Early-life Family Socioeconomic and Educational Impacts on Adult EEG and Behavioral Signatures of Visual Cognitive Function

Early-life Family Socioeconomic and Educational Impacts on Adult EEG and Behavioral Signatures of Visual Cognitive Function | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Early-life Family Socioeconomic and Educational Impacts on Adult EEG and Behavioral Signatures of Visual Cognitive Function Shuxuan Mao, Xuan Zhang, Fengrui Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7239089/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 20 You are reading this latest preprint version Abstract Socioeconomic and educational factors are known to influence cognitive development, but the specific neural mechanisms through which early-life conditions shape adult brain function remain unclear. In this study, we investigated how early-life socioeconomic and educational environments relate to behavioral performance and neural activity in early adulthood during two visual cognitive tasks, i.e., the flanker task and the visual search task. 73 participants completed the tasks while electroencephalography (EEG) data were recorded. We extracted time-domain event-related potentials (P1, P2, N2, P3) and time-frequency features (alpha, beta, and gamma band power) to assess attention, cognitive control, and perceptual processing. Four SES-related variables, subjective family SES, household income, participants’ own education duration, and parental education, were analyzed. Behavioral results showed that individuals from more advantaged early-life backgrounds exhibited faster and more accurate task performance. Neural data further revealed that enhanced ERP amplitudes and oscillatory activity were associated with higher SES and educational levels. Finally, canonical correlation analysis (CCA) quantified the relative influence of each SES factor, identifying household income and subjective SES as most strongly associated with the integrated brain–behavior profile. These findings provide converging behavioral and neurophysiological evidence that early-life socioeconomic context leaves measurable imprints on cognitive and neural function in adulthood, offering biological insight into how social experience becomes embedded in the brain. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Socioeconomic status EEG Visual cognition Flanker Visual search Development Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Socioeconomic status (SES) and educational attainment are essential determinants of cognitive development and brain function across the lifespan 1 . SES indicators, such as parental education level and home income have been shown to play a particularly influential role in shaping early developmental contexts 2 , 3 , affecting not only access to cognitive enrichment but also broader environmental conditions that support healthy neurocognitive growth 4 . Prior research has linked early-life SES to structural and functional brain differences, including altered gray matter volume, cortical thickness, and neural efficiency in adolescence and adulthood 5 . Electroencephalography (EEG) has also been used to study SES-related brain function, with studies showing associations between childhood SES and electrophysiological responses such as mismatch negativity during oddball and go/no-go paradigms 6 , 7 . Despite these advances, several key questions remain unresolved. First, most prior studies have focused on single or narrowly defined neural or behavioral indexes 8 , 9 , rather than integrating multiple neurophysiological markers to comprehensively assess how SES influences cognitive function. As a result, the broader biological profile through which socioeconomic factors shape brain function remains under-characterized. Second, previous research has rarely quantified the relative importance of different SES factors (e.g., parental education, household income, subjective SES) in shaping neural activity. Without such comparative analysis, it remains unclear which aspects of socioeconomic context exert the strongest influence on brain function and cognitive development. To address these gaps, the present study focused on visual cognitive function, a domain that is particularly important for human survival and interaction 10 . Visual information accounts for an estimated 80% of all sensory input processed by the human brain in daily life 11 . Therefore, understanding how SES influences visual cognition provides a valuable window into brain–environment interactions. We selected two well-established visual cognitive paradigms: the flanker task (FL) and the visual search task (VS). The FL task is widely used to assess attentional control and executive inhibition by introducing conflict between target and distractor stimuli 12 , 13 . In contrast, the VS task targets selective attention and perceptual processing, requiring participants to detect relevant visual targets among distractors 14 . Together, these tasks provide complementary measures of visual attention, conflict monitoring, and top-down control. Using EEG, we examined both time-domain event-related potential (ERP) components and time-frequency dynamics to identify neural markers associated with socioeconomic and educational variables. Specifically, we focused on ERP components such as P2, N2, P3, and P1, as well as oscillatory activity in the alpha, beta, and gamma frequency bands, each reflecting distinct aspects of attention, inhibition, and executive control. Notably, four key socioeconomic and educational indicators during early-life period were included in the analysis: subjective family SES, household income, participants’ own education duration, and their parental education. To further integrate behavioral and neural data, we applied canonical correlation analysis (CCA) to quantify the relative contributions of these factors to variations in cognitive and neural functioning. By combining two complementary visual paradigms with EEG and multivariate modeling, this study aimed to clarify how early-life social and educational environments continue to shape cognitive function in early adulthood, and to provide concrete biological evidence for the embodied impact of socioeconomic context on the human brain. Methods Participants and procedure The present study involved 73 participants (43 females, 30 males), aged between 18 and 30 years. Participants were recruited through flyers posted on a university campus and at community centers in the Western United States, as described in a previous study 7 . Subjective childhood’s family SES, household income, participants’ education duration, and their parental education duration of each participant were collected and transmitted into ordinal levels for analysis 15 . Specifically, 1 represents high school or less, 2 represents high school, 3 represents some college or associate degree, and 4 represent bachelor’s degree or higher. Each participant completed two visual cognitive tasks: a Eriksen-flanker task (hereafter referred to as FL) and a visual search task (hereafter referred to as VS), both described in prior research 16 . Behavioral performance was assessed via reaction time and accuracy in both tasks. For the FL task, incompatible (ICm) and compatible (Cm) conditions were analyzed separately. EEG preprocessing EEG data were recorded during two tasks and obtained from an open-access dataset published by Isbell et al. on OpenNeuro 17 and Scientific Data 15 . Preprocessing was conducted using MATLAB and the EEGLAB toolbox 18 . EEG signals were band-pass filtered between 1 and 45 Hz and re-referenced to the average of the LM and RM electrodes. Channels with abnormal spectral power (defined as values exceeding ± 3 standard deviations from the mean across channels) were identified as noisy and corrected via spherical spline interpolation. Eye movement artifacts were removed using independent component analysis (ICA), following established procedures 19 . Event-related potential analyses In order to obtain event-related potentials (ERPs), EEG signals were segmented into 1500-ms epochs (500 ms pre-event and 1000 ms post-event), low-pass filtered at 30 Hz, and baseline-corrected using the pre-event interval. ERP components were extracted for both tasks. For the FL task, the P2 component was extracted from electrodes F3, Fz, and F4 electrodes, while the N2 and P3 components were obtained from P3, Pz, and P4 electrodes 20 . For the VS task, the P1 and P3 components were extracted from P7, P3, Pz, P4, and P8 electrodes 21 ; the N1 component was extracted from O1, Oz, and O2 electrodes 21 ; and the P2 component was measured from frontal-parietal electrodes including F3, Fz, F4, FC1, and FC2 electrodes 22 . The peak amplitudes of each ERP component were extracted for each participant and used in subsequent analyses. Time-frequency analyses were conducted using the short-time Fourier transform, following procedures described in previous study 23 . For the FL task, time-frequency distributions (TFDs) were calculated at F3, Fz, F4, P3, Pz, and P4 electrodes. and the mean power within the beta (14 to 30 Hz) and gamma (31 to 45 Hz) frequency bands was extracted from the post-stimulus window of 200 to 600 ms. For the VS task, TFDs were computed using data from CP5, CP1, CP2, CP6, P7, P3, P4, and P8 electrodes, and post-stimulus power in the alpha (8 to 13 Hz) and beta (14 to 30 Hz) bands