www.ijcrsee.com

Ermakov, P. et al. (2025). Evoked Brain Activity in Food Preference Decisions: Links to Eating Behavior and General Nutritional

Knowledge, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 15-31.

Original scientific paper

Received: February 27, 2025.

Revised: April 18, 2025.

Accepted: April 22, 2025.

UDC:

159.91

612.82-008.6

10.23947/2334-8496-2025-13-1-15-31

Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Corresponding author: edenisova@donstu.ru

Abstract: The article explores the characteristics of evoked brain activity during food preference decisions, emphasiz-

ing the role of psychological and neurophysiological mechanisms. The relevance of studying eating behavior as a multifaceted

phenomenon is highlighted, with attention to the cognitive, emotional, and physiological factors that influence food preferences.

The study involved 40 participants (70% female). Psychological testing included the Dutch Eating Behavior Questionnaire (Rus-

sian version by I.G. Malkina-Pykh, 2007), the Thought and Behavior Questionnaire (adapted by A.V. Anikina and T.A. Rebeko,

2009), the Three-Factor Eating Questionnaire (Russian version, 2018), and the General Nutrition Knowledge Questionnaire

(translated version of Kliemann, 2016). Neurophysiological data were collected using EEG tasks based on a Go/NoGo paradigm.

Mathematical and statistical methods included the Shapiro-Wilk test, Mann-Whitney U test, Student’s t-test, and k-means cluster

analysis. The study revealed significant differences in brain activity between groups with varying psychological characteristics

and levels of nutritional knowledge. These findings align with previous research, confirming the link between cognitive control,

impulsivity, and food preferences. Enhanced activation in the temporo-occipital regions was observed in participants with higher

nutritional awareness. The role of psychological traits was found to outweigh knowledge levels in shaping dysfunctional eating

patterns, highlighting the need for individualized approaches in prevention and treatment. The limitations, including the sample

size and absence of participants with clinical eating disorders, are discussed alongside recommendations for future research.

Keywords: eating behavior, rational consumption, cognitive control, evoked activity, ERP, Food Preference.

Pavel Ermakov

1, 2

, Ekaterina Denisova

, Daria Kirpu

, Anastasia Gosteva

, Nadejda Sylka

Department of Psychophysiology and Abnormal Psychology, Don state technical university, Rostov-on-Don,

Russian Federation, e-mail: paver@sfedu.ru, edenisova@donstu.ru, d.kirpu@list.ru, agosteva@donstu.ru, gramtysh99@gmail.com

Regional Scientific Center of the Russian Academy of Education, Southern Federal University, Rostov-on-Don,

Russian Federation,

e-mail: paver@sfedu.ru

Evoked Brain Activity in Food Preference Decisions: Links to Eating

Behavior and General Nutritional Knowledge

Introduction

In recent years, research on the psychophysiological mechanisms of eating behavior has expand-

ed into several distinct directions. Numerous studies highlight distinctive patterns of brain activity in indi-

viduals with eating disorders, with meta-analyses identifying key research areas (Wonderlich et al., 2021).

One of these focus areas includes examining cognitive control and reward system mechanisms through

neuroscience-derived approaches.

fMRI studies have shown the significance of the reward system in regulating eating behavior. Altera-

tions in the reward system have been observed in individuals with compulsive overeating, characterized by

heightened responsiveness to food stimuli. This sensitivity is associated with stereotypical behavioral patterns

and increased susceptibility to external food cues (Leenaerts et al., 2022; Vrieze and Leenaerts, 2023)

. It was

also shown that the activation patterns of the reward system in response to food stimuli can predict not only

food preferences but also Body Mass Index (BMI) (Cosme and Lopez, 2020). These findings are supported

by twin studies, which reveal that childhood impulsivity and reward system activity correlate with BMI in

adulthood. This highlights the long-term influence of early reward system functionality on eating behavior

(Kan et al., 2020). Variations in reward processing and cognitive control play a significant role in shaping

food preferences and obesity risk. Dysfunctions in the orbitofrontal cortex, which integrates food reward

signals, have been linked to heightened responsiveness to food cues and overeating (Rolls, 2021).

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Ermakov, P. et al. (2025). Evoked Brain Activity in Food Preference Decisions: Links to Eating Behavior and General Nutritional

Knowledge, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 15-31.

