Individual Differences In Cognitive Performance Research Articles

Multiple group membership and executive function in a socioeconomically diverse sample

Belonging to multiple groups is an important feature of our social lives. However, it is largely unknown if it is related to individual differences in cognitive performance. Given that changing self-identities linked to each group requires cognitive operations on knowledge bases associated with each group, the extent to which people belong to multiple groups may be related to individual differences in cognitive performance. Therefore, the main objective of this study was to test if multiple group membership is related to executive function task performance. A socioeconomically diverse sample of 395 individuals in Indonesia participated in this study. Our results show that multiple group membership was positively related to the 3-back working memory performance. However, we also found that this relationship was significant only among participants with high (not median or low) SES. We also observed that Contact diversity was negatively related to working memory performance among participants with low SES. Our results show that the complexity of our social lives is related to individual differences in executive function performance, although this seems to be constrained by SES.

Individual peak alpha frequency does not index individual differences in inhibitory cognitive control.

Previous work has indicated that individual differences in cognitive performance can be predicted by characteristics of resting state oscillations, such as individual peak alpha frequency (IAF). Although IAF has previously been correlated with cognitive functions, such as memory, attention, or mental speed, its link to cognitive conflict processing remains unexplored. The current work investigated the relationship between IAF and incl-established conflict tasks, Stroop and Navon task, while also controlling for alpha power, theta power, and the 1/f offset of aperiodic broadband activity. In Bayesian analyses on a large sample of 127 healthy participants, we found substantial evidence against the assumption that IAF predicts individual abilities to spontaneously exert cognitive control. Similarly, our findings yielded substantial evidence against links between cognitive control and resting state power in the alpha and theta bands or between cognitive control and aperiodic 1/f offset. In sum, our results challenge frameworks suggesting that an individual's ability to spontaneously engage attentional control networks may be mirrored in resting state EEG characteristics.

Age-related effects on a hierarchical structure of canine cognition.

The current study investigates whether there are statistically independent age-related influences on the canine cognitive structure and how individual factors moderate cognitive aging on both cross-sectional and longitudinal samples. A battery of seven tasks was administered to 129 pet dogs, on which exploratory and confirmatory factor analyses were employed to unveil the correlational structure underlying individual differences in cognitive performance. The best-fitting model featured a hierarchical structure with two first-order cognitive domains (individual problem solving, learning) and a second-order common factor. These higher order factors exhibited consistency over a period of at least 2.5years. External validation linked the common factor positively to discrimination and reversal learning performance, exploration, neophilia, activity/excitability, and training level while negatively to cognitive dysfunction symptoms, suggesting that it is a good candidate for a general cognitive factor (canine g). Structural equation models identified three distinct age-related influences, operating on associative learning, on memory, and on canine g. Health status moderated the negative age-canine g relationship, with a stronger association observed in dogs with poorer health status, and no relationship for dogs in good health. On a longitudinal sample (N = 99), we showed that the direction and magnitude of change in canine g over up to 3years is affected by various interactions between the dogs' age, communication score, baseline performance, and time elapsed since the baseline measurement. These findings underscore the presence of a general cognitive factor in dogs and reveal intriguing parallels between human and canine aging, affirming the translational value of dogs in cognition and aging research.

General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning.

Gene expression varies across the brain. This spatial patterning denotes specialised support for particular brain functions. However, the way that a given gene's expression fluctuates across the brain may be governed by general rules. Quantifying patterns of spatial covariation across genes would offer insights into the molecular characteristics of brain areas supporting, for example, complex cognitive functions. Here, we use principal component analysis to separate general and unique gene regulatory associations with cortical substrates of cognition. We find that the region-to-region variation in cortical expression profiles of 8235 genes covaries across two major principal components: gene ontology analysis suggests these dimensions are characterised by downregulation and upregulation of cell-signalling/modification and transcription factors. We validate these patterns out-of-sample and across different data processing choices. Brain regions more strongly implicated in general cognitive functioning (g; 3 cohorts, total meta-analytic N = 39,519) tend to be more balanced between downregulation and upregulation of both major components (indicated by regional component scores). We then identify a further 29 genes as candidate cortical spatial correlates of g, beyond the patterning of the two major components (|β| range = 0.18 to 0.53). Many of these genes have been previously associated with clinical neurodegenerative and psychiatric disorders, or with other health-related phenotypes. The results provide insights into the cortical organisation of gene expression and its association with individual differences in cognitive functioning.

