IMPLICIT NEED FOR AFFILIATION AND PROCESSING OF EMOTIONAL IMAGES: EVENT-RELATED POTENTIAL CORRELATES.
Implicit motives reflect recurrent unconscious preferences for particular qualities of the affective learning experience (Schultheiss, 2008). In accordance with this general definition, the implicit need for affiliation motive (nAff) refers to a nonconscious concern for having warm, close relationships with others (Heyns, Veroff, & Atkinson, 1958). As such, individuals high in nAff derive pleasure from close, harmonious contact with other people.As stated by McClelland (1987), implicit motives cause people to be sensitive to cues predicting motive-specific incentives and disincentives. Such cues can represent particular salient stimuli that attract the attention of a person automatically. Although the power of motivational incentives to catch and hold one's attention has been well documented in the motivation literature, there are few studies in which the role of nAff in attentional orienting to incentive cues has been explored. Atkinson and Walker (1956) examined the influence of nAff on attention by conducting a recognition task in which social (human faces) and nonsocial (furniture) information was presented below the recognition threshold. They found that high-affiliation individuals were better at recognizing faces than were low-affiliation individuals, indicating that nAff is related to a perceptual vigilance toward affiliative stimuli. In contrast, we designed our experiment to assess perceptual thresholds for motive-related incentives, focusing specifically on affiliative versus nonaffiliative information instead of varying the valence of each stimulus (i.e., affiliative-relevant positive and negative information).
Building on Atkinson and Walker's (1956) work, Schultheiss and Hale (2007) conducted a more direct investigation, using a dot-probe task to assess the effects of nAff on attentional deployment. Their results revealed that individuals with high, in comparison to low, affiliation were more oriented toward both happy faces (a high affiliative signal) and angry faces (a low affiliative signal). This finding may seem counterintuitive, because angry faces are aversive disincentives and should be avoided by affiliation-motivated individuals, but Schultheiss and Hale suggested that greater fear of rejection can characterize the affiliation motive, thus making individuals who are affiliation-motivated more sensitive to signals of rejection (cf. Boyatzis, 1973). However, their results across the two studies they conducted were inconsistent; thus, investigations in which other measures of attention are used may be needed.
Until now, Schultheiss and Hale (2007) have been the only researchers to examine the relationship between nAff and the spatial orienting of attention toward motive-related incentives. Thus, we focused on replicating and extending their results by using different types of stimuli and different attentional tasks. Furthermore, although evidence has been obtained from behavioral studies, the neural correlates in this relationship were not defined (Atkinson & Walker, 1956; Schultheiss & Hale, 2007). Therefore, we used event-related potentials (ERPs) to examine affiliation-related emotional information processing. The late positive potential (LPP) component, a positive wave with a central-parietal scalp distribution, is highly sensitive to motivationally relevant stimuli, and is considered to reflect motivational engagement and allocation of attentional resources (Pedersen & Larson, 2016; Schupp et al., 2000). Thus, increases in the motivational relevance of stimuli engender more motivated attention and are reflected in larger LPP amplitudes. We aimed to utilize the LPP to examine how nAff impacts the allocation of attentional resources to affiliative-relevant positive and negative information.
On the basis of the findings outlined above (Atkinson & Walker, 1956; Schultheiss & Hale, 2007), we expected that, relative to low-nAff individuals, high-nAff individuals would orient their attention toward positive (high affiliation) and negative (low affiliation) social images. Thus, we anticipated observing larger LPP amplitudes for positive and negative, but not neutral, social images among high-nAff, compared to low-nAff, participants.
Method
Participants
This study was approved by the local review board for human participant research and written informed consent was obtained prior to the study from all participants. The experiment was conducted with 60 college students from Chengdu Medical College in China. All participants were right-handed, had normal or corrected-to-normal vision, and were asked to confirm that they did not have any neurological diseases. Two participants were excluded because of excessive eye movement artifacts, and the remaining 58 (43 women and 15 men) had an average age of 20.93 years (SD = 1.21).
Materials
Picture Story Exercise. The Picture Story Exercise is a standard story-writing measure of motivational needs that are not accessible through self-report. We assessed nAff by having participants view each picture cue for 30 s and then spending 5 min writing an imaginative story about the picture (Schultheiss & Pang, 2007). Stories were later coded for nAff imagery using Winter's (1994) Manual for Scoring Motive Imagery in Running Text. Two trained scorers, who had previously exceeded 85% for interscorer agreement on training material, independently coded the content of each story. Interrater reliability for nAff was r = .87. Because nAff scores, summed across all picture stories (M = 4.20, SD = 1.98), were significantly and positively correlated (r = .29, p < .05) with the mean total word count (M = 878, SD = 138), we corrected nAff scores by conducting a regression for word count and converting the residuals to z scores.