Building on the understanding of the reward system’s role in eating behavior, cognitive control has

also emerged as a critical factor influencing maladaptive weight-loss strategies in eating disorders. Deficits

in cognitive control are closely linked to impulsivity, a key predictor of pathological overeating (Пичиков et al.,

2018; Oliva et al., 2019). Studies demonstrate that individuals with compulsive overeating exhibit reduced

inhibitory control and attentional selectivity, which also impacts cognitive task performance. For instance,

differences in brain activity during Go/NoGo tasks have been observed predominantly in individuals with

severe disorders manifestations (Berner et al., 2023; Lyu et al., 2018). Moreover, cognitive control levels

have been shown to predict treatment outcomes in cognitive-behavioral therapy (CBT) for eating disorders,

particularly binge eating and obesity, as well as the durability of remission. For example, individuals strug-

gling with interference control in the Stroop task exhibited lower treatment efficacy (Hamatani et al., 2021).

ERP studies have highlighted distinct neural correlations of eating behavior in individuals with

excess weight, particularly the P300, N200, and P200 components. These components reflect variations

in cognitive control, behavioral regulation, and appetite response. A systematic review (Chami, 2019) of

ERP studies of neural responses to food and non-food stimuli among individuals with eating and weight

disorders revealed an enhanced attentional bias toward food stimuli, as evidenced by elevated P300 and

LPP amplitudes, across groups of individuals with excess weight from those of healthy weight. However,

among individuals with obesity, the N200 amplitude positively correlated with caloric intake, while the

P300 amplitude was sensitive to hunger levels (Chami, 2019).

P300 amplitude increase in response to food stimuli, regardless of type, has been observed in groups

with excess weight. Interestingly, the presence of this component, coupled with a preference for low-calorie

food in experimental settings, also predicted snack frequency within the sample. Similarly, the emergence of

the N200 component has been associated with low levels of eating behavior control in obesity (Biehl et al.,

2020). The P200 component has also been highlighted in several studies, showing increased prevalence in

individuals with excess weight in response to food stimuli (Liu et al., 2020; Schwab et al., 2021). This com-

ponent has been linked to appetite suppression when a discrepancy arises between expected and actual

food stimuli (Schwab et al., 2021). However, the relationship between cognitive control and obesity remains

ambiguous, as some animal studies suggest that rather than cognitive control deficits causing obesity, obe-

sity itself might lead to diminished cognitive control (Davidson et al., 2019). At the same time, lifestyle factors

may amplify or modify these neural predispositions. Research suggests that diets rich in ultra-processed

foods not only distort reward system functioning but also reinforce habitual overeating, creating a feedback

loop that exacerbates obesity risk (Edwin and Tittgemeyer, 2020). Moreover, the interaction between be-

havioral patterns and reward system activity may perpetuate maladaptive eating behaviors. For instance,

repetitive exposure to specific food-related cues can alter the reward system’s sensitivity, maintaining dis-

ordered eating tendencies and shaping long-term dietary habits (Frank et al., 2021). So, these findings may

also suggest that while neural activity in response to food cues is heightened in individuals with eating and

weight disorders, its manifestation may depend on specific states (e.g., hunger) or traits (e.g., restrained

eating). Furthermore, prolonged exposure to food-related cues has been linked to weakened cognitive con-

trol, potentially contributing to overeating and other maladaptive behaviors.

Several other studies have demonstrated that cognitive control weakens in individuals with obesity

or dieting tendencies under prolonged exposure to triggers, leading to deteriorations in eating behavior,

such as overeating (Schienle et al., 2019). Simultaneously, the anticipation of food rewards decreases

attention and increases impulsivity (Schiff et al., 2021), which is supported by improvements in cognitive

control when the reward anticipation is more distant. The automatic and controlled aspects of eating

behavior are interrelated, and avoiding contextual triggers can improve cognitive control and lead to

healthier eating behavior (Fürtjes et al., 2020). Additionally, cognitive-behavioral therapy (CBT) integrated

with cognitive training exercises has shown promise in improving inhibitory control, a key factor in prevent-

ing compulsive overeating (Manasse et al., 2020). Emerging evidence also supports the effectiveness of

combining cognitive training and CBT for regulating eating behaviors, including those within normative

ranges (Yang et al., 2019). Additionally, interventions such as guided self-help CBT have shown potential

in reducing maladaptive eating behaviors, highlighting the impact of cognitive and behavioral regulation

on managing these risks (Setsu et al., 2018).