was extracted within the same 200 to 600 ms window 24 . Canonical correlation analysis To examine the multivariate relationship between SES and neurocognitive functions, canonical correlation analysis (CCA) was conducted between a set of demographic variables and a set of neurobehavioral variables derived from EEG and task behavioral indexes 25 , 26 . The demographic variable set included four predictors: family SES, household income level, participant’s education level, and parental education level. EEG and behavioral features were first standardized (z-scored). Principal component analysis (PCA) was then used to reduce dimensionality and multicollinearity, conducted separately on EEG features (10 variables: FL-N2, FL-P2, difference of Cm and ICm for FL-P2 and P3, VS-P1, VS-alpha power, and VS-beta power) and behavioral measures (4 indices: FL-reaction time of both ICm and Cm, FL-accuracy of ICm, and VS-reaction time). The first 2 EEG principal components (PC1 and PC2) and the first behavioral principal component (PC1) were retained based on the proportion of explained variance and combined into a three-dimensional neurobehavioral variable set. Finally, the 4 demographic variable and 3 neurobehavioral variable sets were applied into CCA, performed via the CCA package in R. Canonical loadings, defined as the correlations between original variables and their corresponding canonical variates, were used to interpret contributions to the first canonical dimension. Statistical analyses Linear regression and Pearson correlation were used to investigate the relationships among EEG features, family SES, household income, participants’ education level, and parental education level. Paired-sample t test was used to compare the differences between ICm and Cm conditions in FL task. Statistical significance of the canonical correlations was assessed using Wilks’ Lambda with Rao’s F-approximation. Results Higher socioeconomic and educational levels are associated with enhanced behavioral performance in visual cognitive tasks. We first visualized the distribution of socioeconomic and educational variables for all participants. Histograms showed variability in subjective family socioeconomic status (SES) and household income in the childhood, parental education, and participants’ own education level (Fig. 1 A). We then analyzed behavioral accuracy and reaction time for both VS (Fig. 1 B) and FL (Fig. 1 E) tasks. In the VS task (Table S1 ), reaction time was negatively correlated with both parental education (r = -0.2465, p = 0.0478; Fig. 1 C) and household income (r = -0.3116, p = 0.0115; Fig. 1 D), indicating that individuals from higher SES backgrounds responded more quickly. In the FL task (Table S1 ), participants exhibited lower accuracy and longer reaction times for ICm compared to Cm conditions (****p < 0.0001; Fig. 1 E), confirming the expected increased cognitive demand of ICm trials 27 , 28 . Moreover, reaction time in both ICm and Cm conditions was negatively associated with household income (Fig. 1 F and G; ICm: r = -0.2362, p = 0.0457; Cm: r = -0.2461, p = 0.0409; r). Accuracy in ICm trials was positively correlated with participants’ own education level (r = 0.2543, p = 0.0311; Fig. 1 H), suggesting higher educational attainment may enhance performance under cognitive conflict. Collectively, these results demonstrate that higher socioeconomic and educational levels are associated with more efficient and accurate performance in visual cognitive tasks. ERP components of flanker task are associated with socioeconomic and educational levels. We examined typical ERP components elicited during the FL task under Cm and ICm conditions 20 , 28 . Clear P2 (Fig. 2 A), N2 (Fig. 2 B), and P3 (Fig. 2 C) components were observed (Figure S1 ), with P2 (~ 170 ms) most prominent over frontal electrodes (F3, Fz, and F4), and N2 (~ 190 ms) and P3 (365 ms for Cm, 445 ms for ICm) over parietal sites (P3, Pz, and P4; Fig. 2 D). Statistical comparisons confirmed significant task-related differences. Specifically, N2 amplitude was significantly larger in ICm compared to Cm trials (p < 0.0001; Fig. 2 G), and P3 amplitude was higher in the Cm condition (p = 0.0298; Fig. 2 H). No significant difference was observed for P2 amplitude between conditions (p = 0.2034; Fig. 2 F). In addition, we also analyzed the time-frequency distributions of frontal and parietal electrodes revealed increased beta and gamma band power following stimulus onset for both Cm and ICm conditions (Fig. 2 E), although neither showed significant condition differences (beta: p = 0.3666; gamma: p = 0.6524; Fig. 2 I, J). We next assessed associations between ERP components and socioeconomic/educational variables (Table S2). We found that P2 amplitude was negatively correlated with family SES level in both ICm and Cm conditions (ICm: r = -0.2454, p = 0.0377; Cm: r = -0.2444, p = 0.0386; Fig. 3 A), indicating that individuals from lower SES backgrounds exhibited stronger early attentional responses 29 . Furthermore, the difference in P2 amplitude difference between Cm and ICm conditions (Cm minus ICm) was negatively correlated with household income (r = 0.2818, p = 0.0157; Fig. 3 B), suggesting greater differentiation in attentional modulation among participants from lower-income families. N2 amplitude also showed significant negative associations with household income in both ICm and Cm conditions (ICm: r = -0.2424, p = 0.0388; Cm: r = -0.2405; p = 0.0404; Fig. 3 C), and with participants’ own education level (ICm: r = -0.2693, p = 0.0212; Cm: r = -0.2706, p = 0.0206; Fig. 3 D). These findings suggest enhanced conflict monitoring in individuals from lower-income or less-educated backgrounds 20 , 30 . P3 amplitude difference (Cm minus ICm) was correlated with household income (r = -2630, p = 0.0246; Fig. 3 E), implying reduced cognitive control modulation in individuals with lower income 30 . Additionally, the difference in gamma-band power between Cm and ICm conditions was negatively associated with household income (r = -0.3101, p = 0.0076; Fig. 3 F), indicating attenuated high-frequency neural responses to cognitive conflict among lower-income participants. Overall, these results demonstrate that ERP components, particularly P2, N2, P3, and gamma-band oscillations, are systematically modulated by socioeconomic and educational factors, revealing that individual differences in cognitive control and attentional engagement during the flanker task are shaped by environmental background. ERP components of visual search task are associated with socioeconomic and educational levels. In the VS task, we analyzed ERP components to characterize neural responses associated with visual attention and target detection (Figure S2). Group-averaged ERP waveforms revealed prominent components including P1 (~ 130 ms), N1/P2 (~ 190 ms), and P3 (~ 400 ms) across expected scalp regions (Fig. 4 A to D). Specifically, P1 and P3 components were most pronounced over parietal region (P7, P3, Pz, P4, and P8 electrodes), while N1 was strongest at occipital area (O1, Oz, and O2 electrodes), and P2 was observed over frontal-central electrodes (F3, Fz, F4, FC1, and FC2 electrodes). In addition, through the time-frequency analysis, we found decrease (event-related desynchronizations) at both beta and alpha band between 200 to 600 ms post-stimulus period (Fig. 4 F). We then explored the relationship between ERP features from the VS task and the socioeconomic factors (Table S3). First, we found P1 amplitude was positively correlated with household income (r = 0.2432, p = 0.0395; Fig. 5 A), indicating that individual from higher-income families exhibited stronger early visual sensory responses 31 . In addition, alpha and beta band oscillatory power were both positively correlated with parental education level (alpha: r = 0.2638, p = 0.0251; beta: r = 0.3144, p = 0.0072; Fig. 5 B and 5 C), indicating the different attentional functions among different SES 32 . Together, these results demonstrate that both early ERP components and oscillatory dynamics in the visual search task are sensitive to socioeconomic context, with enhanced neural responses observed in participants from higher SES and more educated family backgrounds. Canonical correlation between socioeconomic and educational factors and neural-cognitive indices To assess the multivariate relationship between socioeconomic/educational background and visual cognitive functioning, we conducted a CCA. EEG features that significantly correlated with socioeconomic and educational variables—namely FL-N2, FL-P2, the difference between Cm and ICm conditions for FL-P2, FL-P3, and FL-gamma, VS-P1, and alpha and beta band power from the VS task—were selected as representative neural indices. Similarly, behavioral measures that showed significant associations with socioeconomic factors, i.e., FL reaction time and accuracy in ICm trials, FL reaction time in Cm trials, and VS reaction time, were included as behavioral indicators of visual cognitive function. In order to optimize the CCA’s reliability, we decreased the dimensions of these features using PCA, resulting in two PCs for EEG signatures and one PC for behavioral results. These three components were then jointly examined as dependent variables against four socioeconomic/educational indicators: family SES level, household income, parental education level, and participant’s