Genotypes of the Polymorphic BDNF Gene Locus, Gender, and Intellectual Activity of Temperament as Factors of Individual Differences in Cognitive Styles

Background. Cognitive styles are innate functional systems that determine differences in how information is processed. However, the biological and psychological factors that underlie these differences are not well understood. The brain-derived neurotrophic factor (BDNF) gene, which is important for brain plasticity, and intellectual activity of temperament (IAT), are two promising candidates for explaining these differences. Objective. To investigate the influence of the BDNF gene (rs6265) genotype, gender, and the Intellectual Activity of Temperament (IAT) on individual differences in twelve cognitive styles, including Field Independence (FI), Wide Range of Equivalence (WRE), Flexibility of Cognitive Control (FCC), Reflectivity (REF), Abstract Conceptualization (AC), Tolerance to Unrealistic Experience (TOL), and their respective opposite poles. Methods. The sample comprised 273 adults (46.52% male) aged 20–54 years. Genotyping for the BDNF Val66Met polymorphism was performed. Cognitive styles were assessed using the Cognitive Personality Style Questionnaire (CPS-Q, Volkova, Rusalov, & Dudnikova, 2022), which measures six bipolar dimensions as independent poles. IAT was measured via temperament inventory (STQ-24, Rusalov, 2012). Data were analyzed using multivariate General Linear Modeling (GLM). Results. Multivariate analysis revealed significant main effects of BDNF genotype (p = .012, ηp² = .098), gender (p = .026, ηp² = .109), and IAT (p = .012, ηp² = .099) on cognitive style expression. Between-subjects analysis showed that IAT significantly influenced FI (p < .001) and FCC (p = .003). Gender affected FI (p = .001), FCC (p = .004), and AC (p = .020). BDNF genotype had a main effect on REF (p = .013). Crucially, significant Gender × BDNF interactions were found for FI (p < .001), WRE (p = .001), FCC (p = .003), and AC (p = .005), indicating that the genetic effect is gender-dependent. IAT × BDNF interactions were significant for WRE (p = .035) and AC (p = .050). Profile analysis revealed that the Val/Met genotype is associated with enhanced cognitive style expression, particularly at high IAT levels, with distinct patterns for males and females. For instance, Val/Met carriers exhibited higher overall FI than Val/Val carriers (MD = 0.84, p = .003), and this effect was moderated by both gender and IAT level. Conclusion. Individual differences in cognitive styles are shaped by a complex interplay of genetic (BDNF), biological (gender), and temperamental (IAT) factors. The effect of the BDNF genotype is not uniform but is moderated by both gender and the level of intellectual activity of temperament, supporting a multi-faceted model of cognitive style determination. These findings underscore the need to integrate neurogenetic and psychological levels of analysis in cognitive style research.

Генотипы полиморфного локуса гена BDNF, пол и интеллектуальная активность темперамента как факторы индивидуальных различий когнитивных стилей

Е.В. Волкова

Институт психологии Российской академии наук, Москва, Россия

Резюме. Актуальность и проблема.  Когнитивные стили — это врожденные функциональные системы, определяющие различия в способах восприятия и обработки информации. Несмотря на значительный рост исследований когнитивных стилей, биологические и психологические факторы, лежащие в основе этих различий, до конца не изучены. Анализ эмпирических данных позволил предположить, что генотипы полиморфных локусов генов BDNF и COMT, пол и интеллектуальная активность темперамента могут выступать в качестве главных факторов индивидуальных различий когнитивных стилей. Цель. Исследовать влияние генотипов генов BDNF (rs6265) и СОМТ (rs4680), пола и интеллектуальной активности темперамента (IAT) на индивидуальные различия когнитивных стилей (поленезависимость (FI), широкий диапазон эквивалентности (WRE), гибкость когнитивного контроля (FCC), рефлективность (REF), абстрактная концептуализация (AC), толерантность к нереалистичному опыту (TOL) и их соответствующие противоположные полюса). Методы и материалы. Выборка включала 273 человека в возрасте от 20 до 54 лет (36.40 ± 9.54 лет), среди них 46.52% мужчин. Биохимические методы: определение генотипов полиморфных локусов генов BDNF (rs6265) и СОМТ (rs4680) в сыворотке крови. Психологические методы: опросник когнитивно-личностных стилей CPS-Q (Волкова, Русалов и Дудникова, 2022); тест интеллекта (СПМ Равена); краткая версия опросника структуры темперамента STQ-24 (Русалов, 2012). Статистические методы: общее линейное моделирование (GLM). Результаты. Многофакторный дисперсионный анализ выявил значимое влияние генотипа гена BDNF (p = .012, ηp² = .098), пола (p = .026, ηp² = .109) и IAT (p = .012, ηp² = .099) на индивидуальные различия когнитивных стилей. Анализ межиндивидуальных различий обнаружил значимое влияния IAT на показатели FI (p <.001) и FCC (p = .003); пола — на показатели FI (p = .001), FCC (p = .004) и AC (p = .020); генотипа гена BDNF — на показатель REF (p = .013). Значимые эффекты взаимодействия между полом и генотипом гена BDNF на вариации показателей FI (p <.001), WRE (p = .001), FCC (p = .003) и AC (p = .005) свидетельствуют, что генетический эффект зависит от пола. Эффекты взаимодействия IAT × BDNF были значимыми для показателей WRE (p = 0,035) и AC (p = 0,050). Анализ профиля показал, что генотип Val/Met связан с повышенной экспрессией когнитивного стиля, особенно при высоких уровнях IAT, с различными паттернами у мужчин и женщин. Например, у носителей Val/Met общий показатель FI был выше, чем у носителей Val/ Val (MD = .84, p = .003), и этот эффект был сопряжен как полом, так и уровнем IAT. Вывод. Индивидуальные различия в когнитивных стилях формируются в результате сложного взаимодействия генетических (BDNF), биологических (пол) и темпераментных (IAT) факторов. Эффект генотипа BDNF не является однородным, он модерируется как полом, так и уровнем интеллектуальной активности темперамента, что поддерживает многомерную модель детерминации когнитивных стилей и указывает на необходимость интеграции нейрогенетического и психологического уровней анализа в исследовании когнитивных стилей.