Thus, the data on the psychophysiological mechanisms of eating behavior confirms the significance

of cognitive control and the reward system in regulating eating habits and their role in the development of

eating disorders. Investigating evoked brain activity in food preference decisions and its relationship to eat-

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Ermakov, P. et al. (2025). Evoked Brain Activity in Food Preference Decisions: Links to Eating Behavior and General Nutritional

Knowledge, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 15-31.

ing behavior and general nutritional knowledge can provide valuable insights into the neural mechanisms

underlying behind rationality of food preferences and consumption. Such an approach will be contributing to

the development of more precise and personalized interventions for preventing and treating eating disorders.

Materials and Methods

The sample consisted of 40 participants aged 18 to 25 years (70% female). EEG data were col-

lected from 26 participants aged 18 to 33 years (80% female). None of the participants reported diag-

nosed disorders or complaints related to eating behavior. Participation was voluntary. All participants were

briefed on the study’s objectives and procedures and provided written informed consent.

Participants were asked to report their gender, age, and answer a series of questions regarding

general nutritional knowledge using the translated version of the General Nutrition Knowledge Question-

naire (Kliemann et al., 2016).

To study the psychological aspects of eating behavior, the following methods were employed: Dutch

Eating Behavior Questionnaire (DEBQ) (T. VanStrien et al., 1986; Russian adaptation by I.G. Malkina-

Pykh, 2007), Thoughts and Behaviors Questionnaire (TBQ) (M. Cooper, G. Todd, R. Woolrich, 2006;

Russian adaptation by A.V. Anikina, T.A. and Rebeko, 2009), Three-Factor Eating Questionnaire (TFEQ)

(A. Stunkard, S. Messic, 1985; Russian version, 2018).

To explore food preferences, a protocol for an experimental psychophysiological study was developed, which

included two types of tasks. Computerized tasks were programmed and performed using the PsychoPy software

and included high-resolution images of food items collected from the Internet (examples are shown in Table 1).

The task was modeled as a variation of the Go/NoGo paradigm. Stimuli consisted of images of

various food items. The test was conducted in two series: the first series involved selecting food stimuli

congruent with the participant’s preferences; the second series involved selecting food stimuli incongruent

with the participant’s preferences. Images of various food items that matched (first series) or did not match

(second series) the participant’s preferences were used as target stimuli with a Go response. Each series

included 150 stimuli presented in random order, with a stimulus exposure time of 500 ms.

Table 1. Examples of stimuli by series

Task Example of stimulus material

2. Go-stimuli are foods congruent with

participant preferences

2. Go-stimuli are foods incongruent with

participant preferences

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Ermakov, P. et al. (2025). Evoked Brain Activity in Food Preference Decisions: Links to Eating Behavior and General Nutritional

Knowledge, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 15-31.

ERP was recorded using a “Neurovisor 136” electroencephalograph, in 128 channels (monopolar

montage with A1 and A2 ear electrodes as a references). EEG data were processed using WinEEG soft-

ware. For ERP pre-stimulus interval was limited to 200 ms, and the post-stimulus interval was set to 700 ms.

Statistical methods: distribution parameter analysis was conducted using the Shapiro-Wilk test.

Comparative analyses were performed using the Mann-Whitney U test and Student’s t-test, with effect

sizes calculated using Cohen’s d and biserial correlation coefficients. Additional analyses included one-

way analysis of variance (ANOVA), Spearman’s rank correlation coefficient, and k-means cluster analysis.

Results

To classify participants based on multiple indicators of eating behavior, a cluster analysis (K-means

clustering) was conducted. The following variables were used for clustering: restrictive, emotional, and ex-

ternal eating behavior (Dutch Eating Behavior Questionnaire); negative, positive, and permissive thoughts;

dieting and overeating; concerns about clothing, weight, and shape; food-related behaviors and the eating

process (Thoughts and Behavior Questionnaire); as well as restraint, disinhibition, and hunger susceptibil-

ity (Three-Factor Eating Questionnaire). As a result, two distinct clusters were identified, differing in the

severity of eating behavior traits, cognitive patterns, and behavioral aspects of nutrition. Cluster 1 (n = 25,

22 women, 3 men, mean age = 29) was characterized by low to moderate levels across these indicators,

reflecting a healthy eating behavior style. Cluster 2 (n = 15, 14 women, 2 men, mean age = 29) exhibited

moderate to high levels of the same indicators, suggesting a tendency toward disordered eating patterns.

