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RESEARCH ARTICLE |
Georgia Institute of Technology
Address correspondence to Fredda Blanchard-Fields, School of Psychology, Georgia Institute of Psychology, Atlanta, GA, 30332-0170. E-mail: fb12{at}prism.gatech.edu
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Abstract |
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THERE is a growing literature suggesting that older adults rely on heuristic processing when making social judgments and thus are more susceptible to judgment biases (Chen & Blanchard-Fields, 2000
; Hess, McGee, Woodburn, & Bolstad, 1998). For example, older adults tend to rely more on easily accessible knowledge structures when forming impressions (Hess & Follett, 1994; Hess et al., 1998); to rely on stereotypes when making source attributions (Mather, Johnson, & De Leonardis, 1999); to be more susceptible to an accessibility bias when making social judgments (Jacoby, 1999); and to have a heightened tendency toward dispositional inferences when making causal attributions (Blanchard-Fields, 1996, 1999).
We have replicated across numerous studies that, in comparison with young adults, older adults are more likely to attribute the cause of a relationship dilemma, such as a marriage in trouble, to personal characteristics of the primary character (e.g., ambition or self-involvement). Hence, they do not give as much weight to other situational or extenuating circumstances (e.g., work pressure or family pressure) that also could have contributed to the outcome (Blanchard-Fields, 1994; Blanchard-Fields, Chen, Schocke, & Hertzog, 1998; Blanchard-Fields & Norris, 1994). In the social psychology literature, this overreliance on dispositional information is referred to as correspondence bias (Gilbert & Malone, 1995). It has been argued that dispositional inferences, as described herein, require little cognitive effort and are typically the initial, spontaneous response of individuals making causal attributions. In contrast, elaborative processing and cognitive effort are required when an individual deliberates on additional information, such as situational constraints, to adjust this initial response (Gilbert & Malone, 1995; Trope & Gaunt, 2000).
At first blush, a plausible explanation for the well-replicated dispositional tendency found in older adults is age-related decline in processing capacity, which may force older adults to rely on easily accessible dispositional information. However, age differences in the dispositional tendency are not observed in all situations. We only observe them in relationship situations that result in a negative outcome, not in achievement-related situations (see Blanchard-Fields, 1999). This suggests that the heightened dispositional tendency is not merely a function of a generalized resource deficit that prevents older adults from engaging in elaborative social inferencing, albeit resource limitations could certainly be operating in conjunction with other factors. It becomes particularly important to identify what it is about relationship situations that make them more likely to produce age differences. A more complete account of age differences in dispositional tendencies in making causal attributions requires addressing the role of content-related, social factors typically linked to relationship situations.
Previous research has demonstrated that the age relevance of the problem situation (Blanchard-Fields, Baldi, & Stein, 1999) and the degree to which a participant focused on a particular character (Blanchard-Fields et al., 1998) influenced dispositional inferences. For example, we demonstrated a black sheep effect within older adults that was similar to past research (Erber, 1999; Erber, Szuchman, & Prager, 1997; Hummert, Garstka, & Shaner, 1997; Marques, Yzerbyt, & Leyens, 1988). Older participants tended to assign greater blame for negative outcomes to characters that belonged to their own age group (Blanchard-Fields et al., 1999). In this case, older adults made harsher judgments for age-relevant cohorts who portray a negative picture of older adulthood. However, it may not simply be the match between age of the participant and the primary character in a hypothetical situation that affects blame attributions. Instead, it could be the degree to which one identifies with one or more characters in the situation that influences dispositional inferences.
The Present Study
In the present study we examine how social factors operate in the context of making evaluative causal attributions in the form of blame attributions. First, we wanted to more fully understand the domain-specificity effect (i.e., age differences found in negative relationship situations only) on blame attributions. Our past studies examined the domain specificity of responding by categorizing problem dilemmas as either achievement or relationship situations. The increased tendency to blame the focal character in relationship situations most likely stems from the inherent transactional nature of this domain in contrast to more achievement-related domains. Relationship dilemmas that are likely to produce age differences are more transactional in nature because they involve the resolution of conflicting perspectives, desires, or goals between two or more people in order to salvage, maintain, or improve the relationship. Thus, such situations involve negotiation between the parties in question, with blame for an outcome attributed to a particular character. In contrast, the achievement dilemmas are often less transactional in nature because the focus is more on the individual's overcoming obstacles in an attempt to accomplish a self-defined goal. However, the problem with this dichotomy is that many achievement situations also reflect, to some extent, attempts to overcome obstacles to maintain or salvage a relationship. Although we typically find no age differences in the dispositional or blaming tendency in achievement situations, age differences did emerge for two achievement situations. Both of these situations reflected the essential criteria for relationship situations: At least part of the outcome involved overcoming obstacles that endangered the integrity of a relationship dyad (Blanchard-Fields et al., 1998). Thus, the relationship–achievement distinction is not sufficient to describe when age differences in blame attributions will occur.