Images. To present the focal images, we adopted a modified oddball paradigm that included six blocks of 100 trials. Each of the blocks consisted of 70 standard images and three conditions of 10 deviant images, all of which were taken from the Chinese Affective Picture System (Bai, Ma, & Huang, 2005). A realistic image of a cup served as the frequent standard picture and 30 pictures grouped as negative, positive, or neutral served as the deviant images. The sequences for deviant and standard pictures were randomized across participants.
Procedure
After participants had completed the Picture Story Exercise, their ERPs were recorded while they took part in a behavioral task involving categorizing images. The task began with a 300 ms presentation of a small white cross on a black background. Next, a blank screen was shown for a random duration ranging between 500 and 1,500 ms, followed by the onset of a picture stimulus. Participants were instructed to press the "F" key as accurately and quickly as possible if a standard image was shown, or to press the "J" key if a deviant image was shown. The stimulus was terminated by pressing the key or when 1,000 ms had elapsed. Each response was followed by a blank screen for 1,000 ms.
ERP recording and analysis. Using tin electrodes that were mounted within an elastic cap (manufactured by Brain Products GmbH, Germany), 32 scalp sites were used to record an electroencephalograph. The left and right mastoids were referenced while a ground electrode was placed on the medial frontal area. Additionally, vertical electrooculograms were recorded infraorbitally and supraorbitally at the left eye. Meanwhile, horizontal electrooculograms were recorded using a left versus right orbital rim. All electrode impedances were kept below 5 k[OMEGA]. Trials with artifacts were excluded from averaging.
The electroencephalograph for each trial was segmented with a 200 ms prestimulus to 800 ms poststimulus epoch. The average included only those trials where the images had been correctly classified. LPP amplitude was calculated for each of the conditions and electrodes, averaging the time window that lasted 300-600 ms poststimulus. The following nine electrode sites were selected for statistical analysis: F3, Fz, F4, C3, Cz, C4, P3, Pz, and P4. A repeated measures analysis of variance (ANOVA) of the amplitude and latency of the LPP component was conducted, with valence (positive, neutral, and negative) and electrode sites (frontal, central, and parietal) as within-subjects factors and nAff scores (above and below median) as the between-subjects factor. The degrees of freedom of the F ratios were corrected using the Greenhouse-Geisser method.
Results
The participants were categorized based on their PSE scores (below or above the median) as low or high in affiliation. The difference in the participants' gender ratio and ages of the group did not reach significance, [chi square](1) < 1, ns; t(56) < 1, ns.
Behavioral Data
The means and standard deviations of response times (RTs) and response accuracy are presented in Table 1. False responses were rare as nearly all participants achieved 100% accuracy for both the standard and deviant pictures. A two-way ANOVA of RT data for the deviant stimuli (valence as a within-subjects factor, nAff as a between-subjects factor) yielded a significant valence effect, F(2, 116) = 4.82, p = .01, [[eta].sup.2.sub.p] = .08. This main effect reflects faster RTs to negative images than to positive (p = .004) and neutral (p = .006) images. There were no significant differences during positive and neutral conditions (p > .97).
ERP Data
The repeated measures ANOVA on LPP amplitudes showed significant main effects of valence, F(2, 112) = 16.36, p < .001, [[eta].sup.2.sub.p] = .23, and electrode sites, F(2, 112) = 105.03, p < .001, [[eta].sup.2.sub.p] = .65. Post hoc Bonferroni comparisons showed that LPPs elicited by negative images (M = 6.32, SE = 0.96) were significantly larger than those elicited by positive (M = 4.87, SE = 0.90, p = .003) and neutral (M = 4.30, SE = 0.90, p < .001) images. However, there was no significant difference between positive and neutral images (p > .47).
LPP amplitude was largest in the parietal sites (M = 11.29, SE = 0.96), significantly smaller in the central sites (M = 4.14, SE = 0.99), and smaller again at the frontal sites (M = 0.06, SE = 1.05). Moreover, the valence x electrode sites interaction was also significant, F(4, 224) = 25.89, p < .001, [[eta].sup.2.sub.p] = .32. The differences between negative images and positive and neutral images were more pronounced at the central sites. Further, there was a significant interaction between nAff and valence, F(2, 112) = 4.05, p = .03, [[eta].sup.2.sub.p] =. 07. Investigation of the valence effect in each group showed that high-nAff participants evidenced a significant valence effect, F(2, 56) = 16.69, p < .001, [[eta].sup.2.sub.p] = .37, with amplitudes being accordingly larger in the negative condition than in the positive (p = .003) and neutral (p < .001) conditions (see Figure 1). There were no significant differences between positive and neutral conditions (p > .70).