Table 2. Results of the analysis of differences between clusters based on the indicators used for clustering

Indicator

Means

Between-

group

variance

Within-group

variance

Significance

level

Cluster 1 Cluster 2

Restrictive eating behavior 1,94 2,91 9,30 23,30 15,57 0,000

Emotional eating behavior 1,78 2,59 6,29 28,47 8,62 0,006

External eating behavior 2,76 3,18 1,68 10,69 6,13 0,018

Negative thoughts 6,46 42,05 12360,95 8351,00 57,73 0,000

Positive thoughts 4,40 30,19 6487,76 5264,44 48,06 0,000

Permitting thoughts 22,23 48,57 6769,97 10269,67 25,71 0,000

Diet 15,00 30,86 2453,85 7996,00 11,97 0,001

Overeating 14,57 38,39 5536,76 4794,88 45,03 0,000

Clothing 12,56 50,88 14322,33 10627,91 52,56 0,000

Weight and shape 7,12 39,25 10071,58 10519,64 37,34 0,000

Behavior associated with food 17,87 32,08 1971,65 12728,11 6,04 0,019

The process of eating 17,20 31,09 1883,28 8466,11 8,68 0,005

Restriction 4,40 7,75 109,49 575,00 7,43 0,010

Disinhibition 2,88 6,44 123,47 244,58 19,69 0,000

Sensitivity to hunger 3,08 4,25 13,36 172,84 3,01 0,090

In the analysis of group differences, significant variations were observed in eating behavior traits, as

measured by the Dutch Eating Behavior Questionnaire (DEBQ) (Table 2). Cluster 1 displayed scores at the

lower boundary of normative values for restrictive and external eating behaviors, with notably low scores

for emotional eating. Restrictive eating scores (1.9) suggest that respondents in this cluster do not make

intentional efforts to lose or maintain weight. Furthermore, their need for food likely aligns more closely with

actual hunger cues (external eating behavior score: 2.76). The low emotional eating score characterizes this

group as exhibiting a healthy eating style, without a tendency to use food as a coping mechanism for stress.

In contrast, Cluster 2 demonstrated elevated scores across all three scales of the DEBQ. The re-

strictive eating score (2.9) indicates a preoccupation with weight gain and attempts to restrict food intake.

Higher emotional eating scores reflect a propensity to use food as a means of coping with emotional

distress. Regarding external eating behavior, participants in this cluster are less guided by internal hunger

signals and more influenced by external food-related stimuli when making decisions about eating.

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Ermakov, P. et al. (2025). Evoked Brain Activity in Food Preference Decisions: Links to Eating Behavior and General Nutritional

Knowledge, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 15-31.

Eating-related thought processes also significantly differed between clusters. Respondents in Clus-

ter 1 scored low across all scales, indicating minimal engagement with negative, positive, or permissive

thoughts regarding food. Conversely, Cluster 2 exhibited substantially higher scores, mostly within aver-

age ranges, suggesting a stronger tendency to associate food with guilt, loss of control, weight gain,

and punishment. Permissive thoughts in Cluster 2 appeared to counterbalance negative ones, allowing

participants to justify food-related behaviors.

Behavioral patterns further highlighted differences: Cluster 1 scored low across all measures, while

Cluster 2 showed higher scores for behaviors such as “Clothing,” “Weight and Shape,” and “Overeating.”

These scores suggest that individuals in Cluster 2 are more prone to overeating and tend to conceal their

bodies with specific clothing to mask their shape.

The Three-Factor Eating Questionnaire (TFEQ) revealed notable differences between clusters as

well. Cluster 1 had consistently low scores across all three factors, whereas Cluster 2 showed scores for

restraint and disinhibition twice as high as those in Cluster 1, although still within the lower end of the

normative range. This suggests that participants in Cluster 2 are more inclined to restrict their food intake

to manage body weight and size and are also more prone to impulsive eating behaviors. The “Hunger

Susceptibility” factor remained within normal limits for both groups, indicating a general ability to manage

hunger and cravings in both clusters.

Considering the absence of high scores across both clusters, it can be concluded that the sample

overall does not exhibit dysfunctional eating patterns.

In summary, the two groups differ significantly in the extent of restrictive, emotional, and external

eating behaviors; the presence of negative, positive, and permissive thoughts; behavioral patterns; and

restraint and disinhibition in eating. Cluster 1 is characterized by a healthier eating style, whereas Cluster

2 shows greater concern with weight management or reduction.

Regarding differences in general awareness and knowledge about nutrition and health, Cluster 2

showed significantly higher levels of both general awareness and specific knowledge about the effects

of food on health compared to Cluster 1, which exhibited lower scores in these areas. The results of the

statistical analysis are presented in Figure 1.

