Findings in recent studies have shown that individual differences in personality, gender, age, and genotype have a potential regulating function on emotion process in the prefrontal lobe and limbic system across multidimensions, such as emotional reactions, emotional memory, and cognition (Hamann & Canli, 2004). As an important factor in individual differences, subjective well-being (SWB) emphasizes an individual’s subjective feelings and has become a comprehensive psychological index (Diener, 1984) and mainly refers to the assessment of emotionality and cognition in life quality. According to the SWB bottom-up theory, people feel happy if their basic needs are fulfilled. This leads to the conclusion that SWB is mainly influenced by external factors such as age, gender, economic income, living conditions, marriage, and religion (Diener, 1984). Although researchers have examined the relationship between emotion processing and some individual personality factors, such as neuroticism, the relationship between SWB and emotion processing has not previously been directly examined by recording event-related potentials (ERPs).

ERPs offer excellent temporal resolution of neural events. They have been regarded as neural manifestations of specific cognition functions, reflecting brain activity from synchronously active populations of neurons. There is ample evidence showing the modulation of emotional stimuli on ERP components. The early posterior negativity that develops around 150 ms after the processing of emotional stimuli may reflect brain selective attention (Schupp, Junghöfer, Weike, & Hamm, 2003, 2004; Schupp, Markus, Weike, & Hamm, 2003), but it is widely accepted that late positive potential (LPP) is related to deep emotion processing, involving arousal, attention, and motivation, of emotional stimuli (Lang et al., 1998). In the present study we recorded and analyzed the LPP component to examine the influence of SWB on emotion processing.

Method

Participants

We assessed the SWB of participants (N = 36, mean age 21.63 years) with the Memorial University of Newfoundland Scale of Happiness (Kozma & Stones, 1980). The participants were divided into two groups comprising 18 in the very happy group (9 females and 9 males) and 18 in the not very happy group (9 females and 9 males). All the participants were right-handed, had no history of mental disease, and had normal or corrected-to-normal vision. They were paid for their participation.

Stimuli

We selected 50 fear-inducing and 50 nonfear-inducing pictures from the International Affective Picture System (Lang, Bradley, & Cuthbert, 1997) and presented these randomly in the center of a 17-inch CRT monitor with 1024 x 768 resolution and 85 Hz refresh rate. Significant differences were found between the valence of the fear-inducing and nonfear-inducing pictures (p < .001), and the arousal score was greater for the fear-inducing (7.31 ± 0.84) than the nonfear- inducing pictures (2.47 ± 0.67; p < .01). The pictures were prime stimuli and two-word Chinese nouns were probe stimuli. All the words (50 positive and 50 negative) were from the Emotional Information Evaluation Form of Two-word Nouns in Modern Chinese Language.

Procedure

The classic emotion-priming paradigm adopted in this study is considered a good option to investigate the relationship between emotions and cognition. Neutral or negative pictures were presented first, followed by the positive or negative target word stimuli. The participants were asked to classify target stimuli according to affective valance. If SWB affected the emotional-priming effect, changes in behavioral results, that is, response time and accuracy rate, and/or in the LPP component could be observed.

A “+” was first presented for 500 ms in the center of the screen followed by an emotional picture (i.e., prime stimulus) for a duration of 500 ms. After an interstimulus interval (ISI) of 250 ms, positive or negative words (i.e., probe stimuli) were presented for 250 ms. Participants were asked to classify the words by emotional valence as accurately and quickly as possible. All the pictures and target words were shown randomly. Participants completed 20 practice trials before the experiment.

Electroencephalogram Recording

The electroencephalogram (EEG) signals were continuously recorded on a Neuroscan NuAmps amplifier, using Quick-Cap with 32-channel Ag/AgCl electrodes according to the extended international 10-20 system. The reference electrode was placed on the left mastoid and re-referenced offline to averaged mastoids. Vertical electrooculogram (EOG) was recorded from two electrodes placed above and below the right eye and horizontal EOG electrodes were placed at the left and right outer canthi of the eyes. Electrode impedance was kept below 5 kΩ. EEG and EOG signals were amplified with a bandpass filter of 0.05-100 Hz at a sampling rate of 500 Hz.

After EOG artifact correction using the method developed by Gratton (Gratton, 1998), the EEG was segmented into the epoch from 200 ms prestimulus to 1000 ms poststimulus. Trials contaminated with artifacts greater than ± 100 μV were rejected before averaging. Trials with targets when participants’ responses were incorrect were rejected from averaging. The EEG segments were averaged separately for the different prime conditions. The ERP data were lowpass filtered at 20 Hz (24 dB/octave) for each condition.

