Relationship of fluid intelligence with other indicators of neurocognitive development in children of senior preschool age

Introduction

Fluid intelligence (FI) is an important predictor of academic success. Research suggests that FI factors such as induction, deduction, classification, general sequential and quantitative thinking determine its important role in an individual's acquisition of new skills and abilities (Rzhanova, Alekseeva, Burdukova, 2020; Johann, 2020; Li, 2021; Passolunghi, 2022; Vernucci, 2021).

According to Cattell, FI is an individual's ability to think logically and solve problems in new, non-standard situations, regardless of previously acquired knowledge (Cattell, 1987). The key tenet of Cattell's theory is the presence of two main factors in the structure of intelligence: FI and crystallized intelligence (the latter is associated with the acquisition of knowledge, sociocultural experience, and the individual's educational environment) (Cattell, 1963). Subsequently, within the framework of Cattell-Horn-Carroll, FI began to be considered one of the key primary cognitive abilities, along with crystallized intelligence (McGrew, 2023).

The issue of the relationship between FI and other indicators of neurocognitive functioning in both adults and children is relevant, since the presence of associations with individual cognitive functions opens up opportunities for developing programs for the development of FI through targeted training of individual cognitive skills.

Particular attention is paid to the relationship between FI and working memory (WM). According to a number of researchers, WM is the most powerful predictor of FI (Luo, 2020). It is an active system that is responsible for storing a limited amount of information and the ability to operate on this information within a short period of time (Rzhanova, Alekseeva, Burdukova, 2020; Nisbett et al., 2012). Kyllonen and Christal were the first to show extremely high correlation coefficients (0,80–0,88) between WM and FI in adults (Kyllonen, Christal, 1990). A strong and stable relationship between WM and FI was confirmed by the results of other studies (Conway et al., 2002; Kane et al., 2004). The close relationship between FI and WM is also supported by the results of neurobiological studies: the same brain structures (prefrontal and parietal cortex) are activated when performing FI and WM tasks (Gray, Chabris, Braver, 2003; Luo, 2020). Overall, the strong correlation between WM and FI in adults is recognized by the scientific community. The determinants of this relationship remain controversial.

Colom et al. (2005, 2006) found that individual differences in FI levels were significantly associated with both WM and short-term memory (STM). In some studies, STM was a stronger predictor of FI than WM, leading the researchers to conclude that the relationship between WM and FI was based on the volume of STM (Colom et al., 2008). However, other studies failed to confirm the mediating role of STM in the relationship between WM and FI (Cochrane, Green, 2021; Conway et al., 2002).

One of the versions explaining the observed relationship between WM and FI is the assumption of the determining role of attentional control mechanisms (Engle, 2010; Schroeders et al., 2016). In FI tasks, cognitive control is necessary for problem analysis, monitoring the solution process, and adapting the solution strategy in accordance with how successfully the subject copes with the task. Similarly, cognitive control is required in tasks assessing WM to maintain representations retrieved from memory in the field of consciousness when faced with interfering phenomena. Two independent studies confirmed the theory of attention as a determinant of the relationship between FI and WM (Colom et al., 2008; Jaeggi et al., 2011). The authors suggest that executive control, which is responsible for the distribution and concentration of attention during problem solving and its switching to more important tasks, plays a fundamental role in the relationship between FI and WM (Kane et al., 2004).

In children, the relationship between WM and FI has been studied significantly less (Erostarbe-Pérez, 2022). A number of studies have shown the influence of STM on the relationship between the studied variables (Hornung et al., 2011; Tillman, Nyberg, Bohlin, 2008). According to the study by Hornung et al. (Hornung et al., 2011; 160 children aged six years), it is the STM component that is associated with FI at this age stage. Other studies have demonstrated the opposite picture, that is, the absence of a significant effect of STM on the relationship between FI and WM (Bayliss et al., 2005; Engel de Abreu, Conway, Gathercole, 2010; Swanson, 2008).

Alekseeva et al. (2018) reported the results of a study examining the relationship between FI and other aspects of cognitive abilities in school-aged children using the Kaufman tests. The main findings highlight the link between FI and memory, both short-term and long-term (LTM), which may indicate the importance of a comprehensive approach to intelligence development.

Of great importance are the data from training experiments demonstrating the possibility of developing FI through WM training (Jaeggi et al., 2008, 2011). However, these data were not confirmed in other studies, including a randomized controlled trial (Redick et al., 2013; Thompson et al., 2013). The discussion about the possibility of influencing FI through WM training remains relevant. The possibility of such training was demonstrated in a meta-analysis of 20 studies (Au et al., 2015), but subsequently in two other studies, including a meta-analysis, these results were criticized and recognized as lacking a serious evidence base (Bogg, Lasecki, 2015; Melby-Lervåg, Hulme, 2016).