Our first goal in the present study was to conceptually extend previous findings by more precisely examining a particular feature of relationship situations that produces age differences in blame attributions. In previous research we found that older adults made higher dispositional attribution ratings when the intentions and causal connections between the main character and the final outcome were ambiguous (e.g., Blanchard-Fields et al., 1998). Again, this age-by-ambiguity effect was only evident in relationship situations and not achievement situations. Therefore, another component of ambiguity may have been operating in these relationship situations, ambiguity of the outcome. To accomplish this goal, we systematically varied the degree to which there is ambiguity regarding a successful resolution of the relationship problem. Low-ambiguous outcomes involve relatively little conflict resolution and a definitive termination of the relationship. For high-ambiguous or uncertain outcomes, the characters attempt to negotiate obstacles and resolve conflicts in order to maintain the relationship. Because the relationship is still in progress in high-ambiguous situations, age-related differences in goals and values about how a relationship can be salvaged may be evoked. This should not occur in low-ambiguous situations. Thus, we hypothesized that a tendency to blame characters on the part of older adults would only emerge in situations that reflected high-ambiguous outcomes.
Given our focus on relationship outcome ambiguity, a second important feature of our design was to focus on blame attribution ratings made by individuals to all of the characters represented in our problem vignettes. In our previous research, ratings such as the degree to which one blames the character or the degree to which the character is responsible for the negative outcome were typically focused on the primary character only. However, interpersonal problem situations always involve more than one person. Thus, our second goal was to determine the degree to which individual differences in blame and responsibility for the negative outcome apply to all characters in the situation.
In our previous research, age differences in blame attributions also varied to the extent that the content of the situation and age of the target were relevant to the age group of the perceiver. In this case, identifying with the same-aged target may have triggered beliefs relevant to how an actor that age should behave in such situations (Blanchard-Fields et al., 1999). Age-relevant characters were blamed more by older adults because the targets violated age-related expectations. However, identification with the character was manipulated as a group characteristic; that is, the age of the target was varied and the age-match effect was examined (e.g., older participant matched with older target). Although shared age-related attributes were the same between target and participant, the degree to which the participant identified with more individualized attributes of the target was not assessed. The latter is an individual-difference variable reflecting idiographic differences in personal identification. This is evident in research in which participants evaluate an individual as opposed to a member of a group (e.g., age group). In this case, the perceiver relies less on accessing one's shared attributes with the group and more on the desirability of the participant's characteristics and on personal identification with the target (Clement & Krueger, 1998; Hamilton & Sherman, 1996). In the latter case, similar targets are characters with which the perceiver shares an identity or sense of "we-ness" (Heider, 1958; Miller, Turnbull, & McFarland, 1988). This form of identification with the target character is strongly linked to one's self-definition and self-worth (Miller et al., 1988) and as a result should have a differential impact on evaluative attributions from our previous findings. Instead of a "harsher" evaluation of a group-related target, a more positive and self-enhancing attribution should be made for a target with whom one individually identifies. Thus, our third goal was to further examine target focus by obtaining ratings of personal identification with each character in every vignette. We predicted that, in interpersonal conflict situations, individuals of any age who identify with a particular character would tend to absolve that character from blame and, instead, blame the opposing character. In other words, characters that are perceived as more dissimilar to the participant are more likely to be blamed for the negative outcome.
In sum, we expected that age differences in the tendency for older adults to blame the primary character for a negative outcome will be most evident in situations with high relationship outcome ambiguity. We also explored the degree to which this applies to the secondary character. Finally, we examined the degree to which identification with a particular character influences the tendency to blame characters for negative outcomes observed in interpersonal situations. We expected that, when participants identify with a particular character, they will absolve that character of blame and in turn blame the opposing character. We explored the degree to which this effect mediates age-related differences in the tendency to blame characters for negative outcomes.
