Social isolation during COVID-19 lockdown impairs cognitive function

Abstract

Studies examining the effect of social isolation on cognitive function typically involve older adults and/or specialist groups (e.g., expeditions). We considered the effects of COVID‐19‐induced social isolation on cognitive function within a representative sample of the general population. We additionally considered how participants ‘shielding’ due to underlying health complications, or living alone, performed. We predicted that performance would be poorest under strictest, most‐isolating conditions. At five timepoints over 13 weeks, participants (N=342; aged 18‐72 years) completed online tasks measuring attention, memory, decision‐making, time‐estimation, and learning. Participants indicated their mood as ‘lockdown’ was eased. Performance typically improved as opportunities for social contact increased. Interactions between participant sub‐groups and timepoint demonstrated that performance was shaped by individuals’ social isolation levels. Social isolation is linked to cognitive decline in the absence of ageing covariates. The impact of social isolation on cognitive function should be considered when implementing prolonged pandemic‐related restrictive conditions.
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Running Head: Social isolation impairs cognitive function
Social isolation during COVID-19 lockdown impairs cognitive function
Joanne Ingram1*, Christopher J. Hand2, and Greg Maciejewski1
1School of Education and Social Science, University of the West of Scotland, Paisley, UK
2Department of Psychology, Glasgow Caledonian University, Glasgow, UK
*Corresponding Author
Joanne Ingram
School of Education and Social Science,
University of the West of Scotland,
Paisley,
PA1 2BE
UK
E-Mail: Joanne.Ingram@uws.ac.uk
Conflict of Interest Statement
We confirm that none of the authors have a conflict of interest.
Data Availability Statement
The data that support the findings of this study are openly available in the Open Science
Framework at https://osf.io/kvpbt/ .

Abstract
Studies examining the effect of social isolation on cognitive function typically involve older
adults and/or specialist groups (e.g., expeditions). We considered the effects of COVID-19-
induced social isolation on cognitive function within a representative sample of the general
population. We additionally considered how participants ‘shielding’ due to underlying health
complications, or living alone, performed. We predicted that performance would be poorest
under strictest, most-isolating conditions. At five timepoints over 13 weeks, participants
(N=342; aged 18-72 years) completed online tasks measuring attention, memory, decision-
making, time-estimation, and learning. Participants indicated their mood as ‘lockdown’ was
eased. Performance typically improved as opportunities for social contact increased.
Interactions between participant sub-groups and timepoint demonstrated that performance
was shaped by individuals’ social isolation levels. Social isolation is linked to cognitive
decline in the absence of ageing covariates. The impact of social isolation on cognitive
function should be considered when implementing prolonged pandemic-related restrictive
conditions.
Keywords cognitive decline; COVID-19; executive function; lockdown; social isolation
1

Introduction
Much of the global population has experienced “lockdown” conditions due to the COVID-19
pandemic. There is growing evidence of the consequences of COVID-19-related social
isolation, confinement, and loneliness on mood and physical health (e.g., Lippi et al., 2020;
Zhang et al., 2020), but no examination of similar changes in cognitive function has been
presented. If “lockdown” conditions lead to cognitive decline – in memory, perceptual ability,
and/or executive function – this has broad impact for education, work, and everyday life, as
well as implications for theories of cognitive decline.
COVID-19 restrictions vary from country-to-country and vary across time within
countries. In Scotland, the strictest conditions permitted leaving home only for societally
essential work, groceries, and solo exercise once a day (or exercise with members of one’s
own household). Entering another home was only permitted in emergencies. Additionally,
approximately 1-in-20 adults were required to ‘shield’ due to pre-existing conditions which
made them vulnerable to COVID-19 infection/complications. Shielding individuals were
required to stay at home, indoors at all times, initially with no exceptions. Effectively,
citizens were left entirely isolated (if living alone) or were restricted to interpersonal contact
with only members of their household.
Except for outdoor exercise, which became unlimited after 49 days, residents of
Scotland spent 66 days under the strictest lockdown conditions. Severe and suddenly-
imposed constraints on interpersonal contact led, for some, to feelings of isolation and
loneliness (Li & Wang, 2020), and to higher levels of negative mood (Ingram et al., 2020).
The relationship between isolation and cognitive decline in certain populations has been well-
documented (see Cacioppo & Hawkley, 2010 for a review); we investigated if isolation due
to COVID-19 restrictions led to a decline in cognitive function in the general population,
with specific consideration of those shielding and/or living alone.
2

