Trends in Cognitive Sciences最新文献

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.

Antisocial behaviour (ASB) incurs substantial costs to the individual and society. Cognitive neuroscience has the potential to shed light on developmental risk for ASB, but it cannot achieve this potential in an 'essentialist' framework that focuses on the brain and cognition isolated from the environment. Here, we present the case for studying the social transactional and iterative unfolding of brain and cognitive development in a relational context. This approach, which we call the study of the 'embedded brain', is needed to fully understand how risk for ASB arises during development. Concentrated efforts are required to develop and unify methods to achieve this approach and reap the benefits for improved prevention and intervention of ASB.

An absence of population-representative participant samples has limited research in healthy brain aging. We highlight examples of what can be gained by enrolling more diverse participant cohorts, and propose recommendations for specific reforms, both in terms of how researchers accomplish this goal and how institutions support and benchmark these efforts.

Our visual system consciously processes only a subset of the incoming information. Selective attention allows us to prioritize relevant inputs, and can be allocated to features, locations, and objects. Recent advances in feature-based attention suggest that several selection principles are shared across these domains and that many differences between the effects of attention on perceptual processing can be explained by differences in the underlying representational structures. Moving forward, it can thus be useful to assess how attention changes the structure of the representational spaces over which it operates, which include the spatial organization, feature maps, and object-based coding in visual cortex. This will ultimately add to our understanding of how attention changes the flow of visual information processing more broadly.

Despite significant improvements in our understanding of brain diseases, many barriers remain. Cognitive neuroscience faces four major challenges: complex structure–function associations; disease phenotype heterogeneity; the lack of transdiagnostic models; and oversimplified cognitive approaches restricted to the laboratory. Here, we propose a synergetics framework that can help to perform the necessary dimensionality reduction of complex interactions between the brain, body, and environment. The key solutions include low-dimensional spatiotemporal hierarchies for brain-structure associations, whole-brain modeling to handle phenotype diversity, model integration of shared transdiagnostic pathophysiological pathways, and naturalistic frameworks balancing experimental control and ecological validity. Creating whole-brain models with reduced manifolds combined with ecological measures can improve our understanding of brain disease and help identify novel interventions. Synergetics provides an integrated framework for future progress in clinical and cognitive neuroscience, pushing the boundaries of brain health and disease toward more mature, naturalistic approaches.