Rejection sensitivity and schema-congruent information processing biases

Journal of Research in Personality xxx (2009) xxx–xxx

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Journal of Research in Personality

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Rejection sensitivity and schema-congruent information processing biases

Nilly Mor *, Mika InbarSchool of Education, Hebrew University, Mount Scopus, Jerusalem 91905, Israel

a r t i c l e i n f o

Keywords:Rejection sensitivityDepressionSocial anxietyInformation processingAttentional biasSelf-referential encodingMemory

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* Corresponding author. Fax: +972 2 588 1311.E-mail address: [email protected] (N. Mor).

Please cite this article in press as: Mor, N., &Research in Personality (2009), doi:10.1016

a b s t r a c t

Rejection Sensitivity (RS) refers to the tendency to anxiously anticipate, readily perceive and overreact torejection. The current research assesses schema-congruent information processing biases related to RS.Specifically, we predicted that high RS individuals would show biases in attention and self-referentialencoding and recall of rejection-relevant information. Similarly, we predicted stronger concordancebetween these biases among high RS than low RS individuals. People high in RS showed biases in self-ref-erential encoding and recall of negative socially relevant material. However, RS was not characterized byan attention bias or by stronger concordance between information processing biases. Implications ofthese findings to the understanding of RS and its long lasting effects are discussed.

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1. Introduction

While everyone experiences rejection, people differ in the man-ner they respond to rejection. Some people respond adaptively,whereas others respond in ways that impair their social relations(Downey, Freitas, Michaelis, & Khouri, 1998) and make them sus-ceptible to low self-esteem, social anxiety and depression (e.g., Ay-duk, Downey, & Kim, 2001; Gailliot & Baumeister, 2007). Individualdifferences in responses to rejection have been construed as a cog-nitive-affective processing disposition, termed rejection sensitivity(RS; Downey & Feldman, 1996). RS refers to the tendency to anx-iously anticipate, readily perceive and overreact to rejection.

The concept of RS is rooted in attachment (Bowlby, 1980) andinterpersonal theories (Horney, 1937; Sullivan, 1953), but socialcognitive models of personality (e.g., Mischel & Shoda, 1995) havebeen central to the conceptualization of RS. These models can beseen (e.g., Caprara & Cervone, 2000) as relying on schema theory toexplain information processing and behavioral patterns in RS.According to schema theory, schemas are organized interconnectedknowledge structures that develop through past experiences andguide the processing and interpretation of new information (Bart-lett, 1932; Fiske & Taylor, 1991; Markus, 1977). People typically holdschemas about themselves, others, and their relationships with oth-ers (i.e., relational schemas; Baldwin, 1999, 2005). Schemas arethought to lead to increased attention allocation, cognitive elabora-tion, and enhanced memory of schema-congruent material. Conse-quently, the activation of schemas is often assessed usinginformation processing tasks that examine attention allocation to

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Inbar, M. Rejection sensitiv/j.jrp.2009.01.001

schema-congruent material (e.g., Ingram, Bernet, & McLaughlin,1994) and incidental recall of self or other-encoded information(e.g., Derry & Kuiper, 1981; Whisman & Delinsky, 2002).

Schema-based information processing, particularly the process-ing of affective, schema-congruent information, has been associatedwith a variety of personality traits. Neuroticism (Chan, Goodwin, &Harmer, 2007), extroversion (Rusting & Larsen, 1998) and traitdepression and anxiety (Rusting, 1998 for a review) have been re-lated to processing of emotional stimuli. This work has suggestedthat individuals selectively attend to, retrieve, and reconstructevents in ways that are consistent with these personality traits. Sim-ilarly, low self-esteem and insecure attachment orientations havebeen linked to biased processing of interpersonal information denot-ing rejection (e.g., Dandeneau & Baldwin, 2004; Dewitte, Koster, DeHouwer, & Buysse, 2007; Gyurak & Ayduk, 2007; Koch, 2002).

Because current models of RS can be construed as relying onschema theory, and given that related personality traits have beenassociated with schema-congruent processing, the aim of the cur-rent research was to examine the link between RS and schema-based processing. The RS model suggests that among people highin RS, the rejection schema is chronically accessible and rejectionis readily primed by interpersonal situations (e.g., Downey, Mou-gios, Ayduk, London, & Shoda, 2004; Pietrzak, Downey, & Ayduk,2005). The heightened accessibility of this schema can lead toschema-congruent information processing, increase sensitivity torejection cues and facilitate the development of anxious expecta-tion of rejection. In turn, these expectations may lead to faultyinterpretations of social situations and eventually to behaviors thatbring about actual rejection (Downey & Feldman, 1996).

