- Email: [email protected]
Cognitive complexity and intergroup perception and evaluation
Person. individ. Diff: Vol. 13, No. 12, pp. 1291-1298, Printed in Great Britain. All rights reserved
1992
0191-8869/92 $5.00 + 0.00 Copyright 0 1992 Pergamon Press Ltd
COGNITIVE COMPLEXITY AND INTERGROUP PERCEPTION AND EVALUATION RACHEL Department
BEN-ARI,
PERI KEDEM
of Psychology,
Bar-Ran
and NAOMI LEVY-WEINER University,
Ramat-Can,
Israel
(Received 25 January 1992) Summary-The experiment tested the relationship between cognitive complexity and intergroup perception and evaluation. Perceived variability within the ingroup and the outgroup, as well as polarity of evaluation of ingroup and outgroup members, were assessed in high school pupils with low- or high-cognitive complexity. In comparison with low-complexity subjects, high-complexity subjects perceived more variability within both the ingroup and the outgroup, in both positive and negative traits. In comparison with low-complexity subjects, high-complexity subjects exhibited a less positive evaluation of the ingroup and a less extreme negative evaluation of the outgroup.
The study of intergroup relations and intergroup bias has a long and rich history in social psychology (Harding, Kutner, Proshansky & Chein, 1954, 1969). Among the major developments in this area has been the growing focus on the cognitive approach to intergroup relations (Stephen, 1985, 1989). This approach emphasizes the operation of cognitive information-processing mechanisms such as attention, encoding, and retrieval, as well as the organization of knowledge about groups into cognitive structures such as schemata, scripts, and prototypes. Within this approach, intergroup bias, i.e. biased evaluations of social groups, is viewed as a consequence of biases in the cognitive processing and organization of social information (e.g. Hamilton, 1981; Markus & Zajonc, 1985; Stephan, 1985, 1989; Wilder, 1981, 1986). One of the central cognitive processes which underlies the organization and evaluation of social information is categorization. Social categorization consists of assigning individuals into social groups (categories) on the basis of defining characteristics of group members. The most emphasized consequence of such categorization is the accentuation of between-group differences and withingroup similarity, or homogeneity (Brewer, 1979; Lilli & Rehm, 1988; Wilder, 1981, 1986). Social categorization typically leads to evaluative bias in favor of ingroup and against outgroup members, further emphasizing the distinction between ingroups and outgroups. In addition, there is evidence that the perception of within-group homogeneity is not identical for ingroups and outgroups. The process of accentuation of within-group similarities operates differently in ingroups and outgroups, with a greater effect in the latter (Chance & Goldstein, 1981; Chance, Goldstein & McBride, 1975; Malpass & Kravitz, 1969). Since individuals interact with a variety of ingroup members under a variety of conditions, they are exposed to individual differences within the ingroup. Consequently members of the ingroup are perceived as more variable, or more differentiated, than members of the outgroup (Linville & Jones, 1980; Quattrone, 1986; Wilder, 1981). Park and Rothbart (1982) showed the information about ingroups is encoded on a subcategorical, differentiated, and individual level, whereas information about outgroups is encoded in superordinate, undifferentiated, and general categories. These authors, as well as Linville and Jones (1980), proposed that ingroups are perceived as more differentiated than outgroups, because people have more complex and differentiated cognitive schemas regarding their own group than other groups. Linville and Jones (1980) also argued that more complex schemas result in more moderate evaluations regarding group members, since such schemas contain a large number of dimensions, some positive and some negative, along which individual group members can be characterized. Conversely, simple, undifferentiated schemas lead to polarized evaluations. Since outgroup schemas are relatively simple, it follows that more extreme evaluations will occur for the outgroup than for the ingroup members. An important qualification of Linville and Jones’s (1980) position is that outgroup members are not uniformly evaluated as more negative than ingroup members. Rather, 1291
RACHEL BEN-ARI et al.
1292
polarized responses can occur for both negative and positive evaluations, depending on whether unfavorable or favorable information is available about outgroup members. In support of their position, Linville and Jones (1980) showed that ingroup members received more moderate evaluations than outgroup members: white Ss rated a well-qualified white applicant less favorably than the well-qualified black applicant, whereas the poorly qualified white applicant was rated less negatively than the poorly qualified black. In subsequent experiments, Linville and Jones (1980) showed that white Ss manifested greater schematic complexity regarding white (ingroup) as compared with black (outgroup) applicants, and that white Ss instructed to evaluate the black applicants’ essays in terms of six dimensions relevant to the evaluation of the essays, exhibited more moderate evaluations than those instructed to evaluate the essays using only two such dimensions. It appears, then, that individuals’ perception and evaluation of ingroups and outgroups is mediated by the relative schematic complexity of the two target groups. However, not all perceivers have the same schemas, as individuals differ in the complexity with which they process and organize information from their social environment (Bieri, 1966; Kelly, 1955; Wilder, 1981). Persons who are high in cognitive complexity construe social behavior in a multidimensional way and, thus, have a more versatile system for perceiving others than individuals with low-cognitive complexity (Bieri, 1966). Such as multidimensional organization of social information allows flexibility and variability in subsequent response to this information (Markus & Zajonc, 1985). As pointed out by Wilder (1981) and Pettigrew (1981), individual differences in cognitive complexity appear to be highly relevant to processes of social categorization. However, research concerned with the direct investigation of this question is scarce, except for some indications that cognitive complexity, or “sophistication,” may exert a moderating influence on intergroup bias (Coffman, 1962; Cohen, 1977; Gardiner, 1972; Glock, Wuthnow, Piliavin & Spencer, 1975; Wagner & Schoenbach, 1984). The present study sought to directly examine the relationship between Ss’ cognitive complexity and two aspects of social categorization: perception of variability within the ingroup and the outgroup, and evaluative polarity of ingroup and outgroup members. We made the following predictions. 1. Since more cognitively complex persons make fine discriminations among organize them in a variety of categories, they will perceive greater variability the ingroup and the outgroup, as compared with low-complexity Ss 2. Given the schematic complexity is associated with more moderate evaluations Jones, 1980), high-complexity Ss will make less polarized evaluations of both and the outgroup members.