Figure 1. Differences Between Clusters in General Awareness of Nutrition and Health (p<0.05)

These defferences align with the hypotheses proposed earlier. The findings likely reflect the char-

acteristics of the clusters. Since Cluster 2 is generally more concerned with weight maintenance and

reduction, as well as dietary control, their higher levels of general awareness of nutrition and health may

reflect a greater interest in these topics.

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Ermakov, P. et al. (2025). Evoked Brain Activity in Food Preference Decisions: Links to Eating Behavior and General Nutritional

Knowledge, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 15-31.

Figure 2. Analysis of Food Preferences Among Respondents

An analysis of average scores for food preferences among respondents in both clusters revealed

a tendency to select healthier options, particularly vegetables and fruits. Respondents from both clusters

showed the least preference for desserts (Cluster 1: 10.08; Cluster 2: 12.36). Differences in food prefer-

ences between the two clusters indicate that respondents in Cluster 2 had higher average scores across

all food categories, with a particularly noticeable gap in the selection of low-calorie foods (Cluster 1: 17.54;

Cluster 2: 22.07) and fruits and vegetables (Cluster 1: 19.71; Cluster 2: 24). Respondents in Cluster 2 also

scored higher in their preference for fatty foods (Cluster 1: 15.42; Cluster 2: 19.07).

Figure 3. Analysis of Food Choices Incogruent to Preferences

When analyzing food choices that diverged from respondents’ stated preferences, Cluster 2 partici-

pants again demonstrated higher average scores across all food categories. For both clusters, desserts

were the least chosen items. The smallest difference between clusters was observed in desserts as a cat-

egory of non-preferred food (Cluster 1: 12.13; Cluster 2: 12.43). Low-calorie foods and fruits/vegetables

were less frequently chosen as non-preferred items, with Cluster 1 participants more frequently selecting

low-calorie foods (Cluster 1: 4.17; Cluster 2: 6.14), while Cluster 2 participants favored fruits and vegeta-

bles (Cluster 1: 3.71; Cluster 2: 7.21). Fatty foods, as non-preferred items, were also selected more often

by Cluster 2 participants than Cluster 1 (Cluster 1: 6.92; Cluster 2: 9.5).

Overall, both clusters showed a general preference for healthier food options, particularly fruits

and vegetables, and the least preference for desserts. However, Cluster 2 participants displayed higher

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Ermakov, P. et al. (2025). Evoked Brain Activity in Food Preference Decisions: Links to Eating Behavior and General Nutritional

Knowledge, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 15-31.

average scores across all food categories, especially in the selection of low-calorie foods and fruits/veg-

etables. These findings suggest that respondents in Cluster 2 are inclined toward more differentiated food

choices, which aligns with the overall characteristics of the cluster.

Heatmap analysis revealed higher amplitudes of evoked potentials during congruent food choice

tasks in the first series for Cluster 2 participants. These increased amplitudes were observed in early- and

mid-latency components across all types of stimuli (Figure 4).

Figure 4. Temporal Dynamics of Average Amplitude of Evoked Potentials (EP) in Food Preference Task 1 in Clus-

ters Based on Psychological Eating Behavior Characteristics (Cluster 1 - Bottom Row, Cluster 2 - Top Row)

Statistically significant differences between the clusters were found in tasks involving congruent

food choices. When accessing low-calorie dishes, differences were observed in the central-parietal region

at 100–200 ms, in the central and temporal regions at 200–300 ms, in the left frontal area at 300–400 ms,

and in the left fronto-central region at 400–500 ms. Similar differences were noted when viewing dessert

images, with distinctions appearing in the temporal region at 100–200 ms, in the central, parietal, and

frontal regions at 200–300 ms, in the frontal region at 300–400 ms, and in the left fronto-temporal and

fronto-central regions at 400–500 ms. For fruits and vegetables, significant differences emerged in the

occipital region at 100–200 ms, in the temporal regions at 200–300 ms, in the temporal region and a single

frontal electrode at 300–400 ms, and at single electrodes in the left fronto-central region at 400–500 ms.

In response to high-calorie foods, differences were detected in the central-parietal region at 100–200 ms,

in the central-frontal and parietal regions at 200–300 ms, in the left frontal area at 300–400 ms, and in the

left fronto-central and right fronto-temporal regions at 400–500 ms.

