Mo. 2022;14(1).
https:// doi. org/ 10. 1002/
dad2. 12340
.
3.
Kiefer M, Martens U. Attentional sensitization of unconscious
cognition: task sets modulate subsequent masked semantic
priming. J Exp Psychol: Gen. 2010;139(3):464–89.
4.
Hung S-M, Wu D-A, Shimojo S. Task-induced attention
load guides and gates unconscious semantic interference.
Nat Commun. 2020;11(1):2088.
5.
Arechavala RJ, Rochart R, Kloner RA, Liu A, Wu D-A,
Hung S-M, et al. Task switching reveals abnormal brain-
heart electrophysiological signatures in cognitively
healthy individuals with abnormal CSF amyloid/tau, a
pilot study. Int J Psychophysiol. 2021;170:102–11.
6.
Arakaki X, Hung S-M, Rochart R, Fonteh AN, Harrington
MG. Alpha desynchronization during Stroop test unmasks
cognitively healthy individuals with abnormal CSF amy
-
loid/tau. Neurobiol Aging. 2022;112:87–101.
7.
Arakaki X, Lee R, King KS, Fonteh AN, Harrington MG.
Alpha desynchronization during simple working memory
unmasks pathological aging in cognitively healthy indi-
viduals. PLoS One. 2019;14(1):e0208517.
8.
Harrington MG, Chiang J, Pogoda JM, Gomez M,
Thomas K, Marion SD, et al. Executive function changes
before memory in preclinical Alzheimer’s pathology: a
prospective, cross-sectional, case control study. PLoS
One. 2013;8(11):e79378.
9.
Lee J, Choi BH, Oh E, Sohn EH, Lee AY. Treatment of
Alzheimer’s disease with repetitive transcranial magnetic
stimulation combined with cognitive training: a prospec-
tive, randomized, double-blind, placebo-controlled study.
J Clin Neurol. 2016;12(1):57.
10.
Vecchio F, Quaranta D, Miraglia F, Pappalettera C, Di Iorio
R, L’Abbate F, et al. Neuronavigated magnetic stimulation
combined with cognitive training for Alzheimer’s patients:
an EEG graph study. GeroScience. 2022;44(1):159–72.
11.
Cotelli M, Manenti R, Cappa SF, Geroldi C, Zanetti O,
Rossini PM, et al. Effect of transcranial magnetic stimula-
tion on action naming in patients with Alzheimer disease.
Arch Neurol. 2006;63(11):1602.
12.
Chang L-H, Shibata K, Andersen GJ, Sasaki Y, Watanabe
T. Age-related declines of stability in visual perceptual
learning. Curr Biol. 2014 Dec;24(24):2926–9.
13.
Yotsumoto Y, Chang L-H, Ni R, Pierce R, Andersen GJ, Wata-
nabe T, et al. White matter in the older brain is more plastic
than in the younger brain. Nat Commun. 2014;5(1):5504.
14.
Gutteling TP, Sillekens L, Lavie N, Jensen O. Alpha oscil-
lations reflect suppression of distractors with increased
perceptual load. Prog Neurobiol. 2022 Jul;214:102285.
GeroScience
1 3
Vol:. (1234567890)
Publisher’s Note
Springer Nature remains neutral with regard
to jurisdictional claims in published maps and institutional
affiliations.