Cognitive control is defined by a set of neural processes that allow us to interact
with our complex environment in a goal-directed manner
1
. Humans regularly challenge these control processes when attempting to simultaneously
accomplish multiple goals (i.e., multitasking), generating interference as the result
of fundamental information processing limitations
2
. It is clear that multitasking behavior has become ubiquitous in today’s technologically-dense
world
3
, and substantial evidence has accrued regarding multitasking difficulties and cognitive
control deficits in our aging population
4
. Here we show that multitasking performance, as assessed with a custom-designed 3-D
video game (NeuroRacer), exhibits a linear age-related decline from 20–79 years of
age. By playing an adaptive version of NeuroRacer in multitasking training mode, older
adults (60–85 y.o.) reduced multitasking costs compared to both an active control
group and a no-contact control group, attaining levels beyond that of untrained 20
year olds, with gains persisting for six months. Furthermore, age-related deficits
in neural signatures of cognitive control, as measured with electroencephalography,
were remediated by multitasking training (i.e., enhanced midline frontal theta power
and frontal-posterior theta coherence). Critically, this training resulted in performance
benefits that extended to untrained cognitive control abilities (i.e., enhanced sustained
attention and working memory), with an increase in midline frontal theta power predicting
the training-induced boost in sustained attention and preservation of multitasking
improvement six months later. These findings highlight the robust plasticity of the
prefrontal cognitive control system in the aging brain, and provide the first evidence
of how a custom-designed video game can be used to assess cognitive abilities across
the lifespan, evaluate underlying neural mechanisms and serve as a powerful tool for
cognitive enhancement.
In a first experiment, we evaluated multitasking performance across the adult lifespan.
174 participants spanning six decades of life (ages 20–79; ~30 individuals per decade)
played a diagnostic version of NeuroRacer to measure their perceptual discrimination
ability (’sign task’) with and without a concurrent visuomotor tracking task (‘driving
task’; see Supplementary Materials for details of NeuroRacer). Performance was evaluated
using two distinct game conditions: 1) ‘Sign Only’- respond as rapidly as possible
to the appearance of a sign only when a green circle was present, and 2) ‘Sign & Drive’-
simultaneously perform the sign task while maintaining a car in the center of a winding
road using a joystick (i.e., ‘drive’; see Figure 1a). Perceptual discrimination performance
was evaluated using the signal detection metric of discriminability (d'). A ‘cost’
index was used to assess multitasking performance by calculating the percentage change
in d’ from ‘Sign Only’ to ‘Sign & Drive’, such that greater cost (i.e., a more negative
% cost) indicates increased interference when simultaneously engaging in the two tasks
(see Methods Summary).
Prior to the assessment of multitasking costs, an adaptive staircase algorithm was
used to determine the difficulty levels of the game at which each participant performed
the perceptual discrimination and visuomotor tracking tasks in isolation at ~80% accuracy.
These levels were then used to set the parameters of the component tasks in the multitasking
condition, so that each individual played the game at a customized challenge level.
This assured that comparisons would inform differences in the ability to multitask,
and not merely reflect disparities in component skills (see Methods, Supplementary
Figures 1 & 2, and Supplementary Materials for more details).
Multitasking performance diminished significantly across the adult lifespan in a linear
fashion (i.e., increasing cost, see Figure 2a and Supplementary Table 1), with the
only significant difference in cost between adjacent decades being the increase from
the 20s (−26.7% cost) to the 30s (−38.6% cost). This deterioration in multitasking
performance is consistent with the pattern of performance decline across the lifespan
observed for fluid cognitive abilities, such as reasoning
5
and working memory
6
. Thus, using NeuroRacer as a performance assessment tool we replicated previously
evidenced age-related multitasking deficits
7,8
, and revealed that multitasking performance declines linearly as we advance in age
beyond our twenties.
In a second experiment, we explored if older adults who trained by playing NeuroRacer
in multitasking mode would exhibit improvements in their multitasking performance
on the game
9,10
(i.e., diminished NeuroRacer costs). Critically, we also assessed if this training
transferred to enhancements in their cognitive control abilities
11
beyond those attained by participants who trained on the component tasks in isolation.