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METHODS |
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; Blanchard-Fields & Norris, 1994; Blanchard-Fields & Robinson, 1987). All vignettes consisted of situations that resulted in negative outcomes. In each situation the primary character played an explicit pivotal role in causing the negative outcome. However, fundamental causality of the negative outcome remained ambiguous. First, although the primary character played the pivotal role, the secondary character played a significant role in the dilemma even though his or her behavior did not explicitly cause the final outcome. Secondary character involvement rendered exclusive culpability on the part of the primary character somewhat ambiguous. A second inherent ambiguity came from the primary character's lack of intentionality in causing the negative outcome (Heider, 1958).
Participants
We used an independent sample of 37 young adults, aged 19–24 years (M = 21.44, SD = 1.07), and 33 older adults, aged 66–75 years (M = 70.05, SD = 2.37), to determine the degree to which each vignette emphasized an ambiguous resolution of a relationship problem. Young adults were recruited through a university participant pool at the Georgia Institute of Technology. Older adults were community dwelling and were recruited through community organizations and newspaper advertisements. All of the participants were required to speak English as their primary language and to be American citizens or have lived in the United States for 10 years. The majority of the sample were Caucasians (73.5%) and from middle-class backgrounds. Participants rated their overall health at the time of participation on a scale of 1 to 4, with 1 indicating poor and 4 indicating excellent. There was a moderate age difference in overall health, F(1, 65) = 4.02, p <.05), with younger adults reporting slightly better overall health (M = 3.25, SD =.60) than older adults (M = 2.94, SD =.68). Participants also indicated their highest level of education. Although young adults were currently in college and the older adults had at least a high school education, age differences in level of education were nonsignificant.
Procedure
After completing a consent form and a demographics questionnaire, participants were asked to read each vignette and then answer the following question: "In this situation, how important is the goal of maintaining a relationship to one or more of the characters?" This reflects the degree to which the targets intend to salvage the relationship, and thus it indicates the degree to which there is ambiguity in resolving the relationship problem (continuing vs terminating the relationship). The question was answered on a 7-point Likert scale, with 1 indicating not at all and 7 indicating a great deal. This procedure was repeated for each of the 40 vignettes.
Results
For the purposes of this study, selected vignettes had two or more characters and did not demonstrate age differences on the rating scale. Of the 40 vignettes, 17 met these criteria. For each of these 17 vignettes we computed a mean rating score aggregated across age groups. We then grouped the 17 vignettes into high or low ROA (i.e., HROA or LROA, respectively). In order to retain an equal number of vignettes at each level of ROA, we excluded the vignette that landed in the center. The mean ratings for the vignettes categorized as having a LROA ranged from 3.09 to 4.17 (M = 3.77, SD =.97). The mean ratings for the vignettes categorized as HROA ranged from 4.37 to 6.06 (M = 5.17, SD =.76).
The vignettes that were categorized as LROA consisted of two vignettes that were previously categorized as achievement situations and six vignettes that were previously categorized as relationship situations. The vignettes that were categorized as HROA consisted of three vignettes that were previously categorized as achievement situations and five vignettes that were previously categorized as relationship situations. As we expected, characters in achievement situations may be perceived as negotiating obstacles to maintain an interpersonal relationship. In addition, characters in vignettes within the relationship domain vary in the degree to which they are successful in resolving relationship problems, or desire to maintain the relationship. For example, many of the relationship vignettes categorized as LROA were vignettes pertaining to a romantic couple that broke up as a result of an interpersonal conflict. In contrast, the relationship vignettes now categorized as HROA consisted of relationships that were troubled, but not ending (see Appendix A for vignette examples).
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THE MAIN STUDY: LEVEL OF ROA AND ATTRIBUTIONAL PROCESSING |
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Interviewers asked all participants to indicate their ethnic group, overall health, and level of education at the time of participation. All of the participants were required to speak English as their primary language and to be American citizens or have lived in the United States for at least 10 years. The sample was primarily Caucasian (73%) and from middle-class backgrounds. Overall health was self-reported on a 4-point scale: 1 = poor, 2 = fair, 3 = good, and 4 = excellent. We analyzed age differences in overall health and level of education by using a one-way analysis of variance (ANOVA). The age group differences for overall health were nonsignificant (M = 3.36, SD =.57). Young adults were currently in college, whereas older adults had at least a high school education.
