where is the number of common features in two aspects; is the total number of features in the referent aspect; is the total number of aspects in the person’s sort and and vary from 0 to ( and unequal).
The Self-Complexity Questionnaire was included in test batteries administered in Studies 1b and 1c.
The participants completed the Self-Incoherence Scale by Styła et al. ( 2010 ). This tool is a self-concept integration measure based on Block’s ( 1961 ) and Donahue et al.’s ( 1993 ) scales. The participants were instructed to rate how descriptive 7 personality traits are of them in each of five different social roles (student, romantic partner, son or daughter, friend, and worker), using a 7-point Likert scale.
The 7 attributes (i.e., active, open-minded, loyal, self-confident, resourceful, independent, direct) were selected after a series of pilot studies. First, a group of psychology students ( N = 24) was asked to read the list of 68 trait adjectives and mark those which describe them. An initial pool of attributes was derived from the Questionnaire of Social Perception (Jarymowicz 2008 ). They were broad personal characteristics and adjective markers that represent the Big Five traits (e.g., creative, outgoing, hardworking, helpful, sensitive). The pilot version of the Self-Incoherence Scale included 27 adjectives most commonly chosen by the students. In succeeding studies, a total of 317 participants completed the pilot version. Then, all items were factor-analyzed to obtain factor scores for each of them. Fourteen items with the greatest factor loadings were selected and split into two parallel versions of the scale. A final study ( N = 94) showed that one of these versions produced better validity coefficients then the other, and was suggested as preferable for scientific research (Styła et al. 2010 ).
Three different indices of self-concept differentiation were computed for each participant from the data generated by this task. The first one was suggested by Styła et al. ( 2010 ), but previously used also by others (e.g., Donahue et al. 1993 ; Goldman 2004 ). This index represents the absolute differences among the roles. In particular, we computed the standard deviation of each of the participant’s personality trait ratings across each role (7 standard deviations in all), and then averaged them. The resulting score (SCD SD ) represents the extent that participants’ personality trait ratings had deviated from one another when describing themselves across their different roles. As the second index of self-concept differentiation we used the average correlation among the roles (SCD R ), as proposed by Campbell et al. ( 2003 ). Correlations between each participant’s five roles (10 correlations in all) were computed on the basis of the adjectives ratings made in each role. This measure provides an inverse measure of self-concept differentiation. The third index was computed by factor-analyzing the correlation matrix, and subtracting the percent of variance accounted for by the first principal component from 100 percent. The resulting score represents the proportion of unshared variance among the roles (SCD VAR ). That is, higher scores on this measure reflect greater differentiation of self. This index was proposed by Block ( 1961 ), and then used by Donahue et al. ( 1993 ). 2
The Self-Incoherence Scale was used in Studies 1a and 1b.
To measure a sense of personal identity, understood as a recurring mode of experiencing oneself-as-subject, extended form of the Multidimensional Questionnaire of Identity (MQI; Pilarska 2012 , 2014a ) was employed. The questionnaire consists of six subscales measuring the degree of accessibility, specificity, separateness, coherence, stability, and valuation of identity content (referred to, respectively, as sense of having inner contents, sense of uniqueness, sense of one’s own boundaries, sense of coherence, sense of continuity over time, and sense of self-worth), including a total of 43 items (e.g., I feel that I was once a very different person than I am now; It happens that I perceive my close one as an important part of my self). All items are evaluated on a four-level scale ranging from “strongly disagree/never” to “strongly agree/always”. In addition, a single composite score for a global sense of identity (GSI) was computed by averaging scores across all identity dimensions. 3 In earlier studies, reliability coefficients for the identity dimensions varied from 0.62 to 0.86, with an average Cronbach’s alpha of 0.74 (e.g., Pilarska 2014a ; Suchańska and Worach 2013 ). For our sample, the standardized Cronbach’s alpha coefficient for the overall scale was 0.90, and ranged from 0.62 to 0.79 (average, 0.72) for the individual subscales.
We included the Multidimensional Questionnaire of Identity in all three studies.
The Identity and Experience Scale (IES) by Whitbourne and collegues ( 2002 ) in its Polish version by Suchańska and Jawłowska (2010, as cited in Jawłowska 2010 ) was used for measuring the identity processes. This tool consists of 33 statements, 11 for each scale: assimilation (e.g., When it comes to understanding myself, I’d rather not look too deeply), accommodation (e.g., Very influenced by what others think), and balance (e.g., Often take stock of what I have or have not accomplished). The participants respond on a seven-point scale from “definitely no” to “definitely yes”. The three subscales demonstrated reasonable internal consistency, with a standardized Cronbach’s alpha of 0.69 for assimilation, 0.83 for accommodation, and 0.83 for balance.
The Identity and Experience Scale was employed in Study 1c.
The Study lb battery contained the following scales measuring selected thinking dispositions.
Need for cognition was assessed via an adapted version of the Need for Cognition Scale (NCS; Cacioppo and Petty 1982 ; Matusz et al. 2011 ). The scale includes 36 items that focus on engagement in and enjoyment of intellectual activities (e.g., I try to avoid situations that require intensive thinking from me; I enjoy broadening my knowledge about things); each evaluated on a five-point scale, ranging from “strongly disagree” to “strongly agree”. Psychometric properties of the Polish version of the NCS are comparable to those of the original version: the reliability and stability indices are α = 0.91 and r = 0.86, respectively (Matusz et al. 2011 ). In the present sample, the internal consistency of this NCS, as measured by Cronbach’s standardized reliability coefficient, was α = 0.88.
Reflection, an openness-related form of self-focused attention, was measured with the 8-item Reflection subscale taken from the Rumination-Reflection Questionnaire – Shortform (RRQ Shortforms) by Trapnell ( 1997 ). Every item (e.g., I love exploring my “inner” self) is presented on a five-point scale, allowing for a range of responses from “strongly disagree” to “strongly agree”. The Cronbach’s standardized reliability coefficient for the translated version of this scale was 0.80.
The Integrative Self-Knowledge Scale (ISK; Ghorbani et al. 2008 ; Polish adaptation by Pilarska 2014b ) assesses a temporally integrated understanding of processes within the self. The scale includes 12 items referring to an individual’s efforts (1) to understand past experience (e.g., If I need to, I can reflect about myself and clearly understand the feelings and attitudes behind my past behaviors), (2) to maintain awareness of the self in the present (e.g., Most of the time, I get so involved in what is going on that I really can’t see how I am responding to a situation), and (3) to move toward desired goals in the future (e.g., By thinking deeply about myself, I can discover what I really want in life and how I might get it). Each item is rated on a five-point Likert scale, ranging from “largely untrue” to “largely true”. The Polish version of the ISK scale has good construct validity, and satisfactory internal consistency (Pilarska 2014b ). In our sample, internal reliability in terms of Cronbach’s standardized alpha was found to be 0.79.
For clarity of presentation, the results with brief comments are presented in five major sections, followed by a more general discussion of the results and their implications. It should be noted that parts of the analyses were performed in the individual samples, while other parts were conducted using the combined data (for the sake of increasing statistical power). In each of the following sections, we state which data set was used.
We started with examining psychometric properties of alternative measures of self-complexity: the dimensionality statistic (H), quantity of self-aspects (NSA) and overlap among them (OL), and a composite index of these two components of self-complexity (SC). The subsequent analyses were based on combined samples for which the appropriate scores were available (i.e., Study 1b and Study 1c samples).
To test the internal consistency of each of the self-complexity measure, split-half reliability coefficients were calculated, following the procedure of Rafaeli-Mor et al. ( 1999 ). Instead of using a participant’s full trait sorting, the relevant measures (NSA, OL, H, and SC) were computed separately on two subsets of traits: the 30 odd-numbered traits and the 30 even-numbered traits. Scores on each measure within one subset of traits were then correlated with the respective scores within the other half of the traits. The resulting split-half correlations were corrected by the Spearman-Brown formula to obtain split-half reliability estimates. The split-half reliability estimate of NSA was the highest ( r = 0.93), followed by Linville’s H statistic ( r = 0.85), OL ( r = 0.69), and Sakaki’s SC statistic ( r = 0.57). Overall, the split-half reliability coefficients were moderate or satisfactory, with the number of self-aspects and the H statistic showing greater reliability. The reliability estimates for Linville’s H measure and NSA matched the values found by Rafaeli-Mor et al. ( 1999 ) in the semi-random splitting. The reliability coefficient for OL was higher than that reported by Rafaeli-Mor et al. ( 1999 ) for the valenced split, but remained below the coefficients that were computed in the semi-random splitting.
The participants used an average of 15.44 ( SD =6.53) trait adjectives in their self-descriptions. The number of self-aspects (NSA) in the present study ranged from 1 to 12, with a mean of 4.90 ( SD =1.93), overlap (OL) ranged from 0.00 to 1.00 with a mean of 0.17 ( SD =0.15), 4 the H statistic ranged from 0.12 to 3.77 with a mean of 1.51 ( SD =0.61), and the SC statistic ranged from 3.08 to 1200.00 with a mean of 45.31 ( SD =71.17). All data were checked for normality. The skewness of the variables ranged from 0.61 to 9.56, and their kurtosis ranged from 0.61 to 136.09. Only the SC scores showed a severe departure from normality based on Kline’s ( 1998 ) rule (i.e., skew index absolute value <3; kurtosis index absolute values <10). There were significant gender differences in the number of traits used, the number of self-aspects, overlap, and Linville’s H statistic. Women (1) used more traits to describe themselves ( M women =16.61 [ SD =6.01] vs. M men =13.78 [ SD =6.89]), U =36987.00, Z = −6.14, p < 0.001, r = 0.24, (2) identified more self-aspects ( M women =5.13 [ SD =1.81] vs. M men =4.58 [ SD =2.06]), U =41133.50, Z = −4.47, p < 0.001, r = 0.17, (3) had more interrelated self-aspects ( M women =0.19 [ SD =0.15] vs. M men =0.15 [ SD =0.16]), U =40603.00, Z = −4.25, p < 0.001, r = 0.17, and (4) had higher H scores ( M women =1.62 [ SD =0.57] vs. M men =1.34 [ SD =0.63]), U =36271.50, Z = −6.44, p < 0.001, r = 0.25, than did men.
The simple correlation analysis revealed that the H statistic had a positive association with the number of self-aspects ( r = 0.59, p < 0.001, r 2 = 0.35), and thus appeared to reflect this element of self-complexity quite well. This is consistent with the findings of Linville ( 1987 ) and others (e.g., Brown and Rafaeli 2007 ; Rafaeli-Mor et al. 1999 ). However, contrary to Linville’s expectation that the H statistic will reflect high distinctiveness among roles, the H statistic and overlap were positively related in this sample ( r = 0.27, p < 0.001, r 2 = 0.08). Similar result was previously reported by Constantino et al. ( 2006 ), Rafaeli-Mor et al. ( 1999 ), and Luo et al. ( 2009 ). We examined the scatterplot of the two variables and used regression analysis to test the significance of a quadratic effect for overlap. The analysis indicated that both linear and nonlinear effects were significant (β =0.15 and β = −0.13, p < 0.001, respectively), suggesting a concave down quadratic trend in the relationship between overlap and the H scores. This observation was even more important since the mean level of overlap in our sample was very low (as was in Rafaeli-Mor et al.’s study, M =0.13, Constantino et al.’s study, M =0.17, and Luo and Watkins’ study, M =0.18) and lied in the 1st theoretical quartile. A high percentage of 96 % of the participants had overlap below, and merely 4 %, above, the theoretical midpoint of 0.50. We used the theoretical midpoint of 0.50 to separate high-OL and low-OL groups, and then performed regression analysis for each group (controlling for the effect of the number of self-aspects and the number of attributes). As predicted, the H statistic was positively predicted by overlap in the low-OL group (β =0.10, p < 0.001), but negatively predicted by overlap in the high-OL group (β = −0.24, p < 0.001). These findings suggest that there were certain circumstances in which greater overlap increased the value of H and other circumstances in which greater overlap reduced its value. More precisely, the relationship between Linville’s H measure and overlap followed an inverted U-shaped function, but because most overlap values were relatively small, the general relationship between them was positive.
Consistent with the findings of Linville ( 1987 ), the SC statistic was found to be positively related to the number of self-aspects ( r = 0.36, p < 0.001, r 2 = 0.13) and negatively associated with overlap ( r = −0.41, p < 0.001, r 2 = 0.17). In agreement with Brown and Rafaeli ( 2007 ), the two component measures of self-complexity (the number of roles and overlap) were unrelated ( r = 0.04, ns ). But controversy emerged as the H statistic and the SC statistic turned out to be unrelated ( r = 0.05, ns ). Since both measures theoretically capture the same construct, this can be only explained by the difference in calculation formula.
