Externalising behaviour from infancy to mid-adolescence: Latent profiles and early predictors

The overall aim of the current study was to identify typical trajectory classes of externalising behaviour, and to identify predictors present already in infancy that discriminate the trajectory classes. 921 children from a community sample were followed over 13 years from the age of 18 months. In a simultaneously estimated model, latent class analyses and multinomial logit regression analyses suggested a five-class solution for developmental patterns of externalising problem behaviours: High stable (18% of the children), High childhood limited (5%), Medium childhood limited (31%), Adolescent onset (30%), and Low stable (16%). Six risk factors measured at 18 months significantly discriminated among the classes. Family stress and maternal age discriminated the High stable class from all the other classes. The results suggest that focusing on enduring problems in the relationship with the partner and partners' health may be important in preventive and early intervention efforts.     

Building tasks from verbal instructions: An EEG study on practice trial exposure and task structure complexity during novel sequences of behavior

Configuring the mind to perform a novel task is an effortful process and one that is related to differences in general intelligence. Previous research has suggested that when participants are given instructions for a future task, representations of the rules contained in the instructions can influence subsequent behavior, even when the rules are not necessary to perform the upcoming task. One hypothesis for the continued activation of rule representations suggests that the practice trials participants perform before the experimental trials may instantiate the unnecessary task rules into participants’ mental model of the task (i.e., the task space). To test this hypothesis, EEGs were recorded as participants (N = 66) completed a multirule task designed to contrast the effects of increasing task structure complexity and practice trial exposure. The results showed that, as was predicted, performance is significantly poorer when more task rules are specified in the task instructions. Practice trials with the extra rule did not affect task performance, indicating that an unacted verbal instruction is sufficient to incorporate the rule into participants’ mental model of the task. The EEG results showed that instruction complexity was linked to a phasic increase in frontal theta synchronization but reduced posterior alpha and beta desynchronization. These changes in synchronization occurred during a time period of low intertrial phase coherence and suggest that participants were “checking the task rules” amidst a trial. This transient neural activity may reflect compensatory mechanisms for dealing with increased mind-wandering that is more likely to occur in complex tasks.     

Fixed- and random-effects meta-analytic structural equation modeling: Examples and analyses in R

Meta-analytic structural equation modeling (MASEM) combines the ideas of meta-analysis and structural equation modeling for the purpose of synthesizing correlation or covariance matrices and fitting structural equation models on the pooled correlation or covariance matrix. Cheung and Chan (Psychological Methods 10:40–64, 2005b, Structural Equation Modeling 16:28–53, 2009) proposed a two-stage structural equation modeling (TSSEM) approach to conducting MASEM that was based on a fixed-effects model by assuming that all studies have the same population correlation or covariance matrices. The main objective of this article is to extend the TSSEM approach to a random-effects model by the inclusion of study-specific random effects. Another objective is to demonstrate the procedures with two examples using the metaSEM package implemented in the R statistical environment. Issues related to and future directions for MASEM are discussed.     

Feedback training for facial image comparison

People are typically poor at matching the identity of unfamiliar faces from photographs. This observation has broad implications for face matching in operational settings (e.g., border control). Here, we report significant improvements in face matching ability following feedback training. In Experiment 1, we show cumulative improvement in performance on a standard test of face matching ability when participants were provided with trial-by-trial feedback. More important, Experiment 2 shows that training benefits can generalize to novel, widely varying, unfamiliar face images for which no feedback is provided. The transfer effect specifically benefited participants who had performed poorly on an initial screening test. These findings are discussed in the context of existing literature on unfamiliar face matching and perceptual training. Given the reliability of the performance enhancement and its generalization to diverse image sets, we suggest that feedback training may be useful for face matching in occupational settings.     

Integrating Theories of Psychological Needs‐as‐Requirements and Psychological Needs‐as‐Motives: A Two Process Model

Two conceptions of psychological needs predominate within contemporary motivational science. Motive disposition theory conceives of needs as behavioral motives which direct behavior (needs‐as‐motives), while self‐determination theory conceives of needs as universally required experiences for optimal functioning (needs‐as‐requirements). Until recently, these perspectives on psychological needs have proceeded without much intersection, despite the fact that they address the same fundamental concept. Here we summarize the Two Process Model of Psychological Needs, which attempts to bridge these two conceptions. We argue that psychological needs are best defined as tendencies to seek out certain basic types of psychosocial experiences, to a somewhat varying extent across individuals, and to feel good and thrive when those basic experiences are obtained, to the same extent across individuals. We suggest that this definition allows a reconciliation of needs‐as‐motives and needs‐as‐requirements perspectives and a more consilient science of human motivation. Empirical support for the TPM is also summarized.     

When past meets present: the relationship between website‐induced nostalgia and well‐being

The present research examined whether social media websites increase feelings of nostalgia, and whether this nostalgic reverie promotes psychological and social health. Specifically, in comparison to control conditions, participants exposed to the websites Dear Old Love and Dear Photograph reported greater feelings of nostalgia (Studies 1–3), positive affect (Studies 1 and 3), life satisfaction (Study 1), and relationship need satisfaction (Study 2). Further, mediational analyses revealed that increased thoughts of nostalgia heightened subjective well‐being and social connectedness. Study 3 showed that the relationship between nostalgia and positive affect was specific to the Dear Photograph website and did not generalize to any website focused on close relationships. The implications of this research for nostalgia, internet use, and well‐being are further discussed.     

The time course of location-avoidance learning in fear of spiders

Two experiments were designed to study the time course of avoidance learning in spider fearfuls (SFs) under controlled experimental conditions. To achieve this, we employed an immersive virtual environment (IVE): While walking freely through a virtual art museum to search for specific paintings, the participants were exposed to virtual spiders. Unbeknown to the participants, only two of four museum rooms contained spiders, allowing for avoidance learning. Indeed, the more SF the participants were, the faster they learned to avoid the rooms that contained spiders (Experiment. 1), and within the first six trials, high fearfuls already developed a preference for starting their search task in rooms without spiders (Experiment 2). These results illustrate the time course of avoidance learning in SFs, and they speak to the usefulness of IVEs in fundamental anxiety research.     

Identity From Variation: Representations of Faces Derived From Multiple Instances

Research in face recognition has tended to focus on discriminating between individuals, or “telling people apart.” It has recently become clear that it is also necessary to understand how images of the same person can vary, or “telling people together.” Learning a new face, and tracking its representation as it changes from unfamiliar to familiar, involves an abstraction of the variability in different images of that person's face. Here, we present an application of principal components analysis computed across different photos of the same person. We demonstrate that people vary in systematic ways, and that this variability is idiosyncratic—the dimensions of variability in one face do not generalize well to another. Learning a new face therefore entails learning how that face varies. We present evidence for this proposal and suggest that it provides an explanation for various effects in face recognition. We conclude by making a number of testable predictions derived from this framework.