According to recent studies, fine motor skills and WM are the cognitive abilities most closely associated with FI in samples of primary school children (Memisevic, Dedic, Malec, 2023). However, other studies have shown a relatively weak relationship between fine motor skills and intelligence in children (Jenni et al., 2013).

Thus, the studies available to date on the relationship between FI and other neurocognitive functions, including in children, are contradictory: the relationship between FI and STM and LTM, attention, fine motor skills remains debatable, as does the question of the possibility of training FI through targeted development of individual cognitive skills.

This inconsistency in the literature may be due to the fact that most studies focus on analyzing isolated associations between FI and specific cognitive functions (WM, attention, or motor skills) across different samples and age groups. A comprehensive analysis of a wide range of neurocognitive indicators in a single sample of older preschool-aged children has not previously been conducted, making it impossible to assess the relative contribution of various functions and their relationship with FI within a single diagnostic paradigm. This necessitated the study presented here, which was based on the following hypothesis: FI in older preschool-aged children is associated with indicators of WM, STM, LTP, attention, and fine motor development. The aim of the study was to evaluate the associations of FI with a number of other indicators of neurocognitive development in older preschool-aged children.

Materials and methods

The children were examined as part of the project "Study of neurobiological predictors of academic success in children." Inclusion criteria: written voluntary consent form a parent; child's age at inclusion: 5 years 10 months - 7 years 4 months; child's ability to understand and follow instructions. Exclusion criteria: previously diagnosed hearing, vision, and motor impairments; severe mental and neurological disorders diagnosed by a psychiatrist and/or neurologist; concussion within the last year, other traumatic brain injury, or neurosurgical intervention on the brain; paroxysmal activity on the EEG; alalia; severe chronic diseases, developmental defects, cachexia, hereditary diseases; chronic mental disorders, alcohol and/or drug addiction in parents.

The Leiter-3 International Performance Scale (4 core subtests of the cognitive block, K) was used to assess FI (n = 169) (Royd, 2014). The Leiter-3 test is designed to assess an individual's ability for abstract thinking, problem solving, and behavioral adaptation, independent of verbal or language skills. It consists primarily of visual-spatial tasks, which minimizes the impact of language barriers. The Leiter-3 demonstrates high validity and is actively used in clinical practice, education, and research (Giofrè et al., 2024; Lichtenstein et al., 2022).

The neuropsychological examination (n = 130) was based on the method of A.R. Luria, adapted for older preschool children aged 6–7 years (Glozman, 2006); the tests used are presented in the table in the Results section.

The assessment of WM, attention, and fine motor skills was conducted using the hardware and software complex (HSC) SHUHFRIED (Vienna Test System, Austria) (n = 114). Three subtests were analyzed:

1. The Tower of London Freiburg Version (TOL-F) test. The main variables assessed are planning ability (the ability to cognitively model alternative solutions and evaluate the consequences of an action before it is performed), WM, and inhibitory control—components of executive functioning (EF) (Berg, Byrd, 2002). The validity of the TOL-F was confirmed in the study by Debelak et al. (2016).
2. The MLS test (Fine Motor Skills Assessment, Short Form according to Sturm and Büssing) includes 8 subtests – 4 for each hand and evaluates the following indicators: purposefulness of movements, calmness of the hands/tremor, precision of hand and wrist movements, dexterity of the hands and fingers, speed of hand and wrist movements, speed of wrist and finger movements.
3. To assess attention, the Determination Test was used: a test of reaction to several stimuli (presentation of color stimuli and sound signals), to which the respondent responds by pressing the corresponding buttons on the response panel and using the pedals. The test requires dividing attention between different stimulus modalities (visual and acoustic), as well as between different response options (pressing a button with the hand or using the pedal).

To date, data on the experience of using the SCHUHFRIED APCS in Russia for carrying out psychological and pedagogical research have been published (Yakimova, Perminov, 2020).

Ninety-eight study participants (68 boys and 30 girls, median age 6.5 [6,0; 7,0] years) completed all assessment procedures. All children were permanent residents of Nizhny Novgorod and Russian-speaking. The study was continuous and cross-sectional. Not all children had completed all planned assessment methods by the time of writing this article (the study is ongoing), and some participants dropped out of the study due to parental refusal to participate. Therefore, the number of children assessed by different specialists varies. The database of children's assessment results is published in the RusPsyDATA repository of psychological research and tools (Zhilyaeva et al., 2025).

Data analysis was performed using StatSoft Statistica 6.0. The data distribution was non-normal (Shapiro-Wilk test), and Spearman's rank correlation coefficient (ρ) was used to assess correlations between variables. Correlations were considered significant at p < 0,05.