Additional analysis showed that the H statistic is problematic for another reason. It was found to be strongly and positively linked to the number of trait adjectives used in the person’s sort ( r = 0.97, p < 0.001, r 2 = 0.94). And this is not without importance, since participants used positive self-descriptors much more frequently than negative ones ( χ 2 (1) =6.25, p = 0.012). This tendency reflects a form of self-enhancement (e.g., Sedikides 1993 ). Expectedly, the H statistic showed a stronger correlation with the number of positive trait adjectives ( r = 0.90, p < 0.001, r 2 = 0.81) than with the number of negative trait adjectives ( r = 0.56, p < 0.001, r 2 = 0.32). The difference between the correlation coefficients was significant ( z = 14.88, p < 0.001). This turned out to be true for NSA ( r = 0.44 and r = 0.25, z = 3.96, p < 0.001) and OL ( r = 0.25 and r = 0.05, z = 3.71, p < 0.001) as well. On the other hand, the same correlation analysis performed for the SC statistic revealed that neither the total number of chosen adjectives nor the number of positive or negative adjectives were related to the SC statistic ( r = 0.04, r = 0.02, and r = 0.05, ns , respectively). 5
To ascertain these relations and determine the most important predictors of both Linville’s H and Sakaki’s SC statistic, simultaneous multiple regression analyses were conducted with the number of self-aspects, overlap, and the number of chosen adjectives as predictors and either the H statistic or the SC statistic as the dependent variable. Squared semipartial correlations were calculated to estimate the unique contribution of each predictor to the variance in self-complexity scores. We also tested for multicollinearity using a rule of thumb associated with the variance inflation factor (VIF <5).
As can be seen in Table Table2, 2 , NAS, OL, and NAT were significant unique predictors of self-complexity, regardless the measure that was used. The number of chosen adjectives emerged as the strongest (and positive) predictor of Linville’s H statistic (β = 0.87, p < 0.001). Positive associations between the H statistic and both number of roles and overlap held even when controlling for all the other predictor variables (β = 0.19, p < 0.001 and β = 0.06, p < 0.001, respectively). The number of self-aspects was the strongest (and positive) predictor (β = 0.42, p < 0.001) of Sakaki’s SC statistic, closely followed by overlap (β = −0.40, p < 0.001), which remained negatively associated with the SC statistic. The regression model ( F (3, 642) =5765.86, p < 0.001) accounted for 96 % of the variance in the H score, with NAT explaining 57.5 % of the variance, NSA approximately 3 %, and OL an additional 0.3 %, as reflected by the squared semipartial correlation. Thus both NSA and OL played only a minor role in self-complexity indicated by the H statistic. For Sakaki’s SC statistic, the regression model accounted for 31 % of the variance ( F (3, 530) =77.80, p < 0.001), with OL and NSA explaining 15.5 % and 13 % of the variance, respectively. The remaining 1.6 % was attributable to NTA. 6
Summary of multiple regression analysis with either the H or SC statistic as the dependent variable
Linville’s H statistic | Sakaki’s SC statistic | |||
---|---|---|---|---|
Variable | β | β | ||
NSA | 0.19 | 22.76*** | 0.42 | 10.01*** |
OL | 0.06 | 7.84*** | −0.40 | −10.90*** |
NAT | 0.87 | 101.46*** | −0.14 | −3.46*** |
Model | = 0.96, (3, 642) =5765.86, < 0.001 | = 0.31, (3, 530) =77.80, < 0.001 |
N = 652
NSA number of self-aspects, OL overlap, NAT number of trait adjectives
*** p <0.001
The above results suggest that the SC statistic serves as a more adequate measure of the self-complexity as conceptualized by Linville ( 1987 ). However, since the equation for computing the SC statistic may cause a divide by zero error, using the two-component approach and analyzing NSA and OL separately seems to be most suitable. It is also worth noting that Linville’s H statistic, reflecting predominantly the quantity of self-descriptions, constitutes the measure of differentiation, and not self-complexity, according to Zajonc’s ( 1960 ) original taxonomy.
The following analyses aimed at providing validity information on the Self-Incoherence Scale and exploring the convergence among different measures designed to assess self-concept differentiation: the average standard deviation of trait ratings across roles (SCD SD ),the average correlation among the roles (SCD R ), and the proportion of unshared variance among the roles (SCD VAR ). The analyses in this section were based on combined samples for which the appropriate scores were available (i.e., Study 1a and Study 1b samples).
Internal consistency reliability of the scale was examined. The standardized Cronbach’s alpha coefficient of the composite index, based on the intercorrelations among trait-specific standard deviations, was 0.83. The mean corrected item-total correlation for the 7 traits was r = 0.57, indicating that those participants who were variable on one trait tended to be variable on the others. Although the reliability coefficient was slightly lower than that of the original study of Styła et al. ( 2010 , α =0.90), it was nevertheless acceptable.
The self-differentiation scores calculated from an average standard deviation (SCD SD ) in the present study ranged from 0.00 to 3.03, with a mean of 0.98 ( SD =0.36), the index based on an average correlation (SCD R ) ranged from 0.10 to 1.00, with a mean of 0.42 ( SD =0.13), and the index derived from factor analysis (SCD VAR ) ranged from 0.00 to 67.92, with a mean of 45.77 ( SD =11.07). 7 For all three variables, skewness and kurtosis were within acceptable limits (skewness: −0.77 to 1.18; kurtosis: 0.75 to 3.67). No significant gender differences were observed.
The zero-order correlation revealed that the SCD index based on an average standard deviation was only weakly correlated with Campbell et al.’s ( 2003 ) index ( r = −0.27, p < 0.001, r 2 = 0.07) and Block’s ( 1961 ) index ( r = 0.27, p < 0.001, r 2 = 0.07). These findings are contrary to those by Donahue et al. ( 1993 ) who reported high degree of convergence between alternative indexes. In accordance with expectations, Campbell et al.’s ( 2003 ) and Block’s ( 1961 ) indexes were strongly associated ( r = −0.95, p < 0.001, r 2 = 0.90).
Additional concerns raised with respect to the fact that all personality traits included in the Self-Incoherence Scale were positively valenced. Thus, it is possible that the SCD score represents the consistency of endorsing desirable traits rather than self-concept differentiation, as conceptualized in the literature (Donahue et al. 1993 ). To test this hypothesis, we compared the SCD scores in two groups: participants who rated themselves highly on the 7 positive traits and those, who claimed they lacked them. For each participant, we averaged their ratings on each trait across the five roles. The compared groups were composed of participants who obtained extremely low and extremely high scores on a given trait. A standard deviation criterion was used as cut-off point. The Mann–Whitney U tests showed there were significant differences in trait variability scores between the two groups on every personality trait analyzed (effect size range r = 0.47 to 0.78, average r = 0.58). Specifically, participants who rated a positive personality trait as highly descriptive of themselves received lower standard deviation scores for that trait , indicating a more stable self-concept (see Table Table3). 3 ). Similar results were obtained in correlation analysis of mean trait rating with cross-role standard deviation for the respective trait. The correlation coefficients ranged from r = −0.32 to r = −0.54, with the average coefficient being approximately −0.39 ( p < 0.001, range of r 2 = 0.10 to 0.29, average r 2 = 0.16).
Trait variability in individuals with high and low ratings on traits included in the Self-Incoherence Scale
Low ratings | High ratings | ||||
---|---|---|---|---|---|
Variable | ( ) | ( ) | Z | Effect size | |
Trait 1 | 1.35 (0.64) | 0.77 (0.36) | 4093.50*** | −7.65 | 0.47 |
Trait 2 | 1.33 (0.62) | 0.57 (0.35) | 2711.50*** | −10.51 | 0.63 |
Trait 3 | 1.27 (0.68) | 0.22 (0.23) | 707.50*** | −12.21 | 0.78 |
Trait 4 | 1.21 (0.69) | 0.57 (0.36) | 3179.00*** | −7.49 | 0.48 |
Trait 5 | 0.99 (0.57) | 0.39 (0.27) | 1600.00*** | −9.59 | 0.65 |
Trait 6 | 1.17 (0.65) | 0.61 (0.38) | 3712.50*** | −7.667 | 0.48 |
Trait 7 | 1.25 (0.65) | 0.50 (0.42) | 3537.50*** | −9.606 | 0.57 |
N = 868
*** p < 0.001
We also performed Mann–Whitney U tests to compare all three SCD indexes between participants who generally viewed themselves positively and those who evaluated themselves more negatively. The two groups were obtained simply by averaging participants’ ratings for all 7 personality traits. A standard deviation criterion was used as cut-off point. Once again, as shown in Table Table4, 4 , self-differentiation index based on an average standard deviation was found to be associated with a tendency to ascribe desirable traits to the self ( U =3025.50, p < 0.001, r = 0.57). 8 The Pearson correlation coefficient of these two variables was r = −0.40 and was statistically significant ( p < 0.001, r 2 = 0,16). This effect, however, was not observed for the other two SCD indexes.
Self-concept differentiation difference between individuals with high and low mean ratings
Low ratings | High ratings | ||||
---|---|---|---|---|---|
Variable | ( ) | ( ) | Z | Effect size | |
SCD | 1.13 (0.45) | 0.69 (0.24) | 3025.50*** | −9.33 | 0.57 |
SCD | 0.44 (0.14) | 0.44 (0.13) | 6268.50 | −0.27 | 0.02 |
SCD | 45.21 (12.57) | 43.89 (11.57) | 5909.00 | −0.99 | 0.07 |
SCD SD average standard deviation of trait ratings across roles, SCD R average correlation among the roles, SCD VAR proportion of unshared variance among the roles
The above results suggest that there is varying correspondence between alternative ways of measuring self-concept differentiation, and, more importantly, that self-concept differentiation – operationalized as the average standard deviation – may not be independent of the contents of the self-concept (see also Locke 2006 ).
We examined whether measures of self-concept structure, reflecting differentiation and integration (unity), were related to one another. These analyses were based on Study 1b sample data.
Correlation analysis (see Table Table5) 5 ) between self-complexity, its components and self-concept differentiation revealed a weak, negative association between overlap and SCD index based on an average standard deviation ( r = −0.13, p = 0.004, r 2 = 0.02). Considering the dependence of SCD SD on mean trait rating (cf. Tables 3 and and4), 4 ), we decided to examine this result further. The zero-order correlation showed that along with OL and SCD SD being correlated to each other, both were significantly correlated to the mean trait rating ( r = 0.12, p = 0.005 and r = −0.40, p < 0.001, respectively). To test for the possible confounding effect of the mean trait rating, a hierarchical regression was used in which the SCD SD score was regressed on overlap (Step 1), and participants’ mean rating for personality traits (Step 2). After entering the mean trait rating in Step 2, the model explained 16.5 % of the variance in the SCD SD score (Δ R 2 = 0.15, F (1, 515) =92.23, p < 0.001). The mean trait rating emerged as a significant predictor (β = −0.39, p < 0.001), whereas the effect of overlap was no longer significant (β = −0.08, ns ). 9
Correlation matrix of measures of self-complexity and self concept differentiation
Variable | OL | H | SC | SCD | SCD | SCD |
---|---|---|---|---|---|---|
NSA | 0.10* | 0.56*** | 0.29*** | −0.03 | −0.01 | 0.02 |
OL | – | 0.33*** | −0.54*** | −0.13** | 0.04 | −0.04 |
H | – | 0.03 | −0.08 | 0.03 | 0.00 | |
SC | – | 0.09 | −0.10 | 0.08 | ||
SCD | – | −0.28*** | 0.28*** | |||
SCD | – | −0.95*** |
N = 521
NSA number of self-aspects, OL overlap, H Linville’s self-complexity index, SC Sakaki’s self-complexity index, SCD SD average standard deviation of trait ratings across roles, SCD R average correlation among the roles, SCD VAR proportion of unshared variance among the roles
*** p < 0.001, ** p < 0.01, * p < 0.05
No further associations between measures of self-complexity and measures of self concept differentiation were found, suggesting that they are, to a large extent, independent constructs. A similar conclusion was drawn by Campbell et al. ( 2003 ), and Lutz and Ross ( 2003 ).
Traditionally, a stable and coherent personal identity is considered to be essential for psychological health and adaptive functioning. According to Erikson ( 1980 ), the subjective experience of personal identity actually gives rise to a preconscious sense of personal well-being. We examined the self structure–identity relationship, operationalizing identity through measures of identity processes and identity senses.
Basic statistical description of identity variables in combined sample is presented in Table Table6. 6 . The levels of skewness and kurtosis exhibited by our data were below those that Kline ( 1998 ) specifies as problematic (skewness: −0.40 to 0.25; kurtosis: −0.25 to 0.26). An analysis of gender differences by means of Mann–Whitney U test indicated that men had a higher sense of inner contents ( U =107833.50, Z = −2.47, p = 0.013, r =0.08), uniqueness ( U =100746.00, Z = −4.00, p < 0.001, r = 0.13), their own boundaries ( U =96659.00, Z = −4.96, p < 0.001, r = 0.16), and self-worth ( U =94079.00, Z = −4.76, p < 0.001, r = 0.15), than women. Moreover, men, as compared to women, had higher scores on overall sense of identity ( U =99912.50, Z = −4.27, p < 0.001, r = 0.14). With respect to identity processes, women endorsed identity accommodation more than men ( U =1098.00, Z = −4.30, p < 0.001, r = 0.03), and men scored higher on identity balance than women ( U =1496.00, Z = −2.23, p = 0.026, r = 0.02).
