Trends in Cognitive Sciences最新文献

Higher levels of physical activity are known to benefit aspects of brain health across the lifespan. However, the role of sedentary behavior (SB) is less well understood. In this review we summarize and discuss evidence on the role of SB on brain health (including cognitive performance, structural or functional brain measures, and dementia risk) for different age groups, critically compare assessment approaches to capture SB, and offer insights into emerging opportunities to assess SB via digital technologies. Across the lifespan, specific characteristics of SB (particularly whether they are cognitively active or cognitively passive) potentially act as moderators influencing the associations between SB and specific brain health outcomes. We outline challenges and opportunities for future research aiming to provide more robust empirical evidence on these observations.

In humans and other animals, individuals can actively respond to the specific needs of others. However, the neural circuits supporting helping behaviors are underspecified. In recent work, Zhang, Wu, and colleagues identified a new role for the anterior cingulate cortex (ACC) in the encoding and regulation of targeted helping behavior (allolicking) in mice.

Is the role of our sensory systems to represent the physical world as accurately as possible? If so, are our preferences and emotions, often deemed irrational, decoupled from these 'ground-truth' sensory experiences? We show why the answer to both questions is 'no'. Brain function is metabolically costly, and the brain loses some fraction of the information that it encodes and transmits. Therefore, if brains maximize objective functions that increase the fitness of their species, they should adapt to the objective-maximizing rules of the environment at the earliest stages of sensory processing. Consequently, observed 'irrationalities', preferences, and emotions stem from the necessity for our early sensory systems to adapt and process information while considering the metabolic costs and internal states of the organism.

Around the world, people engage in social protests aimed at addressing major societal problems. Certain protests have led to significant progress, yet other protests have resulted in little demonstrable change. We introduce a framework for evaluating the effectiveness of social protest made up of three components: (i) what types of action are being considered; (ii) what target audience is being affected; and (iii) what outcomes are being evaluated? We then review relevant research to suggest how the framework can help synthesize conflicting findings in the literature. This synthesis points to two key conclusions: that nonviolent protests are effective at mobilizing sympathizers to support the cause, whereas more disruptive protests can motivate support for policy change among resistant individuals.

We explore meta-perceptions (i.e., what we think others think about reality), their impact on intergroup conflict, and the interventions correcting these often-erroneous perceptions. We introduce a two (direct or indirect) by two (with or without framing) framework classifying these interventions, and we critically assess the benefits and constraints of these approaches.

Survival is a fundamental physiological drive, and neural circuits have evolved to prioritize actions that meet the energy demands of the body. This fine-tuning of goal-directed actions based on metabolic states ('allostasis') is deeply rooted in our brain, and hindbrain nuclei orchestrate the vital communication between the brain and body through the vagus nerve. Despite mounting evidence for vagal control of allostatic behavior in animals, its broader function in humans is still contested. Based on stimulation studies, we propose that the vagal afferent pathway supports transitions between survival modes by gating the integration of ascending bodily signals, thereby regulating reward-seeking. By reconceptualizing vagal signals as catalysts for goal-directed behavior, our perspective opens new avenues for theory-driven translational work in mental disorders.

Psychology is crucial for understanding human history. When aggregated, changes in the psychology of individuals - in the intensity of social trust, parental care, or intellectual curiosity - can lead to important changes in institutions, social norms, and cultures. However, studying the role of psychology in shaping human history has been hindered by the difficulty of documenting the psychological traits of people who are no longer alive. Recent developments in psychology suggest that cultural artifacts reflect in part the psychological traits of the individuals who produced or consumed them. Cultural artifacts can thus serve as 'cognitive fossils' - physical imprints of the psychological traits of long-dead people. We review the range of materials available to cognitive and behavioral scientists, and discuss the methods that can be used to recover and quantify changes in psychological traits throughout history.

We examine the opportunities and challenges of expert judgment in the social sciences, scrutinizing the way social scientists make predictions. While social scientists show above-chance accuracy in predicting laboratory-based phenomena, they often struggle to predict real-world societal changes. We argue that most causal models used in social sciences are oversimplified, confuse levels of analysis to which a model applies, misalign the nature of the model with the nature of the phenomena, and fail to consider factors beyond the scientist's pet theory. Taking cues from physical sciences and meteorology, we advocate an approach that integrates broad foundational models with context-specific time series data. We call for a shift in the social sciences towards more precise, daring predictions and greater intellectual humility.

In this article we investigate the societal implications of empathic artificial intelligence (AI), asking how its seemingly empathic expressions make people feel. We highlight AI's unique ability to simulate empathy without the same biases that afflict humans. While acknowledging serious pitfalls, we propose that AI expressions of empathy could improve human welfare.

Humans possess a remarkable ability to make decisions within real-world environments that are expansive, complex, and multidimensional. Human cognitive computational neuroscience has sought to exploit reinforcement learning (RL) as a framework within which to explain human decision-making, often focusing on constrained, artificial experimental tasks. In this article, we review recent efforts that use naturalistic approaches to determine how humans make decisions in complex environments that better approximate the real world, providing a clearer picture of how humans navigate the challenges posed by real-world decisions. These studies purposely embed elements of naturalistic complexity within experimental paradigms, rather than focusing on simplification, generating insights into the processes that likely underpin humans' ability to navigate complex, multidimensional real-world environments so successfully.