Neuroefficiency and the Ability to Classify Stimulus-Objects of Chemistry

The issue of neural processes occurring during the performance of classification tasks is still open. This article is devoted to the study of neuroefficiency and the ability to classify. The aim of the study is to identify changes in brain rhythm patterns when performing classifications of stimuli-objects of chemistry at various levels of complexity. The empirical study involved 251 people aged 13 to 27. The set of methods included the Amthauer intelligence test (I-S-T 2000-R) and the Great Chemist computer test, synchronized with the EEG. It is shown that as classification problems become more complex, the accuracy decreases and the values of the EEG power spectrum parameters increase. Men are more successful in simple classifications of chemical stimulus-objects, while women are more successful in complex ones. More successful respondents in performing classification tasks are distinguished by a significantly higher speed and accuracy of solving classification problems and lower values of the EEG power spectrum compared to less successful ones.

Introduction

1.1.           The problem of notion of the Ability to Classify Objects

Despite the widespread use of the concept of the Ability to Classify Objects, there is still no generally accepted definition of this term in psychology.           E. Rosch pointed out that the concepts “classification” and “categorization” are used to refer to the same phenomenon (Rosch et al., 1976, p. 384). The lack of differentiation that often occurs between these words is reinforced using “concept” yet one more synonym of category (e.g.,

Gardner, 1987, p. 340).  E. Roche argues that the classification and categorization systems are the mechanisms that set order through the grouping of related phenomena, and yet fundamental differences between the two systems affect how that order is exercised, thereby affecting the informational contexts that are set in each system. E.C. Jakob identifies six fundamental differences between classification and categorization (Jacob, 2004).

Table 1. Comparative analysis of categorization and classification (Jacob, 2004)

John St. Mill wrote that the very process of “naming” things is the classification, the distribution or grouping of the well-known objects, that humanity had already assigned to the objects listed in each list a particular common name, by repeating that operation over and over, until it invented all the common names that language is made up of. According to Mill, classification is a logic process that helps us to discover the truth: “Classification is a means to bring into order the ideas we have about objects: it is the reason why the ideas accompany one another or follow one another in the order that gives us the greatest power over the knowledge we have previously gained and the most direct way to acquire new knowledge” (Mill, 1882, p.870). The author emphasized relativity of generic relations, i.e., any class, while being a gender with respect to the species, itself may be a species with respect to a broader gender. The author emphasized relativity of generic relations, i.e., any class, while being a gender with respect to the species, itself may be a species with respect to a broader gender. The basis for the classification must be such distinctive features, that serve as the causes of the other features and are capable of being recognized in this class, in other words, the grouping of objects in that order, that is the most beneficial for recollection and determination of the features of the objects.

The purpose of classification as a cognitive tool of nature is to mentally compare and order several important common features that facilitate inductive reasoning (Chelpanov, 1994; Mill, 2011). According to J.St. Mill, G.I. Subbotin, and G.I. Chelpanov, classification requires the use of induction as it identifies the general features that enable objects to be assigned to a general class (Chelpanov, 1994; Mill, 2011; Subbotin, 2001).

H.W.F. Hegel emphasized the great importance of the ability to classify in the conceptual thinking. He noted that the classification that does not include a concept “represents a game of the arbitrariness, that decides what part or aspect of the particular to retain and to carry out the classification accordingly” (Hegel, 1972, vol. 3, p. 266). Yet we cannot totally absolutize the classification. Relative nature of the classification is determined by the relativity of the human knowledge as well as because in nature there are no strict borders between the separate kinds of species.

On the other hand, Sue Batley defines classification as the innate ability to classify things to simplify the world and its meaning. The author identifies such levels of classification as: individual, general, and informal (Batley, 2014). The ability to classify, by J. Bruner, is the ability to organize elements into groups that are similar in only one aspect (Bruner, 1977).

V.D. Shadrikov defines classification as a form of intellectual operation that is responsible for the allocation of any subjects, phenomena, concepts into classes, groups, categories based on general attributes (Shadrikov, 2014).

The ability to classify requires the development of the associative abilities of the nervous system, and the further structurization of the individual memory. The conceptual classification not only depends on some subject features, but it also depends on the state of need, on the motive, within which context the purpose of action and possible ways of its achievement. Conceptual classifications, as a rule, are given their verbal labels in the memory of a human being. The features of any object make possible multiple classification of any objects, which can be transferred to concepts themselves as substrate of certain properties (Klix, 1983). J. Lakoff (1986), A.R. Luria (1932), J. Bruner (1977), and F. Klix (1983) pointed to cross-cultural features of classification, due to the sphere of human experience, which determines the direction of classification and the construction of culturally specific world models.