Data Analysis

Accuracy rates and reaction times (RTs) from the stimulus onset were recorded and analyzed using a mixed-model analysis of variance (ANOVA) with group (high and low happiness) as between-groups factor and prime type (fear-inducing and nonfear-inducing) and probe type (positive and negative words) as the within-participants factors.

The amplitudes of the LPP were measured and analyzed by a similar mixed-model ANOVA with the addition of one within-subject factor, that is, site (see below). Because the LPP peak of many participants was not easily discernible at all sites and in each condition, this analysis was based on the mean amplitude calculated between 450 and 750 ms (based on the group average waveforms) only at midline electrode sites Fz, Cz, and Pz. Degrees of freedom were corrected when necessary using the Greenhouse-Geisser epsilon correction factor.

Results

Eight participants (four in the very happy group) were excluded from the analysis owing to the low accuracy rate (mean, 8.3%) or excessive EEG artifacts. Therefore, 14 very happy and 14 not very happy participants are compared in the following results.

Behavioral Data

There was no significant group difference, regardless of RTs (1233 ± 215 ms and 1193 ± 198 ms for very happy and not very happy groups, respectively; F< 1) or accuracy (69.7 ± 10.9% and 72.4 ± 19.1% for very happy and not very happy groups, respectively; F < 1). Positive words were classified faster (1146 ± 153 ms) and more accurately (81.3 ± 12.8%) than negative words (1280 ± 201 ms, p < 0.01, and 60.8 ± 15.9%, p < .001). No other main effects and interactions reached a significant level (p > 0.1).

Event-related Potential Data

As shown in Figure 1, LPP elicited by probe words decreased in the fear-inducing priming condition and this negative priming effect was more evident for negative probe words and for not very happy participants.

The four factors ANOVA showed that, overall, LPP amplitude did not differ between two groups (F < 1). The main effect of the prime type factor was significant, F(1, 26) = 22.4, p < .001, indicating that for both probe type word conditions, the fear-inducing condition reduced LPP amplitudes (1.2 ± 0.5 uV) over the neutral condition (2.8 ± 1.6 uV). We were interested to find that the interaction of the prime type and group factors was also significant, F(1, 26) = 4.5, p < .05. Post hoc comparison for this interaction revealed that although there was no significant group difference for each priming condition (p > .01), the priming effect was more conspicuous for participants in the not very happy group (neutral minus fear-inducing conditions, 2.5 ± 1.0 uV) rather than the very happy group (1.3 ± 0.4 uV; p < .05). We were also interested to find that the interaction of the four factors of group, prime type, probe type, and site was also significant, F(2, 52) = 5.2, p < .02. Further analysis revealed that regardless of whether participants were in the very happy or the not very happy group, at the Pz site the negative priming effect was more evident for negative (neutral minus fear-inducing conditions, 2.9 ± 1.1 uV) than for positive probe words (1.3 ± 0.3 uV; p < .05). No other main effects and interactions reached a significant level (p > .1).

Discussion

Using the emotion-priming paradigm, we examined the neural mechanisms underlying the relationship between SWB and the processing of emotional stimuli. For both low and high SWB groups, the classification advantage for positive words was demonstrated, that is, they were classified faster and more accurately. LPP amplitude elicited by emotional words significantly decreased in the fear-inducing priming condition. This priming effect was more prominent in the not very happy group, indicating that these participants were more sensitive, and subject to, the influence of external stimuli (particularly negative emotional stimuli).