The observed temporal and topographical differences in processing various food types (low-calorie,

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Ermakov, P. et al. (2025). Evoked Brain Activity in Food Preference Decisions: Links to Eating Behavior and General Nutritional

Knowledge, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 15-31.

high-calorie, desserts, fruits, and vegetables) suggest variability in cognitive and emotional responses de-

pending on the food category. More pronounced differences in the temporal and frontal regions for desserts

and high-calorie foods may indicate greater involvement of reward-related and cognitive control areas.

Figure 5. Analysis of Differences in Average EP Amplitude in Food Preference Task 1 in Clusters Based on Psy-

chological Eating Behavior Characteristics (p<0.05)

When performing the task of making food choices incongruent with dietary preferences in the sec-

ond series, a higher amplitude of evoked potentials (EP) of early and middle latency was also observed

in the second cluster, regardless of stimulus type (Figure 6).

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Ermakov, P. et al. (2025). Evoked Brain Activity in Food Preference Decisions: Links to Eating Behavior and General Nutritional

Knowledge, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 15-31.

Figure 6. Temporal Dynamics of Average Amplitude of Evoked Potentials (EP) in Food Preference Task 2 in Clus-

ters Based on Psychological Eating Behavior Characteristics (Cluster 1 - Bottom Row, Cluster 2 - Top Row)

Statistically significant differences between clusters were identified during the task of making food

choices incongruent with individual preferences for specific stimulus types. High-calorie food images elic-

ited differences at 100–200 ms in the central-parietal and left temporal regions. The 200–300 ms interval

showed differences in the frontal-central, left temporal, and temporo-parietal regions. Between 300 and

400 ms, significant activity appeared in the left frontal and central regions, while the 400–500 ms interval

involved the left frontal-temporal region and a single right frontal-temporal channel. For dessert images,

no differences were observed at 100–200 ms; however, significant activity emerged during the 200–300

ms interval in the central, left-lateralized frontal, and parietal regions, followed by activity in the frontal

region at 300–400 ms and the left frontal-central and temporal regions at 400–500 ms. Low-calorie food

images showed differences during the 100–200 ms interval in the central-parietal region. At 200–300 ms,

significant activity was detected in the left-lateralized frontal, central, and temporal regions. By 300–400

ms, activity persisted in the frontal and central regions, with left lateralization, and at 400–500 ms, in the

left frontal-temporal region. For fruits and vegetables, differences were first noted during the 100–200 ms

interval in the central-parietal region. Subsequent differences were observed at 200–300 ms in the left-

lateralized frontal, central, and temporal regions, followed by activity at 300–400 ms in the left-lateralized

frontal and central regions, and at 400–500 ms in the left frontal-temporal region.

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Ermakov, P. et al. (2025). Evoked Brain Activity in Food Preference Decisions: Links to Eating Behavior and General Nutritional

Knowledge, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 15-31.

Figure 7. Analysis of differences in averaged EP amplitude values in Food Preference Task 2 in Clusters Based

on Psychological Eating Behavior Characteristics (p<0,05)

Overall, significant differences between clusters identified by eating behavior traits were predomi-

nantly observed in the temporo-occipital regions across all stimulus types. For high-calorie food, Cluster 1

showed higher amplitudes during early processing stages (100–200 ms), while Cluster 2 exhibited greater

amplitudes at later intervals (200–300 ms and 400–500 ms). For low-calorie food and fruits/vegetables,

significant differences were primarily localized in the parietal-occipital regions, with Cluster 1 demonstrat-

ing consistently higher amplitudes in early intervals (100–300 ms). These findings suggest distinct neural

processing patterns between clusters, reflecting differences in attentional or evaluative responses to food

stimuli. These findings highlight the role of cognitive control and attentional modulation in decisions that

deviate from habitual food preferences, reflecting their importance in regulating eating behavior.

Next, to test the hypothesis that brain activity characteristics in food preference choice tasks may

differ depending on the level of awareness in nutrition and health, a comparative analysis was conducted

between the group with above-average results (group 1) and the group with below-average results (group

2). Respondents with average results were excluded from the sample during the calculations.

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Ermakov, P. et al. (2025). Evoked Brain Activity in Food Preference Decisions: Links to Eating Behavior and General Nutritional

Knowledge, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 15-31.

Visual differences between the heatmaps of the groups with different levels of awareness in nutri-

tion and health are minimal. However, an increase in the amplitude of event-related potentials (ERPs) was

observed in the first group of participants, characterized by higher levels of awareness, in the occipital

area between 100-400 ms during the first series, regardless of the type of stimulus (Figure 8).

