A time to scatter stones and a time to gather them

Ecclesiastes 3:5

Natural Systems of Mind
Journal
Natural Systems Of Mind No 2
Inductive Reasoning and Neural Efficiency December 2021

Inductive Reasoning and Neural Efficiency

Dennis A. Dokuchaeva, Natalia E. Volkovaa
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Abstract

Abstract

25 December 2021 199 views 30

A brief review of studies that considers the issues of cognitive performance allows us to identify two sets of data, some studies confirm the Neural Efficiency Hypothesis (NEH), others testify in favor of the Efficiency Paradox Hypothesis. We assumed that the possible reasons for the controversial outcomes could be not only in the task complexity, but also the heterogeneity of the samples. Therefore, we compared the effectiveness of inductive reasoning in samples that are homogeneous according (1) sex, (2) age, as well as the (3) speed and accuracy of solving elementary logical problems. The study involved 251 respondents aged 13 to 27 years (M/F = 118/133, mean age 15.64±2.65 years). Behavioral and EEG data were collected. Significant differences in the performance of elementary logical operations between female and male were not found. However, the values of EEG Power Spectrum in female were significantly higher than in male. The neuro-efficiency hypothesis was confirmed only in the group of “slow” responders. Ceteris paribus, the fast persons expend more energy than the slow ones, the accurate people more than the inaccurate ones, women compared to men.

Introduction

A phenomenon called cognitive neuroefficiency [20], i.e., more efficient use of the cortex (or even whole brain) in people with high intelligence compared to people with low intelligence, was first obtained in the of R. J. Haier’s work using positron emission tomography (PET). In the study examining the relationship between intellectual task performance and neural activation levels, R. J. Haier found an inverse relationship between brain glucose metabolism levels and intelligence test scores. Participants with high IQs consumed less energy and worked faster than participants with lower IQs. This fact allowed the authors to suggest that higher intelligence was associated with neural circuits that worked faster and more efficiently. Neural efficiency manifests itself in more clearly localized brain activation areas during cognitive tasks in more capable individuals versus less capable individuals in each brain areas. This effect has been confirmed in many studies using different neurophysiological measurement methods and a wide range of different cognitive tasks. More successful subjects showed less brain activation when solving mental tasks than less successful subjects [6,9,10,12,36-38,47]. Several studies demonstrated the non-linear nature of the correlations between the physiological reactions of the body and the results observed during mental activity. Of great importance is the problem of the ratio of physiological “costs” and the success of the thought process, which can be considered in terms of operational efficiency [5].

1.1. Intelligence and the phenomenon of neuroefficiency

J. Haier ‘s followers Niebauer and Fink in a comprehensive review of intelligence and the phenomenon of neuroefficiency reported 29 studies in support of this hypothesis, while 18 studies provided partial support, and 9 studies provided negative results. These scientists suggested that these controversial results could be explained by the fact that some studies included tasks that may not have been demanding enough, while others were too complex and required more cortical resources, leading to a positive correlation between brain function and cognitive ability [12,34,41].

Similar controversial data were revealed in a systematic review of neural efficiency in the athletic brain [31]. Longxi Li and Daniel M. Smith assumed neural that efficiency in athletes is an integration of neuroanatomical structure changes (neural plasticity) and neural proficiency of higher cognitive processing and neural network through long-term training in specific sports. They examined a wide range of sport-specific videos and Multiple Object Tracking (MOT) specific to 18 different sports and utilized Blood Oxygenation-Level Dependent (BOLD) functional Magnetic Resonance Imaging (fMRI), functional Near-InfraRed Spectroscopy (fNIRS), and ElectroEncephaloGram (EEG). They extracted both supporting and conflicting evidence of the Neural Efficiency Hypothesis (NEH). The researchers summarized studies that typically report a negative association between brain activation and optimal task performance. These studies indicated that experts use their brains more efficiently with less energy consumed (a fewer resources are used) than novices or non-athletes for performance of a task. These scientists revealed studies that reported only partial support for the NEH; that is, only for certain categories, under specific conditions/tasks, or for specific brain regions. Moreover, they found studies which presented the opposite point of view: studies on complex visuospatial, visual search, motion observation, and cognitive tasks have shown that athletes’ parietal, central, and other areas have higher cortical activation, which is inconsistent with the NEH.

1.2. Efficiency Paradox Hypothesis

T. Mann, A. Wright, and C. M. Janelle claim that the neural efficiency hypothesis (NEH) is both scientifically and intuitively simplistic, and underlying mechanisms that correlate with this hypothesis remain speculative. They put forward the efficiency paradox hypothesis: “longer is better” that runs contrary to the NEH [32]. As paradoxical it may seem, extensive evidence shows that even for fast ballistic tasks such as table tennis, soccer, badminton, and archery, the duration of QE (Quiet Eye) tracking is longer for successful shots than for unsuccessful ones, in which the cortex activation in an expert is greater than in a novice [2,3,4,8,13,16].

Longxi Li and Daniel M. Smith explain the paradox of neuroefficiency by the fact that with limited cognitive capabilities of the human brain, a person finds ways to navigate complex spatial information earlier and to maintain their focus under the most challenging situations. More often, at the highest level of sport competition, athletes are faced with a large number of factors that can be difficult to control. These researchers emphasized that the NEH is a dynamic and situational concept that depends on several factors including movement characteristics (e.g., side of the movement), hemisphere, and athletes’ personality traits. They believe that the various perspectives associated with NEH need to be considered in a concrete situation [31].

1.3. Neuronal mechanisms of inductive reasoning

Qiu, Y. Pi, K. Liu, H. Zhu, X. Li, J. Zhang, et al showed that higher neural efficiency is a bidirectional phenomenon encompassing both decreased activation of task-related areas and decreased deactivation of areas associated with irrelevant information processing [42]. It is also noted that the correlations between intelligence and brain activation differed depending on the intelligence component. For instance, when studying Event-Related Desynchronization (ERD) alpha activity of the EEG, it was found that during the performance of a reasoning task, neuroefficiency was more pronounced in fluid intelligence compared to crystallized (according to Cattell) [34].