own education level (Fig. 6 , top panels). The analysis revealed a first canonical correlation of r = 0.513 (p = 0.0005), indicating a moderate-to-strong multivariate association between socioeconomic background and the combined neural-behavioral profile (Fig. 6 , center panel; Table S4). Canonical loadings showed that all four socioeconomic variables contributed meaningfully to the canonical variate, with the strongest contributions from household income (0.658), followed by family SES level (0.481). Participant’s own education (0.407) and parental education (0.388) also contributed, though to a lesser extent. These results highlight a robust systems-level link between socioeconomic context and individual differences in brain function and cognitive behavior, supporting the idea that socioeconomic background shapes integrated neural and behavioral signatures of visual cognitive performance. Discussion This study aimed to investigate the influence of socioeconomic and educational background on visual cognitive functions by integrating behavioral performance, EEG responses, and multivariate analysis. Participants performed a FL task and a VS task while EEG was recorded. Results showed that individuals from higher socioeconomic and educational backgrounds demonstrated better behavioral performance, i.e., faster reaction times and higher accuracy. Electrophysiological results revealed that higher SES and education were associated with enhanced attentional and executive processes, as reflected in larger P2 and P3, reduced N2, and stronger gamma activity in the FL task, as well as greater P1 amplitude and stronger alpha and beta oscillations in the VS task. By integrating ERPs, time-frequency analyses, and multivariate modeling via CCA, we found that household income and family SES were the most strongly associated factors with individual differences in neural and behavioral indicators, highlighting the influential role of early environmental conditions in shaping cognitive function. Socioeconomic and educational background shapes cognitive development through environmental enrichment One noteworthy finding from the demographic analysis is that parental education level was significantly correlated with subjective family SES. Specifically, behavioral results from both the FL and VS tasks revealed a consistent pattern: individuals from families with higher SES and education backgrounds demonstrated better performance, characterized by higher accuracy and faster reaction times. In the VS task, these individuals responded more quickly, and in the FL task, they showed more efficient processing especially under high-conflict (ICm) conditions. These findings suggest that a more enriched family environment, marked by higher income, education, and SES, may provide children with more diverse and stimulating developmental experience 33 , 34 . Such environments likely support the acquisition of cognitive skills crucial for attentional control, conflict monitoring, and goal-directed behavior 35 . Neural correlates of cognitive advantage in high-SES individuals Electrophysiological findings from both tasks further reveal that socioeconomic and educational factors shape cognitive functions. In the FL task, higher SES and education levels were associated with greater P2 amplitudes, suggesting enhanced early attentional processing 29 . Increased N2 amplitudes, particularly among individuals from lower-income and less-educated backgrounds, may reflect a compensatory increase in conflict monitoring effort 20 . The P3 component, a marker of cognitive control and evaluation 30 , showed larger amplitudes in higher-SES individuals, reflecting more efficient allocation of executive resources 25 . Additionally, gamma-band activity, typically linked to high-level cognitive integration and allocation 36 , was stronger in participants from higher-income families, highlighting enhanced information processing capacity. In the VS task, P1 amplitude which is associated with processing of visual sensory and external stimulation 37 , 38 , was positively correlated with household income, suggesting heightened perceptual sensitivity in individuals from more advantaged backgrounds. Moreover, alpha and beta band power, which are often interpreted as indices of attentional engagement and top-down control 32 , 39 , 40 , were also stronger in participants whose parents had higher educational attainment. These results indicate that higher SES and education levels support the development of key neural systems involved in attention regulation, cognitive inhibition, and executive control. Canonical correlation reveals the role of socioeconomic factors in neurocognitive outcomes The CCA revealed that the first canonical correlation was statistically significant (r = 0.513, p = 0.0005), suggesting a moderate linear relationship between a weighted combination of demographic variables and a composite index of neurocognitive function. Among the demographic variables, household income and subjective socioeconomic status showed the strongest canonical loadings (-0.658 and − 0.481, respectively), indicating that these two factors contributed most substantially to the canonical dimension. While the negative sign of the loadings does not affect interpretation, given the arbitrary directionality in CCA, the magnitude underscores the relative influence of these variables. In contrast, participants' and parental education levels had weaker loadings, suggesting a less prominent role in the observed multivariate relationship. These findings align with previous literature emphasizing the role of socioeconomic factors in shaping cognitive and brain development 41 , 42 . Notably, most prior research has focused on children, whereas the current study extends this relationship to young adults, providing evidence that socioeconomic disparities may continue to influence neural and behavioral function beyond early developmental periods. Limitations While this study provides important insights into how socioeconomic and educational backgrounds shape visual cognitive functioning at both behavioral and neural levels, several limitations should be acknowledged. First, the participants in this study were mainly raised and educated within the United States, which may limit the generalizability of the findings to populations in different cultural, educational, and socioeconomic systems. Cultural values, schooling structures, and family dynamics in other societies may moderate the relationship between SES and cognitive development 1 . Second, the study focused exclusively on two visual cognitive paradigms, i.e., the flanker and the visual search tasks, both of which primarily target attentional control and perceptual processing. Therefore, conclusions drawn here may not extend to other cognitive domains, such as language, memory, or social cognition. Third, while this study identifies significant associations between socioeconomic background and cognitive-neural measures, it does not directly address the broader systemic and structural factors that may underlie these associations. Socioeconomic status is not merely an individual or family-level characteristic, but is deeply embedded in social structures that distribute resources, opportunities, and adversity unequally. Factors such as intergenerational inequality, structural discrimination, and unequal access to early educational enrichment may mediate or confound the observed relationships. Thus, the findings may reflect not only differences in individual development, but also broader patterns of social advantage and disadvantage that raise important questions about fairness, opportunity, and the reproduction of inequality through cognitive pathways 1 . Conclusion The present study demonstrated that socioeconomic and educational backgrounds are closely associated with individual differences in visual cognitive performance and its neural correlates. By integrating behavioral measures, electrophysiological data, and multivariate modeling, we show that early life environments leave measurable imprints on attentional and executive functions. Importantly, our canonical correlation analysis quantitatively assessed the relative influence of different socioeconomic and educational factors, offering concrete biological evidence for how social environments shape physiological functioning. These findings deepen our understanding of the neurocognitive embodiment of social experience and contribute to broader efforts to bridge neuroscience with social and developmental sciences. Declarations Ethics Statement This study was approved by the University of California Institutional Review Board. The EEG data analyzed were obtained from a previously published, open-access dataset (Isbell et al., 2023, OpenNeuro accession number: ds004217; Isbell et al., 2025, scientific data.). The original study received approval from the Institutional Review Board at the University of Oregon, and all procedures involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Participant consent In the original study from which the EEG data used in this manuscript were obtained (Isbell et al., 2023, OpenNeuro accession number: ds004217; Isbell et al., 2025, scientific data), all participants were fully informed about the study procedures, potential risks, and their rights prior to enrollment. Written informed consent to participate in the study and for the subsequent use and publication of their anonymized data was obtained from each participant in accordance with the Institutional Review Board (IRB) at the University of Oregon. For the current secondary analysis, no new human data were collected, and the authors only accessed and analyzed de-identified, open-access data. Conflicts of interest : None. Funding: The authors received no funding for this work. Author Contribution Fengrui Zhang designed study. Shuxuan Mao, Fengrui Zhang, and Xuan Zhang analyzed the data. Shuxuan Mao and Fengrui Zhang wrote the manuscript. Shuxuan Mao and Xuan Zhang revised the manuscript. Acknowledgement We thank Dr. Elif Isbell, Dylan Richardson, and the IDEA research group for the creation and distribution of the publicly available dataset and securely sharing a more comprehensive de-identified dataset that could not be publicly shared. Data Availability The dataset used in this study is publicly available on OpenNeuro (https://openneuro.org/datasets/ds006018/versions/1.2.2). All analysis scripts are available from the corresponding author upon reasonable request. References Schneider, J. M., Behboudi, M. H. & Maguire, M. J. 