Influence of warmth and competence on the promotion of safe in-group selection. Stereotype content model and social categorization of faces

Categorizing an individual as a friend or foe plays a pivotal role in navigating the social world. According to the Stereotype Content Model, social perception relies on two fundamental dimensions, Warmth and Competence, which allow us to process the intentions of others and their ability to enact those intentions, respectively. Social cognition research indicates that, in categorization tasks, people tend to classify other individuals as more likely to belong to the out-group than the in-group (In-group Overexclusion Effect, IOE) when lacking diagnostic information, probably with the aim of protecting in-group integrity. Here, we explored the role of Warmth and Competence in group-membership decisions by testing 62 participants in a social-categorization task consisting of 150 neutral faces. We assessed whether (i) Warmth and Competence ratings could predict the in-group/out-group categorization, and (ii) the reliance on these two dimensions differed in low-IOE vs. high-IOE participants. Data showed that high ratings of Warmth and Competence were necessary to categorize a face as in-group. Moreover, while low-IOE participants relied on Warmth, high-IOE participants relied on Competence. This finding suggests that the proneness to include/exclude unknown identities in/from one's own in-group is related to individual differences in the reliance on SCM social dimensions. Furthermore, the primacy of Warmth effect seems not to represent a universal phenomenon adopted in the context of social evaluatio

Affect and Group Attachments: The Role of Shared Responsibility

This paper theorizes the role of shared responsibility in the development of affective group attachments, interweaving ideas from social exchange and social identity theories. The main arguments are that (1) people engaged in task interaction experience positive or negative emotions from those interactions; (2) tasks that promote more sense of shared responsibility across members lead people to attribute their individual emotions to groups or organizations; and (3) group attributions of own emotions are the basis for stronger or weaker group attachments. The paper suggests that social categorization and structural interdependence promote group attachments by producing task interactions that have positive emotional effects on those involved

How Long Does it Take to Integrate? Employment Convergence of Immigrants and Natives in Sweden.

This study examines employment convergence between immigrants and natives, by gender and region of origin, using data with annual information (1990-2000) on more than 200,000 individuals of which over 19,000 were born abroad. Duration of residence is found to have a significant effect on employment chances up to and including the first 25 years in Sweden but with greater explanatory power for East- and non-European immigrants. Assuming homogeneous human capital and time effects, immigrant groups with over twenty years residency continue to show a significant employment gap to natives. No notable gender differences in employment convergence patterns are found.Immigration; Employment; Discrimination; Gender

Does Coarse Thinking Matter for Option Pricing? Evidence from an Experiment

Mullainathan et al [Quarterly Journal of Economics, May 2008] present a model of coarse thinking or analogy based thinking. The essential idea behind coarse thinking is that people put situations into categories and the values assigned to attributes in a given situation are affected by the values of corresponding attributes in other co-categorized situations. We test this hypothesis in an experiment on financial options against the benchmark of arbitrage-free pricing. Firstly, we test whether a financial option is priced in analogy with its underlying stock (transference). Secondly, we test for whether variations in the analogy between a financial option and its underlying stock matter (framing). We find evidence in support of both transference and framing.Coarse Thinking, Financial Options, Arbitrage-Free Pricing

A quantum geometric model of similarity

No other study has had as great an impact on the development of the similarity literature as that of Tversky (1977), which provided compelling demonstrations against all the fundamental assumptions of the popular, and extensively employed, geometric similarity models. Notably, similarity judgments were shown to violate symmetry and the triangle inequality, and also be subject to context effects, so that the same pair of items would be rated differently, depending on the presence of other items. Quantum theory provides a generalized geometric approach to similarity and can address several of Tversky’s (1997) main findings. Similarity is modeled as quantum probability, so that asymmetries emerge as order effects, and the triangle equality violations and the diagnosticity effect can be related to the context-dependent properties of quantum probability. We so demonstrate the promise of the quantum approach for similarity and discuss the implications for representation theory in general

Critical Race Science and Critical Race Philosophy of Science

Over several decades, feminist philosophy of science has revealed the ways in which much of science has proceeded from “mainstream” assumptions that privilege men and other hierarchically superordinate groups and existing socially constructed conceptions of gender. In doing so, it has produced a research program that, while rooted in the post- Kuhnian philosophy and sociology of science that has been taken up by many students of scientific method more generally, has been used to critique great swathes of modern science and to reveal both the biases of the mainstream, and the transformative potential of a science that proceeds from the epistemic standpoints of women as well as men and from the research questions and concerns that arise from the goal of promoting equality between men and women

Explaining Explanation

It is not a particularly hard thing to want or seek explanations. In fact, explanations seem to be a large and natural part of our cognitive lives. Children ask why and how questions very early in development and seem genuinely to want some sort of answer, despite our often being poorly equipped to provide them at the appropriate level of sophistication and detail. We seek and receive explanations in every sphere of our adult lives, whether it be to understand why a friendship has foundered, why a car will not start, or why ice expands when it freezes. Moreover, correctly or incorrectly, most of the time we think we know when we have or have not received a good explanation. There is a sense both that a given, successful explanation satisfies a cognitive need, and that a questionable or dubious explanation does not. There are also compelling intuitions about what make good explanations in terms of their form, that is, a sense of when they are structured correctly

Predicting Category Intuitiveness With the Rational Model, the Simplicity Model, and the Generalized Context Model

Naïve observers typically perceive some groupings for a set of stimuli as more intuitive than others. The problem of predicting category intuitiveness has been historically considered the remit of models of unsupervised categorization. In contrast, this article develops a measure of category intuitiveness from one of the most widely supported models of supervised categorization, the generalized context model (GCM). Considering different category assignments for a set of instances, the authors asked how well the GCM can predict the classification of each instance on the basis of all the other instances. The category assignment that results in the smallest prediction error is interpreted as the most intuitive for the GCM—the authors refer to this way of applying the GCM as “unsupervised GCM.” The authors systematically compared predictions of category intuitiveness from the unsupervised GCM and two models of unsupervised categorization: the simplicity model and the rational model. The unsupervised GCM compared favorably with the simplicity model and the rational model. This success of the unsupervised GCM illustrates that the distinction between supervised and unsupervised categorization may need to be reconsidered. However, no model emerged as clearly superior, indicating that there is more work to be done in understanding and modeling category intuitiveness