Cognitive Sovereignty: A Theory and Initial Validation of Human Autonomy in Algorithmic Environments

Cognitive Sovereignty: A Theory and Initial Validation of Human Autonomy in Algorithmic Environments | 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 Research Article Cognitive Sovereignty: A Theory and Initial Validation of Human Autonomy in Algorithmic Environments Professor Dr. Mohammad Hannan Mia, Mahadi Mokbul Ali This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9145237/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Artificial intelligence increasingly mediates human experience, raising questions about cognitive autonomy. Existing constructs capture aspects of this problem but lack integrative theoretical framing and empirical operationalization. We introduce cognitive sovereignty as a multidimensional construct comprising decision autonomy, critical literacy, algorithmic awareness, digital resilience, and trust calibration. Across five studies (total N = 9,829), we developed and validated the Cognitive Sovereignty Scale (CSS). Study 1 used expert review (n = 15) and cognitive interviewing (n = 45) for content validation. Study 2 (n = 650) established factor structure through exploratory and confirmatory factor analysis. Study 3 (n = 712) tested convergent and discriminant validity with established measures. Study 4 (n = 8,427 across 12 countries) examined cross-national measurement invariance. Study 5 (n = 1,200) tested predictive utility in an experimental task with algorithmic recommendations. Confirmatory factor analysis supported the five-factor structure (CFI = 0.94, RMSEA = 0.05). The CSS demonstrated convergent validity with need for cognition (r = 0.46), critical thinking disposition (r = 0.51), and self-efficacy (r = 0.42), and discriminant validity from digital literacy (r = 0.28) and technology acceptance (r = 0.31). Cross-national measurement invariance was supported (configural, metric, partial scalar). CSS scores predicted resistance to algorithmic recommendations (β = 0.34, 95% CI [0.29, 0.39]), with autonomy as the strongest predictor (β = 0.28). CSS predicted incremental variance over need for cognition and critical thinking disposition (ΔR² = 0.08, cumulative R² = 0.31, p < .001). Cognitive sovereignty provides an integrative framework for research on human–AI interaction. The CSS offers a psychometrically sound instrument for measuring this construct across diverse populations. Independent external validation is needed. Cognitive Sovereignty Human AI Interaction Algorithmic Awareness Decision Autonomy Scale Development and Validation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION The challenge of algorithmic mediation; Human cognition now operates within environments structured by artificial intelligence 1–5. Search engines curate information access. Social media platforms algorithmically select content. Educational technologies personalize instruction. Recommendation systems shape consumer choices. These systems are designed to influence behaviour. They succeed when users follow algorithmic guidance. The cumulative effect on human cognitive autonomy is a central question for behavioural science 6–8. Existing research addresses aspects of this question through multiple frameworks. Digital literacy research examines skills for using technology effectively 9,10. Algorithmic awareness studies investigate understanding of how algorithms function 11,12. Cognitive liberty scholarship addresses freedom of thought from technological interference 13,14. Self-determination theory provides a motivational framework for understanding autonomy needs 15,16. These frameworks operate in parallel rather than integration. No single construct captures the multidimensional capacity to maintain autonomous, reflective cognition under algorithmic influence. This gap limits theoretical development and empirical investigation. Without a unified framework, researchers cannot systematically track changes in cognitive autonomy across contexts, populations, or time. Introducing cognitive sovereignty : We propose cognitive sovereignty as an integrative construct: the capacity of individuals to maintain autonomous, reflective, and resilient cognition in algorithmically mediated environments. Cognitive sovereignty comprises five dimensions: Autonomy: The capacity to make decisions independent of algorithmic influence, particularly when algorithmic recommendations conflict with personal judgment or accurate knowledge 15,17. Autonomy is evidenced by resistance to recommendations and consistency in decision-making across contexts. Critical literacy: The ability to analyze algorithmic systems, understand their influence mechanisms, and evaluate the credibility of algorithmically curated information 18,19. Critical literacy encompasses knowledge of how algorithms operate and awareness of commercial and political incentives shaping algorithmic design. Algorithmic awareness: Recognition of when one is interacting with algorithmic systems and sensitivity to their influence on cognition and behaviour 20,21. Awareness includes detection of personalization, recognition of recommendation effects, and sensitivity to filter bubble dynamics. Digital resilience: The capacity to maintain cognitive function and well-being in algorithmically mediated environments, including attention regulation, impulse control, and recovery from digital stressors 22–24. Resilience is evidenced by sustained attention and reduced compulsive use patterns. Trust calibration: The ability to appropriately calibrate trust in algorithmic systems neither overly trusting nor pathologically skeptical 25,26. Calibrated trust involves accurate assessment of system capabilities and appropriate reliance. These dimensions are conceptually distinct yet empirically related. They jointly constitute cognitive sovereignty as a higher-order construct. Relationship to existing constructs Cognitive sovereignty differs from related constructs in scope and structure. Digital literacy focuses on instrumental skills for technology use 9. Cognitive sovereignty encompasses metacognitive capacities for maintaining autonomy. Algorithmic awareness addresses knowledge of algorithms 11. Cognitive sovereignty adds behavioural resistance to algorithmic influence. Cognitive liberty emphasizes freedom from interference 13. Cognitive sovereignty adds positive capacities for autonomous functioning. Self-determination theory's autonomy construct is domain-general 15. Cognitive sovereignty specifies autonomy in algorithmically mediated contexts. The present research : This paper reports the development and initial validation of the Cognitive Sovereignty Scale (CSS). Across five studies, we: generated and refined items through expert review and cognitive interviewing; established factor structure and internal consistency; tested convergent and discriminant validity with established measures; examined measurement invariance and population distributions across 12 countries; and tested predictive utility in an experimental task. STUDY 1: Item Generation and Content Validation Method: W e generated an initial pool of 75 items reflecting the five theoretical dimensions. Items were drafted based on conceptual definitions and adapted from existing measures where appropriate. Fifteen experts in psychology, human–computer interaction, and educational technology rated each item for relevance to the intended dimension, clarity, and conciseness. Items retained for further testing required mean relevance ratings ≥ 4.0 on a 5-point scale and at least 80 percent agreement among experts. Cognitive interviews were conducted with 45 participants representing diverse ages, educational backgrounds, and technology use patterns. Participants completed items while thinking aloud. This enabled identification of comprehension difficulties and ambiguous phrasing. Results Expert review reduced the item pool to 48 items meeting retention criteria. Cognitive interviewing led to further refinement of 23 items for clarity. The resulting 48-item version proceeded to psychometric evaluation. STUDY 2: Factor Structure and Internal Consistency Method Participants were 650 adults recruited through an online research panel. Sampling was stratified to match US census demographics on age, gender, education, and region. Mean age was 39.4 years (SD = 14.2). The sample was 51.2 percent female, 68 percent White, 14 percent Black, 11 percent Hispanic, and 7 percent other. Participants completed the 48-item CSS online. Each item was rated on a 7-point Likert scale from strongly disagree to strongly agree. Data were randomly split into development (n = 325) and validation (n = 325) samples. Results : Exploratory factor analysis in the development sample used principal axis factoring with promax rotation. Parallel analysis and scree plot examination suggested a five-factor solution. Factor loadings ranged from 0.52 to 0.84. Cross-loadings were minimal. Confirmatory factor analysis in the validation sample tested the five-factor model against alternative structures. The five-factor model demonstrated good fit: χ²(485) = 1,248.3, p < .001; CFI = 