The various models are used to explain the neuronal mechanisms of inductive reasoning, most of which follow the principle of complete similarity [22,39,44,51,52]. These researchers believe that categorization, induction, and recognition are closely related conceptual abilities that enable people to draw conclusions from observation and previous experience. As Evan Heit and Brett K. Hayes pointed out, similarities are potentially the common “currency” that links all three cognitive activities [22]. According to the SINC model by V. Slutsky and A. Fisher (A Model of Similarity-Based Induction in Young Children), “categorization and induction in young children are processes based on similarity” that are calculated on visual and linguistic signals” [52]. The data obtained by these researchers indicated that categorization and induction in young children (4–5 years old) depended on visual coincidences between stimuli, as well as auditory coincidences from the point of view of speech categories. The data of a large-scale experiment, in which 3600 respondents of three age groups (5-8 years old, 11-13 years old, 14-16 years old) took part, aimed at the formation of the ability of the operation of inductive reasoning, showed that the formation of the ability for elementary logical operations led to an improvement in academic achievement and fluid intelligence growth [25]. The Similarity-Based Model is based on perceptual similarity [52]. The Concept-Based Model [18] is based on existing knowledge or concepts. A prerequisite for the performance of the reasoning process in the Similarity Model is the similarity between the objects of thought or the general membership in the category, i.e., there is a definite relationship between objects in a semantic context, is based on taxonomic similarities: an overlap of the features or meanings of words that includes elements of the same generic category (e.g., a mammal with members such as panda, antelope, dog, cat, cow etc.) [48]. Other studies have also shown that taxonomic relationships in the process of reasoning are formed more easily than thematic relationships [49].

The Conceptual Model of inductive reasoning assumes that inductive reasoning is based on a common conceptual property, while perceptual similarities between objects are ignored [17]. One of the conceptual properties is a thematic relationship which is based on complementary related elements in scenarios or events that have a common conceptual feature. It was shown that the processing of thematic relationship requires fewer cognitive resources than that of a taxonomic relationship in reasoning tasks [32,48]. Apparently, that is why, inductive logical reasoning based on conceptual information is often used in everyday life [29].

The existing studies of inductive reasoning have primarily adopted imaging methods to explore the neural difference between taxonomic- and thematic based-inductive reasoning [23,24,26,58]. Fangfang Liu, Jiahui Han, Lingcong Zhang and Fuhong Li tested whether or not distance effects on the processing of taxonomic- and thematic-based semantic relations in inductive reasoning were differently reflected in brain [30]. Event-related potential (ERP) results showed that the effect of context (taxonomic versus thematic) was initially observed in the P2 component; while the distance effect (far versus near) was observed in N400 and later components. It should be noted that distance effects for inductive reasoning based on thematic relationships were found in the frontal and frontal-central regions, while the distance effect on inductive reasoning based on taxonomic relationships was observed in the parietal and central parietal areas.

However, the neural efficiency of inductive reasoning associated with the difference in intelligence and temperament traits remains unaddressed. Thus, the purpose of the present study was to investigate the relations among inductive reasoning, EEG power spectrum, intelligence, and temperament traits. In order to achieve our goal, we need to solve the following tasks:

  • to compare indicators of temperament, intelligence and EEG power spectrum when solving elementary logical problems in samples of female and male;
  • to compare indicators of temperament, intelligence, and EEG power spectrum when solving elementary logical problems in 13-14-year-old, 15-17-year-old and 18-27-year-old respondents;
  • to compare indicators of temperament, intelligence, and EEG power spectrum when solving elementary logical problems in groups of slow and accurate, slow and inaccurate, fast and accurate, fast and inaccurate respondents.

Method

2.1. Participants

The present research was administered to 251 subjects, aged from 13 to 27 (M/F = 118/133, mean age 15.64±2.65 years) including early adolescents (13.73±0.45 years), middle adolescents (15.46±0.66 years), and late adolescents (21.11±2.87 years). This study involved respondents from Moscow and Ufa within Russian Federation. All participants were right-handed and without any obvious signs of psychiatric or neurological disorders. They had normal vision. The participants gave informed consent before starting data acquisition experiment. Their participation in the studies were free.

It is well known that at the age of 13-14 years, the formation of secondary sexual characteristics begins, an unstable hormonal profile is observed, and the structures of the brain (cerebral cortex) are in the process of formation. At the age of 15-17 years, the hormonal profile is also unstable, but the cerebral cortex (except for the frontal lobes) and other regulatory structures of the central nervous system (CNS) are formed. At the age of 18-27 years, secondary sexual characteristics have formed, the hormonal profile is stable, the formation of all structures of the central nervous system, including the frontal cortex, has completed [15].

2.2. Measures

Prior to the EEG sessions participants were screened with respect to their intellectual abilities by means of a well-known Standard Progressive Matrixes (SPM) [43] and the “Intelligence Structure Test 2000-R” (I-S-T 2000-R) [1] in Russian adaptation by L.A. Yasyukova [59].

The Standard Progressive Matrixes (SPM) is a well-validated measure of fluid reasoning ability (gF). The Raven’s Standard Progressive Matrixes contain 60 nonverbal items. Each item consists of 3 × 3 matrix with a missing piece to be completed by selecting an answer from six or eight alternatives. Time completing the tasks is 20 minutes.

Intelligence Structure Test (IST) is based on Thurstone’s and Cattell’s intelligence theories and measures verbal, numerical, and figural-spatial abilities. The composite score indicates general reasoning ability which is closely tied to general intelligence. Each verbal, numerical, and figural-spatial tasks consist of 20 items.

  • Verbal Intelligence scale includes such tasks as Sentence Completion (SC), Verbal Analogies (VA), Verbal Similarities (VS), Odd One Out (OOD). These scales measure the reasoning ability within a verbal context.
  • Numerical Intelligence scale consists of Calculations (CA) and Number Series (NS) tasks. These scales measure calculation skills and numerical reasoning (the ability to make logical connections between numbers).
  • Figural-Spatial Intelligence scale covers such tasks as Figure Selection (FS), Cubes (CU), and Verbal Memory (VM). These scales assess the ability to process figural-spatial material (two- and three-dimensional figures) as well as the ability to assess proportions of surfaces and volumes, figural reasoning, the ability to reveal logical relationships among figures.

The Elementary Logical Operations (ELO) test is implemented in both computer and paper-and-pencil versions [46]. The ELO has 24 statements assigned to evaluate the ability to perform elementary logical operations. Respondents are offered to compare the ratio between the values of A, B and C as fast and as accurately as possible and evaluate the truth or falseness of the logical conclusion.

For example, if A is equal to B and B is equal to C then “C is equal to A”. This conclusion is true. But the conclusion “C is not equal to A” under the given conditions is false. There are four possible answers: (1) C is equal to A; (2) C is not equal to A; (3) C is greater than A; (4) C is less than A. Only those problems were offered that had only one correct answer.

The time of the ELO test in paper-and-pencil version was limited to four minutes. Decision time of the ELO test in the computer version was unlimited and the tasks were provided randomly. Decision time and total score are automatically recorded by a special Software InTesting.

Raw scores were converted to S-scores through percentile standardization procedure. The ability to perform Elementary Logical Operations was considered to be high if S-score ≥ 7 and low if S-score ≤ 3.

The Structure Temperament Questionnaire (STQ-S) [45] was used for evaluation of Motor (MA), Intellectual (IA), and Communicative (CA) Activity. Shortened version of the STQ-S contains 26 items. STQ-S has a high correlation with full version of the STQ questionnaire. The IA-scale is thought to be temperamental scale of intelligence measured by Wechsler test [45].