Ключевые слова: ген BDNF, пол, темперамент, когнитивный стиль, поленезависимость, широкий диапазон эквивалентности, гибкость когнитивного контроля, рефлексивность, абстрактная концептуализация, толерантность

Information about the author

Elena V. Volkova, Doctor of Psychology, Head of the V.N. Druzhinin Laboratory of Psychology of Abilities and Mental Resources, Institute of Psychology of the Russian Academy of Sciences; 13/1 Yaroslavskaya St., 129366, Moscow, Russian Federation; ORCID: 0000-0003-3809-3639, e-mail: volkovaev@ipran.ru

Информация об авторе

Елена Вениаминовна Волкова, доктор психологических наук, заведующий лабораторией психологии способностей и ментальных ресурсов им. В.Н. Дружинина, Институт психологии Российской академии наук; Российская Федерация, 129366, Москва, ул. Ярославская 13/1; ORCID: 0000-0003-3809-3639, e-mail: volkovaev@ipran.ru

Introduction

Cognitive styles are mechanisms that regulate cognitive processes, ensuring their conformity and consistency with an individual’s needs and the demands of the environment (Klein, 1970). The variability and severity of cognitive styles lead to differences in the ways of perception, analysis, structuring, categorization, evaluation of information and the use of the information received to organize and manage their own behavior.

Modeling the evolution of cognitive styles has demonstrated that they are not unique to humans but can be observed across a vast diversity of animal species, highlighting an important ecological and evolutionary dimension. The manifestation of cognitive styles is critically dependent on (1) environmental conditions, (2) personality traits, (3) cognition, and (4) the brain. Rapidly changing environmental conditions favor the development of response plasticity and flexibility, whereas slowly changing conditions favor rigidity of cognitive control. A reduction in predation pressure facilitates the development of fast-learning styles to a high level. Conversely, when investments in learning become less profitable, cognitive styles characterized by slow learning become more competitive. The combination of factors such as learning ability, exploratory tendency, and selectivity leads to the emergence of phenotypes adapted to local conditions (Liedtke & Fromhage, 2019).

Despite a significant increase in research on cognitive styles, efforts to formulate a unified and coherent theory have thus far proven unsuccessful (Cools, 2009). Several questions remain open and debated, including the ontological status of cognitive styles (Cools, 2009; Grigorenko & Sternberg, 1995), the methods for their measurement, their structure, and their number. For instance, Hayes and Allinson (1994) identified 29 styles, Armstrong (1999) 54, Coffield et al. (2004) 71, and Curry (2000) over 100. In some studies, cognitive style is conceptualized as a bipolar construct (Cools, 2009; Grigorenko & Sternberg, 1995; Klein, 1970; Kushnir et al., 2024), in others as a quadripolar construct (Kholodnaya, 2025), and in still others, each pole is treated as an independent psychological entity (Volkova & Volkova, 2025; Rusalov & Volkova, 2015; Volkova & Rusalov, 2016; Volkova et al., 2022). Furthermore, while some researchers argue that cognitive styles are dynamic and can be changed (Zhang, 2013), others present evidence suggesting they are stable traits resistant to modification (Clapp, 1993).

One source of the methodological challenges in the field of cognitive styles is the gap between studies on cognitive styles and research on brain function. In this regard, the hierarchical theory of Czesław S. Nosal presents a promising perspective. Within this framework, a style is viewed as a specific functional invariant that links the basic neurobiological level with cognitive and behavioral levels. The articulation of a style involves three levels of manifestation of cognitive-stylistic regulation: neurobiological modules, the organization of the cognitive “holon,” and the behavioral expression of styles. The “holon” is a functional system that filters and hierarchically organizes chaotic information as it moves from perception to memory and thought. Within this system, each style performs a specific function in the structure of higher cognitive processes. For example, field dependence/independence and impulsivity/reflectivity reflect the mode of information perception, while narrow/wide range of equivalence and concrete/abstract conceptualization reflect the mode of concept formation. Nosal (2010) posits that all styles share a common mechanism for forming and scanning the information field, a process induced and stimulated by the situation, and that differences in how this process is carried out depend on the range of conceptual equivalence and behavioral control. His model situates cognitive styles within the system of cognitive processes rather than treating them as a separate cognitive function. However, this theory requires the development of adequate methods and more comprehensive empirical validation.

In this regard, cognitive neuroscience opens up new perspectives: initial data from this discipline demonstrate a link between cognitive style, brain function, and behavioral manifestations (Bendall et al., 2016). Individual differences in brain structure and function predetermine the cognitive style of field dependence–independence (Hao et al., 2013), which, in turn, determines the prioritized type of information during processing (Mawad et al., 2015). Research by Kraemer et al. (2009) showed that a preference for a visual or verbal thinking style (a cognitive style) is associated with activity in different brain regions: the visual style is linked to an image-processing area (the fusiform gyrus), and the verbal style to a speech-processing area (the supramarginal gyrus). Moreover, visualizers create mental images even for words, while verbalizers mentally articulate even pictures. Targeted transcranial stimulation of the supramarginal gyrus (SMG) led to impaired performance on a verbal processing task, with the magnitude of this effect depending on the participants’ baseline level of the verbal cognitive style (Kraemer et al., 2014).