1. Wittgenstein emphasized that an extensive classification must be the basis of a “conceptual calculus”, where one signifier always corresponds to one and only one signifier (Wittgenstein, 2010). According to L.S. Vygotsky, J. Piaget, and B. Inelder’s works, we can distinguish age-specific stages of development of the ability to classify:
2. The stage of figurative aggregates.
3. The stage of non-figurative aggregates or non-figurative sets.

III. The stage of logical classification.

As N.I. Chuprikova notes that at the stage of figurative sets the inseparability of generic and species attributes in children’s classifications …, is the main reason for children’s failure to understand the attitude of inclusion of classes, since here it is required to compare in volume one class formed on the genus attribute, and the second class formed on the species attribute (Chuprikova, 2007). At the non-figurative stage, “general features of objects are already distinguished, but not yet separated in the cognition from species, whereat the elements of cognition are complexes of generic and species features” (Chuprikova, 2007, p. 183). Many primitive languages contain numerous terms to designate varieties based on such complexes.

There are studies that define the classification in terms of generative processes (Feldman, 1992; Feldman, 1997; Kemp, Bernstein, & Tenenbaum, 2005; Lake, Salakhutdinov, & Tenenbaum, 2015) and in the domain of semantic knowledge (Jern & Kemp, 2013; Kemp & Jern, 2009; Lake, Salakhutdinov, & Tenenbaum, 2013; Xu & Tenenbaum, 2007).

Studying the peculiarities of the development of classification operations in junior high school students with mental retardation, N.V. Fedorova identified a low level of formation of classification operations (40% of the subjects). They are characterized by the absence of a hierarchy of features of thought objects, and an insufficient supply of generalizing specific and generic concepts (Fedorova, 2016).

T.A. Ratanova (Ratanova, 2011) shows the results of a large series of studies on the relationship between the speed of classifying stimulus-objects and intelligence, academic performance, and special abilities. Thus, it is noted the more complex the classification and the more time it requires, the more significant are the differences in response speed between more and less capable respondents. Following E.I. Boyko (Boyko, 1976) and N.I. Chuprikova (Chuprikova, 1995), T.A. Ratanova assumes that the basis of individual differences of the ability to classify is the brain’s discriminative ability, i.e., the brain’s ability to discriminate, separate, differentiate complex ensembles of stimulation that are the result of interaction and synthesis of direct signal afferents and verbal signals of prior instruction.

Studies performed on different samples (preschoolers; younger, middle and older adolescents; students of various specialties; gifted students and students with mental retardation; older adolescents in art and music schools) showed:

• Respondents with higher intelligence, high academic achievement showed faster speed in classifying stimulus objects compared to lower intelligence and low academic achievement respondents.
• Statistically significant differences between more successful and less successful students are observed in the semantic classification, which indicates the key role of the discriminatory ability of the brain in building meaningful relationships for successful learning.
• The ability to differentiate identity-difference is important for the learning success of second graders with mental retardation, while perceptual differentiation is significant for physics students.

T.A. Ratanova, comparing the results of studies performed on different age groups, comes to the conclusion that the discriminatory ability of the brain of younger students in relation to sensory-perceptual signals and the discriminatory ability of the brain in relation to semantic signals are not yet separated from each other and form a single distinguishing ability of the brain; by the middle adolescence, these types of abilities begin to differentiate, and by the older adolescence, two relatively independent abilities are formed (Ratanova, 2011).

Roland W. Fleming & Filipp Schmidt showed that participants’ ability to classify objects is a result of their ability to discriminate stimuli using the statistical shape features that are associated with different generative processes (Fleming & Schmidt; 2019).

E.V. Volkova, analyzing the peculiarities of the thinking of chemists, suggested that when performing simple classifications of chemical compounds, the mechanisms of verbal intelligence are mainly involved, and when performing a complex classification, verbal and non-verbal intelligence are involved. She suggested the existence of the so-called “intellectual threshold: IQ 125-129”, above which intelligence and chemical intelligence can be considered as separate independent factors. Her research has shown that more capable chemists have more subtly differentiated structures of representing chemical knowledge (significant differences in the most complex chemical classifications) and more hierarchically ordered.  Her research suggests that when the cognitive structures of chemistry are formed, there is a decrease in the correlations between the time of chemical classifications and IQ (Volkova, 2011; Volkova, 2014).