2.3. EEG procedure

The EEG session started with the mounting of the electrodes and checking the electrode impedances. Participants were in a comfortable EEG recording room. We record two 2-min EEG sequences under resting conditions to determine the neurological profile of the respondents: (a) eyes closed, (b) eyes opened. Then experimental session started the total time of which was about 45 min. The EEG -recorder was synchronized with the InTesting computer diagnostic complex to analyze the change in the EEG power spectrum when solving Elementary Logical Operations. The problems randomly appeared on the screen. The respondent was to evaluate the truth or falsity of the conclusions as quickly and as accurately as possible and press the appropriate key: true or false.

2.4. Apparatus/EEG recording

We used portable electroencephalograph Encephalan-EEGR-19/26 Medicom MTD (European certificate CE 538571 of the British Standards Institute, BSI). The EEG was measured by means of silver electrodes (9 mm diameter) located in an electrode cap in 30 positions (according to the international 10–20 system with interspaced positions). A ground electrode was located on the forehead. Reference electrodes were placed on the left and right earlobes. Electrodes O2-A2, O1-A1, Oz-A2 correspond to occipital lobe; P4-A2, P3-A1, C4-A2, C3-A1, Pz-A1, Cz-A2, CP3-A1, Cpz-A1, CP4-A2 – parietal lobe; F4-A2, F3-A1, Fp2-A2, Fp1-A1, F8-A2, F7-A1, Fz-A1, Fpz-A2, Fcz-A1, FT8-A2 – frontal lobe; T6-A2, T5-A1, T4-A2, T3-A1 – temporal lobe; FC3-A1, FC4-A2 – fronto-parietal lobe; TP7-A1, TP8-A2 – temporo-parietal lobe. The EEG signals were filtered between 0.5 Hz and 50 Hz; an additional 50 Hz notch filter was applied to avoid power line contamination. Electrode impedances were kept below 5 kΩ for the EEG. The sweep rate was 30 mm/s.  The EEG recording was scanned for artifacts. The epochs for analysis were selected after the artifacts were removed manually. The duration of one epoch was 10 seconds. We used for the analysis five epochs of the performing ELO test: two epochs at the beginning, one in the middle, and two at the end. The spectral amplitude is an average value over the time interval under consideration. The mathematical basis of spectral analysis is the Fourier transform of the initial EEG data, which was carried out automatically by the Medicom-MTD program. We used the EEG power spectrum that denotes the squared value of the amplitude of the EEG signal. This parameter provides an increase in the stability of the data obtained due to increasing in the strongest differences and the leveling of weak differences [27].

2.5. Statistical Procedures

Statistical treatment of empirical data included descriptive statistics of raw data (Means, SD, Skewness, and Kurtosis). The test scores corresponded to the normal distribution (Skewness and Kurtosis =±1). Reliability statistics (Cronbach’s alphas) for the both ELO (Paper-and-pencil version) and ELO (Computer version) was conducted. The statistical analyses involved mixed-design ANOVAs and t-tests. Post-hoc comparisons with Bonferroni corrections were made where it was necessary. The indicators of the ELO test were converted into S-scales. Hierarchical Cluster Analysis (HCA, Ward Method) was used to identify relatively homogeneous groups of respondents based on S-scales of the ELO test.

 

 

Results

3.1. Behavioral data

Descriptive statistics are presented in Table 2. Test scores corresponded to the normal distribution. Means and standard deviations (SD) are reported for the male and female sub-samples and for the total sample. The reliability measured by the Cronbach Alpha coefficients were within the acceptable range (0.84-0.91). These results testify that the ELO (Computer version) and the ELO (Paper-and-pencil version) scales had sufficient internal consistency. There were no significant differences in the ability to perform elementary logical operations in male and female samples. However, the female sample was characterized by significantly higher rates of intellectual tests such as Odd One Out Task (OOD), Verbal Similarities (VS), Figure Selection (FS), and Verbal Memory (VM).

 

The data presented in table 3 indicated an increase in the ability to perform elementary logical operations from Early Adolescence to Late Adolescence. Of particular interest is the fact that the speed of making a decision about the truth or falsity of conclusions is significantly higher in late adolescence compared to middle adolescence.

As to the temperamental traits, the values of Motor Ergonicity, Intellectual Tempo and Motor Activity in Middle Adolescence were significantly higher than in Late Adolescence. To clarify the courses of the finding obtained, it was advisable to compare the Behavioral data between homogeneous groups that differ in the speed of decision making and accuracy.

 

The raw scores of decision-making speed and accuracy were converted to S-scales. Then four relatively homogeneous groups of respondents were identified based on Hierarchical Cluster Analysis (Ward Method) which were conditionally named Slow & Inaccurate, Slow & Accurate, Fast & Inaccurate, Fast & Accurate. Thus, we got two groups with the same speed, but different decision-making accuracy, and two groups with the same accuracy, but different decision-making speed (Table 4)

 

According to the data presented in Table 4, the sample of respondents who are equally slow, but differ in the accuracy of decision-making, differ significantly only on the Number Series scale (NS).

In the sample of Fast & Accurate respondents compared with Fast & Inaccurate respondents, we revealed the following significant differences: the higher scores on the Calculations (CA), Cubes (CU), and Motor Ergonicity scales, but the lower scores on the Motor Activity scale.

We found only one significant difference on the Cubes scale (CU) between the samples of equally accurate respondents, but differing in the speed of decision-making. Also, one significant difference was revealed on the Motor Emotionality scale between the samples of respondents, which are equally inaccurate, but differ in the speed of decision-making. It should be noted that Fast & Accurate respondents had higher IQs, while Fast & Inaccurate respondents had lower IQs.

3.2. EEG data

We compared Means of the EEG power spectrum (mV2) during performance of the Elementary Logical Operations between males and females (see Table 5).

 

According to the data presented in Table 5, the power spectrum of the EEG signal under 18 electrodes when solving elementary logical problems were significantly higher in female than in male. At the same time as is seen in Table 2, there were no significant differences in the speed and accuracy of solving elementary logical problems in samples of female and male. The findings testified that female respondents had to expend more “electrophysiological energy” to achieve the same results compared to male respondents. It should also be pointed out that both in males and females, the highest values of the EEG power spectrum were observed under the F3_A1 electrode, and the lowest under the T6-A2 and T5-A1 electrodes.

 

 

We found more significant differences in EEG power spectrum by age than by sex (24 vs 18). Two types of age-related changes in the parameters of the EEG power spectrum were revealed: descending and U-shaped. Power spectrum values continually decreased from Early Adolescence to Late Adolescence under the following Electrodes: O2, O1, Oz, (occipital lobe); P4, P3, C3, Pz, CP3, Cpz (parietal lobe); T6 (temporal lobe); and F7, Fcz, FC4, FT8 (frontal and frontotemporal lobe).

The U-shaped changes in the EEG power spectrum were found in two subtypes:

(a) The power spectrum in Early Adolescence was higher than in Late Adolescence under electrodes C4, F4, Fp1, CP4.