A review of cognitive style research covering the period from 2003 to 2023 presents experimental evidence linking cognitive styles to brain functions and behavior (Ansar & Ganesh, 2023). Based on these findings, it can be hypothesized that different cognitive styles are underpinned by the innate specialization of specific cortical areas (i.e., modality-specific activity). Furthermore, there is evidence for BDNF genotype-related differences in EEG spectral amplitudes, associated with increased cortical excitability in carriers of the Met allele. The Val66Met (rs6265) polymorphism is a functional variant in codon 66 of the BDNF gene that influences neuroplasticity and is associated with cognitive abilities, as well as brain structure and function. Eyes-open resting-state EEG recordings showed focal increases in right fronto-parietal delta power and decreases in alpha-1 and alpha-2 amplitudes in the right hemisphere in Met/Met carriers compared to Val/Val and Val/Met carriers. In Val/Met carriers, a more pronounced frontal topography of beta-1 and beta-2 activity was observed relative to the Val/Val group (Roy et al., 2020). Eyes-closed resting-state EEG analysis revealed inter-genotype differences in focal asymmetry EEG power spectrum indices of delta, theta, beta-1, and beta-2: in Val/Val carriers, power in the central temporal regions of the right hemisphere was higher than in the left, whereas in Met allele carriers, the opposite pattern was observed. For the beta-2 rhythm, similar asymmetry differences were also characteristic of the parieto-occipital regions (Volf & Privodnova, 2023).

Cognitive styles are influenced by both cognitive and temperamental components (Klimov, 1969; Nosal, 2010; Rusalov & Volkova, 2015; Volkova & Rusalov, 2016). At the same time, sex-specific relationships between temperamental traits and cognitive styles have been noted (Semyashkin, 2013). High temperamental activity provides a broad spectrum of cognitive- stylistic regulation (Volkova, Kalugin, & Rusalov, 2022). A study by Nurgalieva et al. (2023) found that polymorphic loci of the BDNF and COMT genes are associated with general temperamental activity, which characterizes an individual’s capacity for work, tempo, and ease of switching from one behavioral program to another (Rusalov, 2022). Neuhaus et al. (2009) reported an association between the COMT Val158 genotype and cognitive flexibility in patients with schizophrenia. The researchers suggest that the presence of the Val allele, likely due to a reduction in dopamine levels in the prefrontal cortex, may be associated with increased cognitive flexibility. However, in another study, no relationship between the flexibility of cognitive control and the COMT genotypes was found (Volkova & Volkova, 2025). Since the current study also did not reveal a significant association between the COMT genotype and cognitive styles, these data were excluded from further analysis.

Xie et al. (2014) found that carriers of the long variant of the DRD4 gene demonstrate a higher ability for speech perception under conditions of interference. The authors suggest that this advantage stems from superior executive functions, particularly executive attention and working memory capacity, which are involved in maintaining information in short-term memory under interference. Compared to homozygotes for the short allele, long-allele carriers may exhibit enhanced attention to goal-relevant information even under interference, implying greater executive attention and working memory capacity, which could be advantageous in tasks requiring similar processes. However, no significant correlation was found between the Stroop test and speech recognition efficiency in masking conditions, which the researchers attribute to the low difficulty of the Stroop tasks.

A study by Volkova & Volkova (2025) revealed that BDNF gene genotypes and sex have a main effect on individual differences in cognitive styles. Furthermore, interaction effects between BDNF genotypes and sex were found on the expression of such cognitive styles as field independence, wide range of equivalence, flexibility of cognitive control, reflectivity, and abstract conceptualization. Of particular interest is the finding that in males, a change in the BDNF genotype (Val/Val, Val/Met, Met/Met) was associated with an increase in intelligence but a decrease in field independence, cognitive control flexibility, and abstract conceptualization. In contrast, females (Val/Val, Val/Met, Met/Met) showed no significant changes in intelligence scores but exhibited an increase in the expression of the aforementioned cognitive styles.

However, data on the genetic correlates of cognitive styles remain minimal. The present study aims to address this gap by investigating the influence of the BDNF genotype, gender, and the Intellectual Activity of Temperament (IAT) on cognitive styles, including field dependence & independence; narrow & wide range of equivalence; rigidity & flexibility of cognitive control; impulsivity & reflectivity; concrete & abstract conceptualization; and intolerance & tolerance to unrealistic experiences.

Method

2.1. Participants

The sample comprised 273 participants aged 20 to 54 years (36.40 ± 9.54). Of these, 46.52% were male. The majority of participants held a higher education degree; however, a significant proportion (57.14%) was not employed in their field of specialization. The range of intelligence scores in the sample varied from low to high and followed a normal distribution. Most participants were carriers of the BDNF gene Val/Val genotype (over 70%), while the Met/Met genotype was the least frequent (2.17%). The proportions of respondents with high and low levels of intellectual activity of temperament (IAT) were approximately equal, whereas those with an intermediate IAT level constituted 65.93% of the total sample. No significant differences in intelligence levels were found between men and women (Mann-Whitney U test, p = .116).