Empirical evidence provides some support for this depiction ofRS. High RS people show increased arousal in the face of rejection-

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related stimuli (Downey et al., 2004) and they interpret ambiguoussocial situations as denoting rejection (Downey & Feldman, 1996).In addition, regulation of attention in the face of rejection cues hasbeen proposed to play a role in RS. Focusing attention away fromarousing aspects of a rejection experience has been found to atten-uate hostile and angry feelings (Ayduk, Mischel, & Downey, 2002).Similarly, the ability to control attention mediates the relationshipbetween RS and negative outcomes such as interpersonal difficul-ties and compromised well-being (Ayduk et al., 2000). Recent neu-roimaging findings indicate that individuals low in RS displaysignificantly more activity in areas responsible for cognitive con-trol when processing rejection-related stimuli, compared to indi-viduals high in RS (Kross, Egner, Ochsner, Hirsch, & Downey,2007). Taken together, available findings link RS and attentionalprocesses and suggest that attention regulation may play a signif-icant role in RS. However, these findings do not follow a unifiedtheoretical framework. In particular, these studies have not fol-lowed a schema-based conceptualization of RS demonstrating di-rectly that high RS individuals show increased attention torejection-related stimuli in their environment.

To summarize, the available work is suggestive of rejection-congruent information processing biases in RS, but several issuesremain unresolved. First, although there is indirect evidence forattentional biases in RS, to date no work has directly examinedwhether high RS individuals indeed direct their attention towardrejection-relevant stimuli and whether they have better memoryfor this information. Second, the specificity of these biases needsto be carefully delineated. Given the links between RS, depression(Ayduk et al., 2001) and social anxiety (e.g., Harb, Heimberg, Fres-co, Schneier, & Liebowitz, 2002), it is important to demonstratethat these biases are characteristic of RS even when controllingfor depression and anxiety. Available research on rejection-relatedbiases rarely examined RS along with competing constructs (seeAyduk et al., 2007 for an exception). Third, specificity of thesebiases to rejection-related content has not been demonstrated.Therefore, investigating the association between RS and informa-tion processing biases toward positive social stimuli as well as to-ward negative but non-social stimuli is necessary for testing theassumption that these biases represent schema-congruent biasesunique to rejection-related content. Finally, schema theory wouldsuggest that a rejection schema should manifest itself not only inbiases in specific processes such as attention, encoding and mem-ory, but also in the coherence among these biases (e.g., Bower,1981). Past research has not examined the concordance betweenthe biases, namely whether people who readily attend to rejec-tion-relevant content, also attribute rejection to themselves andrecall rejection-related content.

The current research was designed to address these issues. Sev-eral hypotheses were examined. We predicted that RS would beassociated with biases in attention, self-referential encoding, andmemory for rejection-related content. We further predicted thatthese biases would be unique to rejection-related content ratherthan negative non-social content, and that these biases will be asso-ciated with RS while controlling for depression and social anxiety.Finally, we predicted that because people high in RS process infor-mation in ways that are congruent with a rejection schema, theassociation between the biases will be stronger among these indi-viduals than among individuals low in RS. Thus, RS will moderatethe association between the various information processing biases.

2. Method

2.1. Participants

Participants were 127 Hebrew-speaking students at the HebrewUniversity of Jerusalem (95 female) who participated in exchange

Please cite this article in press as: Mor, N., & Inbar, M. Rejection sensitivResearch in Personality (2009), doi:10.1016/j.jrp.2009.01.001

for course credit or payment. Participants’ ages were between 18and 38 years (M = 24, SD = 3.2).

2.2. Questionnaires

The Rejection Sensitivity Questionnaire (RSQ; Downey & Feld-man, 1996) was used to measure anxious expectations of rejection.It consists of 18 hypothetical situations in which rejection by a sig-nificant other is possible. For each situation, participants rate on asix-point scale, their anxiety from the expected outcome as well asthe perceived likelihood of rejection. Following an expectancy-va-lue model of anxious expectations of rejection, the score for eachsituation is calculated as the product of the rating of anxiety elic-ited by possible rejection, and the degree to which the person ex-pects rejection to occur. The total RS score is the sum of the scoresacross all items divided by 18, the number of items, with a possiblerange of 1–36. The mean RSQ score in the current sample was 8.21(SD = 4.06), with a range of 1.39–26.83. The coefficient a for thecurrent sample was 0.93.

The Inventory to Diagnose Depression (IDD; Zimmerman & Cor-yell, 1987; Zimmerman, Coryell, Corenthal, & Wilson, 1986), awidely used 22-item self-report questionnaire, was used to mea-sure symptoms of depression. Each item on the IDD consists of fivestatements, presented in ascending order of severity, that cover adepressive symptom. Responders are asked to indicate whichstatement best describes their experiences over the precedingweek. Scores on the IDD range from 0 to 88. For ethical reasons,we removed a question assessing suicidal ideation from the ques-tionnaire. In this sample the mean IDD score was 12.89 (SD = 11),with a range of 0–54. The coefficient a was 0.92.