Descriptive statistics and gender differences for identity senses and identity processes
Variable | ( ) | ( ) | ( ) | Z | Effect size | |
---|---|---|---|---|---|---|
SIC | 2.18 (0.48) | 2.15 (0.46) | 2.22 (0.51) | 107,833.50* | −2.47 | 0.08 |
SU | 1.71 (0.48) | 1.66 (0.49) | 1.78 (0.47) | 100,746.00*** | −4.00 | 0.13 |
SOB | 1.50 (0.45) | 1.44 (0.44) | 1.60 (0.44) | 96,659.00*** | −4.96 | 0.16 |
SC | 1.95 (0.46) | 1.93 (0.44) | 1.97 (0.49) | 112,046.00 | −1.55 | 0.05 |
SCT | 1.90 (0.41) | 1.88 (0.40) | 1.92 (0.44) | 113,717.00 | −1.14 | 0.04 |
SSW | 1.95 (0.49) | 1.89 (0.48) | 2.04 (0.49) | 94,079.00*** | −4.76 | 0.15 |
GSI | 1.86 (0.33) | 1.82 (0.31) | 1.92 (0.34) | 99,912.50*** | −4.27 | 0.14 |
IAS | 3.59 (0.87) | 3.63 (0.91) | 3.58 (0.84) | 1943.00 | −0.16 | 0.00 |
IAC | 3.79 (1.08) | 4.20 (1.05) | 3.34 (0.94) | 1098.00*** | −4.30 | 0.03 |
IBL | 4.90 (0.93) | 4.74 (0.80) | 5.08 (1.05) | 1496.00* | −2.23 | 0.02 |
Descriptive statistics for identity senses are based on 1001 participants; descriptive statistics for identity processes are based on 127 participants
SIC sense of having inner contents, SU sense of uniqueness, SOB sense of one’s own boundaries, SC sense of coherence, SCT sense of continuity over time, SSW sense of self-worth, GSI global sense of identity, IAS assimilation, IAC accommodation, IBL balance
*** p < 0.001, * p < 0.05
Table Table7 7 contains the zero-order correlations between self-complexity, self concept differentiation, and identity measures. All observed correlations were rather weak ( r ≤0.34) and seemingly inconsistent. Of all self-complexity measures, relatively the strongest correlation emerged between the H statistic and identity balance (Study 1c: r = 0.31, p < 0.001, r 2 = 0.10). Linville’s measure was also related to most of the identity senses (average r = 0.11, average r 2 = 0.02) and global sense of identity (average r = 0.16, average r 2 = 0.03). These correlations were all positive with the exception of the one with sense of one’s own boundaries. Overlap showed significant associations with some of the identity senses (average r = 0.02, average r 2 = 0.01) and global sense of identity (average r = 0.02, average r 2 = 0.01). Though the direction and significance of these associations was not consistent across the two studies. The only significant correlation of the number of self-aspects was a negative one with sense of one’s own boundaries (Study 1b: r = −0.12, r 2 = 0.01), and the only significant correlation of Sakaki’s SC statistic was a negative one with identity accommodation (Study 1c: r = −0.21, r 2 = 0.05). Negative associations emerged between SCD SD and most of the identity senses (average r = −0.18, average r 2 = 0.05) and global sense of identity (average r = −0.24, average r 2 = 0.07). Clearly weaker correlations were found between identity senses and SCD R (average r = 0.03, average r 2 = 0.004) and SCD VAR (average r = −0.04, average r 2 = 0.005). Both of these self-concept differentiation indices were uncorrelated with the global sense of identity score (on average, r = 0.04, r 2 = 0.002 and r = −0.06, r 2 = 0.004, respectively).
Correlation matrix of measures of self-complexity, self-concept differentiation, and identity
Sample | SIC | SU | SOB | SC | SCT | SSW | GSI | IAS | IAC | IBL |
---|---|---|---|---|---|---|---|---|---|---|
NSA | ||||||||||
Study 1b | 0.03 | −0.02 | −0.12** | 0.01 | 0.04 | 0.06 | 0.00 | |||
Study 1c | −0.01 | 0.06 | 0.07 | −0.05 | 0.04 | 0.10 | 0.05 | −0.01 | −0.07 | 0.08 |
OL | ||||||||||
Study 1b | 0.08 | 0.01 | −0.05 | 0.09* | 0.16*** | 0.09* | 0.09* | |||
Study 1c | −0.09 | −0.08 | 0.10 | 0.03 | −0.07 | −0.06 | −0.04 | −0.15 | −0.08 | 0.06 |
H | ||||||||||
Study 1b | 0.11* | 0.06 | −0.09* | 0.06 | 0.11* | 0.14*** | 0.09* | |||
Study 1c | 0.14 | 0.22* | 0.15 | 0.06 | 0.17 | 0.23* | 0.23* | 0.10 | −0.05 | 0.31*** |
SC | ||||||||||
Study 1b | 0.01 | −0.06 | −0.02 | −0.01 | −0.06 | 0.00 | −0.03 | |||
Study 1c | 0.13 | 0.02 | −0.12 | 0.06 | 0.04 | 0.11 | 0.06 | −0.04 | −0.21* | 0.06 |
SCD | ||||||||||
Study 1a | −0.34*** | 0.01 | −0.13* | −0.39*** | −0.34*** | −0.31*** | −0.34*** | |||
Study 1b | −0.18*** | −0.02 | −0.01 | −0.19*** | −0.11* | −0.12** | −0.15*** | |||
SCD | ||||||||||
Study 1a | 0.06 | 0.00 | −0.04 | 0.13* | 0.11* | 0.02 | 0.06 | |||
Study 1b | −0.01 | −0.05 | 0.04 | 0.05 | 0.06 | −0.02 | 0.01 | |||
SCD | ||||||||||
Study 1a | −0.09 | 0.01 | 0.02 | −0.14* | −0.13* | −0.05 | −0.08 | |||
Study 1b | −0.01 | 0.04 | −0.06 | −0.07 | −0.06 | 0.01 | −0.03 |
NSA number of self-aspects, OL overlap, H Linville’s self-complexity index, SC Sakaki’s self-complexity index, SCD SD average standard deviation of trait ratings across roles, SCD R average correlation among the roles, SCD VAR proportion of unshared variance among the roles, SIC sense of having inner contents, SU sense of uniqueness, SOB sense of one’s own boundaries, SC sense of coherence, SCT sense of continuity over time, SSW sense of self-worth, GSI global sense of identity, IAS assimilation, IAC accommodation, IBL balance
*** p ≤ 0.001, ** p ≤ 0.01* p ≤ 0.05
To ascertain the extent to which each of the self complexity and self-concept differentiation measures exhibited associations with identity variables that were independent of their associations with other variables, regression analyses were conducted. Identity scores were regressed on self-structure variables that were available in the data set. Due to the fact that specific identity senses were correlated with one another (average r = 0.41, p < 0.001), we only used the global sense of identity scores. Also, there were a number of cases for which the SC scores and scores on the two SCD indices based on the cross-role correlation matrix could not be obtained because of calculation problems (i.e., a division by zero error and an undefined correlation with zero variance). Since the missing cases may have caused the samples to be biased, we excluded these measures from the regression analyses described below. 10 Lastly, since some of the self-structural measures have been shown to be sensitive to variations in self-contents variables (e.g., the proportion of positive traits adjectives used in the sort and the mean trait rating), we performed regressions including them as predictors to control for their respective effects.
In all models, multicollinearity was examined to determine if any of the independent or control variables were a significant function of each other. As expected, since the H score and the number of traits were very highly correlated, the tolerances for these variables were very low and the VIFs exceed the value of 10. In order to avoid the multicollinearity problem, only the number of traits has been included in the analysis. The tolerance and the variance inflation factors in the final regression analyses were well within the acceptable range (tolerance >0.20, VIF <5).
In general, control variables (i.e., related to richness and favorability of self-depiction) were more consistent predictors of personal identity than variables related to self-concept structure (see Table Table8 8 ). 11 Individuals with a strong sense of identity and who were identity balanced were more likely to have very rich self-defining aspects and to describe themselves with more positive attributes.
Summary of beta weights for regression analyses with identity measures as the dependent variable
GSI | IAS | IAC | IBL | |||
---|---|---|---|---|---|---|
Source | Study 1a | Study 1b | Study 1c | Study 1c | Study 1c | Study 1c |
NSA | −0.07 | −0.11 | −0.11 | −0.12 | −0.10 | |
OL | 0.00 | −0.12 | −0.19* | −0.07 | −0.03 | |
SCD | −0.22*** | −0.03 | ||||
NAT | 0.11* | 0.39*** | 0.19 | −0.05 | 0.44*** | |
PPAT | −0.01 | 0.29*** | 0.12 | −0.22* | 0.31*** | |
MTR | 0.30*** | 0.33*** | ||||
Model | = 0.19, (2, 330) =39.00, < 0.001 | = 0.14, (6, 504) =13.25, < 0.001 | = 0.17, (4, 114) =5.84, < 0.001 | = 0.06, (4, 111) =1.68, | = 0.07, (4, 111) =2.01, | = 0.21, (4, 110) =7.53, < 0.001 |
NSA number of self-aspects, OL overlap, SCD SD average standard deviation of trait ratings across roles, NAT number of trait adjectives, PPAT proportion of positive trait adjectives, MTR mean trait rating, GSI global sense of identity, IAS assimilation, IAC accommodation, IBL balance
*** p ≤ 0.001,** p < 0.01, * p < 0.05
With this in mind, it is possible to interpret psychologically the obtained results. The global sense of identity was negatively predicted by SCD SD (Study 1a: β = −0.22, p < 0.001), showing that a differentiated self-concept was associated with a weakening of a sense of identity. Approximately 4 % of the GSI variance was explained by SCD SD . OL emerged as a significant predictor of IAS (Study 1c: β = −0.19, p = 0.046), suggesting that a unified self-structure was related to lesser use of assimilation, probably since there was hardly any discrepancy that needed to be resolved. About 3.5 % of the variance in the IAS scores was attributable to OL.
Overall, these correlation and regression findings would support the assumption that a sense of identity is related to coherence across self-aspects rather than their complexity and diversity. At the same time, they suggest that certain relationships of structural variables with well-being outcomes could be explained by content-based variables.
Drawing on the ideas of positive psychology, we posit that psychological well-being includes the development of various reflective and self-reflective capacities. Therefore, in this section, we address the issue of adaptive significance of structural aspects of self by exploring their relationships with need for cognition, reflection, and integrative self-knowledge. The following analyses were based on Study 1b sample data.
In our sample, the scores on need for cognition ranged from 56.00 to 175.00 ( M =127.39, SD =17.92), on reflection ranged from 1.00 to 5.00 ( M =3.26, SD =0.77), and for integrative self-knowledge ranged from 0.00 to 4.00 ( M =2.36, SD =0.63). For the current data set, the skewness of the variables ranged from −0.18 to 0.09, and the kurtosis ranged from −0.16 to 0.38. These estimates did not identify any serious violations of normality. To examine gender differences, for each variable, an independent Mann–Whitney U test was computed comparing the male and female scores. Women ( M =3.21, SD =0.76) scored significantly lower than men ( M =3.34, SD =0.77) on reflection ( U =31578.00, Z = −2.12, p = 0.034, r = 0.09). In terms of other variables, no significant gender differences were observed.
The observed correlations between measures of self-complexity and self-concept differentiation, and thinking dispositions are given in Table Table9. 9 . Need for cognition correlated with all three indices of self-concept differentiation. The strongest association was observed between NCS and SCD SD ( r = −0.29, p < 0.001, r 2 = 0.09). Reflection correlated with the number of self-aspects, Linville’s H statistic, and self-concept differentiation index calculated from an average standard deviation. The latter association was the strongest ( r = −0.18, p < 0.001, r 2 = 0.03). Finally, integrative self-knowledge correlated with overlap, the H and the SC statistics, and self-concept differentiation index based on an average standard deviation. The association of greatest magnitude was between ISK and OL ( r = 0.21, p < 0.001, r 2 = 0.04). It should be pointed out that all of the correlation coefficients were rather small, even when statistically significant; the largest explained about 9 % of the variance.