In designing the multitasking training version of NeuroRacer, steps were taken to
maintain both equivalent difficulty and engagement in the component tasks to assure
a prolonged multitasking challenge throughout the training period: difficulty was
maintained using an adaptive staircase algorithm to independently adjust the difficulty
of the ‘sign’ and ‘driving’ tasks following each 3-min run based on task performance,
and balanced task engagement was motivated by rewards given only when both component
tasks improved beyond 80% on a given run.
We assessed the impact of training with NeuroRacer in a longitudinal experiment that
involved randomly assigning 46 naïve older adults (60–85yrs: 67.1yrs ± 4.2) to one
of three groups: Multitasking Training (MTT; n=16), Singletask Training (STT; n=15)
as an active control, or No-Contact Control (NCC; n=15). Training involved playing
NeuroRacer on a laptop at home for 1 hour a day, 3 times a week for 4 weeks (12 total
hours of training), with all groups returning for a 1 month Post-training and a 6
month follow-up assessment (Figure 1b). The MTT group played the ‘Sign & Drive’ condition
exclusively during the training period, while the STT group divided their time between
a “Sign Only” and a “Drive Only” condition, and so were matched for all factors except
the presence of interference. In addition to a battery of cognitive control tests
used to assess the breadth of training benefits (see Supplementary Table 2), the neural
basis of training effects was evaluated using electroencephalography (EEG) recorded
at Pre- and Post-training visits while participants performed a neural assessment
version of NeuroRacer.
Analysis showed that only the MTT group’s multitasking performance index significantly
improved from Pre- (−64.2% cost) to Post-training (−16.2% cost; Figure 2b), thus supporting
the role of interference during game play as a key mechanistic feature of the training
approach. In addition, although cost reduction was observed only in the MTT group,
equivalent improvement in component task skills was exhibited by both STT and MTT
(see Supplemental Figures 4 and 5). This indicates that enhanced multitasking ability
was not solely the result of enhanced component skills, but a function of learning
to resolve interference generated by the two tasks when performed concurrently. Moreover,
the d' cost improvement following training was not the result of a task tradeoff,
as driving performance costs also diminished for the MTT group from Pre- to Post-training
(see Supplementary Materials). Notably in the MTT group, the multitasking performance
gains remained stable 6 months after training without booster sessions (at 6 months:
−21.9% cost). Interestingly, the MTT group’s Post-training cost improved significantly
beyond the cost level attained by a group of 20-year-olds who played a single session
of NeuroRacer (−36.7% cost; Experiment 3; p< .001).
Next, we assessed if training with NeuroRacer led to generalized enhancements of cognitive
control abilities that are known to be impaired in aging (e.g., sustained attention,
divided attention, working memory; see Supplementary Table 2)
12
. We hypothesized that being immersed in a challenging, adaptive, high-interference
environment for a prolonged period of time (i.e., MTT) would drive enhanced cognitive
performance on untrained tasks that also demanded cognitive control. Consistent with
our hypothesis, significant group X session interactions and subsequent follow-up
analyses evidenced Pre- to Post-training improvements in both working memory (delayed-recognition
task with and without distraction
7
; Figure 3a,b) and sustained attention (i.e. vigilance; Test of Variables of Attention
(TOVA)
13
) only for the MTT group (Figure 3c; see Supplementary Table 2). In addition, there
were several statistical trends suggestive of improved Post-training performance on
other cognitive control tasks (dual-tasking, useful field of view, and change detection
task; see ANCOVAs in Supplementary Table 2). Note that although the working memory
and sustained attention improvements were documented as more rapid responses to test
probes, neither impulsivity (assessed with the alternate version of the TOVA) nor
accuracy results showed significant group differences, revealing that training effects
were not the result of a speed/accuracy trade-off. Importantly, these cognitive improvements
were specific to working memory and sustained attention processes, and not the result
of generalized increases in speed of processing, as no group X session interactions
were found on two processing speed tasks (a stimulus detection task and the digit
symbol substitution task; see Supplementary Table 2). Finally, only the MTT group
exhibited a significant correlation between multitasking cost reduction (assessed
with NeuroRacer) and improvements on an untrained cognitive control task (delayed-recognition
with distraction) from Pre- to Post-training (Figure 3d).
