Long-term Impact of COVID-19 on Cognition and Mental Health in an Indian Cohort

Long-term Impact of COVID-19 on Cognition and Mental Health in an Indian Cohort | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Long-term Impact of COVID-19 on Cognition and Mental Health in an Indian Cohort Aruneek Biswas , Vivek Pamnani , Priyanka Srivastava , View ORCID Profile Sreekumar Vishnu doi: https://doi.org/10.1101/2025.11.17.25340384 Aruneek Biswas 1 Cognitive Science Lab, International Institute of Information Technology , Hyderabad, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vivek Pamnani 1 Cognitive Science Lab, International Institute of Information Technology , Hyderabad, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site Priyanka Srivastava 1 Cognitive Science Lab, International Institute of Information Technology , Hyderabad, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sreekumar Vishnu 1 Cognitive Science Lab, International Institute of Information Technology , Hyderabad, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sreekumar Vishnu For correspondence: vishnu.sreekumar{at}iiit.ac.in Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Long-term cognitive effects of COVID-19 remain poorly characterized in South Asian populations. We assessed 201 Indian adults at least six months after infection using standardized tests of working memory, selective attention, and pattern separation, alongside validated mental-health scales. Disease severity was defined clinically rather than by hospitalization, capturing individuals who experienced severe illness outside hospital care. Those with moderate-to-severe COVID-19 showed pronounced working-memory deficits compared with mildly infected and uninfected controls, while selective attention and pattern separation were comparable across groups. Vaccination at the time of infection predicted better pattern separation, suggesting selective hippocampal protection. Notably, nearly all participants with moderate-to-severe illness reported a complete recovery despite continuing to exhibit measurable cognitive deficits. However, we did not observe any robust differences in affect or in the propensity toward depression and anxiety. Taken together, our findings demonstrate that hospitalization is an inadequate proxy for disease severity and that subjective recovery is not an appropriate yardstick for assessing Long-COVID, underscoring the need for context-sensitive, population-based assessments of post-COVID sequelae. 1. Introduction The COVID-19 pandemic has precipitated a global health crisis, with the long-term sequelae of SARS-CoV-2 infection emerging as a significant public health challenge. Beyond the acute respiratory illness, a substantial number of individuals experience protracted symptoms, a condition commonly referred to as “Long COVID” ( Greenhalgh et al., 2024 ). Among the most persistent and debilitating of these symptoms are the neurocognitive and psychiatric manifestations ( Natarajan et al., 2023 ; Aretouli et al., 2025 ). Patients frequently report “brain fog,” characterized by difficulties with attention, concentration, and memory, which can profoundly impact daily functioning and quality of life ( Aretouli et al., 2025 ; Jain et al., 2023 ). A large-scale study by Taquet et al. (2022) assessed neurological and psychiatric risks after COVID-19 of over a billion patients from their health records by propensity-matching them to controls with respiratory infections and identified significantly higher risks of cognitive deficit in adults, even at two years post-infection. Likewise, Elboraay et al. (2025) , in a recent meta-analysis, reported that approximately 27.1% of patients exhibited cognitive impairment and 27.8% exhibited memory disorders six months or more after infection. Latifi and Flegr (2023) found that young-adults recovering from severe COVID-19 exhibited fatigue, cognitive slowing, and deteriorated mental health months after infection; similarly, Diana et al. (2023) provide longitudinal evidence of cognitive and psychological alterations in COVID-19 up to a year after infection. So, while considerable research has been dedicated toward understanding these impairments, the majority of studies have been focused on European and American populations, leaving a critical gap in the understanding of the long-term impact on the vast and diverse populations of the Global South ( Jassat et al. (2023) ; Fig. 1B in Hou et al. (2025) ). This context is particularly important for developing tailored interventions and healthcare policies, as their unique demographic, genetic ( Niemi et al., 2022 ), and socioeconomic factors may modulate the disease’s long-term trajectory. Therefore, a focused investigation into the long-term cognitive and mental health effects of COVID-19 within S. Asian populations is imperative. Download figure Open in new tab Figure 1: Overview of the experimental design and the three cognitive tasks. (a) Sequence of the administered battery. (b) n-back task schematic illustrating the 0-back, 1-back, and 2-back conditions. Letters highlighted in green indicate trials where participants were expected to press a key; this color-coding is for illustration only and was not present during the actual task. (c) Modified Stroop Task showing example stimuli from the congruent, neutral, and neutral conditions. (d) Mnemonic Similarity Task (MST) showing the encoding and subsequent retrieval phases. Colored box boundaries denote stimulus types (targets, lures, foils) for illustration purposes only and were not visible to participants during the task. To date, only a handful of studies have examined the long-term cognitive and mental health sequelae of COVID-19 in Indian or broader South Asian cohorts, and their findings remain inconsistent. Chakrabarty et al. (2024) reported substantial rates of depression, anxiety, and insomnia among hopsitalised COVID-19 survivors in Kolkata, a year after their initial infection, alongside measurable cognitive deficits on the ACE-III and Trail Making Test, which were strongly correlated with psychiatric symptoms. In contrast, Gopinath et al. (2025) found minimal long-term impairments in a young adult cohort tested nearly two and a half years post-infection, with only subtle inefficiencies in working memory and executive control. Crucially, however, both studies relied on hospitalization status as a proxy for disease severity, a major limitation in the Indian context, where, during peak pandemic waves, many clinically severe cases went unhospitalized due to the overwhelming strain on the healthcare system. Thus, in this study, we aimed to assess the severity-specific long-term effects of COVID-19 on working memory, pattern separation, and attentional control, at least six months post-infection, using a clinically validated measure of disease severity independent of hospitalization status. We employed a battery of cognitive tasks alongside mental health questionnaires measuring anxiety, depression, and affect to obtain a comprehensive assessment of cognitive function and mental well-being. Given that our comparison involves subtler gradations of disease severity (rather than a stark hospitalized vs. non-hospitalized contrast), we were uncertain whether clear severity-dependent cognitive deficits would emerge. Nevertheless, we expected that at least some cognitive and mental health measures would show persistent impairment proportional to the severity of the acute infection. Further, while the efficacy of vaccines in preventing severe COVID-19 is well established ( Yang et al., 2023 ), their role in mitigating long-term cognitive and mental health sequelae (independent of their effect on disease severity) remains largely unexplored. In particular, few studies have used objective measures to assess these outcomes, contributing to this gap. Therefore, we also sought to examine whether vaccination provides additional protection against potentially enduring cognitive impacts of the virus. 