In the 1950s, E.I. Boyko’s laboratory (Boiko E.I., Chuprikova N.I., Ushakovа T.N., Vlasova M.M., and others) developed the test-stimulus method that allows to study the mechanisms of the analytic-synthetic brain activity.  In the 1970s the Western psychologists adopted the similar research technique called “the mental chronometry” to study the structures of semantic information storage in the long-term memory and the dynamics of its work (M. Posner, T. Marcel, B. Forin, R. Warren, D. Morton, D. Stroop).

Nowadays there is a considerable evidence in the literature on the connection between the person’s reaction rate and the indices of intelligence tests: the higher the intelligence, the faster are many reactions that require differentiation, identifying, finding properties and differences of different kinds of stimulus-objects              (Chuprikova, 1995; Chuprikova, Ratanova, 1995; Cohn, Carlson, Jensen, 1985; Eysenck, 1995; Hunt, 1980; Keating, Bobit, 1978).

T.A. Ratanova (1989, 1996) showed that the majority of subjects with a higher level of intelligence have a shorter time of speed classifications than those with a lower level of intelligence. These differences are always statistically significant.

1.2.           Neuronal mechanisms of the Ability to Classify Objects

The relationship between perceived shape similarity and the organization of object-selective cortex is not easily captured by existing biologically plausible object recognition models. To classify and categorize objects, people rely heavily on the similarity of the shapes of objects.  Multiple studies with fMRI (functional magnetic resonance imaging) have demonstrated that the large area of the occipitotemporal cortex in humans processes the shape of both familiar and unfamiliar objects.

Op de Beeck, J. Wagemans and R. Vogels performed a single-unit experiment in the IT cortex of monkeys that demonstrated a strong relation between tuning of the individual IT neurons and the perceived shape similarity. Scientists suggested that shape-selective areas in primates are functionally organized in terms of shape properties that differentiate perceptually distinct shapes, and that the individual neurons in those areas differentiate objects with respect to more subtle metrical shape differences in addition to the selectivity for the perceptually more distinct shapes (Op de Beeck, et al., 2001).

The Belgian group of scientists (Op de Beeck, Torfs, Wagemans; 2008) revealed that the unfamiliar object classes assessed to be of similar shape were associated with a very similar response pattern distributed throughout the object-selective cortex, whilst the object classes that were assessed to be very different in shape were associated with a rather different response pattern. Object-selective cortex exhibited high sensitivity to differences in object shape characteristics, including straight, curved, as well as “sharp-edged” edges, more than to differences in overall envelope shape. The response patterns in the retinotopic areas (V1, V2, and V4) were unrelated to the perceived shape. The functional organization in V3 area was partly related to the perceived shape, yet with no stronger sensitivity to shape features. Consequently, the organization of the human object-selective cortex is closely related to perceived form for unfamiliar objects, and that form-based organization becomes progressively evident throughout the object-vision process.

A.V. Gladysh, A.S. Gorev, and D.A. Farber’s (1995) neurophysiological studies that compared the EEG parameters in two groups of nine-year-olds that differed in the differentiation rate of perceptual and semantic signals determined by the stimulus-object speed classification technique revealed significant differences in the power spectrograms of most sub-bands of both theta and alpha rhythms in the most of the leads for the benefit of the higher scores in the group of children that showed higher signal differentiation rate. Elementary schoolchildren with higher scores in the speed classification technique display an intercentral EEG organization that is more complex topographically and more differentiated by the alpha and theta rhythm leads and frequency subranges.

Other than a few early cases (Martindale & Hines, 1975; Martindale & Hasenfus, 1978), the use of electroencephalography (EEG) has only recently become regular in the study of classifications. The increased interest and use of EEG to assess this type of activity related to the ability to generate new and useful ideas is largely due to the superior temporal resolution of EEG, as it enables the study of event-related potentials (ERPs), as well as temporal network coherence and phase-synchronized activity (Sternberg & Lubart,1996; Stevens & Zabelina, 2019).

