The reduced cognitive load of strong self-related information’s orientation attention revealed by ERP Running title: Cognitive Load of Self-Related Information

doi:10.21203/rs.3.rs-4723179/v1

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Research Article

The reduced cognitive load of strong self-related information’s orientation attention revealed by ERP Running title: Cognitive Load of Self-Related Information

https://doi.org/10.21203/rs.3.rs-4723179/v1

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Background

Event-related potentials (ERPs) can demonstrate the dynamic nature of individual cognitive processing in real time through changes in brain neurons. Previous studies have shown that self-related information (SRI) has the advantage of orientation attention. P2, N2, and P300 are the main components related to the orientation attention of the SRI.

Methods

This study adopted the oddball paradigm of novel stimuli combined with time-domain and frequency-domain ERP analysis. 35 freshmen were recruited for this study.

Results

Compared with low SRI, high and medium SRI elicited similar temporal-occipital N170. High SRI produced N2/N250 components in the left temporal occipital region while medium SRI induced larger temporal-occipital P2. However, high and medium SRIs failed to elicit the P3a associated with orientation attention. There were significant differences in differing degrees of SRI in the delta and beta2 bands.

Conclusion

Birthplace, as a high SRI, can be automatically processed because it is not limited to word processing. Rather, it relies on emotional activation. High SRI needs less activation at a perceptual level, suggesting that it requires a smaller cognitive load.

ERPs

orientation attention

self-related information

frequency-domain analysis

cognitive processing

Self-related information (SRI) is regarded as being related to the individual ego but unrelated to others outside the individual. SRI has individual meaning and social value, and it is marked by different processing characteristics, compared with other information [1]). Specifically, individuals have a strong orientation to capture physiological and psychological information about themselves, including self-face, voice, name, and autobiographical memory, according to different SRI cues. This indicates strong self-recognition and self-awareness [2–4].

SRI has the advantage of orientation attention, which enables individuals to transfer their attention to SRI more quickly. Evidence from search and matching studies showed that SRI was searched or matched faster and with higher accuracy [5, 6]. These results indicated SRI has an orientation advantage in attentional capture, and limited attentional resources are allocated to SRI because its entry into conscious processing is preferred [7]. Additionally, in both stimulus-related and stimulus-unrelated tasks, SRI (i.e., self-face, name, and even screen name) could be identified faster and more accurately [8–11]. Moreover, when SRI was presented as a distraction, it would attract attention more automatically, leading to a decline in main task performance [12]. Therefore, SRI may be involuntary, uncontrollable, and unconsciously automated.

The cocktail party effect is an example of such automation. A person can recognize their own name quickly, even if it is mentioned at a noisy cocktail party [13]. Even infants as young as five months already show a preference for their own name [14]. This study further investigates the orientation attention mechanism of SRI using event-related potentials (ERPs), a biomarker reflecting pre-attentional change detection under non-attentional conditions [15]. Previous ERP studies on SRI involving the attention mechanism mainly focused on components such as P2, P300, and N2.

P2 is a positive wave that reaches its peak in the 200-250ms range of visual stimulus recognition [16]. This activity is mainly located in the anterior and central areas of the brain and is associated with attention distribution and stimulus processing and analysis [17, 18], especially perceptual analysis and memory processing [19, 20]. P2 can be divided into anterior P2, which is thought to be associated with early recognition of visual stimuli and posterior P2, which is associated with the early processing of verbal information.

On the one hand, P2 latency represents the time required for early processing stimulus capture and perception analysis [21]. The shorter the latency, the earlier individuals notice the target stimulus. Moreover, the longer the latency, the lower the efficiency of information processing [22]. On the other hand, P2 amplitude might indicate that the participants are already classifying and attending to stimuli based on different visual features in the early stages [23]. The larger the amplitude, the more attention resources would be consumed. Additionally, larger P2 amplitude elicited by semantic recognition might indicate that semantic processing involvement has begun on the basis of distinguishing visual features [24]. Furthermore, evidence has shown that P2 in the anterior and central areas is larger when the stimulus contains target features [25], possibly reflecting the rapid detection of the stimulus features [26, 27], although the demonstrated detection lacked advanced cognitive processing and allocation of control resources [28, 29].

