The neural correlates of implicit self-relevant processing in low self-esteem: An ERP study

brain research 1471 (2012) 75–80

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The neural correlates of implicit self-relevant processing in low self-esteem: An ERP study Juan Yanga,b,n, Lili Guana,b, Katarina Dedovicc, Mingming Qia,b, Qinglin Zhanga,b a

Department of Psychology, Southwest University, Chongqing 400715, China Key Laboratory of Cognition and Personality, Southwest University, Chongqing 400715, China c Social and Affective Neuroscience Laboratory, Department of Psychology, UCLA, Los Angeles, United States b

art i cle i nfo

ab st rac t

Article history:

Previous neuroimaging studies have shown that implicit and explicit processing of self-

Accepted 24 June 2012

relevant (schematic) material elicit activity in many of the same brain regions. Electro-

Available online 3 July 2012

physiological studies on the neural processing of explicit self-relevant cues have generally

Keywords:

supported the view that P300 is an index of attention to self-relevant stimuli; however,

Implicit self-relevant processing

there has been no study to date investigating the temporal course of implicit self-relevant

Unconsciousness

processing. The current study seeks to investigate the time course involved in implicit self-

Attentional allocation

processing by comparing processing of self-relevant with non-self-relevant words while

Event-related potentials (ERP)

subjects are making a judgment about color of the words in an implicit attention task.

P2

Sixteen low self-esteem participants were examined using event-related potentials technology (ERP). We hypothesized that this implicit attention task would involve P2 component rather than the P300 component. Indeed, P2 component has been associated with perceptual analysis and attentional allocation and may be more likely to occur in unconscious conditions such as this task. Results showed that latency of P2 component, which indexes the time required for perceptual analysis, was more prolonged in processing self-relevant words compared to processing non-self-relevant words. Our results suggested that the judgment of the color of the word interfered with automatic processing of self-relevant information and resulted in less efficient processing of self-relevant word. Together with previous ERP studies examining processing of explicit self-relevant cues, these findings suggest that the explicit and the implicit processing of self-relevant information would not elicit the same ERP components. & 2012 Elsevier B.V. All rights reserved.

1.

Introduction

The concept of self is constructed as the conscious reflection of one’s own being or identity, as an object separate from other or from the environment (Huitt, 2009). The neural basis of explicit self-processing has been mainly studied using functional magnetic resonance imaging techniques; this line n

of research reveals a remarkably consistent body of results showing that cortical midline structures are implicated in self-referential thought and self-reflection (Macrae et al., 2004; Northoff and Bermpohl, 2004). Neuroimaging investigations have also found that implicit processing of visual selfrelevant material elicited activity in many of the same regions as the explicit self-relevant processing, including

Corresponding author at: Department of Psychology, Southwest University, Chongqing 400715, China. Fax: þ86 23 6825 3304. E-mail address: [email protected] (J. Yang).

0006-8993/$ - see front matter & 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.brainres.2012.06.033

