The Information Processing Flexibility of Individuals with Obsessive- Compulsive Tendency: An Event-related Potentials Study

doi:10.21203/rs.3.rs-4996738/v1

Download PDF

Research Article

The Information Processing Flexibility of Individuals with Obsessive- Compulsive Tendency: An Event-related Potentials Study

https://doi.org/10.21203/rs.3.rs-4996738/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

Obsessive-compulsive disorder (OCD) is a common clinical psychological disease. A large number of studies have shown that OCD patients have cognitive dysfunction, mainly in attention, decision-making, information processing, and so on. Previous studies of OCD information processing focus on processing ways, but studies of its flexibility are limited. As information processing flexibility is a component of executive function and OCD patients have a deficiency in executive function, information processing flexibility could indirectly reflect that OCD patients may be insufficient in information processing flexibility. Hence, we must explore its characteristics in flexible information processing.

Methods

To eliminate the effects of other comorbid factors, we use the Padua Inventory to screen high obsessive-compulsive tendency (HOC) and low obsessive-compulsive tendency (LOC) individuals as the research object university students. The final sample includes 16 HOC and 15 LOC participants. To make the experiment more in line with ecological validity, the study used a single probability-adjusted Flanker task (i.e., a cue of 20%, 50%, and 80% probability was given before each trial)， combined with an ERP study to investigate information processing flexibility and its corresponding brain mechanisms in individuals with obsessive-compulsive tendencies.

Results

(1)In the stage of cue presentation, there were no significant differences in P2 (150-250) among individuals with HOC under the three cue probability conditions, but there were significant differences among those with LOC. In the stage of stimulus presentation, there were no significant differences in N2 (100-200) and N3 (250-300) components among individuals with HOC under the three cues, while there were significant differences among those with LOC, and the amplitudes under 80% probability conditions were greater than those under 20% probability conditions; (2)The N2 amplitude of individuals with LOC under effective cues was significantly greater than that under ineffective cues, while there was no significant differences among individuals with HOC.

Conclusion

Individuals with HOC were less responsive to situational changes and had defects in information processing flexibility, while individuals with LOC were more flexible in information processing, which was manifested in both the cue presentation stage and the stimulus response stage;Individuals with HOC were not easily affected by cues and less strategic in regulating and controlling information processing.

Obsessive-predisposition

information processing flexibility

concentrated processing

event-related potentials

Obsessive-compulsive disorder, as a debilitating chronic mental illness, has received increasing attention from researchers since its introduction. Researchers have studied the characteristics and pathogenesis of OCD from many angles to provide evidence for its treatment. Although in recent years, the research on OCD has been deepening from the technical means (ERP, fMRI), research content (attention, memory, decision-making, etc.) [1–3], there is still a lack of systematic and comprehensive understanding of the possible brain dysfunction in OCD patients. A large number of studies on the neuropsychology of OCD patients have initially shown that there is cortico-striatal-thalamo- cortical circuit dysfunction in patients with OCD [4–7]. The abnormalities mainly exist in the frontal cortex. The frontal cortex mainly included the orbitofrontal cortex (OFC) and the anterior cingulate cortex (ACC). The striatum and thalamus dominate the subcortical brain structure [2][8]. Abnormalities in these brain regions lead to an individual's cognitive deficit in executive control [9–11], attentional inhibition [12], allocation of cognitive resources [13], decision making [14], and so on. Purcell (1988) compared the data of OCD patients with normal individuals through the ERP study and found that OCD patients had insufficient prefrontal attentional inhibition; Bokura [15] conducted an ERP study on OCD patients using the Go/No-Go paradigm and found that the lateral orbitofrontal lobe (OFC) of OCD patients showed significant N2 and P3 wave response when the response was inhibited under NO-GO condition.

Information processing flexibility refers to the transformation of the information processing mode (centralized processing and parallel processing) used by the individual for the stimulus task under different cue scenarios, that is, the transformation between the centralized processing mode and the parallel processing mode under different task scenarios. The flexibility of information processing is a part of the executive function, which mainly reflects the inhibition control and cognitive transfer ability of an individual, realizing the conversion of different cognitive perspectives in the same cognitive activity or the flexible conversion from one cognitive activity to another cognitive activity [16]. Its essence is the conversion of rules [17–18].

Numerous studies have shown that patients with OCD have cognitive dysfunction in cognitive tasks such as attention and decision-making [19–21]. Executive dysfunction is an essential aspect of cognitive dysfunction in OCD patients, which is mainly manifested by deficiencies in mode-switching ability and response inhibition [22]. The impairment of executive function in OCD patients indirectly reflects the lack of flexibility in information processing. Some previous studies on OCD also preliminarily reflected the insufficiency of information processing flexibility in OCD individuals [23–26]. Soref (2008) conducted a study on individuals with obsessive-compulsive tendencies and found that individuals with high obsessive-compulsive tendencies reflected the inertia of their information-processing methods to a certain extent. Miao [27] also found in their initial study that individuals with a high compulsive tendency lacked flexibility in information processing. Although previous studies have reflected information processing deficits in individuals with obsessive- compulsive tendencies to a certain extent, their flexibility in scenario setting and ecological validity was still insufficient, which could not fully explain the characteristics of individuals with obsessive- compulsive tendencies in information processing flexibility. Moreover, there are insufficient ERP studies on information processing flexibility. By creating more flexible experimental scenarios and combining ERP technology, this study aims to explore further the characteristics of information processing flexibility of individuals with obsessive- compulsive tendencies and provide a certain basis for further research on patients with OCD in the future.

In the study, we take the Flanker task as the research paradigm and investigate the participants' information processing flexibility by setting different probabilistic scenario tasks. According to Coles [28] and Gratton [29], there are two stages in the Flanker task: the parallel processing stage and the centralized processing stage. The parallel processing stage is relatively early, and the reaction time is short, while the centralized processing stage is relatively late, and the reaction time is extended. Gratton (1988) found that participants were strategic in their response choice of parallel or centralized processing; that is, they would choose the processing mode according to different contexts.