The Liebowitz Social Anxiety Scale (LSAS; Liebowitz, 1987) wasused to measure symptoms of social anxiety. The LSAS assessesfear and avoidance in 24 situations that are likely to elicit socialanxiety. Scores on the LSAS range from 0 to 144. In the presentstudy the mean LSAS score was 45.49 (SD = 23.03), with a rangeof 0–112. The coefficient a for the total score was 0.96.

2.3. Cognitive tasks

2.3.1. Self referential encoding and incidental recall task (SRET)The SRET is a commonly used task that assesses incidental recall

of self-encoded information (e.g., Derry & Kuiper, 1981; Joormann,Dkane, & Gotlib, 2006; Rogers, Kuiper, & Kirker, 1977; Whisman &Delinsky, 2002). The current version of the SRET was an adaptationof the procedure used by Gotlib et al. (2004). First, on each of 80trials, the words ‘‘describes me or relates to me?” appear for500 ms in the center of the screen followed by a 250 ms pause.Then, a word appears in the center of the screen. Participants pressa key to indicate whether the displayed word describes them. Thisself-encoding phase is followed by a 3-min distraction task. Subse-quently, participants are asked to write as many words as they canrecall from the self-referential encoding phase, independent ofwhether they endorsed the words as self-descriptive or not. Partic-ipants are allotted 3 min for the recall phase.

The stimuli for the task were based on a Hebrew translation ofwords from the ANEW list (Bradley & Lang, 1999). Words wereclassified into four categories, with 20 words in each category:rejection (e.g., lonely), acceptance (e.g., popular) and non-socialnegative and positive (e.g., wound and pleasure, respectively).Words in the different lists were matched for length and frequencyof use in Hebrew (Frost & Plaut, 2005). The word categories werechosen to distinguish between word content and valance. Becausepilot work suggested that many negative adjectives are perceivedas indicative of rejection, we added a similar number of nouns tothe stimuli pool of each category to allow discrimination betweenthe categories. Words were divided into the content categories by

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four graduate students. Only words for which there was a consen-sus regarding their classification were included as stimuli.

2.3.2. Attentional probe taskThe attentional probe task has been commonly used in the

study of attentional biases in psychopathology (e.g., Lubman, Pe-ters, Mogg, Bradley, & Deakin, 2000; MacLeod & Mathews, 1988).In this task, participants are first presented with a fixation pointin the center of the screen, followed by a centrally presented pairof words, one below the other. Each pair consists of a target anda neutral word (MacLeod, Mathews, & Tata, 1986). A probe, whichparticipants are asked to identify, then replaces one of the words.Faster reaction times to the probe when it follows the target com-pared to the neutral word indicate that attention was drawn to thelocation of the target word. In the current study the fixation pointappeared for 500 ms, followed by a relatively long presentationtime of 1000 ms of the word pair. This presentation time elicitsconscious responses, and it was chosen based on recent work onattentional biases in depression (Bradley, Mogg, & Lee, 1997; Got-lib, Krasnoperova, Neubauer Yue, & Joormann, 2004). The locationof the target and neutral words and of the probe, the category ofthe target word, and the type of probe were all counterbalancedacross trials. Response latency and accuracy were recorded bythe computer.

Target words in this task belonged to three categories: rejection,acceptance and non-social negative words, with 32 words in eachcategory. Words were chosen in the same manner as in the SRET.Despite the content similarity, words in the two tasks did not over-lap. To prevent fatigue we shortened the task by not including afourth category of non-social positive words. Neutral words werecategorized, household terms (Mogg, Bradley, Williams, & Math-ews, 1993) and were matched to target words by length and fre-quency of use in Hebrew (Frost & Plaut, 2005).

2.4. Procedure

Participants were seated at 60 cm viewing distance from thecomputer monitor. To avoid priming effects of words from theattentional probe task on the recall task, participants first com-pleted the SRET. On the attentional probe task, participants firstperformed a training phase (eight pairs of neutral words), and onceit was ensured that they understood the instructions, they contin-ued to the task itself. Finally, participants completed the RSQ, IDDand LSAS, which were displayed on the computer screen in a ran-dom order.

1 The proportion of endorsed rejection-related words was positively skewed.Therefore, we used a square-root transformation of this variable. However, the resultsusing the transformed variable were very similar to the results using the untrans-formed proportion. Thus, for clarity purposes, we report the analyses using theuntransformed proportion of endorsed rejection words.

