Genome-wide association study of major anxiety disorders in 122,341 European-ancestry cases identifies 58 loci and highlights GABAergic signaling.

The major anxiety disorders (ANX; including generalized anxiety disorder, panic disorder, and phobias) are highly prevalent, often onset early, persist throughout life, and cause substantial global disability. Although distinct in their clinical presentations, they likely represent differential expressions of a dysregulated threat-response system. Here we present a genome-wide association meta-analysis comprising 122,341 European ancestry ANX cases and 729,881 controls. We identified 58 independent genome-wide significant ANX risk variants and 66 genes with robust biological support. In an independent sample of 1,175,012 self-report ANX cases and 1,956,379 controls, 51 of the 58 associated variants were replicated. As predicted by twin studies, we found substantial genetic correlation between ANX and depression, neuroticism, and other internalizing phenotypes. Follow-up analyses demonstrated enrichment in all major brain regions and highlighted GABAergic signaling as one potential mechanism underlying ANX genetic risk. These results advance our understanding of the genetic architecture of ANX and prioritize genes for functional follow-up studies.

Cognitive ability in early life and risk of depression in adulthood: A systematic review and meta-analysis.

BACKGROUND: There is an established association between cognitive ability and risk of depression, though the direction of this association is unclear. Measuring cognitive ability in childhood, prior to the diagnosis of depression, could help to understand whether childhood cognitive ability is associated with a later diagnosis of depression. This systematic review and meta-analysis explored the association between childhood cognitive ability and risk of depression in adulthood. METHODS: We searched five databases to January 2024. We included studies that assessed cognitive ability in childhood (<18 years) and depression in adulthood. We excluded studies with very specific populations. We pooled each study's most-adjusted correlation coefficient in a random-effects meta-analysis. When studies reported a dichotomous outcome (depression/no depression), we converted the effect size to a correlation coefficient. Subgroup analyses were performed to explore sources of heterogeneity. RESULTS: 18 articles (19 cohorts) were included. There was no association between childhood cognitive ability and depression in adulthood (20 sample populations, N = 45,786, r = -0.04, 95 % CI = -0.09 to 0.01, p = 0.09). Neither age at cognitive assessment, length of follow-up, using a continuous/categorical measure of depression, or sex, significantly influenced the association. We rated most studies as having moderate risk of bias. LIMITATIONS: We limited the literature search to studies written in English. Existing studies were also heterogeneous, often adjusting for a variety of covariates. CONCLUSIONS: Our meta-analysis found no association between childhood cognitive ability and depression in adulthood. Future, longitudinal population-level studies should endeavour to control for potential mediators across the life-course (e.g., demographic and environmental factors).

Childhood cognitive ability and self-harm and suicide in later life.

Background: Self-harm and suicide remain prevalent in later life. For younger adults, higher early-life cognitive ability appears to predict lower self-harm and suicide risk. Comparatively little is known about these associations among middle-aged and older adults. Methods: This study examined the association between childhood (age 11) cognitive ability and self-harm and suicide risk among a Scotland-wide cohort (N = 53037), using hospital admission and mortality records to follow individuals from age 34 to 85. Multistate models examined the association between childhood cognitive ability and transitions between unaffected, self-harm, and then suicide or non-suicide death. Results: After adjusting for childhood and adulthood socioeconomic conditions, higher childhood cognitive ability was significantly associated with reduced risk of self-harm among both males (451 events; HR = 0.90, 95% CI [0.82, 0.99]) and females (516 events; HR = 0.89, 95% CI [0.81, 0.98]). Childhood cognitive ability was not significantly associated with suicide risk among those with (Male: 16 events, HR = 1.05, 95% CI [0.61, 1.80]; Female: 13 events, HR = 1.08, 95% CI [0.55, 2.15]) or without self-harm events (Male: 118 events, HR = 1.17, 95% CI [0.84, 1.63]; Female: 31 events, HR = 1.30, 95% CI [0.70, 2.41]). Limitations: The study only includes self-harm events that result in a hospital admission and does not account for self-harm prior to follow-up. Conclusions: This extends work on cognitive ability and mental health, demonstrating that these associations can span the life course and into middle and older age.

Cohort profile: The Scottish SHARE Mental Health (SHARE-MH) cohort - linkable survey, genetic and routinely collected data for mental health research.

