Psychological essentialism, implicit theories, and intergroup relations

Research on implicit person theories shows that beliefs about the malleability of human attributes have important implications for social cognition, interpersonal behavior, and intergroup relations. We argue that these implications can be understood within the framework of psychological essentialism, which extends work on implicit theories in promising directions. We review evidence that immutability beliefs covary with a broader set of essentialist beliefs, and that these essentialist beliefs are associated with stereotyping and prejudice. We then present recent studies indicating that associations between implicit person theories and stereotyping may be explained in terms of essentialist beliefs, implying a significant role for these beliefs in the psychology of group perception. Finally, we propose ways in which research and theory on essentialist beliefs might clarify and advance research on implicit person theories.

The implicit structure of positive characteristics

The implicit structure of positive character traits was examined in two studies of 190 and 100 undergraduates. Participants judged the pairwise covariation or semantic similarity of 42 positive characteristics using a sorting or a rating task. Characteristics were drawn from a new classification of strengths and virtues, the Five-Factor Model, and a taxonomy of values. Participants showed consistent patterns of perceived association among the characteristics across the study conditions. Multidimensional scaling yielded three consistent dimensions underlying these judgments (“warmth vs. self-control,” “vivacity vs. decency,” and “wisdom vs. power”). Cluster analyses yielded six consistent groupings—“self-control,” “love,” “wisdom,” “drive,” “vivacity,” and “collaboration”—that corresponded only moderately to the virtue classification. All three taxonomies were systematically related to this implicit structure, but none captured it satisfactorily on its own. Revisions to positive psychology’s classification of strengths are proposed.

Implicit and multigrid procedures for steady-state computations with upwind algorithms

A computational study for the convergence acceleration of Euler and Navier-Stokes computations with upwind schemes has been conducted in a unified framework. It involves the flux-vector splitting algorithms due to Steger-Warming and Van Leer, the flux-difference splitting algorithms due to Roe and Osher and the hybrid algorithms, AUSM (Advection Upstream Splitting Method) and HUS (Hybrid Upwind Splitting). Implicit time integration with line Gauss-Seidel relaxation and multigrid are among the procedures which have been systematically investigated on an individual as well as cumulative basis. The upwind schemes have been tested in various implicit-explicit operator combinations such that the optimal among them can be determined based on extensive computations for two-dimensional flows in subsonic, transonic, supersonic and hypersonic flow regimes. In this study, the performance of these implicit time-integration procedures has been systematically compared with those corresponding to a multigrid accelerated explicit Runge-Kutta method. It has been demonstrated that a multigrid method employed in conjunction with an implicit time-integration scheme yields distinctly superior convergence as compared to those associated with either of the acceleration procedures provided that effective smoothers, which have been identified in this investigation, are prescribed in the implicit operator.

Bottleneck assignment problems under categorization

This paper considers two special cases of bottleneck grouped assignment problems when n jobs belong to m distinct categories (m < n). Solving these special problems through the available branch and bound algorithms will result in a heavy computational burden. Sequentially identifying nonopitmal variables, this paper provides more efficient methods for those cases. Propositions leading to the algorithms have been established. Numerical examples illustrate the respective algorithms.

Young at heart, old at work? Relations between age, (meta-)stereotypes, self-categorization, and retirement attitudes

The aims of this study were to examine how workers' negative age stereotypes (i.e., denying older workers' ability to develop) and negative meta-stereotypes (i.e., beliefs that the majority of colleagues feel negative about older workers) are related to their attitudes towards retirement (i.e., occupational future time perspective and intention to retire), and whether the strength of these relationships is influenced by workers' self-categorization as an “older” person. Results of a study among Dutch taxi drivers provided mixed support for the hypotheses. Negative meta-stereotypes, but not negative age stereotypes, were associated with fewer perceived opportunities until retirement and, in turn, a stronger intention to retire. Self-categorization moderated the relationships between negative age (meta-)stereotypes and occupational future time perspective. However, contrary to expectations, the relations were stronger among workers with a low self-categorization as an older person in comparison with workers with a high self-categorization in this regard. Overall, results highlight the importance of psychosocial processes in the study of retirement intentions and their antecedents.