2.2. Procedure

The study was conducted from October 2023 to August 2025 in Moscow and Yekaterinburg. The researchers informed the participants about the purpose, procedures, potential benefits, and risks of the study. The procedure began at 8:00 a.m. Participants completed the following documentation: (1) a screening questionnaire to confirm eligibility; (2) informed consent for voluntary, unpaid participation in scientific research; (3) consent for venous blood collection; and (4) consent for personal data processing. Venous blood samples were collected from fasting participants at approximately 8:10 a.m., prior to testing. The total duration of the assessment was approximately 45 minutes per participant.

2.3. Methods

2.3.1. Biochemical analysis methods

Venous blood samples were collected from the cubital vein. All genetic analyses were performed by the clinical diagnostic laboratory “DNKOM”. Genotyping of the BDNF gene (rs6265) was performed to identify the Val/Val, Val/Met, and Met/Met genotypes. DNA was isolated using column-based sorption (DNA extraction kit REF ALC-002, “Allel” manufacturer). Genotyping was conducted via pyrosequencing (QIAGEN PyroMark Q24 instrument No. 48068-10, PyroMark Q24 software No. FSZ 2010/08544).

2.3.2. Psychological Methods

• Raven’s Standard Progressive Matrices (SPM).
• The shortened Temperament Structure Questionnaire (STQ-24, Rusalov, 2012).
• Cognitive Personality Style Questionnaire (CPS-Q; Volkova, Rusalov, Dudnikova, 2022).

The CPS-Q is based on behavioral descriptions of individuals with pronounced manifestations of specific cognitive styles: field dependence/independence; narrow/wide range of equivalence; rigidity/flexibility of cognitive control; impulsivity/reflectivity; concrete/abstract conceptualization; intolerance/tolerance to unrealistic experience. Unlike traditional bipolar models that treat a cognitive style as a single continuum between two poles, this questionnaire treats both poles as independent scales. Scores on each scale range from 5 to 25 points.

The definitions of the cognitive style poles measured by the CPS-Q are as follows:

Field Dependence (FD) – Reliance on others’ opinions when solving problems and greater trust in external impressions when evaluating situations.

Field Independence (FI) – A tendency to rely on one’s own knowledge and experience, and ease in resisting the influence of others.

Narrow Range of Equivalence (NRE) – An orientation toward features that distinguish one object from another, demonstrating high sensitivity to details

Wide Range of Equivalence (WRE) – A tendency to seek a general strategy based on generalized categorical grounds.

Flexibility of Cognitive Control (FCC) – Ease of switching between different cognitive functions during an activity and a high degree of automation in analyzing complex material.

Rigidity of Cognitive Control (RCC) – Strict adherence to a predetermined plan and instructions regardless of circumstances, with difficulty changing information processing strategies when solving complex, ambiguous tasks.

Impulsivity (IMP) – Spontaneous decision-making in complex, uncertain situations, primarily based on emotionally significant cues.

Reflectivity (REF) – A slow pace of decision-making, characterized by thorough, step-by-step verification of facts.

Concrete Conceptualization (CC) – Dependence on the status and authority of an information source, intolerance of ambiguity, stereotypical decisions, and situational behavior.

Abstract Conceptualization (AC) – Ease in establishing various connections and relationships between objects, and a preference for non-standard problem-solving methods.

Tolerance to Unrealistic Experience (TUE) – Openness to new experiences, even if they contradict previously formed ideas.

Intolerance to Unrealistic Experience (IUE) – A tendency to perceive events primarily in terms of the expected and ordinary, blocking information that contradicts existing personal experience.

2.4. Statistical Data Processing

Statistical analysis included descriptive statistics (mean, standard deviation, skewness, kurtosis, Cronbach’s alpha), percentile-based standardization (Sten transformation) of raw scores, and General Linear Modeling (GLM) multivariate analysis (Pillai’s Trace; F-test for between-group effects).

Nominal independent variables were coded as follows: BDNF genotype (Val/Val; Val/Met; Met/Met), gender (male, female), and Intellectual Activity of Temperament (IAT; high – 8-10 Stens, medium – 5-7 Stens, low – 1-4 Stens). The dependent variables were metric scales of the 12 cognitive styles (above-average expression – 8-10 Stens, average – 5-7 Stens, below-average – 1-4 Stens). The assumption of homogeneity of covariance matrices for GLM was met (Box’s M test=522.223, p = .225), as was the assumption of homogeneity of variances (Levene’s test, p > .05).

Results

3.1. Descriptive Analysis

The assumption of univariate normality was tested by examining skewness and kurtosis. Values between –1 and +1 for these statistics indicate an acceptable approximation to a normal distribution, justifying the application of parametric statistical methods. The internal consistency, assessed using Cronbach’s alpha, was acceptable for all questionnaire scales within the present sample. Therefore, all scales were deemed suitable for the interpretation of the results obtained.

3.2. General Linear Modeling (Multivariate Approach)

To evaluate the influence of the BDNF genotype, gender, and Intellectual Activity of Temperament (IAT) on cognitive styles, a series of seven hypotheses (H1–H7) were tested for each of the 12 CPS-Q scales using a General Linear Model (GLM). These hypotheses proposed main effects for each factor (H1: BDNF genotype; H2: gender; H3: IAT level) and their two-way (H4–H6) and three-way (H7) interactions.

(H1): There is a main effect of the BDNF genotype, which consists of significant differences in the severity of cognitive styles depending on the BDNF genotypes (Val/Val, Val/Met, Met/Met).

(H2): There is a significant main effect of gender (male, female).

(H3): There is a significant main effect of IAT level (low, medium, high).

(H4): There is a significant two-way BDNF genotype × gender interaction.