The EEG-based research serves well to reveal the complex dynamics of classification tasks and provide valuable information in this area (Stevens & Zabelina, 2019). Furthermore, the EEG-based result research assumes most consistently that better performance on divergent thinking tasks is associated with increased alpha-band (8-13 Hz) activity (Benedek, 2018; Fink & Benedek, 2014). For instance, the higher task-related power in alpha band was found to be higher in frontal and parietal regions during classification tasks, where faster and more accurate responses were identified (Jauk, et al., 2012). The positive relationship between alpha power and creativity has also been found in other tasks that feature novel and normal response conditions (Zhou, et al., 2018). Test-level individual differences have also been reported, as more accurate respondents demonstrated higher alpha band power than less accurate people (Fink & Neubauer,2008). Specifically, individuals who performed better at classifications demonstrated more lasting increases in frontal and parietal alpha channel power (Camarda, et al., 2018). Evidence suggests that the relationship between alpha band power and accuracy on classification tasks is thought to be attributable, at least in part, to decreased attention to external stimuli in favor of more internal attention (Benedek, 2018).

Participants can demonstrate greater accuracy and alpha power because they successfully block out information that is irrelevant to the assigned task. In fact, as attention is directed outward (rather than inward), alpha decreases (Benedek, et al., 2011). Alpha, however, is not the only frequency that is related to successful work with classifications. There have also been reported variations in delta and theta band activities (Boot, et al., 2017). Theta appears to have a strong correlation with divergent thinking tasks and classification tasks, as indicated by increased theta-frontal-occipital functional connectivity in more (compared to less) precise respondents (Wokke, et al., 2018). Considering the role of attention (internal and external) in tasks, this finding indicates a theta-dependent suppression of visual functions by the frontal executive areas. To put it another way, theta activity may indicate downward processing of creative cognitive and classification tasks This point of view is corroborated by the involvement of the EEG slow-wave spectrum in long-range communication between brain zones (Clayton, et al., 2015).

Systems research and cognitive neuroscience depend significantly on the study of neural systems as they perform different tasks. The task usually refers to the set of calculations that the system must perform to achieve a goal optimization, such as reward or classification accuracy. The random point motion problem is a classical cognitive task that requires the agent/subject to determine the direction of motion of a coherently moving group of dots among a group of randomly moving dots (Britten, et. all, 1992). Neurobiology and cognitive science usually have each task designed to shed light on the neural mechanism of a particular function. The neurophysiological data suggest that the process of object recognition can be divided into different levels based on a hierarchy, with a series of stages, which create more complex representations at the higher levels as the hierarchy progresses. These are the low-, mid-, and high-level stages of the information processing (Groen, et al., 2017; Groen, et al., 2017). High-level processing proceeds information from the extrastriar cortex toward the inferior temporal cortex, through which visual patterns are identified and then the set of features associated with an ability to classify objects is categorized. It means, that the set of labels derived from the visual features that are related to the object is in a class (either new or existing); thus, there is a relation between the placement (retinotopic information) and the emergence of these features (i.e., the boundaries and shape relations). Hence, these classes that represent the known world can later be used as the information for the object recognition task, where the activation of cortical areas associated with declarative memory contributes to the familiarity, retrieval, or storage of the marked and thus perceived object.

The purpose of the study is to clarify the change in the patterns of brain rhythms while performing classifications of various levels of complexity.

Method

2.1. The participants

Table 2. Profile of the sample respondents

2.3. EEG procedure

The experimental study was carried out in a specially equipped laboratory, where the respondents were conveniently located. The EEG session began with the installation and verification of the electrodes’ resistance. The international 10-20 electrodes fixation system was used. The electrodes O2-A2, O1-A1, and Oz-A2 matched the occipital zone; P4-A2, P3-A1, C4-A2, C3-A1, Pz-A1, Cz-A2, CP3-A1, Cpz-A1, CP4-A2 – the parietal zone; F4-A2, F3-A1, Fp2-A2, Fp1-A1, F8-A2, F7-A1, Fz-A1, Fpz-A2, Fcz-A1, FT8-A2 – frontal; T6-A2, T5-A1, T4-A2, T3-A1 – temporal; FC3-A1, FC4-A2 – frontoparietal; TP7-A1, TP8-A2 – temporoparietal zone. The ground electrode was placed on the forehead (near FPz) and the reference on the right and/ or left earlobe.

The two-minute EEG recording of the eyes-closed to the eyes-open state in resting condition did not reveal any epileptiform or epileptic graphic elements. The overall duration of the experimental session was 45 minutes. The EEG recorder has been synchronized with the InTesting computer program (Volkova & Nilopets, 2015; Volkova, 2020), which initiated the sequential stimulus presentation of chemical formulae at different levels of complexity: global, basic, and detailed.