Heritability of cognitive performance in wild Western Australian magpies.

Individual differences in cognitive performance can have genetic, social and environmental components. Most research on the heritability of cognitive traits comes from humans or captive non-human animals, while less attention has been given to wild populations. Western Australian magpies (Gymnorhina tibicen dorsalis, hereafter magpies) show phenotypic variation in cognitive performance, which affects reproductive success. Despite high levels of individual repeatability, we do not know whether cognitive performance is heritable in this species. Here, we quantify the broad-sense heritability of associative learning ability in a wild population of Western Australian magpies. Specifically, we explore whether offspring associative learning performance is predicted by maternal associative learning performance or by the social environment (group size) when tested at three time points during the first year of life. We found little evidence that offspring associative learning performance is heritable, with an estimated broad-sense heritability of just -0.046 ± 0.084 (confidence interval: -0.234/0.140). However, complementing previous findings, we find that at 300 days post-fledging, individuals raised in larger groups passed the test in fewer trials compared with individuals from small groups. Our results highlight the pivotal influence of the social environment on cognitive development.

Gray and White Matter Metrics Demonstrate Distinct and Complementary Prediction of Differences in Cognitive Performance in Children: Findings from ABCD (N = 11,876).

Individual differences in cognitive performance in childhood are a key predictor of significant life outcomes such as educational attainment and mental health. Differences in cognitive ability are governed in part by variations in brain structure. However, studies commonly focus on either gray or white matter metrics in humans, leaving open the key question as to whether gray or white matter microstructure plays distinct or complementary roles supporting cognitive performance. To compare the role of gray and white matter in supporting cognitive performance, we used regularized structural equation models to predict cognitive performance with gray and white matter measures. Specifically, we compared how gray matter (volume, cortical thickness, and surface area) and white matter measures (volume, fractional anisotropy, and mean diffusivity) predicted individual differences in cognitive performance. The models were tested in 11,876 children (ABCD Study; 5,680 female, 6,196 male) at 10 years old. We found that gray and white matter metrics bring partly nonoverlapping information to predict cognitive performance. The models with only gray or white matter explained respectively 15.4 and 12.4% of the variance in cognitive performance, while the combined model explained 19.0%. Zooming in, we additionally found that different metrics within gray and white matter had different predictive power and that the tracts/regions that were most predictive of cognitive performance differed across metrics. These results show that studies focusing on a single metric in either gray or white matter to study the link between brain structure and cognitive performance are missing a key part of the equation.

Intra- and inter-individual cognitive variability in schizophrenia and bipolar spectrum disorder: an investigation across multiple cognitive domains

There is substantial cognitive heterogeneity among patients with schizophrenia (SZ) and bipolar disorders (BD). More knowledge about the magnitude and clinical correlates of performance variability could improve our understanding of cognitive impairments. Using double generalized linear models (DGLMs) we investigated cognitive mean and variability differences between patients with SZ (n = 905) and BD spectrum disorders (n = 522), and healthy controls (HC, n = 1170) on twenty-two variables. The analysis revealed significant case-control differences on 90% of the variables. Compared to HC, patients showed larger intra-individual (within subject) variability across tests and larger inter-individual (between subject) variability in measures of fine-motor speed, mental processing speed, and inhibitory control (SZ and BD), and in verbal learning and memory and intellectual functioning (SZ). In SZ, we found that lager intra -and inter (on inhibitory control and speed functions) individual variability, was associated with lower functioning and more negative symptoms. Inter-individual variability on single measures of memory and intellectual function was additionally associated with disorganized and positive symptoms, and use of antidepressants. In BD, there were no within-subject associations with symptom severity. However, greater inter-individual variability (primarily on inhibitory control and speeded functions) was associated with lower functioning, more negative -and disorganized symptoms, earlier age at onset, longer duration of illness, and increased medication use. These results highlight larger individual differences in patients compared to controls on various cognitive domains. Further investigations of the causes and correlates of individual differences in cognitive function are warranted.

