Expert Performance Research Articles

Advocating individual-based profiles of elite athletes to capture the multifactorial nature of elite sports performance

Elite athletes are high-performance outliers within their specific sports. Even though science seeks to understand the nature of expertise and elite performance, much knowledge remains compartmentalized within subdisciplines. Despite this multidimensionality being acknowledged, an interdisciplinary approach to understanding elite athletes is still rare. This paper synthesizes insights across scientific domains in order to describe the population and individual characteristics of elite athletes. We analyzed diagnostic data from approximately 300 German squad athletes across eight different sports (e.g., gymnastics, volleyball, ice hockey, 3 × 3 basketball etc., agefemale = 18.95 ± 4.84 years, agemale = 19.32 ± 4.19 years) with expertise values ranging from 2 (low expertise) to 16 (high expertise). Data covered muscular strength, lower-body dynamics, muscle-power genetics, blood micronutrients, basic cognitive function, mental health, social support, and training conditions. Results of logistic regressions identified basic cognitive function (B = 0.89) and well-balanced blood micronutrients (B = 1.22) as critical factors distinguishing elite athletes. Additionally, multiple linear regressions suggested that lower-body dynamics (ß = 0.72) is related to increasing expertise values. We examined interactions between determinants of elite performance, and found that social support is positively associated with mental health and training conditions, whereas muscular strength correlates with lower-body dynamics. Focusing on top elite athletes in contrast to semi-elite athletes, we found higher within-group similarities in basic cognitive function and blood micronutrients. Findings indicate the need for a systemic, individualized, and comprehensive model using individual-based profiles.

Influence of Teachers’ Experience in Information Communication Technology and Students’ Academic Performance in Mathematics in Public Secondary Schools of Rwanda: A Case of Kayonza District

The study evaluated the impact of teachers' ICT experience on students' math performance in public secondary schools in Kayonza District, Rwanda. Involving 259 participants (223 teachers and 36 head teachers), data were gathered through questionnaires, interviews, and observations, and analyzed using SPSS Version 20.0. Findings revealed that 83.5% of respondents strongly agreed that teachers skilled in MS Excel, 85.9% in PowerPoint, and 83.9% in preparing teaching materials with ICT tools positively influenced students' performance. Additionally, 82.3% noted that teachers trained in exam preparation with ICT tools demonstrated their ICT experience. Improved student outcomes were reported, with 79.4% noting better numeracy, 80.2% improved grades, and 78.3% enhanced skills in experimenting with MS Excel. A significant positive correlation was found between teachers' ICT expertise and students' math performance. The study suggests that proficiency in ICT tools enhances students' academic performance. The Ministry of Education should support ICT integration to boost academic achievement, with superintendents and head teachers optimizing ICT training and lab usage. Teachers should share ICT skills to improve instruction and student outcomes. Future research should investigate the broader effects of ICT in education on academic achievement in mathematics.

Development of an Artificial Intelligence System for Distinguishing Malignant from Benign Soft Tissue Tumors Using Contrast-Enhanced MR Images.

The integration of artificial intelligence (AI) into orthopedics has enhanced the diagnosis of various conditions; however, its use in diagnosing soft-tissue tumors remains limited owing to its complexity. This study aimed to develop and assess an AI-driven diagnostic support system for magnetic resonance imaging (MRI)-based soft-tissue tumor diagnosis, potentially improving accuracy and aiding radiologists and orthopedic surgeons. Experienced orthopedic oncologists and radiologists annotated 720 images from 77 cases (41 benign and 36 malignant soft-tissue tumors). Eleven tumor subtypes were identified and classified into benign and malignant groups based on histological diagnosis. Utilizing the standard machine learning classifier pipeline, we examined and down-selected imaging protocols and their predominant radiomic features within the tumor's three-dimensional region to differentiate between benign and malignant tumors. Among the scan protocols, contrast-enhanced T1 weighted fat-suppressed images showed the most accurate classification based on radiomics features. We focused on the two-dimensional features from the largest tumor boundary surface and its neighboring slices, leveraging texture-based radiomic and deep convolutional neural network features from a pretrained VGG19 model. The test data comprised 44 contrast-enhanced images (22 benign and 22 malignant soft-tissue tumors) containing six malignant and five benign subtypes distinct from the training data. We compared expert and non-expert human performances against AI by assessing malignancy detection and the time required for classification. The AI model showed comparable accuracy (AUC 0.91) to that of radiologists (AUC 0.83) and orthopedic surgeons (AUC 0.73). Notably, the AI model processed data approximately 400 times faster than its human counterparts, showcasing its capacity to significantly boost diagnostic efficiency. We developed an AI-driven diagnostic support system for MRI-based soft-tissue tumor diagnosis. While additional refinement is necessary for clinical applications, our system has exhibited promising potential in differentiating between benign and malignant soft-tissue tumors based on MRI.