P300 is a classic and widely used electrophysiological index for cognitive processing and brain mechanism study, and can be divided into sub-components, P3a and P3b. These have different distributions, different latency periods, and independent neural origins, reflecting different levels of neural processing [30]. P3a is an early sub-component of P300 with an incubation period of 220-280ms. It is mainly distributed in the anterior central area, and its maximum amplitude is located near the Fz point (posterior frontal lobe), reflecting the time from stimulus presentation to stimulus detection and processing. It is the main marker of orientation response, reflecting the change in attention allocation and working memory representation in response to the stimulus [13, 31]. Studies have shown that the processing of self-related stimuli elicited larger P2 and P3 amplitudes than that of non-self-related stimuli [32]. For example, when individuals process their own names, they react faster, have higher accuracy, and elicit larger P2 and P3 amplitudes [33].

N2 (also known as N200) is a composite negative wave occurring after P2. It appears around 200ms after stimulus presentation, representing the transition between automatic and controlled processing [34]. Studies have found that N2 is closely related to explicit knowledge; moreover, it reflects stimulus recognition, attention transfer, conflict monitoring, novelty or mismatch detection, and cognitive control function [35–37]. As such, it may be related to self-processing [36].

Evidence from face recognition studies shows a significant negative wave in the temporo-occipital region, named N170, is elicited during an incubation period of 170ms. In addition, Bentin and Deouell (2000) found that N170 could only be elicited by human faces under different kinds of stimuli (including non-face images), suggesting that N170 might reflect the specific neural mechanism of human face processing. Additionally, Caharel et al.'s (2002) study provided evidence that viewing self or familiar faces under passive conditions elicited larger N170 than did viewing non-self-related or unfamiliar faces. Furthermore, Keyes et al.’s (2010) group found that the self-face elicited a larger N170 and VPP amplitude in the posterior and anterior middle areas of the brain. However, Peng’s (2003) group found that not all participants elicited N170 in the “learning-recognition” experiment using face pictures as stimuli. Sui et al’s (2006) group found no difference in the amplitude and latency of the N170 elicited by self and other faces in the orientation judgment of self and familiar faces under both attentional and non-attentional conditions; that is, the relevance and familiarity of faces have no effect on N170.

Although there have been some ERP studies on SRI regarding orientation attention, no unified conclusion has been reached. However, there is no existing research on birthplace, a type of SRI with strong social attributes, as a stimulus material. Therefore, in this study, we used the oddball paradigm combined with ERPs to analyze the N170, P2, and N2 components to provide further electrophysiological evidence that SRI could attract orientation attention and, thus, further enrich the connotation of SRI.

2.1 Participants

35 right-handed students (30 males and 5 females, aged 18–25 years old, with an average age of 20.09 ± 1.85 years) were recruited from China’s Air Force Medical University. All participants volunteered to participate in the experiment, and their written informed consent was obtained according to the Declaration of Helsinki. They had normal naked or corrected vision, normal mental health status (based on their mental health records), no physical discomfort, and no history of neurological diseases or mental illness. This study was approved by the Clinical Trial Ethics Committee of Xijing Hospital, Air Force Military Medical University (No.KY20182047-F-1).

2.2 Materials and Procedures

The experimental stimulus materials were city names representing different SRIs, including high SRI (each participant’s birthplace), medium SRI (Xi’an, the city where the university is located), and low SRI (a totally unrelated city).We conducted a preliminary interview on the consistency of the participants’ past childhood experiences to ensure that they did not frequently change their place of residence from birth to growing up. Then, each participant was asked to list 25 city names, including their birthplace, which were all double characters. A total of 161 city names was obtained, and the City Relevance Questionnaire was completed. Each participant was then asked to rate their self-relevance on a scale of 1–7, 1 being very irrelevant and 7 being truly relevant.

This study adopted the oddball paradigm as a novel stimulus. Flower pictures were used as the target stimulus, low SRI was used as the non-target stimulus, and high and medium SRIs were used as the novel stimulus. Participants were asked to count the pictures of flowers and report the total. Other stimuli were to be ignored.

All stimuli were presented at the center of the screen, 70 cm away from the participants, and the visual angle of the stimuli was 3.28°×1.30°. The presentation time for each stimulus was 300ms, and the stimulus interval (ISI) was 600ms. Low SRI was presented 420 times with a probability of 70%; high and medium SRIs and target stimuli were presented 60 times with a probability of 10%. The first 10 stimuli were non-target stimuli, and there were no less than two non-target stimuli between the novel and target stimuli. A schematic of the experimental process is shown in Fig. 1. The formal experiment took about nine minutes from beginning to end.