2. Method 2.1. Materials 2.1.1. Overall Assessment Participants completed an unsupervised computerized cognitive and mental health assessment battery using an experiment designed in PsyToolkit ( Fig. 1a ), either on-site at IIIT premises or remotely via PsyToolkit Cloud using a non-mobile device. The data collection commenced on October 4, 2022 and concluded on October 6, 2023. Socio-demographic details were collected at the outset (see Appendix A.1 for the questions). Participants completed three cognitive tasks — the n-back Task ( n = 0, 1, 2) ( Miller et al., 2009 ; Frost et al., 2021 ), Stroop Task ( MacLeod, 1992 ; Algom et al., 2021 ), and Mnemonic Similarity Task (MST; ( Stark et al., 2019 )) — designed to assess working memory, selective attention, and pattern separation, respectively. Practice rounds were administered for all the tasks. Following this, they completed standard mental health assessments — Patient Health Questionnaire (PHQ; ( Kroenke et al., 2003 )), Generalized Anxiety Disorder (GAD; ( Kroenke et al., 2010 )), and Positive and Negative Affect Schedule (PANAS; ( Watson et al., 1988 )) — which measure mood, anxiety, and affect, respectively. Additionally, participants provided information about their personal and familial history of mental health disorders, details of any COVID-19 infections (including timing, severity, hospitalization status, and recovery duration), physical comorbidities, and their vaccination status at the time of the survey and their first infection (see Appendix A.2 for the questions). 2.1.2. Cognitive Tests and Performance Metrics (i) Working memory (n-back task) : In this study, we implemented three variations of the delayed-match-to-sample n-back task (NB-DMS; Fig. 1b ): a 0-back condition ( n = 0), as well as 1-back and 2-back conditions ( n = 1, 2). Two blocks for each variant was administered and the order of the variants was chosen at random. Each block comprised a sequence of 21 letters and there was a 33% probability that the current stimulus matched the one n trials back. Each letter was presented for 0.5 s, followed by a jittered inter-trial interval (ITI) ranging between 1.9 and 2.1 s; the response window extended across both the presentation window and ITI. The 0-back task operated as a go/no-go paradigm, requiring participants to press a button whenever a predetermined target letter, specified at the beginning of the block, appeared on the screen. This condition serves to measure sustained attention ( Miller et al., 2009 ). In contrast, the 1-back and 2-back conditions required participants to respond when the currently displayed letter matched the one presented one or two trials earlier, respectively; they serve to measure unstructured working memory span ( Frost et al., 2021 ). For all the variants, we computed d ′ (theoretical range: (−∞, ∞)) to assess task performance ( Haatveit et al., 2010 ). It quantifies sensitivity by measuring the normalized distance between the probability distributions of hits and false alarms. The higher the metric, the better the performance. (ii) Selective Attention (Serial Stroop Color-Word Interference Test) : There exists no consensus on the design of a digitally administrable Stroop task ( Brunetti et al., 2021 ); so, we roughly reproduced Exp. 1B in ( Salo et al., 2001 ) but switched the response modality from vocal to manual, for easier administration. This task serves as a reliable measure of selective attention but in light of recent evidence, we do not claim the task to be a good measure of conflict monitoring ( Algom et al., 2021 ). Our task consisted of 120 trials, divided equally under three conditions ( Fig. 1c ): (i) a color word being displayed in its corresponding ink color (congruent condition), (ii) a non-color word being displayed in one of the ink colors (neutral condition), and (iii) a color word being displayed in a non-matching ink color (incongruent condition). The trials were randomly intermixed and administered serially. For all trials, participants had to identify the ink color by pressing one of four buttons corresponding to Red, Green, Blue, or Yellow. Each stimulus was displayed for 2 s, during which participants could respond. This was followed by a jittered delay (0.45–0.55 s) to avoid entrainment effects, a fixation cross (0.2 s), and a blank screen (0.1 s) before presentation of the next stimulus, resulting in an inter-trial interval of 0.75–0.85 s. To assess task performance, we computed the percentage increase in reaction time (RT-IE; theoretical range: (−100, ∞)) for attempted incongruent trials compared to attempted congruent trials, where lower values indicate better inhibitory control. Notably, the serial version of the task produces a smaller magnitude of interference effect than the manually administered blocked variant ( Penner et al., 2012 ; Brunetti et al., 2021 ); further, the modality of manual response has been shown to reduce the interference effect compared to the classical vocal modality ( Augustinova et al., 2019 ). (iii) Pattern Separation (Mnemonic Similarity Task) : This task ( Fig. 1d ) involved two phases: the encoding phase and the testing phase. In the encoding phase, participants classified 80 stimuli as indoor or outdoor objects. In the retrieval phase, 120 stimuli were presented in random order — 40 being exact repetitions of images displayed in the previous phase (targets), 40 being new images not previously seen (foils), and the remaining being perceptually similar to those seen during the study phase, but not identical (lures; classified into five levels of increasing non-similarity from L1 to L5). Participants had to classify each stimulus as “old,” “similar,” or “new.” The task is considered to be a reliable assessor of finer pattern separation abilities and thus, hippocampal functioning ( Stark et al., 2019 ). In the encoding phase, each image was shown for up to 2 s, followed by a 0.5 s gap for the participant to make a classification choice; if participants responded early, a blank screen was displayed for the remaining time. Likewise, in the retrieval phase, each stimulus was displayed for 2 s, followed by a 0.5 s gap for the participant to log their response. As a metric of task performance, we computed the Lure Discrimination Index (LDI; theoretical range: [−1, 1]), which is the difference between the probability of providing a “similar” response to the lures and the probability of providing a “similar” response to the foils ( Stark et al., 2019 ). The higher the metric, the better the performance. 2.1.3. Mental health surveys and metrics (i) The Patient Health Questionnaire (PHQ) is a self-report Likert-type questionnaire to assess the severity of depression symptoms ( Kroenke et al., 2001 ). We initially administered the two-item version (PHQ-2) to approximately half of the participants, later switching to the more comprehensive nine-item version (PHQ-9). To ensure comparability, PHQ-9 scores were linearly scaled to match the PHQ-2 scoring range ( Kroenke et al., 2003 ). Higher scores reflect greater severity and likelihood of depression; a score of 2 or more on the PHQ-2 is suggested as a clinical cutoff for identifying individuals at risk of major depressive disorder ( Manea et al., 2016 ; Levis et al., 2020 ). (ii) The Generalized Anxiety Disorder (GAD) is a self-report Likert-type questionnaire assessing anxiety symptoms over the past two weeks, derived from the DSM-IV criterion ( Spitzer et al., 2006 ). We initially administered the GAD-2 (items 1 and 2 of the GAD-7; score range: 0–6) to approximately half of the participants, later switching to the GAD-7 (7 items; score range: 0–21) for a more comprehensive assessment. Scores were evaluated and scaled for the two-question variant. Higher scores indicate greater severity of anxiety symptoms; a score of 3 or more on the GAD-2 is suggested as a clinical cutoff for identifying individuals at risk of generalized anxiety disorder ( Plummer et al., 2016 ). (iii) The Positive and Negative Affect Schedule (PANAS) is a self-report scale test that contains ten questions and is used to assess positive and negative affect ( Watson et al., 1988 ). Low PA is strongly linked to depression; high NA is weakly linked to both depression an anxiety ( Watson and Levin-Aspenson, 2017 ). 