Forty-two chemical compound formulae were randomly generated from the whole stimulus database on the computer screen at each difficulty level of chemical stimulus-object classification. The task of the participants was to distribute the chemical formulas into groups as quickly and correctly as possible in accordance with the instructions:

1. Simple classification, i.e. formulas of substances in a random order appear on the screen. The respondent should divide them into “simple” or “complex” compounds. Typically, this is done by recognizing the symbols of the chemical elements in the compound formula and matching them to each other. That is, the identity or difference of the selected features of chemical elements is established (A is identical to A or A is identical to A and a is identical to a) or difference (A1 is not identical to A2 or A1 is identical to A1, but a1 is not identical to a2). In the case of establishing the identity of the sign of the chemical element, the compound is considered as simple, in the case of a difference – complex.
2. The complex classification refers to the identification of the classes of inorganic compounds: Oxides, Acids, Bases, and Salts, through qualitative and quantitative analysis of the substance’s composition.

For example, oxides consist of two elements, one of which is oxygen (CO₂, Al₂O₃, P₂O₅). As for acids, bases and salts, the best solution is to “splitting” the compound into functional groups: hydrogen cation, metal cation, hydroxide anion and anions of acid residues, which is carried out through a kind of “chemical” logic of qualitative-quantitative relations and through the internal structure of atoms, groups atoms in the universal hierarchy of substances. The hydrogen anion is located at the beginning of the compound formula – most likely it is an acid (HCl, H₂SO₄, H₃PO₄). The hydroxide anion is located at the end of the chemical compound formula – most likely it is a base (NaOH, Ba(OH)₂, Al(OH)₃).

1. The most complex classification requires more profound classification to predict more effectively the possibility and direction of chemical processes. In addition to the class “oxides”, it is also necessary to specify what class – basic, acidic, or amphoteric; for hydroxides, it is necessary to distinguish – alkaline, basic, or amphoteric hydroxides; as well as to classify salts and acids into groups. The classification of chemical compounds is carried out within the original unit, by searching for similarities, differences, and regularities.

The InTeresting software provided fixing the time and accuracy of solving problems for the classification of chemical stimulus-objects at all levels of complexity. The level of classification considered as formed only if the correct number of answers was ≥95%.

2.4. Apparatus/EEG recording

The Encephalan-EEGR-19/26 Medicom MTD EEG recorder (CE 538571 European certificate of the British Standards Institute, BSI) was used to register the bioelectric brain activity and the system was synchronized with a desktop computer (Windows 10 OS). The LCD monitor with a screen refresh rate of 100 Hz was used on which stimulus material was presented via the computer program InTesting (Volkova, Nilopets, 2015). The EEG signal was filtered between 0.5 Hz and 50 Hz. Impedance of the EEG electrodes was kept below 5 kOhm during the recording. During the further recording processing the artifacts were manually removed, and then the epochs were selected for analysis.

Each epoch lasted 10 seconds, the number of epochs was 5 (two epochs in the beginning, one in the middle, and two at the end of each level separately).

To quantify changes in the bioelectrical activity of the brain, the EEG power spectrum was decomposed into basic rhythms using the Fourier transform: delta (frequency 0.5–4 Hz, amplitude 40–300 μV), theta (frequency 4–7 Hz, amplitude 10–100 µV), alpha (frequency 8-13 Hz, amplitude up to 100 µV), beta1 (frequency 13-24 Hz, amplitude up to 30 µV) and beta2 (24-35 Hz, amplitude up to 35 µV).

2.5. Statistical Procedures

The statistical data were analyzed with IBM SPSS Statistics 26. Statistical treatment included Descriptive Statistics (Mean,       Standard Deviation, Skewness, Kurtosis) and Compare Mean Statistics (One-Way ANOVA, Kruskal-Wallis Test, Factor – Sample, Sex, Complexity Level of Classification; dependent variables – Classification Time (sec), Classification Accuracy (score), Power Spectrum EEG (Hz)).

