On the limitations of fixed‐step‐size adaptive methods with response confidence

The family of (non‐parametric, fixed‐step‐size) adaptive methods, also known as ‘up–down’ or ‘staircase’ methods, has been used extensively in psychophysical studies for threshold estimation. Extensions of adaptive methods to non‐binary responses have also been proposed. An example is the three‐category weighted up–down (WUD) method (Kaernbach, 2001) and its four‐category extension (Klein, 2001). Such an extension, however, is somewhat restricted, and in this paper we discuss its limitations. To facilitate the discussion, we characterize the extension of WUD by an algorithm that incorporates response confidence into a family of adaptive methods. This algorithm can also be applied to two other adaptive methods, namely Derman's up–down method and the biased‐coin design, which are suitable for estimating any threshold quantiles. We then discuss via simulations of the above three methods the limitations of the algorithm. To illustrate, we conduct a small scale of experiment using the extended WUD under different response confidence formats to evaluate the consistency of threshold estimation.     

How Do Consumers Transfer Existing Knowledge? A Comparison of Analogy and Categorization Effects

The trend in recent consumer research has been to emphasize the similarities between analogy and categorization. In this investigation, we merge the literature on analogy, categorization, and structure mapping theory to reach a better understanding of their differences. In 3 experiments, we compare consumers’ responses to analogy and categorization cues and find that analogy places much greater constraints on knowledge transfer than categorization by focusing consumers on relational similarities. Illustrating this, the analogy group in Study 1 was just as likely as the categorization group to generate relational inferences, but much less likely to generate attribute inferences. Likewise, the results of Study 2 indicate that the analogy group restricted their processing of features lying outside the common relational system, leading to inferior recall relative to the categorization group. Building on these findings, Study 3 demonstrates that, under certain circumstances, analogy and categorization work together syn‐ergistically to enhance consumer memory.     

On Silencing and Systematicity: The Challenge of the Drowning Case

Silencing is a speech‐related harm. We here focus on one particular account of silencing offered by Jennifer Hornsby and Rae Langton. According to this account, silencing is systematically generated, illocutionary‐communicative failure (of a very specific sort). We here raise an apparent challenge to that account. In particular, we offer an example—the drowning case—that meets these conditions of silencing but does not intuitively seem to be an instance of it. First, we explore several conditions one might add to the Hornsby‐Langton account, but we argue that none are satisfactory. Then, we further explore the systematicity condition, which is insufficiently characterized in the current literature. Although we explore several promising ways to further characterize this condition, we ultimately conclude that more work needs to be done. Consequently, because this systematicity condition is under‐specified, the Hornsby‐Langton account of silencing is incomplete.     

Reaching Across the DMZ: Identity Uncertainty and Reunification on the Korean Peninsula

Drawing on uncertainty‐identity theory (Hogg, 2012), we explore the effects of uncertainty concerning a specific social identity on group identification and attitudes toward subgroup integration and separation in South Koreans' nested identity context (N = 148). All variables were measured. Path analysis revealed, as predicted, that superordinate identity uncertainty weakened superordinate identification and subgroup identity uncertainty weakened subgroup identification. We also found that subgroup identity uncertainty strengthened superordinate identification. This effect was stronger for those who perceived their superordinate group prototype and subgroup prototype to be distinct and nonoverlapping. Furthermore, superordinate identity uncertainty decreased reunification intentions by weakening superordinate identification. Subgroup identity uncertainty increased reunification intentions by strengthening superordinate identification only for those who perceived their superordinate group prototype and subgroup prototype to be distinct and nonoverlapping. Implications for uncertainty identity theory and intergroup relations are discussed.     

Influence analysis for the factor analysis model with ranking data

Influence analysis is an important component of data analysis, and the local influence approach has been widely applied to many statistical models to identify influential observations and assess minor model perturbations since the pioneering work of Cook (1986) . The approach is often adopted to develop influence analysis procedures for factor analysis models with ranking data. However, as this well‐known approach is based on the observed data likelihood, which involves multidimensional integrals, directly applying it to develop influence analysis procedures for the factor analysis models with ranking data is difficult. To address this difficulty, a Monte Carlo expectation and maximization algorithm (MCEM) is used to obtain the maximum‐likelihood estimate of the model parameters, and measures for influence analysis on the basis of the conditional expectation of the complete data log likelihood at the E‐step of the MCEM algorithm are then obtained. Very little additional computation is needed to compute the influence measures, because it is possible to make use of the by‐products of the estimation procedure. Influence measures that are based on several typical perturbation schemes are discussed in detail, and the proposed method is illustrated with two real examples and an artificial example.