Coles (1985) and Gratton (1988) believe that individuals would choose the mode of centralized or parallel processing based on the relative effect of the stimulus situation. Accordingly, if experimental conditions are set, subjects can choose the processing mode by anticipating the relative effects of the two stages. When individuals expect that the parallel phase will likely point to the correct response, they will rely more on parallel processing because the evidence from the parallel phase is the first effective, and the response is faster. If the individual anticipates a response that is likely to point to an incorrect response in the parallel phase, more reliance will be placed on concentrated processing to avoid errors. Soref (2008) argue that parallel processing is more appropriate for consistent stimuli, and concentrated processing is better for inconsistent stimuli. Therefore, different expected scenarios can be set on the Flanker task to test the individual's changes in his choice of centralized or parallel processing mode under different scenarios so as to test his flexibility in information processing. One scenario induces the subject to expect the trial to be consistent, while the other induces the subject to expect the trial to be inconsistent. If the expectation of consistent and inconsistent trials influences the choice of parallel or concentrated processing preferences, then the responses of individuals will vary under different expected scenarios.

Strategy adjustment is related to the symbolic representation of the task situation constructed by the subjects, and the strategy changes with the change of the symbolic symbols [30]. After the symbolic representation was established, subjects were allowed to rapidly change their strategies even without explicit descriptions of stimuli and responses. In other words, if environmental cues provide information about subsequent changes in the type of stimulus, individuals should choose a more appropriate way to process that stimulus. That is, on a context-driven basis, subjects should be able to change their processing strategies from top to bottom. However, the global probability adjustment experiment (setting probabilistic scenario changes between blocks) could not prove the characteristics of processing strategy adjustment on the Flanker task. In this experimental condition, the adjustment effect may be sequential. The sequential effect here refers not to the repetition of the target letters but to the repetition of the noise levels, i.e., the trials in which most of a block is consistent, or all is inconsistent after overall probability adjustment, resulting in their noise levels being more repetitive and enhancing a particular stimulus-response connection. The global probability adjustment is not flexible in the setting of the situation, which is far from the real situation, and it mainly reflects the immediate response of the subject to the current stimulus. It has certain advantages in testing the tendency of individuals with obsessive-compulsive tendencies to process information, but it has disadvantages in testing the flexibility of their information processing.

To address the above deficiencies, this study manipulates the probability of consistent and inconsistent stimuli in each trial of Flanker's task. This approach makes each stimulus appear randomly, more consistent with the actual situation and avoids the sequential effect caused by repeated noise levels in the overall probability adjustment. At the same time, after the pre-cue manipulates the subjects' information processing mode in advance, the characteristics of individual information processing flexibility can be detected by comparing the changes of individual information processing modes under different cues. The experiment includes three kinds of cues: In half of the participants, "+" means that the stimulus is 80% likely to be consistent, "?" means there is a 50% chance of agreement, and" -" means there is a 20% chance of agreement. In the other half of the subjects, "+" and "-" have opposite meanings. According to the top-down view, individuals can adjust their processing strategies according to the cues. The experimental hypotheses are as follows:

The differences in EEG component response between the two groups are reflected in both the cue presentation and the stimulus presentation stage.
There are no significant response differences among individuals with a higher obsessive-compulsive tendency under the condition of cue probability, while there are significant differences among individuals with a lower tendency.
There are differences between the two groups in response to the validity of the cues.

The differences among individuals in the higher-tendency group are insignificant with valid and invalid cues, while the differences in the lower-tendency group are more significant.

All procedures of this study were based on the ethical principles of the Declaration of Helsinki and its amendments for human subjects and were reviewed and approved by the University's Ethics Committee.In the study, data collection and follow-up study were carried out after the participants themselves signed the informed consent form, and all procedures were completed by the rigorously trained principal experimenter.

2.1 Participants and Measurement

Four hundred copies of the Padua Inventory (PI,[31]) (Chinese version) were distributed. The participants were informed that the questionnaire was designed to gather insights into everyday experiences. They were encouraged to provide honest responses based on their unique circumstances, and some severe participants would be randomly selected to participate in a follow-up experiment. The α coefficient of PI was 0.89. We invited individuals who scored in the top 10% and bottom 10% on the PI test to form high and low-propensity groups, respectively. Finally, 31 (16 females, 15 males) students aged 19–23 volunteered to participate in the experiment (M = 20.2, SD = 1.94). The mean PI score of the high-propensity group (8 males and 8 females) was 109.91 (SD = 25.31), and the low-propensity group (7 males and 8 females) was 13.30 (SD = 2.95) with F(1,29) = 143.28, p < .01,η²=.88, 1-β = 1.00. Informed consent was signed before the experiment. All subjects had not participated in similar experiments before and were right-handed, with normal or corrected vision. After the experiment, they were given a reward of ¥ 10.

2.2 Stimulation and materials

The string stimulus consists of one target letter (H or S) and four interfering letters (S or H). These stimuli include consistent (SSSSS\HHHHH) and inconsistent (SSHSS\HHSHH). The Angle of view of the letters that make up the stimulus is about 5°, and the Angle of view of the entire string is about 2.5°.

2.3 Experimental design

The study adopted a three-factor mixed experimental design of 2×2×3. The intra-group variables were the type of stimulus (consistent versus inconsistent) and the probability of cue (80%, 50%, 20%), while the inter-group variables were the group (high propensity, low propensity). The dependent variables were mainly response time. In the formal experiment, the occurrence probability of both the target letters H and S is 50%.

2.4 Experimental procedure

The experimental program was written using E-Prime 2.0 software, and the process is shown in Fig. 1.