3. Results

3.1. Self-referential encoding and incidental recall

RS, depression, and social anxiety were moderately correlated(r(125) = 0.40, r(125) = 0.45, r(125) = 0.38 for RS and depression,RS and social anxiety, and depression and social anxiety, respec-tively). However, because there was no indication of multicolline-arity between these predictors, we included all three predictors inall of the models.

3.2. Self-referential encoding

Based on prior work using the SRET (e.g., Joormann et al., 2006)endorsement was calculated as the ratio of endorsed words fromeach content category (i.e., acceptance, rejection, non-social nega-tive and positive) divided by the overall number of endorsedwords. This ratio was computed to control for differences in overalltendencies to self-endorse descriptors.

Please cite this article in press as: Mor, N., & Inbar, M. Rejection sensitivResearch in Personality (2009), doi:10.1016/j.jrp.2009.01.001

The proportions of endorsed words in the four content catego-ries sum up to 1.0 and are not independent of one another. Becauseof this interdependence and because all our predictors were con-tinuous, we first conducted an overall MANCOVA, examining theassociation between RS and a bias in self-referential encoding.We followed up this analysis with hierarchical regression analysesusing a Bonferroni correction for multiple comparisons (signifi-cance required p 6 .0125).1

In a MANCOVA predicting the proportion of self-endorsedwords in each content category, standardized scores on the IDD,RSQ and LSAS, were entered as continuous variables. In a secondanalysis, we added the two-way interactions among depression,social anxiety and RS (the product terms of RSQ, IDD and LSAS) intothe MANCOVA model.

When only main effects were entered into the model, the over-all effects of RS and depression were significant (F(3,121) = 13.99,p < .001, g2 = 0.26 for RS; F(3,121) = 15.26, p < .001, g2 = 0.27 fordepression). In the second analysis, although accounting for asmaller percentage of the variance than depression or RS, the inter-action of social anxiety and depression was significant (F(3,118) =3.41, p < .03, g2 = 0.08).

We conducted four follow up hierarchical regression analyses,predicting the proportions of words endorsed in each of the con-tent categories. RSQ, IDD and LSAS were entered as predictors inthe first model, and their interaction terms were entered in a sub-sequent model (see Table 1). These analyses indicated that RS anddepression significantly predicted the proportion of self-endorsedwords in all four content categories. However, when the interac-tion terms were added, RS was no longer a significant predictorof the proportion of endorsed words in the non-social negativecontent category. Thus, as can be seen in Table 1, higher levels ofRS and depression predicted a higher proportion of endorsementof rejection-related words and a lower proportion of endorsementof acceptance and non-social positive words. Higher levels ofdepression were uniquely associated with a higher proportion ofendorsement of non-social negative words. In addition, unexpect-edly, the interaction between social anxiety and depression posi-tively and significantly predicted the proportion of self-endorsedwords in the rejection category. We probed this interaction usingsimple slope analyses (Aiken & West, 1991). These analyses indi-cated that depression positively predicted the endorsement ofrejection-related words when social anxiety was conditioned at1 SD above its mean (b = 0.06, t = 4.70, p < .001), but not when so-cial anxiety was conditioned at 1 SD below its mean (b = 0.007,t = 0.62, p > .05).

We conducted additional analyses to further address the depen-dence of the proportions of endorsement of words in the differentcontent categories, and to examine the specificity of the associa-tion between RS and encoding of rejection-related content (as op-posed to non-social negative content). In these analyses wecomputed the difference in the proportion of endorsement of rejec-tion-related and non-social negative descriptors (rejection bias).We conducted a hierarchical regression analysis with rejection biasas the dependent variable. Standardized scores on the RSQ, LSASand IDD were entered as predictors in the first model, and thetwo-way interaction terms were entered as predictors in a subse-quent model. The results of this analysis are presented in Table2. The first model accounted for 28% of the variance(F(3,123) = 15.93, p < .001). The second model accounted for 10%of the variance and significantly added to the prediction of the bias

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Table 1Proportion of self-endorsed words for each category, as predicted by RSQ, IDD, LSASand the interactions among them (N = 127).

Dependentvariable

Model Independentvariable

B SE b t

Self attributereject

1 RSQ 0.06 0.009 0.47 6.33*

IDD 0.05 0.009 0.38 5.35*

LSAS �0.00 0.009 �0.02 �0.182 RSQ 0.05 0.009 0.41 5.56*

IDD 0.04 0.009 0.29 3.82*

LSAS 0.01 0.009 0.05 0.67RSQ � IDD 0.00 0.005 �0.00 �0.05RSQ � LSAS 0.01 0.008 0.08 1.07IDD � LSAS 0.03 0.010 0.23 2.83*

Self attributeaccept

1 RSQ �0.04 0.009 �0.34 �4.08*

IDD �0.04 0.008 �0.38 �4.70*

LSAS 0.00 0.008 0.02 0.252 RSQ �0.03 0.009 �0.27 �3.13*

IDD �0.03 0.009 �0.27 �3.07*

LSAS �0.00 0.009 �0.05 �0.62RSQ � IDD �0.00 0.005 �0.07 �0.80RSQ � LSAS �0.00 0.008 �0.05 �0.58IDD � LSAS �0.02 0.010 �0.22 �2.37