These important ‘transfer of benefits’ suggest that a common, underlying mechanism
of cognitive control was challenged and enhanced by MTT with NeuroRacer. To assess
this further, we examined the neural basis of training effects by quantifying event-related
spectral perturbations (ERSP) and long-range phase coherence time-locked to the onset
of each sign presented during NeuroRacer both Pre- and Post-training. We specifically
assessed midline frontal theta (4–7Hz), a well-described EEG measure of cognitive
control (e.g., working memory
14
, sustained attention
15
, and interference resolution
16
) localized to the medial prefrontal cortex. In addition, we analyzed long-range theta
coherence between frontal and posterior brain regions, a functional connectivity measure
also associated with cognitive control (e.g., working memory
14
and sustained attention
15
). Separate ANOVAs for theta power and coherence each revealed significant 3-way interactions
of condition (‘Sign & Drive’, ‘Sign Only’) X session (pre, post) X group (MTT, STT,
NCC; see Supplementary Figure 6). Further analysis revealed that for the ‘Sign & Drive’
condition, only the MTT group demonstrated a significant increase from Pre- to Post-training
in both neural measures (see Figure 4a & b). These findings are consistent with other
reports of training-driven modulations in prefrontal cortical activity of older adults
9,17
. Furthermore, the coherence results demonstrate for the first time modulation of
a neural network in response to cognitive training in older adults. These findings
evidence a shift in the rapid engagement of prefrontal cognitive control processes
less than 400 ms after a sign appears and prior to the motor response (see Supplementary
Figure 7 and Supplementary Table 1b), supporting training-induced neuroplasticity
as the mechanistic basis of these training effects.
As described above, both MTT and STT resulted in equivalent improvements on the NeuroRacer
component tasks (see Supplementary Table 2 and Supplementary Figure 4), while only
MTT led to broad enhancements both behaviorally (i.e., diminished multitasking costs,
improved sustained attention and working memory) and neurally (i.e., enhanced midline
frontal theta power and long-range coherence). This indicates that the training factor
driving these effects was the interference generated when participants were motivated
to engage in the two tasks simultaneously. Given that there were no clear differences
in sustained attention or working memory demands between MTT and STT, transfer of
benefits to these untrained tasks must have resulted from challenges to overlapping
cognitive control processes. Of note, the use of a 3-D immersive and fun video game
for training (see Methods) diverges from the sparse environments typically utilized
in dual-task training studies
9,10
, which have not documented a similar degree of far transfer
10
.
Coupled with previous findings of increased midline frontal theta on a variety of
cognitive control tasks
18
, the current results support a common neural basis of cognitive control processes,
which can be enhanced by immersion in an adaptive, high-interference environment.
This interpretation is bolstered by evidence here indicating that MTT-induced increases
in midline frontal theta power during “Sign & Drive” were positively correlated with
both: i) sustained multitasking performance improvements (6 month – Post performance,
Figure 4c), and ii) improvements in TOVA response times (Figure 4d). Thus, MTT-induced
enhancement of midline frontal theta power was associated with the preservation of
multitasking performance over time and with generalized benefits on an untrained cognitive
control task, reflecting its utility as a neural signature of plastic cognitive control
processes.
Finally, we questioned if these neural measures that exhibited training effects in
older adults were actually altered at baseline compared to younger adults, or if training
boosted non-deficient neural processes. In a third experiment, we compared midline
frontal theta power and long-range coherence from older adults prior to training to
a naïve group of younger adults who were not trained (n=18; 20–29 y.o. (24.1 ± 2.9)).
The multitasking costs for each group replicated findings of age-matched cohorts from
Experiment 1. Both neural measures showed a main effect of group (see Supplementary
Figure 8), indicating less theta power and coherence in older adults when processing
signs in either condition (“Sign & Drive” depicted in Figure 4a, b). The absence of
a significant condition X age group interaction for either neural measure (see Supplementary
Figure 8) revealed that aging was associated with a general reduction in theta power
and coherence when older adults discriminate visual stimuli, regardless of whether
they are multitasking or singletasking. Notably, MTT led to changes in the neural
processing of signs during “Sign & Drive” that reached a level comparable to neural
activity patterns observed in younger adults.