Social isolation and cognitive decline are typically assessed in older adults. Findings
are frequently inconsistent as measurement of social activity is variable (Evans et al., 2019a),
and social isolation is difficult to rigorously control. However, isolation has been shown to
influence cognitive functioning (Evans et al., 2019a) and decline (Kuiper et al., 2016). Living
alone and having no close relationships, or having a limited or poor social network have been
linked to increased risk of dementia (Fratiglioni et al, 2000), whilst poorer cognitive ability in
the absence of dementia has been predicted by lower levels of emotional support (Seeman et
al,. 2001). Social engagement during recreational activities enhances memory (Richards et
al., 2003), and protects against cognitive decline (Bassuk et al., 1999). Younger participants
who were experimentally induced to envisage a future of social isolation were impaired on
general mental ability, self-regulation, and reasoning (Baumeister & DeWall, 2005;
Baumeister et al., 2005; Twenge et al., 2001). Recent research suggests that when controlling
for age, gender, education level, and physically-limiting health conditions, social isolation
(the absence of social relationships and disengagement from community; Nicholson, 2009) is
associated with level of cognitive function (Evans et al., 2018a; Evans et al., 2019b).
Associations between social isolation and cognitive function are frequently linked to
cognitive reserve (Stern, 2009). Social interactions with others involve mental stimulation,
hence frequent social interaction may protect or enhance cognitive function (van Gelder et
al., 2006). Reserve and maintenance of cognitive function may be protected through regular
effortful social interactions which require engagement of complex cognitive processes
(Barnes et al., 2004; Fratiglioni et al, 2004). However, research in this area is limited due to
the inability to establish the directional link between cognitive and social decline – that is,
those experiencing greater decline may be unable to maintain social interactions. Studies
have generally controlled for this using baseline measurements (Barnes, et al., 2004;
Fratiglioni et al., 2004; Zunzunegui et al., 2003) and existing cohort data (Gow et al., 2007).
3

However, research has indicated causal links using cross-lagged modelling (Thomas, 2011)
and latent change score modelling (Read et al., 2020). These studies found differential effects
of social isolation on cognitive impairment across males and females. Whilst neurological
and situational ageing effects are impossible to accurately imitate, enforced lockdown
conditions afforded a unique opportunity to replicate certain social and physical restrictions
often experienced only by older adults. Further issues with measurement of social contacts
and networks (Evans et al., 2019a) were mitigated by the blanket rule of no-contact across the
region.
Social isolation effects have also been assessed naturalistically during scientific
expeditions. A study of prolonged Antarctic isolation yielded varied results, with clear
functional detriment only evident at the very end of the isolation period (Khandelwal et al.,
2017). However, while isolated from broader society, that expedition facility housed 26 team-
members, allowing for extensive, varied face-to-face interpersonal contact. Examination of a
solitary participant during a seventeen-day expedition on-foot through the Simpson desert
indicated substantial cognitive deterioration over time, which resolved fully once the
expedition was complete (Maruff et al., 2006).
A review of Antarctic expeditions (Zimmer et al., 2013) noted that 63.6% of studies
reported cognitive impairment, with a variety of aetiologies suggested, including stress and
fatigue, and low environmental stimulation. However, other studies (e.g., John Paul et al.,
2010; Palinkas et al., 2005) demonstrate maintained or even improved cognitive performance
over extended periods in polar environments. Studies of spaceflight have yielded mixed
evidence; detriment is typically attributed to effects of microgravity or environmental
stressors as opposed to social isolation (Kanas & Manzey, 2008). Deficits in attentional
processing (Pattyn et al., 2005) and concurrent task-management (Manzey & Lorenz, 1998)
have been found, but individual effects of social isolation or stressful environment are rarely
4

demonstrated. Collectively, the results of studies on the effects of social isolation on cognitive
function during expeditions show mixed results or no detriment to cognition. However, it is
important to note that astronauts and polar explorers are carefully selected against specific
criteria and undergo rigorous medical and psychological screening (De La Torre et al., 2012;
John Paul et al., 2010). Space expeditions are generally short, allowing little time to
experience effects of isolation. Polar expeditions often involve a larger number of
individuals, which perhaps provides sufficient social contact to maintain function. Finally,
these individuals have consented to enter a restrictive environment; thus, these
groups/findings may not be representative when considering the effects of pandemic-related
social isolation.
Disentangling the effect of social isolation on human cognitive function is difficult,
but we can draw parallels with animal studies. Rats reared in isolation demonstrate deficits in
cognitive flexibility (Amitai et al., 2014); isolating animals impairs reversal learning,
regardless of inanimate stimulation, suggesting isolation effects on prefrontal cortico-striatal
pathways (Schrijver et al., 2004). Studies further demonstrate that social isolation leads to
permanent neurochemical, behavioural, and neurostructural changes in rodents (Jones et al.,
2011; Schubert et al., 2009).
Research involving older adults or expeditions suggest that cognitive function can be
improved or restored through cognitive plasticity. Research on plasticity in older adults ties
closely with the notion of cognitive reserve already discussed (See Hertzog et al., 2009 for a
discussion of cognitive enrichment). Studies have shown that with cognitive and/or physical
training or intervention, cognitive function can be maintained or improved in the ageing brain
(e.g., Bherer, 2015; Karbach & Verhaeghen, 2014). Cognitive decline seen in expeditioners
has been found to resolve quickly after the expedition was complete (Maruff et al., 2006;
Ratino et al., 1988), suggesting that short-term periods of isolation do not impact cognitive
5