(b) The power spectrum in Late Adolescence was higher than in Early Adolescence under electrodes F3, F8, Cz, Fz, Fpz, TP8.

Comparison of the data presented in Tables 3 and Tables 6 indicated that the decrease in Motor Activity indices corresponded to a decrease in the values of EEG power spectrum in the motor cortex of the right hemisphere (leads FC4, FT8).

Let us describe at first peculiarities of accurate respondents with different speed of solving elementary logical tasks. As it shown in Table 3 there are no significant differences in the accuracy of solving elementary logical problems in the samples of Accurate & Fast and Accurate & Slow respondents. At the same time, the values of the EEG power spectrum under the electrodes F3, Fp2, Fp1, Cz, Fz, TP8 (frontal, parietal and parietotemporal lobes of the cerebral cortex) in Accurate & Slow respondents were significantly lower than in Accurate & Fast ones. Thus, data supported the Efficiency Paradox Hypothesis. Whereas the values of the EEG power spectrum under the electrodes O2 and Oz (occipital lobe) were significantly higher. These results are consistent with the Neural Efficiency Hypothesis.

Then let us consider fast responders with different accuracy of solving elementary logical tasks. The values of the EEG power spectrum on the electrodes F3, Fp2, Fp1, Cz, Fz, TP8 (frontal, parietal and parietotemporal lobes of the cerebral cortex) in Inaccurate & Fast respondents were significantly lower than in Accurate & Fast ones. These results correspond to the Efficiency Paradox Hypothesis. Whereas the values of the EEG power spectrum under the electrodes O2 and Oz (occipital lobe) were significantly higher.

Slow responders with different accuracy of solving elementary logical tasks showed the follow results. The values of the EEG power spectrum were significantly lower under all mentioned in Table 7 electrodes in Accurate & Slow respondents as compared as Inaccurate & Slow subjects. Thus, the results obtained as a whole support the Neural Efficiency Hypothesis.

 

 

 

Discussion

A brief review of the studies that consider the issues of cognitive performance allows us to identify two sets of data, some of the studies confirm the Neural Efficiency Hypothesis (NEH), others testify in favor of the Efficiency Paradox Hypothesis. According to Callan and Naito [7], four neural mechanisms such as neural efficiency, cortical expansion, specialized processes, and internal models provide the superiority of experts over novices. The decrease in brain activity and pronounced localization in the more capable compared to the less capable is explained by the continuous adaptation / neuroplasticity of the cerebral cortex, which in each specific area of human competence correlates with skillful control of cognitive and motor activity [4,36]. The increase in the activity of brain regions when performing cognitive tasks in the more capable compared to the less capable, identified in a large number of studies [31], is explained by an increase in the activation of the Default Mode Network (DMN) which leads to an increase in the activity of all neural networks, to the reorganization of old cortical circuits and to the creation of new ones in the course of cognitive development [53,57]. A possible reason for the controversial results could be the variability in task complexity, since neural efficiency was mostly observed for low-to-moderately difficult tasks [38]. Longxi Li and Daniel M. Smith [31] pointed out the heterogeneity of outcomes and emphasized that NEH is a dynamic and situational concept that depends on several factors including task complexity, movement patterns, hemisphere, personality traits, and others.  Therefore, in this work, we studied the performance of solving elementary logical problems (simple tasks) in female and male; in the Early, Middle and Late adolescence taking into account the peculiarities of intelligence and temperament traits.

4.1. Neural efficiency of inductive reasoning in female and male

C. Neubauer, R. H. Grabner, A. Fink & C. Neuper, studying the influence of task content and sex on the brain–IQ relationship, found that in the female sample when performing figurative-spatial tasks, and in male when performing verbal tasks, brain activity was lower regardless of the level of intelligence [36]. Due to the earlier onset of the formation of brain structures in female, their IQ-level are usually higher than in male [14]. In our study, female also demonstrated higher IQs compared to male. Significant differences in the performance of elementary logical operations between female and male were not found. However, the values of EEG Power Spectrum in female subjects were significantly higher than in male subjects. It means that in order to achieve the same result of inductive reasoning the female subjects spend more resources compared to the male subjects. The highest values of the EEG power spectrum both in female and male were observed on lead F3 (left frontal lobe, field 46 according to Brodman). The field 46 correlates with the motor function of the muscles of the eyeball and the combined movements of the head and eyes, as well as with the comparison of visual information and movements necessary to grasp the field of view. That is, the respondents expend the most energy (in terms of the EEG power spectrum) during the performance of the task. The lowest values of the EEG power spectrum in female and male subjects are also observed under the same electrodes (T5, T6). These electrodes are located above the projection of 20-24 Brodmann fields which are responsible for comparing new data with the information previously received, as well as for consolidating and storing memory. Apparently, the respondents use memory storage structures to a less extend when solving this type of logical problems. The EEG-data show, regardless of sex, the greatest activity is observed under the frontal electrodes, i.e. each task is analyzed using structures responsible for logical operations (analysis, synthesis, generalization, abstraction, comparison and judgment).

4.2. Neural efficiency of inductive thinking in Early, Middle, and Late Adolescence

The data of this study indicate an increase in the accuracy of solving elementary logical problems from Early to Late adolescence. This pattern is confirmed in many studies of cognitive performance. Of particular interest are two types of age-related changes in the EEG power spectrum associated with an increase in the efficiency of solving elementary logical problems: descending and U-shaped changes. The age-related decline in the EEG power spectrum is usually explained by an increase in the stability of hormonal profile and by the maturation of the frontal cortex which is responsible for information processing.  The U-shaped age-related change in EEG power spectrum in our opinion is associated with a significant increase in the speed of solving elementary logical problems from Middle to Late adolescence. Apparently, high speed requires additional energy expenditures of the cerebral cortex in terms of Power Spectrum EEG. To confirm this assumption, we compared the EEG power spectrum in groups of respondents who differed in the speed and accuracy of solving elementary logical problems.

 

4.3. Neural efficiency of inductive reasoning in Samples of Equally Accurate and Equally Fast Respondents.

The NEH holds that the person with high IQs consumes less energy and operates faster than person with lower IQs. In our study, the most “neuroefficient subjects” were those respondents who were noted for higher accuracy, but lower speed of solving elementary logical problems (in terms of the EEG power spectrum). Thus, we received additional evidence that U-shaped age-related change in EEG power spectrum may be associated with an increase in the speed of solving elementary logical problems. It should be emphasized that traditional intelligence tests usually have time limits, i.e., faster responders score more points. Therefore, the inconsistency of our data may be due to both (1) variations in cognitive complexity and (2) time limitations. Obviously, the fast persons expend more energy than the slow ones, and the accurate people expend more energy than the inaccurate ones, and women use more energy than men. These findings confirm every day observations. Apparently, the neuroefficiency hypothesis as well the Efficiency Paradox Hypothesis need to be redefined and the limit of their applicability clarified.