2.2. Participants Any Indian citizen, aged 18 to 60 years, was eligible for enrollment in our study. We did not explicitly target COVID patients to avoid self-selection bias. Color-blind subjects were screened out via the Ishihara test ( Ishihara, 1917 ). Participants were recruited via multiple sources, including social media, email, snowball sampling, and a participant recruitment platform, CloudResearch 2 . Participants recruited via CloudResearch were compensated with 5 USD (all of them were from India), while those recruited through other sources received INR 200 for their time. This study was approved by the IIIT Hyderabad Research Ethics Committee, and participants provided informed consent via the study website before starting the assessment. A total of 256 participants were recruited through social media, email, and CloudResearch ( Hartman et al., 2023 ). 2.2.1. Groups The sample was broadly categorized into four groups. One group consisted of participants who had no history of diagnosis or infection with COVID-19. The other three groups included participants with a history of COVID-19 symptoms, ranked by the severity of those symptoms (reproduced verbatim from our survey; ( COVID-19 Treatment Guidelines Panel, 2019 )): Mild Illness: Individuals who exhibit various signs and symptoms of COVID-19 (e.g., fever, cough, sore throat, malaise—a general feeling of discomfort, illness, or uneasiness—headache, muscle pain, nausea, vomiting, diarrhea, loss of taste and smell) but do not experience short-ness of breath, dyspnea, or abnormal chest imaging. Moderate Illness: Individuals who show evidence of lower respiratory disease during clinical assessment or imaging and have an oxygen saturation (SpO 2 ) ≥ 94% of room air at sea level. Severe Illness: Individuals with (SpO 2 ) < 94% on room air at sea level, a ratio of arterial partial pressure of oxygen to fraction of inspired oxygen (PaO 2 /FiO 2 ) 30 breaths/min, or lung infiltrates > 50%. In total, 256 participants had completed the survey, but 55 participants had to be excluded for failing quality control checks and/or giving inconsistent data and/or being unqualified for our assessments, as elaborated in Appendix B . We merged the moderate and severe categories into one group ( mdrt+ henceforth) due to the extremely low number of severely infected participants; eleven asymptomatic patients were precluded from any analysis for the same reason ( Table 1 ). Further, since our analysis focuses on the long-term effects of COVID-19, we exclude six patients who were infected less than six months before the survey, three patients who did not report the probable time of infection, and three patients who gave an implausible infection date; all of them had reported a mild severity. View this table: View inline View popup Download powerpoint Table 1: Medical severity profile of participants These severity categories were meant to be used by physicians, than self-diagnosis by patients, but we believe the language to have been lucid enough to trust the mild/mdrt+ distinction that we build upon. 2.3. Statistical Methods The data was pre-processed in Python using Statsmodels ( Seabold and Perktold, 2010 ) and SciKit ( Pedregosa et al., 2011 ), and visualized using PtitPrince ( Allen et al., 2021 ). Regression modeling and diagnostics were done exclusively in R using default functions, and the following packages: Ordinal ( Christensen, 2023 ), boot (Angelo Canty and B. D. Ripley, 2024), and gofcat ( Ugba, 2022 ). Participant data for a cognitive task were excluded if they exhibited excessive non-responses (>50% of trials; except in the n-back task) or unusually slow mean response times ( z -score > 3), which likely reflected impaired task engagement or lack of diligence. To evaluate whether performance was above chance, we conducted a one-sided Wilcoxon signed-rank test (Q–Q plots revealed substantial deviations from normality in almost all cases), with the null hypothesis that the median of each metric distribution was equal to zero, tested at the 5% significance level. 2.3.1. Composite Scores To assess global cognitive performance across the tested domains, as in Hampshire et al. (2021) , we extracted the Cognitive Composite Score (CCS) by applying Principal Component Analysis (PCA) on the standardized (z-scored) cognitive metrics, using Singular Value Decomposition, and taking the first component (explained ≈ 43% of the variance): CCS = −0.01×Stroop-RT-IE + 0.39× d ′ (0-back) + 0.60× d ′ (1-back) + 0.62× d ′ (2-back) + 0.33 × MST-LDI. Cognitive Composite Scores were calculated for only those participants who attempted all the five tasks. A higher CCS score corresponded to better cognition. Likewise, we extracted the Mental Composite Score (MCS) from the standardized (z-scored) mental health metrics described above (explained ≈ 60% of the variance): MCS = 0.30 × PANAS PA Score − 0.55 × PHQ-2 score − 0.55 × GAD-2 score − 0.55 × PANAS NA Score. A higher MCS score signifies better mental health. 2.3.2. Regression Modelling (i) Cognitive Metrics: To assess the impact of COVID-19 status and severity on our individual cognitive outcomes and Cognitive Composite Score (CCS), we modeled the metrics as follows: (a) d ′ (n-back Task), RT-IE (Stroop Task), and Cognitive Composite Score: Being unbounded and continuous, these metrics were modeled using Multiple Linear Regression Models (MLRM). (b) LDI (MST): We applied a linear transformation to map the outcome variables onto the interval (0, 1) and ran a Beta Regression Model (BRM) ( Geissinger et al., 2022 ) with a logit link. Numerous alternatives exist for modeling continuous bounded out-comes ( Korkmaz, 2019 ), most are seldom used, lack mature software implementations, and require a level of theoretical grounding beyond the present scope; hence, we adopted the well-established Beta regression framework. We applied bias correction ( Grün et al., 2012 ) to obtain more reliable estimates of the regression parameters. Two sets of comparisons were conducted: (a) Infected participants vs. healthy controls (b) Mdrt+ vs. mild cases, with additional control for time since first infection, number of infections, and vaccination status at the time of the first infection. This also enabled us to evaluate the protective effects of vaccination on cognition—beyond those attributable to reduced disease severity—and to determine whether any observed impairments were chronic or showed signs of improvement over time. We included age (modeled as a quadratic polynomial; see Ferrer et al. 2004 ), gender, education, region, presence of physical comorbidities, and GAD and PHQ scores (as proxies for anxiety and depression, respectively) as nuisance variables. These covariates were selected based on prior literature (see Pliatsikas et al. (2018) , Nikolin et al. (2021) , and Lisica et al. (2022) for evidence of their relevance to the n-back task, and Van der Elst et al. (2006) , Epp et al. (2012) , and Menon et al. (2024) for their relevance to the Stroop task). Given the paucity