PURPOSE: The SHARE Mental Health (SHARE-MH) cohort was established to address the paucity of clinical and genetic data available for mental health research. The cohort brings together detailed mental health questionnaire responses, routinely collected electronic health data and genetic data to provide researchers with an unprecedented linkable dataset. This combination of data sources allows researchers to track mental health longitudinally, across multiple settings. It will be of interest to researchers investigating the genetic and environmental determinants of mental health, the experiences of those interacting with healthcare services, and the overlap between self-reported and clinically derived mental health outcomes. PARTICIPANTS: The cohort consists of individuals sampled from the Scottish Health Research Register (SHARE). To register for SHARE, individuals had to be over the age of 16 years and living in Scotland. Cohort participants were recruited by email and invited to take part in an online mental health survey. When signing up for SHARE, participants also provided written consent to the use of their electronic health records and genetic data-derived from spare blood samples-for research purposes. FINDINGS TO DATE: From 5 February 2021 to 27 November 2021, 9829 individuals completed a survey of various mental health topics, capturing information on symptoms, diagnoses, impact and treatment. Survey responses have been made linkable to electronic health records and genetic data using a single patient identifier. Linked data have been used to describe the cohort in terms of their demographics, self-reported mental health, inpatient and outpatient hospitalisations and dispensed prescriptions. FUTURE PLANS: The cohort will be improved through linkage to a broader variety of routinely collected data and to increasing amounts of genetic data obtained through blood sample diversion. We see the SHARE-MH cohort being used to drive forward novel areas of mental health research and to contribute to global efforts in psychiatric genetics.

Childhood intelligence and risk of depression in later-life: A longitudinal data-linkage study.

Background: Lower childhood intelligence test scores are reported in some studies to be associated with higher risk of depression in adulthood. The reasons for the association are unclear. This longitudinal data-linkage study explored the relationship between childhood intelligence (at age ∼11) and risk of depression in later-life (up to age ∼85), and whether childhood family structure and adulthood socio-economic and geographical factors accounted for some of this association. Methods: Intelligence test scores collected in the Scottish Mental Survey 1947 were linked to electronic health records (hospital admissions and prescribing data) between 1980 and 2020 (n = 53,037), to identify diagnoses of depression. Mixed-effect Cox regression models were used to explore the relationship between childhood intelligence test scores and risk of depression in later-life. Analyses were also adjusted for childhood family structure (size of family) and adulthood socio-economic and geographical factors (Carstairs index, urban/rural). Results: Twenty-seven percent of participants were diagnosed with depression during follow-up (n = 14,063/53,037). Greater childhood intelligence test scores were associated with a reduced risk of depression in an unadjusted analysis (HR = 0.95, 95% CI = 0.93 to 0.97, P < 0.001), and after adjustment for factors experienced in childhood and adulthood (HR = 0.95, 95% CI = 0.91 to 1.00, P = 0.032). When identifying depression using only hospital admissions data, greater childhood intelligence test scores were associated with a reduced risk of depression following unadjusted analysis (HR = 0.86, 95% CI = 0.82 to 0.90, P < 0.001), and after adjusting for risk factors in childhood and adulthood (HR = 0.94, 95% CI = 0.89 to 0.99, P = 0.026). There was no association between childhood cognitive test scores and depression when identifying cases of depression using only prescribed drugs data. Conclusions: This study provides additional evidence suggesting that higher childhood intelligence predicts reduced risk of later-life depression only when depression is assessed based on hospital admission records. Childhood family structure and adulthood socio-economic and geographical factors did not seem to be substantial confounders.

Pictorial depth cues always influence reaching distance.

We report five experiments to test the influence of pictorial depth on reaching. Our core method is to project a wide-field background of linear perspective and/or texture gradient onto a tabletop, and to measure the amplitude of reaches made to targets within it. In 63 healthy participants performing immediate open-loop reaches across Experiments 1-4, we observed a clear effect of pictorial depth. This effect was driven specifically by the convergence of the background pattern at the target position: for each additional degree of pictorial convergence, reaching distance increased by half a millimetre. In the individual experiments, we applied manipulations that might be expected to modify the influence of pictorial depth. We found no evidence that the effect was modified with monocular viewing, or when participants responded with the left hand, or if a memory delay was inserted before the response. Nor did participants become less susceptible to pictorial depth when visual feedback of terminal reaching errors was provided, although visual feedback during the reach did mitigate the influence of pictorial depth. Finally, the visual form agnosic patient DF showed an entirely normal effect of pictorial depth cues, which leads us to question the idea that this effect emanates from visual analyses of size and shape in the ventral stream, rather than from the dorsal stream, or from earlier stages of visual processing.