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medial prefrontal cortex, posterior cingulate/precuneus, etc. (Moran et al., 2009; Rameson et al., 2010). Therefore, processing self-relevant material recruited similar neural networks regardless of whether the self-relevance was made explicit or not. With respect to the time course of these events, electrophysiological studies have focused on examining processing of explicit self-relevant cues (Berlad and Pratt, 1995; Gray et al., 2004; Tacikowski and Nowicka, 2010). However, to authors’ knowledge, no study to date investigated the temporal course of implicit self-relevant processing. Therefore, in the present study, we aim to investigate the temporal course of processing implicit self-relevant cues. Electrophysiological studies on the neural processing of explicit self-relevant cues have generally supported the view that P300 is an index of attention to self-relevant stimuli (Tacikowski and Nowicka, 2010). For example, hearing one’s own name elicits a robust electrophysiological P300 response (Berlad and Pratt, 1995; Perrin et al., 1999). Preserved P300 response to subjects’ own name has also been obtained in visual presentation (Berlad and Pratt, 1995; Gray et al., 2004; Matsuda et al., 1990; Perrin et al., 2005). In addition, it has been suggested that the enhanced amplitude of P300 may reflect that more attentional resources are engaged in selfrelevant processing (Gray et al., 2004). However, some early event-related potential (ERP) components, such as P2, were also found to be related to feature analysis during visual search (Luck and Hillyard, 1994; Thorpe et al., 1996) and attentional allocation (Carretie et al., 2001; Yuan et al., 2009). The P2 component is evoked about 200 ms post-stimulus and its amplitude is modified by the intensity of perceptual processing which requires attention allocation to function (Thorpe et al., 1996; Yuan et al., 2011). Specifically, in Thorpe et al. (1996) study, visual processing of animal stimuli elicited a much more frontal positive component than non-animal stimuli from 150 ms and reached its peak amplitude at about 200 ms after stimulus onset. Since neurophysiological measurements of the latencies of visual responses can be used to provide estimates of visual processing time (Donchin, 1981; Thorpe et al., 1996), the peak latency of frontal P2 is taken as an indication of the time required for perceptual analysis, while slower latency to peak P2 has been suggested to be related to less efficient processing of visual information at a relatively early stage (Burden et al., 2009; Yuan et al., 2011). Furthermore, it has been proposed that P2 component might reflect the early-stage information processing, possibly driven by an automatic mechanism (Huang and Luo, 2007). For example, in a study investigating individuals who suffered from post-traumatic stress disorder after having experienced the great Sichuan earthquake in 2008, the P2 component was speculated to be related to unconscious attentional resource allocation to the earthquake-related words (Yun et al., 2011). Therefore, while both P300 and P2 have been associated with attention, a brief review of event-related brain potentials (ERPs) as indices of cortical information processing suggested that ERP components such as P2 (and N1) were more likely to occur in unconscious conditions, whereas all varieties of the late posterior positive ERP waves (e.g., P300, late positive complex) were more difficult to be elicited in the unconscious condition (Kotchoubey, 2005).

Implicit self-processing has been defined as associations about the self that are relatively automatic and that occur below the level of conscious awareness (Rameson et al., 2010). In order to examine aspects of implicit self-processing, in the current study, we exposed low self-esteem participants to both self-relevant and non-self-relevant words. However, we asked them to make a non-self-referential judgment about these words (‘is the word green or red?’), thereby avoiding the participants explicitly processing self-relevant information. Self-relevant words were adjectives that were congruent with subjects’ personality style (self-esteem), and non-selfrelevant words were incongruent. Indeed, studies have suggested that possessing a self-schema for a particular personality is effectively equivalent to being highly identified with that personality (Baumeister, 2003). For example, high self-esteem individuals have a positive self-view, which is associated with optimism, successful coping, and positive emotions; low self-esteem individuals, on the contrary, have a negative self-view, which is related to depression, fearfulness, shyness, and loneliness (Baumeister, 2003; Brown, 1986). As previously mentioned, we focused on individuals with low self-esteem in this study. This is because it has been suggested that although high self-esteem individuals report favorable feelings of self-worth, they may simultaneously hold unfavorable feelings of self worth of which they are unaware (Kernis, 2003). With respect to naturally occurring discrepancies, high explicit self-esteem paired with negative implicit self-esteem is probably more common than low explicit self-esteem paired with positive implicit self-esteem (Epstein, 1983). Therefore, in this study, we focus on individuals with low self-esteem, whose self-relevant personality words belong to the same category. Since participants’ task was to judge the color of personality words and the colors for both self-relevant words and non-self-relevant words were the same, we would hypothesize that the magnitude of frontal P2, which is considered to be an index of the intensity of perceptual processing (Luck and Hillyard, 1994; Thorpe et al., 1996; Yuan et al., 2011), would show no difference between self-relevant words and non-self-relevant words. However, making the judgment about the word color should interfere with automatic processing of self-relevant information in the implicit selfprocessing, and therefore we would hypothesize that the latency of frontal P2, which is considered to be an indication of the time required for perceptual processing (Burden et al., 2009; Donchin, 1981; Thorpe et al., 1996), would be delayed in response to the self-relevant words, compared to the nonself-relevant words. Furthermore, since P300 is more difficult to be elicited in unconscious condition, we hypothesize that the amplitude of P300 component for the self-relevant and non-self-relevant words would show no difference in this implicit information processing task.