Conclusions

Funding. We thank students from Moscow and Ufa schools and universities who voluntarily participated in this research.

Author contributions.

Competing interests. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

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A brief review of studies that considers the issues of cognitive performance allows us to identify two sets of data, some studies confirm the Neural Efficiency Hypothesis (NEH), others testify in favor of the Efficiency Paradox Hypothesis. We assumed that the possible reasons for the controversial outcomes could be not only in the task complexity, but also the heterogeneity of the samples. Therefore, we compared the effectiveness of inductive reasoning in samples that are homogeneous according (1) sex, (2) age, as well as the (3) speed and accuracy of solving elementary logical problems. The study involved 251 respondents aged 13 to 27 years (M/F = 118/133, mean age 15.64±2.65 years). Behavioral and EEG data were collected. Significant differences in the performance of elementary logical operations between female and male were not found. However, the values of EEG Power Spectrum in female were significantly higher than in male. The neuro-efficiency hypothesis was confirmed only in the group of “slow” responders. Ceteris paribus, the fast persons expend more energy than the slow ones, the accurate people more than the inaccurate ones, women compared to men.

A phenomenon called cognitive neuroefficiency [20], i.e., more efficient use of the cortex (or even whole brain) in people with high intelligence compared to people with low intelligence, was first obtained in the of R. J. Haier’s work using positron emission tomography (PET). In the study examining the relationship between intellectual task performance and neural activation levels, R. J. Haier found an inverse relationship between brain glucose metabolism levels and intelligence test scores. Participants with high IQs consumed less energy and worked faster than participants with lower IQs. This fact allowed the authors to suggest that higher intelligence was associated with neural circuits that worked faster and more efficiently. Neural efficiency manifests itself in more clearly localized brain activation areas during cognitive tasks in more capable individuals versus less capable individuals in each brain areas. This effect has been confirmed in many studies using different neurophysiological measurement methods and a wide range of different cognitive tasks. More successful subjects showed less brain activation when solving mental tasks than less successful subjects [6,9,10,12,36-38,47]. Several studies demonstrated the non-linear nature of the correlations between the physiological reactions of the body and the results observed during mental activity. Of great importance is the problem of the ratio of physiological “costs” and the success of the thought process, which can be considered in terms of operational efficiency [5].

1.1. Intelligence and the phenomenon of neuroefficiency

J. Haier ‘s followers Niebauer and Fink in a comprehensive review of intelligence and the phenomenon of neuroefficiency reported 29 studies in support of this hypothesis, while 18 studies provided partial support, and 9 studies provided negative results. These scientists suggested that these controversial results could be explained by the fact that some studies included tasks that may not have been demanding enough, while others were too complex and required more cortical resources, leading to a positive correlation between brain function and cognitive ability [12,34,41].

Similar controversial data were revealed in a systematic review of neural efficiency in the athletic brain [31]. Longxi Li and Daniel M. Smith assumed neural that efficiency in athletes is an integration of neuroanatomical structure changes (neural plasticity) and neural proficiency of higher cognitive processing and neural network through long-term training in specific sports. They examined a wide range of sport-specific videos and Multiple Object Tracking (MOT) specific to 18 different sports and utilized Blood Oxygenation-Level Dependent (BOLD) functional Magnetic Resonance Imaging (fMRI), functional Near-InfraRed Spectroscopy (fNIRS), and ElectroEncephaloGram (EEG). They extracted both supporting and conflicting evidence of the Neural Efficiency Hypothesis (NEH). The researchers summarized studies that typically report a negative association between brain activation and optimal task performance. These studies indicated that experts use their brains more efficiently with less energy consumed (a fewer resources are used) than novices or non-athletes for performance of a task. These scientists revealed studies that reported only partial support for the NEH; that is, only for certain categories, under specific conditions/tasks, or for specific brain regions. Moreover, they found studies which presented the opposite point of view: studies on complex visuospatial, visual search, motion observation, and cognitive tasks have shown that athletes’ parietal, central, and other areas have higher cortical activation, which is inconsistent with the NEH.

1.2. Efficiency Paradox Hypothesis

T. Mann, A. Wright, and C. M. Janelle claim that the neural efficiency hypothesis (NEH) is both scientifically and intuitively simplistic, and underlying mechanisms that correlate with this hypothesis remain speculative. They put forward the efficiency paradox hypothesis: “longer is better” that runs contrary to the NEH [32]. As paradoxical it may seem, extensive evidence shows that even for fast ballistic tasks such as table tennis, soccer, badminton, and archery, the duration of QE (Quiet Eye) tracking is longer for successful shots than for unsuccessful ones, in which the cortex activation in an expert is greater than in a novice [2,3,4,8,13,16].

Longxi Li and Daniel M. Smith explain the paradox of neuroefficiency by the fact that with limited cognitive capabilities of the human brain, a person finds ways to navigate complex spatial information earlier and to maintain their focus under the most challenging situations. More often, at the highest level of sport competition, athletes are faced with a large number of factors that can be difficult to control. These researchers emphasized that the NEH is a dynamic and situational concept that depends on several factors including movement characteristics (e.g., side of the movement), hemisphere, and athletes’ personality traits. They believe that the various perspectives associated with NEH need to be considered in a concrete situation [31].

1.3. Neuronal mechanisms of inductive reasoning

Qiu, Y. Pi, K. Liu, H. Zhu, X. Li, J. Zhang, et al showed that higher neural efficiency is a bidirectional phenomenon encompassing both decreased activation of task-related areas and decreased deactivation of areas associated with irrelevant information processing [42]. It is also noted that the correlations between intelligence and brain activation differed depending on the intelligence component. For instance, when studying Event-Related Desynchronization (ERD) alpha activity of the EEG, it was found that during the performance of a reasoning task, neuroefficiency was more pronounced in fluid intelligence compared to crystallized (according to Cattell) [34].

The various models are used to explain the neuronal mechanisms of inductive reasoning, most of which follow the principle of complete similarity [22,39,44,51,52]. These researchers believe that categorization, induction, and recognition are closely related conceptual abilities that enable people to draw conclusions from observation and previous experience. As Evan Heit and Brett K. Hayes pointed out, similarities are potentially the common “currency” that links all three cognitive activities [22]. According to the SINC model by V. Slutsky and A. Fisher (A Model of Similarity-Based Induction in Young Children), “categorization and induction in young children are processes based on similarity” that are calculated on visual and linguistic signals” [52]. The data obtained by these researchers indicated that categorization and induction in young children (4–5 years old) depended on visual coincidences between stimuli, as well as auditory coincidences from the point of view of speech categories. The data of a large-scale experiment, in which 3600 respondents of three age groups (5-8 years old, 11-13 years old, 14-16 years old) took part, aimed at the formation of the ability of the operation of inductive reasoning, showed that the formation of the ability for elementary logical operations led to an improvement in academic achievement and fluid intelligence growth [25]. The Similarity-Based Model is based on perceptual similarity [52]. The Concept-Based Model [18] is based on existing knowledge or concepts. A prerequisite for the performance of the reasoning process in the Similarity Model is the similarity between the objects of thought or the general membership in the category, i.e., there is a definite relationship between objects in a semantic context, is based on taxonomic similarities: an overlap of the features or meanings of words that includes elements of the same generic category (e.g., a mammal with members such as panda, antelope, dog, cat, cow etc.) [48]. Other studies have also shown that taxonomic relationships in the process of reasoning are formed more easily than thematic relationships [49].