Correlation matrix of measures of self-complexity, self-concept differentiation, and thinking dispositions
Variable | NCS | RQ | ISK |
---|---|---|---|
NSA | 0.02 | 0.11* | 0.00 |
OL | 0.08 | 0.06 | 0.21*** |
H | 0.07 | 0.12** | 0.15*** |
SC | 0.00 | 0.06 | −0.11* |
SCD | −0.29*** | −0.18*** | −0.18*** |
SCD | 0.10* | 0.06 | 0.07 |
SCD | −0.11* | −0.05 | −0.07 |
NSA number of self-aspects, OL overlap, H Linville’s self-complexity index, SC Sakaki’s self-complexity index, SCD SD average standard deviation of trait ratings across roles, SCD R average correlation among the roles, SCD VAR proportion of unshared variance among the roles, NCS need for cognition, RQ reflection, ISK integrative self-knowledge
*** p ≤ 0.001, ** p < 0.01* p < 0.05
Although the zero-order correlations are instructive, they may be misleading. Therefore, a subsequent series of regression analyses was performed to examine the independent effects of measure of self-complexity and self-concept differentiation on each cognitive variable. The rationale guiding inclusion of the predictor variables was similar to the analyses in the previous section. Sakaki’s SC measure, the SCD VAR index, and the SCD R index were excluded to avoid bias in the sample and to retain sample size. 12 Linville’s H measure was excluded to avoid multicollinearity. Three variables related to richness and favorability of self-concept were included in the models to control for their potential effects. The beta weights (standardized regression coefficients) are reported in Table Table10 10 .
Summary of beta weights for regression analyses with thinking dispositions as the dependent variable
Source | NCS | RQ | ISK |
---|---|---|---|
NSA | −0.01 | 0.07 | −0.08 |
OL | 0.01 | −0.01 | 0.16*** |
SCD | −0.19*** | −0.16*** | −0.11* |
NAT | 0.05 | 0.11* | 0.13** |
PPAT | 0.05 | 0.10* | −0.01 |
MTR | 0.22*** | −0.02 | 0.15** |
Model | = 0.14, (6, 510) =13.57, < 0.001 | = 0.06, (6, 510) =5.60, < 0.001 | = 0.11, (6, 509) =10.81, < 0.001 |
NSA number of self-aspects, OL overlap, SCD SD average standard deviation of trait ratings across roles, NAT number of trait adjectives, PPAT proportion of positive trait adjectives, MTR mean trait rating, NCS need for cognition, RQ reflection, ISK integrative self-knowledge
The obtained results indicated, again, that the control variables were more strongly related to thinking dispositions than were the main predictors. Individuals who gave more elaborated self-descriptions and expressed more favorable self-evaluations used adaptive cognitive thinking to a greater degree. Of the included structural features of the self-concept, self-concept differentiation emerged as a significant predictor of need for cognition (β = −0.19, p < 0.001) as well as reflection (β = −0.16, p ≤0.001), and integrative self-knowledge (β = −0.11, p = 0.016), accounting for, respectively, 3 %, 2 %, and 1 % of the criterion variance. These effects suggested that active cognitive processing accompanied less differentiated self-structure. Overlap was independently associated with integrative self-knowledge (β =0.16, p < 0.001), showing that a tendency to integrate past, present, and desired future self-experience into a meaningful whole was directly linked to a unified self-structure. The predictive value of overlap for integrative self-knowledge was approximately 2.4 %. 13
Altogether, the obtained results indicated that the thinking dispositions of interest were not substantially predicted from the included structural features of the self-concept. Although relatively small in magnitude, these effects suggested that engaging in functionally adaptive cognitive processing was associated with a more coherent self-concept.
Individual differences in the self-concept structure have been a topic of considerable interest for at least three decades and have been debated as to their validity and adaptive significance. Thus, in this paper, we tackled issues related to the validity of operationalization and adaptive value of self-complexity and self-concept differentiation. The results of the present investigation can be summed up as follows:
Correlation and regression analyses showed that the H score was positively associated with the number of self-aspects as well as with the overlap among them. Thus, in contrast to Linville’s ( 1985 , 1987 ) prediction, not only numerous self-aspects but also more role overlap strengthen one’s H score. The effect of overlap proved particularly inconsistent across different studies, ranging from positive (e.g., Constantino et al. 2006 ; Luo and Watkins 2009 ; Rafaeli-Mor et al. 1999 ) to even nonsignificant (e.g., Brown and Rafaeli 2007 ; Engįn 2004 ; Heath 2011 ; Rafaeli-Mor and Steinberg 2002 ), with no consensus on its association to Linville’s H measure. These potentially conflicting findings could all be correct. Inspection of our data indicated that the relationship between the H statistic and overlap was, in fact, inverted U-shaped (see also Luo and Watkins 2008 , 2009 ; Luo et al. 2009 ). That is, an increase in overlap initially increases the H score, but when overlap exceeds a certain threshold, the H score decreases. Thus, depending on range of overlap values observed, the H statistic function takes on a different shape. The most possible explanation for our overall positive result is that the range of overlap in our data was sufficiently low, so that we did not observe the decrease in the H score – only the increase. The same explanation could account for previous failures (e.g., Constantino et al. 2006 ; Luo and Watkins 2009 ; Rafaeli-Mor et al. 1999 ) to identify the theoretically assumed negative effect of overlap on Linville’s H measure.
Moreover, the H statistic turned out to be influenced by the number of traits used in self-definition. In fact, the total number of traits used in the trait-sort task appeared to be the most important predictive factor for the H score, and a significant mediator in the NSA–H and OL–H relationships. Taken together, these data indicate that Linville’s H statistic, at best, could be considered an indirect measure of role quantity or simply a measure of the number of traits endorsed (see also Zajonc 1960 ). Corresponding conclusions were drawn by Locke ( 2003 ), Rafaeli-Mor et al. ( 1999 ), and Solomon and Haaga ( 2003 ). In addition, since the H statistic is highly sensitive to the number of traits, and people tend to ascribe far more positive traits than negative traits to the self, the H statistic will somewhat reflect self-esteem. This was previously recognized by Woolfolk et al. ( 1995 ) and Campbell et al. ( 2003 ), who found that self-complexity was influenced by the evaluative composition of the attributes sorted (i.e., the ratio of positive to negative attributes). Our analyses revealed that both component measures of self-complexity were also affected by the number of traits used, although to a much lesser extent than the H statistic. In addition, overlap was also found to be dependent on the favorability of the self-concept. This agrees with the results reported by Campbell et al. ( 2003 ) for the average correlation among self-aspects. One important question which we should address at this point is, whether we should interpret the obtained results as an indication of a positive self-complexity–self-esteem relationship or as a result of the method of calculation. While not entirely conclusive, our data, and those cited above, point to the second conclusion.
As for Sakaki’s SC statistic, it was found to be related positively to the number of self-aspects utilized and negatively to the overlap among these self-aspects, as Linville argued that it should. The amount of the SC statistic variance captured by the number of traits used in the sort was so small as to suggest that it probably was not meaningful. However, the mathematical formula of the SC statistic can potentially require division by zero and thus may yield uninterpretable results. Surprising as it may seem, neither Sakaki ( 2004 , 2006 ) nor other authors that used this index (e.g., Borawski 2011 ) commented on this issue. 14
Given the above and in agreement with Rafaeli-Mor and colleagues ( 1999 ), the two component measures of self-complexity, rather than singular measures, emerge as a more reasonable alternative in self-complexity assessment. Results showed that the component measures of self-complexity were uncorrelated with each other, thus providing support for their functional independence and for the two-dimensional nature of self-complexity, comprising both differentiation and a form of integration.
Although Campbell et al.’s ( 2003 ) and Block’s ( 1961 ) indices of self-concept differentiation were strongly intercorrelated, contrary to Donahue et al.’s ( 1993 ) finding, they had a weaker correlation with the measure recommended by Styła et al. ( 2010 ). We also found evidence for the non-convergence of these indices through an examination of their patterns of relationships with identity and cognitive variables. In particular, associations between indices derived from cross-role correlations and these other variables were considerably weaker and less consistent.
Furthermore, the SCD SD index turned out to be biased, since participants rating positive traits as highly descriptive of themselves obtained considerably lower self-concept differentiation scores. Hence, it could be argued that the SCD SD index reflects self-esteem (or self-esteem enhancement) in addition to self-concept differentiation. Similar concerns have been raised previously by Baird et al. ( 2006 ). Their studies provided evidence that the cross-role standard deviation conflate mean-level information with variability in trait expression. Baird et al. ( 2006 ) strongly suggested that this association is attributable to the constraints on the bivariate distribution and not to any underlying psychological process. They also pointed out that the relation of mean level and variability of traits will be more evident if the distribution of means is skewed. Because the Self-Incoherence Scale is restricted to positively valenced traits, and people generally view themselves positively, the means distribution in our data was skewed toward higher values ( Sk = −0.49, SES =0.08). Thus, in regression analyses we controlled for trait mean levels and examined whether variability per se predicted well-being outcomes.
As to indices based on cross-role correlation matrix, they can only reflect the covariance rather than consistency between role-based personalities, meaning that they do not exactly reflect the theoretically defined construct of self-concept differentiation (Donahue et al. 1993 ). Moreover, as such they are sensitive to the within role variance, and thence any role identity that has no within variation has to be dropped from calculations or the indices remain undefined in such circumstances (see also Baird et al. 2006 ; Locke 2006 ). The chance of this occurring is lower when the number of self-descriptive traits is greater. However, with only seven self-descriptors, as in the Self-Incoherence Scale, this became an important issue. Regardless, this source of variance is not even relevant to what Donahue and colleagues ( 1993 ) have conceptualized as self-concept differentiation.
Our overall results were comparable to those previously reported (e.g., Campbell et al. 2003 ; Constantino et al. 2006 ; Lutz and Ross 2003 ), as we found no unique associations between measures of self-complexity and self-concept differentiation. Correlation and regression analyses of self-complexity, its components, and self-concept differentiation on identity and cognitive variables yielded rather inconclusive results (see later in the text). Still, it seems safe to say that the patterns of associations for different structural features were neither similar, nor opposite. These findings support the postulate that complexity of the self-concept and self-concept differentiation are not the same phenomena. Moreover, since the two integration measures, namely overlap and self-concept differentiation, also appeared not to be uniquely related to one another, we might need to assume that they reflect different forms of integration. The above observations can also be understood in terms of the nature of the technique used. As noted in the introduction, the self-complexity task allows individuals to define their self-aspects in idiosyncratic ways, whereas in the self-concept differentiation task participants are constrained by limited and predefined roles. As pointed out by some authors (e.g., Constantino et al. 2006 ; Koch and Shepperd 2004 ; Lutz and Ross 2003 ), this distinction is important in that the former task may draw participants’ attention to more pleasant feelings of social specialization (a flexibility–rigidity dimension), whereas the latter task may promote focusing attention on rather unpleasant feelings of wearing many masks (an integration–fragmentation dimension).
The focus of the current paper was to consider the relations between structural characteristics of the self-concept and two broad aspects of psychological adjustment: personal identity and thinking dispositions. The former was operationalized in terms of identity senses (Pilarska 2012 , 2014a ) and identity processes (Whitbourne et al. 2002 ). The latter were operationalized as individual differences in need for cognition (Cacioppo et al. 1996 ), reflection (Trapnell and Campbell 1999 ), and integrative self-knowledge (Ghorbani et al. 2008 ); all of which can be considered indicative of adaptive cognitive thinking.
As previously reported, several structural features of the self-concept appeared to be a function of the quantity and positivity of the self-descriptions. Not surprisingly, our results concerning the relationships of self-complexity and self-concept differentiation with adaptive outcomes were affected by self-contents related variables. Participants’ mean ratings of personality traits mediated the effects of self-concept differentiation on global sense of identity, need for cognition, and integrative self-knowledge. Also, the number of traits used in the sort mediated the effects of overlap on integrative self-knowledge. With this in mind, below we discuss the results after controlling for these variables.
Among the set of self-structure variables, self-concept differentiation showed negative association with global sense of personal identity, suggesting that describing oneself differently across social contexts was indicative of a weakened sense of identity. These results are in line with other reports suggesting that self-concept differentiation serves as a sign of psychological maladjustment (e.g., Campbell et al. 2003 ; Diehl and Hay 2010 ; Diehl et al. 2001 ; Donahue et al. 1993 ; Lutz and Ross 2003 ; McReynolds et al. 2000 ) and identity struggle (e.g., Block 1961 ; Goldman 2004 ; Sheldon et al. 1997 ). Note, however, that self-concept differentiation did not share unique variance with global sense of identity when it was entered simultaneously with other self-structure variables (see Table Table8, 8 , Study 1b). Overlap emerged as a negative predictor for assimilation, thus indicating that greater unity in the self-concept structure was associated with less reliance on identity assimilation. It would seem that the identity processes of assimilation in itself does not serve any purpose, unless there is a dissonance within the self-concept, motivating an individual to reduce existing discrepancies. This is consistent with Whitbourne et al. ( 2002 ) descriptions of identity processes as different approaches to processing identity-discrepant experiences, different modes in dealing with changes in one’s life.