stimuli and within both (Linville & the ingroup
METHOD
Subjects The Ss were 234 Jewish pupils attending 1lth and 12th grades (ages 16-18). In order to obtain a wide range of cognitive complexity, data were collected from Ss attending three different high schools and specializing in five majors. Dejinition of research variables The independent
variables are:
1. Ss’ cognitive complexity-high or low; 2. Type of group-ingroup or outgroup. The dependent variables were: 1. Perceived within-group variability; 2. Polarity of evaluation of ingroup and outgroup members. Instruments Cognitive complexity. The cognitive complexity questionnaire was designed on the basis of an index developed by Scott (1962) and elaborated by Scott, Osgood and Peterson (1979). This instrument examines the number of independent dimensions on which a set of specific elements is
Cognitive complexity
1293
judged. Ss were required to complete a sorting task in which they grouped their acquaintances that belong together into groups. Each S was asked to list 20 acquaintances from different areas of life (school, family, friends, etc). After listing the names, the S was given a page with 18 empty squares. He was asked to list inside each square acquaintances who possess a common attribute, and to name the common attribute at the bottom of each square. (In a square drawn across the original square, the S was asked to name the opposite attribute without naming people. The content of this square was not taken into account in the calculation of the complexity score, and was aimed at preventing the S from using opposite attributes which inflate the complexity score artificially.) The Ss were instructed to generate as many groups as they could. They were told that each acquaintance can be placed in as many groups (squares) as desired. In other words, in forming each group, Ss used the pool of all 20 acquaintances. The complexity score was not calculated directly on the basis of the number of groups formed by the S, but on the basis of “attribution category,” defined as the combination of traits assigned to each acquaintance. For example, if a S generated two groups (intelligent, lazy), this would create four attribution categories in which the object (acquaintance) could be placed: intelligent but not lazy, lazy but not intelligent, both lazy and intelligent, and neither lazy nor intelligent. If a S generated four groups (e.g. handsome, intelligent, sensitive, and tall), this would produce 16 possible attribution categories. By this method, the complexity measure is made less dependent on the raw number of groups generated by the S and more dependent on the Ss capacity to differentiate among types of persons according to different traits. Thus, it is possible that two Ss will generate four groups (traits), but one of them will differentiate between two types of persons (will create 2 attribution categories) while the other will differentiate among five types of persons (will create 5 attribution categories), yielding different levels of cognitive complexity. The complexity score, H, for each S, was calculated using the following formula: H=log,N-kxNilog,Ni
(see Scott et al., 1979, p. 105) where N = total number of acquaintances (here, 20); Ni = number of acquaintances that appear in each combination of attribution categories. The calculation was carried out across all attribution categories, i. Complexity scores ranged from 0 (low-complexity) to 4.32 (high-complexity). Ss were divided into two groups according to their score: high-(above mean complexity score) and low-(below mean complexity score) complexity. Cronbach’s alpha was 0.64. Perception of ingroup variability. Perception of variability was measured by having the Ss indicate their beliefs about how members of a group are distributed along each of several personality traits. The questionnaire included 19 personality traits. The Ss were asked to mark the number of people in the evaluation group (ingroup and outgroup) for whom the trait was applicable, along the following scale: no one; few; less than half; half of this kind and half of another; more than half; almost everyone; everyone. Factor analysis carried out on the questionnaire items for both groups yielded two main factors: (a) “Positive content” factor which included 11 items (e.g. handsome, intelligent, smart, friendly). Tests of within-factor item consistency yielded reliability alpha = 0.8 1. (b) “Negative content” factor which included 8 traits (e.g. stupid, primitive, ungrateful, ugly). Tests of within-factor item consistency yielded reliability alpha = 0.63. A positive and negative variability score was calculated by summing the number of times the S chose the middle category answers (“less than half, ” “half of this kind and half of another,” “more than half’), dividing this sum by the number of items in the respective factor. Thus, the variability score consisted of the number of choices of high-variability statements out of all possible choices, with higher scores indicating higher variability. Polarity of evaluation. The polarity questionnaire included 20 bipolar adjectives describing traits situated at endpoints of 7-point scales. The Ss were asked to mark the point on the scale that best described the evaluated person for each of the 20 traits. Interitem consistency yielded reliability alpha = 0.87. Polarity score was calculated using the number of ratings Ss placed near the extreme ends of the scales, i.e. 1 and 2 (positive endpoints) or 6 and 7 (negative endpoints).
RACHEL BEN-ARI et al.
1294
Procedure The questionnaires were administered in the class. The pupils were told that the questionnaires are administered in different high schools in the country with the aim of examining individual differences in perception. The anonymity of the respondents and the importance of honest answers were emphasized. Likewise, the pupils were told that there were no right or wrong answers. The cognitive complexity questionnaire was administered first, followed by the remaining questionnaires. For the perceived variability and polarity questionnaires, the Ss were asked to respond twice, once relating to Jews (ingroup) and once relating to Arabs (outgroup). RESULTS Perceived
mriabilit~
Variability scores were analyzed using a three-way ANOVA with a “between” effect of cognitive complexity (high, low) and two “within” effects: target group (ingroup, outgroup), and trait content (positive, negative). Table 1 presents the results of the ANOVA. As can be seen, the ANOVA revealed that the three main effects were significant. Highcomplexity Ss perceived higher variability than low-complexity Ss (X = 0.71 vs X = 0.57, respectively). Perceived variability within negative traits was lower than within positive traits (z = 0.61 vs R = 0.70, respectively) and was higher for the ingroup than for the outgroup (X = 0.69 vs a = 0.61, respectively). The absence of Complexity x Group and Complexity x Trait interactions indicates that differences in perceived variability between low- and high-complexity Ss were not influenced by either the group or by the favorability of the traits. Thus, Simple Main Effects, showed that in comparison to low-complexity Ss, high-complexity Ss perceived higher variability within both the ingroup (x = 0.66 vs I= 0.73, respectively), and the outgroup (x = 0.57 vs B = 0.66, respectively) with the positive (x = 0.66 vs x = 0.74, respectively), as well as negative (I= 0.58 vs x = 0.65, respectively) traits. The ANOVA did yield a significant Group x Trait interaction. Table 2 presents the means and standard deviations of perceived variability scores for the ingroup and the outgroup both for the negative and the positive traits. Observation of the means in Table 2 and Simple Main Effects for repeated measures analyses (Winer, 1971) revealed the source of this interaction: for the positive traits, mean variability score of the ingroup was significantly higher than that of the outgroup, whereas for the negative traits, mean variability score of the outgroup was significantly higher than that of the ingroup. Polarity
of eoaluation
Polarity scores were analyzed using a three-way ANOVA with a “between” effect of cognitive complexity (high, low) and two “within” effects: target group (ingroup, outgroup), and trait content (positive, negative). The results of the ANOVA are presented in Table 3. As can be seen, the analysis yielded a significant main effect of Complexity, reflecting the fact that low-complexity Ss made more extreme ratings (8 = 4.53) than high-complexity Ss (3 = 3.49) and a significant main effect of Trait content reflecting more extreme ratings of the positive endpoints (a = 4.46) than of the negative endpoints (X = 3.60). The absence of Complexity x Trait
Table I. Results of ANOVA performed on perceived variabilities with mam factors of positive and negative traits, Ingroup-outgroup and cognittve complexity level Source of variance Complexity Trait content Group Complexity x Trait Complexity x Group Trait x Grout Complexity ; Trait x Group Residual
df
F
P
I I I I I I I 232
1.61 49.09 21.04 0.04 0.18 106.27 0.26
0.006 0.001 0.001 NS NS 0.001 NS
Table 2. Means and standard deviations of positive and negative perceived variability scores for the ingroup and the outgroup Trait content Group Outgroup IngrOUp
Negative
Positive
0.64 (31) 0.59 (25)I
0.59 (31) 0.80 (22) . ,
The higher the score, the greater the perceived variability. Numbers in parentheses indicate the standard deviations.
Cognitive
complexity
Table 3. Results of ANOVA performed on polarity scores with main factors of cognitive comolexitv. erouo and trait content Source of variance
df
Complexity Trait content Group Complexity x Group Complexity x Trait content Trait content x Group Complexity x Trait content x Group Residual
I I I I I I I 232
F
P
19.30 13.47 0.20 1.93 0.36 237.90 19.18
0.001 0.001 NS NS NS 0.001 0.001
1295 Table 4. Means and standard deviations of positive and negative polarity scores toward the ingroup and the outgroup Trait content Group
Negative
Positive
5.33 (4.56) I .88 (1.76)
2.68 (2.50) 6.25 (3.84)
Outgroup Ingroup
The higher the score, the more extreme the polarity. Numbers in parentheses indicate the standard deviations.