Artificial intelligence for dementia-Applied models and digital health.

INTRODUCTION: The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available. METHODS: This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors. RESULTS: This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health. DISCUSSION: Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry).

Childhood cognitive ability and self-harm and suicide in later life.

Self-harm and suicide remain prevalent in later life. For younger adults, work has highlighted an association between higher early-life cognitive ability and lower self-harm and suicide risk. Comparatively little is known about its association with self-harm and suicide among older adults. Furthermore, most work has measured cognitive ability in early adulthood, raising issues of potential confounding by emerging psychiatric conditions. The present study examined the association between childhood (age 11) cognitive ability and self-harm and suicide risk among a Scotland-wide cohort of older adults (N = 53037), using health data linkage to follow individuals from age 34 to 85. Self-harm events were extracted from hospital admissions and suicide deaths were extracted from national mortality records. Multistate models were used to model transitions between unaffected, self-harm, and then suicide or non-suicide death, and to examine the association between childhood cognitive ability and each transition. After adjusting for childhood and adulthood socioeconomic conditions, higher childhood cognitive ability was significantly associated with reduced risk of self-harm among older females (N events = 516; HR = 0.90, 95% CI = [0.81, 0.99]). A similar, though non-significant, association was observed among older males (N events = 451; HR = 0.90, 95% CI = [0.82, 1.00]). Although suicide risk was higher among older adults experiencing self-harm, childhood cognitive ability was not significantly associated with suicide risk among either older adults experiencing no self-harm events (Male: N events = 118, HR = 1.17, 95% CI = [0.84, 1.63]; Female: N events = 31, HR = 1.30, 95% CI = [0.70, 2.41]) or those experiencing a self-harm event during follow-up (Male: N events = 16, HR = 1.05, 95% CI = [0.61, 1.80]; Female: N events = 13, HR = 1.08, 95% CI = [0.55, 2.14]). Higher suicide risk was significantly associated with covariates including higher adulthood deprivation and longer time in the self-harm state. These results extend work on cognitive ability and mental health, demonstrating that these associations can span across the life course and into older age.

Intergenerational Social Mobility and Health in Later Life: Diagonal Reference Models Applied to the Lothian Birth Cohort 1936.

OBJECTIVE: Although commonly used to model associations between intergenerational social mobility and health, linear regression cannot estimate the contributions of origin, destination, and mobility independently. Nonlinear diagonal reference models (DRMs) have become a popular alternative and have been applied to various health outcomes, though few studies examine the impact of social mobility on later-life health. METHODS: This study revisits health outcomes examined in the Lothian Birth Cohort 1936, using DRMs to assess the association between intergenerational social mobility and satisfaction with life, self-rated health, depression, and mortality from age 68-82 years. RESULTS: After adjusting for sex, age, education, and childhood cognitive ability, there was no evidence of an association between intergenerational social mobility and later-life health; participants experiencing upward or downward mobility had similar odds of poor health outcomes as non-mobile participants. However, those from higher occupational social classes exhibited lower odds of mortality (p = .01), with a stronger contribution of adult (own) than of childhood (father's) social class (weights = 0.75 vs. 0.25). No other outcomes demonstrated significant associations with socioeconomic position. DISCUSSION: This adds to evidence that social mobility does not influence variation in later-life health once other factors-including socioeconomic origins and destinations-are accounted for.

Co-development of a Best Practice Checklist for Mental Health Data Science: A Delphi Study.