Implicit and explicit interactions in video mediated collaboration

In this paper we report the results of a study comparing implicit-only and explicit-only interactions in a collaborative, video-mediated task with shared content. Expanding on earlier work which has typically only evaluated how implicit interaction can augment primarily explicit systems, we report issues surrounding control, anxiousness and negotiation in the context of video mediated collaboration. We conclude that implicit interaction has the potential to improve collaborative work, but that there are a multitude of issues that must first be negotiated.

The fully implicit stochastic-alpha method for stiff stochastic differential equations

A fully implicit integration method for stochastic differential equations with significant multiplicative noise and stiffness in both the drift and diffusion coefficients has been constructed, analyzed and illustrated with numerical examples in this work. The method has strong order 1.0 consistency and has user-selectable parameters that allow the user to expand the stability region of the method to cover almost the entire drift-diffusion stability plane. The large stability region enables the method to take computationally efficient time steps. A system of chemical Langevin equations simulated with the method illustrates its computational efficiency.

Place categorization and semantic mapping on a mobile robot

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot with-out environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot’s behaviour during navigation tasks. The system is made available to the community as a ROS module.

Activation and persistence of implicit causality information in spoken language comprehension

A visual world eye-tracking study investigated the activation and persistence of implicit causality information in spoken language comprehension. We showed that people infer the implicit causality of verbs as soon as they encounter such verbs in discourse, as is predicted by proponents of the immediate focusing account (Greene & McKoon, 1995; Koornneef & Van Berkum, 2006; Van Berkum, Koornneef, Otten, & Nieuwland, 2007). Interestingly, we observed activation of implicit causality information even before people encountered the causal conjunction. However, while implicit causality information was persistent as the discourse unfolded, it did not have a privileged role as a focusing cue immediately at the ambiguous pronoun when people were resolving its antecedent. Instead, our study indicated that implicit causality does not affect all referents to the same extent, rather it interacts with other cues in the discourse, especially when one of the referents is already prominently in focus.

The approximate parametrization of the curve of intersection of implicit surfaces

A clear definition of an approximate parametrization of the curve of intersection of (n-1) implicit surfaces in Rn is given. It is justified that marching methods yield such an approximation.

Similarity relations in visual search predict rapid visual categorization

How do we perform rapid visual categorization?It is widely thought that categorization involves evaluating the similarity of an object to other category items, but the underlying features and similarity relations remain unknown. Here, we hypothesized that categorization performance is based on perceived similarity relations between items within and outside the category. To this end, we measured the categorization performance of human subjects on three diverse visual categories (animals, vehicles, and tools) and across three hierarchical levels (superordinate, basic, and subordinate levels among animals). For the same subjects, we measured their perceived pair-wise similarities between objects using a visual search task. Regardless of category and hierarchical level, we found that the time taken to categorize an object could be predicted using its similarity to members within and outside its category. We were able to account for several classic categorization phenomena, such as (a) the longer times required to reject category membership; (b) the longer times to categorize atypical objects; and (c) differences in performance across tasks and across hierarchical levels. These categorization times were also accounted for by a model that extracts coarse structure from an image. The striking agreement observed between categorization and visual search suggests that these two disparate tasks depend on a shared coarse object representation.

Implicit scheme for meshless compressible Euler solver

In this paper, an implicit scheme is presented for a meshless compressible Euler solver based on the Least Square Kinetic Upwind Method (LSKUM). The Jameson and Yoon's split flux Jacobians formulation is very popular in finite volume methodology, which leads to a scalar diagonal dominant matrix for an efficient implicit procedure (Jameson & Yoon, 1987). However, this approach leads to a block diagonal matrix when applied to the LSKUM meshless method. The above split flux Jacobian formulation, along with a matrix-free approach, has been adopted to obtain a diagonally dominant, robust and cheap implicit time integration scheme. The efficacy of the scheme is demonstrated by computing 2D flow past a NACA 0012 airfoil under subsonic, transonic and supersonic flow conditions. The results obtained are compared with available experiments and other reliable computational fluid dynamics (CFD) results. The present implicit formulation shows good convergence acceleration over the RK4 explicit procedure. Further, the accuracy and robustness of the scheme in 3D is demonstrated by computing the flow past an ONERA M6 wing and a clipped delta wing with aileron deflection. The computed results show good agreement with wind tunnel experiments and other CFD computations.