Procedure
After completing a consent form and demographics questionnaire, participants were given the vignettes on separate sheets of paper. The vignettes were randomized and presented to participants in a set order. After reading each vignette, participants immediately made attribution ratings in a set order. Although a larger attribution rating questionnaire was presented to the participants (see Blanchard-Fields et al., 1998, for the entire questionnaire), of interest to this study were the evaluative attributions of responsibility and blame as well as the target focus variable, personal identification. We assessed the degree to which individuals felt the characters were responsible for a given outcome by having participants rate, "How much do you think the character is responsible for the outcome?" We assessed the degree of blame in a similar manner: "How much do you think the character is to blame for the outcome?" For each question, we substituted the character's name and the specific outcome for the story. In order to assess the degree to which individuals identified with each character, after making attributional ratings, participants were asked, "To what degree do you identify with the character in this situation?" Again, the characters' names were given in the question. Each scale was rated on a 7-point Likert scale, with 1 indicating not at all and 7 indicating a great deal.
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RESULTS |
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=.67 to =.92, with a mean of =.83. In addition, ANOVAs revealed a similar pattern of age effects for both responsibility and blame. Therefore, for each participant, we computed a mean score by averaging the responsibility and blame ratings for each character, and we conducted the remaining analyses on this composite responsibility–blame (RB) index.
Analytic Approach
We conducted two analyses. The first analysis addressed our first goal, that level of ROA determines when age differences in the RB index would be observed. In addition, it addressed our second goal, to explore individual and age-related differences in the tendency to blame secondary characters. The second analysis addressed our third goal, to explore the predictive utility of personal identification as well as to see if it would account for observed age differences.
As previously noted, within each level of ROA there were eight vignettes; within each vignette there was a primary and a secondary character, and for each character we obtained measurements of the RB index and personal identification. The RB index was the dependent variable, whereas the remaining variables (level of ROA, character, and personal identification) were within-subject predictors. In addition, the between-subjects predictors were age group and gender. Although a traditional mixed-model ANOVA would have been appropriate for the first analysis, for the second analysis (when personal identification was included), we needed to allow for both the multilevel structure of the data as well as the simultaneous analysis of both continuous and nominal repeated-measures predictors. Thus, we used SAS PROC MIXED to conduct mixed-model regression analyses (Littell, Milliken, Stroup, & Wolfinger, 1996) for both of the analyses. In reporting the analyses, we report group effects with an F value testing the effect and each group's respective means in order to interpret the effect. The effects for continuous variables are represented by the respective F value testing the effect. In order to interpret the effect, we present the regression parameter estimates. These demonstrate the linear effect of the predictor after we control for the other variables in the model.
The Role of Level of ROA
Again, our first and second goals were to explore the effect of level of ROA on the RB index and to explore blame tendencies toward secondary characters, respectively. In order to accomplish these goals, we computed a 2 (age group) x 2 (level of ROA) x 2 (character) repeated-measures ANOVA by using a linear mixed model using SAS PROC MIXED (Littell et al., 1996).
Given that initial analyses indicated that gender effects were nonsignificant, we collapsed the analyses across gender. Although the original model included all possible interactions, we removed higher-order nonsignificant interactions. Therefore, the final model included the following effects: level of ROA, character, age group, Character x Level of ROA, Age group x Level of ROA, Age group x Character, and Age group x Level of ROA x Character.
There were significant main effects for age group, F(1, 104) = 4.19, p <.05; level of ROA, F (1, 104) = 9.43, p <.01; and character, F(1, 104) = 29.35, p <.001. Older adults blamed characters more (M = 4.54, SE =.09) than did young adults (M = 4.28, SE =.09). Individuals, across age groups, blamed characters more in LROA vignettes (M = 4.49, SE =.07) than in HROA vignettes (M = 4.33, SE =.07). Finally, individuals blamed primary characters (M = 4.55, SE =.07) more than secondary characters (M = 4.27, SE =.07). These effects, however, were qualified by higher-order interactions. The level of ROA x Character interaction was significant, F(1, 104) = 56.19, p <.001. In LROA, individuals blamed primary (M = 4.44, SE =.08) and secondary characters (M = 4.54, SE =.08) about equally; however, in HROA, primary characters (M = 4.66, SE =.08) were blamed significantly more than secondary characters (M = 4.00, SE =.08). A significant Age group x Level of ROA x Character interaction, F(1, 104) = 5.55, p <.05, further qualified this effect. See Table 1 for the respective means.