The mechanism underlying these neural findings are informed by a growing literature
that shows deactivation of medial prefrontal cortical activity (suppression of a node
of the ‘default network’
19
) during cognitively demanding tasks is associated with reduced susceptibility to
internal distraction and better task performance
20
. Given that medial prefrontal activity is inversely correlated with midline frontal
theta power
21
, increased levels of midline frontal theta exhibited by older adults following MTT
may reflect more deactivation of medial prefrontal activity. And so, NeuroRacer training
may benefit cognitive control abilities by improving the ability of older adults to
suppress the default network during task engagement, a process known to be compromised
in aging
22
. Future studies utilizing neurochemical and physiological manipulations are warranted
to inform the causal nature of the relationship between medial prefrontal activity
and training-induced performance effects observed here.
This study offers neural and behavioral evidence of generalized positive effects from
video game training on cognitive control abilities of older adults, with enhancements
comparable to those observed in younger adults who are habitual action video game
players; i.e., interference resolution
23
, working memory
24
and sustained attention
25
. Although reports of transfer of benefits following cognitive training in the older
population are relatively rare
11,26
, the observed generalization supports the results of larger-scale training studies
that demonstrate some degree of transfer to: i) untrained cognitive tasks
27,28
, and ii) subjective measures of daily living.
29
In contrast to these studies, and most other cognitive training experiments on older
adults that report small to medium effect sizes for untrained tasks, the current findings
document medium to large effect sizes (all > .50–1.0 (using Cohen’s d, see Methods))
for both cognitive control performance and neural measures versus either control group.
The sustained multitasking cost reduction over time and evidence of generalizability
to untrained cognitive control abilities provide optimism for the use of an adaptive,
interference-rich, video game approach as a therapeutic tool for the diverse populations
that suffer from cognitive control deficits (e.g., ADHD, depression, dementia). These
findings stress the importance of a targeted training approach, as reinforced by a
recent study that observed a distinct lack of transfer following non-specific online
cognitive exercises
30
. In conclusion, we provide evidence of how a custom-designed video game targeting
impaired neural processes in a population can be used to diagnosis deficits, assess
underlying neural mechanisms, and enhance cognitive abilities.
Methods Summary
All participants had normal or corrected vision, no history of neurological, psychiatric,
or vascular disease, and were not taking any psychotropic or hypertension medications.
In addition, they were considered ‘non-gamers’ given that they played less than 2
hours of any type of video game per month. For NeuroRacer, each participant used their
left thumb for tracking and their right index finger for responding to signs on a
Logitech (Logitech, USA) gamepad controller. Participants engaged in three 3-minute
runs of each condition in a randomized fashion. Signs were randomly presented in the
same position over the fixation cross for 400 msec every 2, 2.5, or 3 seconds, with
the speed of driving dissociated from sign presentation parameters. The multitasking
cost index was calculated as follows: [(‘Sign & Drive’ performance - ‘Sign Only’ performance)
/ ‘Sign Only’ performance] * 100. EEG data for 1 MTT Post-training participant and
1 STT Pre-training participant were corrupted during acquisition. 2 MTT participants,
2 STT participants, and 4 NCC participants were unable to return to complete their
6-month follow-up assessments. Critically, no between-group differences were observed
for neuropsychological assessments (p= .52) or Pre-training data involving: i) NeuroRacer
thresholding for both Road (p= .57) and Sign (p= .43), ii) NeuroRacer component task
performance (p> .10 for each task), iii) NeuroRacer multitasking costs (p= .63), iv)
any of the cognitive tests (all ANOVAs at Pre-training: p≥ .26), v) ERSP power for
either condition (p≥ .12), and, vi) coherence for either condition (p≥ .54).