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07:56:55","extension":"html","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":119299,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7239089/v1/14f6a6d2e5844bf86c945564.html"},{"id":91962644,"identity":"9c60382e-0687-4ddd-884c-1ffb09329047","added_by":"auto","created_at":"2025-09-23 07:56:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2711876,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVisual task behaviors are correlated to socioeconomic and educational factors.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Histograms for subjective family socioeconomic status (SES) level, household income level, parental education level, and participant’s own education level. (\u003cstrong\u003eB\u003c/strong\u003e) Behavioral accuracy (left) and reaction time (right) of visual search (VS) task. (\u003cstrong\u003eC\u003c/strong\u003eand \u003cstrong\u003eD\u003c/strong\u003e) Correlations between reaction time and parental education level (C), household income level (D). (\u003cstrong\u003eE\u003c/strong\u003e) Behavioral accuracy (left) and reaction time (right) of Flanker (FL) task for incompatible (ICm) and compatible (Cm) conditions. (\u003cstrong\u003eF\u003c/strong\u003e and \u003cstrong\u003eG\u003c/strong\u003e) Correlations between reaction time of reaction time and household income level for ICm (F) and Cm (G) conditions. (\u003cstrong\u003eH\u003c/strong\u003e) Correlation between behavioral accuracy of ICm condition and participant’s own education level. Statistical analyses utilized Pearson’s correlation (C, D, and F to H) and paired-sample \u003cem\u003et\u003c/em\u003e test (E). ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7239089/v1/30e7aa3516e1496c448fbc1d.png"},{"id":91960848,"identity":"35276c77-9203-40b7-874a-8495c2a0467b","added_by":"auto","created_at":"2025-09-23 07:48:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5109869,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eERP components of flanker task.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e to \u003cstrong\u003eC\u003c/strong\u003e) Group-mean waveform of event-related potential (ERP) for P2 (A, from F3, Fz, and F4 electrodes), N2 (B, from P3, Pz, and P4 electrodes), and P3 (C, from P3, Pz, and P4 electrodes) components. Solid line indicates compatible condition, and dashed line indicates incompatible conditions. (\u003cstrong\u003eD\u003c/strong\u003e) Group-mean scalp topographies of P2, N2 and P3 components. (\u003cstrong\u003eE\u003c/strong\u003e) Group-mean time-frequency distributions (TFDs) of compatible and incompatible conditions. TFDs were from F3, Fz, F4, P3, Pz, and P4 electrodes. Back frames indicate the region of interest (ROI) of beta and gamma band oscillation power. (\u003cstrong\u003eF\u003c/strong\u003e to \u003cstrong\u003eJ\u003c/strong\u003e) Comparisons of P2 amplitude (F), N2 amplitude (G), P3 amplitude (H), beta band power (I), and gamma band power (J) for compatible and incompatible conditions. Statistical analyses utilized paired-sample \u003cem\u003et\u003c/em\u003e test. *p\u0026lt;0.05; ****p \u0026lt; 0.0001; ns, no significance.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7239089/v1/14992acf1bae92e166fd03e4.png"},{"id":91962643,"identity":"71979db5-8ba4-4c3f-b79e-81792d3af1b5","added_by":"auto","created_at":"2025-09-23 07:56:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2055282,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations between flanker’s ERP components and SES factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Correlation between P2 amplitude and family SES level for both ICm (red) and Cm (blue) conditions. (\u003cstrong\u003eB\u003c/strong\u003e) Correlation between differences of P2 amplitude (Cm minus ICm condition) and household income level. (\u003cstrong\u003eC\u003c/strong\u003e) Correlation between N2 amplitude and household income level for both ICm (red) and Cm (blue) conditions. (\u003cstrong\u003eD\u003c/strong\u003e) Correlation between N2 amplitude and participant’s education level for both ICm (red) and Cm (blue) conditions. (\u003cstrong\u003eE\u003c/strong\u003e) Correlation between differences of P3 amplitude (Cm minus ICm condition) and household income level. (\u003cstrong\u003eF\u003c/strong\u003e) Correlation between differences of gamma band power (Cm minus ICm condition) and household income level. Statistical analyses utilized Pearson’s correlation.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7239089/v1/02fb1adc6c1a2653115a8ea9.png"},{"id":91962645,"identity":"7309313c-810d-4977-8273-0167edba5ecd","added_by":"auto","created_at":"2025-09-23 07:56:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2547620,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eERP components of visual search task.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e to \u003cstrong\u003eD\u003c/strong\u003e) Group-mean waveform of ERP for P1 (A, P7, P3 Pz, P4, and P8 electrodes), N1 (B, from O1, Oz, and O2 electrodes), P2 (C, from F3, Fz, F4, FC1, and FC2 electrodes), and P3 (D, from P7, P3 Pz, P4, and P8 electrodes) components. (\u003cstrong\u003eE\u003c/strong\u003e) Group-mean scalp topographies of P1, N1/P2 and P3 components. (\u003cstrong\u003eF\u003c/strong\u003e) Group-mean TFDs of visual research ERP. TFDs were from CP5, CP1, CP2, CP6, P7, P3, P4, and P8 electrodes. Back frames indicate ROIs of alpha and beta band oscillation power.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7239089/v1/7d6d90294260ef7229b08517.png"},{"id":91960855,"identity":"5a10fb6a-c2c4-4e54-86d9-86f12dd9276b","added_by":"auto","created_at":"2025-09-23 07:48:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":367093,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations between visual search’s ERP components and family socioeconomic condition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Correlation between P1 amplitude and household income level. (\u003cstrong\u003eB\u003c/strong\u003e) Correlation between alpha band power and parental education level. (\u003cstrong\u003eC\u003c/strong\u003e) Correlation between beta band power and parental education level. Statistical analyses utilized Pearson’s correlation.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7239089/v1/93f9b801cc1398dd55ce4c3f.png"},{"id":91960854,"identity":"a2d222b1-5480-4b0d-8b6b-b07d90d9ee34","added_by":"auto","created_at":"2025-09-23 07:48:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1952679,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCanonical correlation between socioeconomic/educational factors and visual cognitive functions derived from EEG and behavioral data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEEG features from flanker and visual search tasks were reduced into two principal components (PC1 and PC2) using principal component analysis (PCA). Behavioral measures were reduced into a single component (PC1). These three components were included as dependent variables in a canonical correlation analysis (CCA) with four demographic variables: family SES level, household income, participant’s education level, and parental education level. The first canonical correlation was 0.513. The scatterplot shows the relationship between the first pair of canonical variates. The right panel displays the absolute canonical loadings of the demographic variables on the first canonical variate.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7239089/v1/e3d7ef23fae43b2104c42a5d.png"},{"id":91965280,"identity":"01eee68d-047e-4ffc-9f87-c11fe95bdb6c","added_by":"auto","created_at":"2025-09-23 08:21:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15293959,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7239089/v1/ee19c4db-0655-4ba9-94c8-d521f93c437a.pdf"},{"id":91960850,"identity":"a98629ed-6e17-4412-aaff-e11d7c746593","added_by":"auto","created_at":"2025-09-23 07:48:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1093094,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7239089/v1/cec67fcb8ef168409b304857.