and Complexity x Group interactions indicates that differences in evaluative polarity between lowand high-complexity Ss holds over trait content and target group. Thus, the low-complexity Ss, in comparison to the high-complexity Ss, made significantly more extreme ratings for both the positive endpoints (x = 4.89 vs z = 4.00, respectively), as well as negative endpoints (X = 4.17 vs x = 2.99, respectively). For the outgroup (_I?= 4.60 vs I= 3.36, respectively) as well as the ingroup (x = 4.46 vs x = 3.63, respectively). The ANOVA did yield a significant Group x Trait interaction. Table 4 presents the means and standard deviations of positive and negative polarity scores for the ingroup and the outgroup. Simple Main Effects and inspection of Table 4 reveals that for the positive endpoints of the scales the ingroup received a significantly more extreme evaluation than the outgroup, whereas for the negative endpoints of the scales the outgroup received more extreme evaluation than the ingroup. Finally, the ANOVA yielded a significant three-way Complexity x Group x Trait interaction. Table 5 presents the means and standard deviations of positive and negative polarity scores of highand low-complexity Ss towards the ingroup and the outgroup. Simple Main Effects and inspection of Table 5 reveals that for the positive endpoints of the scales low-complexity Ss made more extreme evaluation of the ingroup than high-complexity Ss, but the two groups of Ss did not differ in their evaluation of the outgroup. In contrast, for negative endpoints, low-complexity Ss were more extreme in their evaluation of the outgroup than high-complexity Ss but the two groups did not differ with respect to the ingroup. DISCUSSION Research on intergroup perception and bias has demonstrated a number of pervasive effects of social categorization. As noted by Pettigrew (1981) most of this research has focused “on the universal and the generic rather than the individual and the unique” (p. 327). This author, as well as Wilder (1986) suggested that individual differences in cognitive complexity should be centrally related to processes of social categorization. The present study demonstrates that individual differences in cognitive complexity modulate two effects of social categorization: perceived variability and evaluative polarity. Perceived variability In support of previous findings (e.g. Jones, Wood & Quattrone, 1981; Park & Rothbart, 1982; Quattrone, 1986; Quattrone & Jones, 1980; Wilder, 1981, 1986), Ss in this study perceived more dimensional variability within the ingroup than within the outgroup. However, this effect was Table 5. Mean of negative and positive polarity scores toward the ingroup and the outgroup in high- and low-complexity Ss Attitude content Positive
Negative Complexity High Low
Ingroup
Outgroup
Ingroup
Outgroup
I .88 (1.72) 1.86 (1.81)
4.09 (3.88) 6.46 (4.84)
5.37 (3.22) 7.04 (4.17)
2.61 (2.61) 2.73 (2.34)
The higher the score, the more extreme the polarity. Numbers in parentheses indicate the standard deviations.
1296
RACHELBEN-AM et al
qualified by the favorability of the evaluated traits, i.e. greater perceived variability within the ingroup was obtained only for positive traits, whereas perceived variability of the negative traits was highly similar within the ingroup and the outgroup. This result is inconsistent with those reported by Park and Rothbart (1982) who found, using male and female Ss, that differences in ingroup-outgroup variability perception were not qualified by the items’ favorability. One reason for this difference may stem from the fact that in the present study, the distinction between the ingroup and the outgroup (Jews and Arabs) is particularly salient, since it is based on religious, economic, and political distinctions, and is further potentiated by an intergroup conflict. Taking into account this realistic and motivationally laden basis for the intergroup distinction, greater perceived variability within the positive traits of the ingroup, as compared with the remaining three categories (positive traits within the outgroup, negative traits within the ingroup, and the outgroup) has interesting implications for the understanding of social categorization processes. As elaborated by the proponents of social identity theory (Brewer & Miller, 1984; Tajfel & Turner, 1986), social categorization leads to evaluative judgements about ingroups and outgroups which serve to maintain and enhance ingroup-outgroup distinctiveness. A central outcome of this process is the differentiation of ingroup members along dimensions which allow comparisons favorable to the ingroup, leading to the well-documented ingroup favoritism. However, this comparison process is said to lead to a homogeneous perception of ingroup members (Brewer & Miller, 1984), whereas, as pointed out in the introduction, social comparison can also result in a more heterogeneous perception of the ingroup than of the outgroup (e.g. Jones et al., 1981; Park & Rothbart, 1982; Quattrone, 1986). Moreover, the latter phenomenon is said to be independent of ingroup favoritism, i.e. ingroup members are perceived as more variable in both positive and negative characteristics (Park & Rothbart, 1982; Quattrone, 1986). Thus, social categorization is said to give rise to a favorable and homogeneous perception of the ingroup on the one hand, and to the heterogeneous perception of the ingroup unrelated to ingroup favoritism, on the other hand. The present results appear to reconcile this contradiction: individuals differentiate ingroup members along favorable dimensions, but attribute higher variability to ingroup members within these dimensions. This pattern of results combines distinct responses to members of the ingroup relative to outgroup, with more heterogeneous responses within the ingroup. Greater perceived variability within the ingroup as compared with outgroup members has been attributed to more complex, or differentiated, schemas of the ingroup (Park & Rothbart, 1982; Quattrone, 1986). Thus, greater perceived variability within the positive but not the negative traits in the ingroup may be seen as reflecting the operation of cognitive factors inherent in intergroup perception which are superimposed on motivational factors underlying the selective nature of intergroup comparisons/perception. The operation of cognitive factors in the intergroup perception is further underscored by the effects of Ss’ cognitive complexity, the major variable of interest in this study, on perceived variability. These results support the idea that individuals with high cognitive complexity, who categorize social stimuli in a multidimensional way, perceive greater within-group variability. Several features of greater perception of variability exhibited by high-complexity Ss are noteworthy. First, it is a general tendency manifest across specific contents, in our case, group (ingroup and outgroup) and trait favourability (negative and positive traits). Second, the increase in perceived variability exhibited by high-complexity Ss within the outgroup indicates that high-complexity individuals are more open to distinctive information about outgroup members and are less prone to categorize them as representatives of an undifferentiated category. Third, despite the above effects of high-complexity, the basic pattern of perceived variability in these Ss was identical to that observed in their low-complexity counterparts, i.e. they perceived greatest variability within the positive traits of the ingroup, as compared with perception of variability within the remaining three categories (ingroup-negative; outgroup-positive; outgroup-negative), which are highly similar. Thus, although high-complexity Ss perceive greater variability within both the ingroup and the outgroup, they continue to exhibit distinct ingroupoutgroup perceptions. Again, then, the operation of cognitive factors may be seen as superimposed on motivational influences, i.e. the differentiation of the ingroup along positive dimensions. Brewer and Miller (1984) argued that reduction of category-based responding can occur via two processes:
Cognitive
complexity
1297
differentiation and personalization, which do not necessarily co-occur. Differentiation refers to perception of intracategory differences which does not eliminate category boundaries that differentiate ingroups from outgroups. Personalization refers to perception of category which involves direct self-other interpersonal comparison and which necessarily crosses category boundaries. In terms of Brewerand Miller, high cognitive complexity enables the process of differentiation, but not of personalization. Evaluation polarity All Ss evaluated ingroup members more extremely than outgroup members on positive traits, whereas outgroup members were evaluated more extremely than ingroup members on negative traits. Linville and Jones (1980) reported that outgroup members were rated more extremely than ingroup members, but they obtained some polarization in both the negative and the positive direction, whereas the present study yielded only the former effect. Since Linville and Jones manipulated the favorability of information presented about the outgroup members, a direct comparison between the two studies cannot be made. However, the pattern of evaluative polarization obtained in the present study is consistent with the widely documented finding that ingroups receive favorable evaluations and outgroups receive negative evaluations (Brewer, 1979). Thus, similarly to perceived variability data, the evaluation polarity data revealed distinct intergroup perception with an ingroup favorability bias. Likewise, in parallel to data on perceived variability, the basic pattern of polarization in high-complexity Ss remained similar to that obtained in low-complexity Ss, i.e. ingroup members were evaluated more positively and outgroup members were evaluated more negatively. However, both evaluations were more moderate in high-complexity Ss than in the low-complexity Ss. Thus, ingroupoutgroup distinctiveness was reduced as a function of Ss’ cognitive complexity. This result points again to an interplay between motivational and cognitive processes in intergroup evaluation. Linville and Jones (1980) argued that complex schemas, by virtue of incorporating a large number of attributes, some positive and some negative, for encoding information, should result in evaluative moderation. However, these authors referred to differences in complexity of ingroup vs outgroup schemas, claiming that the former are more complex than the latter. Consequently, a corollary prediction of their position was that people tend to make more moderate evaluations regarding ingroups than regarding outgroups. As discussed above, this prediction was not supported in the present study, since extreme favorable evaluations were obtained from the ingroup. However, the more general prediction derivable from Linville and Jones’s position, namely, that individuals with complex schemas should generate more moderate evaluations, was fully supported in this study. Importantly, such moderation is obtained for both the ingroup and the outgroup. It is interesting to consider the relationship between cognitive complexity, perceived variability, and evaluative polarization. More complex schemas (of the ingroup) are said to result in greater variability perception (Park & Rothbart, 1982) and greater evaluative moderation (Linville & Jones, 1980). This relationship was not evident when the entire sample of Ss was considered: Thus, Ss perceived greater variability within the positive traits of the ingroup, yet also assigned more extreme positive ratings to the ingroup. However, the relationship was obtained in high-complexity Ss: they perceived greater variability and assigned less polarized appraisals, for both the ingroup and the outgroup. In sum, the present results indicate that individuals’ cognitive complexity is an important mediating variable in intergroup perception and evaluation. High-cognitive complexity exerted a moderating effect, increasing perception of differences within groups and decreasing perception of differences between groups, as well as decreasing evaluative polarity towards the ingroup and the outgroup. In this regard, it must be emphasized that the present results were obtained with two groups which are in an intense conflict. It is reasonable to assume that cognitive complexity would exert a much stronger moderating effect on intergroup perception and evaluation of groups which are not characterized by an intense intergroup conflict. Acknowledgemenf-This of Bar-Han University.