The Role of Affective Labour in Expertise: Bringing Emotions Back into Expert Practices

In recent years, a lot of scholarly attention has been devoted to how practices of digitalisation and datafication require medical professionals to work together with different stakeholders, and to how such collaborations shape expertise (Stevens, Wehrens, and de Bont 2020; Carboni et al. 2024). STS scholars have generally approached expertise as an epistemic and social endeavor, but they have tended to neglect the role affects and emotions play in its development and performance. In this paper, we provide a theoretical reflection on the relation between affective labour and expertise building upon Egher’s (2023) conceptualisation of expertise as a practical achievement realised through coordination and affective labour. Based on ethnographic fieldwork conducted in various medical settings, including digital pathology, psychiatry, and datafication in intensive care, we explore what types of affective labour are conducted in digital healthcare, by whom, and with what consequences. We show how affective labour mediates both epistemic and relational practices. We argue that different affects and emotions are mobilised in these practices, which impacts the development and effective performance of expertise.

Boardroom tenure, financial expertise and bank performance: a nonlinear dynamic framework

ABSTRACT This research investigates the influence of boardroom tenure and financial expertise on US bank performance in a nonlinear dynamic framework. To this aim, we utilize a sample of 305 banks during the 2010–2021 period, and we implement three empirical methodologies: GMM analysis, threshold technique, and quantile approach. Our findings reveal that the individual and interaction effects of boardroom financial expertise and tenure on bank performance are positive. Also, we provide evidence of an inverted U-shaped relation between boardroom tenure and bank performance, and between the board’s financial expertise and bank performance. Moreover, threshold analysis shows the presence of threshold effects in the relationship between boardroom tenure, expertise, and performance. Quantile results demonstrate that boardroom financial expertise improves the performance of high-performing banks. In contrast, boardroom tenure enhances the performance of low-performing banks but diminishes the performance of their high-performing counterparts.

Utilizing ChatGPT in Systematic Reviews and Meta-Analyses

The current uses, potential risks, and practical recommendations for using chat generative pre-trained transformers (ChatGPT) in systematic reviews (SRs) and meta-analyses (MAs) are reviewed in this article. The findings of prior research suggest that, for tasks such as literature screening and information extraction, ChatGPT can match or exceed the performance of human experts. However, for complex tasks such as risk of bias assessment, its performance remains significantly limited, underscoring the critical role of human expertise. The use of ChatGPT as an adjunct tool in SRs and MAs requires careful planning and the implementation of strict quality control and validation mechanisms to mitigate potential errors such as those arising from artificial intelligence (AI) 'hallucinations'. This paper also provides specific recommendations for optimizing human-AI collaboration in SRs and MAs. Assessing the specific context of each task and implementing the most appropriate strategies are critical when using ChatGPT in support of research goals. Furthermore, transparency regarding the use of ChatGPT in research reports is essential to maintaining research integrity. Close attention to ethical norms, including issues of privacy, bias, and fairness, is also imperative. Finally, from a human-centered perspective, this paper emphasizes the importance of researchers cultivating continuous self-iteration, prompt engineering skills, critical thinking, cross-disciplinary collaboration, and ethical awareness skills with the goals of: continuously optimizing human-AI collaboration models within reasonable and compliant norms, enhancing the complex-task performance of AI tools such as ChatGPT, and, ultimately, achieving greater efficiency through technological innovative while upholding scientific rigor.

Ultrasomics differentiation of malignant and benign focal liver lesions based on contrast-enhanced ultrasound