Methods
Participants
All participants were recruited through online and newspaper advertisements. For Experiment
1, 185 (90 male) healthy, right-handed individuals consented to participate according
to procedures approved by the University of California at San Francisco. For Experiment
2 & 3, 60 (33 males) older adult individuals and 18 (9 male) young adult individuals
participated without having been a part of Experiment 1 (see Supplementary Table 3
for demographic descriptions and Supplementary Figure 9 for Experiment 2 participant
enrollment). Participants who were unable to perform the tasks, as indicated by tracking
performance below 15% (6 individuals from Experiment 1, 8 individuals from Experiment
2), or a false positive rate greater than 70% (5 individuals from Experiment 1, 6
individuals from Experiment 2) during any one visit or across more than 4 individual
training sessions, were excluded.
Thresholding
Prior to engaging in NeuroRacer, participants underwent an adaptive thresholding procedure
for discrimination (nine 120 sec runs) and tracking ability (twelve 60 sec runs) to
determine a ‘sign’ and ‘drive’ level that each participant would perform at ~80% accuracy
(see Supplementary Figures 1 & 2). Having individuals engage each condition in their
own ‘space’ following thresholding procedures facilitated a fairer comparison across
ages and abilities. This is a frequently omitted procedure in other studies, and leads
to difficulty interpreting performance differences (especially multitasking) as being
the result of differences in interference processing or due to differences in component
task skills.
For the perceptual discrimination thresholding, each participant’s performance for
a given run was determined by calculating a proportion correct score involving: i)
correctly responding to targets, ii) correctly avoiding non-targets, iii) late responses
to targets, and iv) responding to non-targets. At the end of each run, if this score
was greater than 82.5%, the subsequent run would be played at a higher level which
had a corresponding shorter time window for responses to targets. More specifically,
the adaptive algorithm would make proportional level changes depending upon participants
performance from this ~80% median, such that each 1.75% increment away from this median
corresponded with a change in level (see Supplementary Figure 1a). Thus, a 90% performance
would lead to a 40msec reduction in the time window, while a 55% (or less) performance
would lead to a 100msec lengthening of said window. Thresholding parameters for road
levels followed a similar pattern with each .58% increment away from the same median
corresponded with a change in level (see Supplementary Figure 1b). These parameters
were chosen following extensive pilot testing to: (1) minimize the number of trial
runs until convergence was reached and (2) minimize convergence instability, while
(3) maximizing sampling resolution of user performance.
The first 3 driving thresholding blocks were considered practice to familiarize participants
with the driving portion of the task and were not analyzed. A regression over the
9 thresholding runs in each a case was computed to select the ideal time window and
road speed to promote a level of ~80% accuracy on each distraction free task throughout
the experiment (see Supplementary Figure 2). All participants began the thresholding
procedures at the same road (level 20) and sign levels (level 29).
Conditions
Following the driving and sign thresholding procedures, participants performed 5 different
three minute 'missions', with each mission performed three times in a pseudo-randomized
fashion. In addition to the ‘Sign Only’, ‘Drive Only’, and ‘Sign & Drive’ conditions,
participants also performed a "Sign With Road" condition where the car was placed
on 'auto pilot' for the duration of the run and participants responded to the signs,
and a ‘Drive with Signs’ condition where participants were told to ignore the presence
of signs appearing that and continue to drive as accurately as possible. Data from
these two conditions are not presented here. Feedback was given at the end of each
run as the proportion correct to all signs presented for the perceptual discrimination
task (although we used the signal detection metric of discriminability (d')
31
to calculate our ‘Cost’ index throughout the study), and percentage of time spent
on the road (see Supplementary Figure 10). Prior to the start of the subsequent run,
participants were informed as to which condition would be engaged in next, and made
aware of how many experimental runs were remaining. Including thresholding, the testing
session encompassed 75min of gameplay.
NeuroRacer training and testing protocol
For Experiment 1, participants were seated in a quiet room in front of an Apple© MacBook
Pro 5.3 laptop computer at an approximate distance of 65 cm from the 15" screen. For
Experiment 2 and 3, participants were seated in a dark room with the screen ~100 cm
from the participants. All training participants trained at their homes using an Apple©
MacBook Pro 5.3 laptop computer while sitting ~60 cm from the screen (see Supplementary
Figure 11a). For Experiment 1, each perceptual discrimination-based experimental run
(180 sec) contained 36 relevant targets (green circles) and 36 lures (green, blue
and red pentagons and squares). For Experiments 2 & 3, the sign ratio was to 24/48.