It is worth noting, that the obtained results could also be helpful for clarifying the relation between the two concepts – self and identity. From the literature review (e.g., Baumeister and Muraven 1996 ; Swann and Bosson 2010 ), one can note that the distinction between self and identity is not consistently well-established and the two concepts are sometimes used interchangeably. Since, in the present studies, the associations between self-structural indices and measures of personal identity had little predictive ability, it is tempting to assume that the way we describe ourselves in different situations or contexts, and the way we experience our selves are different phenomena. This conclusion is in line with authors who argue that the distinction between self and identity should be made and maintained (e.g., Berzonsky 2005 ; Katzko 2003 ; Oleś 2008 ). It should also be noticed that corresponding results, essentially no correlation between self-complexity and identity, were obtained by Suchańska and Ligocka ( 2011 ), whose study used different measures of identity, namely identity status and identity style approaches.
The results concerning the relationships of various measures of self-complexity and self-concept differentiation with thinking dispositions indicated self-concept differentiation to be the most important variable in predicting adaptive cognitive endeavors. It showed unique associations with need for cognition, reflection, and integrative self-knowledge. The direction of these effects was by no means constant and suggested that a strong, curiosity-driven, desire to engage in effortful thinking was associated with greater unity in the self-concept. In line with the above results, integrative self-knowledge appeared also to be positively related to overlap. Once again, this result indicated that an adaptive capacity to understand and integrate self-experience across time was associated with a more unified self-structure. Our results can also be interpreted as providing evidence that active cognitive processing serves as a means of uniting self-experience and reducing discrepancies within the self. As such, they support theoretical expectations derived from the existing literature (e.g., Berzonsky 2008 ; Campbell et al. 1996 ; Ghorbani et al. 2008 ; Njus and Johnson 2008 ; Trapnell and Campbell 1999 ).
We will sum up with two concluding remarks: (1) measures of self-complexity and self-concept differentiation do not necessary measure what they have been purported to measure, (2) results concerning the relationships of self-complexity and self-concept differentiation with adaptive outcomes are generally affected by self-contents related variables; when the confounding factors were taken into account, the true effects of structural features of the self-concept, while suggesting that psychological well-being is associated with a stable and coherent self-concept, were of minor significance.
Though the present paper is unique in its consideration of various measures of self-structure and exploring areas that had not previously been investigated (e.g., examining the impact of self-concept structure on cognitive processing), our findings are not the first to indicate that the commonly used indices of self-complexity and self-concept differentiation may lack validity (e.g., Baird et al. 2006 ; Locke 2003 , 2006 ; Luo et al. 2009 ; Rafaeli-Mor et al. 1999 ; Solomon and Haaga 2003 ). Indeed, the reasonable degree of consensus reflected in the studies cited above argues for the invalidity, rather than the validity, of the measures employed here. Yet, these measures have been and are still being used as indices of the constructs they supposedly tap, thereby introducing potential artifacts. As it stands, there seems to be a gap between the available research evidence and using this evidence to change the measurement practice. Until this gap is filled, it would be premature to draw any definite conclusions about the relationship between the structure of the self-concept and important outcomes.
The current investigation has a few limitations that merit discussion. First, the present studies included Polish participants only. On the one hand, the relative homogeneity of the samples studied here was a strength, as there were likely to be fewer confounding variables. On the other hand, it raises questions regarding the generalizability of the findings, for example, whether the results would hold for individuals with different cultural backgrounds. A number of cultural psychologists have pointed out that people in collectivistic cultures (or those with a predominantly interdependent self-construal) are expected to show less cross-situational consistency in their behavior (e.g., Choi and Choi 2002 ; Church et al. 2008 ; English and Chen 2007 ; Markus and Kitayama 1991 ). Moreover, self-consistency is believed to be central to optimal functioning in individualistic cultures, but not in collectivistic cultures (e.g., Cross et al. 2003 ; Pilarska 2014a ; Suh 2002 ). It should be noted that, with regard to cultural dimensions, Poland is one of the countries which Hofstede ( 1984 ) identified as an exception – while it is considered as an individualistic society, it also has high scores on both power distance and uncertainty avoidance (see also Minkov 2013 ; Murdoch 2009 ). As argued by Reykowski ( 1994 , 1998 ), despite popular belief that there has been a major change toward individualism in Poland, strong collectivistic elements have persisted.
There was also another limitation in relation to the samples. Given the developmental changes taking place during emerging adulthood (i.e., ages 18–25; Arnett 2000 ), the inclusion criteria could have been stricter or age could have been introduced as an additional independent variable. Since identity exploration – an active experimentation with different social roles – is thought to be an important feature of emerging adulthood (e.g., Arnett 2000 ; Schwartz et al. 2005 ), there may be a theoretical reason to expect age-related differences in the structure of self-concept among our participants. Diehl et al. ( 2001 ) obtained some evidence that self-concept differentiation was related to age, and that the association between self-concept differentiation and psychological well-being was moderated by age. More precisely, the negative effect of self-concept differentiation on psychological well-being was more pronounced in older adults than in younger adults.
Finally, an additional limitation of our studies could be the use of the Self-Incoherence Scale. This measure is similar to the one used by Donahue et al. ( 1993 ), but with considerably fewer adjectives, all of which are positively valenced. Since the indices of self-concept differentiation are sensitive to the within-role variation and the mean trait rating, perhaps using a longer and more balanced list of traits could reduce (but by no means eliminate) their limitations.
The conflation of self-concept structure and self-concept content evidenced by our results not only presents a problem for research, but also challenges the underlying theoretical models themselves. Both Linville’s ( 1987 ) and Donahue et al.’s ( 1993 ) models assume that structure and content are independent and, more specifically, that the valence of self-content is unrelated to structure. While various authors have reported on the inability to distinguish between structure and content (valence) by the applied measures, the possibility that the assumptions behind these models may themselves be the problem has been given less attention. Yet, research by Woolfolk and colleagues ( 1995 , 2004 ) demonstrated that evaluative valence may affect self-complexity, and identified two partially independent dimensions of self-complexity, namely positive self-complexity and negative self-complexity. In a similar vein, Locke ( 2006 ) provided support for the relative independence of positive and negative self-concept differentiation. The possible effects of features of the self-knowledge, other than valence, on the structure of the self-concept seem to deserve further investigation. Meanwhile, whether it is just that the currently available self-structural measures are vulnerable to self-enhancement and social desirability biases (e.g., the over-endorsement of positive traits) or the contents of the self-concept have an influence on the way they are organized, the inclusion of a measure of self-esteem should be considered standard practice in future studies utilizing measures of self-structure.
Moreover, the search for potential moderators of the effects of self-concept structure should continue. Only a few previous studies (besides those mentioned earlier) have examined whether the relationship between the structure of the self-concept and psychological well-being was conditional on other variables. McConnell et al. ( 2005 ) found support for a moderating role of self-aspects control, meaning that the positive relation between self-complexity and poor well-being was evident among those with low perceived control over their self-aspects. In another study, McConnell et al. ( 2006 ) found interactions between self-complexity and three of the Big Five major personality traits (openness, conscientiousness, and agreeableness) in accounting for differences in well-being. The findings of Diehl and Hay ( 2011 ) revealed that the relationship of self-concept differentiation and well-being was qualified by self-concept clarity. Other relevant factors that might moderate the effects of self-concept structure could include, for example, importance (centrality) of one’s self-aspects and internalization of one’s self-aspects (Ryan and Deci 2003 ).
Future research should also examine potential antecedents of different dimensions of self-structure. There is very little empirical evidence on what factors actually lead to individual differences in the self-concept structure. To our knowledge, except for cross-cultural studies, there have been only few investigations on this topic. Using a developmental perspective, Evans and Seaman ( 2000 ) proposed that individual differences in self-complexity could be explained by differences in maturity of defense mechanisms. Also encouraging are the findings by Lutz and Ross ( 2003 ) that link self-concept differentiation to aspects of parental bonding. Moreover, conclusions drawn from the longitudinal study of Donahue et al. ( 1993 ) support the view that psychological adjustment may be a causal antecedent to self-concept differentiation.
This work was partially supported by an internal grant for young scientists at the Institute of Psychology at the University of Adam Mickiewicz.
1 We used Styła et al.’s ( 2010 ) tool called Self-Incoherence Scale to assess self-concept differentiation. Therefore, the terms self-concept differentiation and self-incoherence will be used interchangeable throughout this paper.
2 In computing the SCD SD score, we allowed for up to seven non-responses (20 %), with no more than one omission on each trait adjective. When calculating SCD indices based on correlation coefficients, we allowed for one role (20 %) to be excluded from the correlation matrix, either because of the zero within variance or because of non-responses on all seven trait adjectives. The remaining six correlations were then averaged or processed by factor analysis to obtain the SCD R and the SCD VAR scores, respectively.
3 We allowed for up to 20 % of non-responses in each subscale and then used a single imputation procedure (person mean substitution). Missing values were replaced with the intraindividual mean of the other items on that subscale. Missing responses to the other multi-item measures used in this study were treated in the same manner unless otherwise specified.
4 A total of 17.3 % of the sample received a zero score on OL thereby causing a divide by zero in further computation of the SC statistic.
5 We performed additional regression analyses to examine the potential confounding of quantity with evaluation. Both the number of chosen adjectives and the proportion of positive traits chosen (PPAT) were entered to assess whether they uniquely contributed to the prediction of the dependent variables. The results showed that the number of attributes uniquely contributed to the prediction of NSA (β = 0.47, p < 0.001), OL (β = 0.25, p < 0.001), and the H score (β = 0.97, p < 0.001), and the proportion of positive attributes uniquely predicted OL (β = 0.11, p < 0.01) and the H score (β = 0.03, p < 0.05). Sakaki’s SC statistic was unrelated to any of these two variables (β = 0.03 and β = −0.04, ns , respectively). It is worth noting that the richness of self-depiction and its favorability were not exactly independent of each other, as demonstrated by a significant correlation between them ( r = −0.18, p < 0.001). Moreover, in the model of the H score, the interaction term of NAT and PPAT appeared to be significant ( F (1, 648) =8.63, p < 0.01). The effect of the number of traits on the H score was consistently positive and increased with increasing proportion of positive traits.
6 We also employed Hayes ( 2012 ) bootstrapping PROCESS tool for SPSS to assess the direct and indirect effects of NSA and OL on either the H or SC scores, with NAT as a mediating variable. The remaining component of self-complexity was used as a covariate and partialed out of all paths in the respective model. A total of 1000 re-samples of the data were executed using Hayes’ SPSS macro. The confidence intervals for the indirect effects of NSA and OL on the H scores via NAT did not include zero (estimate =0.12, 95 % CI =0.09, 0.14 and estimate =0.73, 95 % CI =0.40, 1.02 for NSA and OL, respectively). Thus, the number of traits mediated the relationships of the number of self-aspects and overlap with the H score. The indirect effect of NSA on the SC score was found to be significant (estimate = −2.89, 95 % CI = −6.70, −0.87). However, there was no indication of a significant indirect effect of overlap on the SC scores through the number of traits (estimate = −4.07, 95 % CI = −18.78, 1.56).
7 For a total of 7.2 % of the sample, the SCD R and SCD VAR scores could not be calculated because of zero within variation for more than one role.
8 The comparison of all three groups (low, medium, and high mean trait ratings) led to the same conclusions. According to Kruskal-Wallis tests, there was a significant effect of the mean self-rating classification on the SCD SD score (χ2(2) =129.62, p < 0.001, E R 2 = 0.15). Subsequent pairwise comparisons, performed using the Mann–Whitney U test, showed that all three groups were significantly different from each other with respect to the SCD SD scores ( p < 0.01).
9 Bootstrapping analysis (Hayes 2012 ) was used to test the mediating role of the mean trait rating. A total of 1000 re-samples of the data were executed using Hayes’ macro. As zero was not in the 95 % confidence interval for the indirect effect, we can conclude that it was indeed significant (estimate = −0.12, 95 % CI = −0.24, −0.03).
10 In each of the samples, we made a series of comparisons between participants for whom we had data and those for whom we did not, to assess whether there was any notable difference between them on any of the outcome measure. Comparisons were performed using the Mann–Whitney U-test. Significant differences were found between those for whom we obtained the SCD VAR and SCD R scores and those for whom we did not in relation to global sense of identity (Study 1a: U =1784.00, p < 0.01, r = 0.15); and between those for whom we obtained the SC scores and those for whom we did not in relation to accommodation (Study 1c: U =436.50, p < 0.05, r = 0.19) and balance (Study 1c: U =299.50, p < 0.001, r = 0.30).