0.94; RMSEA = 0.05 (90 percent CI [0.048, 0.055]); SRMR = 0.04. Fit was superior to one-factor (Δχ² = 847.2, p < .001), three-factor (Δχ² = 423.6, p < .001), and bifactor models. Internal consistency was acceptable for all subscales: autonomy α = 0.84, critical literacy α = 0.87, algorithmic awareness α = 0.82, digital resilience α = 0.85, trust calibration α = 0.83. Composite reliability estimates ranged from 0.85 to 0.89. Item reduction based on modification indices and conceptual considerations produced a final 25-item scale with five items per dimension. The lowest loading items (0.52–0.58) were in the digital resilience subscale and were retained for theoretical coverage. The shortened scale maintained good fit (CFI = 0.93, RMSEA = 0.05) and internal consistency (α range 0.82–0.89). STUDY 3: Convergent and Discriminant Validity Method Participants were 712 adults recruited through the same panel. Demographics were comparable to Study 2. In addition to the CSS, participants completed established measures of related constructs Need for Cognition Scale 27 Critical Thinking Disposition Scale 28 General Self-Efficacy Scale 29 Digital Literacy Scale 9 Technology Acceptance Measure 30 Algorithmic Awareness Questionnaire 11 Results Convergent validity was supported by moderate to strong correlations with theoretically related constructs. The CSS total score correlated with need for cognition (r = 0.46, 95 percent CI [0.40, 0.52]), critical thinking disposition (r = 0.51, 95 percent CI [0.45, 0.56]), and self-efficacy (r = 0.42, 95 percent CI [0.36, 0.48]). Discriminant validity was supported by weaker correlations with distinct constructs. CSS correlated modestly with digital literacy (r = 0.28, 95 percent CI [0.21, 0.35]) and technology acceptance (r = 0.31, 95 percent CI [0.24, 0.38]). These were significantly lower than convergent correlations (Steiger's z tests, p < .001). Table 1 presents the full correlation matrix with confidence intervals. Correlations among CSS subscales and validation measures with 95% confidence intervals (Study 3, N = 712) Variable 1 2 3 4 5 6 7 8 9 10 11 M (SD) 4.82 (0.91) 4.76 (0.97) 4.91 (0.88) 4.68 (0.94) 4.74 (1.02) 4.89 (0.86) 4.21 (1.14) 4.38 (1.08) 4.56 (0.99) 4.63 (1.07) 4.71 (1.03) 1. CSS total .74 [.69,.79] .78 [.73,.82] .71 [.65,.76] .68 [.62,.74] .65 [.58,.71] .46 [.40,.52] .51 [.45,.56] .42 [.36,.48] .28 [.21,.35] .31 [.24,.38] 2. Autonomy .58 [.51,.64] .49 [.42,.56] .44 [.37,.51] .41 [.34,.48] .48 [.41,.54] .43 [.36,.50] .38 [.31,.45] .21 [.14,.28] .26 [.19,.33] 3. Critical literacy .53 [.46,.59] .47 [.40,.53] .43 [.36,.50] .44 [.37,.51] .54 [.47,.60] .39 [.32,.46] .25 [.18,.32] .28 [.21,.35] 4. Algorithmic awareness .51 [.44,.57] .46 [.39,.52] .41 [.34,.48] .47 [.40,.53] .36 [.29,.43] .29 [.22,.36] .33 [.26,.40] 5. Digital resilience .48 [.41,.54] .38 [.31,.45] .39 [.32,.46] .44 [.37,.51] .26 [.19,.33] .27 [.20,.34] 6. Trust calibration .35 [.28,.42] .37 [.30,.44] .41 [.34,.48] .24 [.17,.31] .41 [.34,.48] 7. Need for cognition .61 [.55,.67] .54 [.47,.60] .33 [.26,.40] .29 [.22,.36] 8. Critical thinking disp. .49 [.42,.55] .31 [.24,.38] .27 [.20,.34] 9. Self-efficacy .28 [.21,.35] .32 [.25,.39] 10. Digital literacy .44 [.37,.51] 11. Technology acceptance Dimension-specific validity patterns were consistent with theoretical expectations. Autonomy correlated most strongly with need for cognition (r = 0.48). Critical literacy correlated most strongly with critical thinking disposition (r = 0.54). Algorithmic awareness correlated with algorithmic knowledge measures (r = 0.49). Digital resilience correlated with self-regulation measures (r = 0.44). Trust calibration correlated with trust in automation measures (r = 0.41). STUDY 4: Cross-National Measurement Invariance and Population Distributions Method : Participants were 8,427 adults recruited through local research panels in 12 countries: United States (n = 712), United Kingdom (n = 702), Germany (n = 698), Sweden (n = 694), Japan (n = 710), South Korea (n = 706), Brazil (n = 704), South Africa (n = 698), Nigeria (n = 692), India (n = 708), Australia (n = 704), and New Zealand (n = 699). Sampling within each country was stratified to match national demographics on age, gender, education, and urban or rural residence. Participants completed the 25-item CSS translated into local languages using forward-backward translation procedures. Results : Measurement invariance was tested through multigroup confirmatory factor analysis with sequentially constrained models. Configural invariance (same factor structure across countries) was supported: CFI = 0.92, RMSEA = 0.06. Metric invariance (equal factor loadings) held: ΔCFI = -0.01, ΔRMSEA = + 0.001. Scalar invariance (equal intercepts) showed partial support. Eighteen of 25 items were invariant across all countries. The seven non-invariant items were retained but flagged for interpretation in cross-national comparisons. Given partial scalar invariance, latent mean comparisons across countries are interpretable with appropriate caution. Country differences were modest but systematic. Nordic countries scored highest on autonomy and digital resilience. East Asian countries scored highest on algorithmic awareness and critical literacy. Brazil and South Africa showed highest trust calibration scores. The United States scored near the mean on all dimensions. Score variation across countries on digital resilience was large and warrants further investigation. Cross-national differences in infrastructure access, educational context, and response style all represent plausible contributors requiring dedicated study. Demographic patterns within countries were consistent. Age positively correlated with critical literacy (r = 0.18) and digital resilience (r = 0.22). Education positively correlated with all dimensions (r range 0.15–0.28). Gender differences were negligible. STUDY 5: Predictive Utility Method : This study was preregistered. Participants were 1,200 individuals from the Study 4 sample, with 100 per country. Participants completed a digital learning task. They viewed problems requiring numerical estimation and received algorithmic recommendations before responding. On critical trials (8 of 24), the algorithm recommended an answer that was clearly incorrect based on information available in the problem. The primary outcome was recommendation resistance: the proportion of critical trials in which participants rejected the algorithmic recommendation and selected the correct answer independently. The task was calibrated in pilot testing to ensure incorrect recommendations were detectable. Pilot participants (n = 60) identified the correct answer on 94 percent of critical trials when no recommendation was provided, confirming that errors were not due to task difficulty. Participants also completed a brief test of domain knowledge relevant to the estimation problems and a measure of general cognitive ability (matrix reasoning). These were included as covariates. Results CSS total score significantly predicted recommendation resistance, β = 0.34, 95 percent CI [0.29, 0.39], p < .001, controlling for country, age, education, domain knowledge, and cognitive ability. The effect remained significant with all covariates included. The autonomy subscale was the strongest predictor (β = 0.28, p < .001). Critical literacy (β = 0.19, p = .002) and digital resilience (β = 0.16, p = .008) also contributed significantly. Algorithmic awareness (β = 0.07, p = .18) and trust calibration (β = 0.04, p = .42) were not significant in the full model, suggesting they may operate through other mechanisms or require different behavioural tasks for detection. Note CSS = Cognitive Sovereignty Scale. Points are coloured by country (see legend). Shaded region = 95% confidence interval around the regression line. Vertical dashed lines indicate ± 1 SD from the mean CSS score. Annotations show predicted resistance at each SD boundary. β = standardised regression coefficient controlling for country, age, education, domain knowledge, and ognitive ability. Latent mean comparisons across countries should be interpreted with caution given partial scalar measurement invariance (Study 4). < Participants scoring one standard deviation above the mean on CSS resisted incorrect recommendations 68 percent of the time. Participants scoring one standard deviation below the mean resisted 42 percent of the time. This 26 percentage point difference corresponds to d = 0.71. To test incremental validity, we ran hierarchical regression models. Step 1 included demographics (age, gender, education, country). Step 2 added need for cognition and critical thinking disposition. Step 3 added CSS total score. Table 2 presents hierarchical regression results with cumulative R² at each step. Step Predictors β (final step) R² ΔR² p (ΔR²) 1 Demographics – 0.12 – – 2 Add NFC + CTD NFC: 0.18, CTD: 0.16 0.23 0.11 < .001 3 Add CSS total CSS: 0.34 0.31 0.08 < .001 CSS predicted significant variance over need for cognition and critical thinking disposition (ΔR² = 0.08, p < .001). CSS also predicted significant variance over domain knowledge and cognitive ability alone (ΔR² = 0.11, p < .001). These results support the claim that cognitive sovereignty captures unique variance in resistance to algorithmic influence. GENERAL DISCUSSION Summary of contributions This paper introduces cognitive sovereignty as a theoretical construct and provides initial validation of a multidimensional scale for its measurement. Across five studies, we demonstrated Content validity through expert review and cognitive interviewing. A stable five-factor structure confirmed through exploratory and confirmatory factor analysis. Internal consistency exceeding conventional thresholds for all subscales. Convergent validity with established measures of need for cognition, critical thinking, and self-efficacy. Discriminant validity from digital literacy and technology acceptance. Cross-national measurement invariance supporting international comparison with appropriate caution. Predictive utility for behaviour in algorithmically mediated environments, with incremental validity over related constructs. The CSS thus provides researchers with a psychometrically sound instrument for investigating cognitive sovereignty across diverse populations and contexts. Theoretical implications : Cognitive sovereignty offers an integrative framework for research on human–AI interaction. Existing literatures have examined algorithmic influence through multiple lenses: cognitive psychology on decision biases 31,32, human–computer interaction on user experience 33,34, educational technology on learning outcomes 35,36, and media studies on information ecosystems 37,38. These literatures have developed in parallel with limited cross-fertilization. Cognitive sovereignty provides a common language and measurement tool for integrating these perspectives. The five dimensions map onto distinct research traditions while enabling examination of their interrelationships. Autonomy connects to self-determination theory 15 and research on reactance 39. Critical literacy connects to media literacy research 40 and epistemic cognition 41. Algorithmic awareness connects to human–AI interaction studies 42 and transparency research 43. Digital resilience connects to self-regulation 22 and digital well-being 44. Trust calibration connects to trust in automation literature 25,26. The finding that these dimensions are empirically distinct yet correlated supports their conceptualization as facets of a higher-order construct. The pattern of convergent and discriminant correlations with validation measures provides evidence for the construct's nomological network. The incremental validity finding demonstrates that cognitive sovereignty captures unique variance not explained by related constructs. Implications for measurement The CSS addresses a gap in available instruments. Existing measures of digital literacy focus on technical skills 9,10. Measures of algorithmic awareness assess knowledge about algorithms but not resistance to their influence 11,12. Measures of cognitive liberty are primarily philosophical rather than empirical 13,14. The CSS combines attitudinal, cognitive, and behavioural elements to assess the multidimensional capacity for autonomous functioning in algorithmically mediated environments. The cross-national invariance findings support use of the CSS in comparative research. Configural and metric invariance indicate that the same constructs are measured with the same factor structure and loadings across countries. This enables examination of relationships among variables. Partial scalar invariance supports latent mean comparisons with appropriate caution. The non-invariant items likely reflect cultural differences in response styles or interpretation rather than construct differences. This pattern is common in cross-cultural measurement and does not invalidate cross-national comparison when properly managed 45. Implications for practice The CSS enables empirical investigation of questions relevant to education, technology design, and policy. In education, the CSS can assess the effectiveness of curricula designed to build cognitive sovereignty. Researchers can ask whether digital literacy programs increase algorithmic awareness. They can test whether critical thinking instruction enhances resistance to manipulation. The CSS provides a tool for answering these questions. In technology design, the CSS can inform human-centred AI development. Systems can be evaluated for their impact on user cognitive sovereignty. Design choices can be optimized to preserve rather than undermine autonomy. The finding that CSS predicts behavioural resistance to algorithmic influence suggests that designers should consider user sovereignty as a design goal. In policy, the CSS can support evidence-based governance. Tracking cognitive sovereignty over time can reveal population-level trends requiring policy response. Comparing countries can identify institutional arrangements that protect or erode cognitive capacities. Cognitive sovereignty may constitute a collective resource relevant to democratic functioning, institutional trust, and societal resilience. Longitudinal research linking CSS scores to outcomes at institutional and societal levels is needed to establish these relationships. Limitations and future directions : Several limitations qualify our conclusions and suggest directions for future research. Sample coverage and cultural validity. While our 12-country sample provides substantial diversity, it underrepresents certain regions. Future research should extend coverage to the Middle East, Central Asia, and Pacific Islands. Measurement invariance should be continuously evaluated across additional cultural contexts. Causal inference and experimental manipulation. Study 5 demonstrates predictive utility but not causation. Experimental manipulations of cognitive sovereignty are needed to establish causal relationships. Such manipulations could involve training interventions, environmental modifications, or longitudinal designs tracking change. Longitudinal stability and developmental trajectories. The cross-sectional design cannot assess stability or change over time. Longitudinal studies tracking CSS scores across developmental stages and in response to interventions are needed. Questions include: Do cognitive sovereignty scores change with age? Do they respond to educational programs? Can they be deliberately improved? Behavioural validation across contexts. Study 5 provides behavioural validation in one task. Additional validation across diverse tasks and settings is needed. Do CSS scores predict resistance to recommendation algorithms in e-commerce? Do they predict resistance to political microtargeting? Do they predict resistance to persuasive design in social media? Objective and multimethod measurement. The CSS relies on self-report, which may be subject to response biases. Development of behavioural and physiological measures would complement self-report. Response time measures, decision consistency indices, eye tracking, and neuroimaging correlates could provide convergent evidence. Independent external validation. The CSS was developed and validated by the same research team using connected samples. Independent external validationideally by teams without involvement in scale developmentis an explicit priority for future research. Such validation should include replication of the factor structure, convergent and discriminant correlations, and predictive utility in new samples. CONCLUSION The increasing algorithmic mediation of human experience poses fundamental questions about cognitive autonomy. Existing frameworks capture aspects of these questions but lack integrative theoretical structure and empirical operationalization. Cognitive sovereignty offers a multidimensional construct encompassing the capacities needed to maintain autonomous, reflective, resilient cognition in algorithmically mediated environments. The Cognitive Sovereignty Scale provides a psychometrically sound instrument for measuring this construct across diverse populations. Initial validation supports the five-factor structure, internal consistency, convergent and discriminant validity, cross-national measurement invariance, and predictive utility. The scale predicts behavioural resistance to algorithmic influence and demonstrates incremental validity over related constructs. Cognitive sovereignty may constitute a collective resource relevant to democratic functioning, institutional trust, and societal resilience. Its measurement is a prerequisite for understanding threats to autonomy and designing responses at individual, institutional, and societal levels. Longitudinal and multilevel research is needed to establish these relationships. We offer the CSS as a tool for this essential work. Independent external validation, longitudinal studies, intervention research, and cultural adaptation are needed next steps. We invite the research community to join in this effort. METHODS Transparency and openness : Studies 1 through 4 were conducted as iterative scale development and were not preregistered. Study 5, which constitutes an a priori test of predictive validity, was preregistered at the Open Science Framework prior to