Prior to training, each participant was given a tutorial demonstrating how to turn
on the laptop, properly setup the joystick, navigate to the experiment, shown what
the 1st day of training would be like in terms of the task, how to interpret what
the feedback provided meant, and were encouraged to find a quiet environment in their
home for their training sessions. If indicated by the participant, a lab member would
visit the participant at their home to help set up the computer and instruct training.
In addition, to encourage/assess compliance and hold participants to a reasonable
schedule, participants were asked to plan their training days & times with the experimenter
for the entire training period and enter this information into a shared calendar.
Each participant (regardless of group) was informed that their training protocol was
designed to train cognitive control faculties, using the same dialogue to avoid expectancy
differences between groups. There was no contact between participants of different
groups, and they were encouraged to avoid discussing their training protocol with
others to avoid potentially biasing participants in the other groups.
Each day of training, the participants were shown a visualization of a map that represented
their ‘training journey’ to provide a sense of accomplishment following each training
session (Supplementary Figure 11b). They were also shown a brief video that reminded
them how to hold the controller, which buttons to use, their previous level(s) reached,
and what the target would be that day for the perceptual discrimination condition.
In addition, the laptop’s built-in video camera was also activated (indicated by a
green light) for the duration of said run, providing i) visual assessment of task
engagement, ii) motivation for participants to be compliant with the training task
instructions, and iii) information about any run where performance was dramatically
poorer than others.
Participants were discouraged from playing 2 days in a row, while they were encouraged
to play at the same time of day. MTT participants were reminded that an optimal training
experience was dependent upon doing well on both their sign and drive performance
without sacrificing performance on one task for the other. While the STT group were
provided a ‘Driving’ or ‘Sign’ score following each training run, the MTT group were
also provided an ‘Overall’ score following each run as a composite of performance
on both tasks (see Supplementary Figures 5 and 11). Following the completion of every
4th run, participants were rewarded with a ‘fun fact’ screen regarding basic human
physiology (http://faculty.washington.edu/chudler/ffacts.html) before beginning their
subsequent training run. To assess if training was a ‘fun’ experience, participants
in each training group rated the training experience on their final visit to the laboratory
on a scale of 1 (minimally) to 10 (maximally) (MTT: 6.5 ± 2.2; STT 6.9 ± 2.4; t= .65,
p= .52). Critically, training groups did not differ on their initial thresholding
values for both Road (F(2,45)= .58, p= .57) and Sign (F(2,45)= .87, p= .43).
Each laptop was configured to transmit NeuroRacer performance data to our secure lab
server wirelessly using DropBox® as each run was completed. This facilitated monitoring
for compliance and data integrity in a relatively real-time fashion, as participants
would be contacted if i) there was a failure to complete all 20 training runs on a
scheduled training day, ii) ‘Sign Only’ and ‘Drive Only’ performance was suggestive
that a problem had occurred within a given training session, and iii) a designated
training day was missed. Individuals without wireless internet in their home were
instructed to visit an open wireless internet location (e.g., coffee shop, public
library) at least once a week to transfer data, and if this was not an option, researchers
arranged for weekly home visits to acquire said data. All participants were contacted
via email and/or phone calls on a weekly basis to encourage and discuss their training;
similarly, in the event of any questions regarding the training procedures, participants
were able to contact the research staff via phone and email.
Pre- and Post-training evaluations involving cognitive testing and NeuroRacer EEG
took place across 3 different days (appointment and individual test order were counterbalanced),
with all sessions completed approximately within the span of a week (total number
of days to complete all Pre-training testing: 6.5 days ± 2.2; Post-training testing:
6.1 days ± 1.5). Participants returned for their 1st Post-training cognitive assessments
2.0 ± 2.2 days following their final training session. While scheduled for 6 months
after their final testing session, the 6 month follow-up visits actually occurred
on average 7.6 months ± 1.1 afterwards due to difficulties in maintaining (and rescheduling)
these distant appointments. Critically, no group differences were present regarding
any of these time-of-testing measures (F< 1.81, p> .18 for each comparison).