The subjects were 60cm away from the computer screen. At the beginning of the experiment, a fixation point "●" of 500ms was presented in the centre of the screen, followed by a prompt cue "+\? \- "for 100ms, and then a 1400ms blank screen. Then, a string stimulus appeared for 100ms, requiring the participants to react quickly and accurately to the target letter in the middle. The participant could respond within the 1500ms, followed by a 500ms blank screen.

The experiment consisted of 3 blocks, each containing 200 stimuli, a total of 600 trials, and there was a rest between each block. The experiment lasted about 30 minutes, and all the stimuli were presented randomly. Both "+" and "-" were used as cues for 240 trials. "?" was used as cues for 120 tries. Half of the subjects had 192 consistent stimuli and 48 inconsistent stimuli under the "+" cue. Under the "?" cue, there were 60 consistent stimuli and 60 inconsistent stimuli. There were 192 inconsistent stimuli and 48 consistent stimuli under the "-" cue. The other half of the participants have the opposite stimuli under the "+" and "-". There were 30 practice tests before the experiment, which were not included in the statistical analysis of the data. In the experiment, half of the subjects were asked to press the 'F' key for the letter S and the 'J' key for the letter H. The other half did the opposite.

After the formal experiment, the participants were asked in an interview if they felt uncomfortable. No subjects reported any discomfort.

2.5 Experimental Equipment

The experiment was conducted in a soundproof EEG laboratory. The experiment setup included two computers (one for recording and storing EEG data with a Pentium IV 1.6GHz processor and 1024MB RAM, a 17 "monitor with a refresh rate of 100Hz and a refresh cycle of about 10ms; and the other for running experimental programs and saving behavioural response data), one monitor with 1024*768 resolution, an observer, an ERP recorder and 64 electrode caps. The stimuli were presented in the centre of the monitor screen, and the subjects sat on a comfortable chair in front of the screen, with the line connecting the eyes and the centre of the monitor parallel to the ground. The visual distance of their eyes was 60cm.

2.6 Electroencephalography recording and processing

The ERP recording and analysis system developed by German Brain Products was used in this study. EEG was recorded using 64 electrode caps extended according to the international 10–20 system. The reference electrodes were connected to the mastoid of the two ears; TP9 and TP10 were the reference electrodes on both sides, and the forehead was grounded. Horizontal oculogram (HEOG) was recorded by an electrode placed on the outside of the right eye, and vertical oculogram (VEOG) was recorded by an electrode placed below the right eye. Scalp resistance at all electrodes was controlled below ten kω. The filter bandpass is 0.01 to 100Hz, and the sampling frequency is 500Hz/ guide. Before the formal recording, the subjects were told how to correctly and reasonably make critical responses in the experiment (keep the head stable, minimize the eye movement, keep the arm and body stable and do not make too much movement), and through practice training, they were familiar with and mastered the correct critical response. After the continuous recording of EEG was completed, the data were processed offline, the continuous EEG data were re-referenced with the average value of bilateral mastoid electrodes (TP9 and TP10), and the FCz electrode points were recovered. The digital filter was 0.01-30Hz, and the average value of EEG signals of 200ms before the occurrence of the cue was taken as the baseline to complete the baseline correction. All EEG with correct responses were superimposed, EOG was automatically corrected, and other artefacts were fully excluded. In the superposition, the amplitude greater than ± 80µV was automatically deleted.

According to the purpose of this study, after the pretreatment of each condition, the superposition average of repeated types of stimuli under each condition was completed according to different cue conditions. The ERP components induced by cue and target stimulus were analyzed, respectively. According to 200ms before the cue, 1000ms after the cue, 200ms before the stimulus and 800ms after the stimulus, the cue-related ERP and stimulation-related ERP of the high and low groups under each condition were extracted, and their amplitude was measured. The key response times were lower than 200ms and higher than 800ms, and the wrong response times were regarded as the wrong response times and deleted from the response data.

Due to the significant EEG fluctuation of one participant in the formal experiment, he was excluded, and finally, the data of 30 participants (15 with HOC and 15 with LOC) were analyzed in the EEG analysis part. SPSS17.0 was used for statistical analysis of the EEG data. Based on past research and the whole brain activity, the position of the electrode to be analyzed was determined. The cue-related and stimulus-related ERP components were extracted under different ex-perimental conditions and the difference ANOVA was used to determine the differences in neural activity between the high and low groups under different cue conditions.

According to the purpose of this study and the total average map, combined with the basis of previous studies [32–33] and the analysis of the brain topographic map, we selected the data of 15 electrode points in the centre line (Fz et al.), bilateral electrodes (F3\4, C3\4, P3\4) and occipital lobe electrodes (PO3, PO4, PO7, PO8) for statistical analysis. At which electrodes the individuals showed significant differences in cue correlation and stimulus correlation were measured respectively.

3.1 Cue-related ERP

Through the analysis and comparison of the relevant components of each electrode point, we found that in the cue stage, when faced with the three probability cues (80%, 50%, 20%), individuals with LOC and HOC showed significant differences in the activation of EEG components at the midline electrode point. Among them, Fz, Cz and FCz were particularly obvious at three electrode points (as shown in Fig. 2 and Fig. 3). It can be found that P2 (150–250) and N2 (300-450ms) and P3 (450–600) components can be observed at the midline electrode points of the frontal-parietal lobe (Fz et al.). Through repeated measurement ANOVA for the total mean amplitude of each electrode point at the midline by groups (HOC, LOC) * cue probability conditions (80%, 50%, 20%), we found that:

In the P2 component, the main group effect is not significant. The main effect of cue probability condition is significant with F(2,36) = 4.43, p = .02, η²=.20,1-β = .73. The interaction effect of clues probability conditions and group is significant with F (2,56) = 4.14, p = .02,η²=.19,1-β = .70 (as shown in Fig. 4). A further simple analysis showed no significant differences in the amplitude under the three cueing probability conditions in the high group. However, in individuals with LOC, there were significant differences in the amplitude under the three cue probability conditions with F(2,28) = 6.79, p = .006,η²=.38,1-β = .87 (Fig. 3).There are significant differences in the average amplitude under 50%. The results indicate that there are significant differences between the two groups under 80% (F (1,28) = 5.12,p = .04,η²=.22,1-β = .57) and 50% (F(1,28) = 5.75, p = .03,η²=.24,1-β = .62) probability conditions (Fig. 2). The amplitude of individuals with LOC was significantly higher than that of individuals with HOC. However, under the 20% probability condition, the difference between the two groups is insignificant.

