Firearm Storage Practices and Risk Perceptions Among a Nationally Representative Sample of U.S. Veterans With and Without Self‐Harm Risk Factors

Despite the disproportionate use of firearms in Veteran suicides and the well‐established link between firearm access and suicide, little is known about how Veterans store their firearms or what they think about the relationship between firearm access and suicide risk. Using data from 2015 nationally representative online survey (response rate 60.9%), we compare characteristics of Veteran firearm owners with and without self‐harm risk factors with respect to how they store their firearms and their beliefs about suicide risk related to firearms. Overall, one in three U.S. Veteran firearm owners store household firearms loaded and unlocked, one in twenty believe that a firearm increases household suicide risk, and one in four consider their loaded and unlocked firearm to be inaccessible to suicidal household members. Storage practices and risk perceptions are similar among those with and without self‐reported suicide risk factors. Affecting risk perceptions may be a critical aspect of interventions addressing lethal means safety among U.S. Veterans.     

Learning How to Generalize

Generalization is a fundamental problem solved by every cognitive system in essentially every domain. Although it is known that how people generalize varies in complex ways depending on the context or domain, it is an open question how people learn the appropriate way to generalize for a new context. To understand this capability, we cast the problem of learning how to generalize as a problem of learning the appropriate hypothesis space for generalization. We propose a normative mathematical framework for learning how to generalize by learning inductive biases for which properties are relevant for generalization in a domain from the statistical structure of features and concepts observed in that domain. More formally, the framework predicts that an ideal learner should learn to generalize by either taking the weighted average of the results of generalizing according to each hypothesis space, with weights given by how well each hypothesis space fits the previously observed concepts, or by using the most likely hypothesis space. We compare the predictions of this framework to human generalization behavior with three experiments in one perceptual (rectangles) and two conceptual (animals and numbers) domains. Across all three studies we find support for the framework's predictions, including individual‐level support for averaging in the third study.     

Procedural learning across the lifespan: A systematic review with implications for atypical development

This systematic review aimed to investigate procedural learning across the lifespan in typical and atypical development. Procedural learning is essential for the development of everyday skills, including language and communication skills. Although procedural learning efficiency has been extensively studied, there is no consensus yet on potential procedural learning changes during development and ageing. Currently, three conflicting models regarding this trajectory exist: (1) a model of age invariance; (2a) a model with a peak in young adulthood; and (2b) a model with a plateau in childhood followed by a decline. The aims of this study were (1) to investigate this debate on procedural learning across the lifespan by systematically reviewing evidence for each model from studies using the serial reaction time task; and (2) to review procedural learning in autism spectrum disorder (ASD) and specific language impairment (SLI), two developmental disorders characterized by deficits in communication skills, in the light of these models. Our findings on typical development strongly support a model of age-related changes (Model 2a or 2b) and show that mixed findings regarding the developmental trajectory during childhood can be explained by methodological differences across studies. Applying these conclusions to systematic reviews of studies of ASD and SLI makes it clear that there is a strong need for the inclusion of multiple age groups in these clinical studies to model procedural learning in atypical development. Clinical implications of the findings are discussed. Future research should focus on the role of declarative learning in both typical and atypical development.     

Vectors into the Future of Mass and Interpersonal Communication Research: Big Data,Social Media,and Computational Social Science

Simultaneous developments in big data, social media, and computational social science have set the stage for how we think about and understand interpersonal and mass communication. This article explores some of the ways that these developments generate 4 hypothetical “vectors”—directions—into the next generation of communication research. These vectors include developments in network analysis, modeling interpersonal and social influence, recommendation systems, and the blurring of distinctions between interpersonal and mass audiences through narrowcasting and broadcasting. The methods and research in these arenas are occurring in areas outside the typical boundaries of the communication discipline but engage classic, substantive questions in mass and interpersonal communication.