research
was supported
by the Institute
for the Advancement
of Social Integration
in the Schools
1298
RACHEL BEN-ARI et al.
REFERENCES Bieri, J. (1966). Cognitive complexity and personality development. In Harvey, 0. J. (Ed.), Experience structure and adaptability. New York: Springer. Brewer, M. B. (1979). Ingroup bias in the minimal intergroup situation: A cognitive motivational analysis. Psychological Bulletin, 86, 3077324. Brewer, M. B. & Miller, N. (1984). Beyond the contact hypothesis: Theoretical perspectives on desegregation. In Miller, N. & Brewer, M. B. (Eds), Groups in contact: The psychology of desegregation. Orlando, FL: Academic Press. Chance, J. E. & Goldstein, A. G. (1981). Depth of processing in response to own and other race faces. Personality and Social Psychology Bulletin, 7, 4755480. Chance, J. E., Goldstein, A. G. & McBride, L. (1975). Differential experience and recognition memory of faces, Journal of Social Psychology, 97, 243-253. Coffman, T. L. (1962). Personality structure, involvement and the consequences of taking a stand. Unpublished doctoral dissertation, Princeton University, Princeton. Cohen, C. E. (1977). Cognitive basis of stereotyping. In Schoenbach, P., Gollwitzer, P., Stiepel, G. & Wagner, U. (Eds), Education and intergroup attitudes. New York: Academic Press. Gardiner, G. S. (1972). Complexity training and prejudice reduction. Journal of Applied Social Psychology, 2, 3266342. Glock, C. Y., Wuthnow, R., Piliavin, J. A. & Spencer, M. (1975). Adolescent prejudice. New York: Harper & Row. Hamilton, D. L. (1981). Cognitive processes in stereofyping and inlergroup behavior. Hillsdale, NJ: Erlbaum. Harding, J., Kutner, B., Proshansky, H. & Chein, I. (1954). Prejudice and ethnic relations. In Lindzey, G. (Ed.), Handbook of .social psychology. Cambridge, MA: Addison-Wesley. Harding, J., Kutner, B., Proshansky, H. SC Chein, I. (1969). Prejudice and ethnic relations. In Lindzey, G. & Aronson, E. (Eds), Handbook of social psychology (Vol. 5). Reading, MA: Addison-Wesley. Jones, E. E., Wood, G. C. & Quattrone, G. A. (1981). Perceived variability of personal characteristics in in-groups and out-groups: The role of knowledge and evaluation. Personality and Socia/ Psychology Bulletin, 7(3), 5233528. Kelly. G. A. (1955). The psychology of personal constructs. New York: Norton. Lilli, W. & Rehm, J. (1988). Judgmental processes as bases of intergroup conflict. In Stroebe, W., Kruglanski, A. W., Bar-Tal, D. & Hewstone, M. (Eds), The social psychology of intergroup conflict. Berlin, Heidelberg: Springer-Verlag. Linville, P. W. & Jones, E. E. (1980). Polarized appraisals of outgroups members. Journal of Personality and Social Psychology, 38, 689-703. Malpass, R. S. & Kravitz, J. (1969). Recognition for faces of own and other race. Journal of Personality and Social Psychology, 13, 330-334. Markus, H. & Zajonc, R. B. (1985). The cognitive perspectives in social psychology. In Lindzey, G. & Aronson, E. (Eds), Handbook of social psychology (3rd Edn). Hillsdale, NJ: Erlbaum. Park, B. & Rothbart, M. (1982). Perception of out-group homogeneity and levels of social categorization: Memory for the subordinate attributes of in-group and out-group members. Journal of Personality and Social Psychology, 42(6), 1051-1068. Pettigrew, T. F. (1981). Extending the stereotype concept. In Hamilton, D. L. (Ed.), Cognitive processes in stereotyping and intergroup behaoior. Hillsdale, NJ: Erlbaum. Quattrone, G. A. (1986). On the perception of a group’s variability. In Worchel, S. & Austin, W. (Eds), Psychology of intergroup rekafions. Chicago, IL: Nelson-Hall. Quattrone, G. A. & Jones, E. E. (1980). The perception of variability within in-groups and out-groups: Implications for the law of small numbers. Journal of Personalify and Social Psychology, 38(l), 141-152. Scott, W. A. (1962). Psychological and social correlates of international images. In Kelman, H. C. (Ed.), Internafional behavior: A social psychology analysis. New York: Holt Rinehart. Scott, W. A., Osgood, D. W. & Peterson, C. (1979). Cognirive structure: Theory and measurement of individual differences. Washington, DC: V. W. Winston. Stephan, W. G. (1985). Intergroup relations. In Lindzey, G. & Aronson, E. (Eds), Handbook of socialpsychology (3rd Edn). Hillsdale, NJ: Erlbaum. Stephan, W. G. (1989). A cognitive approach to stereotyping. In Bar-Tal, D., Graumann, C. F., Kruglanski, A. W. & Stroebe, W. (Eds), Stereotyping a&prejudice: Changing conceptions. New York: Springer-Verlag. _ Tajfel, H. & Turner, J. (1986). The social identity theory of intergroup behavior. In Worchel, S. & Austin, W. (Eds), Psychology of intergroup relations. Chicago, IL: Nelson-Hall. Wagner, U. & Schoenbach; P. (1984). Links between educational status and prejudice: Ethnic attitudes in West Germany. In Miller, N. & Brewer, M. B. (Eds), Groups in contact: The psychology of desegregation. Orlando, FL: Academic Press. Wilder, D. A. (1981). Perceiving persons as a group: Categorization and intergroup relations. In Hamilton, D. L. (Ed.), Cognitice processes in stereotyping and intergroup behavior. Hillsdale, NJ: &lb&m. Wilder, D. A. (1986). Social categorization: Imolications for creation and reduction of intergroup bias. In Berkowitz. L. (Ed.), Advances in experimental social psychology. 19. New York: Academic Press. . Winer, B. J. (1971). Statistical principles in experimenfal design. New York: McGraw-Hill.