Researchers have found that emotionally positive facial expressions are recognized more rapidly than emotionally negative facial expressions (Ducci, 1981). In our examination of the relationship between SWB and emotion processing by recording (ERPs) we found the same pattern for positive words. We were interested to find that, according to our results, although the RTs and accuracy of the words did not show significant valence effects, the LPP in the participants’ lexical decision was evidently less in the fear-inducing priming condition than in the neutral condition. It has been documented that the latter portion of the ERP waveform over a broad latency interval demonstrated elevated positivity to high arousal stimuli (Cuthbert, Schupp, Bradley, Birbaumer, & Lang, 1999). We have shown that the LPP component is dominated by the P3 component and the following positive slow wave (for a review, see Olofsson, Nordin, Sequeira, & Polich, 2008). Generally, the P3 component indexes attentional and initial memory storage events, whereas subsequent slow-wave activity appears related to task demands involving working memory operations (for a review, see Polich, 2007). In addition, it has been suggested in several studies that the P3/LPP amplitude is positively correlated with the amount of attention resources allocated to the eliciting stimulus (Strayer & Kramer, 1990; Wickens, Kramer, & Donchin, 1984; Wickens, Kramer, Vanasse, & Donchin, 1983). Although the behavioral performance did not show significant priming effects, greater LPP amplitudes in the neutral priming condition may mean that probe words attract more attention in a neutral rather than a fear-inducing priming condition. This interpretation may differ from the view that negative emotional stimuli are automatically processed to a deeper level.

The important result was that SWB significantly influenced the LPP priming effect, that is, the negative emotional priming effect was greater in participants in the not very happy group. The findings indicated that the participants who were not very happy showed stronger sensitivity to stimuli that were strongly fear inducing and were more easily activated by negative emotional stimuli. Previous researchers have observed higher P3/LPP amplitude in introverts than in extroverts (Ditraglia & Polich, 1991; Stelmack & Houlihan, 1995). This has been attributed to greater flexibility in extroverts (Hofer et al., 2006). According to Eysenck’s theory of personality, extroverts have a tendency towards enjoyment of communication, excitement, novelty, change, and enthusiasm, correlating with positive emotion, whereas nervousness is relevant to negative emotions like fear, anxiety, anger, and sorrow (Eysenck, 1967; Eysenck & Eysenck, 1985). Notwithstanding the relationship between SWB and introverted and extroverted personality traits, we are the first researchers to demonstrate that negative emotional processing varied according to level of SWB. It has been confirmed in many studies that an individual trait was closely related to negative emotion not to positive emotion (Rusting & Larsen, 1997; Watson & Clark, 1992), regardless of brain imaging research (Canli et al., 2001) or ERP studies (Canli, et al., 2001; Schmidtke & Heller, 2004). Our findings further demonstrate an intimate connection between individual traits and emotion processing, particularly negative emotion.

In sum, our findings have added to the existing evidence of the relationship between SWB and emotion processing, demonstrating that as a personality trait dimension, SWB has a profound influence in human emotion processing.

There are two limitations in this study. Firstly, we did not compare effect of priming at different levels, such as for pictures that would induce fear at high, mid, and low levels. Second, the number of participants was small. Further studies should be conducted with larger numbers of participants to generalize our results.

References

Canli, T., Zhao, Z., Desmond, J. E., Kang, E., Gross, J., & Gabrieli, J. D. E. (2001). An fMRI study of personality influences on brain reactivity to emotional stimuli. Behavioral Neuroscience, 115, 33-42. http://doi.org/cj37b6

Cuthbert, B. N., Schupp, H. T., Bradley, M. M., Birbaumer, N., & Lang, P. J. (1999). Brain potentials in affective picture processing: Covariation with autonomic arousal and affective report. Biological Psychology, 52, 95-111. http://doi.org/bz7qdv

Diener, E. (1984).Subjective well-being. Psychological Bulletin, 95, 542-575. http://doi.org/bw5wxd

Ditraglia, G. M., & Polich, J. (1991). P300 and introverted/extraverted personality types. Psychophysiology, 28, 177-184. http://doi.org/hmd

Ducci, L. (1981). Reaction times in the recognition of facial expressions of emotion. Italian Journal of Psychology, 8, 183-193.

Eysenck, H. J. (1967). The biological basis of personality. Springfield, IL: Thomas.

Eysenck, H. J., & Eysenck, M. W. (1985). Personality and individual differences: A natural science approach. New York: Plenum Press.

Gratton, G. (1998). Dealing with artifacts: The EOG contamination of the event-related brain potential. Behavior Research Methods, Instruments & Computers, 30, 44-53. http://doi.org/bmtkwt

Hamann, S., & Canli, T. (2004). Individual differences in emotion processing. Current Opinion in Neurobiology, 14, 233-238. http://doi.org/bnf6bc

Hofer, A., Siedentopf, C. M., Ischebeck, A., Rettenbacher, M. A., Verius, M., Felber, S., & Fleischhacker, W. (2006). Gender differences in regional cerebral activity during the perception of emotion: A functional MRI study. NeuroImage, 32, 854-862. http://doi.org/cbnb9h

Kozma, A., & Stones, M. J. (1980). The measurement of happiness: The development of the Memorial University of Newfoundland Scale of Happiness (MUNSH). The Journal of Gerontology, 35, 906-912.

Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1997). International Affective Picture System (IAPS): Technical manual and affective ratings. University of Florida, FL: NIMH Center for the Study of Emotion and Attention.

Lang, P. J., Bradley, M. M., Fitzsimmons, J. R., Cuthbert, B. N., Scott, J. D., Moulder, B., & Nangia, V. (1998). Emotional arousal and activation of the visual cortex: An fMRI analysis. Psychophysiology, 35, 199-210. http://doi.org/bwg669

Olofsson, J. K., Nordin, S., Sequeira, H., & Polich, J. (2008). Affective picture processing: an integrative review of ERP findings. Biological Psychology, 77, 247-265. http://doi.org/bbdfp6

Polich, J. (2007). Updating P300: An integrative theory of P3a and P3b. Clinical Neurophysiology, 118, 2128-2148. http://doi.org/hmf

Rusting, C. L., & Larsen, R. J. (1997). Extraversion, neuroticism, and susceptibility to positive and negative affect: A test of two theoretical models. Personality and Individual Differences, 22, 607-612. http://doi.org/b4f

Schmidtke, J. I., & Heller, W. (2004). Personality, affect and EEG: Predicting patterns of regional brain activity related to extraversion and neuroticism. Personality and Individual Differences, 36, 717-732. http://doi.org/hmg

Schupp, H. T., Junghöfer, M., Weike, A. I., & Hamm, A. O. (2003). Attention and emotion: an ERP analysis of facilitated emotional stimulus processing. Neuroreport, 14, 1107-1110. http://doi.org/hmh

Schupp, H. T., Junghöfer, M., Weike, A. I., & Hamm, A. O. (2004). The selective processing of briefly presented affective pictures: An ERP analysis. Psychophysiology, 41, 441-449. http://doi.org/hmk

Schupp, H. T., Markus, J., Weike, A. I., & Hamm, A. O. (2003). Emotional facilitation of sensory processing in the visual cortex. Psychological Science, 14, 7-13. http://doi.org/hmj

Stelmack, R. M., & Houlihan, M. (1995). Event-related potentials, personality, and intelligence: Concepts, issues, and evidence. In D. H. Saklofske & M. Zeidner (Eds.), International handbook of personality and intelligence (pp. 349-365). New York: Plenum Press.

Strayer, D. L., & Kramer, A. F. (1990). Attentional requirements of automatic and controlled processing. Journal of Experimental Psychology: Learning, Memory and Cognition, 16, 67-82. http://doi.org/b9w6j5

Watson, D., & Clark, L. A. (1992). On traits and temperament: General and specific factors of emotional experience and their relation to the five-factor model. Journal of Personality, 60, 441-476. http://doi.org/b66q35

Wickens, C., Kramer, A., & Donchin, E. (1984). The event-related potential as an index of the processing demands of a complex target acquisition task. Annals of the New York Academy of Sciences, 425, 295-299. http://doi.org/hmm

Wickens, C., Kramer, A., Vanasse, L., & Donchin, E. (1983). Performance of concurrent tasks: A psychophysical analysis of the reciprocity of information-processing resources. Science, 221, 1080-1082. http://doi.org/cr895c

Canli, T., Zhao, Z., Desmond, J. E., Kang, E., Gross, J., & Gabrieli, J. D. E. (2001). An fMRI study of personality influences on brain reactivity to emotional stimuli. Behavioral Neuroscience, 115, 33-42. http://doi.org/cj37b6

Cuthbert, B. N., Schupp, H. T., Bradley, M. M., Birbaumer, N., & Lang, P. J. (1999). Brain potentials in affective picture processing: Covariation with autonomic arousal and affective report. Biological Psychology, 52, 95-111. http://doi.org/bz7qdv

Diener, E. (1984).Subjective well-being. Psychological Bulletin, 95, 542-575. http://doi.org/bw5wxd

Ditraglia, G. M., & Polich, J. (1991). P300 and introverted/extraverted personality types. Psychophysiology, 28, 177-184. http://doi.org/hmd

Ducci, L. (1981). Reaction times in the recognition of facial expressions of emotion. Italian Journal of Psychology, 8, 183-193.

Eysenck, H. J. (1967). The biological basis of personality. Springfield, IL: Thomas.