(H5): There is a significant two-way BDNF genotype × IAT interaction.

(H6): There is a significant two-way gender × IAT interaction.

(H7): There is a significant three-way BDNF genotype × gender × IAT interaction.

The initial multivariate analysis (Table 1) revealed statistically significant main effects for BDNF genotype (F = 1.801, p = .012, ηp² = .098), gender (F = 2.000, p = .026, ηp² = .109), and IAT (F = 1.805, p = .012, ηp² = .099), with medium effect sizes, thereby supporting H1, H2, and H3. No significant interaction effects were detected at this multivariate level. Thus, BDNF genotype, gender, and the Intellectual Activity of Temperament have a statistically significant influence on individual differences in cognitive style scores.

Table 1. Multivariate Tests (Pillai’s Trace)

3.3. General Linear Modeling (Between-Subjects Effects)

For a detailed interpretation of the effects on individual cognitive styles, between-subjects effects were analyzed (Table 2). This analysis included only scales for which the overall models were statistically significant. The results are further illustrated by profiles showing mean Sten scores for each cognitive style across groups (Figure (panels a-l)).

Notably, all cognitive styles presented in Table 2 correspond to a single, “productive” pole—a theoretical construct substantiated by M.A. Kholodnaya (2025). According to Ch.S. Nosal (2010), a wide range of equivalence reflects fluency in abstraction and the ability to focus on common features, whereas a narrow range is associated with difficulties in generalization, attention to differences, and deficits in abstract thinking. Thus, a wide range of equivalence may also be considered a productive style. It should be noted that the notion of cognitive style “productivity” remains conceptually ambiguous and debated. On one hand, the term itself lacks clear definition; on the other, the idea of a bipolar continuum may reflect a methodological simplification rather than a psychological reality. The present findings are partially consistent with those of Volkova N.E. and Volkova E.V. (2025), who also reported associations between BDNF genotype, sex, and the expression of cognitive styles

The between-subjects analysis (Table 2) revealed more nuanced effects. Corrected models for six dependent variables were significant (p < .001), with the model for Field Independence (FI) showing the largest effect size (ηp² = .247).

Main Effects: IAT had a significant independent effect on FI (p < .001) and Flexible Cognitive Control (FCC) (p = .003). Gender significantly affected FI (p = .001), FCC (p = .004), and Abstract Conceptualization (AC) (p = .020). BDNF genotype showed a main effect only on Reflectivity (p = .013), with a trend for FI (p = .055).

Interaction Effects: Contrary to the initial multivariate result, significant two-way interactions were identified. The IAT × BDNF interaction was significant for Wide Range of Equivalence (WRE) (p = .035) and approached significance for Abstract Conceptualization (p = .050). Most robustly, a significant Gender × BDNF interaction was found for FI (p < .001), WRE (p = .001), FCC (p = .003), and AC (p = .005). No significant Sex × IAT or three-way interaction was observed.

2.4. General Linear Modeling (Detailed Profile Analysis)

Figure (panels a-l) presents cognitive style profiles for men and women with BDNF Val/Val and Val/Met genotypes across low, medium, and high levels of intellectual activity of temperament.

Supporting the interaction effects, the relationship between the BDNF gene genotype and cognitive style varied by intellectual activity of temperament. In both men and women (Figure: panels a, b), increased intellectual activity is associated with greater Field Independence (Sidak: Mean Difference (low – medium) = -.9432, p = .003; Mean Difference (medium – high) = -1.225, p < .001) in carriers of both the BDNF Val/Val and Val/Met genotypes. Moreover, Val/Met carriers exhibited higher overall Field Independence than Val/Val carriers (Sidak: Mean Difference (Val/ Met – Val/Val) = 0.8401, p = .003). A similar trend of increased Flexibility of Cognitive Control, Reflectivity, Abstract Conceptualization, and Tolerance to Uncertainty with increasing intellectual activity of temperament is observed in Val/Val carriers of both sexes; however, for the Wide Range of Equivalence, this pattern was found only in women (Figure: panels c-l).

The results were more complex for individuals with the Val/Met genotype of the BDNF gene. For instance, men carriers of this genotype scored higher on a Wide Range of Equivalence at the average level of the IAT, but lower on both low and high levels of the IAT (Figure: panel c). A similar pattern was also observed for women carriers of this genotype in terms of Tolerance to Uncertainty (Figure: panel l). As for the cognitive style “Abstract Conceptualization”, men with the Val/Met genotype of the BDNF gene showed higher scores in low and average levels of intellectual activity of temperament (Figure: panel i).

The study demonstrates a positive association between the level of temperamental intellectual activity and the majority of cognitive style dimensions, specifically among carriers of the BDNF Val/Val genotype. Crucially, the effect of the BDNF genotype (Val/Met versus Val/Val) is not uniform but rather is moderated by the level of this activity and by gender, indicating a complex, trait-specific interaction.

Discussion

The current study aimed to explore the complex interplay between the BDNF gene (rs6265), gender, and intellectual activity of temperament (IAT) in shaping individual differences in cognitive styles. The findings provide robust evidence that cognitive styles are not merely descriptive psychological categories but are grounded in a multifaceted biological and temperamental architecture. The results confirm and extend the theoretical frameworks proposed by Nosal (2010), which posit that cognitive styles are functional invariants linking neurobiology to higher cognition.