11 The PROCESS macro (Hayes 2012 ) was used to test for mediation and moderation effects. We found evidence for mediation of the relationship between self-concept differentiation and global sense of identity, through the mean trait ratings (estimate = −0.11, 95 % CI = −0.17, −0.06 and estimate = −0.10, 95 % CI = −0.14, −0.06 for the indirect effects of SCD SD in Study 1a and Study 1b, respectively).
12 There were significant differences with regard to the cognitive variables among participants for whom we had complete data and those for whom we did not, suggesting that a bias would be present. Those for whom we did not obtain the SCD VAR and SCD R scores had lower levels of integrative self-knowledge than did those for whom we could calculate both scores ( U =9722.50, p < 0.05, r = 0.11). The same was true in case of the SC scores ( U =16487.00, p < 0.01, r = 0.13).
13 Using PROCESS bootstrapping macro (Hayes 2012 ), we found that the effect of self-concept differentiation on need for cognition as well as on integrative self-knowledge was mediated by the mean trait ratings (estimate = −3.89, 95 % CI = −5.79, −2.42 and estimate = −0.09, 95 % CI = −0.15, −0.04 for the indirect effect of SCD SD on NCS and ISK, respectively). Also, the number of traits used in the sort served as a significant mediator between overlap and integrative self-knowledge (estimate =0.13, 95 % CI =0.03, 0.28 for the indirect effect of OL).
14 In a private correspondence, Michiko Sakaki herself has suggested the use of overlap and the number of aspects participants reported as alternative measures of self-complexity. One strategy, that we felt might be useful for overcoming division by zero would be adding a small constant (for example, 0.001) to the divisor. The majority of previous findings were replicated when this correction was used, except that the correlation between accommodation and the SC score was lost (Study 1c, r = 0.18, ns ), whereas the correlation between balance and the SC score was significant (Study 1c, r = −0.27, p < 0.01). However, in the simultaneous regression, the SC score was unrelated to either accommodation or balance (β = 0.14 and β = −0.10, ns , respectively). Full results are available on request.
Does healthy narcissism exist? In recent years, the notion of “healthy narcissism” has gained traction in popular culture and self-help circles. Proponents of this concept argue that a certain degree of self-interest and self-focus is necessary for personal growth and success. However, this idea is not only misleading but also potentially harmful, as it contradicts established neuropsychological research findings.
Instead of embracing the myth of “healthy narcissism,” it is more beneficial to cultivate self-realization, a concept rooted in Abraham Maslow’s hierarchy of needs theory.
Let’s dive in…
Narcissistic personality disorder (NPD) is not a mere personality quirk or a continuum of self-absorption. Recent neuroimaging studies have revealed striking similarities between the brains of narcissists and psychopaths, suggesting that NPD is a more malignant condition than previously believed.
Brain scans of individuals with NPD show structural and functional abnormalities in regions associated with empathy, emotional regulation, and social cognition, mirroring the neural deficits observed in psychopaths. Specifically, narcissists exhibit reduced gray matter volume in the insular cortex and prefrontal areas, which are crucial for empathy and emotional processing.
Moreover, research has found that narcissists, like psychopaths, have an overactive striatum, a brain region involved in reward processing and decision-making. This striatal hyperactivity is linked to the impulsive, reward-seeking behavior and lack of consideration for consequences exhibited by both narcissists and psychopaths.
Contrary to the belief that narcissists struggle with self-loathing or shame, neuroimaging studies suggest that narcissists lack the capacity for genuine remorse or empathy. Their brain abnormalities, particularly in the amygdala and prefrontal cortex, impair their ability to experience and process emotions like guilt, shame, or remorse.
Furthermore, the deceptive nature of narcissists, often characterized by grandiose lies and manipulation, is reflected in their brain activity patterns. Functional MRI studies have shown that when narcissists lie or engage in deception, they exhibit reduced activity in the prefrontal cortex, a region associated with moral reasoning and decision-making.
These neurobiological findings challenge the notion of a “healthy narcissism” continuum. Instead, they suggest that narcissism is a distinct and severe personality disorder with profound neurological underpinnings, akin to the brain abnormalities observed in psychopathy.
Rather than experiencing self-loathing or remorse, narcissists lack the neurological capacity for genuine empathy, guilt, or shame. Their brain abnormalities facilitate a persistent pattern of grandiosity, exploitation, and a lack of concern for others, making NPD a more malignant condition than previously believed.
It is important to note that narcissism is not a personality trait that exists on a continuum, with “healthy” levels at one end and “unhealthy” levels at the other. Rather, it is a distinct personality disorder that can have significant negative impacts on those closely involved with these individuals.
While some highly-esteemed psychologists propose the idea of narcissism existing on a continuum, neuroimaging studies do not support this notion. Brain scans of individuals with narcissistic personality disorder (NPD) reveal distinct structural and functional abnormalities that differentiate them from those without the disorder. There is no evidence of a gradual continuum of brain changes corresponding to varying levels of narcissistic traits. Specifically, individuals diagnosed with NPD exhibit reduced gray matter volume in key brain regions like the insular cortex and prefrontal areas involved in empathy, emotional regulation, and social cognition. These structural deficits are not observed in a milder form among those without a clinical diagnosis, suggesting a clear neurobiological distinction between narcissists and non-narcissists.
Furthermore, the concept of a “narcissistic continuum” is difficult to validate due to the inherent lack of transparency and honesty in narcissistic individuals. Narcissists are known to be deceptive and manipulative, often presenting an inflated or distorted view of themselves on self-report assessments. This tendency to lie and exaggerate their positive qualities makes it challenging to accurately measure and quantify narcissistic traits, undermining attempts to place individuals on a continuum based on such measures.
Therefore, while the idea of a narcissistic continuum may be theoretically proposed, it lacks empirical support from neuroimaging studies and is confounded by the deceptive nature of narcissists themselves. Brain scans indicate a clear neurobiological distinction between those with NPD and those without, suggesting that narcissism is a categorical disorder rather than a spectrum of traits.
The concept of “healthy narcissism” is an oxymoron that contradicts the very definition and diagnostic criteria of narcissistic personality disorder. It suggests that a certain degree of self-absorption and self-centeredness can be beneficial, which is not supported by empirical evidence or scientific research. Proponents of “healthy narcissism” often conflate self-love and self-confidence with narcissistic traits. However, these are distinct concepts. Self-love and self-confidence are positive qualities that involve self-acceptance, self-respect, and a realistic assessment of one’s strengths and weaknesses.
Narcissism, on the other hand, is characterized by an inflated and distorted sense of self-importance, a lack of empathy, and a tendency to exploit others. Furthermore, the notion of “healthy narcissism” lacks empirical support and has not been extensively researched or validated by scientific studies. It is a theoretical construct proposed by some psychoanalysts, but it does not have a strong foundation in neuropsychology.
Instead of embracing the myth of “healthy narcissism,” it is more beneficial to cultivate self-realization, a concept rooted in Abraham Maslow’s hierarchy of needs theory. Self-realization, also known as self-actualization, refers to the process of realizing one’s full potential and becoming the best version of oneself. According to Maslow, self-realization is the highest level of human motivation and personal growth. It involves the pursuit of meaningful goals, the development of positive qualities such as creativity and spontaneity, and a concern for the well-being of others.
Unlike narcissism, which is characterized by self-absorption and a lack of empathy, self-realization emphasizes self-awareness, personal growth, and the development of positive qualities that contribute to the greater good. Maslow’s theory of self-realization is supported by extensive research and has been widely accepted and incorporated into various fields of psychology, including humanistic psychology, positive psychology, and personal growth theories.
In our image-obsessed world, the idea of “healthy narcissism” has seduced many into believing that a little self-love and self-promotion is not only acceptable but necessary for success. But what if this widely embraced concept is nothing more than a dangerous delusion, a wolf in sheep’s clothing that threatens to devour our very souls?
The narcissistic mindset is a seductive siren’s call, luring us with the promise of unwavering confidence, unshakable self-belief, and the ability to unapologetically pursue our desires. Yet, beneath this alluring facade lies a sinister truth – narcissism, even in its supposed “healthy” form, is a toxic force that erodes our humanity, corroding empathy, authenticity, and genuine connection.
Proponents of “healthy narcissism” would have us believe that a touch of self-absorption is harmless, even beneficial. But this is a lie, a carefully crafted illusion designed to justify and normalize a deeply destructive mindset. For narcissism, in any guise, is a malignant force that breeds emotional detachment, exploitation, and a callous disregard for the needs and feelings of others.
The path to true self-worth and fulfillment lies not in the empty promises of “healthy narcissism” but in the transformative power of self-realization. This journey requires us to shed the masks we wear, to confront our deepest vulnerabilities, and to embrace our authentic selves – flaws and all.
The concept of “healthy narcissism” is a flawed and potentially harmful notion that contradicts established neuropsychological theories and research findings.
Instead of embracing self-absorption and self-centeredness, it is more beneficial to cultivate self-realization, a concept rooted in Abraham Maslow’s hierarchy of needs theory. Self-realization emphasizes personal growth, self-awareness, and the development of positive qualities that contribute to the greater good. It involves the pursuit of meaningful goals, the cultivation of empathy and compassion, and a commitment to continuous self-improvement.
By rejecting the myth of “healthy narcissism” and embracing self-realization, individuals can embark on a journey of personal growth, self-acceptance, and positive impact on the world around them. It is a path that leads to a more fulfilling and meaningful life, grounded in self-awareness, empathy, and a genuine concern for the well-being of others.
Kim Saeed is a leading voice in the field of narcissistic abuse recovery. Drawing from her 13+ years of extensive expertise, she guides survivors to reclaim their power and rebuild their lives after enduring the trauma of psychological abuse and manipulation. If you’d like to work with Kim, visit the Schedule a Session Page.
Psychological Medicine, Volume 41 , Issue 8 , August 2011 , pp. 1641 – 1650
nature.com/articles/s41598-021-94920-z
Do Psychopaths Have Emotions?
https://www.researchgate.net/publication/239521124_Gray_matter_abnormalities_in_patients_with_narcissistic_personality_disorder
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10605183/
https://scitechdaily.com/scientists-have-established-a-key-biological-difference-between-psychopaths-and-normal-people/
5 Ways Shame Binds Us To Toxic Relationships
Can Neuroplasticity Help Narcissists Develop Empathy? What You Need To Know
The Twin Flame Myth Is Spiritual Narcissism: What You Need To Know
Save my name, email, and website in this browser for the next time I comment.
At NC State University, the N.C. Plant Sciences Initiative is leading efforts aimed at helping producers put artificial intelligence to work for increased yields, efficiency and sustainability.
Two new tools will propel its work in AI for agriculture forward: A robot named BenchBot 3.0 has taken its place on an empty field adjacent to the Plant Sciences Building on NC State’s Centennial Campus. It’s just one of the data-gathering devices that will be tied into the power of N.C. PSI’s newly acquired supercomputer, the NVIDIA Grace Hopper 200, to create AI for agricultural applications.
One of the Grace Hopper’s first tasks will be to begin making sense of the half million plant photos that BenchBot will take as it passes repeatedly over 500 pots of different plant species.
Those photos, along with ones taken by earlier versions of the BenchBot, will help feed what N.C. PSI Platform Director for Resilient Agriculture Chris Reberg-Horton says will be the world’s largest open-source agricultural image repository.
Reberg-Horton and his colleagues in the Precision Sustainable Agriculture network will use the collected photos to develop software and tools that can help farmers make all kinds of decisions about their crops: when and where to harvest, spray for weeds or insects, fertilize and more.
Getting from photos to those applications will take powerful AI, and the NVIDIA Grace Hopper, with its ability to handle five terabytes of data per second, is just the right tool, says Jevon Smith , N.C. PSI’s research computing manager.
The most powerful computer of its kind on NC State’s campus, the machine enables teams working on interdisciplinary plant sciences research to take on large datasets, like the image repository, to make more complex models and come up with solutions faster than ever.
Getting computers to recognize plants, weeds and other stressors is the first step toward getting machines to detect problems or map performance, says Reberg-Horton, the Blue Cross and Blue Shield of North Carolina Foundation/W.K. Kellogg Distinguished Professor in Sustainable Community-Based Food Systems in the Department of Crop and Soil Sciences .
He likens what he and his colleagues are doing to build the image repository and create grower decision tools to what car companies have done to make self-driving cars a reality.
“If you’ve got a company that is trying out some new technology for self-driving cars, the first thing they have got to do is to train computers to understand and recognize the things a car sees, and so if you’ve ever done the CAPTCHA stuff — identify the bicycles, identify the pedestrians — you’re basically helping train AI because you’re labeling images so that computers can learn to recognize, ‘That’s a person. Don’t hit the gas now,’” he says.
And it’s not just self-driving cars that use such image-based AI.
“In a lot of economic sectors, we have literally millions of images labeled so that we can train computers on tasks like driving around a city,” he says. “We don’t have that in agriculture.”
The agricultural image repository is designed to help technology developers fill that gap. Ag technology companies large and small will have access, as will anyone who uses the internet.