In N2 and P3, the main effects of group and cue probability conditions were not significant, and the interaction effect was not significant either. The results showed that there were significant differences among individuals in both groups in N2 (F(1,28) = 6.48, p = .02,η²=.27,1-β = .67) and P3 (F(1,28) = 5.46, p = .03,η²=.23,1-β = .60) components under the 80% cue probability condition and the amplitude of the low-propensity individuals was significantly higher than that of the high-propensity individuals. In contrast, there were no significant differences among individuals in both groups in N2 and P3 components under 50% and 20% probability conditions.

3.2 Stimulus-related ERP

(1) Comparison of EEG differences between the two groups under three kinds of cue probability cues

In the stimulation stage, through the analysis and comparison of the relevant components of each electrode point, we found that the EEG response of individuals from the high and low groups under the three kinds of cue probability prompts would activate different EEG components at different stages, and the EEG differences under different conditions were particularly significant in different electrode points. By observing the average ERP waveform of the high and low groups under three kinds of cue conditions, it was found that there were prominent P2 (100–200), N3 (250–300), P3 (300–450) components in the frontal-parietal region, and obvious N2 (100–200), N3 (250–300) and P3 (300–450) components in the temporal occipital region. Through repeated measurement ANOVA for the total mean of the amplitude of each component at each brain region by groups (HOC, LOC) *cue probability conditions (80%, 50%, 20%), we found that:

In the N2 component of the posterior occipital region (PO7, PO8, POZ) (Fig. 5, taking PO8 as an example), the main group effect was insignificant, and the main cue effect was insignificant. The interaction effect of clues and group was effect with F(2,56) = 2.76, p = .07,η²=.13, 1-β = .51( Fig. 6). Further simple analysis showed that the amplitudes of the three kinds of cues were not significantly different in the high-propensity individuals. However, the amplitudes under the three cue probability conditions were significantly different among individuals with HOC with F(2,28) = 4.34, p = .03,η²=.33, 1-β = .68, and the amplitudes under 80% probability conditions were significantly higher than those under 20% probability conditions. The results showed no significant differences among individuals in both groups under the three cue probability prompts.

In the N3 component of the midline region (FZ, CZ, FCz) (Fig. 7, taking Cz as an example), the main group effect was not significant, and the main cue effect was not significant either. The interaction effect of clues and group was significant with F (2,56) = 4.92, p = .01,η²=.22,1-β = .77 (Fig. 8). Further simple analysis showed no significant differences among individuals with HOC under the three cue probability conditions. However, the differences among the individuals with LOC were significant with F(2,28) = 4.07, p = .04,η²=.31, 1-β = .65. The amplitude under the 80% probability condition was significantly higher than that under the 20% probability condition. The results showed no significant differences among individuals in both groups under the three cue probability prompts.

The analysis of P3 components showed that there were differences in multiple brain regions, and the differences were mainly reflected in the differences between groups, especially the electrodes in the left brain region (F3, C3, P3) and the occipital lobe (PO3, PO4, POz) (Fig. 9, C3 and PO3 as examples). ANOVA for the total mean of the amplitude of P3 components in these two brain regions showed that there were no significant differences in the main effect of cues and interaction in the occipital region, while the main group effect was significant with F(1,28) = 4.57, p = .046,η²=.20, 1-β = .53. Further analysis showed that under 80% (t = 2.19,p = 0.04) and 50% (t = 2.32,p = 0.03) probability conditions, the differences among individuals in both groups were significant. The amplitude of individuals with LOC was significantly more extensive than that of individuals with HOC, but the differences were insignificant under the 20% probability condition. In the left brain region, the main effect of cue and interaction was not significant, but the main effect of the group was significant with F(1,28) = 9.01, p = .01,η²=.33,1-β = .81. Further analysis showed that under the three probability conditions (80%: t = 2.88,p = 0.01; 50%: t = 3.11,p = 0.01; 20%:t = 2.57,p = 0.02),there were significant differences among individuals in both groups and the amplitude of individuals with LOC was significantly greater than that of individuals with HOC.

(2) Comparison of EEG differences between the two groups under cue validity

By observing the average ERP waveform of the two groups under both effective and ineffective cues, it was found that there were prominent P2 (120–200), N3 (230–300) and P3 (300–450) components in the frontal-parietal region. N2 (120–200), N3 (250–300), P3 (300–450) were found in the temporal occipital region. The analysis of these EEG components in both groups under the effective and ineffective cues showed that there were no significant differences in P2, N3 and P3 components, but there was a difference in N2, which was mainly reflected in the electrode points in the temporal occipital region (PO3, PO4, PO7 and PO8) (see Fig. 10). Through repeated measurement ANOVA for the total mean amplitude of N2 in this region by groups (HOC, LOC) * cue validity (valid cue, invalid cue), it was found that:

The main effect of the group was not significant, and the interaction effect of cue validity and group was not significant. However, the main effect of cue validity was significant with F(1,28) = 13.83, p = .002,η²=.43,1-β = .94. Further simple analysis showed that there were no significant differences in the amplitude of N2 components induced by effective cues and ineffective cues among individuals with HOC. However, there were significant differences among individuals with LOC in the amplitude of N2 induced by effective cues and ineffective cues with F(1,14) = 40.84, p < .001,η²=.82,1-β = 1.00, and the amplitude of N2 induced by effective cues was more significant than that induced by ineffective cues. The two groups had no significant differences under both effective and ineffective cues.