2.3 Electroencephalograms recording and analysis

A Mitsar EEG acquisition and analysis system was used as the EEG equipment. Thirty-four electrode positions (FP1, FP2, AFz, F7, F3, Fz, F4, F8, FP3, FPz, FP4, T3, C3, Cz, C4, T4, CP3, CPz, CP4, P3, Pz, P4, P7, P8, PO7, PO3, POz, PO4, PO8, O1, Oz, O2, M1, and M2) were recorded using the international 10–20 system standard AG/AgCI electrode cap. The reference electrode was placed at the apex nasi. Vertical electrooculographic (VEOG) electrodes were placed 1 cm above and 1 cmbelow the right orbit. Horizontal electrooculographic (HEOG) electrodes were placed 1 cm outside both the right and left eyes. The impedance of each electrode was maintained below 5 kΩ. Directcurrent sampling, sampling rate, bandwidth 0.01Hz ~ 100Hz.

The ERP data were analyzed offline using WinEEG software. Data were imported in EDF format into MATLAB2013b. The loaded raw ERP data were previewed; then, channel location detection was conducted, and the useless electrodes were removed before carrying out the reference point determination. The bilateral papillae were used as the average reference. Filtering (30 Hz low-pass filtering, 1 Hz high-pass filtering) was undertaken to eliminate the power frequency interference of 50 Hz. Then, baseline correction was performed following the segment. The epoch duration ranged from 200ms before the stimulus to 800ms after the stimulus, and bad segments and interpolated bad conductors were removed. Then, VEOG, HEOG, EKOG, and EMOG were removed by ICA. Finally, the average was stacked. Each type of stimulus consisted of at least 40 trials.

As shown in Fig. 2, compared with the low SRI, high and medium SRIs showed no significant differences in P3a; that is, neither elicited P3a related to orientation attention. Therefore, this study focused on the analysis of N170 and P2, which reflected the early processing of the temporo-occipital region distribution.

The peak and latency electrodes of N170 were composed of six leads, P3, P4, P7, P8, O1, and O2. The measurement window was 120ms ~ 200ms following the stimulation. Since P2 did not show a clear peak in each participant, only the mean amplitude of P2 was measured and analyzed, and the measurement window was 230ms ~ 280ms after stimulation.

A three-factor repeated measures analysis of variance (ANOVA) was performed for all the above measurements: correlation degree (high and medium SRI) × brain region (left and right) × channel location (P3/4, P7/8, O1/2).

2.4 Statistical analysis

Statistical Package for the Social Sciences, version 19.0 statistical software was used, and all data were tested for normal distribution and homogeneity of variance. At-test and repeated measures ANOVA were used, and p < .05 was used as the level of statistical significance.

2.5 Cortical traceability analysis

Extrapolating the cortical origin of the ERP effect (inverse computation) provided some useful hypotheses regarding the brain's involvement in the respective cognitive processes. In this study, we used standardized low-resolution electromagnetic tomography (sLORETA). The cortical origins of N170 and P2 elicited by high and medium SRIs were analyzed. The sLORETA method uses a standardized BEM volume conduction model and extended electrode coordinates (Montreal Neurological Institute, MNI, stereotaxic coordinates) based on an effective head surface positioning system to obtain a standard current density image with zero localization error.

2.6 Frequency-domain analysis

Frequency-domain analysis is also known as the power spectral density analysis. First, a data segment was defined. We extracted the time window with a baseline of -200 to ~ 0ms from the ERP signal after preprocessing analysis. Second, the Fourier transform generates a spectrum of the specified frequency range. The ERP signal matrix for each participant, each condition, and each electrode was Fourier transformed, and the time-domain signal of the ERP data was converted into a frequency domain signal. µV was used as the unit for the ERP power spectral density map. Third, the ERP spectrum was averaged across participants to obtain a group-level ERP spectrum. Under each condition, the average spectrum value was obtained for a single participant. Fourth, traditional ANOVA and a data-driven point-to-point t-test method were used for the statistical analysis.

As shown in Fig. 2, N170, P2, and P3, which reflect the distribution of the temporo-occipital region in early perception processing, are elicited by different levels of SRI. However, compared with low SRI, the P3 elicited by high and medium SRI did not show a significant difference; that is, neither elicited P3a was related to orientation attention.