of literature on the MST, the same covariates were assumed to be relevant for it as well. We avoided automatic selection techniques like stepwise regression due to their tendency to produce biased estimates and inflated p-values, among other issues, as described in Harrell (2015b) . Interaction terms, though plausible, were not included due to the sample size being insufficient to test interaction effects reliably ( Gelman, 2018 ). (ii) Mental Health Metrics: To assess the impact of COVID-19 status and severity on our individual mental health metrics and Mental Composite Score (MCS), we modeled the metrics as follows: (a) PANAS PA and NA, GAD, and PHQ: These metrics, being ordinal, were modeled using Ordered Logit Models (OLM) (i.e. Proportional Odds Logit Model). Additionally, GAD and PHQ scores, having a clinical cutoff at 3, were binarized and modeled using Logit Models (LM); to address separation issues, we applied mean bias reduction ( Kosmidis et al., 2019 ) and obtained more stable estimates of the regression parameters. (b) Mental Composite Score: The metric, being continuous and unbounded, was modeled using a Multiple Linear Regression Model (MLRM). The same two-tier comparison strategy, i.e., (a) infected participants vs. healthy controls and (b) Mdrt+ vs. mild cases, with the same control variables, was applied as in the case of cognitive metrics. As before, we avoided automatic methods ( Hosmer et al., 2013c ) and relied on the clinical literature ( Akhtar-Danesh and Landeen (2007) ; Kessler et al. (2001) ; Jayasankar et al. (2023) ) to include age, gender, region, education, and presence of physical comorbidities as nuisance variables. 2.3.3. Model Assumptions and Validation (i) MLRM : Multiple Linear Regression Model requires (a) independence of observations, (b) homogeneity of residual variance, and (c) normality of the pooled residuals, in order of decreasing priority ( Zuur et al., 2009 ). Additionally, the model must exhibit acceptable goodness-of-fit. Independence is ensured by the structure of our dataset, where each subject contributes only one observation. For the remaining assumptions, we avoid blunt-edged statistical tests ( Zuur et al., 2009 ) and instead rely on visual checks. Except for d ′ (0-back), Q-Q plots of the residuals for any of the metrics in our models did not suggest any significant deviation from normality; simulation studies and practical experience across empirical studies attest that minor violations of the normality assumption, as seen in our examples, are allowed ( Knief and Forstmeier, 2021 ). Likewise, plots of standardized residuals versus fitted values and scale-location plots showed no evidence of heteroskedasticity. We also verified that our models were not unduly influenced by outliers. To assess goodness-of-fit, we conducted F-tests; all models were significant at α = 0.05. (ii) BRM: The Beta Regression Model requires that observations are independent and that the dependent variable is continuous and restricted to the open unit interval (0, 1) ( Ferrari and Cribari-Neto, 2004 ). Independence of observations is, again, ensured by the study design. Model adequacy was assessed using the six standard diagnostic plots ( Cribari-Neto and Zeileis, 2010 ). Across all cases, the Pearson residuals did not show any obvious pattern, with no evidence of misspecification (i.e., lack of GoF), heteroskedasticity beyond that accounted for by the precision parameter, or observations exerting disproportionate influence. (iii) OLM: Ordered (ordinal) Logit Model requires that observations are independent and that the proportional odds assumption holds. Additionally, the model must demonstrate an acceptable goodness-of-fit. The first requirement continues to be met by the very nature of our data while the second is typically evaluated by Brant’s Wald test ( Hosmer et al., 2013b ). In our case, the test returned p-values less than 0.05 for all the models; however, given the very low expected frequencies in the contingency structure of our data (80% cells had values less than 0.01) due to sparse outcomes in many of our covariates, the statistical test could not be relied upon to evaluate our models ( Conover, 1999 ). When we plotted observed and model-estimated means of each predictor across outcome levels of the ordinal outcome, albeit in a simple univariate way, we found a total lack of monotonicity and overlap between the curves, indicating potentially severe violations of the proportional odds assumption ( Harrell, 2015c ). Indeed, plots of score residuals (a U-shaped fit is expected) and partial residuals (roughly parallel lines are expected) from the model show severe violations ( Harrell, 2015c ). So, we rely on the observation from simulation studies that violations of the proportional odds assumption are usually not fatal for the model but nonetheless suggest caution when interpreting the results ( Harrell, 2020 ). (iv) LM: The Logit Model requires independence of observations and a linear relationship between the log odds of the dependent variable and each of the continuous covariates ( Harrell, 2015a ). Additionally, the model must demonstrate an acceptable goodness-of-fit. The first requirement continues to be met by the very nature of our data. The second requirement could not be resolved despite adding spline terms for our continuous variables (which improved the fit of our model only in a single case) and adding pairwise interactions ( Harrell, 2015a ). Thus, instead of odds ratios, we only report the effect size (Average Marginal Effects; see Section 2.3.5 for details) as they are not only more robust to small sample size and nonlinear predictor functions ( Bergtold et al., 2017 ) but also less sensitive to the sampled data and the model specification ( Norton and Dowd (2017) ; but see Harrell (2021) for concerns on biases arising out of sample unrepresentativeness). We also verified that our models were not unduly influenced by outliers ( Hosmer et al., 2013a ). To evaluate goodness-of-fit (and diagnostic discrimination), rather than relying on the widely criticized Hosmer-Lemeshow test, we calculated the area under the Receiver Operating Curve (ROC) ( Harrell, 2015a ; Hosmer et al., 2013a ). The area for each model exceeded 0.5. For all diagnostic plots, see our code repository. 2.3.4. Bootstrapping and Confidence Interval We sampled paired response–predictor observations with replacement 2000 times ( Fox, 2015 ), used these resampled datasets to refit the model and obtain a distribution of regression coefficients, and used the bias-corrected and accelerated (BCa) bootstrap method ( Efron, 1987 ) to construct our Confidence Interval (CI). 2.3.5. Effect Size Indices For categorical variables modeled using Multiple Linear Regression Models (MLRM), the regression coefficients themselves represent level-specific effect sizes and following Fox and Weisberg (2019) , we do not standardize them. To quantify the contribution of each categorical variable as a whole, we report the partial omega squared ( ), which estimates the expected proportion of population variance uniquely explained by that variable ( Kroes and Finley, 2025 ; Richardson, 2011 ). For categorical variables modeled using Ordinal Logit Models (OLM) or Logit Models (LM), we report the Average Marginal Effects (AME), i.e., the difference in model-predicted means between levels of a categorical variable on a perfectly balanced grid of categorical predictors with numeric predictors held at their means, as a raw indicator of group mean differences. Additionally, since for LMs, we report the AME in lieu of the regression coefficients, we also report the corresponding p-values and confidence intervals using the BCa method. For categorical variables modeled using Beta Regression Models (BRM), we do not report any effect size due to the lack of established metrics in the available literature. 