Background: Mental health research is commonly affected by difficulties in recruiting and retaining participants, resulting in findings which are based on a sub-sample of those actually living with mental illness. Increasing the use of Big Data for mental health research, especially routinely-collected data, could improve this situation. However, steps to facilitate this must be enacted in collaboration with those who would provide the data - people with mental health conditions. Methods: We used the Delphi method to create a best practice checklist for mental health data science. Twenty participants with both expertise in data science and personal experience of mental illness worked together over three phases. In Phase 1, participants rated a list of 63 statements and added any statements or topics that were missing. Statements receiving a mean score of 5 or more (out of 7) were retained. These were then combined with the results of a rapid thematic analysis of participants' comments to produce a 14-item draft checklist, with each item split into two components: best practice now and best practice in the future. In Phase 2, participants indicated whether or not each item should remain in the checklist, and items that scored more than 50% endorsement were retained. In Phase 3 participants rated their satisfaction with the final checklist. Results: The final checklist was made up of 14 "best practice" items, with each item covering best practice now and best practice in the future. At the end of the three phases, 85% of participants were (very) satisfied with the two best practice checklists, with no participants expressing dissatisfaction. Conclusions: Increased stakeholder involvement is essential at every stage of mental health data science. The checklist produced through this work represents the views of people with experience of mental illness, and it is hoped that it will be used to facilitate trustworthy and innovative research which is inclusive of a wider range of individuals.

Apolipoprotein E e4 allele status and later-life depression in the Lothian Birth Cohort 1936.

BACKGROUND: Previous results have been mixed regarding the role of the apolipoprotein E e4 (APOE e4) allele in later-life depression: some studies note that carriers experience greater symptoms and increased risk while others find no such association. However, there are few prospective, population-based studies of the APOE e4-depression association and fewer that examine depressive symptom trajectory and depression risk longitudinally. We examined the association between APOE e4 allele status and longitudinal change in depressive symptoms and depression risk in later-life, over a 12-year follow-up period. METHODS: We used data from 690 participants of the Lothian Birth Cohort 1936 who took part in the Scottish Mental Survey 1947 (aged 11) and were followed-up in later-life over five waves from 2004 to 2019 (aged 70-82). We used APOE e4 allele status to predict longitudinal change in depressive symptom scores and risk of depression (defined by a symptom score threshold or use of depression-related medication). Models were adjusted for sex, childhood cognitive ability, childhood social class, education, adult social class, smoking status and functional limitations at baseline. RESULTS: Depressive symptom scores increased with age. Once adjusted for covariates, APOE e4 allele status did not significantly predict symptom score trajectories or depression risk. Greater functional limitations at baseline significantly predicted poorer symptom score trajectories and increased depression risk (defined by medications). APOE e4 allele status did not significantly moderate the contribution of sex, education or functional limitations. CONCLUSIONS: There was no evidence that APOE e4 carriers experience an increased risk for later-life depression.

Generational differences in loneliness and its psychological and sociodemographic predictors: an exploratory and confirmatory machine learning study.

BACKGROUND: Loneliness is a growing public health issue in the developed world. Among older adults, loneliness is a particular challenge, as the older segment of the population is growing and loneliness is comorbid with many mental as well as physical health issues. Comorbidity and common cause factors make identifying the antecedents of loneliness difficult, however, contemporary machine learning techniques are positioned to tackle this problem. METHODS: This study analyzed four cohorts of older individuals, split into two age groups - 45-69 and 70-79 - to examine which common psychological and sociodemographic are associated with loneliness at different ages. Gradient boosted modeling, a machine learning technique, and regression models were used to identify and replicate associations with loneliness. RESULTS: In all cohorts, higher emotional stability was associated with lower loneliness. In the older group, social circumstances such as living alone were also associated with higher loneliness. In the younger group, extraversion's association with lower loneliness was the only other confirmed relationship. CONCLUSIONS: Different individual and social factors might underlie loneliness differences in distinct age groups. Machine learning methods have the potential to unveil novel associations between psychological and social variables, particularly interactions, and mental health outcomes.

Do childhood socioeconomic circumstances moderate the association between childhood cognitive ability and all-cause mortality across the life course? Prospective observational study of the 36-day sample of the Scottish Mental Survey 1947.