3.1 N170 comparison of related cities of different SRI degrees

Table 1 shows the N170 amplitude (µV) and latency (ms) elicited by the high and medium SRIs. The results of the variance analysis were as follows:

Table 1

N170 and P2 elicited by high and medium **SRI** (n = 35, ±SD)
Self-relevance	Channel location	N170 peak(µV)	N170 latency(ms)	P2 amplitude(µV)
High	P3 P4 P7 P8 O1 O2	-2.02 ± 3.50 -1.81 ± 3.99 -3.43 ± 4.10 -2.68 ± 4.25 -1.57 ± 3.69 -1.46 ± 3.54	148.48 ± 18.50 149.66 ± 20.31 154.83 ± 15.46 152.07 ± 21.09 146.86 ± 16.35 146.24 ± 19.01	4.45 ± 5.12 4.41 ± 5.93 5.43 ± 5.22 4.85 ± 6.44 7.14 ± 5.37 6.48 ± 4.90
Medium	P3 P4 P7 P8 O1 O2	-1.31 ± 2.41 -0.78 ± 2.87 -2.45 ± 3.78 -1.61 ± 3.82 -0.77 ± 3.71 -0.58 ± 3.27	149.10 ± 19.21 149.55 ± 18.64 151.83 ± 16.14 150.10 ± 18.14 149.48 ± 20.25 149.93 ± 21.59	6.57 ± 3.73 5.97 ± 4.54 7.23 ± 5.24 6.13 ± 5.17 8.76 ± 5.75 7.93 ± 4.90

The main effect of the N170 peak channel location was significant (F = 6.66, p < .01, η² = .19). The N170 peak at P7/8 in the temporal region (-2.54µV) was significantly larger than that at P3/4 in the parietal region (-1.48µV, p = .04) and O1/2 in the occipital region (-1.10µV, p < .01).The other main effects and interactions were not significant.

The main effect of channel location on N170 peak latency was significant (F = 3.64, p = .04, η² = .12). The N170 latency of the temporal region P7/8 (152.21ms) was slower than that of the parietal P3/4(149.20ms, p = .06) and occipital O1/2(148.13ms, p = .05) regions. The other main effects and interactions were not significant.

3.2 P2 comparison of related cities of different SRI degrees

Table 1 shows the P2 average amplitude (µV) elicited by the high and medium SRIs. The results of the variance analysis were as follows:

The main effect of self-relevance was significant (F = 5.09, p = .03, η² = .15), and the mean amplitude of P2 elicited by medium SRI (7.10µV) was significantly larger than that of the P2 elicited by high SRI (5.40µV).The main effect of channel location was significant (F = 8.58, p < .01, η² = .23). The P2 average amplitude of O1/2 in the occipital region (7.58µV) was significantly larger than that of P3 in the parietal region (5.35µV, p < .01) and P7/8 in the temporal region (5.91µV, p < .01). The other main effects and interactions were not significant (see Fig. 2).

3.3 N2 comparison of related cities of different SRI degrees

Compared with medium SRI, high SRI produced N2/N250 components in the left temporal occipital region (see Fig. 3).

3.4 Traceability analysis

The results of the vortex traceability analysis are presented in Fig. 4. The source of N170 elicited by the medium SRI was mainly located in the left temporo-occipital region. The maximum current density was located in the region of the Broca 22 area, in the left superior temporal gyrus. In contrast, the source of N170 elicited by the high SRI was mainly located in the right temporo-occipital region. The maximum current density was located in the region of Broca 37 area, in the right middle temporal gyrus. Although the source of P2 elicited by high and medium SRI was located in the left temporo-occipital region (the maximum current density was located in Broca 39 area, in the left middle temporal gyrus), the P2 current density elicited by high SRI was greater than that elicited by medium SRI.

3.5 Frequency-domain analysis

A Fourier transform was used to analyze the frequency domain of the preprocessed ERP original signal, and the power spectrum(µV) was obtained for each frequency band of the ERP. After preprocessing, the transformed ERP power at each frequency was divided into five bands: delta (1–3 Hz), theta (4–7 Hz), alpha (8–12 Hz), beta (13–30 Hz), and gamma (> 30 Hz).

EEGs contain specific frequency bands of different physiological significance; moreover, the characteristics of different frequency bands are of great significance for characterizing different brain activities. In this study, ERP preprocessing signals were defined as six frequency bands: delta (1–3 Hz), theta (4–7 Hz), alpha1 (8–10 Hz), alpha2 (11–12 Hz), beta1 (13–20 Hz), and beta2 (20–30 Hz).

To further understand the variation of the frequency-domain fast Fourier transform (FFT) in the whole brain region, we averaged the FFT values of 16 channels in six frequency bands. The traditional ANOVA and the data-driven point-to-point t-test methods were used for the statistical analysis, as shown in Table 2. Figure 5 shows the channel distribution for the FFT.