2.3.6. Reporting of Statistical Significance While recognizing the limitations of exclusive reliance on p-values ( Wasserstein and Lazar, 2016 ), we consider it to be a good yardstick in line with the latest statement by American Statistical Association ( Benjamini et al., 2021 ) which suggests supplementing it with other measures. However, we acknowledge that the conventional 0.05 threshold is somewhat arbitrary ( Amrhein and Greenland, 2017 ). Thus, while we treat results as significant only when the two-tailed p-value is below 0.05, we also report results with p-values near this threshold to highlight variables of interest for future research. Further, we supplement all our results with the confidence interval (CI). So, overall, we mention the following statistics when reporting any finding: (a) for GLMs, BRMs, and OLMs, unstandardized regression coefficient; for LMs, the Average Marginal Effect, (b) the corresponding two-tailed p-value, and (c) the corresponding bootstrapped 95% confidence interval (CI), rounded to two decimal places. 3. Results Participants were distributed across a wide age range ( M = 30.85 ± 0.71; Fig. 2a ). The groups were balanced w.r.t gender and education levels ( Table 2 ). The timing of first infection among our participants was broadly spread across the COVID-19 pandemic, with the peaks roughly aligning with surges in India, particularly in mid-2021 and early 2022 ( Fig. 2b ). This congruence not only suggests that our findings are unlikely to be driven by a particular viral strain but also that the self-reported infection dates were largely accurate. Cases across all severity groups were distributed across India (except for the North and North-Eastern Cultural Zones (Ministry of Culture, Government of India, 2025)) indicating a spatially diverse cohort ( Fig. 2c ). Physical comorbidities were rare across all the severity groups ( Fig. 2d ). Download figure Open in new tab Figure 2: Demographic and clinical overview of study participants. (a) Age distribution of participants, grouped by COVID-19 severity (or lack thereof). (b) Self-reported timing of first infection from infected participants, grouped by COVID-19 severity. A kernel density estimate (KDE) of pan-India COVID-19 cases (sourced from WHO data) is overlaid to contextualize w.r.t the infection waves. (c) Geographic spread of participants, grouped by COVID-19 severity. (d) Prevalence of physical comorbidities across severity groups. The mirrored layout was adopted for visual compactness and does not reflect any data transformation. View this table: View inline View popup Download powerpoint Table 2: Demographic profile of participants (%) across the severity groups It is prudent to note that the vast majority of our infected participants would not have met the criteria for Long COVID, which requires symptoms persisting for at least three months after the initial infection and not attributable to alternative diagnoses ( Greenhalgh et al., 2024 ). Most of our participants (87% of mild patients; 96% of mdrt+ patients) reported to have “fully recovered” at the time of the survey, with the preponderant majority (81% of mild patients; 74% of mdrt+ patients) claiming to have recovered within 2 months of being infected. Overall, those who contracted COVID-19 of moderate-and-above severity fared the worst in all tasks. Participants performed well above chance across groups in all tasks ( Table 3 ). In the following sections, we first report the general impact of infection severity on overall cognitive performance and mental health for our overall cohort, as measured with the composite scores, and then report the effect on performance in individual tasks and questionnaires only if they are statistically significant. We also describe how vaccination confers some protective effects against the impact of COVID-19 on cognition. View this table: View inline View popup Download powerpoint Table 3: Average cognitive task scores and one-sided Wilcoxon signed-rank p-values, testing whether median scores were greater than zero (chance-level performance). N.B.: Not all participants attempted all tasks. View this table: View inline View popup Download powerpoint Table 4: Regression results comparing cognitive and mental health metrics across COVID-19 groups. Significant results ( p < 0.05) are boldfaced. The regression β comes from different models; for LDI, the coefficient of the transformed variable (( LDI + 1) / 2) is reported. 3.1. Cognitive Performance Patients who experienced COVID-19 of moderate and above severity (Mdrt+) demonstrated poorer global cognition compared to both healthy controls (β = −1.31; p < 0.005; 95% C.I. = [−1.94, −0.60]; = 0.07) and mildly infected patients (β = −1.30; p < 0.005; 95% C.I. = [−2.08, −0.42]; = 0.11), as shown in Fig. 3a . In contrast, mildly infected patients did not demonstrate any significant decline w.r.t healthy controls (β = 0.07; p = 0.75; 95% C.I. = [−0.38, 0.52]). These results suggest that greater COVID-19 severity is associated with substantial impairments in overall cognitive functioning and the moderate effect sizes ( Field, 2019 ) indicate that severity differences accounted for a meaningful and reliable portion of the variance in cognitive performance. Download figure Open in new tab Figure 3: Impairments in cognitive performance across COVID-19 severity and vaccination status. All p-values are from our GLM models. (a) Cognitive Composite Scores were significantly lower in the Mdrt+ group compared to both healthy controls and mildly infected participants. (b) Severely deteriorated performance in the 1-back task in the Mdrt+ group compared to both healthy controls and mildly infected participants. (c) Severely deteriorated performance in the 2-back task in the Mdrt+ group compared to both healthy controls and mildly infected participants. (d) Individuals vaccinated, as of the infection, show better performance on the MST (Lure Discrimination Index), regardless of severity. To further understand the specific domains that were affected, we performed regressions on the individual tasks and found that this decline was primarily driven by poor performance on the 1-back task (Mdrt+ vs Healthy: β = −1.30; p < 0.005; 95% C.I. = [−2.13, −0.33]; = 0.03 and Mdrt+ vs Mild: β = −1.10; p = 0.03; 95% C.I. = [−2.15, 0.12]; = 0.04) and 2-back task (Mdrt+ vs Healthy: β = −1.36; p < 0.005; 95% C.I. = [−1.97, −0.77]; = 0.07 and Mdrt+ vs Mild: β = −1.36; p < 0.005; 95% C.I. = [−2.08, −0.63]; = 0.10), as shown in Fig. 3b and Fig. 3c , respectively. This drop in performance was not due to a speed-accuracy tradeoff where people who respond faster tend to be less accurate; for both the tasks, there were no significant differences between the average time taken by patients across the three groups. Thus, taken together, the observed cognitive decline in the Mdrt+ group can be primarily attributed to decline in working memory. 