We further calculated the correlation between the continuous nAff score and the negative-neutral versus negative-positive difference in LPP amplitude. The Pearson correlation coefficients were both significant (negative-neutral: r = .37, p = .004; negative--positive: r = .27, p = .04). In contrast, the valence effect was not significant in the low-nAff participants, F(2, 56) = 2.33, p > .12. There were also no significant differences between the groups in terms of LPP peak latency.
Discussion
We examined in this study whether nAff modulates the processing of affiliative-relevant positive and negative information. The main result was that, compared to low-nAff participants, high-nAff participants had a larger LPP amplitude to negative images. As previously mentioned, a heightened LPP amplitude indicates an increase in motivational engagement and allocation of attentional resources (Schupp et al., 2000). Our results regarding the attentional processing of negative pictures are in line with, and support the reliability and generalizability of, those of Schultheiss and Hale (2007).
The attentional pattern discovered in our study and by Schultheiss and Hale may be able to be explained by referring to the defensive nature of high-nAff individuals. There is a lot of evidence that nAff is a rejection-sensitive motive. For instance, Byrne (1961) reported that nAff was associated with social anxiety, and Gable and Berkman (2008) argued that nAff is best conceptualized as measuring fear of rejection and/or affiliative anxiety. People characterized by high nAff seem to experience more fear of being alone and/or rejected than they do pleasure from being with others (Gable & Impett, 2012). Their overwhelming fear of rejection and greater dependence make high-nAff individuals particularly sensitive to low affiliative signals that fulfill the affiliation-motivated person's expectation that he or she will be rejected. That is, people are quick to recognize a low affiliative signal they are chronically primed for and only later learn what it was that preceded this fulfilled expectation.
Our second hypothesis, that high-nAff individuals would also orient their attention toward positive images, was not supported. This result is also consistent with Schultheiss and Hale's (2007; Study 2) finding that high-nAff individuals showed a nonsignificant tendency to attend to happy faces. When combined, these findings help to reinforce the notion that affiliation-motivated individuals can be sensitive when it comes to interpersonal cues that may signal rejection. However, the combined findings also suggest that these individuals may be less sensitive than originally expected to high-affiliation signals. The latter conclusion has also been confirmed in several other studies. For example, Schultheiss, Pang, Torges, Wirth, and Treynor (2005) failed to find a reinforcing effect of happy faces on implicit learning behavior in high-nAff individuals. Further, Kordik, Eska, and Schultheiss (2012) found that nAff was not associated with corrugator activity in a positive social interaction.
It must be pointed out, though, that the lack of attentional bias toward positive information in high-nAff participants could also be due to the scoring system used. Winter's (1994) scoring system combines the duality of affiliation motive, namely, fear of rejection and desire for closeness, into one motive. The differences between them have, therefore, been folded into a single system. Future researchers might do well to differentiate between the two types of affiliation to see whether they coexist within individuals to roughly equivalent degrees or whether people tend to be characterized by one or the other.
Our findings are an initial step in the process of illuminating the precise mechanisms that underlie affiliation-related emotional processing. However, as we measured individual differences in nAff, we are not able to make a strong inference regarding the causality of nAff. Future researchers should attempt to experimentally manipulate nAff to ascertain its causal role in emotional processing. Further, although all the target images used in this study were of people, they did not all represent affiliation-related content. Additional insight into specific affiliation-based differences may be derived by using only affiliation-themed images as targets (e.g., discord and rejection, interpersonal affiliation).
References
Atkinson, J. W., & Walker, E. L. (1956). The affiliation motive and perceptual sensitivity to faces. The Journal of Abnormal and Social Psychology, 53, 38-41. https://doi.org/dfn4b7
Bai, L., Ma, H., & Huang, Y.-X. (2005). The development of the Chinese Affective Picture System: A pretest with 46 college students [In Chinese]. Chinese Mental Health Journal, 19, 719-722.
Boyatzis, R. E. (1973). Affiliation motivation. In D. C. McClelland & R. S. Steele (Eds.), Human motivation: A book of readings (pp. 252-276). Morristown, NJ: General Learning Corporation.