2.

Results

2.1.

Behavioral results

As intended, participants were highly accurate in judging the color of the adjectives (M ¼ 97.5%, SD ¼ 3.2%). Accuracy of

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responses did not differ for self-relevant and non-self-relevant adjectives (M ¼ 97.5% vs. 97.5%), nor did reaction times (M ¼ 475 ms vs. 475 ms).

2.2.

Electrophysiological scalp data

A repeated measures ANOVA of P2 amplitudes showed significant main effects of electrode location, F(4,60) ¼ 4.77, p o 0.05, Zp2 ¼ 0.24. The amplitudes of P2 were more pronounced at electrode sites of FCz and Cz, p o 0.05. The analysis of latency to P2 revealed a significant effect of stimulus type, F(1,15) ¼ 5.32, p o 0.05, Zp2 ¼ 0.26. The latency to P2 in response to selfrelevant stimuli (M ¼ 180.3 ms, SD ¼ 13.4 ms) occurred significantly later than to non-self-relevant stimuli (M ¼ 174.8 ms, SD ¼ 14.9 ms). The ANOVA of the P300 amplitude revealed neither main effects of electrode location and stimulus type, nor their interaction.

3.

Discussion

The current experiment was designed to investigate the neural correlates of implicit self-processing. This research question is driven by two lines of thought. On the one hand,

larger traditional social cognition research has often shown that implicit and explicit measurements of self-relevant concept (e.g., self-esteem) tend to show weak or inconsistent correlation with each other (Hofmann et al., 2005; Rameson et al., 2010). On the other hand, with the development of neuroimaging technology, it has been found that processing self-relevant material recruits similar neural networks regardless of whether the self-relevance is made explicit or not (Rameson et al., 2010). Our event-related potential results showed that implicit self-relevant word processing elicited significantly prolonged peak latency of P2 component compared to implicit non-self-relevant word processing, but we did not observe any differences with respect to the P300 component. This is consistent with the literature where a prolonged amplitude of P300 component was previously reported to be elicited by explicit self-processing (Tacikowski and Nowicka, 2010). The P2 component observed in this study was more pronounced at the frontal and central sites than at the parietal scalp sites, regardless of stimulus type (see Fig. 1), which fits the classical scalp distribution of P2 that is related to perceptual analysis and attention allocation (Bigman and Pratt, 2004; Carretie et al., 2001; Luck, 2005; Thorpe et al., 1996). The magnitude of frontal P2 activity is considered as an FCz -4

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Fig. 1 – Grand average waveforms of self-relevant stimuli, grand average maps of non-self-relevant stimuli and topographic map of P2 in implicit self-reflection task.