3.2. Mental Health Patients who contracted COVID-19 of a moderate and above severity did not show a statistically significant difference in overall mental health when compared to healthy controls (Mdrt+ vs Healthy: β = −0.06; p = 0.87; 95% C.I. = [−0.69, 0.54]; = 0.00) or mildly infected patients (Mdrt+ vs Mild: β = −0.24; p = 0.52; 95% C.I. = [−0.98, 0.52]; = 0.00). Expectedly, mildly infected patients did not show any difference when compared to the healthy controls (β = 0.12; p = 0.62; 95% C.I. = [−0.36, 0.60]). To understand if COVID-19 affects any specific aspect of mental health, we ran regressions on individual task-scores from the PHQ, GAD, and PANAS questionnaires but found no significant differences between any of the infected groups. To ensure that the presence of physical comorbidities was not masking potential mental health effects, we re-ran our regression models for both the composite score and individual question-naires without controlling for comorbidities. This step addressed the possibility that comorbidities acted as mediators—lying on the causal pathway from disease severity to mental health outcomes (for instance, COVID-19 increasing the risk of physical illness, which in turn affects mental health)—rather than as independent confounders. The results remained unchanged, indicating that the absence of group differences in mental health outcomes was not an artifact of adjusting for comorbidity. However, when modeling GAD as a binary outcome using the clinical threshold (score ≥ 3), we found statistically significant elevation in the likelihood of anxiety among patients with moderate and above severity compared to healthy controls (Mdrt+ vs Healthy: AME = 0.25; p = 0.02; 95%C.I. = [0.03, 0.46]). The difference in results from the OLM, most likely arose due to the sparsity across individual response levels (which can substantially affect statistical power) and/or the extensive violations of the proportional odds assumption. Nonetheless, given that the assumptions of the Logistic model were violated too ( Section 2.3.3 ), we suggest extreme caution in interpreting these findings. 3.3. Protective effect of vaccination on cognition Vaccination not only confers protection against severe illness, thereby lowering the risk of developing long-COVID, but also mitigates long-term symptom severity ( Watanabe et al., 2023 ; Maier et al., 2024 ). Therefore, we examined whether being vaccinated could mitigate long-term cognitive decline following infection. 17% of our Mdrt+ patients and 50% of our midly infected patients had received at-least one vaccination, as of their first infection — notably, this corresponds to a Relative Risk (RR) of 0.29 (Fisher’s exact test, p = 0.01), indicating that vaccinated individuals had a 71% lower risk of experiencing moderate or severe COVID-19 and further reinforcing the clinical consensus on the significant protective benefits of vaccination. Vaccinated patients reported a marginally significant improvement in global cognition compared to unvaccinated patients (Vaxxed vs None: β = 0.68; p = 0.05; 95% C.I. = [−0.06, 1.32]; = 0.01). This arose from their superior performance in the MST Task over unvaccinated counterparts (β = 0.26; p = 0.04; 95% C.I. = [−0.01, 0.46]), as shown in Fig. 3d . The improvement was not due to a speed-accuracy tradeoff; for both the tasks, there were no significant differences between the average time taken by patients across the three severity groups. Thus, vaccination appeared to have conferred a protective effect, selectively on hippocampal performance. Owing to the skewed distribution of vaccination status within the moderate-and-above (Mdrt+) group, in which most participants were unvaccinated, we examined whether our findings could have been driven by differences in infection severity (despite its inclusion as a covariate in the regression model). To address this, we repeated the analysis within a sub-cohort comprising only mildly infected participants and successfully replicated our results (see Appendix C ), indicating that the observed benefits of vaccination on hippocampal performance are robust and generalize across severity levels. Furthermore, to ensure that our findings were not artefacts of our choice of model, we re-ran all our analyses using a Multiple Linear Regression Model (MLRM) instead of a Beta Regression Model (BRM) and replicated the results (see Appendix C ). 4. Discussion Adjudged within the local context of S. Asia, our findings add to the scarce literature (see Chakrabarty et al. (2024) and Gopinath et al. (2025) for exceptions) on the long-term cognitive performance of survivors of COVID-19. In line with existing evidence from other regions ( Cui et al., 2023 ; Aretouli et al., 2025 ), we observe that individuals who experienced moderate-to-severe COVID-19 exhibit persistent deficits in working memory—as assessed by the 1-back and 2-back tasks—well beyond the acute phase and extending at least six months post-infection. Notably, these impairments were absent among mildly infected individuals, suggesting that the long-term impact of COVID-19 is primarily driven by illness severity. Importantly, time since infection did not significantly predict working memory performance, indicating that the observed deficits may be persistent rather than progressively improving, consistent with findings from the latest meta-analysis on long-COVID ( Hou et al., 2025 ) but contrary to the trends reported in ( Cui et al., 2023 ). However, unlike multiple studies that document impaired selective attention of patients with a history of COVID-19 upon administration of the Stroop Task (see Panagea et al. (2024) and Table 3 in Nasir et al. (2025) for an overview), we do not observe any such deterioration. Likewise, although studies employing the Mnemonic Similarity Task (MST) in COVID-19 populations are extremely rare, our findings diverge from Meyer and Zaiser (2025) , who reported significant hippocampal deficits in COVID-19 patients compared to healthy controls. Further, we did not observe any significant cognitive slowing (see Zhao et al. (2024) and Weinerova et al. (2025) for mixed evidence) in either of the infected groups w.r.t healthy controls on any cognitive task. These differences, we believe, stem not from minor variations in analytical approaches or demographic composition, but primarily from two factors: (a) the non-standard criteria used by us to classify disease severity (many studies equate severe cases exclusively with ICU admission, thereby increasing the likelihood of detecting cognitive deficits) and (b) our non-standard participant recruitment strategy (most prior studies tested participants within six months of infection or specifically targeted those with long-COVID; among the Stroop studies summarized in Nasir et al. (2025) , Bungenberg et al. (2022) employed the recruitment strategy most comparable to ours and, like us, reported no corresponding impairment). We elaborate on both the aspects below. Much of the existing literature on COVID-19-related cognitive dysfunction (see Voruz et al. 2022 , Vakani et al. 2023 , de Pádua Serafim et al. 2024 , Wood et al. 2024 , and Demmer et al. 2025 for individual studies; Austin et al. 2024 and Aretouli et al. 2025 for meta-analyses) classifies disease severity according to the latest WHO guidelines ( WHO Working Group on the Clinical Characterisation and Management of COVID-19 infection, 2020 ), wherein the moderate and severe categories are operationalized based on hospitalization status. Correspondingly, these studies report more significant cognitive impairments across multiple domains among hospitalized patients compared to non-hospitalized ones. Our study diverges in an important methodological respect: we adopted an older classification schema which assessed severity without wholly relying on hospitalization (≈ 87% of our moderate-to-severe cases were managed outside of hospital settings). This enabled us to detect substantial working memory impairments in patients who had not been hospitalized yet had otherwise borne the brunt of the infection, while at the same time possibly ex-plaining why we did not observe any significant impairment in selective attention or hippocampal integrity within our cohort. Our findings thereby reveal an important blind spot in current research paradigms and prompt further reflection. During the height of COVID-19 waves in India and other low-resource settings, hospitalization was not always feasible even for severe cases due to healthcare