4.1. Main Effects and Theoretical Implications

The significant main effects of IAT, gender, and BDNF genotype on cognitive style expression (Table 1) align with previous research emphasizing the multi-determined nature of these constructs (Volkova, Kalugin, & Rusalov, 2022). The influence of IAT on Field Independence (FI) and Flexible Cognitive Control (FCC) is particularly noteworthy. Intellectual activity, characterized by a high endurance (capacity for work), rapid tempo, and ease of switching between behavioral programs (Rusalov, 2022), appears to facilitate the cognitive operations central to these styles: resisting irrelevant cues (FI) and adapting information processing strategies (FCC). This suggests a shared foundation in the efficiency of executive functions, likely supported by prefrontal cortical networks.

The main effect of gender on FI, FCC, and Abstract Conceptualization (AC) replicates earlier findings (Semyashkin, 2013) and underscores the necessity of considering gender as a biological variable in cognitive style research. These differences may arise from organizational effects of sex hormones on brain development or from differential socialization, though the interaction effects with BDNF point toward a biological basis.

4.2. The Moderating Role of Gender and IAT on BDNF Effects

Perhaps the most compelling findings are the significant two-way interactions. The absence of a significant multivariate interaction for Gender × BDNF was superseded by robust univariate interaction effects for specific styles (FI, WRE, FCC, AC). This indicates that the influence of the BDNF genotype is not monolithic but is expressed differently in males and females depending on the specific cognitive domain. For instance, the crossover interaction patterns observed in profile analysis (see Figure) suggest that the same genetic variant can confer an advantage in one sex under specific IAT conditions while being neutral or even disadvantageous in another. This aligns with the growing recognition in psychiatric genetics that many genetic effects are sex-dependent (e.g., Volf & Privodnova, 2023).

The significant IAT × BDNF interactions for WRE and AC indicate that the functional Val66Met polymorphism moderates the relationship between temperamental activity and cognitive style. Specifically, the Val/Met heterozygotes appeared to benefit more from high IAT levels, showing steeper increases in productive styles compared to Val/Val homozygotes (Figure: panel a, b, d, f). This supports the idea of differential susceptibility (Belsky & Pluess, 2009), where certain genotypes are more responsive to both positive and negative environmental or internal conditions. In this case, the intermediate dopamine and BDNF signaling associated with the Val/Met genotype (Egan et al., 2003) may create a neurochemical environment that is optimally poised to translate high cognitive-motivational drive (IAT) into efficient cognitive structuring and flexibility. This statement is consistent with the understanding of intellectual activity of temperament as a biologically determined need for intellectual activity (Rusalov and Dudin, 2024) (Rusalov, Dudin, 2024)

4.3. The Val/Met Advantage and Neurobiological Plausibility

The observation that individuals with the Val/Met genotype often outperform those with the Val/Val genotype on measures such as Field Independence (in men and women) and Wide Range of Equivalence (in women), particularly at higher IAT levels, is intriguing. The Val66Met polymorphism affects activity-dependent secretion of BDNF, with the Met allele associated with reduced trafficking and secretion (Egan et al., 2003). While Met/Met homozygotes are often linked to poorer cognitive outcomes, in some contexts, the heterozygous state may confer an optimal level of neuroplasticity—sufficient for adaptive reorganization, yet not excessive enough to introduce instability. This aligns with the findings of Roy et al. (2020), who reported distinct EEG profiles for Val/Met carriers, suggesting a characteristic cortical excitation-inhibition balance that may facilitate flexible cognitive processing.

4.4. The Question of Cognitive Style “Productivity”

The concentration of significant effects on the “productive” poles (FI, WRE, FCC, Reflectivity, AC) deserves theoretical consideration. While the concept of a “productive” cognitive style (Kholodnaya, 2025) is debated, our data suggest that these poles are more sensitive to genetic and temperamental variation. This may be because they rely more heavily on fluid cognitive abilities and executive functions, which are known to be heritable and dependent on prefrontal dopamine and BDNF signaling. In contrast, the less “productive” poles (e.g., Field Dependence, Rigidity) may be more influenced by environmental factors, learning history, or motivational states not captured by IAT. This finding supports a nuanced view where the two poles of a cognitive style dimension are not merely opposites but may have distinct etiologies.

4.5. Limitations and Future Directions

Several limitations should be acknowledged. First, the sample size, while adequate for the main analyses, resulted in a small number of Met/Met homozygotes, precluding a full three-group comparison in interaction models. Future studies should oversample for this rare genotype. Second, the cross-sectional design prevents causal inference; longitudinal studies are needed to track how the interplay of genes and temperament shapes cognitive style development over time. Third, the study focused on a single candidate gene; genome-wide approaches or polygenic scores could provide a more comprehensive picture. Fourth, while the CPS-Q demonstrates good internal consistency, its validity across different cultural contexts requires further examination.

Future research should integrate neuroimaging (e.g., resting-state fMRI, EEG) to directly link the observed BDNF × Sex × IAT interactions to brain network dynamics. Additionally, investigating other neurotransmitter systems (e.g., dopaminergic genes like COMT and DRD4) in conjunction with BDNF would help build a polygenic model of cognitive style determination.

Conclusions

This study demonstrates that individual differences in cognitive styles are significantly influenced by the BDNF Val66Met genotype, gender, and temperamental intellectual activity, both independently and through their interactions. The effect of the BDNF gene is not direct or uniform but is moderated by gender and the individual’s level of intellectual activity, revealing a complex, trait-specific pattern of interplay. The findings provide empirical support for hierarchical models that link neurobiological substrates to higher-order cognitive control processes. By moving beyond univariate approaches, this research underscores the necessity of adopting a multi-level, integrative perspective in differential psychology. Understanding how genetic variability interacts with temperament and gender to shape cognitive processing has profound implications for personalized education, occupational guidance, and clinical interventions targeting cognitive remediation.