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The RB index for secondary characters revealed a different pattern of results. Older adults blamed secondary characters more than young adults in both levels of ROA. In addition, individuals, irrespective of age, blamed secondary characters more in LROA than in HROA. Older adults and younger adults responded similarly to different levels of ROA for secondary characters.
In sum, older adults blamed primary characters more than young adults did in situations high in ROA; however, age differences did not appear in situations low in ROA. We did not find this differential effect of ROA for secondary characters. It should also be noted that, for LROA situations, both age groups blamed primary and secondary characters equivalently. However, for HROA vignettes, individuals, across age groups, focused on the primary character as the causal factor for the negative outcome.
The Role of Personal Identification
Our third goal in the current study was to examine the role of personal identification in predicting blame attributions. First, we anticipated that if individuals identified with a given character, they would absolve that character and blame the opposing character. In addition, we wanted to explore whether personal identification would account for age differences in the tendency to blame.
To accomplish our goal, we used SAS PROC MIXED to analyze the degree to which personal identification accounted for variance in blame ratings for both primary and secondary characters. This analysis yielded parallel findings for both blame ratings, so we present only the findings for the primary characters. This decision is justified for two reasons. First, age-related differences in blame varied by level of ROA for primary characters but not for secondary characters. Second, the primary characters explicitly played a causal role in the negative outcome.
The model for the RB index for primary characters consisted of a 2 (age group: young, older adult) x 2 (level of relationship outcome ambiguity: low, high) repeated-measures mixed linear model. In this analysis, we included personal identification with the primary characters and personal identification with the secondary characters as two continuous predictors. Given that there was a moderate correlation between personal identification with the primary character and personal identification with the secondary character (r =.22, p <.001), we centered the identification variables, that is, by subtracting their respective mean values. The dependent variable was the RB index for the primary characters.
The original model included all possible interactions among age group, level of ROA, personal identification with the primary characters, and personal identification with the secondary characters; however, we trimmed the nonsignificant higher-order interactions from the model. Among the nonsignificant items that were removed were all interactions including age group and personal identification. Personal identification (for both the primary and secondary characters) did not vary by age group. The final model included the following effects: age group, level of ROA, Age group x Level of ROA, personal identification with the primary characters (ID primary), personal identification with the secondary characters (ID secondary), ID primary x ID secondary, ID primary x Level of ROA, and ID secondary x Level of ROA.
Including personal identification in the model did not meaningfully change the three effects examined in the previous analysis (age group, level of ROA, and the Age group x Level of ROA interaction) for primary characters (as presented in Table 1). Given that personal identification neither interacted with age group nor altered the original age effects, we can conclude that personal identification did not mediate age differences in the RB index. Hence, we focus on only the effects of personal identification.
Although personal identification for primary and secondary characters acted as reliable predictors of the RB index, that is, F(1, 1583) = 80.06, p <.001; b = –.14, SE =.03 for primary characters and F(1, 1583) = 67.64, p <.001; b =.04, SE =.03 for secondary characters, both of these effects were qualified by significant higher-order interactions. First, the effect of the two types of personal identification interacted with each other, F(1, 1583) = 3.94, p <.05. In order to interpret this interaction, we examined the slope of ID primary at three levels of ID secondary (Aiken & West, 1991): not at all, a moderate amount, and a great deal. At each level of ID secondary, we ran a PROC MIXED analysis. The predictors in each of the three models were age group, level of ROA, Age group x Level of ROA, and ID primary. The dependent variable was the RB index for the primary character. We adjusted alpha to.017 by using a Bonferroni correction.
Greater identification with the primary character significantly predicted tendencies to absolve the primary character of blame in two of the analyses. ID primary was a significant predictor of the RB index for the primary character when individuals identified with the secondary character either not at all, F(1, 231) = 32.67, p <.001; b = –.27, SE =.05, or a moderate amount, F(1, 287) = 25.96, p <.001; b = –.24, SE =.05. However, when individuals identified with the secondary character a great deal, the predictive utility of ID primary was nonsignificant. Thus, although ID primary is a potent predictor of RB, its predictive utility depended on the degree to which individuals identify with the secondary character.