system overload ( Zargar, 2021 ; Arora, Neha and Ravikumar, Sachin, 2021 ). Relying exclusively on hospitalization as a marker of severity can therefore systematically exclude a vulnerable segment of the population who experienced the full severity of illness without access to hospital-based care. Even beyond the constraints of such contexts, select prior studies report cognitive impairments in non-hospitalized individuals: He et al. (2023) reported cognitive deterioration even among non-hospitalized patients with clinically moderate or severe COVID-19; Kirchberger et al. (2023) found similar impairments in patients with a mild disease profile; while, Miskowiak et al. (2023) found no significant difference in the extent of impairment between hospitalized and non-hospitalized patients approximately seven months after infection. Our findings align with the thrust of these results, and the effect sizes of global cognitive impairment we observe for the moderate-to-severe group are comparable to those reported for hospitalized samples in studies such as Ollila et al. (2022) . Collectively, these results caution against using hospitalization as a proxy for illness severity in studies of long-term cognitive sequelae. Another methodological distinction laid in how participants were recruited. Most studies which focus on the long-term effects of COVID-19 (e.g., Herrera et al. 2023 , Hampshire et al. 2024 , Boutet et al. 2025 ; particularly see Recommendation 1 in Becker et al. (2023) on selection bias) recruit individuals who self-report cognitive complaints in tune with the definition of long-COVID. Such studies, if used to draw conclusions about the long-term impact of COVID-19 in the broader population, can not only inflate the extent of impairments but also obscure subtler dysfunctions. Further, our findings—alongside those of Ariza et al. (2023) ; Meyer and Zaiser (2025) ; Voruz et al. (2022 )—suggest that individuals who consider themselves fully recovered (to reiterate, 96% of Mdrt+ patients reported to have “fully recovered” at the time of the survey) can show measurable deficits in cognitive domains; this points to a significant disconnect between subjective recovery and true cognitive functioning, and suggests that self-perception may not be a reliable indicator when screening for long-COVID. Importantly, as noted above, we did not observe any deficit in Mnemonic Similarity Task (MST) performance among individuals who had recovered from COVID-19 compared with healthy controls. However, vaccination at the time of infection was associated with better MST performance six months or more post-infection, suggesting a potential protective effect on hippocampal-dependent pattern separation. At first glance, this may appear counterintuitive, i.e., if MST performance is not measurably reduced as a function of COVID-19 severity in our sample, why would vaccination confer protection? One possibility is that the post-COVID group contains a mix of people: some more vulnerable to hippocampal disruption and others less so, regardless of clinical severity. If vaccination protected the more vulnerable subgroup, then a benefit would emerge only when comparing vaccinated with unvaccinated individuals, even if mean MST performance by severity alone appears unaffected. This interpretation is speculative, and given the limited literature addressing cognitive protection conferred by COVID-19 vaccines ( Zhao et al., 2023 ), we urge caution. Nonetheless, our finding is broadly consistent with recent proposals that vaccination may modulate neuroinflammatory responses in a way that preserves or enhances adult hippocampal neurogenesis ( Kumar et al., 2023 ; Goldstein, 2023 ), a mechanism that could support improved pattern separation. These converging observations, while preliminary, point to a potentially important and currently underexplored avenue for future research on long-term neurocognitive outcomes following COVID-19. Although our results provide important initial insights, several limitations must be acknowledged. First, our sample size may not have been sufficient for capturing more subtle cognitive differences and might explain the inability to observe any impairment among the mildly infected patients. Second, a larger sample size would have allowed us to probe interaction effects. Third, although we controlled for major demographic confounds, pre-infection cognitive baselines were not available, limiting our ability to conclusively attribute observed differences to COVID-19 rather than pre-existing variation. Finally, while the N-back, Stroop, and MST tasks index important cognitive domains, they may not capture the full spectrum of cognitive deficits, such as verbal fluency, planning, or processing speed, which are also affected in post-COVID populations. Further while our results show no evidence of enduring affective or mood disturbances across groups (but see Rudenstine et al. (2022) and Vallée et al. (2025) ) and support prior reports that cognitive and psychiatric sequelae can be dissociable, the violations of our model assumptions concerning those for mental health do not allow firm conclusions. Future research, especially in the subcontinent, should prioritize retrospective or cohort-based longitudinal approaches, leveraging existing health records to reconstruct pre- and post-infection trajectories where possible. Studies that implement repeated follow-ups in previously infected co-horts ( Liu et al., 2022 ; Taquet et al., 2024 ; Liu et al., 2024 ) remain valuable for tracking recovery trajectories and identifying delayed-onset impairments. There is also a need for broader cognitive assessments that includes more comprehensive test-batteries and, optimally, coupled with EEG and/or fMRI studies. Also, sampling strategies should actively include non-hospitalized populations who are often overlooked in studies run out of clinics and hospitals, and, if possible, should focus on recruiting in large numbers that make fine-grained comparisons across the different viral strains possible. Overall, our results challenge the growing public and institutional tendency to forget the COVID-19 pandemic. While acute symptoms may fade and many recover without complications, our findings reinforce that moderate-to-severe COVID-19 can have chronic cognitive consequences that persist well beyond subjective assessments of recovery. This underscores the importance of sustained vigilance, cognitive screening, and possible rehabilitation support (see Thams et al. (2022) for a trial approach to improve working memory in long-COVID patients) even in a post-pandemic world. Disregarding these risks could leave a substantial portion of the population under-served and unrecognized in their cognitive struggles, with potentially far-reaching consequences for individual productivity and national economy. Data Availability Anonymized raw data and code will be provided to reviewers and released upon publication. Data and Code Availability Anonymized raw data and code will be provided to reviewers and released upon publication. Contributions Aruneek Biswas : Writing – original draft; Formal Analysis (lead); Data Curation; Visualization Vivek Pamnani : Investigation; Formal Analysis (supporting); Software; Writing – Review & Editing (supporting) Priyanka Srivastava : Conceptualization; Funding Acquisition; Supervision; Project Administration; Writing – Review & Editing (supporting) Vishnu Sreekumar : Conceptualization; Funding Acquisition; Supervision; Project Administration; Writing – Review & Editing (lead) Acknowledgements We would like to thank Sriya Ravula and Pritha Ghosh for providing useful feedback on earlier versions of the manuscript. We acknowledge IHub-Data, IIIT Hyderabad (IIIT-H/IHub/Project/Healthcare/2021-22/H2-004; PS