BACKGROUND: There is growing evidence that higher childhood cognitive ability predicts lower all-cause mortality risk across the life course. Whereas this association does not appear to be mediated by childhood socioeconomic circumstances, it is unclear whether socioeconomic circumstances moderate this association. METHODS: The moderating role of childhood socioeconomic circumstances was assessed in 5318 members of the 36-day sample of the Scottish Mental Survey 1947. Univariate, sex-adjusted and age-adjusted, and mutually adjusted Cox models predicting all-cause mortality risk up to age 79 years were created using childhood IQ scores and childhood social class as predictors. Moderation was assessed by adding an interaction term between IQ scores and social class and comparing model fit. RESULTS: An SD advantage in childhood IQ scores (HR=0.83, 95% CI 0.79 to 0.86, p<0.001) and a single-class advantage in childhood social class (HR=0.92, 95% CI 0.88 to 0.97, p<0.001) independently predicted lower mortality risk. Adding the IQ-social class interaction effect did not improve model fit (χ2Δ=1.36, p=0.24), and the interaction effect did not predict mortality risk (HR=1.03, 95% CI 0.98 to 1.07, p=0.25). CONCLUSIONS: The present study demonstrated that the association between higher childhood cognitive ability and lower all-cause mortality risk is not conditional on childhood social class. Whereas other measures of socioeconomic circumstances may play a moderating role, these findings suggest that the benefits of higher childhood cognitive ability for longevity apply regardless of the material socioeconomic circumstances experienced in childhood.

Using a knowledge exchange event to assess study participants' attitudes to research in a rapidly evolving research context.

Background: The UK hosts some of the world's longest-running longitudinal cohort studies, which make repeated observations of their participants and use these data to explore health outcomes. An alternative method for data collection is record linkage; the linking together of electronic health and administrative records. Applied nationally, this could provide unrivalled opportunities to follow a large number of people in perpetuity. However, public attitudes to the use of data in research are currently unclear. Here we report on an event where we collected attitudes towards recent opportunities and controversies within health data science. Methods: The event was attended by ~250 individuals (cohort members and their guests), who had been invited through the offices of their participating cohort studies. There were a series of presentations describing key research results and the audience participated in 15 multiple-choice questions using interactive voting pads. Results: Our participants showed a high level of trust in researchers (87% scoring them 4/5 or 5/5) and doctors (81%); but less trust in commercial companies (35%). They supported the idea of researchers using information from both neonatal blood spots (Guthrie spots) (97% yes) and from electronic health records (95% yes). Our respondents were willing to wear devices like a 'Fit-bit' (88% agreed) or take a brain scan that might predict later mental illness (73%). However, they were less willing to take a new drug for research purposes (45%). They were keen to encourage others to take part in research; whether that be offering the opportunity to pregnant mothers (97% agreed) or extending invitations to their own children and grandchildren (98%). Conclusions: Our participants were broadly supportive of research access to data, albeit less supportive when commercial interests were involved. Public engagement events that facilitate two-way interactions can influence and support future research and public engagement efforts.

Navigating the landscape of non-health administrative data in Scotland: A researcher's narrative.

Background: There is growing interest in using routinely collected data for research purposes. Following the success of research using routinely collected healthcare data, attention has turned to leveraging administrative data derived from systems providing other services to the population (e.g., education, social security) to conduct research on important social problems. In Scotland, specialised organisations have been set up to support researchers in their pursuit of using and linking administrative data. The landscape of administrative data in Scotland, however, is complex and changeable, and is often difficult for researchers to navigate. Purpose: This paper provides a researcher's narrative of the steps required to gain the various approvals necessary to access and link administrative data for research in social and cognitive epidemiology. Findings: This paper highlights the problems, particularly regarding the length and complexity of the process, which researchers typically face, and which result in a challenging research environment. The causes of these problems are discussed, as are potential solutions. Conclusions: Whereas the potential of administrative data is great, more work and investment are needed on the part of all those concerned - from researchers to data controllers - in order to realise this potential.

Early-life predictors of retirement decisions and post-retirement health.