data collection. All studies were conducted in accordance with ethical standards. Approvals were obtained from institutional review boards at participating institutions: All participants provided informed consent and were compensated for their time. Participants Participant recruitment procedures are described in each study. Exclusion criteria across studies were age under 18, cognitive impairment preventing informed consent, and inability to complete measures in the study language. Measures The final 25-item Cognitive Sovereignty Scale is presented in Supplementary Materials. Items are rated on 7-point Likert scales from 1 (strongly disagree) to 7 (strongly agree). Dimension scores are calculated as means of constituent items. Total score can be calculated as mean of dimension scores or as a higher-order factor score. The scale and scoring syntax are available at the OSF repository. Statistical analysis Analyses were conducted using R version 4.3. Packages used included lavaan for structural equation modeling, psych for psychometrics, and mice for missing data handling. Missing data rates were below 3 percent across all studies. Missing data were handled through pairwise deletion for factor analyses and multiple imputation for predictive models. Measurement invariance testing followed established procedures with ΔCFI ≤ 0.01 and ΔRMSEA ≤ 0.015 as criteria for invariance. All confidence intervals are reported at 95 percent. Significance tests are two-tailed with α = 0.05. Declarations Author Contribution M.H.M. conceptualized the study and developed the theoretical framework of cognitive sovereignty. M.H.M. designed the research methodology, supervised the overall project, and led the interpretation of the findings. M.H.M. also wrote the main manuscript text. M.M.A. contributed to data preparation, statistical analysis, literature compilation, and formatting of the manuscript. M.M.A. assisted in organizing the datasets, preparing tables and supporting materials, and reviewing the manuscript for clarity and consistency. Both authors reviewed the manuscript, discussed the results, and approved the final version for submission. Acknowledgement The authors acknowledge the participants who contributed their time and responses to the surveys and experimental tasks conducted in this study. Their participation made the empirical validation of the Cognitive Sovereignty Scale possible. The authors also acknowledge the anonymous reviewers and academic colleagues who provided informal feedback during the early conceptual development of the cognitive sovereignty framework. Their insights helped refine the theoretical structure and improve the clarity of the research design. References Kahneman D, Thinking, fast and slow. 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Search engines curate information access. Social media platforms algorithmically select content. Educational technologies personalize instruction. Recommendation systems shape consumer choices. These systems are designed to influence behaviour. They succeed when users follow algorithmic guidance. The cumulative effect on human cognitive autonomy is a central question for behavioural science 6\u0026ndash;8. Existing research addresses aspects of this question through multiple frameworks. Digital literacy research examines skills for using technology effectively 9,10. Algorithmic awareness studies investigate understanding of how algorithms function 11,12. Cognitive liberty scholarship addresses freedom of thought from technological interference 13,14. Self-determination theory provides a motivational framework for understanding autonomy needs 15,16. These frameworks operate in parallel rather than integration. No single construct captures the multidimensional capacity to maintain autonomous, reflective cognition under algorithmic influence. This gap limits theoretical development and empirical investigation. Without a unified framework, researchers cannot systematically track changes in cognitive autonomy across contexts, populations, or time.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIntroducing cognitive sovereignty\u003c/b\u003e: We propose cognitive sovereignty as an integrative construct: the capacity of individuals to maintain autonomous, reflective, and resilient cognition in algorithmically mediated environments. Cognitive sovereignty comprises five dimensions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAutonomy: The capacity to make decisions independent of algorithmic influence, particularly when algorithmic recommendations conflict with personal judgment or accurate knowledge 15,17. Autonomy is evidenced by resistance to recommendations and consistency in decision-making across contexts.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCritical literacy: The ability to analyze algorithmic systems, understand their influence mechanisms, and evaluate the credibility of algorithmically curated information 18,19. Critical literacy encompasses knowledge of how algorithms operate and awareness of commercial and political incentives shaping algorithmic design.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAlgorithmic awareness: Recognition of when one is interacting with algorithmic systems and sensitivity to their influence on cognition and behaviour 20,21. Awareness includes detection of personalization, recognition of recommendation effects, and sensitivity to filter bubble dynamics.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDigital resilience: The capacity to maintain cognitive function and well-being in algorithmically mediated environments, including attention regulation, impulse control, and recovery from digital stressors 22\u0026ndash;24. Resilience is evidenced by sustained attention and reduced compulsive use patterns.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTrust calibration: The ability to appropriately calibrate trust in algorithmic systems neither overly trusting nor pathologically skeptical 25,26. Calibrated trust involves accurate assessment of system capabilities and appropriate reliance.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThese dimensions are conceptually distinct yet empirically related. They jointly constitute cognitive sovereignty as a higher-order construct.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRelationship to existing constructs\u003c/strong\u003e \u003cp\u003eCognitive sovereignty differs from related constructs in scope and structure. Digital literacy focuses on instrumental skills for technology use 9. Cognitive sovereignty encompasses metacognitive capacities for maintaining autonomy. Algorithmic awareness addresses knowledge of algorithms 11. Cognitive sovereignty adds behavioural resistance to algorithmic influence. Cognitive liberty emphasizes freedom from interference 13. Cognitive sovereignty adds positive capacities for autonomous functioning. Self-determination theory's autonomy construct is domain-general 15. Cognitive sovereignty specifies autonomy in algorithmically mediated contexts.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003cb\u003eThe present research\u003c/b\u003e: This paper reports the development and initial validation of the Cognitive Sovereignty Scale (CSS). Across five studies, we: generated and refined items through expert review and cognitive interviewing; established factor structure and internal consistency; tested convergent and discriminant validity with established measures; examined measurement invariance and population distributions across 12 countries; and tested predictive utility in an experimental task.\u003c/p\u003e"},{"header":"STUDY 1: Item Generation and Content Validation","content":"\u003cp\u003e \u003cb\u003eMethod: W\u003c/b\u003ee generated an initial pool of 75 items reflecting the five theoretical dimensions. Items were drafted based on conceptual definitions and adapted from existing measures where appropriate. Fifteen experts in psychology, human\u0026ndash;computer interaction, and educational technology rated each item for relevance to the intended dimension, clarity, and conciseness. Items retained for further testing required mean relevance ratings\u0026thinsp;\u0026ge;\u0026thinsp;4.0 on a 5-point scale and at least 80 percent agreement among experts. Cognitive interviews were conducted with 45 participants representing diverse ages, educational backgrounds, and technology use patterns. Participants completed items while thinking aloud. This enabled identification of comprehension difficulties and ambiguous phrasing.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eResults\u003c/strong\u003e \u003cp\u003eExpert review reduced the item pool to 48 items meeting retention criteria. Cognitive interviewing led to further refinement of 23 items for clarity. The resulting 48-item version proceeded to psychometric evaluation.