The Conceptual Model of inductive reasoning assumes that inductive reasoning is based on a common conceptual property, while perceptual similarities between objects are ignored [17]. One of the conceptual properties is a thematic relationship which is based on complementary related elements in scenarios or events that have a common conceptual feature. It was shown that the processing of thematic relationship requires fewer cognitive resources than that of a taxonomic relationship in reasoning tasks [32,48]. Apparently, that is why, inductive logical reasoning based on conceptual information is often used in everyday life [29].

The existing studies of inductive reasoning have primarily adopted imaging methods to explore the neural difference between taxonomic- and thematic based-inductive reasoning [23,24,26,58]. Fangfang Liu, Jiahui Han, Lingcong Zhang and Fuhong Li tested whether or not distance effects on the processing of taxonomic- and thematic-based semantic relations in inductive reasoning were differently reflected in brain [30]. Event-related potential (ERP) results showed that the effect of context (taxonomic versus thematic) was initially observed in the P2 component; while the distance effect (far versus near) was observed in N400 and later components. It should be noted that distance effects for inductive reasoning based on thematic relationships were found in the frontal and frontal-central regions, while the distance effect on inductive reasoning based on taxonomic relationships was observed in the parietal and central parietal areas.

However, the neural efficiency of inductive reasoning associated with the difference in intelligence and temperament traits remains unaddressed. Thus, the purpose of the present study was to investigate the relations among inductive reasoning, EEG power spectrum, intelligence, and temperament traits. In order to achieve our goal, we need to solve the following tasks:

  • to compare indicators of temperament, intelligence and EEG power spectrum when solving elementary logical problems in samples of female and male;
  • to compare indicators of temperament, intelligence, and EEG power spectrum when solving elementary logical problems in 13-14-year-old, 15-17-year-old and 18-27-year-old respondents;
  • to compare indicators of temperament, intelligence, and EEG power spectrum when solving elementary logical problems in groups of slow and accurate, slow and inaccurate, fast and accurate, fast and inaccurate respondents.

2.1. Participants

The present research was administered to 251 subjects, aged from 13 to 27 (M/F = 118/133, mean age 15.64±2.65 years) including early adolescents (13.73±0.45 years), middle adolescents (15.46±0.66 years), and late adolescents (21.11±2.87 years). This study involved respondents from Moscow and Ufa within Russian Federation. All participants were right-handed and without any obvious signs of psychiatric or neurological disorders. They had normal vision. The participants gave informed consent before starting data acquisition experiment. Their participation in the studies were free.

It is well known that at the age of 13-14 years, the formation of secondary sexual characteristics begins, an unstable hormonal profile is observed, and the structures of the brain (cerebral cortex) are in the process of formation. At the age of 15-17 years, the hormonal profile is also unstable, but the cerebral cortex (except for the frontal lobes) and other regulatory structures of the central nervous system (CNS) are formed. At the age of 18-27 years, secondary sexual characteristics have formed, the hormonal profile is stable, the formation of all structures of the central nervous system, including the frontal cortex, has completed [15].

2.2. Measures

Prior to the EEG sessions participants were screened with respect to their intellectual abilities by means of a well-known Standard Progressive Matrixes (SPM) [43] and the “Intelligence Structure Test 2000-R” (I-S-T 2000-R) [1] in Russian adaptation by L.A. Yasyukova [59].

The Standard Progressive Matrixes (SPM) is a well-validated measure of fluid reasoning ability (gF). The Raven’s Standard Progressive Matrixes contain 60 nonverbal items. Each item consists of 3 × 3 matrix with a missing piece to be completed by selecting an answer from six or eight alternatives. Time completing the tasks is 20 minutes.

Intelligence Structure Test (IST) is based on Thurstone’s and Cattell’s intelligence theories and measures verbal, numerical, and figural-spatial abilities. The composite score indicates general reasoning ability which is closely tied to general intelligence. Each verbal, numerical, and figural-spatial tasks consist of 20 items.

  • Verbal Intelligence scale includes such tasks as Sentence Completion (SC), Verbal Analogies (VA), Verbal Similarities (VS), Odd One Out (OOD). These scales measure the reasoning ability within a verbal context.
  • Numerical Intelligence scale consists of Calculations (CA) and Number Series (NS) tasks. These scales measure calculation skills and numerical reasoning (the ability to make logical connections between numbers).
  • Figural-Spatial Intelligence scale covers such tasks as Figure Selection (FS), Cubes (CU), and Verbal Memory (VM). These scales assess the ability to process figural-spatial material (two- and three-dimensional figures) as well as the ability to assess proportions of surfaces and volumes, figural reasoning, the ability to reveal logical relationships among figures.

The Elementary Logical Operations (ELO) test is implemented in both computer and paper-and-pencil versions [46]. The ELO has 24 statements assigned to evaluate the ability to perform elementary logical operations. Respondents are offered to compare the ratio between the values of A, B and C as fast and as accurately as possible and evaluate the truth or falseness of the logical conclusion.

For example, if A is equal to B and B is equal to C then “C is equal to A”. This conclusion is true. But the conclusion “C is not equal to A” under the given conditions is false. There are four possible answers: (1) C is equal to A; (2) C is not equal to A; (3) C is greater than A; (4) C is less than A. Only those problems were offered that had only one correct answer.

The time of the ELO test in paper-and-pencil version was limited to four minutes. Decision time of the ELO test in the computer version was unlimited and the tasks were provided randomly. Decision time and total score are automatically recorded by a special Software InTesting.

Raw scores were converted to S-scores through percentile standardization procedure. The ability to perform Elementary Logical Operations was considered to be high if S-score ≥ 7 and low if S-score ≤ 3.

The Structure Temperament Questionnaire (STQ-S) [45] was used for evaluation of Motor (MA), Intellectual (IA), and Communicative (CA) Activity. Shortened version of the STQ-S contains 26 items. STQ-S has a high correlation with full version of the STQ questionnaire. The IA-scale is thought to be temperamental scale of intelligence measured by Wechsler test [45].

