No face-like processing for objects-of-expertise in three behavioural tasks

In the debate between expertise and domain-specific explanations of "special" processing for faces, a common belief is that behavioural studies support the expertise hypothesis. The present article refutes this view, via a combination of new data and review. We tested dog experts with confirmed good individuation of exemplars of their breed-of-expertise. In all experiments, standard results were confirmed for faces. However, dog experts showed no face-like processing for dogs on three behavioural tasks (inversion; the composite paradigm; and sensitivity to contrast reversal). The lack of holistic/configural processing, indicated in the first two of these tests, is shown by review to be consistent rather than inconsistent with previous studies of objects-of-expertise.     

Combining versus analyzing multiple causes: how domain assumptions and task context affect integration rules

In everyday life, people typically observe fragments of causal networks. From this knowledge, people infer how novel combinations of causes they may never have observed together might behave. I report on 4 experiments that address the question of how people intuitively integrate multiple causes to predict a continuously varying effect. Most theories of causal induction in psychology and statistics assume a bias toward linearity and additivity. In contrast, these experiments show that people are sensitive to cues biasing various integration rules. Causes that refer to intensive quantities (e.g., taste) or to preferences (e.g., liking) bias people toward averaging the causal influences, whereas extensive quantities (e.g., strength of a drug) lead to a tendency to add. However, the knowledge underlying these processes is fallible and unstable. Therefore, people are easily influenced by additional task-related context factors. These additional factors include the way data are presented, the difficulty of the inference task, and transfer from previous tasks. The results of the experiments provide evidence for causal model and related theories, which postulate that domain-general representations of causal knowledge are influenced by abstract domain knowledge, data-driven task factors, and processing difficulty.     

A new perspective on visual word processing efficiency

As a fundamental part of our daily lives, visual word processing has received much attention in the psychological literature. Despite the well established advantage of perceiving letters in a word or in a pseudoword over letters alone or in random sequences using accuracy, a comparable effect using response times has been elusive. Some researchers continue to question whether the advantage due to word context is perceptual. We use the capacity coefficient, a well established, response time based measure of efficiency to provide evidence of word processing as a particularly efficient perceptual process to complement those results from the accuracy domain.     

The Chinese Version of the Revised Creativity Domain Questionnaire (CDQ‐R): First Evidence for its Factorial Validity and Systematic Association with the Big Five

The present study examines the factor structure of a Chinese version of the Revised Creativity Domain Questionnaire (CDQ‐R; Kaufman, Waterstreet, Ailaouni, Whitcomb, Roe, & Riggs, 2009) as well as its relation to Big Five personality traits within a Chinese sample (N = 787). Analyses indicate the appropriateness of the Chinese version of the CDQ‐R in terms of internal consistency, factorial validity as well as convergent and divergent validity concerning the Big Five personality factors. Revealing some culture‐specific variation, confirmatory factor analysis indicated a slight superiority of a five‐factor model for this Chinese sample over the existing four‐factor model established with American samples. This higher level of differentiation in terms of one factor of the creativity domain could be explained on the basis of the specific characteristics of the Chinese culture.     

Delimiting Family in Syntheses of Research on Childhood Chronic Conditions and Family Life

Synthesis of family research presents unique challenges to investigators who must delimit what will be included as a family study in the proposed review. In this paper, the authors discuss the conceptual and pragmatic challenges of conducting systematic reviews of the literature on the intersection between family life and childhood chronic conditions. A proposed framework for delimiting the family domain of interest is presented. The framework addresses both topical salience and level of relevance and provides direction to future researchers, with the goal of supporting the overall quality of family research synthesis efforts. For users of synthesis studies, knowledge of how investigators conceptualize the boundaries of family research is important contextual information for understanding the limits and applicability of the results.     

Knowledge as a formative construct: A good alpha is not always better

Measurements of domain knowledge very often use and report Cronbach's alpha or similar indicators of internal consistency for test construction. In this short article, we argue that this approach is often at odds with the theoretical conception of knowledge underlying the measure. While domain knowledge is usually described as a formative construct (formed by the manifest observations) theoretically, the use of Cronbach's alpha to construct and evaluate an empirical measure implies a reflective model (the construct reflects in manifest behaviors). After illustrating the difference between reflective and formative models, we illustrate how this mismatch between theoretical conception and empirical operationalization can have substantial implications for the assessment and modeling of domain knowledge. Specifically, the construct may be operationalized too narrowly or even be misinterpreted by applying criteria for item selection that focus on homogeneity such as Cronbach's alpha. Rather than maximizing items internal consistency, researchers constructing measures of domain knowledge should, therefore, make strong arguments for the theoretical merit of their items even if they are not correlated to each other.