Dementia Risk Factors Predict Individual Differences in Cognitive Function

AbstractBackgroundTwelve modifiable risk factors have been estimated to account for 40% of dementia incidence worldwide (Livingston et al., 2020). Growing understanding of these risk factors presents the possibility that dementia risk can be partially mitigated via non‐pharmacological means, such as changes in lifestyle (e.g., alcohol consumption, social activity). We sought to provide the first systematic investigation of whether this set of risk factors also contributes to individual differences in cognitive function in a cognitively‐normal sample, and which risk factors provide the strongest contribution.MethodWe investigated cross‐sectionally the association between eight of these risk factors and cognition in 246 cognitively‐normal adults, aged 50‐90 from the Dallas Lifespan Brain Study. Risk factors included alcohol use (drinks per week), body mass index (BMI), cigarettes smoked per day, depressive symptoms (CESD scale), years of education, exercise frequency (20+ min sessions), pulse pressure, and frequency of social activity. A general measure of cognition, fluid ability, was used as the outcome variable and it was an average of six commonly used assessments of processing speed, working memory, and reasoning.ResultIn a multiple regression controlled for age and sex, 15.6% of fluid ability variance was accounted for by years of education (R 2 = .040, p = .002), frequency of social activity (R 2 = .034, p = .004), BMI (R 2 = .030, p = .008), cigarettes smoked per day (R 2 = .020, p = .032), and depressive symptoms (R 2 = .017, p = .047), as well as non‐significant contributions from alcohol use, exercise frequency, and pulse pressure. Further analysis examining cortical amyloid‐beta burden as a predictor and moderating effects of age will be discussed.ConclusionThe findings illustrate that levels of these modifiable risk factors affect cognition, even in a large sample of adults unselected for AD risk. We report differences in magnitude of effects for adults who showed cognitive decline over 10 years compared to those who were stable.

JS03.5.A CLINICALLY COLLECTED VARIABLES FAIL TO RELIABLY PREDICT COGNITIVE FUNCTIONING IN UNTREATED GLIOMA PATIENTS

Abstract BACKGROUND Previous research has identified several clinically collected variables (e.g., age and WHO grade) that are significantly related to impairments in cognitive functions before surgery in patients with a glioma. The degree to which clinically collected variables can collectively explain differences in cognitive functioning between patients, however, remains unclear, as most studies did not evaluate the total amount of variability between patients explained by a set of predictors. Furthermore, only linear or rank-based methods with a small number of variables have been used. In this study, we used various machine learning models to quantify the amount of variability in cognitive functioning we can explain using a comprehensive set of variables as identified from the literature that were collected as part of clinical care. The extent to which cognitive functioning can be reliably predicted reveals the degree to which these variables can explain individual differences between patients. MATERIAL AND METHODS We trained 11 machine learning models to predict pre-surgical cognitive function in patients with a glioma (n=354) across eight cognitive tests. Included variables comprised sociodemographics, tumor characteristics, medication, presenting symptoms, ASA score, comorbidities, and anxiety and depression scores. Model performance was evaluated on four objectives 1) predicting impairment on at least one cognitive test, 2) predicting the number of tests on which a patient is impaired, 3) predicting impairment for each test separately, and 4) predicting cognitive function for each test separately. Model performance, hyperparameter selection, and feature importance were analyzed and clinical implications were discussed. RESULTS Best-performing models demonstrated above-random performance for most objectives. Performance, however, was unreliable with poor predictions for a substantial number of patients on most objectives. Best-performing models were relatively simple and used most variables for prediction while not relying strongly on any specific variable. CONCLUSION Our results show that the clinically collected variables included in this study fail to reliably predict cognitive functioning. This indicates that these variables are unable to explain a considerable amount of variability between patients, despite many of these variables being significantly related to cognitive function in previous studies. Our results suggest that a multi-parametric (i.e., more holistic) view of an individual patient may be necessary to explain differences in cognitive functioning, ideally considering imaging markers, biological markers, sociodemographics, and clinical characteristics. Finally, our results stress the need to collect larger cross-center and multimodal datasets to better explain and predict individual differences in cognitive function.

Cortical thickness, gray matter volume, and cognitive performance: a crosssectional study of the moderating effects of age on their interrelationships.