function over the longer-term. Considering the evidence for plasticity of cognitive function, it
was expected that any cognitive decline resulting from COVID-19 restrictions to social
contact would resolve as restrictions were relaxed.
Societal “lockdown” conditions within the UK (beginning in March 2020) provided a
valuable opportunity to assess social isolation effects on cognitive function across a large
representative sample, with minimal limitations (e.g., older-adult-only sample, extreme
environments). To compare cognitive function during stricter and more-liberal societal
conditions (i.e., when extra-household face-to-face social contact was permitted), participants
completed multiple cognitive tasks across five timepoints. Tasks assessed a range of cognitive
functions, examined previously in relation to social isolation (e.g., Beneke et al., 1993; John
Paul et al., 2010; Kelly et al., 2005; Ratino et al., 1988). These included: attention (Flanker
Task: Wylie et al., 2007), working memory (Digitized-Digit Symbol Substitution Task;
Chatterjee et al., 2019), decision making (Iowa Gambling Task, Bechara et al., 1994), time
perception, (modified version of Time Production Task; Tortello et al., 2020) and learning
(Symbol Learning; Yang et al., 2017).
The initial timepoint (Week 1) aligned with participants living under the most-
restrictive conditions – leaving the house was allowed only for non-shielding individuals for
essential work which could not be completed from home, for essential groceries, or for
individual outdoor exercise (which had become unlimited, after initially being restricted to
once per day). Participants completed the task battery at four further timepoints. Restrictions
were eased across this period as follows. At Week 3, unless self-isolating / shielding, meeting
outside with one other household was allowed. At Week 5, one household could meet with
people from up to two households out-of-doors, those in the shielding group could go
outdoors for exercise. At Week 9, people could meet with others from up to two households
indoors or outdoors, and retail, hospitality, hairdressers, and cultural venues re-opened. At
6

Week 13, in addition to the expansion at Week 9, children had returned to nurseries and
schools.
We predicted that performance on all tasks would be poorest at timepoint 1, with
gradual improvement as restrictions were eased. We predicted that due to differing levels of
isolation, shielding participants would show differential effects to non-shielding participants,
and that those who lived alone would show differential effects to those who co-habited.
Method
Participants
342 Scottish nationals/long-term residents (56.7% female, 41.5% male, 0.6% non-
binary, 0.9% transgender) aged 18-72 years old (Mage=32.1 years, SDage=11.2) participated. An
a priori power analysis anticipating small effect sizes (f=.10, α=.05, power=.95) suggested a
target sample of 188; thus, our sample was ample. Participants who identified as Scottish
were recruited using Prolific Academic (https://prolific.co) and first took part in an additional
study on the effects of COVID-19 restrictions on healthy behaviours (CURRENT
AUTHORS, 2020). 399 eligible participants took part in the additional study by CURRENT
AUTHORS. Participants who were native English-speakers with no vision/attention/learning
impairment or prior knowledge of Mandarin characters (used in the symbol-learning task; N
= 342), were then immediately invited to take part in the current study. A breakdown of key
participant demographic characteristics is presented in Table 1. There are two rows of data in
Table 1; one represents the main dataset, and the other represents the subset dataset (please
see Data Analysis section for further details). Crucially, there are very few differences in the
demographic make-up of the sub-sample, relative to the global sample.
<<Table 1 about here>>
7

All experienced social isolation during lockdown; 14.9% (nshield = 51) of participants
identified as having ‘shielded’ throughout lockdown. Approximately 3% of the general
Scottish adult population were ‘required’ to shield (Scottish Government, 2020). 12% of
participants lived alone during lockdown – in the broader Scottish context approximately
15% of people live alone (National Records Scotland, 2018). There was moderate participant
dropout across timepoints (328 participants remained after Week 3, 275 after Week 5, 228
after Week 9, and 203 after Week 13). No participants were excluded during the study. A
small number of participants were not included in certain sub-analyses; specific details can be
found under Data Analysis.
Measures & Procedure
We examined participants’ performance on five cognitive tasks. These included the
Iowa gambling task (adapted from Bechara et al., 1994) as a measure of decision making, a
flanker task (adapted from Wylie et al., 2007) as a measure of selective attention, a symbol-
learning task (adapted from Yang et al., 2017) as a measure of learning ability, a digit-symbol
substitution task (Chatterjee et al., 2019, Version 1) as a measure of working memory, and a
time production task (adapted from Tortello et al., 2020) as a measure of time estimation. As
negative mood has been shown to correlate with poorer performance on some cognitive tasks
(see Chepenik et al., 2007 for a review), we measured and controlled for participants’
negative mood when examining potential changes in cognitive function. Ten negative items
from Grove and Prapavessis’ (1992) abbreviated Profile of Mood State (POMS) scale were
used. For more information about the tasks and measures, please see the Supporting
Information.
8