Already, with earlier versions of the BenchBot, Reberg-Horton says the team “has tackled about 30 of the most common weed species in North America. For cash crops, we have imaged corn, soybeans, cotton, wheat, rye, barley and oats.”
“In theory we can do about 1,000 plant pots per day, but we will test logistics slowly before going that big,” Reberg-Horton adds. “This summer will be just weeds. From October to March, we will image winter cover crop species.”
Meanwhile, two other BenchBots are being deployed to add images from different locations. One is at the U.S. Department of Agriculture’s Agricultural Research Center in Beltsville, Maryland, while a third will be deployed soon at College Station, home of Texas A&M University.
The images the BenchBots collect will help overcome a bottleneck in fully realizing the concept of precision farming, Reberg-Horton notes.
In precision farming, producers deliver exactly what a plant needs, precisely when and where and in what amounts it’s needed. For example, rather than spraying an entire field for insects, which would be expensive, they could spray just the areas where they are a problem. That would protect crops, limit chemical use and safeguard the environment from excess application.
Thanks to information technology, the concept of precision farming, forged in the 1980s, has taken off since the 2000s.
Smith says that while AI has become controversial, its rapid development is pushing precision farming technology ahead faster than ever.
“You can’t turn on the TV without hearing two letters — AI,” Smith says. “There are so many bad stories about AI out there, but the applications in the agriculture sector include increasing yields, reducing waste, helping reduce carbon emissions, and more. It can be used for great good.”
And it already is.
There are self-driving tractors and combines, and some are equipped with variable rate equipment that can sense where to apply chemicals and where to plant seeds — and do those tasks. Artificial intelligence is also used in farming for water management, crop rotation, harvest timing, optimal planting and more.
“We’ve been talking about precision agriculture for decades,” Reberg-Horton says. “It’s really been aspirational for the most part because we have to have lots of knowledge about a field to be able to manage each piece of the field optimally. The smart equipment is available now to apply most of our inputs variably. But we have been stuck on creating enough intelligence to tell that equipment what to do.
“Computer vision is the technology that can do it, and we will start seeing cameras on all of our agricultural machinery.”
The N.C. PSI has made significant headway in applying AI in agriculture. For example, to allow growers to know precisely when to plant soybeans and how to manage soybeans in counties across the state, Rachel Vann — has worked alongside computer engineering experts to lead a team developing an AI-powered web application that will be released this fall. Vann is an NC State Extension soybean specialist, assistant professor in the Department of crop and Soil Sciences and N.C. PSI platform director for extension outreach and engagement.
Her colleague, Cranos Williams — N.C. PSI’s platform director for data analytics and Goodnight Distinguished Professor of Agricultural Analytics in the Department of Electrical and Computer Engineering — is among the leaders of N.C. PSI’s Sweet-APPS team. The team is working to reduce labor in the post-harvest handling of sweetpotatoes by helping producers use sensors and machines to sort and grade the vegetables by size, shape and other characteristics.
And to increase soybean resilience to climate change, N.C. PSI’s Ross Sozzani , professor in the Department of Plant and Microbial Biology and platform director for plant improvement, is working with U.S. Department of Agriculture scientist Anna Locke , as well as colleagues at the VIB, a life sciences institute in Belgium. The team is marrying machine learning, crop physiology and phosphoproteomics to evaluate the plant’s temperature stress regulators and develop a test to rapidly screen soybean genetics for temperature tolerance. The data they generate will allow the breeders to identify temperature-tolerant soybean varieties more efficiently.
Meanwhile, crop scientist Reberg-Horton is making progress with several AI-powered tools, some that rely on images in the repository he’s helping build. One tool uses cameras and AI-trained software to allow growers to map where they have herbicide-resistant weeds, such as the notoriously noxious palmer amaranth.
He calls the weed “sticky” because it tends to come back to the same area where it’s grown the previous year. Having a map of where palmer amaranth grew one year would help the farmer know where to use pesticides or rotate crops in the following year.
“It may sound trivial, but you’ve got to remember that the farmers that I deal with — big row crop farmers — might have 5,000 acres spread over multiple counties,” he says. “Given the growth that’s occurred in the size of farms, thinking they know where everything is at and where the bad spots are is just not true anymore.”
Another tool Reberg-Horton’s working on is designed for those who grow cover crops, which are planted in the off-season on land where cash crops are grown. They help manage soil quality, fertility, erosion and pests such as weeds and insects. Because cover crops have environmental benefits, the federal government offers incentives for planting them.
“Increasingly the Natural Resources Conservation Service is encouraging mixtures of species. So you might have a legume and a grass in a mix. Sometimes we even see complicated mixes where you might have five or six species,” he says.
“Applying the right amount of the fertilizer nitrogen onto crops like corn is already tricky because soils are not uniform across a field,” he explains. “Cover crop mixtures can amplify that variation because legumes in the mix will supply nitrogen to the corn, while other cover crops in the mix will not. We have to map the growth of each species to account for that. In essence, we are asking AI to recognize plant species.”
Using that information to reduce the need for nitrogen fertilizer saves money for farmers and reduces the environmental impact: Nitrates are lead contaminants of groundwater, where they can cause drinking water problems, and can run into surface water, where they can cause algal blooms.
In supporting the development of smart tools for agriculture, the Grace Hopper will give the N.C. PSI an advantage. The computer is named for a computer programming pioneer who began her U.S. Navy career during World War II.
To secure and deploy the Grace Hopper, N.C. PSI has partnered with Cambridge Computing, an NVIDIA reseller that specializes in consulting with universities and research institutions. N.C. PSI secured one of 50 university seed grants to get the supercomputer.
“We already have two machine learning systems, and the Grace Hopper is a new tool on a level we haven’t had before,” Smith says. “It gives us the capability to train even larger-scale models to become more accurate and more predictive in nature and come up with solutions to more complicated challenges.”
One of the top challenges has meaning for farmers and consumers alike. “Growers are faced with needing to meet a growing population’s need for more food at the same time that climate change is altering growing conditions,” Smith says.
U.S. farmers are also facing serious labor shortages. While one of the fears surrounding AI is that it’ll take away jobs, supporters say it may hold promise for easing agricultural worker shortages, especially with the labor-intensive crops that North Carolina is known for — sweetpotatoes and tobacco, for starters.
As Reberg-Horton notes, “We’ve been talking about precision agriculture for decades. We know what we want, but it’s hard to get there. The AI revolution has been a missing piece that’ll help us get there.”
This post was originally published in Plant Sciences Initiative.
Do bugs burp, fanning the flames of textile science, catalyzing creative concepts to solve commercial challenges.
As the world economy starts to emerge from the COVID-19 crisis, the time will soon come for leaders to look beyond safeguarding lives and livelihoods and to set their sights on a more profound challenge: bettering them. This societal challenge might be ten times as big as the pandemic and last ten times as long. The three goals we have in mind—growth, sustainability, and inclusion—buttress one another yet don’t always pull in the same direction; we see powerful reinforcing as well as counteracting loops among them (exhibit). And so, while many might broadly agree on the aspiration, there’s a very tough question lurking in the background: How do we go about building a future that delivers growth and sustainability and inclusion?
Full disclosure: we’re not going to offer an answer. Instead, we propose a way for changemakers in business, government, and society to explore the problem, a mental model that might offer the best chance to reach the answer. It starts with this: we believe the ands are crucial and that they are in fact the means to the end . The three elements of growth, sustainability, and inclusion are deeply connected and cannot be viewed as trade-offs. Consider this: without growth, how could we achieve prosperity and well-being or pay for the transitions needed to make the economy more sustainable and inclusive? Without sustainability, how could we fashion growth for the current generation and the ones to follow? Without inclusion—an opportunity for productive work and a satisfying life for all citizens—how could we ensure the demand needed to propel growth? Indeed, getting to and —moving to a world in which growth and sustainability and inclusion form a powerful dynamic—is the imperative for the next era of business.
But before we get to the challenge of and , let’s face facts: hastening growth, sustainability, and inclusion are incredibly difficult challenges in their own right. Fortunately, thinkers, strategists, activists, and many others around the world—dreamers and doers—are working on it. We are too. In our view, the world will need to confront three problems simultaneously:
And that’s just the start: as we explain in this article, even if the global economy were to get these three goals notionally right, there are contingencies among them that, if left unresolved, could wreck any progress made.
Here, we seek to frame the debate about achieving sustainable, inclusive growth in a clear-eyed way, laying out the aspiration but also the toughest problems that need to be solved to achieve this growth, with some illustrations as to their size. Good strategy should always start with asking the right questions. For today’s leaders, the questions are vast and profound— and soluble.
Good strategy should always start with asking the right questions. For today’s leaders, the questions are vast, profound— and soluble.
What do we mean by sustainable, inclusive growth? There are many ideas associated with these words. We aim for broad rather than narrow interpretations:
These three goals are daunting. Fortunately, they can strengthen and reinforce one another:
These three goals—sustainability, inclusion, growth—are daunting. Fortunately they can strengthen and reinforce each other.
If only each element of the circle of sustainable, inclusive growth created purely positive reinforcements to the others, the way forward would be clear. But the reality is that sustainability, inclusion, and growth also counteract. Squaring this circle means combating three sets of potential counterforces, which could be just as powerful as the reinforcing loops.
A McKinsey Live event on 'Charting a sustainable, inclusive, and growing future'
Growth imposes two major challenges. First is the persistent rise in inequality, which could worsen with growth. Already, 70 percent of the global population live in countries where inequality is mounting. Second is rising resource consumption and emissions.
Trillions in capital are needed for energy investment to achieve the goal of net-zero emissions by 2050. If consumers and businesses shoulder the burden, near-term growth and inclusion could suffer, even though the longer-term benefits are clear. If costs are passed on to consumers, energy prices could rise well before the gains are eventually reaped, and if costs are passed on to businesses, the profitability of whole sectors could suffer.
This dynamic sets up the potential for two counteractions: uneven distribution of impact and a challenge to the goal of inclusion.
The positive spillovers of inclusion are indisputable and well documented: greater workforce participation, higher creativity, more capital allocated to children’s needs. However, poorly conceived measures to boost inclusion can have unintended negative consequences that can include distorted product markets, reduced investment, or faster environmental depletion. For example, in developing economies, free or highly subsidized nonvolumetric pricing of electricity used to pump water can lead to groundwater depletion . 12 Bekele Shifraw, “Addressing groundwater depletion: Lessons from India, the world’s largest user of groundwater,” World Bank Independent Evaluation Group, August 23, 2021, ieg.worldbankgroup.org. Efforts to achieve equality can also backfire if they become a box-ticking exercise, or a quota-driven program, which may fail to address the root causes of inequality. As a result, the goal of achieving a fairer workplace or society may not be achieved, and outcomes may even worsen for certain groups.
As in the pandemic, we will need multiple experiments, unprecedented speed in scaling successful ones, and broad participation across actors.
Achieving a future that is sustainable and inclusive and growing is so compelling an idea that today’s leaders owe it to future generations to act immediately. Such a feat cannot be left to enlightened self-interest: if it were that easy, the problem would already have been solved. We see six key challenges that will need to be tackled—with success or failure hinging on how effectively these challenges are met.
Answering these six questions would negate the counterforces mentioned earlier and allow the virtuous cycle to flow unimpeded. But important obstacles, linked to incentives, stand in the way. First is what Mark Carney has called “the tragedy of horizons” : today’s leaders collectively need to take action today for returns that will accrue only over time. 13 “Breaking the tragedy of the horizon—climate change and financial stability—speech by Mark Carney,” Bank of England, September 29, 2015, bankofengland.co.uk. Second is the tragedy of the commons: for collective action, especially on environmental sustainability, all invested parties must look past their parochial interests and fight for the common good.
No stakeholder can solve all these problems on their own. A clear road map, with buy-in from others, is paramount, as is a framework of incentives that balance short- and long-term horizons and interests across value-chain elements, economic sectors, countries, and regions. As in the case of the pandemic, tackling these challenges successfully will require multiple experiments, unprecedented speed in scaling successful ones, and broad participation across actors.
Governments will need to orchestrate a resilient transition—to manage risks, smooth costs, and avoid cascading crises in response to actions taken. On the business side, more companies and CEOs will need to enter the arena, to engage deeply in the design of policies, and to contribute their market knowledge. They will need to be open and realistic about the challenges, while also setting ambitious goals to create positive impact for their customers, workforces, societies, and the environment. Their capacity for innovation can and must be harnessed to shift the frontier of what’s possible and to help achieve what may seem unachievable. If companies don’t engage well and honestly, younger generations of workers will hold them accountable.
When it comes to achieving sustainable, inclusive growth, it is crucial first to fully recognize both the reinforcing as well as the counteracting loops. Then the conversation must move from agreeing on the targets—for who would not agree to such a tantalizing vision—to understanding how to solve the tough problems that stand in the way.