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early-life Family Socioeconomic and Educational Impacts on Adult EEG and Behavioral Signatures of Visual Cognitive Function","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSocioeconomic status (SES) and educational attainment are essential determinants of cognitive development and brain function across the lifespan\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. SES indicators, such as parental education level and home income have been shown to play a particularly influential role in shaping early developmental contexts\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, affecting not only access to cognitive enrichment but also broader environmental conditions that support healthy neurocognitive growth\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Prior research has linked early-life SES to structural and functional brain differences, including altered gray matter volume, cortical thickness, and neural efficiency in adolescence and adulthood\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Electroencephalography (EEG) has also been used to study SES-related brain function, with studies showing associations between childhood SES and electrophysiological responses such as mismatch negativity during oddball and go/no-go paradigms\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite these advances, several key questions remain unresolved. First, most prior studies have focused on single or narrowly defined neural or behavioral indexes\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, rather than integrating multiple neurophysiological markers to comprehensively assess how SES influences cognitive function. As a result, the broader biological profile through which socioeconomic factors shape brain function remains under-characterized. Second, previous research has rarely quantified the relative importance of different SES factors (e.g., parental education, household income, subjective SES) in shaping neural activity. Without such comparative analysis, it remains unclear which aspects of socioeconomic context exert the strongest influence on brain function and cognitive development.\u003c/p\u003e\u003cp\u003eTo address these gaps, the present study focused on visual cognitive function, a domain that is particularly important for human survival and interaction\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Visual information accounts for an estimated 80% of all sensory input processed by the human brain in daily life\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Therefore, understanding how SES influences visual cognition provides a valuable window into brain\u0026ndash;environment interactions. We selected two well-established visual cognitive paradigms: the flanker task (FL) and the visual search task (VS). The FL task is widely used to assess attentional control and executive inhibition by introducing conflict between target and distractor stimuli\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In contrast, the VS task targets selective attention and perceptual processing, requiring participants to detect relevant visual targets among distractors\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Together, these tasks provide complementary measures of visual attention, conflict monitoring, and top-down control.\u003c/p\u003e\u003cp\u003eUsing EEG, we examined both time-domain event-related potential (ERP) components and time-frequency dynamics to identify neural markers associated with socioeconomic and educational variables. Specifically, we focused on ERP components such as P2, N2, P3, and P1, as well as oscillatory activity in the alpha, beta, and gamma frequency bands, each reflecting distinct aspects of attention, inhibition, and executive control. Notably, four key socioeconomic and educational indicators during early-life period were included in the analysis: subjective family SES, household income, participants\u0026rsquo; own education duration, and their parental education. To further integrate behavioral and neural data, we applied canonical correlation analysis (CCA) to quantify the relative contributions of these factors to variations in cognitive and neural functioning. By combining two complementary visual paradigms with EEG and multivariate modeling, this study aimed to clarify how early-life social and educational environments continue to shape cognitive function in early adulthood, and to provide concrete biological evidence for the embodied impact of socioeconomic context on the human brain.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eParticipants and procedure\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe present study involved 73 participants (43 females, 30 males), aged between 18 and 30 years. Participants were recruited through flyers posted on a university campus and at community centers in the Western United States, as described in a previous study\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Subjective childhood\u0026rsquo;s family SES, household income, participants\u0026rsquo; education duration, and their parental education duration of each participant were collected and transmitted into ordinal levels for analysis\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Specifically, 1 represents high school or less, 2 represents high school, 3 represents some college or associate degree, and 4 represent bachelor\u0026rsquo;s degree or higher. Each participant completed two visual cognitive tasks: a Eriksen-flanker task (hereafter referred to as FL) and a visual search task (hereafter referred to as VS), both described in prior research\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Behavioral performance was assessed via reaction time and accuracy in both tasks. For the FL task, incompatible (ICm) and compatible (Cm) conditions were analyzed separately.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEEG preprocessing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEEG data were recorded during two tasks and obtained from an open-access dataset published by Isbell et al. on \u003cem\u003eOpenNeuro\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003eScientific Data\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Preprocessing was conducted using MATLAB and the EEGLAB toolbox\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. EEG signals were band-pass filtered between 1 and 45 Hz and re-referenced to the average of the LM and RM electrodes. Channels with abnormal spectral power (defined as values exceeding\u0026thinsp;\u0026plusmn;\u0026thinsp;3 standard deviations from the mean across channels) were identified as noisy and corrected via spherical spline interpolation. Eye movement artifacts were removed using independent component analysis (ICA), following established procedures\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEvent-related potential analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn order to obtain event-related potentials (ERPs), EEG signals were segmented into 1500-ms epochs (500 ms pre-event and 1000 ms post-event), low-pass filtered at 30 Hz, and baseline-corrected using the pre-event interval. ERP components were extracted for both tasks. For the FL task, the P2 component was extracted from electrodes F3, Fz, and F4 electrodes, while the N2 and P3 components were obtained from P3, Pz, and P4 electrodes\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. For the VS task, the P1 and P3 components were extracted from P7, P3, Pz, P4, and P8 electrodes\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e; the N1 component was extracted from O1, Oz, and O2 electrodes\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e; and the P2 component was measured from frontal-parietal electrodes including F3, Fz, F4, FC1, and FC2 electrodes\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The peak amplitudes of each ERP component were extracted for each participant and used in subsequent analyses.\u003c/p\u003e\u003cp\u003eTime-frequency analyses were conducted using the short-time Fourier transform, following procedures described in previous study\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. For the FL task, time-frequency distributions (TFDs) were calculated at F3, Fz, F4, P3, Pz, and P4 electrodes. and the mean power within the beta (14 to 30 Hz) and gamma (31 to 45 Hz) frequency bands was extracted from the post-stimulus window of 200 to 600 ms. For the VS task, TFDs were computed using data from CP5, CP1, CP2, CP6, P7, P3, P4, and P8 electrodes, and post-stimulus power in the alpha (8 to 13 Hz) and beta (14 to 30 Hz) bands was extracted within the same 200 to 600 ms window\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCanonical correlation analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo examine the multivariate relationship between SES and neurocognitive functions, canonical correlation analysis (CCA) was conducted between a set of demographic variables and a set of neurobehavioral variables derived from EEG and task behavioral indexes\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The demographic variable set included four predictors: family SES, household income level, participant\u0026rsquo;s education level, and parental education level. EEG and behavioral features were first standardized (z-scored). Principal component analysis (PCA) was then used to reduce dimensionality and multicollinearity, conducted separately on EEG features (10 variables: FL-N2, FL-P2, difference of Cm and ICm for FL-P2 and P3, VS-P1, VS-alpha power, and VS-beta power) and behavioral measures (4 indices: FL-reaction time of both ICm and Cm, FL-accuracy of ICm, and VS-reaction time). The first 2 EEG principal components (PC1 and PC2) and the first behavioral principal component (PC1) were retained based on the proportion of explained variance and combined into a three-dimensional neurobehavioral variable set. Finally, the 4 demographic variable and 3 neurobehavioral variable sets were applied into CCA, performed via the CCA package in R. Canonical loadings, defined as the correlations between original variables and their corresponding canonical variates, were used to interpret contributions to the first canonical dimension.