2.3. EEG procedure

The EEG session started with the mounting of the electrodes and checking the electrode impedances. Participants were in a comfortable EEG recording room. We record two 2-min EEG sequences under resting conditions to determine the neurological profile of the respondents: (a) eyes closed, (b) eyes opened. Then experimental session started the total time of which was about 45 min. The EEG -recorder was synchronized with the InTesting computer diagnostic complex to analyze the change in the EEG power spectrum when solving Elementary Logical Operations. The problems randomly appeared on the screen. The respondent was to evaluate the truth or falsity of the conclusions as quickly and as accurately as possible and press the appropriate key: true or false.

2.4. Apparatus/EEG recording

We used portable electroencephalograph Encephalan-EEGR-19/26 Medicom MTD (European certificate CE 538571 of the British Standards Institute, BSI). The EEG was measured by means of silver electrodes (9 mm diameter) located in an electrode cap in 30 positions (according to the international 10–20 system with interspaced positions). A ground electrode was located on the forehead. Reference electrodes were placed on the left and right earlobes. Electrodes O2-A2, O1-A1, Oz-A2 correspond to occipital lobe; P4-A2, P3-A1, C4-A2, C3-A1, Pz-A1, Cz-A2, CP3-A1, Cpz-A1, CP4-A2 – parietal lobe; F4-A2, F3-A1, Fp2-A2, Fp1-A1, F8-A2, F7-A1, Fz-A1, Fpz-A2, Fcz-A1, FT8-A2 – frontal lobe; T6-A2, T5-A1, T4-A2, T3-A1 – temporal lobe; FC3-A1, FC4-A2 – fronto-parietal lobe; TP7-A1, TP8-A2 – temporo-parietal lobe. The EEG signals were filtered between 0.5 Hz and 50 Hz; an additional 50 Hz notch filter was applied to avoid power line contamination. Electrode impedances were kept below 5 kΩ for the EEG. The sweep rate was 30 mm/s.  The EEG recording was scanned for artifacts. The epochs for analysis were selected after the artifacts were removed manually. The duration of one epoch was 10 seconds. We used for the analysis five epochs of the performing ELO test: two epochs at the beginning, one in the middle, and two at the end. The spectral amplitude is an average value over the time interval under consideration. The mathematical basis of spectral analysis is the Fourier transform of the initial EEG data, which was carried out automatically by the Medicom-MTD program. We used the EEG power spectrum that denotes the squared value of the amplitude of the EEG signal. This parameter provides an increase in the stability of the data obtained due to increasing in the strongest differences and the leveling of weak differences [27].

2.5. Statistical Procedures

Statistical treatment of empirical data included descriptive statistics of raw data (Means, SD, Skewness, and Kurtosis). The test scores corresponded to the normal distribution (Skewness and Kurtosis =±1). Reliability statistics (Cronbach’s alphas) for the both ELO (Paper-and-pencil version) and ELO (Computer version) was conducted. The statistical analyses involved mixed-design ANOVAs and t-tests. Post-hoc comparisons with Bonferroni corrections were made where it was necessary. The indicators of the ELO test were converted into S-scales. Hierarchical Cluster Analysis (HCA, Ward Method) was used to identify relatively homogeneous groups of respondents based on S-scales of the ELO test.

 

 

3.1. Behavioral data

Descriptive statistics are presented in Table 2. Test scores corresponded to the normal distribution. Means and standard deviations (SD) are reported for the male and female sub-samples and for the total sample. The reliability measured by the Cronbach Alpha coefficients were within the acceptable range (0.84-0.91). These results testify that the ELO (Computer version) and the ELO (Paper-and-pencil version) scales had sufficient internal consistency. There were no significant differences in the ability to perform elementary logical operations in male and female samples. However, the female sample was characterized by significantly higher rates of intellectual tests such as Odd One Out Task (OOD), Verbal Similarities (VS), Figure Selection (FS), and Verbal Memory (VM).

 

The data presented in table 3 indicated an increase in the ability to perform elementary logical operations from Early Adolescence to Late Adolescence. Of particular interest is the fact that the speed of making a decision about the truth or falsity of conclusions is significantly higher in late adolescence compared to middle adolescence.

As to the temperamental traits, the values of Motor Ergonicity, Intellectual Tempo and Motor Activity in Middle Adolescence were significantly higher than in Late Adolescence. To clarify the courses of the finding obtained, it was advisable to compare the Behavioral data between homogeneous groups that differ in the speed of decision making and accuracy.

 

The raw scores of decision-making speed and accuracy were converted to S-scales. Then four relatively homogeneous groups of respondents were identified based on Hierarchical Cluster Analysis (Ward Method) which were conditionally named Slow & Inaccurate, Slow & Accurate, Fast & Inaccurate, Fast & Accurate. Thus, we got two groups with the same speed, but different decision-making accuracy, and two groups with the same accuracy, but different decision-making speed (Table 4)

 

According to the data presented in Table 4, the sample of respondents who are equally slow, but differ in the accuracy of decision-making, differ significantly only on the Number Series scale (NS).

In the sample of Fast & Accurate respondents compared with Fast & Inaccurate respondents, we revealed the following significant differences: the higher scores on the Calculations (CA), Cubes (CU), and Motor Ergonicity scales, but the lower scores on the Motor Activity scale.

We found only one significant difference on the Cubes scale (CU) between the samples of equally accurate respondents, but differing in the speed of decision-making. Also, one significant difference was revealed on the Motor Emotionality scale between the samples of respondents, which are equally inaccurate, but differ in the speed of decision-making. It should be noted that Fast & Accurate respondents had higher IQs, while Fast & Inaccurate respondents had lower IQs.

3.2. EEG data

We compared Means of the EEG power spectrum (mV2) during performance of the Elementary Logical Operations between males and females (see Table 5).

 

According to the data presented in Table 5, the power spectrum of the EEG signal under 18 electrodes when solving elementary logical problems were significantly higher in female than in male. At the same time as is seen in Table 2, there were no significant differences in the speed and accuracy of solving elementary logical problems in samples of female and male. The findings testified that female respondents had to expend more “electrophysiological energy” to achieve the same results compared to male respondents. It should also be pointed out that both in males and females, the highest values of the EEG power spectrum were observed under the F3_A1 electrode, and the lowest under the T6-A2 and T5-A1 electrodes.

 

 

We found more significant differences in EEG power spectrum by age than by sex (24 vs 18). Two types of age-related changes in the parameters of the EEG power spectrum were revealed: descending and U-shaped. Power spectrum values continually decreased from Early Adolescence to Late Adolescence under the following Electrodes: O2, O1, Oz, (occipital lobe); P4, P3, C3, Pz, CP3, Cpz (parietal lobe); T6 (temporal lobe); and F7, Fcz, FC4, FT8 (frontal and frontotemporal lobe).

The U-shaped changes in the EEG power spectrum were found in two subtypes:

(a) The power spectrum in Early Adolescence was higher than in Late Adolescence under electrodes C4, F4, Fp1, CP4.

(b) The power spectrum in Late Adolescence was higher than in Early Adolescence under electrodes F3, F8, Cz, Fz, Fpz, TP8.