For our part, we have put our hypotheses on those problems at the top of our research agenda and look to learn even more from the leaders of the global organizations we work with who are “making a dent in the universe” through sustainable, inclusive growth. We hope that the ways in which we’ve sketched out the forces and counterforces here contributes to our collective understanding. With that, it may be possible to start to move toward a sustainable and inclusive and growing global economy.
If we don’t focus on the and , we won’t achieve the end.
The authors wish to thank Peter Gumbel and Daniel Pacthod for their contributions to this article. This is the first in a series of articles devoted to sustainable and inclusive growth.
This article was edited by Mark Staples, an executive editor in the New York office.
Related articles.
You’ve likely heard that a picture is worth a thousand words, but can a large language model (LLM) get the picture if it’s never seen images before?
As it turns out, language models that are trained purely on text have a solid understanding of the visual world. They can write image-rendering code to generate complex scenes with intriguing objects and compositions — and even when that knowledge is not used properly, LLMs can refine their images. Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) observed this when prompting language models to self-correct their code for different images, where the systems improved on their simple clipart drawings with each query.
The visual knowledge of these language models is gained from how concepts like shapes and colors are described across the internet, whether in language or code. When given a direction like “draw a parrot in the jungle,” users jog the LLM to consider what it’s read in descriptions before. To assess how much visual knowledge LLMs have, the CSAIL team constructed a “vision checkup” for LLMs: using their “Visual Aptitude Dataset,” they tested the models’ abilities to draw, recognize, and self-correct these concepts. Collecting each final draft of these illustrations, the researchers trained a computer vision system that identifies the content of real photos.
“We essentially train a vision system without directly using any visual data,” says Tamar Rott Shaham, co-lead author of the study and an MIT electrical engineering and computer science (EECS) postdoc at CSAIL. “Our team queried language models to write image-rendering codes to generate data for us and then trained the vision system to evaluate natural images. We were inspired by the question of how visual concepts are represented through other mediums, like text. To express their visual knowledge, LLMs can use code as a common ground between text and vision.”
To build this dataset, the researchers first queried the models to generate code for different shapes, objects, and scenes. Then, they compiled that code to render simple digital illustrations, like a row of bicycles, showing that LLMs understand spatial relations well enough to draw the two-wheelers in a horizontal row. As another example, the model generated a car-shaped cake, combining two random concepts. The language model also produced a glowing light bulb, indicating its ability to create visual effects.
“Our work shows that when you query an LLM (without multimodal pre-training) to create an image, it knows much more than it seems,” says co-lead author, EECS PhD student, and CSAIL member Pratyusha Sharma. “Let’s say you asked it to draw a chair. The model knows other things about this piece of furniture that it may not have immediately rendered, so users can query the model to improve the visual it produces with each iteration. Surprisingly, the model can iteratively enrich the drawing by improving the rendering code to a significant extent.”
The researchers gathered these illustrations, which were then used to train a computer vision system that can recognize objects within real photos (despite never having seen one before). With this synthetic, text-generated data as its only reference point, the system outperforms other procedurally generated image datasets that were trained with authentic photos.
The CSAIL team believes that combining the hidden visual knowledge of LLMs with the artistic capabilities of other AI tools like diffusion models could also be beneficial. Systems like Midjourney sometimes lack the know-how to consistently tweak the finer details in an image, making it difficult for them to handle requests like reducing how many cars are pictured, or placing an object behind another. If an LLM sketched out the requested change for the diffusion model beforehand, the resulting edit could be more satisfactory.
The irony, as Rott Shaham and Sharma acknowledge, is that LLMs sometimes fail to recognize the same concepts that they can draw. This became clear when the models incorrectly identified human re-creations of images within the dataset. Such diverse representations of the visual world likely triggered the language models’ misconceptions.
While the models struggled to perceive these abstract depictions, they demonstrated the creativity to draw the same concepts differently each time. When the researchers queried LLMs to draw concepts like strawberries and arcades multiple times, they produced pictures from diverse angles with varying shapes and colors, hinting that the models might have actual mental imagery of visual concepts (rather than reciting examples they saw before).
The CSAIL team believes this procedure could be a baseline for evaluating how well a generative AI model can train a computer vision system. Additionally, the researchers look to expand the tasks they challenge language models on. As for their recent study, the MIT group notes that they don’t have access to the training set of the LLMs they used, making it challenging to further investigate the origin of their visual knowledge. In the future, they intend to explore training an even better vision model by letting the LLM work directly with it.
Sharma and Rott Shaham are joined on the paper by former CSAIL affiliate Stephanie Fu ’22, MNG ’23 and EECS PhD students Manel Baradad, Adrián Rodríguez-Muñoz ’22, and Shivam Duggal, who are all CSAIL affiliates; as well as MIT Associate Professor Phillip Isola and Professor Antonio Torralba. Their work was supported, in part, by a grant from the MIT-IBM Watson AI Lab, a LaCaixa Fellowship, the Zuckerman STEM Leadership Program, and the Viterbi Fellowship. They present their paper this week at the IEEE/CVF Computer Vision and Pattern Recognition Conference.
IMAGES
VIDEO
COMMENTS
Abstract: In sociology and social psychology, self-concept refers to the thoughts, feelings, and evaluations of individuals about themselves. Researchers distinguish between multiple. types (e.g ...
The self-concept is an organized system that shapes how individuals feel about themselves, other individuals, and their social relationships (Leary & Tangney, 2011; Vazire & Wilson, 2012).Generally speaking, individuals who have more positive beliefs about themselves tend to report higher levels of self-esteem (Showers, 1992).However, there is more to the link between the self-concept and self ...
The self-concept is often used in such research with reference to its content and structural properties. It is hoped the broad overview of theory and applications of the concept provides readers with a framework for appreciating the diverse perspectives on the self-concept, and its utility as a focus in psychological investigations. ...
Self-concept is typically defined as the set of thoughts and feelings that the person has about self. The multiple self-construals, or selves, making up a person's self-concept are not " . . . a laundry list (of) . . . randomly scattered elements . . . " (M. Rosenberg, 1979, p. 17). Instead, the individual makes her or his multifaceted ...
Self-Concept. Self-concept encompasses an individual's cumulative knowledge and perceptions of themselves, encompassing various facets such as their physical being, academic pursuits, abilities, personality traits, ambitions, relationships with both the environment and others, and more. A collection of these self-concepts coalesces into what ...
How do we develop a stable and coherent self-concept in contemporary times? Susan Harter's original work The Construction of Self (1999; 2012) argues that cognitive and social processes are building blocks for developing a coherent sense of self, resulting in self-concept clarity across various domains in life (e.g., (pro-)social, academic, and physical).
In what has become a very influential review of research on self-concept, Shavelson, Hubner, and Stanton (1976; see Fig. 8.1) differentiated between four large domains of self-concept: academic, social, physical self-concept, and emotional. Within the domain of academic self-concept, they further differentiated between self-concepts in various ...
The structure of academic self-concept (ASC) is assumed to be multidimensional and hierarchical. This methodological review considers the most central models depicting the structure of ASC: a higher-order factor model, the Marsh/Shavelson model, the nested Marsh/Shavelson model, a bifactor representation based on exploratory structural equation modeling, and a first-order factor model.
Self-concept, self-esteem, and self-perception are interrelated and condition the lifestyle and health-related habits of individuals, particularly of adolescents. ... The research by Molero D, et al. states that physical self-concept is related to the age of the subject. Scores on this concept improve over the years, being higher at the ...
Self-concept clarity also has been associated with mindfulness and may characterize individuals higher in self-connection (Hanley & Garland, 2017). Additionally, consistent with SDT and Sheldon's (2004) ... As part of such research on self-connection and well-being, it also would be useful to examine whether high self-connection carries less ...
The reciprocally causal relationship between academic self-concept and achievement has also been widely tested and supported in the past three decades of research in academic self-concept (e.g. Guay et al., Citation 2003; H. Wu et al., Citation 2021; Marsh & Martin, Citation 2011).
The causes of low self-concept clarity have been theorized to be due to a discrepancy between one's current self-views and the social feedback one has received in childhood (Streamer & Seery, 2015). Both high self-concept certainty and self-concept clarity are associated with higher self-esteem (Campbell, 1990).
Self-concept research has typically focused on children's capacity to describe and rate . themselves across multiple dimensions. For example, by second grade children can report on .
In Sum. Our self-concept is an important guiding principle that helps us navigate the world and understand our role in it. Parts of our self-concept may be good or not-so-good for our well-being ...
Research on the relation between the structure of the self-concept and psychological well-being has yielded seemingly inconsistent and even conflicting results. This article presents studies that examined the validity of often-used measures of self-complexity and self-concept differentiation and tested their ability to predict personal identity and active cognitive processing.
Self-Concept, Personality and EI by Gender and Cultural Group. The analysis of variance results (see Supplementary Table S1) showed that there were significant differences as a function of gender for self-concept, more specifically in academic self-concept, with the girls achieving higher grades in post hoc comparisons using the Bonferroni test, t = 0.667, p = 0.007, and self-esteem, t = 1.139 ...
Self-concept in psychology refers to an individual's self-perceived knowledge, beliefs, and feelings about themselves, encompassing elements like self-worth, self-image, and self-esteem. It's formed through experiences, interactions, and reflections, and plays a pivotal role in influencing behavior, emotions, and interpersonal relationships. A healthy self-concept promotes well-being, while a ...
Research on Self-Concept. Given the marked interest in this topic within sociology and psychology, there is quite a bit of research out there on the subject. Here are a few of the most interesting and impactful findings on self-concept. Self-Concept in Marketing and How it Influences Consumer Behavior.
Psychologist Bruce A. Bracken had a slightly different theory and believed that self-concept was multidimensional, consisting of six independent traits: Academic: Success or failure in school. Affect: Awareness of emotional states. Competence: Ability to meet basic needs. Family: How well you work in your family unit.
Self-Complexity Provides a Buffer Against Negative Emotions. The self-concept is a rich and complex social representation. In addition to our thoughts about who we are right now, the self-concept includes thoughts about our past self—our experiences, accomplishments, and failures—and about our future self—our hopes, plans, goals, and possibilities (Oyserman, Bybee, Terry, & Hart-Johnson ...
The self-concept: theory and research By Sunil S. Bhar , Michael Kyrios Edited by Michael Kyrios , Australian National University, Canberra , Richard Moulding , Deakin University, Victoria , Guy Doron , Sunil S. Bhar , Swinburne University of Technology, Victoria , Maja Nedeljkovic , Swinburne University of Technology, Victoria , Mario Mikulincer
Introduction. The majority of contemporary theorists and researchers in personality psychology agree that self-concept is a dynamic and multifaceted phenomenon (e.g., Greenwald and Pratkanis 1988; Markus and Wurf 1987; Roberts 2007; Suszek 2007; Swann and Bosson 2010).The notion of the self as plural allows distinguishing between its content (i.e., what one thinks one is like) and structural ...
Journal of Applied Research in Intellectual Disabilities (JARID) is a learning disabilities journal covering topics ranging from quality of life to medication & services. Abstract Background This study aimed to identify perspectives of relatives and healthcare professionals regarding self-determination support for people with severe or profound ...
The concept of "healthy narcissism" is a flawed and potentially harmful notion that contradicts established neuropsychological theories and research findings. Instead of embracing self-absorption and self-centeredness, it is more beneficial to cultivate self-realization, a concept rooted in Abraham Maslow's hierarchy of needs theory.
"If you've got a company that is trying out some new technology for self-driving cars, the first thing they have got to do is to train computers to understand and recognize the things a car sees, and so if you've ever done the CAPTCHA stuff — identify the bicycles, identify the pedestrians — you're basically helping train AI because ...
As the world economy starts to emerge from the COVID-19 crisis, the time will soon come for leaders to look beyond safeguarding lives and livelihoods and to set their sights on a more profound challenge: bettering them. This societal challenge might be ten times as big as the pandemic and last ten times as long. The three goals we have in mind—growth, sustainability, and inclusion—buttress ...
The prospect of EVs and self-driving vehicles—and plain old changing tastes—has car designers and executives weighing what could replace the sport-utility vehicle. Violet Frances.
Research show submenu for "Research ... (CSAIL) observed this when prompting language models to self-correct their code for different images, where the systems improved on their simple clipart drawings with each query. ... recognize, and self-correct these concepts. Collecting each final draft of these illustrations, the researchers trained a ...
06/12/2024 June 12, 2024. To counteract market dumping, the European Union will slap tariffs of up to 38% on electric vehicles from China. This puts additional strain on China's EV manufacturers ...
It was observed that males tended to score higher on Inattention/Memory Problems while females scored higher on Problems with Self-Concept. Conclusion This research establishes the psychometric properties of the CAARS-S:S, placing greater confidence in using it to screen for ADHD symptoms in emerging adults living in a Westernized cultural context.