Both ID primary and ID secondary also interacted with level of ROA in predicting the RB index for the primary characters, that is, F(1, 1583) = 6.81, p <.01 for the ID primary interaction and F(1, 1583) = 46.86, p <.001 for the ID secondary interaction. In order to decompose these interactions, we ran two post hoc PROC MIXED analyses (one at each level of relationship outcome ambiguity). In each analysis, ID primary and ID secondary were predictors of RB. We adjusted alpha to.025 for each analysis by using a Bonferroni correction.
For LROA vignettes, the effect of ID primary, F(1, 740) = 82.70, p <.001; b = –.24, SE =.03, and ID secondary, F(1, 740) = 115.81, p <.001; b =.31, SE =.03, produced strong effects. Identifying with the primary character led to absolving the primary characters whereas identifying with the secondary characters led to blaming the primary characters. In contrast, the effect of ID primary was much lower in magnitude for HROA, F(1, 740) = 19.26, p <.001; b = –.13, SE =.03, than for LROA, b = –.24. In addition, the effect of ID secondary was nonsignificant.
In sum, identifying with the primary characters led to absolving the primary characters of blame. Conversely, identifying with the secondary characters led to blaming the primary characters. However, the predictive utility of personal identification varied by level of ROA. Personal identification played a large role in determining the degree to which the primary characters were to blame for the negative outcome in LROA vignettes. In contrast, in HROA, personal identification played only a minimal role in explaining individual differences in blame attributions. Personal identification did not influence age differences in the RB index for the primary characters in the high level of ROA.
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DISCUSSION |
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, 1996; Blanchard-Fields et al., 1998). One of our main goals in the current study was to probe further into what it is about relationship situations that produces these age differences, specifically by examining ROA. The major findings from this study suggest that, when relationship outcome ambiguity is high (i.e., the outcome of the relationship is uncertain and individuals are in the process of salvaging the relationship), age is predictive of the tendency to blame the primary character. However, when relationship outcome ambiguity is low, target focus in the form of personal identification is the critical social factor accounting for the tendency to blame the primary characters across all age groups. We also found differential patterns of age effects depending on whether the primary or secondary character was the target of focus. We discuss each of these factors in terms of how they help explain under what conditions age differences in blaming tendencies will occur.
ROA and Blame Attributions
Expanding on earlier research, the present study suggests that age differences in blame attributions do not emerge in relationship situations simply because they necessitate transactional involvement. Instead, the extent to which the characters are attempting to salvage the relationship may better capture what influences older adults to blame the characters more than young adults do. Indeed, this was the case when the blaming tendencies for the primary character were examined (which was the focus in our previous work). Older adults blamed the primary characters more than young adults when the characters involved in the interpersonal interaction were trying to overcome obstacles to maintain or salvage the relationship (HROA). However, when the relationship was unsalvageable and terminated (LROA), young and older adults blamed the primary character similarly.
What is it about situations that involve characters who are attempting to preserve the relationship that impels older adults to blame the primary character more than young adults? It may be that, when the relationship is perceived as ongoing and potentially salvageable, age differences in the ways in which goals and values about the importance of maintaining a relationship are triggered. Although this particular belief system has not been studied in this context, there is some evidence to suggest that older adults place greater emphasis on relationship beliefs and values in some situations and not in others (Blanchard-Fields, 1996). For example, Blanchard-Fields (1996) found that specific vignettes evoked social rules (e.g., marriage is more important than a career) as a function of the particular cohort or life stage confronting different age groups. She suggested that cohort differences influenced the differential accessibility of strong relationship schemas or social rule systems that in turn could influence attributional judgments. In general, age differences in the content and accessibility of relationship maintenance schemas may be more evident in HROA situations, and, in turn, they may have influenced older adults' blaming tendencies. This warrants further research.
In fact, there is evidence in the extant literature demonstrating that older adults' attitudes and beliefs do affect their behavior. For example, older adults rely on beliefs about the rules of social behavior in forming impressions of others (Hess & Auman, 2001); older adults' attitudes toward aging influence their impressions of other older adults (Chasteen, 2000); and older adults rely more on emotion information in decision making (Mather & Johnson, 2000).