Results

3.1. Behavioral data

Table 2 presents information on the success of the classification of chemical stimulus objects of different levels of complexity (global, basic and detailed), that is, on the degree of formation of the ability to classify in different samples of the study. As noted earlier, the classification level is considered formed if the accuracy of the answers exceeds 95%. According to the data obtained, 70% of respondents are not able to classify simple and complex chemical compounds with sufficient accuracy (simple or global level of classification complexity). The global level is formed in 21% of the sample. The global and basic levels are formed in 6% of the sample, that is, only 6% of the respondents are able to successfully classify both simple and complex compounds, as well as classes of inorganic compounds (oxides, acids, bases, and salts). The ability to classify (all three-complexity level of classification) is fully formed in 3% of the sample. According to the inter-sex differences in the ability to classify, global level is formed in men better than in women. However, basic and detailed levels in women are formed better.

Table 3. The success of the classification of chemical stimulus-objects of different levels of complexity in men and women

The results of a comparative analysis of the success of classifying chemical compounds according to their formulas and the Amthauer intelligence test are presented in Table 3. It should be noted that the table shows only those scales of the Amthauer intelligence test that reached the statistical significance of the differences. As the classification tasks become more complex, the accuracy of answers decreases (ANOVA, p <0.05). The higher is the level of ability to classify, the faster is the respondents cope with the tasks (ANOVA, p<0.05). Significant differences in calculus (CA) and mathematical intelligence according to the Amthauer intelligence test were revealed (ANOVA, p<0.05). As classification ability increases, the scores on these scales increase.

Table 4. Classification of chemical compounds by their formulas     

¹No level formed

²Global level formed

³Global and basic levels formed

⁴Global, base and detailed levels are formed

The results of the Multiple Comparisons (test Scheffe) testify that first sample (the ability to classify chemical stimulus objects is not formed) are significantly differ from the results of all other groups: the accuracy of classification and scores on the intelligence test are lower, and the time spent is higher. Likewise, the results of the second sample are statistically different from the results of the third sample. There are significant differences between the results of the first group and all others, the second group and the third, and the second group   and     the fourth at the detailed level of complexity classification tasks. Based on the time spent on the tasks, the statistically significant data were only obtained in the global level of complexity.  There are significant differences on Calculations (CA) between the first sample with not formed abilities to classify and respondents in the third sample.  No data are available on the Amthauer intelligence test in the respondents with detailed level formed.

3.2. EEG data 

The analysis of the delta power spectrum revealed statistically significant variations of the given dimension in 26 EEG leads out of 30 involved (Table 5). Among the variations two patterns were revealed both U-shaped and the increase of the power spectrum value from a minimum at the level of the simple classification to a maximum at the level of the complex one (9 leads).The U -shaped variation is characteristic of the occipital leads (O2, O1, Oz); parietal leads (P4, P3, C3, Pz, Cz, CP3, Cpz, CP4); frontal lobe leads (F4, F3, Fp2, F7, Fpz); temporal lobe leads (T5, T4); frontoparietal leads (FC3, FC4) and leads located in the temporoparietal region (TP7, TP8). They include leads with a maximum in the complex classification (13 leads) and a maximum in the simple (9 leads). The increase of the power spectrum value from the level of simple classification to the most complex, is observed in the central frontal lead (Fz), two temporal leads (T6 – right and T3 – left) and in the lead located on the temporal-frontal area (FT8).

Table 5. Statistically significant variation of the delta power spectrum,

The statistically significant power spectrum changes in the theta range were revealed in 20 leads (Table 6). The power spectrum in the theta range changes according to a U-shaped pattern in three types of changes only. The first is the same reading of the global and detailed levels (2 leads – O1- and O2-occipital lobe on the left and right). The second is with a maximum at the level of the most difficult classification (12 leads). The given electrodes are located at the parietal lobe of the large hemisphere – P4, C3, CP3, Cpz; temporal – T6, T4, T3; occipital – Oz (central line of the occipital lobe); frontal – F7, Fcz; temporoparietal – TP7 and TP8, frontoparietal – FC3 (left). The third pattern is highest when passing the level of simple classification (6 leads). These are CP4, Cpz, Cz, P3 at the parietal lobe of the large hemispheres; F8 at the frontal lobe and FC4 at the fronto-parietal lobe.

Table 6. Statistically significant variation of the theta power spectrum

The alpha range power spectrum was significantly different in 9 leads (Table 7). The power spectrum values decreased with increasing difficulty level (8 leads: in the occipital lobe – O2, in the parietal region – P3, C4, Pz, Cpz; in the frontal region – F4; in the temporal region – T3 and in the frontotemporal region – FT8. An inverse U-shape is observed in the left temporal lead (T5).