The tasks were designed and administered online, using the Gorilla Experiment
Builder (https://gorilla.sc; for information about stimulus and response timing precision, see
Anwyl-Irvine et al., 2019; Bridges et al., 2020). The tasks were administered in the same
order at each timepoint (Iowa gambling, flanker, symbol-learning, time production, digit-
symbol substitution, mood rating). At the end of timepoint 5 (Week 13), could disclose
whether they had cheated on any of the tasks (e.g., using an online translator to determine
Mandarin character meanings). Participants received £5 for completing each session; sessions
took, on average, 20 minutes to complete. The study was approved by the lead institution
ethics committee, following British Psychological Society (2014) guidelines.
Data analysis
Cognitive task data were analysed with four logit/linear mixed-effects models, using
the “lme4” package (Bates et al., 2011) in R (R Development Core Team, 2004). Fixed effects
were tested using maximum likelihood-ratio tests comparing full and reduced models. The
first model (henceforth referred to as the ‘main’ model) included Time (Weeks 1, 3, 5, 9, 13)
as a fixed effect and Negative Mood Rating (NMR), Age, and Gender as covariates. Within
Time, repeated coding was used to define four planned contrasts that compared consecutive
timepoint pairings (Week 1 vs. 3, Week 3 vs. 5, Week 5 vs. 9, Week 9 vs. 13). Note that NMR
was removed due to model non-convergence1 for the Iowa gambling, flanker (accuracy but
not RT), symbol-learning, and digit-symbol substitution (accuracy) tasks. For the same
reason, Age was removed for the Iowa gambling, symbol-learning (only analyses involving
interaction terms), and digit-symbol substitution tasks (accuracy), whilst Gender was
removed for the Iowa gambling (only analyses involving interaction terms), flanker
(accuracy), symbol-learning, and digit-symbol substitution (accuracy) tasks. NMR and Age
were significant covariates of RT in the flanker and digit-symbol substitution tasks. Age was
9

also a significant covariate of accuracy in the digit-symbol substitution task. Gender was a
significant covariate of RT in the flanker and DSST tasks and the number of advantageous
deck selections in the Iowa gambling task.
The second model (henceforth referred to as the ‘subset’ model) was identical to the
main model, except that it included only those participants who completed all sessions (203
out of 342). The rationale for a second model was that due to participant dropout, estimates
for each timepoint in the main model could be biased as they were based on all participants
who completed a given session, rather than those who completed all sessions. This is because
mixed-effects models ignore missing observations (unlike general linear models which delete
them listwise). However, we demonstrate below that for each task, the results of the subset
model corroborated those of the main model, confirming that the latter were not driven by a
subset of participants at a particular timepoint.
The third model (henceforth referred to as the ‘shielding status’ model) examined
differences between shielding (n=51) and non-shielding participants (n=288). Note that three
participants were excluded from these analyses as they did not disclose their shielding status.
The fourth model (henceforth referred to as the ‘living status’ model) examined differences
between participants who lived alone during lockdown (n=41) and those who co-habited
(n=301). The shielding status and living status models were identical to the main model, but
additionally included Group (shielding, non-shielding / solitary, non-solitary) and Group ×
Time as additional fixed effects. For significant interactions, follow-up comparisons
examined Group differences for each pair of consecutive timepoints separately. The purpose
of supplementary by-groups models was to demonstrate that the predicted gradual
improvement in task performance was due to the easing of lockdown restrictions and
differential social isolation, rather than due to simple practice effects.
10

NMR data were analysed using a linear mixed-effects model with Time as a fixed
effect. All models included a random intercept by-participants. The random by-participants
slope for Time was significant in all models but was removed due to model non-convergence
after fixed effects and covariates were added.
Two of the 342 participants were excluded from the symbol-learning task analyses;
although they did not understand Mandarin, they reported knowing certain characters because
they are also used in Japanese Kanji. Finally, RT analyses of flanker and digit-symbol
substitution tasks excluded incorrect responses (3.0% and 2.2% of responses, respectively),
and excluded correct responses ±2 SDs from a participant’s mean at each timepoint (4.0%
and 4.7% of correct responses, respectively).
All data and analysis scripts are openly available (Ingram et al., 2021).
Results
Iowa gambling task
Time had a significant effect on the number of advantageous deck selections
[χ2(4)=1835.90, p<.001; see Figure 1].
<<Figure 1 about here>>
Planned contrasts showed improvement (i.e., higher number of advantageous
selections) from Week 1 to 3 (z=40.74, p<.001), from Week 3 to 5 (z=37.53, p<.001), from
Week 5 to 9 (z=26.74, p<.001), and from Week 9 to 13 (z=19.72, p<.001). The subset model
showed qualitatively the same results. The shielding status model revealed a significant
Group × Time interaction [χ2(4)=11.83, p<.05]. This was solely due to a greater improvement
from Week 9 to 13 for shielding [χ2(1)=27.72, p<.001] than non-shielding participants
[χ2(1)=34.84, p<.001]. The living status model also revealed a significant Group × Time
interaction [χ2(4)=79.55, p<.001]. This was due to a greater improvement from Week 1 to 3
11