Results

The table presents the results of the analysis of correlations of the integral indicator FI – IQ – with the results of the assessment of other neurocognitive functions.

Table / Таблица

Correlations (Spearman) of the fluid intelligence quotient with the results of assessment of other neurocognitive functions

Корреляции (Спирмена) коэффициента флюидного интеллекта с результатами оценки других нейрокогнитивных функций

Примечание: АПК – аппаратно-программный комплекс. Результаты нейропсихологической оценки кодировались таким образом: чем больше отклонений от нормы, тем выше выставлялся балл. Поэтому коэффициенты корреляций показателей нейропсихологической батареи с IQ отрицательные.

Note: HSC – hardware and software complex. The results of the neuropsychological assessment are coded in such a way: the more deviations from the norm, the higher the score. Therefore, the correlation coefficients of the neuropsychological battery indicators with the IQ are negative.

Discussion

According to the obtained results (see Table), in older preschool children, FI does not have significant associations with the working memory indicator (10-word test, memorization capacity at 1 presentation). The overwhelming majority of data on a close correlation between FI and WM were obtained in samples of adults (Conway et al., 2002; Engle, 2010; Gray, Chabris, Braver, 2003; Kane et al., 2004; Kyllonen, Christal, 1990). In the only published study we found on the relationship between WM and FI in children, the authors concluded that in childhood, the relationship between FI and WM is based on cognitive control mechanisms, but not STM (Engel de Abreu, Conway, Gathercole, 2010).

Moreover, we obtained a highly significant correlation between visual memory and FI (ρ = –0,199; p = 0,000094). It is likely that visual working memory is most involved in completing the Leiter-3 tasks. Verbal working memory, which has a different neurobiological basis, apparently does not contribute to the FI indicator in older preschool age. At the same time, the verbal mechanical and LTM indicators in our study correlate with FI, albeit weakly. This is consistent with the data of other researchers (Alekseeva, Rzhanova, Burdukova, 2018; Hornung et al., 2011; Tillman, Nyberg, Bohlin, 2008).

Significant correlations of attention indices with FI, obtained using both neuropsychological assessment and the SCHUHFRIED HSC (determination test), are consistent with each other, as well as with the data of other researchers who have demonstrated a connection between attention indices and FI (Colom et al., 2008; Engle, 2010; Jaeggi et al., 2011; Schroeders et al., 2016). The authors of the cited studies explain the connection between attention and FI in the context of a close relationship between attention and IF. In our study, IF was tested using the TOL test of the SCHUHFRIED HSC, and the number of correct decisions in this test also correlated with FI (ρ = 0,20; p = 0,035).

Thus, the results obtained in this study indicate the existence of a relationship between various components of memory (except verbal working memory), attention, EF and FI, previously presented in the literature.

However, it is noteworthy that the relationship between FI and other indicators of children's neurocognitive development is closer and more significant. Thus, FI has significant correlations (see table) with dynamic praxis, the ability to plan and create a copying strategy based on analytical and holistic components of perception, hand coordination, fine motor skills, the interaction of the afferent and efferent links of optical-constructive activity, the kinesthetic organization of speech movements, acoustic gnosis, awareness of body schema, spatial organization of movement, kinesthetic praxis, as well as comprehension of instructions, performance, and perseverance. Furthermore, a large number of significant weak correlations were obtained between FI and fine motor skills, assessed by the MLS test of the SCHUHFRIED HSC. While conflicting data on the relationship between fine motor skills and FI in children are presented in the literature (Jenni et al., 2013; Memisevic, Dedic, Malec, 2023), there is no data on the relationship between FI and other indicators of neurocognitive functioning described above in the available sources.

Conclusions

According to the data obtained in our study, FI in older preschool children correlates with a wide range of neurocognitive development indicators, including various types of praxis, gnosis, visuospatial functions, and fine motor skills. However, significant links with verbal working memory are absent, which may indicate the leading role of visuospatial systems in the implementation of FI at this age. Thus, it is not possible to single out a single target for FI training, which is consistent with the concept of FI as a complex dynamic system based on the interaction of various cognitive processes and neural networks. To achieve results in improving FI in older preschool children, it is likely necessary to consider integrative training approaches encompassing a wide range of cognitive functions.

Limitations. The associations obtained in this cross-sectional study cannot be interpreted as cause-and-effect relationships, nor can it be concluded that the development of neurocognitive traits associated with FI will contribute to the development of FI. A reverse relationship cannot be ruled out: children with more developed FI have higher scores on other tests due to their more highly developed FI. However, the obtained results create the basis for further prospective experimental studies that will be able to confirm or refute the hypothesis that FI can be developed through comprehensive training of several neurocognitive development indicators.

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