4.1 Deficits of Individuals with HOC in the Flexibility of Information Processing

The analysis of EEG data showed that N2 (100–200), N3 (250–300) and P3 (300–450) components were induced in both the high and low groups after stimulus presentation. The EEG response components of the high and low groups under the influence of three kinds of cues were analyzed. It was found that there were significant differences in N2 components in the posterior occipital lobe; that is, individuals with LOC had significant differences in N2 components in response to stimuli under three kinds of cues, mainly manifested as that the amplitude under 80% conditions was significantly greater than that under 20% conditions, while there were no significant differences in N2 components when individuals with HOC responded to stimuli among under three kinds of cues. The N2 component, as a conflict monitoring mechanism [34–35], reflects the higher cognitive function of individuals to a certain extent. However, the deficiency of higher cognitive function or cognitive resources in individuals with HOC reduces their processing and transformation ability; that is, their executive control ability is weakened. As a result, there was no difference in the activation of the electrical components of the brain in response to different stimuli. However, individuals with LOC, because their cognitive ability was not impaired, showed differences in the stimulating brain components after different cues, indicating that individuals with LOC are more flexible in information processing. Significant differences in N3 components were found in the midline of the parietal and frontal lobes. Significant differences in N3 components were also found in individuals with LOC under three kinds of cue conditions, mainly manifesting that the amplitude was significantly greater under 80% conditions than under 20%.In comparison, no differences in N3 components under the three kinds of cues were found in individuals with HOC. The N3 component with an incubation period of 200-350ms is very similar to the N2 component proposed by Veen & Carte, which is latent in 340-380ms. Its distribution area is characterized by the distribution of the frontal central scalp, which is consistent with our research results. Its source is located in the anterior cingulate gyrus [36], which may reflect the cognitive processing of an individual paying attention to one object while ignoring another. Individuals with LOC have significant differences in the stimulus-induced N3 components under the three cue conditions, indicating that individuals with low propensity take different cognitive processing processes in response to stimuli under different conditions. The amplitudes under 80% conditions are significantly greater than those under 20%, indicating that parallel processing may require more psychological resources. However, there was no significant difference in N3 in individuals with HOC under the three cue conditions, indicating that their cognitive processing methods were more consistent. All these indicate from the neural mechanism that individuals with HOC have deficiencies in the flexibility of information processing, while individuals with LOC have relatively flexible information processing.

We analyzed EEG components in the stimulation stage and found significant differences in P3 components between the high and low groups in the occipital lobe and the left brain region. In the occipital lobe, there were significant differences in P3 components between the high and low groups after 80% and 50% cue probability conditions, and the amplitude of individuals with LOC was significantly greater than that of individuals with HOC. However, at 20%, there were no significant differences in the P3 component. These electrical differences were consistent with behavioural responses. Further, from the brain's neural mechanism, individuals with high compulsive tendencies were less flexible in information processing than those with low compulsive tendencies. In the left brain region, there were significant differences in P3 components in both the high and low groups under the three cues, and the P3 amplitude of the individuals with LOC was still significantly greater than that of individuals with HOC. Polich [37] believed that the amplitude of P3 was directly proportional to the attention level of the participants, and the early P3 component reflected the activation of the attention-switching mechanism. In the process of information processing, if attention resources are not allocated effectively, the available attention resources will be reduced, which is reflected in the amplitude reduction in P3 [13]. The differences in P3 between the two groups indicated that the individuals with LOC paid more attention to the stimuli after the cue than individuals with HOC, which also reflected the lack of attention ability of the individuals with HOC compared with individuals with LOC. The differences in the responses of individuals in different brain regions indicate that the attention processing of individuals is the result of the joint action of multiple brain regions, and different brain regions have different processing abilities for different stimuli.

4.2 Individuals with HOC were not easily affected by cueing

EEG data analysis showed that after effective and ineffective cues, there were differences in N2 components in the electrodes in the temporal and occipital region between individuals with HOC and LOC. There were significant differences in N2 components in individuals WITH LOC under effective and ineffective cues. The amplitudes under effective cues were more significant than those under ineffective cues, while there were no significant differences in N2 amplitudes between high-propensity individuals under effective cues and ineffective cues. N2 reflects a monitoring mechanism [38], which refers to an individual's effective monitoring of the prompt cues in our study. The N2 component reflects an individual's sensitivity to matching cues and responses [39], and there were significant differences in the activation of the N2 component in individuals with LOC under effective and ineffective cues. However, there were no differences between individuals with high and low propensity. This result indicates that individuals with LOC have a high degree of dependence on cues and closely monitor whether cues and stimuli match each other. Therefore, prompt cues significantly impact individuals with LOC, while individuals with HOC lack dependence on cues.

By analyzing the EEG data in the cue stage, we also found that the individuals with HOC were not easily affected by the prompt cues and were less flexible in changing the information processing mode predicted by the cue, which indirectly reflected the lack of flexibility in information processing of the individuals with HOC. The results showed that P2, N2 and P3 were induced in individuals with HOC and LOC during the cue presentation stage, and there were differences in P2 and N2 components in both groups at the midline of the frontal-parietal lobe (Fz et al.). Below 80% and 50%, the differences between high and low groups were significant, and the amplitude of individuals with LOC was significantly greater than that of individuals with HOC. However, under the 20% condition, the differences between the high and low groups were insignificant. As an exogenous component, P2 is related to the allocation of attention resources, and the more attention resources are consumed, the greater the volatility of P2 [40]. This indicates that individuals with LOC need to consume more attention resources to process cue stimuli under the probability of 80% and 50% cues, while the two groups consume similar attention resources under the probability of 20% cues, which also indicates that individuals from different groups have different pre-preparation mechanisms when faced with different cues. Through the analysis of the EEG components of the individuals from the same group under the three cue conditions, we also found differences in P2 components. There were no significant differences in P2 components under the three cue probability conditions in the individuals with HOC, while there were significant differences in P2 amplitudes under the three cue probability conditions in individuals with LOC, and this difference was mainly reflected in the 50% probability condition. In other words, there were no differences in the amount of attention resources consumed or allocated by individuals with HOC when faced with three different cues, while individuals with LOC gave different attention resources when faced with different cues. Under the 50% uncertainty condition, they needed more attention resources. The differences in P2 between the two groups in the cue presentation stage indicated differences in the expected processing of cues between individuals with HOC and LOC.