Cognitive Battery
The cognitive battery (see Supplementary Table 2) consisted of tasks spanning different
cognitive control domains: sustained attention (TOVA; see Supplementary Figure 12a),
working memory (delayed-recognition- see Supplementary Figure 12b); visual working
memory capacity (see Supplementary Figure 13), dual-tasking (see Supplementary Figure
14), useful field of view (UFOV; see Supplementary Figure 15), and two control tasks
of basic motor and speed of processing (stimulus detection task, digit symbol substitution
task; see Supplementary Figure 16). Using the analysis metrics regularly reported
for each measure, we performed a mixed model ANOVA of Group (3: MTT, STT, NCC) X Session
(2: pre, post) X Cognitive test (11; see Supplementary Table 2), and observed a significant
3-way interaction (F(20, 400)= 2.12, p= .004) indicative that training had selective
benefits across group and test. To interrogate this interaction, each cognitive test
was analyzed separately with Session X Group ANOVAs to isolate those measures that
changed significantly following training. We also present the p-value associated with
the ANCOVAs for each measure in Supplementary Table 2 (dependent measure = Post-training
performance, covariate = Pre-training performance), which showed a similar pattern
of effects as most of the 2-way ANOVAs. The ANCOVA approach is considered to be a
more suitable approach when post-test performance that is not conditional/predictable
based on pre-test performance is the primary outcome of interest following treatment,
as opposed to characterizing gains achieved from Pre-training performance (e.g., group
X session interaction(s))
32
; however, both are appropriate statistical tools that have been used to assess cognitive
training outcomes
27,33
(see Supplementary Figure 17 as an example).
EEG Recordings and Eye Movements
Neural data were recorded using an Active Two head cap (Cortech-Solutions) with a
BioSemiActiveTwo 64-channel EEG acquisition system in conjunction with BioSemiActiView
software (Cortech-Solutions). Signals were amplified and digitized at 1024 Hz with
a 16-bit resolution. Anti-aliasing filters were used and data were band-pass filtered
between 0.01–100 Hz during data acquisition.
For each EEG recording session, the NeuroRacer code was modified to flash a 1x1” white
box for 10msec at one of the corners on the stimulus presentation monitor upon the
appearance of a sign. A photodiode (http://www.gtec.at/Products/Hardware-and-Accessories/g.TRIGbox-Specs-Features)
captured this change in luminance to facilitate precise time-locking of the neural
activity associated with each sign event. During the experiment, these corners were
covered with tape to prevent participants from being distracted by the flashing light.
To ensure that any training effects were not due to changes in eye movement, electrooculographic
data were analyzed as described by Berry and colleagues
34
. Using this approach, vertical (VEOG = FP2-IEOG electrodes) and horizontal (HEOG=
REOG-LEOG electrodes) difference waves were calculated from the raw data and baseline
corrected to the mean prestimulus activity. The magnitude of eye movement was computed
as follows: (VEOG2 + HEOG2)1/2. The variance in the magnitude of eye movement was
computed across trials and the mean variance was specifically examined from −200 to
1000msec post-stimulus onset. The variance was compared i) between sessions for each
group’s performance on the ‘Sign and Drive’ and ‘Sign Only’ conditions, ii) between
groups at each session for each condition, and iii) between young and older adults
on each condition. We used two-tailed t-test that were uncorrected for multiple comparisons
at every msec time point to be as conservative as possible. There was no session difference
for any group on the ‘Sign Only’ condition (p> .05 for each group comparison); similarly,
there were no differences for the MTT or NCC groups on the ‘Sign & Drive’ condition
(p> .30 for each comparison), with the STT group showing more variance following training
(p= .01). With respect to Experiment 3, there were also no age differences on either
condition (p> .45 for each comparison). This indicates that the training effects observed
were not due to learned eye movements, and that the age-effects observed were also
not a function of age-related differences in eye movements as well.
EEG analysis
Preprocessing was conducted using Analyzer software (Brain Vision, LLC) then exported
to EEGLAB
35
for event-related spectral perturbations (ERSP) analyses. ERSP is a powerful approach
to identifying stable features in a spontaneous EEG spectrum that are induced by experimental
events, and have been used to successfully isolate markers of cognitive control
36,37
. We selected this approach because we felt that a measure in the frequency domain
would be more stable than other metrics given the dynamic environment of NeuroRacer.