In a sample comprising younger, middle-aged, and older cognitively healthy adults (N = 375), we examined associations between mean cortical thickness, gray matter volume (GMV), and performance in 4 cognitive domains-memory, speed, fluency, and crystallized intelligence. In almost all cases, the associations were moderated significantly by age, with the strongest associations in the older age group. An exception to this pattern was identified in a younger adult subgroup aged <23 years when a negative association between cognitive performance and cortical thickness was identified. Other than for speed, all associations between structural metrics and performance in specific cognitive domains were fully mediated by mean cognitive ability. Cortical thickness and GMV explained unique fractions of the variance in mean cognitive ability, speed, and fluency. In no case, however, did the amount of variance jointly explained by the 2 metrics exceed 7% of the total variance. These findings suggest that cortical thickness and GMV are distinct correlates of domain-general cognitive ability, that the strength and, for cortical thickness, the direction of these associations are moderated by age, and that these structural metrics offer only limited insights into the determinants of individual differences in cognitive performance across the adult lifespan.

Cognitive reserve and individual differences in brain tumour patients.

The aim of the paper is to determine the effects of the cognitive reserve on brain tumour patients' cognitive functions and, specifically, if cognitive reserve helps patients cope with the negative effects of brain tumours on their cognitive functions. We retrospectively studied a large sample of around 700 patients, diagnosed with a brain tumour. Each received an MRI brain examination and performed a battery of tests measuring their cognitive abilities before they underwent neurosurgery. To account for the complexity of cognitive reserve, we construct our cognitive reserve proxy by combining three predictors of patients' cognitive performance, namely, patients' education, occupation, and the environment where they live. Our statistical analysis controls for the type, side, site, and size of the lesion, for fluid intelligence quotient, and for age and gender, in order to tease out the effect of cognitive reserve on each of these tests. Clinical neurological variables have the expected effects on cognitive functions. We find a robust positive effect of cognitive reserve on patients' cognitive performance. Moreover, we find that cognitive reserve modulates the effects of the volume of the lesion: the additional negative impact of an increase in the tumour size on patients' performance is less severe for patients with higher cognitive reserve. We also find substantial differences in these effects depending on the cerebral hemisphere where the lesion occurred and on the cognitive function considered. For several of these functions, the positive effect of cognitive reserve is stronger for patients with lesions in the left hemisphere than for patients whose lesions are in the right hemisphere. The development of prevention strategies and personalized rehabilitation interventions will benefit from our contribution to understanding the role of cognitive reserve, in addition to that of neurological variables, as one of the factors determining the patients' individual differences in cognitive performance caused by brain tumours.

Genetic variation in the dopamine system is associated with mixed-strategy decision-making in patients with Parkinson's disease.

Decision-making during mixed-strategy games requires flexibly adapting choice strategies in response to others' actions and dynamically tracking outcomes. Such decisions involve diverse cognitive processes, including reinforcement learning, which are affected by disruptions to the striatal dopamine system. We therefore investigated how genetic variation in dopamine function affected mixed-strategy decision-making in Parkinson's disease (PD), which involves striatal dopamine pathology. Sixty-six PD patients (ages 49-85, Hoehn and Yahr Stages 1-3) and 22 healthy controls (ages 54-75) competed in a mixed-strategy game where successful performance depended on minimizing choice biases (i.e., flexibly adapting choices trial by trial). Participants also completed a fixed-strategy task that was matched for sensory input, motor outputs and overall reward rate. Factor analyses were used to disentangle cognitive from motor aspects within both tasks. Using a within-subject, multi-centre design, patients were examined on and off dopaminergic therapy, and genetic variation was examined via a multilocus genetic profile score representing the additive effects of three single nucleotide polymorphisms (SNPs) that influence dopamine transmission: rs4680 (COMT Val158 Met), rs6277 (C957T) and rs907094 (encoding DARPP-32). PD and control participants displayed comparable mixed-strategy choice behaviour (overall); however, PD patients with genetic profile scores indicating higher dopamine transmission showed improved performance relative to those with low scores. Exploratory follow-up tests across individual SNPs revealed better performance in individuals with the C957T polymorphism, reflecting higher striatal D2/D3 receptor density. Importantly, genetic variation modulated cognitive aspects of performance, above and beyond motor function, suggesting that genetic variation in dopamine signalling may underlie individual differences in cognitive function in PD.

Explaining individual differences in cognitive performance: The role of anxiety, social support and living arrangements during COVID-19

The present study investigated the relationship between anxiety, social support, living arrangements and cognitive performance of university students during the global pandemic. Two hundred and fifteen students participated by completing online questionnaires. Separate moderated multiple regression models were used to test whether social support (Family, Friends, Significant Other subscales of the Multidimensional Scale of Perceived Social Support) moderated the relationship between anxiety (Anxiety subscale of Depression, Anxiety Stress Scale), living arrangements (Living Alone vs Living with Friends and Family) and cognitive performance (Cognitive Failures Questionnaire), after controlling for comorbid depression. The results for each level of perceived social support suggested that anxiety was negatively associated with cognitive performance. Our most significant finding was that for students living alone, social support from a significant other offered a protective factor, whereby buffering the anxiety related cognitive deficits prevalent in those who reported lower social support. These data have important practical implications for supporting the social-emotional and academic needs of university students during the global pandemic.