Figure 8. Temporal Dynamics of Average Amplitude of Evoked Potentials (EP) in Food Preference Task 1 in

groups depending on the level of awareness in nutrition and health (results for group 1 in the lower row of im-

ages, for group 2 in the upper row).

Since the distribution in the selected groups for several indicators differed from normal (Shapiro-

Wilk test), the analysis was conducted using both a parametric criterion (Student’s t-test) and a non-

parametric criterion (Mann-Whitney U-test). Significant differences between the first and second groups

in the congruent food choice task during the first series, when demonstrating images of high-calorie foods,

were found in the 100-200 ms interval in the temporal areas, in the 200-300 ms interval in the left and right

temporal areas, and in the 400-500 ms interval in the left central-temporal area. No statistically significant

differences were found in the 300-400 ms interval (Figure 9).

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Ermakov, P. et al. (2025). Evoked Brain Activity in Food Preference Decisions: Links to Eating Behavior and General Nutritional

Knowledge, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 15-31.

Figure 9. Analysis of Differences in Average EP Amplitude in Food Preference Task 1 in groups depending on the

level of awareness in nutrition and health (p < 0.05).

Statistically significant differences in averaged ERP amplitude values across participant groups

depending on the level of awareness in nutrition and health were predominantly localized in the temporo-

occipital regions. For dessert images at 100–200 ms, differences emerged in the parietal-occipital regions,

in the 200–300 ms interval, significant activity was detected at a single electrode in the fronto-temporal

and temporal regions, while at 400–500 ms, differences were observed in the left temporo-parietal, fronto-

temporal, and right frontal regions. For low-calorie dishes, significant differences appeared at 100–200 ms

in the right temporo-parietal region. During the 200–300 ms interval, differences were noted at individual

electrodes in the central, parietal, and left parieto-occipital regions. By 400–500 ms, significant differ-

ences were observed at individual electrodes in the temporal and fronto-central regions. When fruits and

vegetables were presented, differences were observed at 100–200 ms at individual electrodes in the left

parieto-occipital, fronto-temporal, and right parieto-occipital regions. At 200–300 ms, significant activity

was seen in the left centro-temporal and right parieto-occipital regions. Finally, during the 400–500 ms

interval, differences were observed at a single electrode in the right temporal region.

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Ermakov, P. et al. (2025). Evoked Brain Activity in Food Preference Decisions: Links to Eating Behavior and General Nutritional

Knowledge, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 15-31.

In the second task, the heatmaps also show an increase in the amplitude of the P300 in the oc-

cipital region during the 100-300 ms interval for the first group of participants (Figure 10).

Figure 10. Temporal Dynamics of Average Amplitude of Evoked Potentials (EP) in Food Preference Task 2 in

groups depending on the level of awareness in nutrition and health (results for group 1 in the lower row of im-

ages, for group 2 in the upper row).

Statistically significant differences in ERP amplitude between the first and second groups during

the incongruent food choice task were predominantly observed in temporo-occipital regions (Figure 11).

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Ermakov, P. et al. (2025). Evoked Brain Activity in Food Preference Decisions: Links to Eating Behavior and General Nutritional

Knowledge, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 15-31.

Figure 11. Analysis of Differences in Average EP Amplitude in Food Preference Task 2 in groups depending on the

level of awareness in nutrition and health (p<0.05).

For high-calorie food, significant differences were found at 100–200 ms in the fronto-central region

(single electrode), at 200–300 ms in the left frontal and right temporo-occipital regions, and at 400–500

ms in the right temporo-parietal region, with no differences at 300–400 ms. For desserts, differences were

detected at 100–200 ms in the left and right centro-parietal regions and at 200–300 ms in the right parieto-

occipital region, with no significant differences at 300–400 ms or 400–500 ms. For low-calorie food, differ-

ences appeared at 100–200 ms in the right temporo-central region (single electrode) and at 200–300 ms

in the left parieto-occipital region, with no differences observed at later intervals. For fruits and vegetables,

significant differences were observed at 100–200 ms in the left parietal, centro-parietal, temporo-parietal,

and parieto-occipital regions, and at 200–300 ms at single electrodes in the left and right temporo-parietal

regions, with no significant differences at 300–400 ms or 400–500 ms.