It remains unclear whether retirement circumstances are associated with better or worse post-retirement health. This is partly due to confounding between measures of retirement circumstances and a tendency to account only for covariates around retirement age. The present study examined the contributions of both retirement age and retirement type, independently, to post-retirement health around age 77 years. It also examined whether these contributions remain once earlier life-course factors - social class, cognitive ability and education - were accounted for. Our sample was 742 Scottish people who took part in the Scottish Mental Survey 1947. In a path model including life-course predictors, retirement type (reason), but not age, significantly predicted post-retirement health, with ill-health retirement associated with poorer physical (ß = 0.455, 95% CI [0.313, 0.597], p < 0.001) and mental health (ß = 0.339, 95% CI [0.191, 0.486], p < 0.001), and redundancy retirement associated with poorer physical health only (ß = 0.200, 95% CI [0.069, 0.331], p = 0.004). Of the life-course predictors, higher adult social class was associated with later retirement (ß = 0.115, 95% CI [0.034, 0.196], p = 0.006) and higher childhood cognitive ability was associated with increased odds of voluntary retirement (OR = 1.054, 95% CI [1.005, 1.105], p = 0.032), but no indirect contribution to health (mediated by retirement circumstances) was significant. At the same time, higher childhood cognitive ability directly predicted better post-retirement physical health (ß = -0.110, 95% CI [-0.216, -0.004], p = 0.041), independently of retirement circumstances. This study demonstrates the importance of considering retirement circumstances beyond age, and of accounting for confounding between retirement circumstances and earlier life-course factors.

Intelligence and all-cause mortality in the 6-Day Sample of the Scottish Mental Survey 1947 and their siblings: testing the contribution of family background.

Background: Higher early-life intelligence is associated with a reduced risk of mortality in adulthood, though this association is apparently hardly attenuated when accounting for early-life socio-economic status (SES). However, the use of proxy measures of SES means that residual confounding may underestimate this attenuation. In the present study, the potential confounding effect of early-life SES was instead accounted for by examining the intelligence-mortality association within families. Methods: The association between early-life intelligence and mortality in adulthood was assessed in 727 members of the 6-Day Sample of the Scottish Mental Survey 1947 and, for the first time, 1580 of their younger siblings. These individuals were born between 1936 and 1958, and were followed up into later life, with deaths recorded up to 2015. Cox regression was used to estimate the relative risk of mortality associated with higher IQ scores after adjusting for shared family factors. Results: A standard-deviation advantage in IQ score was associated with a significantly reduced mortality risk [hazard ratio = 0.76, p < 0.001, 95% confidence interval (CI) (0.68-0.84)]. This reduction in hazard was only slightly attenuated by adjusting for sex and shared family factors [hazard ratio = 0.79, p = 0.002, 95% CI (0.68-0.92)]. Conclusions: Although somewhat conservative, adjusting for all variance shared by a family avoids any potential residual confounding of the intelligence-mortality association arising from the use of proxy measures of early-life SES. The present study demonstrates that the longevity associated with higher early-life intelligence cannot be explained by early-life SES or within-family factors.

Intergenerational social mobility and subjective wellbeing in later life.

Whereas a great deal of literature has been devoted to investigating the link between intergenerational social mobility and health, the few studies that have examined the association between social mobility and life satisfaction have produced conflicting findings. In the present study, we attempt to rectify several shortcomings common to previous work by examining the association between intergenerational social mobility and both life satisfaction and self-rated health as measured in later-life. Our sample consisted of individuals born in Scotland in 1936, who took part in the Scottish Mental Survey 1947 and were subsequently followed-up into later-life. Regression analyses demonstrated that satisfaction with life at age 78 was not significantly predicted by childhood or adulthood socioeconomic status, or by the amount of social mobility experienced from parental occupational social class. In contrast, self-rated health at age 78 was significantly predicted by adult socioeconomic status and by education, but not by social mobility from parental occupational social class. These results suggest that efforts to promote upwards social mobility may not result in better subjective wellbeing, despite the apparent benefits for health.

Goal neglect, fluid intelligence and processing speed: Manipulating instruction load and inter-stimulus interval.

Goal maintenance is the process where task rules and instructions are kept active to exert their control on behavior. When this process fails, an individual may ignore a rule while performing the task, despite being able to describe it after task completion. Previous research has suggested that the goal maintenance system is limited by the number of concurrent rules which can be maintained during a task, and that this limit is dependent on an individual's level of fluid intelligence. However, the speed at which an individual can process information may also limit their ability to use task rules when the task demands them. In the present study, four experiments manipulated the number of instructions to be maintained by younger and older adults and examined whether performance on a rapid letter-monitoring task was predicted by individual differences in fluid intelligence or processing speed. Fluid intelligence played little role in determining how frequently rules were ignored during the task, regardless of the number of rules to be maintained. In contrast, processing speed predicted the rate of goal neglect in older adults, where increasing the presentation rate of the letter-monitoring task increased goal neglect. These findings suggest that goal maintenance may be limited by the speed at which it can operate.