for solitary [χ2(1)=75.70, p<.001] than non-solitary participants [χ2(1)=290.92, p<.001], an
improvement from Week 3 to 5 for non-solitary [χ2(1)=221.44, p<.001] but not solitary
participants [χ2(1)=0.13, p=.71], an improvement from Weeks 5 to 9 for solitary [χ2(1)=29.95,
p<.001] but not non-solitary participants [χ2(1)=0.26, p=.61], and an improvement from Week
9 to 13 for non-solitary participants [χ2(1)=112.93, p<.001] but a deterioration for solitary
participants [χ2(1)=36.00, p<.001].
Flanker task
Time had a significant effect on the number of correct responses [χ2(4)=24.19, p<.001;
see Figure 2].
<<Figure 2 about here>>
Planned contrasts showed no differences between Week 1 and 3 (z=0.80, p=.43), a
deterioration from Week 3 to 5 (z=-2.98, p<.01), a further deterioration from Week 5 to 9 (z=-
3.36, p<.001), and no differences between Week 9 and 13 (z=-1.80, p=.072). The subset
model showed qualitatively the same results. We could not test the Group × Time interaction
in the shielding status model due to model non-convergence. The living status model revealed
a significant Group × Time interaction [χ2(4)=18.08, p<.01]. This was solely due to an
improvement from Week 1 to 3 for non-solitary participants [χ2(1)=23.54, p<.001] but a
deterioration for solitary participants [χ2(1)=8.47, p<.01].
Time also had a significant effect on RT [χ2(4)=453.02, p<.001; see Figure 2]. Planned
contrasts showed speeding-up from Week 1 to 3 (t=-20.52, p<.001), slowing from Week 3 to
5 (t=-9.69, p<.001), speeding-up from Week 5 to 9 (t=-7.31, p<.001) and from Week 9 to 13
(t=-6.26, p<.001). The subset model showed qualitatively the same results. The shielding
status model revealed a significant Group × Time interaction [χ2(4)=41.84, p<.001]. This was
due to a greater speeding-up from Week 1 to 3 for shielding [χ2(1)=134.73, p<.001] than non-
12

shielding participants [χ2(1)=332.40, p<.001], a greater slowing from Week 3 to 5 for
shielding [χ2(1)=31.15, p<.001] than non-shielding participants [χ2(1)=4.79, p<.05], and a
greater speeding-up from Week 5 to 9 for shielding [χ2(1)=27.22, p<.001] than non-shielding
participants [χ2(1)=3.71, p=.054]. The living status model also revealed a significant Group ×
Time interaction [χ2(4)=88.49, p<.001]. This was due to a significant slowing from Week 3 to
5 for non-solitary [χ2(1)=21.94, p<.001] but not solitary participants [χ2(1)=0.20, p=.65], a
speeding-up from Week 5 to 9 for non-solitary participants [χ2(1)=11.87, p<.001] but a
slowing for solitary participants [χ2(1)=53.78, p<.001], and a significant speeding-up from
Week 9 to 13 for solitary [χ2(1)=38.30, p<.001] but not non-solitary participants [χ2(1)=3.37,
p=.07]. Similar results were obtained when Trial (congruent, incongruent) was included as an
additional fixed effect.
Symbol-learning task
Time had a significant effect on the number of correctly recalled meanings
[χ2(4)=25.32, p<.001; see Figure 3].
<<Figure 3 about here>>
Planned contrasts showed an improvement from Week 1 to 3 (z=4.03, p<.001), from
Week 3 to 5 (z=4.12, p<.001), from Week 5 to 9 (z=3.94, p<.001), and a non-significant
deterioration from Week 9 to 13 (z=1.04, p=.30). The subset model showed similar results,
except there was no difference between Week 1 and 3 (z=1.49, p=.14). The Group × Time
interaction was non-significant in both the shielding status [χ2(4)=1.27, p=.87] and living
status models [χ2(4)=5.12, p=.28].
Time production task
13

Time had a significant effect on time deviation score, or the numerical difference
between participants’ RT and the amount of time they were asked to estimate/produce
[χ2(4)=58.13, p<.001; see Figure 4].
<<Figure 4 about here>>
Planned contrasts showed a shift towards less underestimation from Week 1 to 3
(t=7.23, p<.001), a shift towards overestimation from Week 3 to 5 (t=6.38, p<.001), and
greater overestimation from Week 5 to 9 (t=5.06, p<.001) and from Week 9 to 13 (t=3.93,
p<.001). The subset model showed qualitatively the same results. The Group × Time
interaction was non-significant in the shielding status [χ2(4)=8.01, p=.09] and living status
models [χ2(4)=7.24, p=.12].
Digit-symbol substitution task
Time had a significant effect on the number of correct responses [χ2(4)=980.84,
p<.001; see Figure 5].
<<Figure 5 about here>>
Planned contrasts showed a significant improvement from Week 1 and 3 (z=28.85,
p<.001), from Week 3 to 5 (z=17.74, p<.01), from Week 5 to 9 (z=10.43, p<.001), and a
deterioration between Week 9 and 13 (z=-2.84, p<.01). The subset model showed
qualitatively the same results. We could not test the Group × Time interaction in the shielding
and living status models due to model non-convergence.
Time also had a significant effect on RT [χ2(4)=7048.80, p<.001; see Figure 4].
Planned contrasts showed a speeding-up from Week 1 to 3 (t=-80.31, p<.001), from Week 3
to 5 (t=-77.13, p<.001), from Week 5 to 9 (t=-57.40, p<.001), and from Week 9 to 13 (t=-
38.24, p<.001). The subset model showed qualitatively the same results. The shielding status
model revealed a significant Group × Time interaction [χ2(4)=30.58, p<.001]. This was due to
14