1992
0191-8869/92 $5.00 + 0.00 Copyright 0 1992 Pergamon Press Ltd
COGNITIVE COMPLEXITY AND INTERGROUP PERCEPTION AND EVALUATION RACHEL Department
BEN-ARI,
PERI KEDEM
of Psychology,
Bar-Ran
and NAOMI LEVY-WEINER University,
Ramat-Can,
Israel
(Received 25 January 1992) Summary-The experiment tested the relationship between cognitive complexity and intergroup perception and evaluation. Perceived variability within the ingroup and the outgroup, as well as polarity of evaluation of ingroup and outgroup members, were assessed in high school pupils with low- or high-cognitive complexity. In comparison with low-complexity subjects, high-complexity subjects perceived more variability within both the ingroup and the outgroup, in both positive and negative traits. In comparison with low-complexity subjects, high-complexity subjects exhibited a less positive evaluation of the ingroup and a less extreme negative evaluation of the outgroup.
The study of intergroup relations and intergroup bias has a long and rich history in social psychology (Harding, Kutner, Proshansky & Chein, 1954, 1969). Among the major developments in this area has been the growing focus on the cognitive approach to intergroup relations (Stephen, 1985, 1989). This approach emphasizes the operation of cognitive information-processing mechanisms such as attention, encoding, and retrieval, as well as the organization of knowledge about groups into cognitive structures such as schemata, scripts, and prototypes. Within this approach, intergroup bias, i.e. biased evaluations of social groups, is viewed as a consequence of biases in the cognitive processing and organization of social information (e.g. Hamilton, 1981; Markus & Zajonc, 1985; Stephan, 1985, 1989; Wilder, 1981, 1986). One of the central cognitive processes which underlies the organization and evaluation of social information is categorization. Social categorization consists of assigning individuals into social groups (categories) on the basis of defining characteristics of group members. The most emphasized consequence of such categorization is the accentuation of between-group differences and withingroup similarity, or homogeneity (Brewer, 1979; Lilli & Rehm, 1988; Wilder, 1981, 1986). Social categorization typically leads to evaluative bias in favor of ingroup and against outgroup members, further emphasizing the distinction between ingroups and outgroups. In addition, there is evidence that the perception of within-group homogeneity is not identical for ingroups and outgroups. The process of accentuation of within-group similarities operates differently in ingroups and outgroups, with a greater effect in the latter (Chance & Goldstein, 1981; Chance, Goldstein & McBride, 1975; Malpass & Kravitz, 1969). Since individuals interact with a variety of ingroup members under a variety of conditions, they are exposed to individual differences within the ingroup. Consequently members of the ingroup are perceived as more variable, or more differentiated, than members of the outgroup (Linville & Jones, 1980; Quattrone, 1986; Wilder, 1981). Park and Rothbart (1982) showed the information about ingroups is encoded on a subcategorical, differentiated, and individual level, whereas information about outgroups is encoded in superordinate, undifferentiated, and general categories. These authors, as well as Linville and Jones (1980), proposed that ingroups are perceived as more differentiated than outgroups, because people have more complex and differentiated cognitive schemas regarding their own group than other groups. Linville and Jones (1980) also argued that more complex schemas result in more moderate evaluations regarding group members, since such schemas contain a large number of dimensions, some positive and some negative, along which individual group members can be characterized. Conversely, simple, undifferentiated schemas lead to polarized evaluations. Since outgroup schemas are relatively simple, it follows that more extreme evaluations will occur for the outgroup than for the ingroup members. An important qualification of Linville and Jones’s (1980) position is that outgroup members are not uniformly evaluated as more negative than ingroup members. Rather, 1291
RACHEL BEN-ARI et al.
1292
polarized responses can occur for both negative and positive evaluations, depending on whether unfavorable or favorable information is available about outgroup members. In support of their position, Linville and Jones (1980) showed that ingroup members received more moderate evaluations than outgroup members: white Ss rated a well-qualified white applicant less favorably than the well-qualified black applicant, whereas the poorly qualified white applicant was rated less negatively than the poorly qualified black. In subsequent experiments, Linville and Jones (1980) showed that white Ss manifested greater schematic complexity regarding white (ingroup) as compared with black (outgroup) applicants, and that white Ss instructed to evaluate the black applicants’ essays in terms of six dimensions relevant to the evaluation of the essays, exhibited more moderate evaluations than those instructed to evaluate the essays using only two such dimensions. It appears, then, that individuals’ perception and evaluation of ingroups and outgroups is mediated by the relative schematic complexity of the two target groups. However, not all perceivers have the same schemas, as individuals differ in the complexity with which they process and organize information from their social environment (Bieri, 1966; Kelly, 1955; Wilder, 1981). Persons who are high in cognitive complexity construe social behavior in a multidimensional way and, thus, have a more versatile system for perceiving others than individuals with low-cognitive complexity (Bieri, 1966). Such as multidimensional organization of social information allows flexibility and variability in subsequent response to this information (Markus & Zajonc, 1985). As pointed out by Wilder (1981) and Pettigrew (1981), individual differences in cognitive complexity appear to be highly relevant to processes of social categorization. However, research concerned with the direct investigation of this question is scarce, except for some indications that cognitive complexity, or “sophistication,” may exert a moderating influence on intergroup bias (Coffman, 1962; Cohen, 1977; Gardiner, 1972; Glock, Wuthnow, Piliavin & Spencer, 1975; Wagner & Schoenbach, 1984). The present study sought to directly examine the relationship between Ss’ cognitive complexity and two aspects of social categorization: perception of variability within the ingroup and the outgroup, and evaluative polarity of ingroup and outgroup members. We made the following predictions. 1. Since more cognitively complex persons make fine discriminations among organize them in a variety of categories, they will perceive greater variability the ingroup and the outgroup, as compared with low-complexity Ss 2. Given the schematic complexity is associated with more moderate evaluations Jones, 1980), high-complexity Ss will make less polarized evaluations of both and the outgroup members.