Byrne, D. (1961). Anxiety and the experimental arousal of affiliation need. Journal of Abnormal and Social Psychology, 63, 660-662. https://doi.org/c76hb2
Gable, S. L., & Berkman, E. T. (2008). Making connections and avoiding loneliness: Approach and avoidance social motives and goals. In A. J. Elliot (Ed.), Handbook of approach and avoidance motivation (pp. 203-216). New York, NY: Psychology Press.
Gable, S. L., & Impett, E. A. (2012). Approach and avoidance motives and close relationships. Social and Personality Psychology Compass, 6, 95-108. https://doi.org/fx6txd
Heyns, R. W., Veroff, J., & Atkinson, J. W. (1958). A scoring manual for the affiliation motive. In J. W. Atkinson (Ed.), Motives in fantasy, action, and society: A method of assessment and study (pp. 205-218). Princeton, NJ: Van Nostrand.
Kordik, A., Eska, K., & Schultheiss, O. C. (2012). Implicit need for affiliation is associated with increased corrugator activity in a non-positive, but not in a positive social interaction. Journal of Research in Personality, 46, 604-608. https://doi.org/f4cn57
McClelland, D. C. (1987). Human motivation. New York, NY: Cambridge University Press. Pedersen, W. S., & Larson, C. L. (2016). State anxiety carried over from prior threat increases late positive potential amplitude during an instructed emotion regulation task. Emotion, 16, 719-729. https://doi.org/b5wq
Schultheiss, O. C. (2008). Implicit motives. In O. P. John, R. W. Robins, & L. A. Pervin (Eds.), Handbook of personality: Theory and research (3rd ed., pp. 603-633). New York, NY: Guilford Press.
Schultheiss, O. C., & Hale, J. A. (2007). Implicit motives modulate attentional orienting to perceived facial expressions of emotion. Motivation and Emotion, 31, 13-24. https://doi.org/d7pnf3
Schultheiss, O. C., & Pang, J. S. (2007). Measuring implicit motives. In R. W. Robins, R. C. Fraley, & R. Krueger (Eds.), Handbook of research methods in personality psychology (pp. 322-344). New York, NY: Guilford.
Schultheiss, O. C., Pang, J. S., Torges, C. M., Wirth, M. M., & Treynor, W. (2005). Perceived facial expressions of emotion as motivational incentives: Evidence from a differential implicit learning paradigm. Emotion, 1, 41-54. https://doi.org/cmpq5s
Schupp, H. T., Cuthbert, B. N., Bradley, M. M., Cacioppo, J. T., Ito, T., & Lang, P. J. (2000). Affective picture processing: The late positive potential is modulated by motivational relevance. Psychophysiology, 37, 257-261. https://doi.org/cf9hfx
Winter, D. G. (1994). Manual for scoring motive imagery in running text (4th ed.). Unpublished manuscript, Department of Psychology, University of Michigan, MI, USA.
JIANFENG WANG, YAN WU, AND LUSHI JING
Chengdu Medical College
Jianfeng Wang, Yan Wu, and Lushi Jing, Department of Psychology, Chengdu Medical College. Jianfeng Wang and Yan Wu contributed equally to this work.
Correspondence concerning this article should be addressed to Lushi Jing, Department of Psychology, Chengdu Medical College, 783 Xin Du Ave, Chengdu 610500, People's Republic of China. Email: [email protected]
https://doi.org/10.2224/sbp.6544
Table 1. Averaged Response Times and Response Accuracy of High- and
Low-nAff Participants in Positive, Neutral, and Negative Conditions
High nAff Low nAff
M SD M SD
Response times (ms)
Positive 545.33 80.44 540.58 68.63
Neutral 544.46 75.28 541.61 61.78
Negative 534.76 68.39 537.01 64.35
Response accuracy (%)
Positive 97.97 1.73 97.60 2.98
Neutral 97.83 2.09 97.47 2.26
Negative 97.90 2.06 97.93 1.72
Note. nAff = implicit need for affiliation.
 Printer friendly
 Cite/link
 Email
 Feedback
| |
| Author: | Wang, Jianfeng; Wu, Yan; Jing, Lushi |
|---|---|
| Publication: | Social Behavior and Personality: An International Journal |
| Article Type: | Report |
| Geographic Code: | 9CHIN |
| Date: | Feb 1, 2018 |
| Words: | 2959 |
| Previous Article: | CAN PITCHER PERFORMANCE REVEAL GAME FIXING IN BASEBALL? DEVELOPING A FORENSIC ANALYSIS DETECTION PROCEDURE. |
| Next Article: | INFLUENCE OF SCARCITY CLAIMS ON PRODUCT EVALUATION: THE ROLE OF REVERSIBILITY OF DECISION. |
| Topics: | |