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLinear regression and Pearson correlation were used to investigate the relationships among EEG features, family SES, household income, participants\u0026rsquo; education level, and parental education level. Paired-sample \u003cem\u003et\u003c/em\u003e test was used to compare the differences between ICm and Cm conditions in FL task. Statistical significance of the canonical correlations was assessed using Wilks\u0026rsquo; Lambda with Rao\u0026rsquo;s F-approximation.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eHigher socioeconomic and educational levels are associated with enhanced behavioral performance in visual cognitive tasks.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe first visualized the distribution of socioeconomic and educational variables for all participants. Histograms showed variability in subjective family socioeconomic status (SES) and household income in the childhood, parental education, and participants\u0026rsquo; own education level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). We then analyzed behavioral accuracy and reaction time for both VS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) and FL (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE) tasks. In the VS task (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), reaction time was negatively correlated with both parental education (r = -0.2465, p\u0026thinsp;=\u0026thinsp;0.0478; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) and household income (r = -0.3116, p\u0026thinsp;=\u0026thinsp;0.0115; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), indicating that individuals from higher SES backgrounds responded more quickly. In the FL task (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), participants exhibited lower accuracy and longer reaction times for ICm compared to Cm conditions (****p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE), confirming the expected increased cognitive demand of ICm trials\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Moreover, reaction time in both ICm and Cm conditions was negatively associated with household income (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF and G; ICm: r = -0.2362, p\u0026thinsp;=\u0026thinsp;0.0457; Cm: r = -0.2461, p\u0026thinsp;=\u0026thinsp;0.0409; r). Accuracy in ICm trials was positively correlated with participants\u0026rsquo; own education level (r\u0026thinsp;=\u0026thinsp;0.2543, p\u0026thinsp;=\u0026thinsp;0.0311; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH), suggesting higher educational attainment may enhance performance under cognitive conflict. Collectively, these results demonstrate that higher socioeconomic and educational levels are associated with more efficient and accurate performance in visual cognitive tasks.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eERP components of flanker task are associated with socioeconomic and educational levels.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe examined typical ERP components elicited during the FL task under Cm and ICm conditions\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Clear P2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), N2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), and P3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) components were observed (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), with P2 (~\u0026thinsp;170 ms) most prominent over frontal electrodes (F3, Fz, and F4), and N2 (~\u0026thinsp;190 ms) and P3 (365 ms for Cm, 445 ms for ICm) over parietal sites (P3, Pz, and P4; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Statistical comparisons confirmed significant task-related differences. Specifically, N2 amplitude was significantly larger in ICm compared to Cm trials (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG), and P3 amplitude was higher in the Cm condition (p\u0026thinsp;=\u0026thinsp;0.0298; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). No significant difference was observed for P2 amplitude between conditions (p\u0026thinsp;=\u0026thinsp;0.2034; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). In addition, we also analyzed the time-frequency distributions of frontal and parietal electrodes revealed increased beta and gamma band power following stimulus onset for both Cm and ICm conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), although neither showed significant condition differences (beta: p\u0026thinsp;=\u0026thinsp;0.3666; gamma: p\u0026thinsp;=\u0026thinsp;0.6524; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI, J).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe next assessed associations between ERP components and socioeconomic/educational variables (Table S2). We found that P2 amplitude was negatively correlated with family SES level in both ICm and Cm conditions (ICm: r = -0.2454, p\u0026thinsp;=\u0026thinsp;0.0377; Cm: r = -0.2444, p\u0026thinsp;=\u0026thinsp;0.0386; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), indicating that individuals from lower SES backgrounds exhibited stronger early attentional responses\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Furthermore, the difference in P2 amplitude difference between Cm and ICm conditions (Cm minus ICm) was negatively correlated with household income (r\u0026thinsp;=\u0026thinsp;0.2818, p\u0026thinsp;=\u0026thinsp;0.0157; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), suggesting greater differentiation in attentional modulation among participants from lower-income families. N2 amplitude also showed significant negative associations with household income in both ICm and Cm conditions (ICm: r = -0.2424, p\u0026thinsp;=\u0026thinsp;0.0388; Cm: r = -0.2405; p\u0026thinsp;=\u0026thinsp;0.0404; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), and with participants\u0026rsquo; own education level (ICm: r = -0.2693, p\u0026thinsp;=\u0026thinsp;0.0212; Cm: r = -0.2706, p\u0026thinsp;=\u0026thinsp;0.0206; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). These findings suggest enhanced conflict monitoring in individuals from lower-income or less-educated backgrounds\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. P3 amplitude difference (Cm minus ICm) was correlated with household income (r = -2630, p\u0026thinsp;=\u0026thinsp;0.0246; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), implying reduced cognitive control modulation in individuals with lower income\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Additionally, the difference in gamma-band power between Cm and ICm conditions was negatively associated with household income (r = -0.3101, p\u0026thinsp;=\u0026thinsp;0.0076; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF), indicating attenuated high-frequency neural responses to cognitive conflict among lower-income participants. Overall, these results demonstrate that ERP components, particularly P2, N2, P3, and gamma-band oscillations, are systematically modulated by socioeconomic and educational factors, revealing that individual differences in cognitive control and attentional engagement during the flanker task are shaped by environmental background.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eERP components of visual search task are associated with socioeconomic and educational levels.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the VS task, we analyzed ERP components to characterize neural responses associated with visual attention and target detection (Figure S2). Group-averaged ERP waveforms revealed prominent components including P1 (~\u0026thinsp;130 ms), N1/P2 (~\u0026thinsp;190 ms), and P3 (~\u0026thinsp;400 ms) across expected scalp regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA to D). Specifically, P1 and P3 components were most pronounced over parietal region (P7, P3, Pz, P4, and P8 electrodes), while N1 was strongest at occipital area (O1, Oz, and O2 electrodes), and P2 was observed over frontal-central electrodes (F3, Fz, F4, FC1, and FC2 electrodes). In addition, through the time-frequency analysis, we found decrease (event-related desynchronizations) at both beta and alpha band between 200 to 600 ms post-stimulus period (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe then explored the relationship between ERP features from the VS task and the socioeconomic factors (Table S3). First, we found P1 amplitude was positively correlated with household income (r\u0026thinsp;=\u0026thinsp;0.2432, p\u0026thinsp;=\u0026thinsp;0.0395; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), indicating that individual from higher-income families exhibited stronger early visual sensory responses\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In addition, alpha and beta band oscillatory power were both positively correlated with parental education level (alpha: r\u0026thinsp;=\u0026thinsp;0.2638, p\u0026thinsp;=\u0026thinsp;0.0251; beta: r\u0026thinsp;=\u0026thinsp;0.3144, p\u0026thinsp;=\u0026thinsp;0.0072; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), indicating the different attentional functions among different SES\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Together, these results demonstrate that both early ERP components and oscillatory dynamics in the visual search task are sensitive to socioeconomic context, with enhanced neural responses observed in participants from higher SES and more educated family backgrounds.