stimuli and within both (Linville & the ingroup
METHOD
Subjects The Ss were 234 Jewish pupils attending 1lth and 12th grades (ages 16-18). In order to obtain a wide range of cognitive complexity, data were collected from Ss attending three different high schools and specializing in five majors. Dejinition of research variables The independent
variables are:
1. Ss’ cognitive complexity-high or low; 2. Type of group-ingroup or outgroup. The dependent variables were: 1. Perceived within-group variability; 2. Polarity of evaluation of ingroup and outgroup members. Instruments Cognitive complexity. The cognitive complexity questionnaire was designed on the basis of an index developed by Scott (1962) and elaborated by Scott, Osgood and Peterson (1979). This instrument examines the number of independent dimensions on which a set of specific elements is
Cognitive complexity
1293
judged. Ss were required to complete a sorting task in which they grouped their acquaintances that belong together into groups. Each S was asked to list 20 acquaintances from different areas of life (school, family, friends, etc). After listing the names, the S was given a page with 18 empty squares. He was asked to list inside each square acquaintances who possess a common attribute, and to name the common attribute at the bottom of each square. (In a square drawn across the original square, the S was asked to name the opposite attribute without naming people. The content of this square was not taken into account in the calculation of the complexity score, and was aimed at preventing the S from using opposite attributes which inflate the complexity score artificially.) The Ss were instructed to generate as many groups as they could. They were told that each acquaintance can be placed in as many groups (squares) as desired. In other words, in forming each group, Ss used the pool of all 20 acquaintances. The complexity score was not calculated directly on the basis of the number of groups formed by the S, but on the basis of “attribution category,” defined as the combination of traits assigned to each acquaintance. For example, if a S generated two groups (intelligent, lazy), this would create four attribution categories in which the object (acquaintance) could be placed: intelligent but not lazy, lazy but not intelligent, both lazy and intelligent, and neither lazy nor intelligent. If a S generated four groups (e.g. handsome, intelligent, sensitive, and tall), this would produce 16 possible attribution categories. By this method, the complexity measure is made less dependent on the raw number of groups generated by the S and more dependent on the Ss capacity to differentiate among types of persons according to different traits. Thus, it is possible that two Ss will generate four groups (traits), but one of them will differentiate between two types of persons (will create 2 attribution categories) while the other will differentiate among five types of persons (will create 5 attribution categories), yielding different levels of cognitive complexity. The complexity score, H, for each S, was calculated using the following formula: H=log,N-kxNilog,Ni
(see Scott et al., 1979, p. 105) where N = total number of acquaintances (here, 20); Ni = number of acquaintances that appear in each combination of attribution categories. The calculation was carried out across all attribution categories, i. Complexity scores ranged from 0 (low-complexity) to 4.32 (high-complexity). Ss were divided into two groups according to their score: high-(above mean complexity score) and low-(below mean complexity score) complexity. Cronbach’s alpha was 0.64. Perception of ingroup variability. Perception of variability was measured by having the Ss indicate their beliefs about how members of a group are distributed along each of several personality traits. The questionnaire included 19 personality traits. The Ss were asked to mark the number of people in the evaluation group (ingroup and outgroup) for whom the trait was applicable, along the following scale: no one; few; less than half; half of this kind and half of another; more than half; almost everyone; everyone. Factor analysis carried out on the questionnaire items for both groups yielded two main factors: (a) “Positive content” factor which included 11 items (e.g. handsome, intelligent, smart, friendly). Tests of within-factor item consistency yielded reliability alpha = 0.8 1. (b) “Negative content” factor which included 8 traits (e.g. stupid, primitive, ungrateful, ugly). Tests of within-factor item consistency yielded reliability alpha = 0.63. A positive and negative variability score was calculated by summing the number of times the S chose the middle category answers (“less than half, ” “half of this kind and half of another,” “more than half’), dividing this sum by the number of items in the respective factor. Thus, the variability score consisted of the number of choices of high-variability statements out of all possible choices, with higher scores indicating higher variability. Polarity of evaluation. The polarity questionnaire included 20 bipolar adjectives describing traits situated at endpoints of 7-point scales. The Ss were asked to mark the point on the scale that best described the evaluated person for each of the 20 traits. Interitem consistency yielded reliability alpha = 0.87. Polarity score was calculated using the number of ratings Ss placed near the extreme ends of the scales, i.e. 1 and 2 (positive endpoints) or 6 and 7 (negative endpoints).
RACHEL BEN-ARI et al.
1294
Procedure The questionnaires were administered in the class. The pupils were told that the questionnaires are administered in different high schools in the country with the aim of examining individual differences in perception. The anonymity of the respondents and the importance of honest answers were emphasized. Likewise, the pupils were told that there were no right or wrong answers. The cognitive complexity questionnaire was administered first, followed by the remaining questionnaires. For the perceived variability and polarity questionnaires, the Ss were asked to respond twice, once relating to Jews (ingroup) and once relating to Arabs (outgroup). RESULTS Perceived
mriabilit~
Variability scores were analyzed using a three-way ANOVA with a “between” effect of cognitive complexity (high, low) and two “within” effects: target group (ingroup, outgroup), and trait content (positive, negative). Table 1 presents the results of the ANOVA. As can be seen, the ANOVA revealed that the three main effects were significant. Highcomplexity Ss perceived higher variability than low-complexity Ss (X = 0.71 vs X = 0.57, respectively). Perceived variability within negative traits was lower than within positive traits (z = 0.61 vs R = 0.70, respectively) and was higher for the ingroup than for the outgroup (X = 0.69 vs a = 0.61, respectively). The absence of Complexity x Group and Complexity x Trait interactions indicates that differences in perceived variability between low- and high-complexity Ss were not influenced by either the group or by the favorability of the traits. Thus, Simple Main Effects, showed that in comparison to low-complexity Ss, high-complexity Ss perceived higher variability within both the ingroup (x = 0.66 vs I= 0.73, respectively), and the outgroup (x = 0.57 vs B = 0.66, respectively) with the positive (x = 0.66 vs x = 0.74, respectively), as well as negative (I= 0.58 vs x = 0.65, respectively) traits. The ANOVA did yield a significant Group x Trait interaction. Table 2 presents the means and standard deviations of perceived variability scores for the ingroup and the outgroup both for the negative and the positive traits. Observation of the means in Table 2 and Simple Main Effects for repeated measures analyses (Winer, 1971) revealed the source of this interaction: for the positive traits, mean variability score of the ingroup was significantly higher than that of the outgroup, whereas for the negative traits, mean variability score of the outgroup was significantly higher than that of the ingroup. Polarity
of eoaluation
Polarity scores were analyzed using a three-way ANOVA with a “between” effect of cognitive complexity (high, low) and two “within” effects: target group (ingroup, outgroup), and trait content (positive, negative). The results of the ANOVA are presented in Table 3. As can be seen, the analysis yielded a significant main effect of Complexity, reflecting the fact that low-complexity Ss made more extreme ratings (8 = 4.53) than high-complexity Ss (3 = 3.49) and a significant main effect of Trait content reflecting more extreme ratings of the positive endpoints (a = 4.46) than of the negative endpoints (X = 3.60). The absence of Complexity x Trait
Table I. Results of ANOVA performed on perceived variabilities with mam factors of positive and negative traits, Ingroup-outgroup and cognittve complexity level Source of variance Complexity Trait content Group Complexity x Trait Complexity x Group Trait x Grout Complexity ; Trait x Group Residual
df
F
P
I I I I I I I 232
1.61 49.09 21.04 0.04 0.18 106.27 0.26
0.006 0.001 0.001 NS NS 0.001 NS
Table 2. Means and standard deviations of positive and negative perceived variability scores for the ingroup and the outgroup Trait content Group Outgroup IngrOUp
Negative
Positive
0.64 (31) 0.59 (25)I
0.59 (31) 0.80 (22) . ,
The higher the score, the greater the perceived variability. Numbers in parentheses indicate the standard deviations.
Cognitive
complexity
Table 3. Results of ANOVA performed on polarity scores with main factors of cognitive comolexitv. erouo and trait content Source of variance
df
Complexity Trait content Group Complexity x Group Complexity x Trait content Trait content x Group Complexity x Trait content x Group Residual
I I I I I I I 232
F
P
19.30 13.47 0.20 1.93 0.36 237.90 19.18
0.001 0.001 NS NS NS 0.001 0.001
1295 Table 4. Means and standard deviations of positive and negative polarity scores toward the ingroup and the outgroup Trait content Group
Negative
Positive
5.33 (4.56) I .88 (1.76)
2.68 (2.50) 6.25 (3.84)
Outgroup Ingroup
The higher the score, the more extreme the polarity. Numbers in parentheses indicate the standard deviations.