Ethics Statement: The study design was approved by the local ethics committee of the Ural State Medical University of the Ministry of Health of the Russian Federation (Protocol No 5 dated 06.16.2023).

CRediT author statement: The author is responsible for submitting the final version of the manuscript for publication. The final version of the manuscript was approved by the author.

Conflict of interest: The author declares no conflicts of interest.

Acknowledgements: The author expresses their gratitude to A.V. Varlamov, I.O. Kuvaeva, N.E. Volkova, and D.A. Dokuchaev for their assistance in data collection and primary processing. She also thanks the study participants for their voluntary participation in the research, which is aimed at promoting the development of science.

Funding: This work was supported by the state assignment of the Ministry of Science and Higher Education of the Russian Federation (Project No. 0138-2025-0016, “Intellectual Systems and Human Abilities”).

Ethics Statement: The study design was approved by the local ethics committee of the Ural State Medical University of the Ministry of Health of the Russian Federation (Protocol No 5 dated 06.16.2023).

CRediT author statement: The author is responsible for submitting the final version of the manuscript for publication. The final version of the manuscript was approved by the author.

Conflict of interest: The author declares no conflicts of interest.

Acknowledgements: The author expresses their gratitude to A.V. Varlamov, I.O. Kuvaeva, N.E. Volkova, and D.A. Dokuchaev for their assistance in data collection and primary processing. She also thanks the study participants for their voluntary participation in the research, which is aimed at promoting the development of science.

Funding: This work was supported by the state assignment of the Ministry of Science and Higher Education of the Russian Federation (Project No. 0138-2025-0016, “Intellectual Systems and Human Abilities”).