Eysenck, H. J., & Eysenck, M. W. (1985). Personality and individual differences: A natural science approach. New York: Plenum Press.

Gratton, G. (1998). Dealing with artifacts: The EOG contamination of the event-related brain potential. Behavior Research Methods, Instruments & Computers, 30, 44-53. http://doi.org/bmtkwt

Hamann, S., & Canli, T. (2004). Individual differences in emotion processing. Current Opinion in Neurobiology, 14, 233-238. http://doi.org/bnf6bc

Hofer, A., Siedentopf, C. M., Ischebeck, A., Rettenbacher, M. A., Verius, M., Felber, S., & Fleischhacker, W. (2006). Gender differences in regional cerebral activity during the perception of emotion: A functional MRI study. NeuroImage, 32, 854-862. http://doi.org/cbnb9h

Kozma, A., & Stones, M. J. (1980). The measurement of happiness: The development of the Memorial University of Newfoundland Scale of Happiness (MUNSH). The Journal of Gerontology, 35, 906-912.

Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1997). International Affective Picture System (IAPS): Technical manual and affective ratings. University of Florida, FL: NIMH Center for the Study of Emotion and Attention.

Lang, P. J., Bradley, M. M., Fitzsimmons, J. R., Cuthbert, B. N., Scott, J. D., Moulder, B., & Nangia, V. (1998). Emotional arousal and activation of the visual cortex: An fMRI analysis. Psychophysiology, 35, 199-210. http://doi.org/bwg669

Olofsson, J. K., Nordin, S., Sequeira, H., & Polich, J. (2008). Affective picture processing: an integrative review of ERP findings. Biological Psychology, 77, 247-265. http://doi.org/bbdfp6

Polich, J. (2007). Updating P300: An integrative theory of P3a and P3b. Clinical Neurophysiology, 118, 2128-2148. http://doi.org/hmf

Rusting, C. L., & Larsen, R. J. (1997). Extraversion, neuroticism, and susceptibility to positive and negative affect: A test of two theoretical models. Personality and Individual Differences, 22, 607-612. http://doi.org/b4f

Schmidtke, J. I., & Heller, W. (2004). Personality, affect and EEG: Predicting patterns of regional brain activity related to extraversion and neuroticism. Personality and Individual Differences, 36, 717-732. http://doi.org/hmg

Schupp, H. T., Junghöfer, M., Weike, A. I., & Hamm, A. O. (2003). Attention and emotion: an ERP analysis of facilitated emotional stimulus processing. Neuroreport, 14, 1107-1110. http://doi.org/hmh

Schupp, H. T., Junghöfer, M., Weike, A. I., & Hamm, A. O. (2004). The selective processing of briefly presented affective pictures: An ERP analysis. Psychophysiology, 41, 441-449. http://doi.org/hmk

Schupp, H. T., Markus, J., Weike, A. I., & Hamm, A. O. (2003). Emotional facilitation of sensory processing in the visual cortex. Psychological Science, 14, 7-13. http://doi.org/hmj

Stelmack, R. M., & Houlihan, M. (1995). Event-related potentials, personality, and intelligence: Concepts, issues, and evidence. In D. H. Saklofske & M. Zeidner (Eds.), International handbook of personality and intelligence (pp. 349-365). New York: Plenum Press.

Strayer, D. L., & Kramer, A. F. (1990). Attentional requirements of automatic and controlled processing. Journal of Experimental Psychology: Learning, Memory and Cognition, 16, 67-82. http://doi.org/b9w6j5

Watson, D., & Clark, L. A. (1992). On traits and temperament: General and specific factors of emotional experience and their relation to the five-factor model. Journal of Personality, 60, 441-476. http://doi.org/b66q35

Wickens, C., Kramer, A., & Donchin, E. (1984). The event-related potential as an index of the processing demands of a complex target acquisition task. Annals of the New York Academy of Sciences, 425, 295-299. http://doi.org/hmm

Wickens, C., Kramer, A., Vanasse, L., & Donchin, E. (1983). Performance of concurrent tasks: A psychophysical analysis of the reciprocity of information-processing resources. Science, 221, 1080-1082. http://doi.org/cr895c

This work was supported by the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China (10XNK011).

Biao Li, School of Journalism and Communication, Renmin University of China, 59 Zhongguancun Street, Haidian District, Beijing 100872, People’s Republic of China. Email: [email protected]