Statistically significant changes of the power spectrum in the beta1 range were revealed in 5 leads (Table 8). The electrodes P4 (right parietal area) and Oz (central occipital area) showed a decrease of the power spectrum from the first level to the third, and the electrode Fp2 (frontopolar lead) demonstrated an increase. The Cz (central-parietal lead), Fz (central-frontal lead) showed a U-shaped change with maximum at the first level in the first case, and at the third level in the second case.

Table 7. Statistically significant variation of the alpha power spectrum

Table 8. Statistically significant variation of the beta-1 power spectrum

The beta-2 power spectrum changes statistically significantly in 14 leads (Table 9). The power spectrum has a U-shaped change in 4 leads (with the maximum in the most difficult classification level – O1 (left occipital lead) and F7 (left frontal lead); with the maximum in the global classification level – Fp2 and Fp1 (frontopolar leads left and right). There is an increase in the power spectrum value from the simple classification level to the most complex in 7 leads, including C3, Cpz and Pz in the parietal region; F3 in the frontal region; T4 in the temporal region; FC3 in the frontoparietal region; and TP7 in the temporoparietal region. The power spectrum has an inverse U- shaped change in three leads. These leads are F8 – frontal right, Fcz – frontal center line, CP3 – parietal left. It was revealed that 15 leads occurred in three or more frequency ranges. The power spectrum in four (delta, theta, alpha, and beta2) ranges has statistically significant differences in the Cpz leads. The O2, P3, and T3 electrodes are found in the delta, theta, and alpha rhythm ranges. The seven leads (O1, C3, CP3, T4, F7, FC3, TP7) are found in the delta, theta, and beta-2 ranges. The two leads (Oz and Cz) are found in the delta, theta, and beta-1 ranges: the Pz lead is in the delta, alpha, and beta-2 bands; and the Fp2 lead is in the delta, beta-1, and beta-2 bands. The leads O2, O1, P3, Cz, and F7 are of great interest, as they have the same tendency in all frequency ranges, based on the level of classification complexity.

Table 9. Statistically significant variation of the beta-2 power spectrum

Discussion

categorization are both rooted in misconceptions. E. Jacob organized the terms and identified the basic criteria needed to distinguish one from the other (Jacob, 2004). However, the classification concept has not been sufficiently explored. Moreover, it is important to emphasize that the classification is a basic cognitive process that highlights the importance of its study.

The question of the neural processes that occur during classification tasks also remains open. The researchers tend to study one EEG rhythm or record the EEG at rest. This study is a comprehensive approach to the issue. The analysis of all EEG frequency ranges was conducted by us.

D.A. Dokuchaev and E.V. Volkova showed that the higher the level of cognitive complexity (the measure of classification hierarchy) of the chemical classification operations, the more time and energy (in terms of EEG power spectrum) is required to complete the tasks and the lower the number of correct answers (Volkova & Dokuchaev, 2020a,2020b).

Analysis of the EEG power spectrum in groups of students and schoolchildren with the same and different measures of hierarchical classification revealed significantly lower EEG power spectrum indicators in more successful students compared to more successful schoolchildren. Similar patterns are   also observed between less successful schoolchildren and students. Students and schoolchildren with higher abilities to classify characterized by greater specialization of brain structures when classifying chemical stimuli-objects, in contrast to less successful respondents, whose brain activity is characterized by global generalized activity. The more successful respondents had significantly lower power spectrum indicators than the less successful respondents, which indicates greater neuroefficiency of the activity (Volkova, Dokuchaev, 2020a). The researchers emphasized the increase of brain activity in the occipital area and the decrease in the frontal area (occipital-frontal gradient) with an increase in the hierarchical classification of chemical stimulus-objects (Volkova, Dokuchaev, 2020 b). The data obtained by the authors testify to the “activation of the “mental map” of chemical compounds, at the analytical level which presents individual sense-distinguishing signs of chemical elements signs (reflecting the properties of chemical elements) and integrative-synthetic level of various combinations (generalized rules of combination of elements for each level of chemical compound classification) – frontal lobe, responsible for establishing logical connections between phenomena/theoretical statements” (Volkova, Dokuchaev, 2020, p. 114). The results obtained by E.V. Volkova and D.A. Dokuchaev demonstrate that the same brain structures are involved in the classification of chemical stimuli and logical operations, but the changes in brain activity are different (Volkova, Dokuchaev, 2021).