a greater speeding-up from Week 1 to 3 for non-shielding [χ2(1)=1146.47, p<.001] than
shielding participants [χ2(1)=87.96, p<.001] and a greater speeding-up from Week 3 to 5 for
shielding [χ2(1)=673.61, p<.001] than non-shielding participants [χ2(1)=239.24, p<.001]. The
living status model also revealed a significant Group × Time interaction [χ2(4)=32.11,
p<.001]. This was due to a greater speeding-up from Week 3 to 5 for non-solitary
[χ2(1)=871.16, p<.001] than solitary participants [χ2(1)=47.64, p<.001], greater speeding-up
from Week 5 to 9 for solitary [χ2(1)=46.28, p<.001] than non-solitary participants
[χ2(1)=52.74, p<.001], and greater speeding-up from Week 9 to 13 for solitary [χ2(1)=49.19,
p<.001] than non-solitary participants [χ2(1)=22.99, p<.001].
Mood rating task
Time had a significant effect on NMR [χ2(4)=10.99, p<.05; see Figure 6]. Planned
contrasts showed an improvement (i.e., lower NMR) from Week 1 to 3 (t=-2.28, p<.05), a
deterioration from Week 3 to 5 (t=-2.28, p<.05), and an improvement from Week 5 to 9 (t=-
3.15, p<.01) and from Week 9 to 13 (t=-2.13, p<.05). The effect of Time was marginal in the
subset model [χ2(4)=8.94, p=.063]. The Group × Time interaction was non-significant in both
the shielding status [χ2(4)=2.14, p= 71] and living status models [χ2(4)=4.29, p=.37].
<<Figure 6 about here>>
Discussion
Our results suggest that prolonged time in a socially-impoverished environment was
detrimental to key aspects of cognitive function. Crucially, Group × Time interactions
indicated that differential social isolation differentially influenced cognitive function.
15

We first consider three of our tasks which most-clearly represent executive function.
Iowa Gambling Task (IGT) selections consistently improved as restrictions were eased,
except for shielding participants. Shielding participant did not show IGT improvement until
between Week 9 and 13 when shielding was “paused” (shielding individuals were required to
follow the same restrictions as other individuals during the pause). Flanker task RT
performance generally improved as restrictions were eased, with a decline in Week 5
corresponding with an increase in negative mood. Digit-symbol substitution showed general
improvement over time; these improvements were greatest for solitary participants in later
weeks, reflecting the broadest re-opening of society between Week 5 and 9. These solitary
participants could now benefit from visiting other people (and having visitors) inside their
homes, as well as the re-opening of many cultural amenities. We additionally tested
participants’ time-estimation and symbol-learning performance. The most-robust finding for
time production was that of a qualitative and quantitative difference in time-estimation as
lockdown conditions eased, from significant underestimation to significant overestimation.
Symbol-learning showed consistent improvement, but no significant Group × Time
interactions for either shielding, non-shielding / solitary, non-solitary dwellers.
Older adults experiencing cognitive decline show riskier decision-making in
comparison to healthy controls (Smart & Krawitz, 2015); age-related decline in cognitive
processing may lead to decision-making deficits as adults age (Beitz et al., 2014). Studies
using rodents demonstrate direct effects of isolation on decision-making ability using an
adapted version of the IGT (Zeeb et al., 2013). Our IGT analyses show that decision-making
ability improved in less-restrictive conditions; this was qualified by an interaction with
shielding status. This suggests that restricting social behaviours due to the COVID-19
pandemic led to poorer, riskier decision-making.
16

Flanker tasks probe selective attention; we observed flanker RT deficit during the
greatest level of isolation. This executive function task-decrement during severe social
restriction is supported by studies involving both older and younger adults (Baumeister &
DeWall, 2005; Cacioppo et al., 2000; Twenge et al., 2001). In one study the mere suggestion
of a future spent alone led to problems with higher-order cognitive and self-regulatory
processes (Baumeister et al., 2005); and so the effect of prolonged time spent in a highly
restricted social environment is reflected in the poorer performance on the flanker task,
particularly at the first timepoint. In addition, fluctuations in flanker task performance
corresponding with negative mood rating in our analyses align with previous research
indicating an effect of depression on selective attention (see Chepenik et al., 2007 for a
review).
Both accuracy and RT data from the digit-symbol substitution task (DSST) support
the hypothesis that cognitive function would be poorer during severe social restrictions.
Whilst research involving space exploration has shown minimal effects using the DSST, these
trips generally lasted less than a week and involved highly trained participants (Kelly et al.,
2005). A decline in cognitive functioning has been linked to prolonged social isolation in
older adults (Evans et al., 2019a). Research using the Symbol Digit Modality Test (SDMT;
Smith 2007) have shown that information processing and working memory components
(similar to those assessed by the DSST) have a reduced rate of decline when older adults
maintain social networks and social engagements (Barnes et al., 2004).
Time perception task analyses demonstrate an interesting effect. Rather than improve
as lockdown conditions eased, participants evolved from underestimating time-elapsed when
restrictions were severe to overestimating time-elapsed when restrictions were most relaxed.
This suggests that participants’ time-estimation had slowed-down as restrictions were eased.
This result reflects early work on cognitive processing in space expeditions (Ratino et al.,
17