Self attributenegative

1 RSQ 0.02 0.005 0.25 2.92*

IDD 0.03 0.005 0.44 5.30*

LSAS �0.00 0.005 �0.07 �0.772 RSQ 0.01 0.005 0.23 2.43

IDD 0.02 0.005 0.42 4.45*

LSAS �0.00 0.005 �0.05 �0.60RSQ � IDD 0.00 0.003 0.11 1.16RSQ � LSAS �0.00 0.005 �0.04 �0.45IDD � LSAS �0.00 0.006 �0.02 �0.23

Self attributepositive

1 RSQ �0.04 0.008 �0.37 �4.39*

IDD �0.03 0.008 �0.33 �4.15*

LSAS 0.00 0.008 0.03 0.412 RSQ �0.04 0.009 �0.37 �4.00*

IDD �0.03 0.009 �0.33 �3.53*

LSAS 0.00 0.009 0.03 0.28RSQ � IDD 0.00 0.005 0.01 0.13RSQ � LSAS �0.00 0.008 �0.02 �0.25IDD � LSAS �0.00 0.010 �0.04 �0.41

Note. RSQ, Rejection Sensitivity Questionnaire; IDD, Inventory to DiagnosisDepression and LSAS, Liebowitz Social Anxiety Scale.* p < .0125.

2 In all analyses pertaining to recall, when we refer to the number of endorsed orendorsed and recalled words in the negative or the rejection content categories, werefer to the transformed and standardized variables.

Table 2Difference in proportion of self-endorsed for rejection-related versus non-socialnegative Descriptors, as predicted by RSQ, IDD, LSAS and the interactions among them(N = 127).

Dependent variable Model Independentvariable

B SE b t

Self attributerejection –negative

1 RSQ 0.04 0.009 0.41 4.55*

IDD 0.02 0.009 0.20 2.30LSAS 0.00 0.009 0.02 0.24

2 RSQ 0.04 0.009 0.36 4.00*

IDD 0.01 0.009 0.10 1.12LSAS 0.01 0.009 0.09 1.00RSQ � IDD �0.00 0.005 �0.07 �0.72RSQ � LSAS 0.01 0.009 0.12 1.31IDD � LSAS 0.03 0.010 0.28 2.89*

Note. RSQ, Rejection Sensitivity Questionnaire; IDD, Inventory to DiagnosisDepression and LSAS, Liebowitz Social Anxiety Scale.* p < .01.

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(F change (3,120) = 6.11, p < .001). As can be seen in the table, RSpositively and significantly predicted the bias toward endorsementof rejection-related as opposed to non-social negative descriptors.Again, unexpectedly, the interaction between social anxiety anddepression also predicted this bias. Simple slope analyses indicatedthat depression positively predicted the endorsement rejectionbias when social anxiety was conditioned at 1 SD above its mean

Please cite this article in press as: Mor, N., & Inbar, M. Rejection sensitivResearch in Personality (2009), doi:10.1016/j.jrp.2009.01.001

(b = 0.04, t = 7.55, p < .001), but not when social anxiety was condi-tioned at 1 SD below its mean (b = �0.019, t = �1.56, p > .05). Theeffects of depression, social anxiety, and the interaction betweendepression and RS and between social anxiety and RS were non-significant (p > .05).

We further examined whether the preferential encoding of so-cial content is unique to rejection-related content, rather than toany social content (positive or negative). Therefore, we computedthe difference between the proportion of endorsed acceptance-re-lated and non-social positive descriptors (acceptance bias). We ex-pected that RS would only be related to a rejection bias and not toan acceptance bias. We conducted a hierarchical regression pre-dicting the acceptance bias from RSQ, IDD and LSAS (included inthe model), and their interactions, (added in the second model).Neither of the models were significant (F(3,123) = 0.07, p > .05 forthe first model, and F(3,120) = 1.31, p > .05 for the second model),nor were any of the individual predictors. Thus, as predicted, rejec-tion sensitivity is uniquely associated with a rejection bias and isnot associated with an acceptance bias.