In the cue presentation stage, we also found differences in the N2 component in both groups. Under the 80% probability condition, the amplitude differences were significant in both groups, but the amplitude of the low-tendency group was significantly greater than that of the high-tendency group. However, the difference between the high and low groups was insignificant under the probability conditions of 50% and 20%. The N2 component reflects attention allocation processing and automatic inhibition processing in the early period [38][41]. The low group exhibited significantly larger N2 amplitudes than the high-tendency group. Under 80% probability cues, individuals with LOC initiated more vital automatic suppression processing ability and prepared for automatically inhibiting and interfering with targets in advance compared with individuals with HOC. In other words, under the 80% probability cue, individuals are more likely to use automated parallel processing, providing a specific prediction for the choice of individual processing modes. However, under 50% and 20% probability conditions, there were no differences in the N2 component between the two groups, indicating that under these two conditions, the two groups of individuals are expected to start processing in similar ways. Researchers believe that P3 is an effective indicator of central information processing in task-related decision-making, and P3 amplitude is inversely proportional to task decision difficulty [42]. The P3 component in the late period is generally associated with decision formation during the processing of an ongoing task [43]. The differences in the P3 component between the two groups indicate the difference in the judgment and decision-making ability to use the processing method under different cues. The larger P3 amplitude of y individuals with LOC indicates that they are easier to make decisions, while individuals with HOC are not good at making decisions. However, this difference was only shown in the 80% cue, which the decision-making mechanism may cause. It is indicated that under the 80% cue, the participants should adopt parallel processing for the subsequent stimulus, and individuals with LOC are prepared for parallel processing according to the prompt cues, while individuals with HOC have a single processing mode and mainly focus on centralized processing, so their ability of decision-making control is insufficient. The 20% probability condition indicates that participants should adopt centralized processing, which is consistent with the processing mechanism of individuals with HOC; the differences in this probability are insignificant. Under the 50% probability condition, the two groups have no significant differences due to their uncertainty, indicating that individuals with LOC are more dependent on the cue.

According to Posner's "Attention-participation theory, "attention is a limited capacity resource distributed to various regions of visual space. The parietal lobe controls cue-induced attention orientation, while the prefrontal lobe controls strategic control in attention orientation [44]. The impairment of the prefrontal brain region in OCD patients [45] may lead to differences in strategic control of attention orientation between individuals with high and low compulsive tendencies and thus lead to different responses to prompt cues between the two groups. Individuals with LOC can have strategic attention control, so when the cue is valid or invalid, they can adjust their processing strategies to respond to the next stimulus according to their cognition of the cues. However, individuals with HOC cannot flexibly adjust their processing strategies due to their limited strategic control ability. Another explanation is that individuals with HOC may have difficulties in decision-making, which leads to their differences in responding to prompt cues from individuals with LOC. Simon's "limited rationality theory" holds that due to people's limited cognitive ability and ability to have access to information, they will deviate from rationality to a large extent when making decisions. However, probability (80%, 50%, 20%) and other mathematical rule models imply many systematic biases, so without the guidance of intelligent decision-making aids, individuals' cognitive biases make them more prone to bias when making judgments. Human rationality is limited, and the judgment of probability under uncertain conditions is subjective and largely depends on external cues [46]. People have formed a certain decision-making attitude based on the effectiveness of prompt cues before the experiment and maintain high stability; that is, there is a phenomenon of "irrational trust" in the prompt cues [47]. In this study, we found that compared with individuals with LOC, individuals with HOC did not show dependence on external cues, which may be because OCD patients or individuals with HOC have decision-making dysfunction and distrust [48][13], leading to differences in cue dependence from individuals with LOC.

The results showed that individuals with a high obsessive-compulsive tendency (HOC) were less responsive to situational changes and had defects in information processing flexibility, while individuals with a low obsessive-compulsive tendency (LOC) were more flexible in information processing, which is manifested in both the cue presentation stage and the stimulus-response stage. In the stage of cue presentation, there was no significant difference in P2 in HOC under the three cue probability conditions, but there was a significant difference in the LOC. In the stage of stimulus presentation, there was no significant difference in the N2 and N3 components of the HOC under the three cues, while there was a significant difference in the LOC, and the amplitudes under 80% probability conditions were more significant than those under 20% probability conditions. Besides, individuals with HOC were less susceptible to cues. They had less strategic control over information processing, which was reflected in the difference in N2 components between individuals with HOC and those with LOC when prompted by cues, and the N2 amplitude of LOC under effective cues was significantly greater than that under ineffective cues. At the same time, there was no significant difference in HOC.

Acknowledgements

Thanks to all the authors and the funder who contributed to this study.

Author contributions

Concept and design: Miao Xiaocui ; data collection and analysis: Miao Xiaocui and Cheng Xiaojuan ;drafting of the article:Miao Xiaocui and Wang Rong; critical revision of the article for important intellectual content: Cheng Hui;study supervision: Xiao Haiyan. All the authors approved the final article.

Funding

The research is supported by the Shanxi Datong University Teaching Reform and Innovation Project(XJG2023202), Shanxi Datong University Ideological and Political Project(2022S10), Shanxi Provincial Universities Philosophy and Social Science Research Project (Ideological and Political Education Project) (2023zsszsx076) and Science and Technology Innovation Program of Universities in Shanxi Province (2023W45).