Table 2

FFT amplitude ANOVA results of six frequency bands among three different SRI.
Frequency band	F	P	η2
Delta	5.15	0.038	0.256
Theta	4.45	0.052	0.229
Alpha1	2.281	0.152	0.132
Alpha2	1.219	0.287	0.075
Beta1	1.881	0.190	0.111
Beta2	11.521	0.00	0.424

The results show that the amplitudes of the three groups of SRI in 16 EEG channels with six frequency bands decreased in conjunction with an increase in frequency (Fig. 6).Especially in the beta band, FFT fluctuated between 0.1 and 0.4uV, which indicates that when the participant was in a static state, most of the energy was concentrated in the lower frequency band.

The results when comparing the FFT amplitudes of the three conditions were as follows:

(1) The FFT amplitudes for the three SRI groups of SRI were significantly different in the delta and beta2 frequency bands. The variation trend of the FFT in other frequency bands was similar, and the intra-group difference was not significant.

(2) In the delta frequency band, the FFT value for high SRI was greater than that of the medium and low SRI.

(3) Delta frequencies were found in the frontal (FP1, FP2, F3, F4, F7, F8) and temporal lobes (T3, T4, T5, and T6).

This study showed that, compared with low SRI, high and medium SRI elicited similar temporal-occipital N170. Furthermore, compared with high SRI, medium SRI induced larger temporal-occipital P2. However, high and medium SRI failed to elicit P3a associated with orientation attention.

In this study, high SRI elicited N170 in the right temporo-occipital region, whereas medium SRI elicited N170 in the left temporo-occipital region. There was no significant difference in the amplitudes of the N170 elicited by high and medium SRI. The N170 of the left and right temporo-occipital regions had different meanings. The temporo-occipital region N170 was the first ERP component sensitive to language materials [38]. The word-induced N170 was mainly distributed in the left temporo-occipital region, while the right temporo-occipital region N170 mainly represented recognition of emotions, faces, objects, etc. [39–41]. The N170 elicited by medium SRI was mainly located in the left temporo-occipital region, indicating that medium SRI belonged to general text perception processing. However, our study found that the N170 elicited by high SRI was activated in the right temporo-occipital region, indicating that the processing of high SRI was not limited to word processing. There are three possible reasons for this finding.

First, we used birthplace as high SRI in this study; however, most of our participants were freshmen, who had been away from their hometown for a long time and could not go home midway through the semester. Therefore, they were full of yearning for their hometowns. When participants saw the name of their birthplaces, their emotions activated, and the N170 elicited by emotional information activation was mainly distributed in the right temporo-occipital region [42, 43].

Second, the N170 of the right temporo-occipital region mainly represented the recognition of faces, familiar objects, and other categories. When participants saw their own birthplaces, this may have trigged feelings of missing their family members, which led to the imagining of their faces or of the landscape in their hometown. This could also explain why the birthplace activated the right temporo-occipital region.

Third, high SRI was of great significance to individuals and may be materialized by participants. For example, when it comes to the birthplace, participants might have referred to a bridge or a house in their hometown, and familiarity with these objects mainly elicited N170 in the right temporo-occipital region [44]. The N170 in the right temporo-occipital region was mainly elicited by text stimuli in illiterate preschool children, whereas the N170 in the left temporo-occipital region was mainly elicited by adults, suggesting that the illiterate children only viewed the words as shape objects [40, 41].

In this study, compared with medium SRI, high SRI elicited a smaller temporal-occipital P2. P2 might reflect an automated process as well as a smaller cognitive load requirement [45, 46]. Our results also found that high SRI could be automatically processed, and a smaller P2 elicited by birthplace indicated a smaller activation at a perceptual level, suggesting that high SRI required a smaller cognitive load. Some studies have found that the amplitude of P2 elicited by faces is inversely proportional to the degree of self-relevance; that is, self-face induced a smaller P2 amplitude than friend and stranger faces [45]. Sawada et al. (2019) obtained similar results using handwriting as experimental material; in other words, compared with other people’s handwriting, participants’ own handwriting induced a smaller P2 in the right posterior occipital lobe. This was consistent with our results.

Previous studies showed that both self-face and name elicited higher P3a [47], which is a typical component of the second stage of the orientation response [48]. However, our study found that high and medium SRIs did not induce a significant P3a. This difference might be due to different experimental paradigms and stimuli materials. In this study, compared with the high probability of low SRI as the standard stimulus, the low probability of high and medium SRI may have relatively insufficient novelty [49].