ObjectivesTo establish a nomogram for differentiating malignant and benign focal liver lesions (FLLs) using ultrasomics features derived from contrast-enhanced ultrasound (CEUS).Methods527 patients were retrospectively enrolled. On the training cohort, ultrasomics features were extracted from CEUS and b-mode ultrasound (BUS). Automatic feature selection and model development were performed using the Ultrasomics-Platform software, outputting the corresponding ultrasomics scores. A nomogram based on the ultrasomics scores from artery phase (AP), portal venous phase (PVP) and delayed phase (DP) of CEUS, and clinical factors were established. On the validation cohort, the diagnostic performance of the nomogram was assessed and compared with seniorexpert and resident radiologists.ResultsIn the training cohort, the AP, PVP and DP scores exhibited better differential performance than BUS score, with area under the curve (AUC) of 84.1-85.1% compared with the BUS (74.6%, P < 0.05). In the validation cohort, the AUC of combined nomogram and expert was significantly higher than that of the resident (91.4% vs. 89.5% vs. 79.3%, P < 0.05). The combined nomogram had a comparable sensitivity with the expert and resident (95.2% vs. 98.4% vs. 97.6%), while the expert had a higher specificity than the nomogram and the resident (80.6% vs. 72.2% vs. 61.1%, P = 0.205).ConclusionsA CEUS ultrasomics based nomogram had an expert level performance in FLL characterization.

Why the post-identification era is long overdue: Commentary on the current controversy over forensic feature comparison as applied to forensic firearms examination

In this commentary, we critically review recurring arguments for and against the discipline of forensic feature comparison as applied to firearms examination from various commentators within and outside forensic science. One of the mainstream criticisms that we address, among others, is that the field cannot demonstrate sufficient proficiency and robustness based on empirical (i.e., black-box) studies. While the lack of empirically demonstrated examiner proficiency is a valid concern and a powerful concept in the short term (e.g., in admissibility proceedings), many critics reduce their discussion of forensic feature comparison solely to the need to measure and demonstrate proficiency through error rates. However, the exclusive focus on aggregate expert performance metrics, here referred to as examiner diagnosticism, remains a surface-level perspective. It provides an incomplete account of the field because these metrics do not represent—but are often confused with—the notion of the evidentiary value of findings, i.e., observations made on examined items in individual cases. We argue that examiner diagnosticism should be contrasted and complemented with the notion of feature selectivity, i.e., the diagnostic capacity of observed marks and features on examined items. We argue that forensic scientists should report and be probed on their ability to quantify feature selectivity (i.e., the probative value of findings). By ceasing to express source attribution opinions (identification/individualisation), which are now widely exposed as unscientific, the forensic feature comparison disciplines could move further into the long-awaited post-identification era pioneered by other fields such as forensic genetics.

Horizontal Transition of Expertise

Expert performance in a domain is often defined as maximal adaptation to stable task constraints. This definition is useful when analyzing the vertical transition when novices become experts. However, many workplaces undergo considerable changes and, thus, task constraints change as well. In this paper, a complementary conceptualization of expertise is offered, one that focuses on expert performance as recurring adaptation to dynamic task constraints. This definition is useful when analyzing the horizontal transition when experts adapt to dynamically changing work contexts. Using the documentary method, the aim of the present study was to analyze cases of horizontal transitions based on qualitative biographical interview data from five experts reconstructing different types of adaptation to technological change in their domains that they have experienced. Implications for studying horizontal transitions at dynamic worksites are discussed.

Flow and Athletic Experts

AbstractPhilosophers frequently refer to ‘flow’ when discussing skill and expertise. This is especially clear when considering the ‘flow argument’ advocated by the Automatic view. Although the Automatic view is a minority position today, many philosophers and scientists endorse the flow argument or central parts of it. The present paper argues that the argument is implausible in the light of current psychological research. It proceeds in two steps. First, the paper argues that the psychological research does not suggest that flow is the only type of psychological state characteristic of optimal expert performance. Experts undergo different states, notably the state of clutch. Second, the paper argues that the psychological research does not suggest that flow states are devoid of conscious supervision in the form of conscious control, self-awareness, or task-related thoughts. Experts execute multiple conscious operations involved in attention control, motivational self-talk, and certain psychological mindsets while being in flow. These considerations oppose the two premises of the flow argument. Moreover, they correct misunderstandings of flow in the broader philosophical and scientific literature.

Experimental insights into cognition, motor skills, and artistic expertise in Paleolithic art

The production of Paleolithic art represents one of the most intricate technical and cognitive endeavors of Homo sapiens, marked by its profound antiquity and vast temporal and spatial framework. Despite its significance, there have been no prior studies aimed at understanding the cognitive and motor skills linked to the creation of realistic images characteristic of this artistic cycle. This research integrates archaeology and experimental psychology, premised on the assumption that the neurological basis of Anatomically Modern Humans has not changed substantially since the Upper Paleolithic. This work employs an innovative interdisciplinary approach, utilizing psychometric tests and drawing and engraving tasks monitored by motion-sensing gloves, to compare the performance of experts and non-experts in visual arts when faced with challenges akin to those of Upper Paleolithic artistic production. The results revealed that expertise in visual arts is linked to enhanced spatial abilities and specific patterns in drawing from memory. Additionally, both experts and non-experts displayed similar motor skills when engraving using Paleolithic techniques, suggesting that these techniques required specialized training in the contemporary experts. In conclusion, this research deepens our understanding of the processes involved in Upper Paleolithic artistic production.