Availability of data and materials

To protect the privacy of students and ensure the confidentiality of the data, the datasets used and analyzed during the study are not publicly available but can be gained from the corresponding author on reasonable request.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Author details

¹ Shanxi Datong University,Datong (037009) , Shanxi Province,China

²Shaanxi institute of technology,xi’an（710300) , Shaanxi Province,China

Soref A, Dar R, Argov G, & Meiran N. (2008). Obsessive–compulsive tendencies are associated with a focuse dinformation processing strategy. Behaviour Research and Therapy, 46(12), 1295- 1299.
Vaghi MM,Vertes PE,Kitzbichler MG,Apergis- Schoute AM,Van Der Flier FE,Fineberg NA,Sule A, Zaman R,Voon V,Kundu P, Bullmore ET, &Robbins TW. (2017). Specific frontostriatal circuits for impaired cognitive flexibility and goal- directed planning in obsessive-compulsive disorder: evidence from resting-state functional connectivity. Biol Psychiatry,81:708-717.
Dajani DR, & Uddin LQ.(2015).Demystifying cognitive flexibility: Implications for clinical and developmental neuroscience. Trends in Neuroscience, 38(9),571-578.
Ruchsow M,Gron G,Reuter K,Spitzer M,Hermle L,&Kiefer M. (2005).Error-Related Brain Activity In Patients with Obsessive-Compulsive Disorder and in Healthy Controls. Journal of Psychophysiology,19(4):298-304.
Bucci P,Mucei A,Volpe U,Merlotti E,Galderisi S,&Maj M.(2004).Executive Hypercontrol in obsessive-compulsive disorde electrophysio- logical and neuropsychological indices Clinical Neurophysiology ,115(6):1340- 1348.
Gehring WJ,Himle J,Nisenson LG.(2010). Action- monitoring dysfunction in obsessive-compulsive disorder. Psychological Science,11(1):1-6.
Rosenberg DR, &Keshavan MS.( 1998).Toward a Neurodevelopmental Model of Obsessive- Compulsive Disorder . Biological Psychiatry,43(9): 623-640.
Tyagi H,Apergis-Schoute AM,Akram H,Foltynie T,Limousin P,Drummond LM,Fineberg NA, Matthews K,Jahanshahi M,Robbins TW,Sahakian BJ, Zrinzo L,Hariz M,& Joyce EM.(2019) .A randomized trial directly comparing ventral capsule and anteromedial subthalamic nucleus stimulation in obsessive-compulsive disorder: clinical and imaging evidence for dissociable effects. Biol Psychiatry, 85:726-734.
Fradkin I,Strauss AY,Pereg M, & Huppert JD. (2018).Rigidly applied rules? Revisiting inflexibility in obsessive compulsive disorder using multilevel meta-analysis. Clinical Psychological Science, 6(4), 481-505.
Aydın PC,Koybasi GP,Sert E,Mete L,&Oyekcin DG.(2014).Executive functions and memory in autogenous and reactive subtype of obsessive- compulsive disorder patients. Comprehensive Psychiatry,55(4):904-911.
Lewin AB,Larson MJ,Park JM,McGuire JF,Murphy TK, &Storch EA.(2014). Neuro- psychological functioning in youth with obsessive compulsive disorder: An examination of executive function and memory impairment.Psychiatry Research,216,108- 115.
Purcell R, Maruff P, & Kyrios M. (1998). Cognitive deficits in obsessive-compulsive disorder on tests of frontal-striatal function[J]. BiolPsychiatry,43(5): 348-357.
Russo PM,Pascalis V,Varriale V,&Barratt ES. (2008).Impulsivity,intelligence,and P300 wave: all empirical study. International Journal of Psyehophysiology,69,112-118.
Admon R,Cohen MB,Weizmant R,Poyurovsky M,Faragian S, &Hendler T.(2012).Functional and structural neural indices of risk aversion in obsessive–compulsive disorder (OCD). Psychiatry Research Neuroimaging, 203(2-3):207-213.
Bokura H,Yamaguchi S ,&Kobayashi S.(2001). Electrophysiological correlates for response inhibition in a Go/NoGo task.Clin NeurophysioL, 112(12):2224-2232.
Li H,&Wang YZ.(2006).On the Relationship between Children’s Cognitive Flexibility and Verbal Ability.　Psychological Science.29(6): 1306-1311.
Liu HH,Fan N,Shen XY,&Ji JY.(2013).Effect of Cognitive Flexibility on Language Switching in Non-proficient Bilinguals: An ERPs Study .Acta Psychologica Sinica,45(6), 636-648.
Vriend C,de Wit SJ,Remijnse PL,van Balkoma AJLM,Veltman DJ, &van den Heuvel OA.(2013). Switch the itch: A naturalistic follow-up study on the neural correlates of cognitive exibility in obsessive-compulsive disorder. Psychiatry Research: Neuroimaging,213,31-38.
Chamberlain SR,& Grant JE.(2019) .Relationship between quality of life in young adults and impulsivity/compulsivity. Psychiatry Res,271:253- 258.
Olatunji BO ,Ciesielski BG,David H, &Zald DH.(2011).A selective impairment in attentional disengagement from erotica in obsessive– compulsive disorder.Progress in Neuro- Psychopharmacology & Biological Psychiatry,35, 1977-1982.
Zhang ZM,Zhang ZL, &Li H. (2010). Double conflicts model and anxiety ratification therapy hypotheses of obsessive-compulsive disorder. Medical Hypotheses,75(6), 586-589.
Labuschagne I,Rossell SL,Dunai J,Castle DJ, &Kyrios M.(2013).A comparison of executive function in Body Dysmorphic Disorder (BDD) and Obsessive-Compulsive Disorder (OCD). Journal of Obsessive-Compulsive and Related Disorders,2, 257-262.
Chamberlain SR,Solly JE,Hook RW,Vaghi MM,&Robbins TW.(2021).Cognitive Inflexibility in OCD and Related Disorders.Current topics in behavioral neurosciences,49: 125-145.