A Systematic Review of Individual Differences in Perception, Action, and Decision Making: Implications for Dance Education.

This systematic bibliometric review summarizes recent neurocognitive research highlighting inter-individual differences in perception, action, and decision making that may have implications for dance education. First, the relevance of individual differences in cognitive functioning for dance education is illustrated by describing how a person's preferred reliance on certain perceptual, motor, or (meta) cognitive skills may be exploited in dance training. Subsequently, we describe the findings of a literature search conducted to identify cognitive neuroscientific publications between 2010 and 2021 that highlight individual differences in cognitive functions that were also found to be supported by structural or functional-connectivity differences in the central nervous system. To cluster the findings of the literature search, we propose a simplified, six-category information processing model. Finally, for each model category, we summarize recent representative findings on salient individual differences and tentatively formulate testable implications for dance education practice with regard to pedagogical and curricula adaptations. Finally, the review also delineates an agenda for lines of research of which the results hopefully will assist dance instructors in the future.

Determinants of Changes in Cognitive Functioning in the Elderly

Questions about cognitive aging are inherent questions about why individuals of different ages or from different subpopulations differ from each other and how these differences change over time. One of the main goals of cognitive gerontology is to describe how cognitive abilities change over time and to link these changes with brain aging. When we talk about the cognitive deterioration in the elderly, we have to consider great individual differences in cognitive functioning, that are greater than in the earlier period of life. In neurophysiology, significant progress has been made in mapping the brain areas responsible for changes in cognitive functioning; whether biological weakening will manifest in our behavior is greatly determined by life experiences and habits. Emotional stability, openness to experience, higher level of education, higher socioeconomic status, enjoyment in intellectual activities through the lifespan, and better physical and mental health are positively correlated with preserved cognitive abilities among older adults.

On the Need to Improve the Way Individual Differences in Cognitive Function Are Measured With Reaction Time Tasks

The measurement of individual differences in specific cognitive functions has been an important area of study for decades. Often the goal of such studies is to determine whether there are cognitive deficits or enhancements associated with, for example, a specific population, psychological disorder, health status, or age group. The inherent difficulty, however, is that most cognitive functions are not directly observable, so researchers rely on indirect measures to infer an individual’s functioning. One of the most common approaches is to use a task that is designed to tap into a specific function and to use behavioral measures, such as reaction times (RTs), to assess performance on that task. Although this approach is widespread, it unfortunately is subject to a problem of reverse inference: Differences in a given cognitive function can be manifest as differences in RTs, but that does not guarantee that differences in RTs imply differences in that cognitive function. We illustrate this inference problem with data from a study on aging and lexical processing, highlighting how RTs can lead to erroneous conclusions about processing. Then we discuss how employing choice-RT models to analyze data can improve inference and highlight practical approaches to improving the models and incorporating them into one’s analysis pipeline.

Correlations between COMT polymorphism and brain structure and cognition in elderly subjects: An observational study.

The catechol-O-methyltransferase (COMT) gene has been noted to play an important role in individual variations in the aging process. We investigated whether COMT polymorphism could influence cognition related to white matter networks. More specifically, we examined whether methionine (Met) allele loading is associated with better individual cognitive performance. Thirty-four healthy elderly participants were recruited; each participant's COMT genotype was determined, and Korean version of Montreal Cognitive Assessment scores and a diffusion tensor image were obtained for all participants. The Met carrier group showed significantly lower mean diffusivity, axial diffusivity, and radial diffusivity values for the right hippocampus, thalamus, uncinate fasciculus, and left caudate nucleus than the valine homozygote group. The Met carrier group also scored higher for executive function and attention on the Korean version of Montreal Cognitive Assessment. Based on these results, we can assume that the COMT Met allele has a protective effect on cognitive decline contributing to individual differences in cognitive function in late life period.

Revisiting the Effect of Retirement on Cognition: Heterogeneity and Endowment.