Goal-directed learning is multidimensional and accompanied by diverse and widespread changes in neocortical signaling.

New tasks are often learned in stages with each stage reflecting a different learning challenge. Accordingly, each learning stage is likely mediated by distinct neuronal processes. And yet, most rodent studies of the neuronal correlates of goal-directed learning focus on individual outcome measures and individual brain regions. Here, we longitudinally studied mice from naïve to expert performance in a head-fixed, operant conditioning whisker discrimination task. In addition to tracking the primary behavioral outcome of stimulus discrimination, we tracked and compared an array of object-based and temporal-based behavioral measures. These behavioral analyses identify multiple, partially overlapping learning stages in this task, consistent with initial response implementation, early stimulus-response generalization, and late response inhibition. To begin to understand the neuronal foundations of these learning processes, we performed widefield Ca2+ imaging of dorsal neocortex throughout learning and correlated behavioral measures with neuronal activity. We found distinct and widespread correlations between neocortical activation patterns and various behavioral measures. For example, improvements in sensory discrimination correlated with target stimulus evoked activations of response-related cortices along with distractor stimulus evoked global cortical suppression. Our study reveals multidimensional learning for a simple goal-directed learning task and generates hypotheses for the neuronal modulations underlying these various learning processes.

Evaluations of solo piano performances: The role of performing with and without a musical score

The act of performing without a musical score is common and expected in many contexts. The purpose of this study was to examine the effects of performing with and without a musical score on listeners’ evaluations of solo pianists. We also investigated whether the effects of score use differed according to the perceived expertise of the performer. Participants ( N = 69) were collegiate music students who observed two video recordings of a pianist performing an excerpt from Schubert’s Impromptu Op. 90, No. 2. In one of the videos, the pianist used a musical score and in the other video the pianist did not use a score. One group of participants was informed at the outset that the pianist was an expert performer and the other group was told that the pianist was a student auditioning for a graduate program as a piano major. Participants rated each performance and indicated which recording they preferred. Results indicated no significant difference in ratings by performance condition overall, but significant interaction effects indicated that the effect of performance condition was dependent upon both the perceived expertise of the performer and presentation order. Chi-square results indicated no significant differences in participants’ preferences between conditions.

INVESTIGATING THE IMPACT OF AUDIT COMMITTEE ATTRIBUTES ON COMPANY PERFORMANCE: A MALAYSIAN FOCUS

Background and Purpose: This study aims to examine the relationship between selected audit committee attributes (gender diversity, size, meeting, independence, expertise) and company performance that are assessed as return on equity (ROE) and return on asset (ROA). As one of the primary corporate governance mechanisms facilitating corporate efficiency, audit committees positively impact company performance. Methodology: This quantitative cross-sectional study underpinned the agency theory through the target population: public listed companies under the Bursa Malaysia. Secondary data were gathered from the corporate annual report of 106 companies and Eikon database between 2018 and 2020 and subsequently analyzed with Pearson Correlation and Multiple Linear Regression using SPSS. Findings: Resultantly, a positive gender diversity-size correlation was identified with company performance as opposed to meeting, independence, and expertise. Furthermore, a significant gender diversity-size relationship was determined with company performance while no substantial counterparts between (i) meeting and independence and (ii) expertise and company performance were identified for the selected public listed companies. In this vein, audit committee attributes (gender diversity and size) could be utilized as a mechanism for high company performance. Contributions: The study outcomes offer useful insights into optimizing current corporate governance structures and regulations for relevant authorities. Keywords: Audit committee, corporate governance, company performance, Malaysia. Cite as: Gregory, V., Yusoff, H., Tapsir, R., &amp; Fauzi, H. (2024). Investigating the impact of audit committee attributes on company performance: a Malaysian focus. Journal of Nusantara Studies, 9(2), 185-217. http://dx.doi.org/10.24200/jonus.vol9iss2pp185-217

Deep learning-based fully automatic Risser stage assessment model using abdominal radiographs.