3.3. Recall

Because self-endorsed words are typically recalled more readilythan non-endorsed words, (Rogers et al., 1977) recall was calcu-lated as the number of words that were endorsed and recalled ineach content category. To demonstrate that recall is independentlyrelated to RS, depression, or social anxiety, it was important to con-trol for the number of endorsed words in the relevant content cat-egory. Therefore, the number of endorsed words in the relevantcontent category was included in all analyses pertaining to recall.An examination of these measures revealed that the recall andendorsement variables were positively skewed in the rejection cat-egory (1.97 [SE = 0.22] for recall, and 1.36 [SE = 0.22] for endorse-ment) and the negative category (1.71 [SE = 0.22] for recall, and1.27 [SE = 0.22] for endorsement). Thus, these variables weresquare-root transformed.2

We conducted four hierarchical regression analyses, predictingthe number of words that were endorsed and recalled in each ofthe four content categories. In each model, RSQ, IDD and LSAS, aswell as the number of endorsed words in the category, were en-tered as predictors in the first model. In a second model, the inter-action terms between RSQ, IDD and LSAS were added into themodel.

In all four regressions analyses, endorsement of words in a cat-egory predicted recall of words in that category. Importantly, aspredicted, RS predicted the recall of rejection-related words, butnot other content categories. Neither depression nor social anxietywere significantly related to recall of words in any of the contentcategories. Given these findings, we further describe only the mod-els predicting the recall of rejection-related words. These modelsare presented in Table 3. The first model, in which only main ef-fects were included, was significant and accounted for 38% of thevariance in recall. In this model, when controlling for level ofendorsement of rejection-related words, higher levels of RS wereassociated with better recall for rejection-related content(b = 0.18, t(124) = 2.07, p < 0.05). The second model, in which theinteraction terms of RS, social anxiety, and depression were added,accounted for an additional 3% of the variance and it failed to reachsignificance. None of the interaction terms predicted recall.

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Table 3The number of rejection-related words that were endorsed and recalled, predicted byRSQ, IDD, LSAS and the interactions among them and rejection-related endorsement(N = 124).

Dependentvariable

Model Independentvariable

B SE b t

Rejection-relatedrecall

1 RSQ 0.19 0.09 0.18 2.07*

IDD 0.01 0.1 0.10 0.12LSAS �0.11 0.09 �0.11 �1.29Endorsement 0.56 0.09 0.55 6.17*

2 RSQ 0.19 0.10 0.17 1.97*

IDD �0.01 0.1 �0.01 �0.07LSAS �0.11 0.09 �0.11 �1.19Endorsement 0.57 0.09 0.56 6.07*

RSQ � IDD 0.06 0.11 0.05 0.53RSQ � LSAS �0.01 0.09 �0.01 �0.06IDD � LSAS 0.01 0.11 0.01 0.05

Note. RSQ, Rejection Sensitivity Questionnaire; IDD, Inventory to DiagnosisDepression; LSAS, Liebowitz Social Anxiety Scale and Endorsement, Number ofEndorsed Rejection-Related Words.* p < .05.

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3.4. Attentional probe task

Outlier responses are a typical problem in RT data. Therefore,we followed standard procedures for data reduction and cleaningin the attentional probe task (e.g., Bradley et al., 1997). Reactiontimes smaller than 100 ms or larger than 2000 ms are oftenthought to be too fast or too slow to be considered true response.Similarly, responses that are more than 2.5 SD from each partici-pant’s mean RT, are considered outlier responses. These responses(3.1% of responses) as well as erroneous responses (2.7%) were ex-cluded from analyses.

Bias scores were computed by subtracting the RT when theprobe replaced the target word from the RT when it replaced thecontrol word. Three bias scores were computed, for rejection,acceptance and negative target words. We ran a MANCOVA withthe three bias scores as the predicted variables. Depression, socialanxiety and RS were the predictors in the first model. In the secondanalysis, the interactions among these predictors were added tothe model. Contrary to our prediction, none of the predictors sig-nificantly predicted attentional biases toward rejection, accep-tance, or non-social negative stimuli (all ps > 0.05). Nevertheless,we conducted follow up regression analyses. These analyses, too,did not yield significant results in predicting an attention bias to-ward rejection, acceptance or non-social negative stimuli.

3.5. Associations among the information processing tasks

Because there was no evidence for an attention bias in RS, weonly examined whether RS moderated the association between re-call and endorsement of rejection-related stimuli. Therefore, weconducted a regression analysis predicting the number of rejec-tion-related words that were endorsed and recalled, from RS, theproportion of endorsed rejection-related words (out of all self-en-dorsed words), and the interaction between the proportion ofendorsement of rejection-related content and RS. The proportionof endorsed words and RS were standardized and centered andthe interaction term was computed as the product of these twostandardized scores. Zero-order correlations were all significantand indicated that the recall of rejection-related words was associ-ated with each of the predictors (r(125) = 0.57, with rejection-re-lated endorsement, r(125) = 0.34 with RS, and r(125) = 0.28 withthe interaction of RS and rejection-related endorsement). However,when these predictors were entered simultaneously into a regres-sion model predicting recall, only the proportion of rejection-re-lated endorsement emerged as a significant predictor (b = 0.63,