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index of intensity of perceptual processing (Luck and Hillyard, 1994; Thorpe et al., 1996; Yuan et al., 2011). In the current study, P2 amplitudes were prominent, but did not differ between selfrelevant processing and non-self-relevant processing. This implies that the intensity of perceptual processing of selfrelevant words is similar to perceptual processing of non-selfrelevant words. This is likely due to the fact that participants’ task was to judge the color of words, and both the self-relevant words and non-self-relevant words were presented in the same colors. However, peak latencies of P2 were delayed for the selfrelevant word processing compared to non-self-relevant word processing. As previously noted, neurophysiological measurements of the latencies of visual responses can be used to provide estimates of visual processing time (Donchin, 1981; Thorpe et al., 1996), therefore, the peak latency of frontal P2 is taken as an indication of the time required for perceptual analysis, with the slower latency to peak P2 reflecting less efficient processing of visual information at a relatively early stage (Burden et al., 2009; Yuan et al., 2011). Our findings imply that when a judgment of color is required, implicit processing of self-relevant words is less efficient than processing of non-self-relevant words. There are two theories that may explain this finding. The load theory proposes that attentional allocation happens in two steps where processing resources are first allocated to task-relevant stimuli and secondly remaining capacity ‘spills over’ to task-irrelevant distracters (Lavie, 1995). In contrast, the Theory of Visual Attention (TVA) assumes that allocation happens in a single step where processing capacity is allocated to all stimuli, both taskrelevant and task-irrelevant, in proportion to their relative attentional weight (Bundesen, 1990). In the current task, the task-relevant information was the color of the adjectives, whereas the task-irrelevant information was the type of the adjectives which were either self-relevant or non-self-relevant. It could be assumed that self-relevant adjectives would carry more relative attentional weight than the non-selfrelevant adjectives. The data showed that the judgment of the word color interfered with the processing of self-relevant information and resulted in less efficient processing of selfrelevant words compared to non-self-relevant words. This finding is in line with previous results supporting the TVA proposal of a single step capacity allocation (Kyllingsbaek et al., 2011). Together with previous ERP studies on explicit processing of self-relevant information (Berlad and Pratt, 1995; Gray et al., 2004; Tacikowski and Nowicka, 2010), the present results suggest that processing explicit and implicit selfrelevant information do not elicit the same ERP components. This could be seen as being in opposition to fMRI neuroimaging findings. However, although both ERP and fMRI studies seek to unravel the functional and structural architecture of higher order cognitive processes (Jamadar et al., 2010), these methods do focus on different aspects of cognitive processes. Therefore, it is no surprise that the implicit and explicit processing of self-relevant information seem not to elicit the same components in the ERP studies as in previous fMRI studies. However, future study should attempt to apply ERP and fMRI methodologies on the same paradigm in order to

take advantages of their strong temporal and spatial resolution, respectively, and thus be able to more fully elucidate the intricacies of neural networks underlying the cognitive processes. A number of limitations are present in the current study. Firstly, participants came from a Normal University in which the numbers of female students were larger than the numbers of male students; therefore, 13 of 16 participants were female in the current study. Secondly, only participants with low self-esteem were recruited into the current study; therefore, the results and conclusions of the current study are restricted to low self-esteem participants. Future studies should aim to test these hypotheses in the high self-esteem participants.

4.

Conclusion

The current experiment was designed to investigate the neural correlates of implicit form of self-processing. Our results were consistent with our hypothesis and showed that the latency of P2 component, which indexes the time required for perceptual analysis, was more prolonged in processing self-relevant words compared to processing nonself-relevant words in the implicit self and non-self processing task.

5.

Experimental procedures

5.1.

Participants

Three months before the experiment, 187 undergraduates (63 males, mean age ¼ 20.6 years) from a Chinese University filled out the Rosenberg self-esteem scale. The Rosenberg self-esteem scale is made up of 10 items such as ‘‘On the whole, I am satisfied with myself.’’ and is coded on a 4-point scale ranging from 1 (strongly disagree) to 4 (strongly agree). It assesses a person’s overall evaluation of his or her selfworth (Rosenberg, 1965). Sixteen low SE participants (self-esteem scales’ mean score: 23.7 7 1.5) from the pool were then selected for the electrophysiological study (13 females; mean age, 21.3 7 0.9 years). None had a history of or currently suffered from neurological or psychiatric conditions (subject self-report). All participants were right-handed and had normal or corrected-to-normal vision. The data and results described in this manuscript were obtained in compliance with the guidelines of APA requirements. Subjects gave their written informed consent prior to participation and were paid for completing the study.

5.2.

