A five-factor measure of schizotypal personality traits

The current study provides convergent, discriminant, and incremental validity data for a new measure of schizotypy from the perspective of the five-factor model (FFM) of general personality structure. Nine schizotypy scales were constructed as maladaptive variants of respective facets of the FFM (e.g., Aberrant Ideas as a maladaptive variant of FFM Openness to Ideas). On the basis of data from 143 undergraduates, the convergent validity of these nine scales was tested with respect to seven established measures of schizotypy and the respective facets of the FFM. Discriminant validity was tested with respect to facets from other FFM domains and components of schizotypy. Incremental validity was tested with respect to the ability of the FFM schizotypy scales to account for variance in two established measures of schizotypy, after variance accounted for by respective FFM facets and other established measures of schizotypy were first removed. The findings support the validity of these new scales as measures of schizotypal traits and as maladaptive variants of the FFM.     

Multiculturalism and Equal Human Dignity: An Essay on Bhikhu Parekh

Bhikhu Parekh is an internationally renowned political theorist. His work on identity and multiculturalism is unquestionably thoughtful and nuanced, benefiting from a tremendous depth of knowledge of particular cases. Despite his work’s many virtues, however, the normative justification for Parekh’s recommendations is at times vague or ambiguous. In this essay, I argue that a close reading of his work, in particular his magnum opus Rethinking Multiculturalism and the selfproclaimed “sequel” A New Politics of Identity, reveals that his claims frequently rely upon a Kantian account of moral dialogue and indeed moral personhood that he remains unwilling to claim. Recognizing this latent Kantianism is essential to a thorough assessment of Parekh’s work on identity, and his criticisms of other theorists. It is only because of his ambiguity that his multiculturalism is able to avoid the sort of charges that he levels against other responses to diversity, including those of such authors as Rawls, Habermas, Kymlicka, and Raz.     

Ulama, the state, & war: community Islamic leaders in the Aceh conflict

In recent years, much has been said of the relationship between the headmasters of Islamic boarding schools (ulama), the state, and war. Hoping to clarify how ulama behave in times of war and why they react as they do, I look to the recent secessionist conflict in Aceh, Indonesia. Based on extensive village fieldwork, I find that in response to the conflict, Aceh’s ulama were divided; some supported the rebels, some supported the state, and some remained neutral. These positions were largely predicted by combatant control, a pragmatic response to conflict dynamics. Sub-regional comparisons, temporal comparisons, and interviews suggest that while many ulama sided with power, they also sided against human rights abuses, a concern for social justice which may be rooted in religious sentiment after all.     

Three ideal observer models for rule learning in simple languages

Children learning the inflections of their native language show the ability to generalize beyond the perceptual particulars of the examples they are exposed to. The phenomenon of “rule learning”—quick learning of abstract regularities from exposure to a limited set of stimuli—has become an important model system for understanding generalization in infancy. Experiments with adults and children have revealed differences in performance across domains and types of rules. To understand the representational and inferential assumptions necessary to capture this broad set of results, we introduce three ideal observer models for rule learning. Each model builds on the next, allowing us to test the consequences of individual assumptions. Model 1 learns a single rule, Model 2 learns a single rule from noisy input, and Model 3 learns multiple rules from noisy input. These models capture a wide range of experimental results—including several that have been used to argue for domain-specificity or limits on the kinds of generalizations learners can make—suggesting that these ideal observers may be a useful baseline for future work on rule learning.     

A probabilistic model of cross-categorization

Most natural domains can be represented in multiple ways: we can categorize foods in terms of their nutritional content or social role, animals in terms of their taxonomic groupings or their ecological niches, and musical instruments in terms of their taxonomic categories or social uses. Previous approaches to modeling human categorization have largely ignored the problem of cross-categorization, focusing on learning just a single system of categories that explains all of the features. Cross-categorization presents a difficult problem: how can we infer categories without first knowing which features the categories are meant to explain? We present a novel model that suggests that human cross-categorization is a result of joint inference about multiple systems of categories and the features that they explain. We also formalize two commonly proposed alternative explanations for cross-categorization behavior: a features-first and an objects-first approach. The features-first approach suggests that cross-categorization is a consequence of attentional processes, where features are selected by an attentional mechanism first and categories are derived second. The objects-first approach suggests that cross-categorization is a consequence of repeated, sequential attempts to explain features, where categories are derived first, then features that are poorly explained are recategorized. We present two sets of simulations and experiments testing the models’ predictions about human categorization. We find that an approach based on joint inference provides the best fit to human categorization behavior, and we suggest that a full account of human category learning will need to incorporate something akin to these capabilities.     

Psychopathic traits from the perspective of self and informant reports: is there evidence for a lack of insight?

It has been suggested that psychopathic individuals are incapable of providing valid reports on their own personality functioning because they are either unwilling (i.e., pathologically lie) or unable (i.e. lack insight) to do so. Despite the long-standing nature of this suggestion, almost no empirical research exists on this topic. In the current study, the authors examined the issue of psychopathy and insight by testing self and informant convergence and mean level differences across 3 indices of psychopathy in a community sample (N=64). Self- and informant-report psychopathy scores were also examined in relation to self and informant reports on traits from the Five-Factor Model (FFM). Convergence was strong across the 3 psychopathy indices and their respective factors (i.e., median r=.64), and there was only modest evidence that individuals rated themselves as less psychopathic than did informants. In addition, the same FFM domains-low Agreeableness and Conscientiousness-characterized individuals with psychopathic traits regardless of reporter. Psychopathic individuals appear capable of reporting accurately on psychopathic traits when there are no direct consequences to accurate reporting (i.e., sentencing). It may be that the lack of concern for the consequences of these traits has been mistaken for a lack of insight into them.     

Learning to learn causal models

Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the objects into categories and specifies the causal powers and characteristic features of these categories and the characteristic causal interactions between categories. A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments. Our results confirm that humans learn rapidly about the causal powers of novel objects, and we show that our framework accounts better for our data than alternative models of causal learning.     

Rater Personality and Performance Dimension Weighting in Making Overall Performance Judgments

Purpose  

The present study examined the effects of rater personality on the performance appraisal process. Specifically, we determined the relative weights that raters place on different performance dimensions when making overall performance evaluations, and examined whether rater personality influenced this weighting process. The literatures on social/political values and mate/friend selection were used as guiding frameworks in developing specific hypotheses.