Based on the data obtained, the smallest number of leads involved was found in the beta-1 range (5 electrodes). Next, the alpha increases in order – 9 electrodes, beta-2 – 14 electrodes, theta – 20 electrodes. The maximum number is 26 in the delta range. This suggests more pronounced delta rhythm in the general power spectrum range and less pronounced fast frequencies.

The large number of leads with statistically significant changes of the power spectrum in the delta range may indicate increased anxiety of the respondents, behavioral lethargy, and wariness for novelty. This can also be seen in delta rhythms in the frontal, central, and parietal leads (O2, O1, P4, P3, C3, F4, F3, Fp2, F7, Oz, Pz, and others) (Smidt, Westenberg 2017). This effect is manifested through the connection of the delta rhythm with subcortical structures and with the amygdala body.  Also, according to the literature analysis (Moralis-Romero, 2015) the presence of a delta rhythm in the parietal leads, that indicates an increased control of attention, whereas Knyzev (Knyzev, 2019) also suggests a delta rhythm in the occipital cortex.

Several patterns were revealed in the change of the EEG power spectrum, based on the classification complexity. The first is U-shaped. It occurs in 21 leads of the delta range, in all thetas leads. It does not occur in the alpha range but is present in beta-1 and beta-2. We can explain this pattern by the fact that there are many chemical element signs that need to be distinguished in a simple level of classifications, which is apparently more difficult to do than to distinguish into four classes based on a small number of strictly defined signs or based on class prototypes.

We should also keep in mind that verbal intelligence is connected to figurative intelligence in the process of performing the basic and detailed level classifications. At the global level (simple classification), apparently, only verbal intelligence is involved. The decrease in the EEG power spectrum in complex classifications can be explained by the synergistic interaction of these two mechanisms, whereas the increase in the power spectrum in the most complex classifications is explained by the increase in the complexity of the tasks. Probably, there is a special mechanism that allows the brain to assess whether there are enough internal resources to cope with the detailed level of complexity of the classification of chemical stimulus-objects, if enough, then an increase in the EEG power spectrum is observed and the respondent copes with the task, if not enough, then a decrease is noted EEG power spectrum and a sharp increase in erroneous decisions. The inversed U-shaped change is similar to this pattern. It occurs in the alpha range – in the left temporal region and beta-2 – in the right frontal, frontoparietal, and parietal lobes. This could be explained that most respondents failed to cope with the third level of difficulty and engaged all their internal resources to go through the difficult classifications.

The delta, beta-1, and beta-2 ranges include leads in which the power spectrum increases from the simplest to the most complex levels of complexity. Beta rhythm is associated with cognitive loads, and the higher they are, the stronger the power spectrum in the beta range. These leads are in the temporal, frontal, and parietal areas. Higher levels of difficulty require more energy to be expended in terms of the EEG power spectrum, to implement logical activities (frontal lobe), construct spatial maps (parietal lobe), and refer to memory (temporal lobe).

The EEG power spectrum decreased in the alpha and beta-1 ranges. High alpha rhythm indicates high memory performance (Toure, Fishbach, 2014), as well as (especially in the frontal regions) internal stimulus processing, which is associated with the DMN system (Soham, Laila, Chai, et al. 2021). Left hemispheric asymmetry of alpha rhythm indicates increased motivation of respondents in passing the task (Boroojerdi, et al., 2001; Harmon-Jones, Gable, Peterson, 2010). Also, the theoretical section cited the facts that high alpha rhythm values indicate the ability to block external irritants. Consequently, a decrease in the EEG power spectrum in this range indicates a decrease in concentration when the level of task difficulty increases. According to the literature the decrease in the alpha rhythm power spectrum that we observe in this study is associated with a decrease in cognitive performance and a decrease in accuracy when the level of difficulty increases.  High beta range power spectrum values in the F3 and F7 leads indicate increased motivation, and right temporal T4 leads indicate negative emotion (Touré-Tillery & Fishbach, 2014). Increased beta rhythm is associated with increased response time to a stimulus (Oschmann, 2019).

It is necessary to point out the limitations of this study. First, the sample is represented by respondents with a low level of ability to classify stimulus-objects of chemistry and an insufficiently high level of intelligence. Second, gender differences in the EEG power spectrum were not considered.

To resolve these issues, further research is required both on samples that are homogeneous in terms of sex and on respondents with a high level of intelligence.

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