Please cite this article in press as: Mor, N., & Inbar, M. Rejection sensitivResearch in Personality (2009), doi:10.1016/j.jrp.2009.01.001

t(121) = 5.86, p < .001). We also examined the magnitude of thecorrelation between endorsement and recall of rejection-relatedcontent in individuals high and low in RS, by using a median split.As we predicted, this correlation was higher among those high inRS (r(59) = 0.60) than among those low in RS (r(64) = 0.42). How-ever, as expected from the results of the regression analysis, thedifference between the correlations was non-significant. Thus, RSdid not moderate the association between endorsement and recallof rejection-related terms.

4. Discussion

A main goal of the current research was to examine the specific-ity of schema-congruent information processing biases to RS. Aninteresting pattern of results that differentiates depression, RSand social anxiety, emerged. RS was associated with a specific biasfor rejection-related content, whereas social anxiety was not char-acterized by endorsement biases, and depression was associatedwith a bias for endorsing any negative content. More importantly,RS but not depression or social anxiety, was associated with arejection-related bias. These findings provide support for our asser-tion that RS is characterized by schema-congruent processing thatcannot be explained by depression or social anxiety.

Our findings regarding the rejection endorsement bias are con-sistent with the definition of RS and with the initial findings ofDowney and Feldman (1996) whereby individuals high in RS tendto attribute rejection to the self, both in hypothetical and in real-life situations. These findings suggest that the self-referentialencoding task may provide an alternative, implicit measure of RS.Using this task can assist in avoiding pitfalls of self-report mea-sures, corroborate findings from self-report, and contribute to anew body of research that utilizes implicit measures of RS, suchas startle response to rejection-related stimuli (e.g., Downeyet al., 2004; Gyurak & Ayduk, 2007).

The link between RS and recall of rejection-congruent self-en-dorsed information provides a stronger test of schematic process-ing in RS. Whereas endorsement of rejection-related terms canseem similar to completing a questionnaire, increased recall ofrejection-related terms, when controlling for endorsement, isreflective of an independent cognitive process. Although endorse-ment and recall were correlated, the unique association of therejection-related recall with RS indicates that endorsement and re-call represent separate processes.

To our knowledge, this study is the first to demonstrate a mem-ory bias in RS. In contrast to previous findings that refer to imme-diate processing that occurs while rejection-related stimuli arepresent, memory biases refer to processing that occurs when thestimuli are no longer present and they involve reflective processesthat go beyond the immediate consequences of perception (John-son & Hirst, 1993). Although no research exists on the relationshipbetween individual differences in RS and memory biases, these re-sults are consistent with recent work that demonstrated that situ-ational activation of relational schemas can affect memory forschema-congruent material (Rowe & Carnelley, 2003). Schema-congruent recall biases are of particular importance, because theymay put individuals high in RS at risk for negative emotional andinterpersonal consequences. Whereas other information process-ing biases such as attention and interpretation play a role duringsocial interactions, memory biases can be more detrimental be-cause they are active long after the social interaction has ended,and thus can lead to enduring maladaptive effects. Thus, a height-ened recall of rejection-related events may contribute to the longlasting effects of RS such as hostility and relationship break-up(e.g., London, Downey, Bonica, & Paltin, 2007) and depression (Ay-duk et al., 2001).

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Contrary to our expectation, the association between rejection-related endorsement and recall was not moderated by RS.Although the correlation between endorsement and recall washigher among those high in RS than those low in RS, and zero-ordercorrelations revealed that recall was correlated with the interac-tion of RS and endorsement, these findings did not reach statisticalsignificance. It is possible that the skewness of the distributions ofthe endorsement and recall measures and the moderately highassociation between these variables are responsible for these nullresults. Further examination of the coherence between thesebiases is warranted.

RS was not associated with an attention bias toward rejection-related content on the attentional probe task. A possible interpre-tation of this null effect is that RS does not fully operate as a sche-ma, because it does not guide attention processes. However, webelieve that this null effect most likely reflects methodological lim-itations associated with the use of the attentional probe task. First,although in frequent use, this task has been questioned in non-clinical populations, due to low estimates of internal consistencyand test–retest reliability (Dewitte et al., 2007; Schmukle, 2005).Second, word stimuli may not be powerful enough to elicit anattentional bias. Indeed, current work on attention biases indepression uses facial stimuli rather than words, because the for-mer are more powerful stimuli (Öhman, 2002) and they yield moreconsistent results than do verbal stimuli (e.g., Gotlib, Krasnoper-ova, et al., 2004; Gotlib et al., 2004).