Situations in which the relationship ends and there is relatively little potential to salvage the relationship (as in the LROA condition) may produce uniformity in young and older adults' beliefs about the causal role of the primary character. In these situations, age-related beliefs about the importance of maintaining the relationship may no longer be relevant or of predictive importance. This of course warrants further research.
Blaming tendencies as a function of HROA versus LROA also become evident when secondary characters are examined. Across age groups, the primary characters were blamed more than the secondary characters in HROA situations. However, in situations with LROA outcomes, when the relationship has ended, both age groups blamed both the primary and secondary characters equivalently. This is surprising given that the primary characters committed the pivotal action causing the negative outcome in all vignettes. Again, it may be the case that individuals of all ages view the dissolution of a relationship as the responsibility of both partners. Nevertheless, focusing on each of the characters involved in a relationship dilemma (e.g., primary vs secondary) is important for us to consider when examining blaming tendencies. In addition to focusing on the characters themselves, the degree to which the perceiver identifies with a particular character may help us further understand the importance of target focus.
The Role of Personal Identification
Target focus, or identifying with a particular character, played an important role in predicting attributions of blame. Initially we were interested in whether personal identification could account for age-related differences in blame attributions for the primary character. This was not the case. Instead, the degree to which individuals identified with the primary and secondary characters acted as potent predictors of individual differences in blame attributions across age groups. Specifically, individuals who identified with the primary characters while at the same time not identifying with the secondary characters at all or only to a moderate degree tended to absolve the primary characters from blame. Furthermore, this relationship between ID primary and ID secondary and blame was robust for LROA vignettes. However, it should be noted that, in HROA vignettes, ID primary was related to blame attributions of primary characters, although minimally.
Thus, an interesting contrast emerges. In HROA vignettes, age becomes central in predicting degree of blame attributions for the primary characters (as already discussed). However, in LROA vignettes, personal identification becomes most important in predicting blame attributions. There are a number of factors that could be contributing to these differential findings. As indicated herein, HROA situations may differentially activate relationship maintenance values in older adults as opposed to younger adults. However, this may not be the case in LROA situations. Instead, in low relationship outcome vignettes, personal identification with characters may evoke individual differences in self-protective needs of the individual. For example, need for self-enhancement may lead individuals to accept the behavior of targets with whom they identify in order to preserve a self-concept that is high in esteem (Jost, Glaser, Kruglanski, & Sulloway, 2003; Miller et al., 1988). Thus, from a self-enhancement perspective, characters engendering low personal identification would be more likely to be blamed for the negative outcome, whereas characters engendering high personal identification would be absolved from blame. This self-oriented need may be overridden in HROA vignettes in that the issue of salvaging a relationship takes precedence over self-concerns, especially for older adults.
Conclusion
Overall, the degree to which an interpersonal situation reflects characters' willingness to maintain the relationship is a key factor in producing age differences in the tendency to blame. It could be that the activation of age-related differences in goals and values related to maintaining intimate interpersonal relationships accounts for this finding. A more precise assessment of this hypothesis is warranted. When the maintenance of the relationship is not at stake, differential target focus or personal identification emerges as the important predictor of when an individual will engage in excessive blame, irrespective of age. Future research has to concentrate on identifying the conditions under which target focus influences evaluative social judgments. In addition, multiple dimensions of target focus have to be examined such as the importance of the situation to the target participant's well-being and experience and familiarity with the target participant's situation. What is particularly important at this juncture is to develop a more precise method for assessing dimensions of target focus operating in relationship situations. In this way, we can better differentiate the conditions under which age differences in blame attribution tendencies emerge.
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Acknowledgments |
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Footnotes |
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Received for publication November 11, 2003. Accepted for publication July 9, 2004.
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 F. Blanchard-Fields, Y. Chen, M. Horhota, and M. Wang Cultural Differences in the Relationship Between Aging and the Correspondence Bias J. Gerontol. B. Psychol. Sci. Soc. Sci., November 1, 2007; 62(6): P362 - P365. [Abstract] [Full Text] [PDF]
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F. Blanchard-Fields and M. Horhota Age Differences in the Correspondence Bias: When a Plausible Explanation Matters J. Gerontol. B. Psychol. Sci. Soc. Sci., September 1, 2005; 60(5): P259 - P267. [Abstract] [Full Text] [PDF]
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