Artificial intelligence has been increasingly used in medical imaging and has demonstrated expert level performance in image classification tasks. To develop a fully automatic approach for determining the Risser stage using deep learning on abdominal radiographs. In this multicenter study, 1,681 supine abdominal radiographs (age range, 9-18years, 50% female) obtained between January 2019 and April 2022 were collected retrospectively from three medical institutions and graded manually using the United States Risser staging system. A total of 1,577 images from Hospitals 1 and 2 were used for development, and 104 images from Hospital 3 for external validation. From each radiograph, right and left iliac crest patch images were extracted using the pelvic bone segmentation model DeepLabv3 + with the EfficientNet-B0 encoder trained with 90 digitally reconstructed radiographs from pelvic computed tomography scans with a pelvic bone mask. Using these patch images, ConvNeXt-B was trained to grade according to the Risser classification. The model's performance was evaluated using accuracy, area under the receiver operating characteristic curve (AUROC), and mean absolute error. The fully automatic Risser stage assessment model showed an accuracy of 0.87 and 0.75, mean absolute error of 0.13 and 0.26, and AUROC of 0.99 and 0.95 on internal and external test sets, respectively. We developed a deep learning-based, fully automatic segmentation and classification model for Risser stage assessment using abdominal radiographs.

A Novel Approach Using Non-Experts and Transformation Models to Predict the Performance of Experts in A/B Tests

A Novel Approach Using Non-Experts and Transformation Models to Predict the Performance of Experts in A/B Tests

External validation of an artificial intelligence model for Gleason grading of prostate cancer on prostatectomy specimens.

To externally validate the performance of the DeepDx Prostate artificial intelligence (AI) algorithm (Deep Bio Inc., Seoul, South Korea) for Gleason grading on whole-mount prostate histopathology, considering potential variations observed when applying AI models trained on biopsy samples to radical prostatectomy (RP) specimens due to inherent differences in tissue representation and sample size. The commercially available DeepDx Prostate AI algorithm is an automated Gleason grading system that was previously trained using 1133 prostate core biopsy images and validated on 700 biopsy images from two institutions. We assessed the AI algorithm's performance, which outputs Gleason patterns (3, 4, or 5), on 500 1-mm2 tiles created from 150 whole-mount RP specimens from a third institution. These patterns were then grouped into grade groups (GGs) for comparison with expert pathologist assessments. The reference standard was the International Society of Urological Pathology GG as established by two experienced uropathologists with a third expert to adjudicate discordant cases. We defined the main metric as the agreement with the reference standard, using Cohen's kappa. The agreement between the two experienced pathologists in determining GGs at the tile level had a quadratically weighted Cohen's kappa of 0.94. The agreement between the AI algorithm and the reference standard in differentiating cancerous vs non-cancerous tissue had an unweighted Cohen's kappa of 0.91. Additionally, the AI algorithm's agreement with the reference standard in classifying tiles into GGs had a quadratically weighted Cohen's kappa of 0.89. In distinguishing cancerous vs non-cancerous tissue, the AI algorithm achieved a sensitivity of 0.997 and specificity of 0.88; in classifying GG ≥2 vs GG 1 and non-cancerous tissue, it demonstrated a sensitivity of 0.98 and specificity of 0.85. The DeepDx Prostate AI algorithm had excellent agreement with expert uropathologists and performance in cancer identification and grading on RP specimens, despite being trained on biopsy specimens from an entirely different patient population.

Assessing GPT-4's Performance in Delivering Medical Advice: Comparative Analysis With Human Experts.