Blinks and eye-movement artifacts were removed through an independent components analysis
(ICA), as were epochs with excessive peak-to-peak deflections (±100 µV). Given the
use of d’, which takes into account performance on every trial, we collapsed across
all trial types for all subsequent analyses. −1000 to +1000msec epochs were created
for ERSP total power analysis (evoked power + induced power), with theta band activity
analyzed by resolving 4–100 Hz activity using a complex Morlet wavelet in EEGLAB and
referenced to a −900 to −700 pre-stimulus baseline (thus relative power (dB)).
Assessment of the “Sign & Drive” ERSP data in 40msec time bins collapsing across all
older adult participants and experimental sessions revealed the onset of peak midline
frontal activity to be between 360–400msec post-stimulus, and so all neural findings
were evaluated within this time window for the older adults (see Supplementary Figure
7 for these topographies). For younger adults, peak theta activity occurred between
280–320 msec, and so for across-group comparisons, data from this time window was
used for younger adults.
The cognitive aging literature has demonstrated delayed neural processing in older
adults using EEG
38,39
. For example, Zanto and colleagues
38
demonstrated that older adults show similar patterns of activity as younger adults
in terms of selective processing, but there is a time shift to delayed processing
with aging. For the data generated in this study, presented topographically in Supplementary
Figure 7, it was clear that the peak of the midline frontal theta was delayed in older
versus younger adults. To fairly assess if there was a difference in power, it was
necessary to select different comparison windows in an unbiased, data-driven manner
for each group.
Coherence data for each channel was first filtered in multiple pass bands using a
two-way, zero phase-lag, finite impulse response filter (eegfilt.m function in EEGLAB
toolbox) to prevent phase distortion. We then applied a Hilbert transform to each
of these time series (hilbert.m function), yielding results equivalent to sliding
window FFT and wavelet approaches
40
, giving a complex time series,
hx
[n] = ax
[n]exp(iϕ
x
[n])
where ax[n] and φx[n] are the instantaneous amplitudes and phases, respectively. The
phase time series φx assumes values within (−π, π] radians with a cosine phase such
that π radians corresponds to the trough and 0 radians to the peak. In order to compute
PLV for theta phase, for example, we extract instantaneous theta phases φθ[n] by taking
the angle of hθ[n]. Event-related phase time-series are then extracted and, for each
time point, the mean vector length Rθ[n] is calculated across trials (circ_r.m function
in CircStats toolbox)
41
. This mean vector length represents the degree of PLV where an R of 1 reflects perfect
phase-locking across trials and a value of 0 reflects perfectly randomly distributed
phases. These PLVs were controlled for individual state differences at each session
by baseline correcting each individual’s PLVs using their −200 to 0 period (thus,
a relative PLV score was calculated for each subject).
Statistical analyses
Mixed model ANOVAs with: i) decade of life (Experiment 1), ii) training group (Experiment
2), or iii) age (Experiment 3) as the between-group factor were used for all behavioral
and neural comparisons, with planned follow-up t-tests and the Greenhouse-Geisser
correction utilized where appropriate. One-tailed t-tests were utilized to interrogate
group differences for all transfer measures given our a priori hypothesis of the direction
of results following multitask training. All effect size values were calculated using
Cohen’s d
42
and corrected for small sample bias using the Hedges and Olkin
43
approach. The neural-behavioral correlations presented included only those MTT participants
who demonstrated increased midline frontal theta power following training (14/15 participants).
For statistical analyses, we created 1 frontal and 3 posterior composite electrodes
of interest (EOI) from the average of the following electrodes: AFz, Fz, FPz, AF3,
and AF4 (medial frontal), PO8, P8, and P10 (right-posterior), PO7, P7, and P9 (left-posterior);
POz, Oz, O1, O2 and Iz (central-posterior), with PLVs calculated for each frontal-posterior
EOI combination separately. For the coherence data, the factor of posterior EOI location
(3) was modeled in the ANOVA, but did not show either a main effect or interaction
with the other factors.
Supplementary Material
1