Emotion, rationality, and social identity: a theoretical–methodological proposal for a cognitive approach

Abstract

Humberto Maturana's biology of cognition and the studies of neuroscientist Antonio Damasio have proposed theories that indicate the role of emotion in cognitive processes. Emotion lies at the basis of any behavior. An emotional transition defines a transition from one domain of actions to the other, while emotions shared in human interaction define collective domains of action and thus also social identity. In this case, they may give rise to operational coherences within the same domain of action which is recognized as “rationality”. The transition from one operational coherence to another within the same rationality does not therefore entail an emotional transition. This article uses this theoretical framework to propose an interpretation of the phenomenon of social identity and distinguishes social identities based only on emotions from those related to operational coherence. We call the former type of social identity “ontological”, while the latter is referred to as “formal”. To empirically prove the theoretical proposal of the article, the concept of metacontrast found in social self-categorization theory and its quantification elaborated by Alberto Voci are used. According to the social self-categorization theory, the greater the distance between two groups’ visions of the world, the greater the metacontrast between them. Using our model, metacontrast must thus be greater for ontological social identity and less for formal social identity. Our hypothesis was confirmed by the data. This outcome allows huge applications of the methodology proposed, as exemplified in the final considerations.

Appendix

This appendix is identical to appendix A presented by Voci (2006: 89–90), except for the context to which the technique is applied, and is included here for the convenience of the reader.

As explained in the Method section, each interviewee was asked to imagine 100 consumers of a brand he or she liked (in-group) and 100 consumers of a brand he or she did not like (out-group). The interviewee was then asked to distribute both groups along a metric 5-point scale based on the degree to which these imaginary consumers belong to consensual domains of actions. For the first study, the domains were Differentiation, Family, and Friendship. For the second study, the domains of Differentiation, Assimilation, Friendship, Family, and Social Network were used.

One participant generated the following distribution for Friendship: 0–0-10–90-0 for the in-group and 0–40-60–0-0 for the out-group. Thus, 10 in-group members are situated in the third cell of the distribution and 90 in the fourth. Concerning the out-group, 40 members are in the second cell and 60 in the third. If we consider the first 10 in-group members, they are different by 1 position from the 40 out-group members and by 0 position from the 60 out-group members. Thus, we have 10 times 40 with a difference of 1 and 10 times 60 with a difference of 0. The other 90 in-group members are 2 positions distant from the 40 out-group members and 1 position from the 30 out-group members. Thus, we have 90 times 40 with a difference of 2, and 90 times 60 with a difference of 1. Inter-group differences are thus $10 \times 40 + 10 \times 60 \times 0 + 90 \times 40 \times 2 + 90 \times 60 = 13000.$ The total number of comparisons made is: $10 \times 40 + 10 \times 60 + 90 \times 40 + 90 \times 60 = 10000.$ Thus, average inter-group differences are $\frac{13000}{{10000}} = 1.30$. In the computation of intra-group differences, only the distribution concerning the in-group is involved. In this case, we have 10 members who are different by 1 position from 90 members. Thus, the differences within the in-group are: $10 \times 90 = 900$. For the intra-group comparison we have: $\frac{{\left[ {100 \times \left( {100 - 1} \right)} \right]}}{2} = 4950$; average intra-group differences are $\frac{900}{{4950}} = 0.18.$ After the constant 1 is added to both terms, the meta-contrast ratio corresponds to: $\frac{2.30}{{1.18}} = 1.94$.