So, differences between groups with varying levels of nutritional and health awareness were pri-

marily observed in temporo-occipital regions across both series. In the first series, higher amplitudes were

noted in the more aware group during early stages of stimulus processing (100–400 ms), while the less

aware group exhibited higher amplitudes at later stages (400–500 ms), regardless of stimulus type. In the

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Ermakov, P. et al. (2025). Evoked Brain Activity in Food Preference Decisions: Links to Eating Behavior and General Nutritional

Knowledge, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 15-31.

second series, the less aware group showed higher amplitudes at 100–200 ms and 400–500 ms for high-

calorie food, whereas the more aware group demonstrated higher amplitudes at 100–200 ms (excluding

high-calorie food) and 200–300 ms, irrespective of stimulus type.

Discussion

The results of this study confirm the importance of psychophysiological mechanisms in the regula-

tion of eating behavior and their relationship with psychological factors, as well as with the level of general

awareness in nutrition and health. The obtained data, demonstrating differences in the characteristics of

brain activity between the identified clusters and respondent groups, align with findings from previous

research. For instance, the observed differences in the average P300 amplitude during the 200–300 ms

interval between clusters support earlier evidence linking cognitive control, impulsivity, and food prefer-

ences. The observed activation in the fronto-central region during the 600–700 ms interval, particularly in

response to non-food stimuli, highlights the role of cognitive control in suppressing impulsive reactions.

Additionally, the more pronounced activation of the temporal-occipital region in participants with a

higher level of nutritional awareness aligns with the idea that greater awareness may facilitate heightened

attentional processing of food-related stimuli. However, the relatively limited number of statistically signifi-

cant differences associated with the level of awareness could reflect the small sample size or lend support

to the hypothesis that psychological traits (such as impulsivity or cognitive control) outweigh knowledge

levels in determining food preferences and potentially in shaping these preferences.

The findings for the second cluster, characterized by heightened sensitivity to external food stimuli,

further support the hypothesis that excessive responses to food triggers may be associated with insuf-

ficient cognitive regulation. This aligns with prior evidence suggesting that individuals who exhibit strong

external sensitivity may struggle with attentional control or emotion regulation in food-related contexts.

Several limitations should be noted. The small sample size may reduce the statistical power of the

results, and the inclusion of individuals without diagnosed eating disorders limits the generalizability of

these findings to clinical populations. While this approach provides valuable insights into eating behavior

in the general population, its applicability to groups with clinical concerns remains unclear.

In conclusion, this study emphasizes the importance of a comprehensive approach to examin-

ing food preferences, integrating cognitive, emotional, and neurophysiological factors. Future research

should aim to expand sample sizes, include clinical populations, and investigate the interplay between

knowledge, psychological traits, and neurophysiological responses in greater detail.

Conclusion

The aim of this study was to investigate the characteristics of brain activity in food preference

tasks, depending on psychological characteristics of eating behavior, as well as the level of general

awareness in proper nutrition.

The identified differences between the clusters indicate that respondents with healthier eating

behaviors demonstrate lower reactivity to external food stimuli, which is related to a higher level of cogni-

tive control. In contrast, respondents with less healthy eating behaviors are characterized by increased

sensitivity to external triggers, which supports the significance of psychophysiological mechanisms in

regulating behavior.

Overall, the differences in brain activity between the first and second clusters, depending on the

psychological characteristics of eating behavior, and between the identified groups of respondents, de-

pending on their level of awareness in nutrition and health, in tasks involving congruent and incongruent

food preference choices, are aligned with both the characteristics of the studied groups and the type of

tasks performed.

The obtained results may be useful for creating prevention and treatment programs for eating

behavior disorders, tailored to the individual characteristics of respondents, and open up opportunities for

further study. In particular, it is important to investigate the nature of the relationship between cognitive

control indicators and various aspects of eating behavior, and whether different forms of interventions

aimed at developing executive functions can influence long-term changes in eating habits.

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Ermakov, P. et al. (2025). Evoked Brain Activity in Food Preference Decisions: Links to Eating Behavior and General Nutritional

Knowledge, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 15-31.

Acknowledgements

The study was carried out with the financial support of a grant for the implementation of research

projects carried out under the supervision of young scientists of DSTU “Science-2030” (project “Psycho-

logical and psychophysiological mechanisms of eating behavior of young people in the context of the

transition to a rational consumption model”, 2023-2024).

Conflict of interests

The authors declare no conflict of interest.

Author Contributions

Conceptualization, E.P.N.; formal analysis, G.A.O.; investigation, K.D.R. and S.N.V.; project admin-

istration, D.E.G.; supervision, E.P.N; writing – original draft, D.E.G., K.D.R. and S.N.V.; writing – review &

editing, D.E.G. and K.D.R. All authors have read and agreed to the published version of the manuscript.

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