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCanonical correlation between socioeconomic and educational factors and neural-cognitive indices\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess the multivariate relationship between socioeconomic/educational background and visual cognitive functioning, we conducted a CCA. EEG features that significantly correlated with socioeconomic and educational variables\u0026mdash;namely FL-N2, FL-P2, the difference between Cm and ICm conditions for FL-P2, FL-P3, and FL-gamma, VS-P1, and alpha and beta band power from the VS task\u0026mdash;were selected as representative neural indices. Similarly, behavioral measures that showed significant associations with socioeconomic factors, i.e., FL reaction time and accuracy in ICm trials, FL reaction time in Cm trials, and VS reaction time, were included as behavioral indicators of visual cognitive function. In order to optimize the CCA\u0026rsquo;s reliability, we decreased the dimensions of these features using PCA, resulting in two PCs for EEG signatures and one PC for behavioral results. These three components were then jointly examined as dependent variables against four socioeconomic/educational indicators: family SES level, household income, parental education level, and participant\u0026rsquo;s own education level (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, top panels).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe analysis revealed a first canonical correlation of r\u0026thinsp;=\u0026thinsp;0.513 (p\u0026thinsp;=\u0026thinsp;0.0005), indicating a moderate-to-strong multivariate association between socioeconomic background and the combined neural-behavioral profile (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, center panel; Table S4). Canonical loadings showed that all four socioeconomic variables contributed meaningfully to the canonical variate, with the strongest contributions from household income (0.658), followed by family SES level (0.481). Participant\u0026rsquo;s own education (0.407) and parental education (0.388) also contributed, though to a lesser extent. These results highlight a robust systems-level link between socioeconomic context and individual differences in brain function and cognitive behavior, supporting the idea that socioeconomic background shapes integrated neural and behavioral signatures of visual cognitive performance.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to investigate the influence of socioeconomic and educational background on visual cognitive functions by integrating behavioral performance, EEG responses, and multivariate analysis. Participants performed a FL task and a VS task while EEG was recorded. Results showed that individuals from higher socioeconomic and educational backgrounds demonstrated better behavioral performance, i.e., faster reaction times and higher accuracy. Electrophysiological results revealed that higher SES and education were associated with enhanced attentional and executive processes, as reflected in larger P2 and P3, reduced N2, and stronger gamma activity in the FL task, as well as greater P1 amplitude and stronger alpha and beta oscillations in the VS task. By integrating ERPs, time-frequency analyses, and multivariate modeling via CCA, we found that household income and family SES were the most strongly associated factors with individual differences in neural and behavioral indicators, highlighting the influential role of early environmental conditions in shaping cognitive function.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSocioeconomic and educational background shapes cognitive development through environmental enrichment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOne noteworthy finding from the demographic analysis is that parental education level was significantly correlated with subjective family SES. Specifically, behavioral results from both the FL and VS tasks revealed a consistent pattern: individuals from families with higher SES and education backgrounds demonstrated better performance, characterized by higher accuracy and faster reaction times. In the VS task, these individuals responded more quickly, and in the FL task, they showed more efficient processing especially under high-conflict (ICm) conditions. These findings suggest that a more enriched family environment, marked by higher income, education, and SES, may provide children with more diverse and stimulating developmental experience\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Such environments likely support the acquisition of cognitive skills crucial for attentional control, conflict monitoring, and goal-directed behavior\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNeural correlates of cognitive advantage in high-SES individuals\u003c/b\u003e\u003c/p\u003e\u003cp\u003eElectrophysiological findings from both tasks further reveal that socioeconomic and educational factors shape cognitive functions. In the FL task, higher SES and education levels were associated with greater P2 amplitudes, suggesting enhanced early attentional processing\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Increased N2 amplitudes, particularly among individuals from lower-income and less-educated backgrounds, may reflect a compensatory increase in conflict monitoring effort\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The P3 component, a marker of cognitive control and evaluation\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, showed larger amplitudes in higher-SES individuals, reflecting more efficient allocation of executive resources\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Additionally, gamma-band activity, typically linked to high-level cognitive integration and allocation\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, was stronger in participants from higher-income families, highlighting enhanced information processing capacity.\u003c/p\u003e\u003cp\u003eIn the VS task, P1 amplitude which is associated with processing of visual sensory and external stimulation\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, was positively correlated with household income, suggesting heightened perceptual sensitivity in individuals from more advantaged backgrounds. Moreover, alpha and beta band power, which are often interpreted as indices of attentional engagement and top-down control\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, were also stronger in participants whose parents had higher educational attainment. These results indicate that higher SES and education levels support the development of key neural systems involved in attention regulation, cognitive inhibition, and executive control.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCanonical correlation reveals the role of socioeconomic factors in neurocognitive outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe CCA revealed that the first canonical correlation was statistically significant (r\u0026thinsp;=\u0026thinsp;0.513, p\u0026thinsp;=\u0026thinsp;0.0005), suggesting a moderate linear relationship between a weighted combination of demographic variables and a composite index of neurocognitive function. Among the demographic variables, household income and subjective socioeconomic status showed the strongest canonical loadings (-0.658 and \u0026minus;\u0026thinsp;0.481, respectively), indicating that these two factors contributed most substantially to the canonical dimension. While the negative sign of the loadings does not affect interpretation, given the arbitrary directionality in CCA, the magnitude underscores the relative influence of these variables. In contrast, participants' and parental education levels had weaker loadings, suggesting a less prominent role in the observed multivariate relationship. These findings align with previous literature emphasizing the role of socioeconomic factors in shaping cognitive and brain development\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Notably, most prior research has focused on children, whereas the current study extends this relationship to young adults, providing evidence that socioeconomic disparities may continue to influence neural and behavioral function beyond early developmental periods.