Materials

The self-descriptiveness of 468 personality-trait adjectives from a database (Huang and Zhang, 1992) was judged by 51 high self-esteem volunteers (self-esteem scales’ mean score: 30.9 7 2.5) and 51 low self-esteem volunteers (self-esteem scales’ mean score: 21.5 7 2.4) on a 5-point scale from 1 (not at all) to 5 (very much) before the experiment. The ratings of

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Table 1 – Mean score and standard deviation (SD) of desirability, meaningfulness, and familiarity of selfrelevant adjectives and non-self-relevant adjectives from the database. Category

Desirability

Meaningfulness

Familiarity

Self-relevant adjectives Non-selfrelevant adjectives

2.77(0.49)

3.23(0.21)

3.00(0.16)

2.65(0.27)

3.22(0.13)

2.98(0.10)

trait adjectives were analyzed by one-way ANOVA between high self-esteem volunteers and low self-esteem volunteers. The results showed that there were 40 adjectives with significantly higher ratings in low self-esteem than in high self-esteem participants (p o 0.05); these adjectives were selected for the self-relevant words. Another 40 comparable adjectives from the database (Huang and Zhang, 1992) with similar desirability, meaningfulness, and familiarity as selfrelevant adjectives were selected to be presented as the nonself-relevant words (see Table 1 and Appendix). Both the selfrelevant adjectives and the non-self-relevant adjectives were presented twice to the participants over the course of the task and were written once in red and once in green. The order of the presentation of adjectives was randomized.

5.3.

Procedure

For this task, participants viewed both self-relevant adjectives and non-self-relevant adjectives, written in red or green, and made a non-self-referential judgment about the word (‘is the word in red or in green?’). For each trial, a fixation sign appeared at the center of the screen for 300–500 ms (durations were varied randomly), and then adjectives were presented for 2 s each. The inter-stimulus-interval was 300–500 ms. Participants were required to press the appropriate keys to indicate the color of the adjectives (‘‘1’’ for red and ‘‘2’’ for green) as soon as the adjectives appeared on the screen.

electromyographic (EMG) activity, or peak-to-peak deflection exceeding 100 mV were excluded from averaging. Following these quality control procedures, there were at least 64 trials left for self-relevant processing and 62 trials left for non-selfrelevant processing. A pre-stimulus period of 200 ms was subtracted as a baseline. The amplitudes at Fz, Fcz, Cz, CPz and Pz were computed on the basis of the signals obtained across cortical midline. The EEG was segmented to obtain epochs extending from 200 ms before to 500 ms after the stimulus onset. Inspection of the averaged ERPs showed a pronounced P2 in the 150–200 ms time interval and a P300 (late positive component) in the 300–500 ms time interval during both conditions (Fig. 1). Therefore, we measured the peak latencies and amplitudes of P2 (150–200 ms) and the averaged amplitudes of P300 (300–500 ms) in each analyzed electrodes and in each subject. Then, repeated measures analysis of variance (ANOVA) were conducted on the peak amplitudes and latencies of P2 and on the averaged amplitudes of P300 component using the following factors as repeated factors: electrode location (Fz, Fcz, Cz, CPz and Pz) and stimulus type (self-relevant vs. non-self-relevant). The p-value for all the analyses was corrected for deviations according to Greenhouse-Geisser.

Acknowledgment This work was supported by the National Natural Science Foundation of China (30900397), the Natural Science Foundation Project of CQ CSTC (2010BB5001), and the Key Discipline Fund of National 211 project, China (NSKD11017).

Appendix A.

Supplementary material

Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.brainres. 2012.06.033.

r e f e r e nc e s 5.4.

Electrophysiological recording and analysis

Brain electrical activity was recorded at 64 scalp sites using tin electrodes mounted in an elastic cap (Brain Products, Germany). It was placed on the scalp according to the 10–20 system positions with the reference on the left and right mastoids and re-referenced to the average mastoid during post-processing. Vertical and horizontal electrooculogram (EOG) were recorded from above and below the right eye and at the right and left outer canthi, respectively. The interelectrode impedance was maintained below 5 kO at all times. The electroencephalogram (EEG) and EOG were amplified using a 0.05–100 Hz bandpass, and continuous sampling was conducted at 500 Hz/channel during on-line recording. We used the Brain Vision Analyzer 1.05 software (Brain Products, Germany) for data analysis. Eye movement artifacts (blinks and other movements) were corrected using a Gratton and Coles-based algorithm off-line (Gratton et al., 1983). Trials with artifacts due to amplifier clipping, bursts of

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