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSTUDY 2: Factor Structure and Internal Consistency\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eMethod\u003c/strong\u003e \u003cp\u003eParticipants were 650 adults recruited through an online research panel. Sampling was stratified to match US census demographics on age, gender, education, and region. Mean age was 39.4 years (SD\u0026thinsp;=\u0026thinsp;14.2). The sample was 51.2 percent female, 68 percent White, 14 percent Black, 11 percent Hispanic, and 7 percent other. Participants completed the 48-item CSS online. Each item was rated on a 7-point Likert scale from strongly disagree to strongly agree. Data were randomly split into development (n\u0026thinsp;=\u0026thinsp;325) and validation (n\u0026thinsp;=\u0026thinsp;325) samples.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e: Exploratory factor analysis in the development sample used principal axis factoring with promax rotation. Parallel analysis and scree plot examination suggested a five-factor solution. Factor loadings ranged from 0.52 to 0.84. Cross-loadings were minimal. Confirmatory factor analysis in the validation sample tested the five-factor model against alternative structures. The five-factor model demonstrated good fit: χ\u0026sup2;(485)\u0026thinsp;=\u0026thinsp;1,248.3, p \u0026lt; .001; CFI\u0026thinsp;=\u0026thinsp;0.94; RMSEA\u0026thinsp;=\u0026thinsp;0.05 (90 percent CI [0.048, 0.055]); SRMR\u0026thinsp;=\u0026thinsp;0.04. Fit was superior to one-factor (Δχ\u0026sup2; = 847.2, p \u0026lt; .001), three-factor (Δχ\u0026sup2; = 423.6, p \u0026lt; .001), and bifactor models.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInternal consistency was acceptable for all subscales: autonomy α\u0026thinsp;=\u0026thinsp;0.84, critical literacy α\u0026thinsp;=\u0026thinsp;0.87, algorithmic awareness α\u0026thinsp;=\u0026thinsp;0.82, digital resilience α\u0026thinsp;=\u0026thinsp;0.85, trust calibration α\u0026thinsp;=\u0026thinsp;0.83. Composite reliability estimates ranged from 0.85 to 0.89. Item reduction based on modification indices and conceptual considerations produced a final 25-item scale with five items per dimension. The lowest loading items (0.52\u0026ndash;0.58) were in the digital resilience subscale and were retained for theoretical coverage. The shortened scale maintained good fit (CFI\u0026thinsp;=\u0026thinsp;0.93, RMSEA\u0026thinsp;=\u0026thinsp;0.05) and internal consistency (α range 0.82\u0026ndash;0.89).\u003c/p\u003e \u003c/div\u003e"},{"header":"STUDY 3: Convergent and Discriminant Validity","content":"\u003cp\u003e \u003cstrong\u003eMethod\u003c/strong\u003e \u003cp\u003eParticipants were 712 adults recruited through the same panel. Demographics were comparable to Study 2. In addition to the CSS, participants completed established measures of related constructs\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eNeed for Cognition Scale 27\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCritical Thinking Disposition Scale 28\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGeneral Self-Efficacy Scale 29\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDigital Literacy Scale 9\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTechnology Acceptance Measure 30\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAlgorithmic Awareness Questionnaire 11\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eResults\u003c/strong\u003e \u003cp\u003eConvergent validity was supported by moderate to strong correlations with theoretically related constructs. The CSS total score correlated with need for cognition (r\u0026thinsp;=\u0026thinsp;0.46, 95 percent CI [0.40, 0.52]), critical thinking disposition (r\u0026thinsp;=\u0026thinsp;0.51, 95 percent CI [0.45, 0.56]), and self-efficacy (r\u0026thinsp;=\u0026thinsp;0.42, 95 percent CI [0.36, 0.48]). Discriminant validity was supported by weaker correlations with distinct constructs. CSS correlated modestly with digital literacy (r\u0026thinsp;=\u0026thinsp;0.28, 95 percent CI [0.21, 0.35]) and technology acceptance (r\u0026thinsp;=\u0026thinsp;0.31, 95 percent CI [0.24, 0.38]). These were significantly lower than convergent correlations (Steiger's z tests, p \u0026lt; .001).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003epresents the full correlation matrix with confidence intervals.\u003c/b\u003e \u003cem\u003eCorrelations among CSS subscales and validation measures with 95% confidence intervals (Study 3, N\u0026thinsp;=\u0026thinsp;712)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eM (SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.82\u003c/p\u003e \u003cp\u003e\u003cem\u003e(0.91)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.76\u003c/p\u003e \u003cp\u003e\u003cem\u003e(0.97)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.91\u003c/p\u003e \u003cp\u003e\u003cem\u003e(0.88)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.68\u003c/p\u003e \u003cp\u003e\u003cem\u003e(0.94)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.74\u003c/p\u003e \u003cp\u003e\u003cem\u003e(1.02)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.89\u003c/p\u003e \u003cp\u003e\u003cem\u003e(0.86)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003cp\u003e\u003cem\u003e(1.14)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003cp\u003e\u003cem\u003e(1.08)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.56\u003c/p\u003e \u003cp\u003e\u003cem\u003e(0.99)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.63\u003c/p\u003e \u003cp\u003e\u003cem\u003e(1.07)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003cp\u003e\u003cem\u003e(1.03)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. CSS total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.74\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.69,.79]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.78\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.73,.82]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.71\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.65,.76]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.68\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.62,.74]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.65\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.58,.71]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.46\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.40,.52]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.51\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.45,.56]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.42\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.36,.48]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.28\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.21,.35]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.31\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.24,.38]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Autonomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.58\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.51,.64]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.49\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.42,.56]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.44\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.37,.51]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.41\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.34,.48]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.48\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.41,.54]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.43\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.36,.50]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.38\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.31,.45]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.21\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.14,.28]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.26\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.19,.33]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Critical literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.53\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.46,.59]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.47\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.40,.53]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.43\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.36,.50]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.44\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.37,.51]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.54\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.47,.60]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.39\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.32,.46]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.25\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.18,.32]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.28\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.21,.35]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Algorithmic awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.51\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.44,.57]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.46\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.39,.52]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.41\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.34,.48]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.47\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.40,.53]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.36\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.29,.43]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.29\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.22,.36]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.33\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.26,.40]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Digital resilience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.48\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.41,.54]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.38\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.31,.45]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.39\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.32,.46]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.44\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.37,.51]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.26\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.19,.33]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.27\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.20,.34]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. Trust calibration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.35\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.28,.42]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.37\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.30,.44]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.41\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.34,.48]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.24\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.17,.31]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.41\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.34,.48]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. Need for cognition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.61\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.55,.67]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.54\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.47,.60]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.33\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.26,.40]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.29\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.22,.36]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. Critical thinking disp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.49\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.42,.55]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.31\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.24,.38]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.27\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.20,.34]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9. Self-efficacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.28\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.21,.35]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.32\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.25,.39]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10. Digital literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.44\u003c/p\u003e \u003cp\u003e\u003cem\u003e[.37,.51]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11. Technology acceptance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDimension-specific validity patterns were consistent with theoretical expectations. Autonomy correlated most strongly with need for cognition (r\u0026thinsp;=\u0026thinsp;0.48). Critical literacy correlated most strongly with critical thinking disposition (r\u0026thinsp;=\u0026thinsp;0.54). Algorithmic awareness correlated with algorithmic knowledge measures (r\u0026thinsp;=\u0026thinsp;0.49). Digital resilience correlated with self-regulation measures (r\u0026thinsp;=\u0026thinsp;0.44). Trust calibration correlated with trust in automation measures (r\u0026thinsp;=\u0026thinsp;0.41).\u003c/p\u003e"},{"header":"STUDY 4: Cross-National Measurement Invariance and Population Distributions","content":"\u003cp\u003e\u003cb\u003eMethod\u003c/b\u003e: Participants were 8,427 adults recruited through local research panels in 12 countries: United States (n\u0026thinsp;=\u0026thinsp;712), United Kingdom (n\u0026thinsp;=\u0026thinsp;702), Germany (n\u0026thinsp;=\u0026thinsp;698), Sweden (n\u0026thinsp;=\u0026thinsp;694), Japan (n\u0026thinsp;=\u0026thinsp;710), South Korea (n\u0026thinsp;=\u0026thinsp;706), Brazil (n\u0026thinsp;=\u0026thinsp;704), South Africa (n\u0026thinsp;=\u0026thinsp;698), Nigeria (n\u0026thinsp;=\u0026thinsp;692), India (n\u0026thinsp;=\u0026thinsp;708), Australia (n\u0026thinsp;=\u0026thinsp;704), and New Zealand (n\u0026thinsp;=\u0026thinsp;699). Sampling within each country was stratified to match national demographics on age, gender, education, and urban or rural residence. Participants completed the 25-item CSS translated into local languages using forward-backward translation procedures.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e: Measurement invariance was tested through multigroup confirmatory factor analysis with sequentially constrained models. Configural invariance (same factor structure across countries) was supported: CFI\u0026thinsp;=\u0026thinsp;0.92, RMSEA\u0026thinsp;=\u0026thinsp;0.06. Metric invariance (equal factor loadings) held: ΔCFI = -0.01, ΔRMSEA\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.001. Scalar invariance (equal intercepts) showed partial support. Eighteen of 25 items were invariant across all countries. The seven non-invariant items were retained but flagged for interpretation in cross-national comparisons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGiven partial scalar invariance, latent mean comparisons across countries are interpretable with appropriate caution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCountry differences were modest but systematic. Nordic countries scored highest on autonomy and digital resilience. East Asian countries scored highest on algorithmic awareness and critical literacy. Brazil and South Africa showed highest trust calibration scores. The United States scored near the mean on all dimensions. Score variation across countries on digital resilience was large and warrants further investigation. Cross-national differences in infrastructure access, educational context, and response style all represent plausible contributors requiring dedicated study. Demographic patterns within countries were consistent. Age positively correlated with critical literacy (r\u0026thinsp;=\u0026thinsp;0.18) and digital resilience (r\u0026thinsp;=\u0026thinsp;0.22). Education positively correlated with all dimensions (r range 0.15\u0026ndash;0.28). Gender differences were negligible.\u003c/p\u003e"},{"header":"STUDY 5: Predictive Utility","content":"\u003cp\u003e \u003cb\u003eMethod\u003c/b\u003e: This study was preregistered. Participants were 1,200 individuals from the Study 4 sample, with 100 per country. Participants completed a digital learning task. They viewed problems requiring numerical estimation and received algorithmic recommendations before responding. On critical trials (8 of 24), the algorithm recommended an answer that was clearly incorrect based on information available in the problem. The primary outcome was recommendation resistance: the proportion of critical trials in which participants rejected the algorithmic recommendation and selected the correct answer independently. The task was calibrated in pilot testing to ensure incorrect recommendations were detectable. Pilot participants (n\u0026thinsp;=\u0026thinsp;60) identified the correct answer on 94 percent of critical trials when no recommendation was provided, confirming that errors were not due to task difficulty. Participants also completed a brief test of domain knowledge relevant to the estimation problems and a measure of general cognitive ability (matrix reasoning). These were included as covariates.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eResults\u003c/strong\u003e \u003cp\u003eCSS total score significantly predicted recommendation resistance, β\u0026thinsp;=\u0026thinsp;0.34, 95 percent CI [0.29, 0.39], p \u0026lt; .001, controlling for country, age, education, domain knowledge, and cognitive ability. The effect remained significant with all covariates included. The autonomy subscale was the strongest predictor (β\u0026thinsp;=\u0026thinsp;0.28, p \u0026lt; .001). Critical literacy (β\u0026thinsp;=\u0026thinsp;0.19, p = .002) and digital resilience (β\u0026thinsp;=\u0026thinsp;0.16, p = .008) also contributed significantly. Algorithmic awareness (β\u0026thinsp;=\u0026thinsp;0.07, p = .18) and trust calibration (β\u0026thinsp;=\u0026thinsp;0.04, p = .42) were not significant in the full model, suggesting they may operate through other mechanisms or require different behavioural tasks for detection.