Contemporary scholarship on mindfulness casts it as a form of purely non-evaluative engagement with experience. Yet, traditionally mindfulness was not intended to operate in a vacuum of dispassionate observation, but was seen as facilitative of eudaimonic mental states. In spite of this historical context, modern psychological research has neglected to ask the question of how the practice of mindfulness affects downstream emotion regulatory processes to impact the sense of meaning in life. To fill this lacuna, here we describe the , from which we derive a novel process model of mindful positive emotion regulation informed by affective science, in which mindfulness is proposed to introduce flexibility in the generation of cognitive appraisals by enhancing interoceptive attention, thereby expanding the scope of cognition to facilitate reappraisal of adversity and savoring of positive experience. This process is proposed to culminate in a deepened capacity for meaning-making and greater engagement with life.

The nature and origin of human diversity has been a source of intellectual curiosity since the beginning of human history. Contemporary advances in cultural and biological sciences provide unique opportunities for the emerging field of cultural neuroscience. Research in cultural neuroscience examines how cultural and genetic diversity shape the human mind, brain and behavior across multiple time scales: situation, ontogeny and phylogeny. Recent progress in cultural neuroscience provides novel theoretical frameworks for understanding the complex interaction of environmental, cultural and genetic factors in the production of adaptive human behavior. Here, we provide a brief history of cultural neuroscience, theoretical and methodological advances, as well as empirical evidence of the promise of and progress in the field. Implications of this research for population health disparities and public policy are discussed.

Many psychological theories suggest a link between self-regulation and identity, but until now a mechanistic account that suggests ways to improve self-regulation has not been put forth. The identity-value model (IVM) connects the idea from social psychology, that aspects of identity such as core values and group affiliations hold positive subjective value, to the process-focused account from decision-making and behavioral economics, that self-regulation is driven by a dynamic value integration across a range of choice attributes. Together, these ideas imply that goal-directed behaviors that are identity-relevant are more likely to be enacted because they have greater subjective value than identity-irrelevant behaviors. A central hypothesis, therefore, is that interventions that increase the degree to which a target behavior is perceived as self-relevant will improve self-regulation. Additionally, identity-based changes in self-regulation are expected to be mediated by changes in subjective value and its underlying neural systems. In this paper, we define the key constructs relevant to the IVM, explicate the model and delineate its boundary conditions, and describe how it fits with related theories. We also review disparate results in the research literature that might share identity-related value as a common underlying mechanism of action. We close by discussing questions about the model whose answers could advance the study of self-regulation.

Causal inference and generalizability matter. Historically, systematic designs emphasize causal inference, while representative designs focus on generalizability. Here, we suggest a transformative synthesis - - concurrently enhancing both causal inference and "built-in" generalizability by leveraging today's intelligent agent, virtual environments, and other technologies. In SRD, a "default control group" (DCG) can be created in a virtual environment by from real-world situations. Experimental groups can be built with systematic manipulations onto the DCG base. Applying (e.g., random assignment to DCG versus experimental groups) in SRD affords valid causal inferences. After explicating the proposed SRD synthesis, we delineate how the approach concurrently advances generalizability and robustness, cause-effect inference and precision science, a computationally-enabled cumulative psychological science supporting both "bigger theory" and concrete implementations grappling with tough questions (e.g., what is context?) and affording rapidly-scalable interventions for real-world problems.

While the past few decades have seen much work in psychopathology research that has yielded provocative insights, relatively little progress has been made in understanding the etiology of mental disorders. We contend that this is due to an overreliance on statistics and technology with insufficient attention to adequacy of experimental design, a lack of integration of data across various domains of research, and testing of theoretical models using relatively weak study designs. We provide a conceptual discussion of these issues and follow with a concrete demonstration of our proposed solution. Using two different disorders - depression and substance use - as examples, we illustrate how we can evaluate competing theories regarding their etiology by integrating information from various domains including latent variable models, neurobiology, and quasi-experimental data such as twin and adoption studies, rather than relying on any single methodology alone. More broadly, we discuss the extent to which such integrative thinking allows for inferences about the etiology of mental disorders, rather than focusing on descriptive correlates alone. Greater scientific insight will require stringent tests of competing theories and a deeper conceptual understanding of the advantages and pitfalls of methodologies and criteria we use in our studies.

The purpose of this comment is to put the Process Overlap Theory of Kovacs and Conway in the broadest possible context. I will briefly discuss the nature of intelligence testing and then relate it to the theory under consideration before making a few concluding comments.

All psychological research on morality relies on definitions of morality. Yet the various definitions often go unstated. When unstated definitions diverge, theoretical disagreements become intractable, as theories that purport to explain "morality" actually talk about very different things. This article argues for the importance of defining morality and considers four common ways of doing so: The , the , the , and the . Each has encountered difficulties. To surmount those difficulties, I propose a , and definition of morality: obligatory concerns with others' welfare, rights, fairness, and justice, as well as the reasoning, judgment, emotions, and actions that spring from those concerns. By articulating workable definitions of morality, psychologists can communicate more clearly across paradigms, separate definitional from empirical disagreements, and jointly advance the field of moral psychology.