References

1. Armstrong, S. J. (1999). Cognitive style and dyadic interaction: A study of supervisors and subordinates engaged in working relationships[Doctoral dissertation, University of Leeds].
2. Ansar, M. R. U., & Ganesh, M. P. (2023). Cognitive styles research: A review of theory, assessments and applications. International Journal of Indian Psychology, 11(1), 924–934. https://doi.org/10.25215/1101.094
3. Belsky, J., & Pluess, M. (2009). Beyond diathesis stress: Differential susceptibility to environmental influences. Psychological Bulletin, 135(6), 885–908. doi: 10.1037/a0017376
4. Bendall, R. C. A., Galpin, A., Marrow, L. P., & Cassidy, S. (2016). Cognitive style: Time to experiment. Frontiers in Psychology, 7, Article 1786. https://doi.org/10.3389/fpsyg.2016.01786
5. Clapp, R. G. (1993). Stability of cognitive style in adults and some implications: A longitudinal study of the Kirton adaption-innovation inventory. Psychological Reports, 73(3, Pt. 2), 1235–1245. https://doi.org/10.2466/pr0.1993.73.3f.1235
6. Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning: A systematic and critical review. Learning & Skills Research Centre.
7. Cools, E. (2009). A reflection on the future of the cognitive style field: A proposed research agenda. Reflection Education, 5(2), 19–34.
8. Curry, L. (2000). Review of learning style, studying approach, and instructional preference research in medical education. In R. J. Riding & S. G. Rayner (Eds.), International perspectives on individual differences: Vol. 1. Cognitive styles(pp. 239–276). Ablex.
9. Egan, M. F., Kojima, M., Callicott, J. H., Goldberg, T. E., Kolachana, B. S., Bertolino, A., Zaitsev, E., Gold, B., Goldman, D., Dean, M., Lu, B., & Weinberger, D. R. (2003). The BDNFVal66Met polymorphism affects activity-dependent secretion of BDNF and human memory and hippocampal function. Cell, 112(2), 257–269. https://doi.org/10.1016/S0092-8674(03)00035-7
10. Grigorenko, E. L., & Sternberg, R. J. (1995). Thinking styles. In D. H. Saklofske & M. Zeidner (Eds.), International handbook of personality and intelligence(pp. 205–229). Plenum Press.
11. Hao, X., Wang, K., Li, W., Yang, W., Wei, D., Qiu, J., & Zhang, Q. (2013). Individual differences in brain structure and resting brain function underlie cognitive styles: Evidence from the embedded figures test. PLOS ONE, 8(12), Article e78089. https://doi.org/10.1371/journal.pone.0078089
12. Hayes, J., & Allinson, C. W. (1994). Cognitive style and its relevance for management practice. British Journal of Management, 5(1), 53–71. https://doi.org/10.1111/j.1467-8551.1994.tb00068.x
13. Kholodnaya, M. A. (2025). Kognitivnaya psikhologiya. Kognitivnye stili: Uchebnik dlya vuzov [Cognitive psychology. Cognitive styles: A textbook for universities] (3rd ed.). Yurait Publishing
14. Klein, G. S. (1970). Perception, motives and personality.
15. Klimov, E. A. (1969). Individual’nyy stil’ deyatel’nosti v zavisimosti ot tipologicheskikh svoystv nervnoy sistemy: K psikhologicheskim osnovam nauchnoy organizatsii truda, ucheniya, sporta[Individual style of activity depending on the typological properties of the nervous system: Towards the psychological foundations of the scientific organization of labor, study, and sports]. Kazan University Press.
16. Kraemer, D. J. M., Hamilton, R. H., Messing, S. B., DeSantis, J. H., & Thompson-Schill, S. L. (2014). Cognitive style, cortical stimulation, and the conversion hypothesis. Frontiers in Human Neuroscience, 8, Article 15. https://doi.org/10.3389/fnhum.2014.00015
17. Kraemer, D. J. M., Rosenberg, L. M., & Thompson-Schill, S. L. (2009). The neural correlates of visual and verbal cognitive styles. Journal of Neuroscience, 29(12), 3792–3798. https://doi.org/10.1523/JNEUROSCI.4635-08.2009
18. Kushnir, A.B., Mikhailova, E.S., Gerasimenko, N.Yu. (2024). The Influence of Sex and Cognitive Style on Eye Movement Patterns during Map Navigation. Experimental Psychology (Russia), 17(2), 10–28. (In Russ.). https://doi.org/10.17759/exppsy.2024170201
19. Liedtke, J., & Fromhage, L. (2019). Modelling the evolution of cognitive styles. BMC Ecology and Evolution, 19, Article 234. https://doi.org/10.1186/s12862-019-1565-2
20. Mawad, F., Trias, M., Gimenez, A., Maiche, A., & Ares, G. (2015). Influence of cognitive style on information processing and selection of yogurt labels: Insights from an eye-tracking study. Food Research International, 74, 1–9. https://doi.org/10.1016/j.foodres.2015.04.023
21. Neuhaus, A. H., Opgen-Rhein, C., Urbanek, C., Hahn, E., Ta, T. M., Seidelsohn, M., Strathmann, S., Kley, F., Wieseke, N., Sander, T., & Dettling, M. (2009). COMTVal158Met polymorphism is associated with cognitive flexibility in a signal discrimination task in schizophrenia. Pharmacopsychiatry, 42(4), 141–144. https://doi.org/10.1055/s-0028-1112132
22. Nosal, C. S. (2010). The structure and regulative function of the cognitive styles: A new theory. Polish Psychological Bulletin, 41(3), 122–126. https://doi.org/10.2478/v10059-010-0016-0
23. Nurgalieva, A. K., Bashkatov, S. A., Volkova, E. V., Petrova, S. G., Takhirova, Z. R., Mustafin, R. N., Fedorova, Y. Y., Prokofyeva, D. S., & Khusnutdinova, E. K. (2023). Analysis of associations of alleles and genotypes of polymorphic loci of a range of candidate genes with phenotypic variations at the level of intelligence. Opera Medica et Physiologica, 10(1), 37–52.
24. Roy, N., Barry, R. J., Fernandez, F. E., & Coyle, K. (2020). Electrophysiological correlates of the brain-derived neurotrophic factor (BDNF) Val66Met polymorphism. Scientific Reports, 10(1), Article 17915. https://doi.org/10.1038/s41598-020-74780-9
25. Rusalov, V. M. (2012). Temperament v strukture individual’nosti cheloveka: Differentsial’no-psikhofiziologicheskie i psikhogeneticheskie issledovaniya [Temperament in the structure of human individuality: Differential-psychophysiological and psychogenetic studies]. Institute of Psychology of the Russian Academy of Sciences.
26. Rusalov, V. M. (2022). Some theoretical problems of constructing of a special theory of human individuality. Natural Systems of Mind, 2(1), 68–81. doi: 10.38098/nsom_2022_02_01_08
27. Rusalov, V. M., & Dudin, S. I. (2024). Temperament i intellekt: Teoretiko-eksperimental’nyye issledovaniya [Temperament and intelligence: Theoretical and experimental studies]. Ikar Publishing.
28. Rusalov, V. M., & Volkova, E. V. (2015). Lichnostno-kognitivnyye stili i ikh svyaz’ s temperamentom i kharakterom cheloveka v period ranney yunosti [Personality-cognitive styles and their relationship with temperament and character in early adolescence]. Psikhologicheskiy Zhurnal, 36(5), 32–42.
29. Volf, N. V., & Privodnova, E. Y. (2023). Background EEG activity mediates the association between the BDNFVal66Met polymorphism and memory during aging. Neuroscience and Behavioral Physiology, 53(9), 1469–1477. https://doi.org/10.1007/s11055-023-01540-3
30. Volkova, E. V., Kalugin, A. Y., & Rusalov, V. M. (2022). Personality traits, attitudes to life and patterns of behavior. Natural Systems of Mind, 2(1), 31–46. doi: 10.38098/nsom_2022_02_01_03
31. Volkova, E. V., & Rusalov, V. M. (2016). Cognitive styles and personality. Personality and Individual Differences, 99, 266–271. https://doi.org/10.1016/j.paid.2016.04.097
32. Volkova, E. V., Rusalov, V. M., & Dudnikova, T. A. (2022). Cognitive and personal styles questionnaire: Validity and reliability of the CPS-Q based on a Russian sample. Natural Systems of Mind, 2(4). https://doi.org/10.38098/nsom_2022_02_04_03
33. Volkova, E.V., Volkova, N.E. (2025). Genotypes of polymorphic locus of BDNF gene and sex as factors of individual differences in cognitive styles. Experimental Psychology (Russia), 18(2), 72—88. (In Russ.). https://doi.org/10.17759/exppsy.2025180204
34. Xie, Z., Maddox, W. T., Knopik, V. S., McGeary, J. E., & Chandrasekaran, B. (2015). Dopamine receptor D4 (DRD4) gene modulates the influence of informational masking on speech recognition. Neuropsychologia, 67, 121–131. https://doi.org/10.1016/j.neuropsychologia.2014.12.013
35. Zhang, L.-F. (2013). The malleability of intellectual styles. Cambridge University Press. https://doi.org/10.1017/CBO9780511973055