Robbins TW,Vaghi MM, &Banca P. (2019). Obsessive-compulsive disorder: Puzzles and prospects. Neuron,102(1), 27-47.
Gruner P, &Pittenger C.(2017). Cognitive inflexibility in obsessive compulsive disorder. Neuroscience, 345, 243-255.
Sarig S, Dar R, & Liberman N.(2012). Obsessive-compulsive tendencies are related to indecisiveness and reliance on feedback in a neutral color judgment task. Journal of Behavior Therapy and Experimental Psychiatry,43,692-697.
Miao XC,Li YJ,Wang MY,Ding B,&Zhang ZM.(2015).The Characteristics of Information Processing Way to Study in the Different Obsessive-compulsive Tendency Individuals. Journal of Psychological Science ,38(2): 474-481.
Coles MGH,Gratton G,Bashore TR,Eriksen CW, & Donchin E.(1985).A psycho- physiological investigation of the continuous flow model of human information processing. Journal of Experimental Psychology: Human Perception and Performance,11, 529-553.
Gratton G,Coles MGH,Sirevaag EJ,Eriksen CW, &Donchin E.(1988).Pre-and post-stimulus activation of response channels: A psycho- physiological analysis. Journal of Experimental Psychology: Human Perception and Performance, 14, 331-344.
Gratton G,Coles MGH, &Donchin E.(1992). Optimizing the use of information: strategic control of activation of responses. Journal of Experimental Psychology: General,121(4), 480-506.
Sanavio E.(1988).Obsessions and compulsions:the Padua Inventory. Behaviour research and therapy, 26(2),169-177.
Zhou LB.(2008).Error-Related Negativity in under- graduates with high or low obsessive compulsive tendency. Master's Thesis. Capital Normal University.
Gohle D,Juckel G, & Mavrogiorgou P.(2008). Electrophysiological evidence for cortical abnormalities in Obsessive-compulsive disorder a replication study using autitory event-related P300 subcomponents.Psychiatr Res,42(4): 297-303.
Yeung N,Botvinick MM,&Cohen JD.(2004).The neural basis of error detection:conflict monitoring and the error-related negativity.Psychological Review,111(4),931-959.
Bartholow BD,Pearson MA,Dickter CL,Sher KJ,Fabiani M,&Grattin G.(2005).Strategic control and medial frontal negativity :beyond errors and response conflict.Psychophysiology,42(1),33-42.
Veen VV, & Carter CS.(2002).The timing of action-monitoring processes in the anterior cingulate cortex. Journal of Cognitive Neuroseienee,14:593-602.
Polich J.(2007).Updating P300：an integrative theory of P3a and P3b．Clinical neurophysiology： official journal of the International Federation of Clinical. Neurophysiology,118(10):2128-2148.
Kanske P, &Kotz SA.(2010).Modulation of early conflict processing:N200 responses to emotional words in a flanker task. Neuropsychotogia , 48(12):3661-3664.
Xiang L.(2010).The Neural Mechanism of Conflict Monitoring:Evidence from ERP.Phd Dissertation. XiNan University.
Luck SJ, &Hillyard SA. (1994). Electro- physiological correlates of feature analysis during visual search. Psychophysiology,31:291-308.
Forster SE,Carter CS,Cohen JD, &Cho RY. (2011).Parametric manipulation of the conflict signal and control-state adaptation. Journal of Cognitive Neuroscienee,23(4):923-935.
Palmer B,Nasman VT, &Wilson GF.(1994).Task decision diffieulty:effeets on ERPs in a same- different letter classification task. Biological Psyehology,38(2-3):199-214.
Vendemia JM,Buzan RF,Green EP,&Schillaci MJ. (2006). Effects of preparedness to Deceive on ERP Waveforms in a Two-Stimulus Paradigm. Journal of Neurotherapy,9(3):45-70.
Posner MI.(1980).Orienting of attention. Quarterly Journal of Experimental Psychology,32, 3-25.
de Wit S J,de Vries FE ,van der Werf YD,Cath DC,Heslenfeld DJ,Veltman EM,van Balkom AJ, Veltman DJ, &Van den Heuvel OA. (2012). Presupplementary motor area hyperactivity during response inhibition: a candidate endophenotype of obsessive-compulsive disorder. American Journal of Psychiatry,169,1100–1108.
Wang XD.(2011).The Effect of Cues on Judgment and Decision-Making under Uncertainty. Master's Thesis.Henan University.
Zhou H.(2012).The Study of Clue Influence on Decision.Master's Thesis .ZheJiang Normal University.
Nedeljkovic M, & Kyrios M.(2007).Confidence in memory and other cognitive processes in obsessive compulsive disorder. Behaviour Research and Therapy.45,2899-2914.

No competing interests reported.

Download PDF

Editorial decision: Revision requested
04 Sep, 2024
Editor assigned by journal
04 Sep, 2024
Submission checks completed at journal
01 Sep, 2024
First submitted to journal
29 Aug, 2024

You are reading this latest preprint version

The Information Processing Flexibility of Individuals with Obsessive- Compulsive Tendency: An Event-related Potentials Study

Status:

Version 1

Abstract

Figures

1 Introduction

2 Experimental Methodology

2.1 Participants and Measurement

2.2 Stimulation and materials

2.3 Experimental design

2.4 Experimental procedure

2.5 Experimental Equipment

2.6 Electroencephalography recording and processing

3 Results and analysis

3.1 Cue-related ERP

3.2 Stimulus-related ERP

4 Discussion

4.1 Deficits of Individuals with HOC in the Flexibility of Information Processing

4.2 Individuals with HOC were not easily affected by cueing

5 Conclusions

Declarations

References

Additional Declarations

Status:

Version 1