1988). Astronauts’ time-estimation was impaired; particularly, in over-estimating brief time
intervals (2 sec) near the end of journey and immediately after landing. This was attributed to
astronauts’ high workload at the end of a mission. However, the greatest difference was
observed in the first time-estimation assessment immediately after landing on Earth. It is
possible that this effect arose from the relaxation or relief associated with successful mission-
accomplishment; this explanation could also apply to the present results. As lockdown
restrictions eased, participants felt more relaxed (as evidenced by lower NMR) and began to
perceive time passing more slowly.
Significant improvement in negative mood rating as lockdown restrictions eased
indicated the benefits of socialisation and freedom of movement. These results support those
of Ingram et al. (2020). Cognitive function, particularly attention, varies with mood in
isolated (polar) conditions, however these changes were previously considered to be linked to
temperature-related hormone changes (Reed et al., 2001). Our results have implications for
research on cognitive ageing, particularly in relation to cognitive reserve.
We have demonstrated that even relatively short-term social isolation – specifically,
reduced social contact with those outside the household – has a negative impact on cognitive
abilities/executive functions. These results are in line with studies which demonstrated a link
between social isolation and cognitive decline in older adults (Evans et al., 2019a; Kuiper et
al., 2016). Social interactions are thought to preserve cognitive abilities through the process
of cognitive reserve (Stern, 2009); however, in traditional ageing research, it is difficult to
differentiate between decline caused by lack of social contact and reduced social contact due
to age-related decline (Gow et al., 2007). The imposed reduction in social contact for our
participants (Mage=32.1 years, SDage=11.2) allows us to attribute poorer cognitive function to
social isolation, as opposed to the reverse. Fluctuations in performance on tasks are also
found when comparing participants who lived alone (12% of sample) during “lockdown” to
18

those who lived with others. Specifically, improvements for participants living alone were
seen between Week 5 and Week 9, which is when those who were living alone could form
“extended households” so that they could visit one other household and be visited by that
same other household. Studies of older adults have shown conflicting results with respect to
the independent influences of living alone and social isolation on cognitive function (see
Evans et al., 2019b for a detailed discussion). Our results support a reduction in cognitive
ability for those living alone; however, note that this is a small sample size, and these
participants had no opportunity to engage in face-to-face social contact to mitigate the
increased social isolation experienced whilst living alone during “lockdown”.
Another factor which may relate to our results is that of constriction of life-space.
Life-space refers to the daily extent of movement throughout the environment; that is, a
physical measure of spaces (e.g., home, neighbourhood, town, etc.) that a person frequents.
Restricted life-space is linked to increased risk of Alzheimer’s dementia (AD) and milder
cognitive impairment in older adults (James et al., 2011). Life-space-constrained participants
– for instance, those who rarely left their home or neighbourhood – were twice as likely to
develop AD than those with larger life-space, controlling for social network size (James et al.,
2011). These results and our own suggest that physically-restrictive conditions can drive
cognitive decline, as opposed to only social restrictions / social isolation. Therefore, strategies
to alleviate cognitive decline should not focus exclusively on encouraging online social
interaction, as this does not expand life-space.
Restrictions to, or reduced, physical activity may also be linked with reduced
cognitive ability. Physical activity has been shown to protect against dementia and benefit
cognition (Fratiglioni et al., 2004). Whilst engaging in aerobic exercise seems to improve
older adults’ abilities on tasks involving executive control (Kramer et al., 1999), it is difficult
in research involving older adults to unpick the relationship between cognitive decline, social
19

interaction, and physical activity (Richards et al., 2003), or between physical function (e.g.
mobility), life-space, and cognitive ability (De Silva et al., 2019). It is a limitation of the
current study that physical activity was not tracked across timepoints. However, at timepoint
1, 52.7% of participants reported having increased or perceived no-change to their level of
physical activity since the “lockdown” conditions were imposed. Therefore, a decrease in
physical activity due to restrictions cannot account for the decline in cognitive function
within this group. These reported changes in physical activity support the conclusion that
reduced social interaction, and life-space, account for our results, with easing of restrictions
leading to graded improvement in performance on cognitive tasks.
Our study is somewhat limited as, due to the immediate instigation of lockdown
measures within the UK, we were unable to gather baseline measures of cognitive function.
As a consequence, it is not possible to show the level of initial cognitive decline, or any
adaptation of cognitive processes to the socially-impoverished conditions. However, the
finding of improvements over time seen across tasks supports theories of cognitive
enrichment and plasticity (Hertzog et al., 2009). Our results demonstrate plasticity of
cognitive function, with graded improvement in tasks as restricted eased qualified by
differing patterns across groups. The effect of practice on task improvements cannot be ruled
out without baseline measures. It is important to note though that the differing patterns of
improvement for the shielding and living alone participants, which correspond with differing
changes to restrictions aligned with these groups, suggest this is not the case. Similarly,
fluctuations in improvements linked to mood further indicate that the observed results are
driven by the very nature of restrictions, rather than repeated testing within our study.
We demonstrate that restrictive living conditions consequent of the COVID-19
pandemic related to poorer cognitive performance. Easing of restrictions allowed more
mobility, and social contact coincided with improvement in a number of tests of cognitive
20

