Trends in Cognitive Sciences最新文献

Advances in artificial intelligence offer an enticing solution to a global problem: perhaps interacting with large language models (LLMs) can help alleviate loneliness. Although promising, evidence from cognitive neuroscience suggests that LLM interactions cannot satisfy psychological and physical needs for proximity. Addressing loneliness requires societal action, not simulating human relationships with artificial surrogates.

Cognitive neuroscience research has made tremendous progress over the past decade in addressing how episodic memory (EM; memory for unique past experiences) supports our ability to understand real-world events. Despite this progress, we still lack a computational modeling framework that is able to generate precise predictions regarding how EM will be used when processing high-dimensional naturalistic stimuli. Recent work in machine learning that augments large language models (LLMs) with external memory could potentially accomplish this, but current popular approaches are misaligned with human memory in various ways. This review surveys these differences, suggests criteria for benchmark tasks to promote alignment with human EM, and ends with potential methods to evaluate predictions from memory-augmented models using neuroimaging techniques.

Neuroscience has probed only a sliver of the rich cognitive, emotional, and social behaviors that enable primates to thrive in the real world. Technological breakthroughs allow us to quantify these behaviors alongside wireless neural recordings. New studies reveal that neural activity is intricately bound to movement and is profoundly modulated by behavioral context, emotional states, and social dynamics. We frame our review of primate neuroethology around Niko Tinbergen's four foundational questions - function, mechanism, development, and evolution - to unify classic ethological insights with modern neuroscience tools. We demonstrate that investigating natural behavior promises deep insights into primate cognition, which are relevant for advanced brain-machine interfaces, improved therapies for neurological disorders, and deeper understanding of natural and artificial intelligence.

A core strength of computational psychiatry is its focus on theory-driven research, in which cognitive processes are precisely quantified using computational models that formalize specific theoretical mechanisms. However, the data used in these studies often come from traditional laboratory-based cognitive tasks, which have unclear ecological validity. In this review we propose that the same theoretical frameworks and computational models can be applied to real-world data such as experience sampling, passive data, and digital-behavior data (e.g., online activity such as on social media). In turn, modeling real-world data can benefit from a theory-driven computational approach to move from purely predictive to explanatory power. We illustrate these points using emerging studies and discuss the challenges and opportunities of using real-world data in computational psychiatry.

Our understanding of the neural basis of consciousness is mostly restricted to large-scale brain activity patterns as measured by methods such as functional magnetic resonance imaging (fMRI) and magneto/electro-encephalography (M/EEG). In contrast, we lack even basic understanding of circuit-level mechanisms supporting consciousness - particularly in humans - despite the fundamental role that such mechanisms likely play in instantiating larger-scale brain activity patterns supporting conscious states and contents. Here, we review what progress has been made on circuit-level theories of consciousness (e.g., apical amplification theory, dendritic integration theory) and argue that such theories can be tested in humans using recently developed, state-of-the-art methods. Doing so will further facilitate translation of consciousness science into clinical settings and strengthen the bridge between circuit- and network-level theories of consciousness.

Working memory (WM) is crucial for planning, reasoning, and learning, and is one of the most extensively studied topics in cognitive psychology and neuroscience. However, the concept of a WM subsystem for motor content - or 'motor working memory' (MWM) - is generally neglected, even though MWM likely plays an important role in everyday action. Here, we synthesize evidence that the brain both prospectively and retrospectively maintains motor content in WM and propose that MWM carries out multiple key computational functions in motor control and skill learning. A focused research program on MWM is overdue and will deepen our understanding of the links between cognition and action.

Over the last decade, new research has shown how human collectives can develop technologies that no single individual could discover on their own. However, this research often overlooks how technology can become so complex that individuals cannot operate it on their own. At this level of technological complexity, distributing cognition is a necessary process for reducing cognitive load on individuals. Yet distributing cognition also imposes coordination costs as technological systems become larger and the individuals in these systems become more specialized. We describe a sprawling set of cultural innovations that facilitate cognitive distribution by reducing cognitive load, reducing coordination costs, or doing both. Preliminary evidence suggests that these cultural innovations co-evolve with technological complexity.

The human olfactory system is unusual. It deviates from the classical structure and function of other sensory cortices, and many of its basic computations remain mysterious. These idiosyncrasies have challenged the development of a clear and comprehensive theoretical framework in olfactory neuroscience. To address this challenge, we develop a theory of olfactory predictive coding that aims to unify diverse olfactory phenomena. Under this scheme, the olfactory system is not merely passively processing sensory information. Instead, it is actively issuing predictions about sensory inputs before they even arrive. We map this conceptual framework onto the micro- and macroscale neurobiology of the human olfactory system and review a variety of neurobiological, computational, and behavioral evidence in support of this scheme.