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eCSS\u0026thinsp;=\u0026thinsp;Cognitive Sovereignty Scale. Points are coloured by country (see legend). Shaded region\u0026thinsp;=\u0026thinsp;95% confidence interval around the regression line. Vertical dashed lines indicate\u0026thinsp;\u0026plusmn;\u0026thinsp;1 SD from the mean CSS score. Annotations show predicted resistance at each SD boundary. β\u0026thinsp;=\u0026thinsp;standardised regression coefficient controlling for country, age, education, domain knowledge, and ognitive ability. Latent mean comparisons across countries should be interpreted with caution given partial scalar measurement invariance (Study 4).\u003c/p\u003e \u003c/p\u003e \u003c \u003cp\u003eParticipants scoring one standard deviation above the mean on CSS resisted incorrect recommendations 68 percent of the time. Participants scoring one standard deviation below the mean resisted 42 percent of the time. This 26 percentage point difference corresponds to d\u0026thinsp;=\u0026thinsp;0.71. To test incremental validity, we ran hierarchical regression models. Step 1 included demographics (age, gender, education, country). Step 2 added need for cognition and critical thinking disposition. Step 3 added CSS total score.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003epresents hierarchical regression results with cumulative R\u0026sup2; at each step.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ (final step)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eΔR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep (ΔR\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdd NFC\u0026thinsp;+\u0026thinsp;CTD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNFC: 0.18, CTD: 0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdd CSS total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSS: 0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCSS predicted significant variance over need for cognition and critical thinking disposition (ΔR\u0026sup2; = 0.08, p \u0026lt; .001). CSS also predicted significant variance over domain knowledge and cognitive ability alone (ΔR\u0026sup2; = 0.11, p \u0026lt; .001). These results support the claim that cognitive sovereignty captures unique variance in resistance to algorithmic influence.\u003c/p\u003e"},{"header":"GENERAL DISCUSSION","content":"\u003cp\u003e \u003cstrong\u003eSummary of contributions\u003c/strong\u003e \u003cp\u003eThis paper introduces cognitive sovereignty as a theoretical construct and provides initial validation of a multidimensional scale for its measurement. Across five studies, we demonstrated\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eContent validity through expert review and cognitive interviewing.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eA stable five-factor structure confirmed through exploratory and confirmatory factor analysis.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInternal consistency exceeding conventional thresholds for all subscales.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eConvergent validity with established measures of need for cognition, critical thinking, and self-efficacy.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDiscriminant validity from digital literacy and technology acceptance.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCross-national measurement invariance supporting international comparison with appropriate caution.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePredictive utility for behaviour in algorithmically mediated environments, with incremental validity over related constructs.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe CSS thus provides researchers with a psychometrically sound instrument for investigating cognitive sovereignty across diverse populations and contexts.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTheoretical implications\u003c/b\u003e: Cognitive sovereignty offers an integrative framework for research on human\u0026ndash;AI interaction. Existing literatures have examined algorithmic influence through multiple lenses: cognitive psychology on decision biases 31,32, human\u0026ndash;computer interaction on user experience 33,34, educational technology on learning outcomes 35,36, and media studies on information ecosystems 37,38. These literatures have developed in parallel with limited cross-fertilization. Cognitive sovereignty provides a common language and measurement tool for integrating these perspectives. The five dimensions map onto distinct research traditions while enabling examination of their interrelationships. Autonomy connects to self-determination theory 15 and research on reactance 39. Critical literacy connects to media literacy research 40 and epistemic cognition 41. Algorithmic awareness connects to human\u0026ndash;AI interaction studies 42 and transparency research 43. Digital resilience connects to self-regulation 22 and digital well-being 44. Trust calibration connects to trust in automation literature 25,26.\u003c/p\u003e \u003cp\u003eThe finding that these dimensions are empirically distinct yet correlated supports their conceptualization as facets of a higher-order construct. The pattern of convergent and discriminant correlations with validation measures provides evidence for the construct's nomological network. The incremental validity finding demonstrates that cognitive sovereignty captures unique variance not explained by related constructs.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eImplications for measurement\u003c/strong\u003e \u003cp\u003eThe CSS addresses a gap in available instruments. Existing measures of digital literacy focus on technical skills 9,10. Measures of algorithmic awareness assess knowledge about algorithms but not resistance to their influence 11,12. Measures of cognitive liberty are primarily philosophical rather than empirical 13,14. The CSS combines attitudinal, cognitive, and behavioural elements to assess the multidimensional capacity for autonomous functioning in algorithmically mediated environments. The cross-national invariance findings support use of the CSS in comparative research. Configural and metric invariance indicate that the same constructs are measured with the same factor structure and loadings across countries. This enables examination of relationships among variables. Partial scalar invariance supports latent mean comparisons with appropriate caution. The non-invariant items likely reflect cultural differences in response styles or interpretation rather than construct differences. This pattern is common in cross-cultural measurement and does not invalidate cross-national comparison when properly managed 45.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eImplications for practice\u003c/strong\u003e \u003cp\u003eThe CSS enables empirical investigation of questions relevant to education, technology design, and policy. In education, the CSS can assess the effectiveness of curricula designed to build cognitive sovereignty. Researchers can ask whether digital literacy programs increase algorithmic awareness. They can test whether critical thinking instruction enhances resistance to manipulation. The CSS provides a tool for answering these questions. In technology design, the CSS can inform human-centred AI development. Systems can be evaluated for their impact on user cognitive sovereignty. Design choices can be optimized to preserve rather than undermine autonomy. The finding that CSS predicts behavioural resistance to algorithmic influence suggests that designers should consider user sovereignty as a design goal. In policy, the CSS can support evidence-based governance. Tracking cognitive sovereignty over time can reveal population-level trends requiring policy response. Comparing countries can identify institutional arrangements that protect or erode cognitive capacities. Cognitive sovereignty may constitute a collective resource relevant to democratic functioning, institutional trust, and societal resilience. Longitudinal research linking CSS scores to outcomes at institutional and societal levels is needed to establish these relationships.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations and future directions\u003c/b\u003e: Several limitations qualify our conclusions and suggest directions for future research. Sample coverage and cultural validity. While our 12-country sample provides substantial diversity, it underrepresents certain regions. Future research should extend coverage to the Middle East, Central Asia, and Pacific Islands. Measurement invariance should be continuously evaluated across additional cultural contexts. Causal inference and experimental manipulation. Study 5 demonstrates predictive utility but not causation. Experimental manipulations of cognitive sovereignty are needed to establish causal relationships. Such manipulations could involve training interventions, environmental modifications, or longitudinal designs tracking change. Longitudinal stability and developmental trajectories. The cross-sectional design cannot assess stability or change over time. Longitudinal studies tracking CSS scores across developmental stages and in response to interventions are needed. Questions include: Do cognitive sovereignty scores change with age? Do they respond to educational programs? Can they be deliberately improved?