Trends in Cognitive Sciences最新文献

One major challenge of neuroscience is identifying structure in seemingly disorganized neural activity. Different types of structure have different computational implications that can help neuroscientists understand the functional role of a particular brain area. Here, we outline a unified approach to characterize structure by inspecting the representational geometry and the modularity properties of the recorded activity and show that a similar approach can also reveal structure in connectivity. We start by setting up a general framework for determining geometry and modularity in activity and connectivity and relating these properties with computations performed by the network. We then use this framework to review the types of structure found in recent studies of model networks performing three classes of computations.

To not only survive, but also thrive, the brain must efficiently orchestrate distributed computation across space and time. This requires hierarchical organisation facilitating fast information transfer and processing at the lowest possible metabolic cost. Quantifying brain hierarchy is difficult but can be estimated from the asymmetry of information flow. Thermodynamics has successfully characterised hierarchy in many other complex systems. Here, we propose the 'Thermodynamics of Mind' framework as a natural way to quantify hierarchical brain orchestration and its underlying mechanisms. This has already provided novel insights into the orchestration of hierarchy in brain states including movie watching, where the hierarchy of the brain is flatter than during rest. Overall, this framework holds great promise for revealing the orchestration of cognition.

Concepts of empathy, sympathy and compassion are often confused in a variety of literatures. This article proposes a pattern-theoretic approach to distinguishing compassion from empathy and sympathy. Drawing on psychology, Western philosophy, affective neuroscience, and contemplative science, we clarify the nature of compassion as a specific pattern of dynamically related factors that include physiological, cognitive, and affective processes, relational/intersubjective processes, and motivational/action tendencies. We also show that the dynamic nature of the compassion pattern is reflected in neuroscientific findings, as well as in compassion practice. The pattern theory of compassion allows us to make several clear distinctions between compassion, empathy, and sympathy.

Anhedonia is a reduction in enjoyment, motivation, or interest. It is common across mental health disorders and a harbinger of poor treatment outcomes. The enjoyment aspect, termed 'consummatory anhedonia', in particular poses fundamental questions about how the brain constructs rewards: what processes determine how intensely a reward is experienced? Here, we outline limitations of existing computational conceptualisations of consummatory anhedonia. We then suggest a richer reinforcement learning (RL) account of consummatory anhedonia with a reconceptualisation of subjective hedonic experience in terms of goal progress. This accounts qualitatively for the impact of stress, dysfunctional cognitions, and maladaptive beliefs on hedonic experience. The model also offers new views on the treatments for anhedonia.

Adolescents growing up in the 21st century face novel challenges that affect today's adolescents differently compared with previous generations. Adolescents' prosocial values and social engagement can contribute in unique ways to combatting societal challenges. Participatory research provides tools to transform adolescents' prosocial motivations into drivers for societal change.

Rapid advancements in artificial intelligence (AI) have driven interest in its potential application for lie detection. Unfortunately, the current approaches have primarily focused on technical aspects at the expense of a solid methodological and theoretical foundation. We discuss the implications thereof and offer recommendations for the development and regulation of AI-based deception detection.