More importantly, because selective attention is not a unitaryconstruct (e.g., LaBerge, 1995; Posner & Rothbart, 2007), it is pos-sible that attentional processes that are relevant to individual dif-ferences in RS are not captured by the attentional probe task. Thisparadigm taps into selective attention to objects’ location in thevisual field. An alternative conceptualization of attention refersto stimulus dimensions rather than spatial locations, and reflectsthe ability to attend to relevant versus irrelevant dimensionsand to ignore some aspects of a stimulus while attending to others(e.g., Shalev & Algom, 2000). In the context of RS, following thisconceptualization, we would expect that among individuals highon RS, rejection-related aspects of stimuli would interrupt pro-cessing of competing information. This notion is consistent withrecent findings that demonstrate that people who get ‘‘stuck” onrejection-related aspects of an event, have more difficulty regulat-ing their emotions (Ayduk et al., 2002). Neuroimaging researchthat demonstrates that high RS individuals show less neural activ-ity in regions involved in the control of attention to rejection-rel-evant stimuli (Kross et al., 2007) can also provide support for thisalternative conceptualization of attention in RS. Furthermore, thelack of an attentional bias in RS may be due to the diversity ofattentional modes that people with high RS use. Ayduk et al.(2002) have documented two styles of processing; a ‘‘hot” process-ing mode whereby people focus on the rejection-related aspects ofa negative event, and a ‘‘cool” mode whereby people focus onalternative and distant aspects. It is possible that only the ‘‘hot”processing mode is associated with attentional inflexibility andbias toward rejection-relevant stimuli. This variability in process-ing modes may explain why no attention bias was found in thecurrent study. Future research should examine whether a ten-dency to engage in ‘‘hot” versus ‘‘cool” mode of processing is asso-ciated with an attentional bias toward rejection-related aspects ofstimuli and with drawing attention away from alternative stimu-lus dimensions.

An unexpected link was found between depression, social anx-iety and an endorsement rejection-related bias, such that the co-occurrence of depression and social anxiety predicted the endorse-ment bias. Although not the focus of the current research, thesefindings are interesting as they fit with prior work on social anxietyand depression. Social anxiety and depression are highly comorbid

Please cite this article in press as: Mor, N., & Inbar, M. Rejection sensitivResearch in Personality (2009), doi:10.1016/j.jrp.2009.01.001

conditions (Brunello et al., 2000) and are characterized by similarprocessing biases (e.g., Dozois & Frewen, 2006; Wilson & Rapee,2005a). Nevertheless, the processing biases in depression and anx-iety are not identical. Depressed individuals show biases in pro-cessing social and non-social information, socially anxious peopleshow biases only in processing social information (e.g., Vocken,Bogels, & Peeters, 2007), and comorbid depression intensifiesbiases in socially anxious individuals (Wilson & Rapee, 2005b).Relatedly our findings suggest that individuals with comorbidanxiety and depression may be characterized by different informa-tion biases than individuals with either depression or anxiety only.Identifying biases that specifically characterize comorbid anxietyand depression can be important, because comorbid depressionmay impede treatment effectiveness in social anxiety (e.g.,Chambless, Tran, & Glass, 1997).

Several limitations of the current research should be noted.First, mean RS scores were lower in the current sample comparedto typical RS scores. This may be due to age and cultural character-istics of the sample. Second, in examining the specificity of the re-ported effects, we attempted to distinguish between social andnon-social stimuli. Because negative adjectives often contain socialinformation, we used a mix of adjectives and nouns as stimuli inthe various tasks, instead of the typical use of adjectives. Similarly,the attentional probe task did not include non-social positivewords. Future research should corroborate our findings using alter-native sets of stimuli, including non-social positive words. Third,the correlational nature of the study precludes causal inferencesregarding the role of RS in information processing biases. There-fore, future research should examine the effects of priming rejec-tion on attention, self-attribution and memory biases. Finally, inthis study, as in the vast majority of studies, RS was measuredusing the RSQ. This exclusive use of the RSQ may limit the gener-alizability of findings regarding RS. Therefore, future researchshould use alternative modes of assessment, possibly implicit onessuch as the SRET.

In sum, the current research is the first to systematically exam-ine the role of schema-congruent information processing biases inRS. Our findings contribute to the understanding of underlyingmechanisms in RS, as well as to the broader issue of the link be-tween personality predispositions and information processing(e.g., Guyll & Madon, 2003; Yovel, Revelle, & Mineka, 2005). Giventhe biases found here, future research should investigate the linksbetween these biases and the emotional (e.g., Ayduk et al., 2002),physiological (Downey et al., 2004) and interpersonal (Downey &Feldman, 1996; London et al., 2007) consequences of RS.

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