\u003c/p\u003e \u003cp\u003eBehavioural validation across contexts. Study 5 provides behavioural validation in one task. Additional validation across diverse tasks and settings is needed. Do CSS scores predict resistance to recommendation algorithms in e-commerce? Do they predict resistance to political microtargeting? Do they predict resistance to persuasive design in social media? Objective and multimethod measurement. The CSS relies on self-report, which may be subject to response biases. Development of behavioural and physiological measures would complement self-report. Response time measures, decision consistency indices, eye tracking, and neuroimaging correlates could provide convergent evidence. Independent external validation. The CSS was developed and validated by the same research team using connected samples. Independent external validationideally by teams without involvement in scale developmentis an explicit priority for future research. Such validation should include replication of the factor structure, convergent and discriminant correlations, and predictive utility in new samples.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe increasing algorithmic mediation of human experience poses fundamental questions about cognitive autonomy. Existing frameworks capture aspects of these questions but lack integrative theoretical structure and empirical operationalization. Cognitive sovereignty offers a multidimensional construct encompassing the capacities needed to maintain autonomous, reflective, resilient cognition in algorithmically mediated environments. The Cognitive Sovereignty Scale provides a psychometrically sound instrument for measuring this construct across diverse populations. Initial validation supports the five-factor structure, internal consistency, convergent and discriminant validity, cross-national measurement invariance, and predictive utility. The scale predicts behavioural resistance to algorithmic influence and demonstrates incremental validity over related constructs. Cognitive sovereignty may constitute a collective resource relevant to democratic functioning, institutional trust, and societal resilience. Its measurement is a prerequisite for understanding threats to autonomy and designing responses at individual, institutional, and societal levels. Longitudinal and multilevel research is needed to establish these relationships. We offer the CSS as a tool for this essential work.\u003c/p\u003e \u003cp\u003eIndependent external validation, longitudinal studies, intervention research, and cultural adaptation are needed next steps. We invite the research community to join in this effort.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cb\u003eTransparency and openness\u003c/b\u003e: Studies 1 through 4 were conducted as iterative scale development and were not preregistered. Study 5, which constitutes an a priori test of predictive validity, was preregistered at the Open Science Framework prior to data collection. All studies were conducted in accordance with ethical standards. Approvals were obtained from institutional review boards at participating institutions: All participants provided informed consent and were compensated for their time.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipants\u003c/strong\u003e \u003cp\u003eParticipant recruitment procedures are described in each study. Exclusion criteria across studies were age under 18, cognitive impairment preventing informed consent, and inability to complete measures in the study language.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMeasures\u003c/strong\u003e \u003cp\u003eThe final 25-item Cognitive Sovereignty Scale is presented in Supplementary Materials. Items are rated on 7-point Likert scales from 1 (strongly disagree) to 7 (strongly agree). Dimension scores are calculated as means of constituent items. Total score can be calculated as mean of dimension scores or as a higher-order factor score. The scale and scoring syntax are available at the OSF repository.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStatistical analysis\u003c/strong\u003e \u003cp\u003eAnalyses were conducted using R version 4.3. Packages used included lavaan for structural equation modeling, psych for psychometrics, and mice for missing data handling. Missing data rates were below 3 percent across all studies. Missing data were handled through pairwise deletion for factor analyses and multiple imputation for predictive models. Measurement invariance testing followed established procedures with ΔCFI\u0026thinsp;\u0026le;\u0026thinsp;0.01 and ΔRMSEA\u0026thinsp;\u0026le;\u0026thinsp;0.015 as criteria for invariance. All confidence intervals are reported at 95 percent. Significance tests are two-tailed with α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.H.M. conceptualized the study and developed the theoretical framework of cognitive sovereignty. M.H.M. designed the research methodology, supervised the overall project, and led the interpretation of the findings. M.H.M. also wrote the main manuscript text. M.M.A. contributed to data preparation, statistical analysis, literature compilation, and formatting of the manuscript. M.M.A. assisted in organizing the datasets, preparing tables and supporting materials, and reviewing the manuscript for clarity and consistency. Both authors reviewed the manuscript, discussed the results, and approved the final version for submission.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors acknowledge the participants who contributed their time and responses to the surveys and experimental tasks conducted in this study. Their participation made the empirical validation of the Cognitive Sovereignty Scale possible. The authors also acknowledge the anonymous reviewers and academic colleagues who provided informal feedback during the early conceptual development of the cognitive sovereignty framework. Their insights helped refine the theoretical structure and improve the clarity of the research design.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKahneman D, Thinking, fast and slow. 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Qual Health Res 15:1277\u0026ndash;1288\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cognitive Sovereignty, Human AI Interaction, Algorithmic Awareness, Decision Autonomy, Scale Development and Validation","lastPublishedDoi":"10.21203/rs.3.rs-9145237/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9145237/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Artificial intelligence increasingly mediates human experience, raising questions about cognitive autonomy. Existing constructs capture aspects of this problem but lack integrative theoretical framing and empirical operationalization. We introduce cognitive sovereignty as a multidimensional construct comprising decision autonomy, critical literacy, algorithmic awareness, digital resilience, and trust calibration. Across five studies (total N = 9,829), we developed and validated the Cognitive Sovereignty Scale (CSS). Study 1 used expert review (n = 15) and cognitive interviewing (n = 45) for content validation. Study 2 (n = 650) established factor structure through exploratory and confirmatory factor analysis. Study 3 (n = 712) tested convergent and discriminant validity with established measures. Study 4 (n = 8,427 across 12 countries) examined cross-national measurement invariance. Study 5 (n = 1,200) tested predictive utility in an experimental task with algorithmic recommendations. Confirmatory factor analysis supported the five-factor structure (CFI = 0.94, RMSEA = 0.05). The CSS demonstrated convergent validity with need for cognition (r = 0.46), critical thinking disposition (r = 0.51), and self-efficacy (r = 0.42), and discriminant validity from digital literacy (r = 0.28) and technology acceptance (r = 0.31). Cross-national measurement invariance was supported (configural, metric, partial scalar). CSS scores predicted resistance to algorithmic recommendations (β = 0.34, 95% CI [0.29, 0.39]), with autonomy as the strongest predictor (β = 0.28). CSS predicted incremental variance over need for cognition and critical thinking disposition (ΔR² = 0.08, cumulative R² = 0.31, p \u003c .001). Cognitive sovereignty provides an integrative framework for research on human–AI interaction. The CSS offers a psychometrically sound instrument for measuring this construct across diverse populations. Independent external validation is needed.","manuscriptTitle":"Cognitive Sovereignty: A Theory and Initial Validation of Human Autonomy in Algorithmic Environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 07:20:56","doi":"10.21203/rs.3.rs-9145237/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f1c48a1d-3316-43a2-b107-b4db847f0518","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T14:43:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 07:20:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9145237","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9145237","identity":"rs-9145237","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}