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhile this study provides important insights into how socioeconomic and educational backgrounds shape visual cognitive functioning at both behavioral and neural levels, several limitations should be acknowledged. First, the participants in this study were mainly raised and educated within the United States, which may limit the generalizability of the findings to populations in different cultural, educational, and socioeconomic systems. Cultural values, schooling structures, and family dynamics in other societies may moderate the relationship between SES and cognitive development\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Second, the study focused exclusively on two visual cognitive paradigms, i.e., the flanker and the visual search tasks, both of which primarily target attentional control and perceptual processing. Therefore, conclusions drawn here may not extend to other cognitive domains, such as language, memory, or social cognition. Third, while this study identifies significant associations between socioeconomic background and cognitive-neural measures, it does not directly address the broader systemic and structural factors that may underlie these associations. Socioeconomic status is not merely an individual or family-level characteristic, but is deeply embedded in social structures that distribute resources, opportunities, and adversity unequally. Factors such as intergenerational inequality, structural discrimination, and unequal access to early educational enrichment may mediate or confound the observed relationships. Thus, the findings may reflect not only differences in individual development, but also broader patterns of social advantage and disadvantage that raise important questions about fairness, opportunity, and the reproduction of inequality through cognitive pathways\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study demonstrated that socioeconomic and educational backgrounds are closely associated with individual differences in visual cognitive performance and its neural correlates. By integrating behavioral measures, electrophysiological data, and multivariate modeling, we show that early life environments leave measurable imprints on attentional and executive functions. Importantly, our canonical correlation analysis quantitatively assessed the relative influence of different socioeconomic and educational factors, offering concrete biological evidence for how social environments shape physiological functioning. These findings deepen our understanding of the neurocognitive embodiment of social experience and contribute to broader efforts to bridge neuroscience with social and developmental sciences.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics Statement This study was approved by the University of California Institutional Review Board. The EEG data analyzed were obtained from a previously published, open-access dataset (Isbell et al., 2023, OpenNeuro accession number: ds004217; Isbell et al., 2025, scientific data.). The original study received approval from the Institutional Review Board at the University of Oregon, and all procedures involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003eParticipant consent In the original study from which the EEG data used in this manuscript were obtained (Isbell et al., 2023, OpenNeuro accession number: ds004217; Isbell et al., 2025, scientific data), all participants were fully informed about the study procedures, potential risks, and their rights prior to enrollment. Written informed consent to participate in the study and for the subsequent use and publication of their anonymized data was obtained from each participant in accordance with the Institutional Review Board (IRB) at the University of Oregon. For the current secondary analysis, no new human data were collected, and the authors only accessed and analyzed de-identified, open-access data.\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConflicts of interest\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003eNone.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThe authors received no funding for this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFengrui Zhang designed study. Shuxuan Mao, Fengrui Zhang, and Xuan Zhang analyzed the data. Shuxuan Mao and Fengrui Zhang wrote the manuscript. Shuxuan Mao and Xuan Zhang revised the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Dr. Elif Isbell, Dylan Richardson, and the IDEA research group for the creation and distribution of the publicly available dataset and securely sharing a more comprehensive de-identified dataset that could not be publicly shared.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset used in this study is publicly available on OpenNeuro (https://openneuro.org/datasets/ds006018/versions/1.2.2). All analysis scripts are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSchneider, J. M., Behboudi, M. H. \u0026amp; Maguire, M. J. 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Sci.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 65\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tics.2008.11.003\u003c/span\u003e\u003cspan address=\"10.1016/j.tics.2008.11.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Socioeconomic status, EEG, Visual cognition, Flanker, Visual search, Development","lastPublishedDoi":"10.21203/rs.3.rs-7239089/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7239089/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSocioeconomic and educational factors are known to influence cognitive development, but the specific neural mechanisms through which early-life conditions shape adult brain function remain unclear. In this study, we investigated how early-life socioeconomic and educational environments relate to behavioral performance and neural activity in early adulthood during two visual cognitive tasks, i.e., the flanker task and the visual search task. 73 participants completed the tasks while electroencephalography (EEG) data were recorded. We extracted time-domain event-related potentials (P1, P2, N2, P3) and time-frequency features (alpha, beta, and gamma band power) to assess attention, cognitive control, and perceptual processing. Four SES-related variables, subjective family SES, household income, participants\u0026rsquo; own education duration, and parental education, were analyzed. Behavioral results showed that individuals from more advantaged early-life backgrounds exhibited faster and more accurate task performance. Neural data further revealed that enhanced ERP amplitudes and oscillatory activity were associated with higher SES and educational levels. Finally, canonical correlation analysis (CCA) quantified the relative influence of each SES factor, identifying household income and subjective SES as most strongly associated with the integrated brain\u0026ndash;behavior profile. These findings provide converging behavioral and neurophysiological evidence that early-life socioeconomic context leaves measurable imprints on cognitive and neural function in adulthood, offering biological insight into how social experience becomes embedded in the brain.\u003c/p\u003e","manuscriptTitle":"Early-life Family Socioeconomic and Educational Impacts on Adult EEG and Behavioral Signatures of Visual Cognitive Function","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 07:48:49","doi":"10.21203/rs.3.rs-7239089/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-19T07:41:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-28T12:46:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-22T11:33:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-17T16:35:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-17T04:08:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-05T14:15:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241538217811095203873732838140851954715","date":"2025-11-29T10:34:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-26T18:43:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40721087345369688309674198302192911187","date":"2025-11-26T09:32:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328708667427405364615655567946134650122","date":"2025-11-26T07:34:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60178093607588174150703707270050227308","date":"2025-11-25T23:21:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216373756424513144235588083337428741277","date":"2025-11-25T05:12:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"13081655553499721812756540213285415070","date":"2025-11-24T12:56:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T10:35:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274344061328132614553640954543546144039","date":"2025-11-24T09:07:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-24T08:54:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-31T05:57:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-30T07:14:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-29T14:53:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-29T05:05:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d50481ae-c6cc-4906-83a2-0c33ed076fc3","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":53595412,"name":"Biological sciences/Neuroscience"},{"id":53595413,"name":"Biological sciences/Psychology"},{"id":53595414,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-04-07T05:38:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 07:48:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7239089","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7239089","identity":"rs-7239089","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}