function. This pattern was reinforced by evidence that individuals who were more isolated
(shielding participants) demonstrated longer-lasting deficits in cognition. Our results support
the theory of cognitive reserve and suggest that maintaining social relationships throughout
the lifespan plays a role in maintaining cognitive ability. Continued restrictions to social
contact and life-space may be highly detrimental to cognitive function. As such, if lockdown
conditions continue to be used in the fight against the COVID-19 pandemic, strategies to
alleviate cognitive decline during prolonged restrictive conditions should be considered. As a
true substitute for social contact and life-space is unlikely to be found, policymakers may
wish to also consider the effect on cognitive function when implementing restrictions. Future
research may wish to address longer-term effects on cognitive function as restrictions
continue to be relaxed and then tightened.
21

Footnotes
1 Mixed-effects models typically fail to converge when testing too many or too complex
effects that are not supported by the underlying data (see Bates et al., 2015), especially when
modelling binary outcome variables.
22

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32

Table 1. Participant sample demographics
Sample N Mean Age
Main 342 32.1 years (SD=11.2)
Subset 203 33.4 years (SD=11.9)
Gender-Sex Female Male Non-Binary Trans
Main 56.6% 41.3% 0.9% 0.6%
Subset 56.2% 42.4% 0.5% 1.0%
Location Town City Suburbs Village Countryside
Main 32.2% 26.6% 22.2% 12.6% 6.4%
Subset 29.6% 27.1% 24.6% 10.8% 7.9%
Relationship Status Single Married In a Relationship Divorced Separated
Main 28.9% 26.6% 41.8% 0.9% 1.8%
Subset 30.1% 25.1% 42.9% 1.0% 1.0%
Household Partner only Parents Partner + Children Living Alone Other Adult Alone +Child(ren)
Main 29.2% 24.9% 21.4% 12.0% 6.4% 3.5%
Subset 24.9% 30.0% 20.8% 16.2% 5.1% 3.1%
Student Status Full-Time Part-Time Non-Student
Main 22.0% 3.5% 74.5%
Subset 18.2% 3.5% 78.3%
Employment Working from Home Unemployed Furloughed Keyworker Carer/Parent Working Away
Main 36.1% 19.9% 21.1% 14.4% 4.7% 2.3%
Subset 35.9% 21.2% 20.7% 13.6% 6.1% 2.5%
Physical Activity A lot less active A little less active About the same A little more active A lot more active
Main 23.8% 22.0% 15.8% 25.8% 12.6%
Subset 22.7% 25.1% 15.3% 22.7% 14.3%

Figure Captions
Figure 1. Iowa Gambling task: Mean proportions of selections from advantageous decks for
shielding vs. non-shielding (Panel A) and solitary vs. non-solitary participants (Panel B).
Error bars show 95% confidence intervals adjusted to remove between-subjects variance
using Morey’s (2008) method.
Figure 2. Flanker task: Mean proportions of correct responses and RTs for shielding vs. non-
shielding (Panels A & B) and solitary vs. non-solitary participants (Panels C & D). Error bars
show 95% confidence intervals adjusted to remove between-subjects variance using Morey’s
(2008) method.
Figure 3. Symbol learning task: Mean proportions of correctly recalled meanings for
shielding vs. non-shielding (Panel A) and solitary vs. non-solitary participants (Panel B).
Error bars show 95% confidence intervals adjusted to remove between-subjects variance
using Morey’s (2008) method.
Figure 4. Time production task: Mean time deviation scores (RT – target duration) for
shielding vs. non-shielding (Panel A) and solitary vs. non-solitary participants (Panel B).
Scores below the dashed line represent underestimation, whereas those above represent
overestimation. Error bars show 95% confidence intervals adjusted to remove between-
subjects variance using Morey’s (2008) method.
Figure 5. Digit-symbol substitution task: Mean proportions of correct responses and RTs for
shielding vs. non-shielding (Panels A & B) and solitary vs. non-solitary participants (Panels C
& D). Error bars show 95% confidence intervals adjusted to remove between-subjects
variance using Morey’s (2008) method.
Figure 6. Mood rating task: Mean ratings for shielding vs. non-shielding (Panel A) and
solitary vs. non-solitary participants (Panel B). Error bars show 95% confidence intervals
adjusted to remove between-subjects variance using Morey’s (2008) method. Higher ratings
denote more-negative mood.