Do wild New Caledonian crows (<Emphasis Type="Italic">Corvus moneduloides</Emphasis>) attend to the functional properties of their tools?

New Caledonian crows are the most proficient non-hominin tool manufacturers but the cognition behind their remarkable skills remains largely unknown. Here we investigate if they attend to the functional properties of the tools that they routinely use in the wild. Pandanus tools have natural barbs along one edge that enable them to function as hooking implements when the barbs face backwards from the working tip. In experiment 1 we presented eight crows with either a non-functional (‘upside-down’) or a functional pandanus tool in a baited hole. Four of the crows never flipped the tools. The behaviour of the four flipping birds suggested that they had a strategy of flipping a tool when it was not working. Observations of two of the eight crows picking up pandanus tools at feeding tables in the wild supported the lack of attention to barb direction. In experiment 2 we gave six of the eight crows a choice of either a barbed or a barbless pandanus tool. Five of the crows chose tools at random, which further supported the findings in experiment 1 that the crows paid little or no attention to the barbs. In contrast, a third experiment found that seven out of eight crows flipped non-functional stick tools significantly more than functional ones. Our findings indicate that the crows do not consistently attend to the presence or orientation of barbs on pandanus tools. Successful pandanus tool use in the wild seems to rely on behavioural strategies formed through associative learning, including procedural knowledge about the sequence of operations required to make a successful pandanus tool. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

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.     

Unveiling the link between facets of positive nonverbal communication and perceived leader effectiveness: A moderated mediation model

Previous experimental research has examined the important role of combined facets of kinesics and paralanguage in engendering perceived leader effectiveness. However, little is known about the distinct impact of these overarching forms of nonverbal communication and the underlying mechanisms that account for this relationship. Hence, conducting a scenario and a survey, we examined the affect of leader positive kinesics and paralanguage on perceived leader effectiveness through trust in leader. Furthermore, we investigated if these indirect effects are contingent on a novel personality trait--namely, suspicion. The present results demonstrated that suspicion moderated the mediating effect of trust in leader on the relationship between both kinesics and paralanguage and perceived leader effectiveness. Implications and future research are also 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.