and Complexity x Group interactions indicates that differences in evaluative polarity between lowand high-complexity Ss holds over trait content and target group. Thus, the low-complexity Ss, in comparison to the high-complexity Ss, made significantly more extreme ratings for both the positive endpoints (x = 4.89 vs z = 4.00, respectively), as well as negative endpoints (X = 4.17 vs x = 2.99, respectively). For the outgroup (_I?= 4.60 vs I= 3.36, respectively) as well as the ingroup (x = 4.46 vs x = 3.63, respectively). The ANOVA did yield a significant Group x Trait interaction. Table 4 presents the means and standard deviations of positive and negative polarity scores for the ingroup and the outgroup. Simple Main Effects and inspection of Table 4 reveals that for the positive endpoints of the scales the ingroup received a significantly more extreme evaluation than the outgroup, whereas for the negative endpoints of the scales the outgroup received more extreme evaluation than the ingroup. Finally, the ANOVA yielded a significant three-way Complexity x Group x Trait interaction. Table 5 presents the means and standard deviations of positive and negative polarity scores of highand low-complexity Ss towards the ingroup and the outgroup. Simple Main Effects and inspection of Table 5 reveals that for the positive endpoints of the scales low-complexity Ss made more extreme evaluation of the ingroup than high-complexity Ss, but the two groups of Ss did not differ in their evaluation of the outgroup. In contrast, for negative endpoints, low-complexity Ss were more extreme in their evaluation of the outgroup than high-complexity Ss but the two groups did not differ with respect to the ingroup. DISCUSSION Research on intergroup perception and bias has demonstrated a number of pervasive effects of social categorization. As noted by Pettigrew (1981) most of this research has focused “on the universal and the generic rather than the individual and the unique” (p. 327). This author, as well as Wilder (1986) suggested that individual differences in cognitive complexity should be centrally related to processes of social categorization. The present study demonstrates that individual differences in cognitive complexity modulate two effects of social categorization: perceived variability and evaluative polarity. Perceived variability In support of previous findings (e.g. Jones, Wood & Quattrone, 1981; Park & Rothbart, 1982; Quattrone, 1986; Quattrone & Jones, 1980; Wilder, 1981, 1986), Ss in this study perceived more dimensional variability within the ingroup than within the outgroup. However, this effect was Table 5. Mean of negative and positive polarity scores toward the ingroup and the outgroup in high- and low-complexity Ss Attitude content Positive
Negative Complexity High Low
Ingroup
Outgroup
Ingroup
Outgroup
I .88 (1.72) 1.86 (1.81)
4.09 (3.88) 6.46 (4.84)
5.37 (3.22) 7.04 (4.17)
2.61 (2.61) 2.73 (2.34)
The higher the score, the more extreme the polarity. Numbers in parentheses indicate the standard deviations.
1296
RACHELBEN-AM et al
qualified by the favorability of the evaluated traits, i.e. greater perceived variability within the ingroup was obtained only for positive traits, whereas perceived variability of the negative traits was highly similar within the ingroup and the outgroup. This result is inconsistent with those reported by Park and Rothbart (1982) who found, using male and female Ss, that differences in ingroup-outgroup variability perception were not qualified by the items’ favorability. One reason for this difference may stem from the fact that in the present study, the distinction between the ingroup and the outgroup (Jews and Arabs) is particularly salient, since it is based on religious, economic, and political distinctions, and is further potentiated by an intergroup conflict. Taking into account this realistic and motivationally laden basis for the intergroup distinction, greater perceived variability within the positive traits of the ingroup, as compared with the remaining three categories (positive traits within the outgroup, negative traits within the ingroup, and the outgroup) has interesting implications for the understanding of social categorization processes. As elaborated by the proponents of social identity theory (Brewer & Miller, 1984; Tajfel & Turner, 1986), social categorization leads to evaluative judgements about ingroups and outgroups which serve to maintain and enhance ingroup-outgroup distinctiveness. A central outcome of this process is the differentiation of ingroup members along dimensions which allow comparisons favorable to the ingroup, leading to the well-documented ingroup favoritism. However, this comparison process is said to lead to a homogeneous perception of ingroup members (Brewer & Miller, 1984), whereas, as pointed out in the introduction, social comparison can also result in a more heterogeneous perception of the ingroup than of the outgroup (e.g. Jones et al., 1981; Park & Rothbart, 1982; Quattrone, 1986). Moreover, the latter phenomenon is said to be independent of ingroup favoritism, i.e. ingroup members are perceived as more variable in both positive and negative characteristics (Park & Rothbart, 1982; Quattrone, 1986). Thus, social categorization is said to give rise to a favorable and homogeneous perception of the ingroup on the one hand, and to the heterogeneous perception of the ingroup unrelated to ingroup favoritism, on the other hand. The present results appear to reconcile this contradiction: individuals differentiate ingroup members along favorable dimensions, but attribute higher variability to ingroup members within these dimensions. This pattern of results combines distinct responses to members of the ingroup relative to outgroup, with more heterogeneous responses within the ingroup. Greater perceived variability within the ingroup as compared with outgroup members has been attributed to more complex, or differentiated, schemas of the ingroup (Park & Rothbart, 1982; Quattrone, 1986). Thus, greater perceived variability within the positive but not the negative traits in the ingroup may be seen as reflecting the operation of cognitive factors inherent in intergroup perception which are superimposed on motivational factors underlying the selective nature of intergroup comparisons/perception. The operation of cognitive factors in the intergroup perception is further underscored by the effects of Ss’ cognitive complexity, the major variable of interest in this study, on perceived variability. These results support the idea that individuals with high cognitive complexity, who categorize social stimuli in a multidimensional way, perceive greater within-group variability. Several features of greater perception of variability exhibited by high-complexity Ss are noteworthy. First, it is a general tendency manifest across specific contents, in our case, group (ingroup and outgroup) and trait favourability (negative and positive traits). Second, the increase in perceived variability exhibited by high-complexity Ss within the outgroup indicates that high-complexity individuals are more open to distinctive information about outgroup members and are less prone to categorize them as representatives of an undifferentiated category. Third, despite the above effects of high-complexity, the basic pattern of perceived variability in these Ss was identical to that observed in their low-complexity counterparts, i.e. they perceived greatest variability within the positive traits of the ingroup, as compared with perception of variability within the remaining three categories (ingroup-negative; outgroup-positive; outgroup-negative), which are highly similar. Thus, although high-complexity Ss perceive greater variability within both the ingroup and the outgroup, they continue to exhibit distinct ingroupoutgroup perceptions. Again, then, the operation of cognitive factors may be seen as superimposed on motivational influences, i.e. the differentiation of the ingroup along positive dimensions. Brewer and Miller (1984) argued that reduction of category-based responding can occur via two processes:
Cognitive
complexity
1297
differentiation and personalization, which do not necessarily co-occur. Differentiation refers to perception of intracategory differences which does not eliminate category boundaries that differentiate ingroups from outgroups. Personalization refers to perception of category which involves direct self-other interpersonal comparison and which necessarily crosses category boundaries. In terms of Brewerand Miller, high cognitive complexity enables the process of differentiation, but not of personalization. Evaluation polarity All Ss evaluated ingroup members more extremely than outgroup members on positive traits, whereas outgroup members were evaluated more extremely than ingroup members on negative traits. Linville and Jones (1980) reported that outgroup members were rated more extremely than ingroup members, but they obtained some polarization in both the negative and the positive direction, whereas the present study yielded only the former effect. Since Linville and Jones manipulated the favorability of information presented about the outgroup members, a direct comparison between the two studies cannot be made. However, the pattern of evaluative polarization obtained in the present study is consistent with the widely documented finding that ingroups receive favorable evaluations and outgroups receive negative evaluations (Brewer, 1979). Thus, similarly to perceived variability data, the evaluation polarity data revealed distinct intergroup perception with an ingroup favorability bias. Likewise, in parallel to data on perceived variability, the basic pattern of polarization in high-complexity Ss remained similar to that obtained in low-complexity Ss, i.e. ingroup members were evaluated more positively and outgroup members were evaluated more negatively. However, both evaluations were more moderate in high-complexity Ss than in the low-complexity Ss. Thus, ingroupoutgroup distinctiveness was reduced as a function of Ss’ cognitive complexity. This result points again to an interplay between motivational and cognitive processes in intergroup evaluation. Linville and Jones (1980) argued that complex schemas, by virtue of incorporating a large number of attributes, some positive and some negative, for encoding information, should result in evaluative moderation. However, these authors referred to differences in complexity of ingroup vs outgroup schemas, claiming that the former are more complex than the latter. Consequently, a corollary prediction of their position was that people tend to make more moderate evaluations regarding ingroups than regarding outgroups. As discussed above, this prediction was not supported in the present study, since extreme favorable evaluations were obtained from the ingroup. However, the more general prediction derivable from Linville and Jones’s position, namely, that individuals with complex schemas should generate more moderate evaluations, was fully supported in this study. Importantly, such moderation is obtained for both the ingroup and the outgroup. It is interesting to consider the relationship between cognitive complexity, perceived variability, and evaluative polarization. More complex schemas (of the ingroup) are said to result in greater variability perception (Park & Rothbart, 1982) and greater evaluative moderation (Linville & Jones, 1980). This relationship was not evident when the entire sample of Ss was considered: Thus, Ss perceived greater variability within the positive traits of the ingroup, yet also assigned more extreme positive ratings to the ingroup. However, the relationship was obtained in high-complexity Ss: they perceived greater variability and assigned less polarized appraisals, for both the ingroup and the outgroup. In sum, the present results indicate that individuals’ cognitive complexity is an important mediating variable in intergroup perception and evaluation. High-cognitive complexity exerted a moderating effect, increasing perception of differences within groups and decreasing perception of differences between groups, as well as decreasing evaluative polarity towards the ingroup and the outgroup. In this regard, it must be emphasized that the present results were obtained with two groups which are in an intense conflict. It is reasonable to assume that cognitive complexity would exert a much stronger moderating effect on intergroup perception and evaluation of groups which are not characterized by an intense intergroup conflict. Acknowledgemenf-This of Bar-Han University.