and VS) and IIIT Hyderabad Faculty Seed Fund (IIIT/R&D Office/Seed-Grant/2021-22/018; VS) for supporting this work. Appendix A. Survey Questionnaire Appendix A.1. Questions on Demographics To which gender identity do you mostly identify? – Man – Woman – Transgender Woman – Transgender Man – Non-Binary Gender – Prefer not to say – Prefer to self-describe: _______ How old are you? – ____(Allowed range: 18–70) What kind of area do you live in? – City – Sub-urban – Rural Choose the state/union territory where you are residing currently: – One had to pick from a list of Indian states and UTs. Choose the option that best describes your employment status: – Working (full time) – Working (part-time) – Not working (on a leave) – Not working (looking for work) – Not working (retired) – Not working (student) – Not working (other) – Volunteer workers – Casual and temporary employees – Home maker – Home-keeping – Trainees/apprentices – Employee on probation – Foreign Employees – Contract / Subcontract workers Select the option which best describes your latest education status: – No Formal School Education – Vocational Education or Skill Training (e.g., Carpentry, Mechanic skill learning) – Level IV/V or lower – Level VI – Level X – Level XI – Level XII or Higher Secondary – Graduation – Post Graduation – Diploma – PhD – Professional Courses (e.g., MBA) Appendix A.2. Select Questions on Infection, Recovery, and Vaccination (Questions in red were displayed if the answer to the question on having symptoms of COVID-19 was answered in affirmative): Please select one option that indicates your current COVID-19 vaccination status: – Not Vaccinated – Partially Vaccinated (one dose only) – Fully Vaccinated – Fully Vaccinated with one booster dose – Fully Vaccinated with two booster doses Have you ever been tested positive for COVID-19? – Yes (when tested using swab/RTPCR/Scan/antigen/others) – No (when tested using swab/RTPCR/Scan/Antigen/others) – Awaiting results (when tested using swab/RTPCR/Scan/Antigen/others) – Did not get tested Have you had or suspect you have had symptoms of COVID-19?: – Yes – No When did you experience COVID-19 infection or symptoms? (If more than once, pick the 2–3 most recent instances.) – Date-time picker – Date-time picker – Date-time picker Please select one of the following according to the severity of your COVID-19 symptoms: – Asymptomatic or Presymptomatic Infection : Individuals who test positive for COVID-19 but who have no symptoms that are consistent with COVID-19. – Mild Illness : Individuals who have any of the various signs and symptoms of COVID-19 (e.g., fever, cough, sore throat, malaise (a general feeling of discomfort, illness, or uneasiness whose exact cause is difficult to identify), headache, muscle pain, nausea, vomiting, diarrhea, loss of taste and smell) but who do not have shortness of breath, dyspnea, or abnormal chest imaging. – Moderate Illness : Individuals who show evidence of lower respiratory disease during clinical assessment or imaging and who have an oxygen saturation (SpO2) 94% on room air at sea level. – Severe Illness : Individuals who have SpO2 < 94% on room air at sea level, a ratio of arterial partial pressure of oxygen to fraction of inspired oxygen (PaO2/FiO2) 30 breaths/min, or lung infiltrates > 50%. – Critical Illness : Individuals who have respiratory failure, septic shock, or multiple organ dysfunction. Please select the appropriate medical course of action(s) that you have experienced. Select one or more of the following: – Home Quarantine/Isolation – Home Stay with Medical Care/Support – Hospitalization: * Hospitalization (General Ward/Special Ward) * Hospitalization with Intensive Care Unit (ICU) / Intensive Treatment Unit (ITU) / Critical Care Unit (CCU) / Intensive Coronary Care Unit (ICCU) – Mechanical Support: * Continuous Oxygen supplement * Oxygen Support but no continuous requirement * Ventilator support / Invasive mechanical ventilator support * High-flow nasal support Do you feel fully recovered?: – Yes – No – Not sure Please mention the duration of post-COVID symptoms (e.g., fatigue, loss of taste and smell, etc.) you experienced since the infection onset. Choose one which represents the duration of recovery most closely.: – I am still experiencing post-COVID symptoms – Less than 1 week – 1–2 weeks – 3–4 weeks – Almost 2 months – Almost 3 months – Almost 4 months – Almost 5 months – Almost 6 months – More than 6 months – More than 12 months – More than 18 months – More than 24 months When did you experience the COVID-19 symptoms? – Before vaccination – After partial vaccination (one dose only) – After full vaccination (two doses) – After full vaccination with one booster dose – After full vaccination with two booster doses Which vaccine did you take? – Covishield – Covaxin – Sputnik-V – Not listed? Enter the name here: _________ Please enter the month and year of your first dose of vaccination to the best of your memory: – _____ If you could not remember the month/year of your first dose of vaccination and skipped the question, consider visiting the official CoWIN portal ( https://selfregistration.cowin.gov.in ) to check your vaccination history.: – _____ Appendix B. Exclusion criterion To ensure data quality, we imposed the following filters, successively: Inattentive and Non-serious Participants : Seven participants were excluded for failing two or more of the eleven cognitive and attention-related checks. These checks included outlying response times (z-score ≥ 2 for the five tasks), task performance outliers (IQR for the five task metrics), and incorrect response to an attention-check question. Inconsistent COVID-19 History : Sixteen participants were excluded for self-reporting a positive COVID-19 test while also indicating that they had never suspected having COVID-19. Invalid Vaccination Year : Seven participants were excluded for reporting a spurious vaccination year, even after being asked to cross-reference with the CoWin database. Contradictory Vaccination and Infection Timeline : Two participants were excluded for reporting a vaccination date that preceded their infection but later claiming to have experienced COVID-19 symptoms before vaccination. Inconsistent COVID-19 Severity and Treatment Reports : Seven participants were excluded for reporting mild or moderate COVID-19 severity but indicating treatment levels inconsistent with their severity classification (e.g., hospitalization or ventilator use for mild cases). Diagnosed with a Mental Disorder : Sixteen participants were excluded for reporting being diagnosed with mental health disorders (Dementia, ADHD, OCD, BPD, Depression, etc.) that usually affect cognitive functioning in a severe manner. While they only performed around chance level at the 1-back task (median d’ = 0; one-sided Wilcoxon signed-rank p = 0.08), we nevertheless excluded them. Appendix C. Robustness Check: Benefit of vaccination for LDI performance View this table: View inline View popup Download powerpoint Table C.1: Note that the regression Betas are not comparable across the two kind of models; for Beta Regression Models (BRMs), the coefficient of the transformed variable (( LDI + 1) / 2) is reported. Footnotes ↵ 2 Can be accessed at https://www.cloudresearch.com as of 13 November 2025. References ↵ Akhtar-Danesh , N. , Landeen , J ., 2007 . Relation between depression and sociodemographic factors . International Journal of Mental Health Systems 1 , 4 . URL : doi: 10.1186/1752-4458-1-4 , doi:10.1186/1752-4458-1-4. OpenUrl CrossRef PubMed ↵ Algom , D. , Fitousi , D. , Chajut , E ., 2021 . Can the stroop effect serve as the gold standard of conflict monitoring and control? A conceptual critique . 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