Accurate medical advice is paramount in ensuring optimal patient care, and misinformation can lead to misguided decisions with potentially detrimental health outcomes. The emergence of large language models (LLMs) such as OpenAI's GPT-4 has spurred interest in their potential health care applications, particularly in automated medical consultation. Yet, rigorous investigations comparing their performance to human experts remain sparse. This study aims to compare the medical accuracy of GPT-4 with human experts in providing medical advice using real-world user-generated queries, with a specific focus on cardiology. It also sought to analyze the performance of GPT-4 and human experts in specific question categories, including drug or medication information and preliminary diagnoses. We collected 251 pairs of cardiology-specific questions from general users and answers from human experts via an internet portal. GPT-4 was tasked with generating responses to the same questions. Three independent cardiologists (SL, JHK, and JJC) evaluated the answers provided by both human experts and GPT-4. Using a computer interface, each evaluator compared the pairs and determined which answer was superior, and they quantitatively measured the clarity and complexity of the questions as well as the accuracy and appropriateness of the responses, applying a 3-tiered grading scale (low, medium, and high). Furthermore, a linguistic analysis was conducted to compare the length and vocabulary diversity of the responses using word count and type-token ratio. GPT-4 and human experts displayed comparable efficacy in medical accuracy ("GPT-4 is better" at 132/251, 52.6% vs "Human expert is better" at 119/251, 47.4%). In accuracy level categorization, humans had more high-accuracy responses than GPT-4 (50/237, 21.1% vs 30/238, 12.6%) but also a greater proportion of low-accuracy responses (11/237, 4.6% vs 1/238, 0.4%; P=.001). GPT-4 responses were generally longer and used a less diverse vocabulary than those of human experts, potentially enhancing their comprehensibility for general users (sentence count: mean 10.9, SD 4.2 vs mean 5.9, SD 3.7; P<.001; type-token ratio: mean 0.69, SD 0.07 vs mean 0.79, SD 0.09; P<.001). Nevertheless, human experts outperformed GPT-4 in specific question categories, notably those related to drug or medication information and preliminary diagnoses. These findings highlight the limitations of GPT-4 in providing advice based on clinical experience. GPT-4 has shown promising potential in automated medical consultation, with comparable medical accuracy to human experts. However, challenges remain particularly in the realm of nuanced clinical judgment. Future improvements in LLMs may require the integration of specific clinical reasoning pathways and regulatory oversight for safe use. Further research is needed to understand the full potential of LLMs across various medical specialties and conditions.

Cardiac sarcoidosis and neurosarcoidosis - multidisciplinary approach for diagnosis.

The current review aims to highlight the role of multidisciplinary approach in the diagnosis of patients with cardiac and neurosarcoidosis. Multidisciplinary approach integrates the available clinical information, imaging and histopathological results aiming to reach a definite or at least provisional diagnosis and allow appropriate management. Multidisciplinary approach is the reference standard for diagnosis of interstitial lung disease and should be strongly considered in complex clinical conditions such as cardiac sarcoidosis (CS) and neurosarcoidosis. Histopathological confirmation of noncaseating granulomatous inflammation provides a definite diagnosis of sarcoidosis involving any organ. However, a provisional high confidence or even definite clinical diagnosis can be reached using multidisciplinary evaluation of all available evidence. The diagnosis of cardiac sarcoidosis and neurosarcoidosis requires the integration of different expertise based on the current diagnostic criteria sets. Identifying typical or at least compatible patterns on advanced imaging modalities (CMR and Fluro-Deoxy-Glucose Positron Emission Tomography (FDG-PET)) seems key for the diagnosis of CS, while a confident diagnosis of extra-cardiac disease supports an at least provisional diagnosis. Similarly, in neurosarcoidosis integrating compatible MRI appearances and cerebrospinal fluid results in patients with systemic sarcoidosis allows an at least provisional diagnosis. Exclusion of alternative differential diagnoses is crucial and requires high clinical suspicion, imaging review expertise and appropriate tests performance. There have been considerable advances in the diagnostic approach of patients with cardiac and neurosarcoidosis. Multidisciplinary approach for both diagnosis and management is required to reach a confident clinical diagnosis and should be applied when possible.

Gene-environment interaction in expertise acquisition: Practice effects on musical expertise vary by polygenic scores for cognitive performance

Expert performance is associated with practice, partly because of causal effects of practice on skill (i.e., learning). However, the practice-expertise association is also influenced by a complex interplay between genes and environment including partly overlapping genetic influences. The importance of cognitive ability in the practice-expertise association is less well understood. Therefore, we first examined whether genetic predisposition for cognitive performance, operationalized as a polygenic score, is associated with music practice and expertise. Next, we tested whether there is evidence for gene × environment interaction, i.e., whether effects of practice on expertise differ depending on an individual's genetic predisposition for cognitive performance. Polygenic scores for cognitive performance (PGScp) and multi-trait cognitive performances, including educational attainment and mathematical performances (PGScps) were calculated for approximately 3800 genotyped Swedish individuals with information available on their cumulative amount of music practice, musical achievement, and musical auditory discrimination. We found that higher PGScp and PGScps were associated with higher levels of achievement, musical auditory discrimination, and more practice, although the association with practice weakened when controlling for education. Music practice was linked to both expertise outcomes, and the effect sizes of these associations varied depending on an individual's PGScp and PGScps (with the exception of PGScp for musical auditory discrimination). These results suggest genetic pleiotropy between cognitive performance and musical expertise. Additionally, they reveal the presence of G × E interaction in skill acquisition, as effects of practice on musical expertise are stronger for individuals with a higher genetic predisposition for cognitive performance.