Comparison of the data presented in Tables 3 and Tables 6 indicated that the decrease in Motor Activity indices corresponded to a decrease in the values of EEG power spectrum in the motor cortex of the right hemisphere (leads FC4, FT8).

Let us describe at first peculiarities of accurate respondents with different speed of solving elementary logical tasks. As it shown in Table 3 there are no significant differences in the accuracy of solving elementary logical problems in the samples of Accurate & Fast and Accurate & Slow respondents. At the same time, the values of the EEG power spectrum under the electrodes F3, Fp2, Fp1, Cz, Fz, TP8 (frontal, parietal and parietotemporal lobes of the cerebral cortex) in Accurate & Slow respondents were significantly lower than in Accurate & Fast ones. Thus, data supported the Efficiency Paradox Hypothesis. Whereas the values of the EEG power spectrum under the electrodes O2 and Oz (occipital lobe) were significantly higher. These results are consistent with the Neural Efficiency Hypothesis.

Then let us consider fast responders with different accuracy of solving elementary logical tasks. The values of the EEG power spectrum on the electrodes F3, Fp2, Fp1, Cz, Fz, TP8 (frontal, parietal and parietotemporal lobes of the cerebral cortex) in Inaccurate & Fast respondents were significantly lower than in Accurate & Fast ones. These results correspond to the Efficiency Paradox Hypothesis. Whereas the values of the EEG power spectrum under the electrodes O2 and Oz (occipital lobe) were significantly higher.

Slow responders with different accuracy of solving elementary logical tasks showed the follow results. The values of the EEG power spectrum were significantly lower under all mentioned in Table 7 electrodes in Accurate & Slow respondents as compared as Inaccurate & Slow subjects. Thus, the results obtained as a whole support the Neural Efficiency Hypothesis.

 

 

 

A brief review of the studies that consider the issues of cognitive performance allows us to identify two sets of data, some of the studies confirm the Neural Efficiency Hypothesis (NEH), others testify in favor of the Efficiency Paradox Hypothesis. According to Callan and Naito [7], four neural mechanisms such as neural efficiency, cortical expansion, specialized processes, and internal models provide the superiority of experts over novices. The decrease in brain activity and pronounced localization in the more capable compared to the less capable is explained by the continuous adaptation / neuroplasticity of the cerebral cortex, which in each specific area of human competence correlates with skillful control of cognitive and motor activity [4,36]. The increase in the activity of brain regions when performing cognitive tasks in the more capable compared to the less capable, identified in a large number of studies [31], is explained by an increase in the activation of the Default Mode Network (DMN) which leads to an increase in the activity of all neural networks, to the reorganization of old cortical circuits and to the creation of new ones in the course of cognitive development [53,57]. A possible reason for the controversial results could be the variability in task complexity, since neural efficiency was mostly observed for low-to-moderately difficult tasks [38]. Longxi Li and Daniel M. Smith [31] pointed out the heterogeneity of outcomes and emphasized that NEH is a dynamic and situational concept that depends on several factors including task complexity, movement patterns, hemisphere, personality traits, and others.  Therefore, in this work, we studied the performance of solving elementary logical problems (simple tasks) in female and male; in the Early, Middle and Late adolescence taking into account the peculiarities of intelligence and temperament traits.

4.1. Neural efficiency of inductive reasoning in female and male

C. Neubauer, R. H. Grabner, A. Fink & C. Neuper, studying the influence of task content and sex on the brain–IQ relationship, found that in the female sample when performing figurative-spatial tasks, and in male when performing verbal tasks, brain activity was lower regardless of the level of intelligence [36]. Due to the earlier onset of the formation of brain structures in female, their IQ-level are usually higher than in male [14]. In our study, female also demonstrated higher IQs compared to male. Significant differences in the performance of elementary logical operations between female and male were not found. However, the values of EEG Power Spectrum in female subjects were significantly higher than in male subjects. It means that in order to achieve the same result of inductive reasoning the female subjects spend more resources compared to the male subjects. The highest values of the EEG power spectrum both in female and male were observed on lead F3 (left frontal lobe, field 46 according to Brodman). The field 46 correlates with the motor function of the muscles of the eyeball and the combined movements of the head and eyes, as well as with the comparison of visual information and movements necessary to grasp the field of view. That is, the respondents expend the most energy (in terms of the EEG power spectrum) during the performance of the task. The lowest values of the EEG power spectrum in female and male subjects are also observed under the same electrodes (T5, T6). These electrodes are located above the projection of 20-24 Brodmann fields which are responsible for comparing new data with the information previously received, as well as for consolidating and storing memory. Apparently, the respondents use memory storage structures to a less extend when solving this type of logical problems. The EEG-data show, regardless of sex, the greatest activity is observed under the frontal electrodes, i.e. each task is analyzed using structures responsible for logical operations (analysis, synthesis, generalization, abstraction, comparison and judgment).

4.2. Neural efficiency of inductive thinking in Early, Middle, and Late Adolescence

The data of this study indicate an increase in the accuracy of solving elementary logical problems from Early to Late adolescence. This pattern is confirmed in many studies of cognitive performance. Of particular interest are two types of age-related changes in the EEG power spectrum associated with an increase in the efficiency of solving elementary logical problems: descending and U-shaped changes. The age-related decline in the EEG power spectrum is usually explained by an increase in the stability of hormonal profile and by the maturation of the frontal cortex which is responsible for information processing.  The U-shaped age-related change in EEG power spectrum in our opinion is associated with a significant increase in the speed of solving elementary logical problems from Middle to Late adolescence. Apparently, high speed requires additional energy expenditures of the cerebral cortex in terms of Power Spectrum EEG. To confirm this assumption, we compared the EEG power spectrum in groups of respondents who differed in the speed and accuracy of solving elementary logical problems.

 

4.3. Neural efficiency of inductive reasoning in Samples of Equally Accurate and Equally Fast Respondents.

The NEH holds that the person with high IQs consumes less energy and operates faster than person with lower IQs. In our study, the most “neuroefficient subjects” were those respondents who were noted for higher accuracy, but lower speed of solving elementary logical problems (in terms of the EEG power spectrum). Thus, we received additional evidence that U-shaped age-related change in EEG power spectrum may be associated with an increase in the speed of solving elementary logical problems. It should be emphasized that traditional intelligence tests usually have time limits, i.e., faster responders score more points. Therefore, the inconsistency of our data may be due to both (1) variations in cognitive complexity and (2) time limitations. Obviously, the fast persons expend more energy than the slow ones, and the accurate people expend more energy than the inaccurate ones, and women use more energy than men. These findings confirm every day observations. Apparently, the neuroefficiency hypothesis as well the Efficiency Paradox Hypothesis need to be redefined and the limit of their applicability clarified.

Funding. We thank students from Moscow and Ufa schools and universities who voluntarily participated in this research.

Author contributions.

Competing interests. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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