Since the seminal paper of Rohwedder and Willis (2010), the effect of retirement on cognition has drawn significant research interest from economists. Especially with ongoing policy discussions about public pension reforms and the increasing burden of dementia, it is indisputably an important research question with significant policy implications. Building on this growing literature, our paper makes two important contributions. First, we explicitly consider cognitive demands of jobs in studying hetereogeneity of the retirement effect. As the primary explanation for the potential adverse effect of retirement is that cognition is better maintained through mental exercise (Salthouse, 2006), by investigating the cognitive demands of the job one retires from we can directly test the hypothesized relationship. Second, we avoid biases associated with omitted variables, particularly by controlling for endowed cognitive ability. While endowed, genetic differences in cognitive ability is an important omitted variable that can explain individual differences in cognitive performance as well as selection into a particular type of job, this inherited characteristic has not been controlled for in the prior literature. Taking advantage of the polygenic risk score of cognition (Davies et al., 2015), we control for individual differences in genetic endowments in estimating the effect of retirement on cognition. We find supporting evidence for differential effects of retirement by cognitive demands of jobs after controlling for innate differences in cognition and educational attainment.

A Coordinated Analysis Examining the Association Between Personality Traits and Cognitive Dispersion

Cognitive dispersion is the degree of within-person variation in performance across cognitive tasks at the same testing occasion. Existing literature indicates that cognitive dispersion may be an early marker of poor brain health, dementia and mortality. Limited research, however, has examined individual differences in cognitive dispersion. Although personality traits are associated with individual differences in cognitive functioning, no research has examined personality and cognitive dispersion. In this project, we execute a pre-registered, coordinated analysis of seven diverse, international longitudinal studies of aging (Ntotal=33,581; mean age range=56.4-71.2) to investigate the extent to which the Big Five personality traits are associated with cognitive dispersion. For methodological approach, see /osf.io/wrnjq/. Cognitive dispersion scores were derived from cognitive test results, and independent linear regression models were fit independently in each study to examine personality traits as predictors of dispersion scores, adjusting for mean cognitive performance and socio-demographics (age, sex, education). Results from individual studies were synthesized using random-effects meta-analyses. Results revealed minimal evidence for associations between cognitive dispersion and personality traits in independent analyses or in meta-analyses. Based on the meta-analytic estimates, only higher levels of openness were associated with greater cognitive dispersion. Mean cognitive scores were negatively associated with cognitive dispersion across the majority of studies, indicating that individuals with higher mean performance had less dispersed cognitive scores. Our study contributes to the replicability and transparency efforts characteristic of open science by pre-registering our study and drawing on the collaborative network of the Integrative Analysis of Longitudinal Studies of Aging and Dementia (IALSA).

Contributions of Parasympathetic Arousal–Related Activity to Cognitive Performance in Patients With First-Episode Psychosis and Control Subjects

BackgroundCognitive impairment is integral to the pathophysiology of psychosis. Recent findings implicate autonomic arousal–related activity in both momentary fluctuations and individual differences in cognitive performance. Although altered autonomic arousal is common in patients with first-episode psychosis (FEP), its contribution to cognitive performance is unknown. MethodsA total of 24 patients with FEP (46% male, age = 24.31 [SD 4.27] years) and 24 control subjects (42% male, age = 27.06 [3.44] years) performed the Multi-Source Interference Task in-scanner with simultaneous pulse oximetry. First-level models included the cardiac-blood oxygen level–dependent regressor, in addition to task (congruent, interference, and error) and nuisance (motion and CompCor physiology) regressors. The cardiac-blood oxygen level–dependent regressor reflected parasympathetic arousal–related activity and was created by convolving the interbeat interval at each heartbeat with the hemodynamic response function. Group models examined the effect of group or cognitive performance (reaction times × error rate) on arousal-related and task activity, while controlling for sex, age, and framewise displacement. ResultsParasympathetic arousal–related activity was robust in both groups but localized to different regions for patients with FEP and healthy control subjects. Within both groups, arousal-related activity was significantly associated with cognitive performance across occipital and temporal cortical regions. Greater arousal-related activity in the bilateral prefrontal cortex (Brodmann area 9) was related to better performance in healthy control subjects but not patients with FEP. ConclusionsAutonomic arousal circuits contribute to cognitive performance and the pathophysiology of FEP. Arousal-related functional activity is a novel indicator of cognitive ability and should be incorporated into neurobiological models of cognition in psychosis.