Stimuli in this study were all written materials, whereas previous studies used shapes or figures that were quite different from words. For example, J. Chen (2011) used large circles as the standard stimulus, and Ninomiya (1998)’s group used red squares as the stimulus. P3a was related to the orientation attention of the novel and unusual stimuli, and the greater the difference between the deviant and standard stimuli, the higher the amplitude of P3a. In this study, both the bias and standard stimuli were text based, and the insufficient novelty of the deviant stimulus might be the main reason for its failure to induce P3a.

Frequency-domain analysis is commonly used in engineering to decompose complex signals into simple ones. In our study, we used the frequency-domain analysis method to decompose ERP. We found that as the frequency increased, the amplitudes of the three groups decreased in 16 EEG channels in six frequency bands. The FFT value of the high SRI was greater than that of the medium and low SRI in the delta frequency band. Delta waves are thought to be associated with an unconscious state [50]; that is, they possess fewer cognitive processing resources. As self-relevance increased, the information required fewer cognitive resources, the FFT value in the delta band was larger.

There are some limitations to note in this study. The participants were mainly male. Since we conducted research in a military academy, it is difficult to balance participants’ gender. Future studies should seek to eliminate possible gender bias. Moreover, ERP technology does not take advantage of space. Further research should be conducted with near-infrared spectroscopy and fMRI to present a more thorough description of the mechanisms involved in these important cognitive processes.

In conclusion, N170 elicited by high SRI was activated in the right temporo-occipital region, indicating that the processing of birthplace as high SRI was not limited to word processing. Rather, it relied on emotional activation. High SRI was automatically processed, and a smaller P2 elicited by the SRI pointed to a smaller activation at the perceptual level, suggesting that high SRI required a smaller cognitive load.

Ethics approval and consent to participate

All participants volunteered to participate in the experiment, and their written informed consent was obtained according to the Declaration of Helsinki. This study was approved by the Clinical Trial Ethics Committee of Xijing Hospital, Air Force Military Medical University (No.KY20182047-F-1).

Consent for publication

Not applicable.

Availability of data and materials

The data that support the findings of this study are available from Air Force Medical University, but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. The data are, however, available from the authors upon reasonable request and with the permission of Air Force Medical University.

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.

Funding

This work was supported by grant funding of Key Project of Air Force Equipment Comprehensive Research, Grant No. KJ2022A000415 and No. 2020-JCJQ-ZD-263-00-04.

Authors’ Contributions

Conceptualization, Qingchen Zhan, Xinhong Li, Xiuchao Wang and Xufeng Liu; Data curation, Yang Cao, Yuting Zhang; Funding acquisition, Xufeng Liu; Investigation, Sizhe Cheng, Rui Qiu, Wanying Xing and Yan Huang; Methodology, Yan Huang, Xiuchao Wang and Xufeng Liu; Project administration, Xinhong Li, Yan Huang, Xiuchao Wang and Xufeng Liu; Resources, Qingchen Zhan, Yan Huang and Xiuchao Wang; Software, Sizhe Cheng, Wanying Xing and Yang Cao; Supervision, Yan Huang, Xiuchao Wang and Xufeng Liu; Validation, Sizhe Cheng, Rui Qiu and Yuting Zhang; Visualization, Yang Cao; Writing – original draft, Sizhe Cheng, Qingchen Zhan and Rui Qiu; Writing – review & editing, Sizhe Cheng, Qingchen Zhan, Rui Qiu and Xufeng Liu. All authors reviewed the manuscript.

Acknowledgments

Thank you to every scientific researcher who helped with this study. We thank Professor Zhao Lun for providing technical guidance. We thank all the participants for their active participation and cooperation.

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The reduced cognitive load of strong self-related information’s orientation attention revealed by ERP Running title: Cognitive Load of Self-Related Information

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Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

1. Introduction

2. Materials and Methods

2.1 Participants

2.2 Materials and Procedures

2.3 Electroencephalograms recording and analysis

2.4 Statistical analysis

2.5 Cortical traceability analysis

2.6 Frequency-domain analysis

3. Results

3.1 N170 comparison of related cities of different SRI degrees

3.2 P2 comparison of related cities of different SRI degrees

3.3 N2 comparison of related cities of different SRI degrees

3.4 Traceability analysis

3.5 Frequency-domain analysis

4. Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1