research
was supported
by the Institute
for the Advancement
of Social Integration
in the Schools
1298
RACHEL BEN-ARI et al.
REFERENCES Bieri, J. (1966). Cognitive complexity and personality development. In Harvey, 0. J. (Ed.), Experience structure and adaptability. New York: Springer. Brewer, M. B. (1979). Ingroup bias in the minimal intergroup situation: A cognitive motivational analysis. Psychological Bulletin, 86, 3077324. Brewer, M. B. & Miller, N. (1984). Beyond the contact hypothesis: Theoretical perspectives on desegregation. In Miller, N. & Brewer, M. B. (Eds), Groups in contact: The psychology of desegregation. Orlando, FL: Academic Press. Chance, J. E. & Goldstein, A. G. (1981). Depth of processing in response to own and other race faces. Personality and Social Psychology Bulletin, 7, 4755480. Chance, J. E., Goldstein, A. G. & McBride, L. (1975). Differential experience and recognition memory of faces, Journal of Social Psychology, 97, 243-253. Coffman, T. L. (1962). Personality structure, involvement and the consequences of taking a stand. Unpublished doctoral dissertation, Princeton University, Princeton. Cohen, C. E. (1977). Cognitive basis of stereotyping. In Schoenbach, P., Gollwitzer, P., Stiepel, G. & Wagner, U. (Eds), Education and intergroup attitudes. New York: Academic Press. Gardiner, G. S. (1972). Complexity training and prejudice reduction. Journal of Applied Social Psychology, 2, 3266342. Glock, C. Y., Wuthnow, R., Piliavin, J. A. & Spencer, M. (1975). Adolescent prejudice. New York: Harper & Row. Hamilton, D. L. (1981). Cognitive processes in stereofyping and inlergroup behavior. Hillsdale, NJ: Erlbaum. Harding, J., Kutner, B., Proshansky, H. & Chein, I. (1954). Prejudice and ethnic relations. In Lindzey, G. (Ed.), Handbook of .social psychology. Cambridge, MA: Addison-Wesley. Harding, J., Kutner, B., Proshansky, H. SC Chein, I. (1969). Prejudice and ethnic relations. In Lindzey, G. & Aronson, E. (Eds), Handbook of social psychology (Vol. 5). Reading, MA: Addison-Wesley. Jones, E. E., Wood, G. C. & Quattrone, G. A. (1981). Perceived variability of personal characteristics in in-groups and out-groups: The role of knowledge and evaluation. Personality and Socia/ Psychology Bulletin, 7(3), 5233528. Kelly. G. A. (1955). The psychology of personal constructs. New York: Norton. Lilli, W. & Rehm, J. (1988). Judgmental processes as bases of intergroup conflict. In Stroebe, W., Kruglanski, A. W., Bar-Tal, D. & Hewstone, M. (Eds), The social psychology of intergroup conflict. Berlin, Heidelberg: Springer-Verlag. Linville, P. W. & Jones, E. E. (1980). Polarized appraisals of outgroups members. Journal of Personality and Social Psychology, 38, 689-703. Malpass, R. S. & Kravitz, J. (1969). Recognition for faces of own and other race. Journal of Personality and Social Psychology, 13, 330-334. Markus, H. & Zajonc, R. B. (1985). The cognitive perspectives in social psychology. In Lindzey, G. & Aronson, E. (Eds), Handbook of social psychology (3rd Edn). Hillsdale, NJ: Erlbaum. Park, B. & Rothbart, M. (1982). Perception of out-group homogeneity and levels of social categorization: Memory for the subordinate attributes of in-group and out-group members. Journal of Personality and Social Psychology, 42(6), 1051-1068. Pettigrew, T. F. (1981). Extending the stereotype concept. In Hamilton, D. L. (Ed.), Cognitive processes in stereotyping and intergroup behaoior. Hillsdale, NJ: Erlbaum. Quattrone, G. A. (1986). On the perception of a group’s variability. In Worchel, S. & Austin, W. (Eds), Psychology of intergroup rekafions. Chicago, IL: Nelson-Hall. Quattrone, G. A. & Jones, E. E. (1980). The perception of variability within in-groups and out-groups: Implications for the law of small numbers. Journal of Personalify and Social Psychology, 38(l), 141-152. Scott, W. A. (1962). Psychological and social correlates of international images. In Kelman, H. C. (Ed.), Internafional behavior: A social psychology analysis. New York: Holt Rinehart. Scott, W. A., Osgood, D. W. & Peterson, C. (1979). Cognirive structure: Theory and measurement of individual differences. Washington, DC: V. W. Winston. Stephan, W. G. (1985). Intergroup relations. In Lindzey, G. & Aronson, E. (Eds), Handbook of socialpsychology (3rd Edn). Hillsdale, NJ: Erlbaum. Stephan, W. G. (1989). A cognitive approach to stereotyping. In Bar-Tal, D., Graumann, C. F., Kruglanski, A. W. & Stroebe, W. (Eds), Stereotyping a&prejudice: Changing conceptions. New York: Springer-Verlag. _ Tajfel, H. & Turner, J. (1986). The social identity theory of intergroup behavior. In Worchel, S. & Austin, W. (Eds), Psychology of intergroup relations. Chicago, IL: Nelson-Hall. Wagner, U. & Schoenbach; P. (1984). Links between educational status and prejudice: Ethnic attitudes in West Germany. In Miller, N. & Brewer, M. B. (Eds), Groups in contact: The psychology of desegregation. Orlando, FL: Academic Press. Wilder, D. A. (1981). Perceiving persons as a group: Categorization and intergroup relations. In Hamilton, D. L. (Ed.), Cognitice processes in stereotyping and intergroup behavior. Hillsdale, NJ: &lb&m. Wilder, D. A. (1986). Social categorization: Imolications for creation and reduction of intergroup bias. In Berkowitz. L. (Ed.), Advances in experimental social psychology. 19. New York: Academic Press. . Winer, B. J. (1971). Statistical principles in experimenfal design. New York: McGraw-Hill.