Trends in Cognitive Sciences最新文献

Disputes between rival theories of consciousness have often centered on whether perceptual contents can be decoded from the prefrontal cortex (PFC). Failures to decode from the PFC are taken to challenge 'cognitive' theories of consciousness such as the global workspace theory and higher-order monitoring theories, and decoding successes have been taken to confirm these theories. However, PFC decoding shows both too much and too little. Too much because cognitive theories of consciousness do not need PFC rerepresentation of perceptual contents since pointers to perceptual representations suffice. Too little because there is evidence that PFC decoding of perceptual content reflects postperceptual cognitive representation, such as thoughts that have those perceptual contents rather than conscious percepts.

Cognitive impairments are common in Parkinson's disease (PD). We have linked this deficit to attenuated midfrontal 1-8-Hz activity that fails to engage cortical cognitive networks. We discuss the consequences of these impairments and how they might be leveraged for PD-specific neurophysiological markers and for novel brain stimulation paradigms.

Emerging evidence highlights the importance of metacognition - the capacity for insight into the reliability and fallibility of our own knowledge and thought - in politically contested domains. The present synthesis elucidates why metacognition matters in politically charged contexts and its potential impact on how individuals form beliefs, process evidence, and make decisions.

The study of human working memory (WM) holds significant importance in neuroscience; yet, exploring the role of the medial temporal lobe (MTL) in WM has been limited by the technological constraints of noninvasive methods. Recent advancements in human intracranial neural recordings have indicated the involvement of the MTL in WM processes. These recordings show that different regions of the MTL are involved in distinct aspects of WM processing and also dynamically interact with each other and the broader brain network. These findings support incorporating the MTL into models of the neural basis of WM. This integration can better reflect the complex neural mechanisms underlying WM and enhance our understanding of WM's flexibility, adaptability, and precision.

Cognitive scientists often infer multidimensional representations from data. Whether the data involve text, neuroimaging, neural networks, or human judgments, researchers frequently infer and analyze latent representational spaces (i.e., embeddings). However, the properties of a latent representation (e.g., prediction performance, interpretability, compactness) depend on the inference procedure, which can vary widely across endeavors. For example, dimensions are not always globally interpretable and the dimensionality of different embeddings may not be readily comparable. Moreover, the dichotomy between multidimensional spaces and purportedly richer representational formats, such as graph representations, is misleading. We review what the different notions of dimension in cognitive science imply for how these latent representations should be used and interpreted.

Humans have a protracted postnatal helplessness period, typically attributed to human-specific maternal constraints causing an early birth when the brain is highly immature. By aligning neurodevelopmental events across species, however, it has been found that humans are not born with especially immature brains compared with animal species with a shorter helpless period. Consistent with this, the rapidly growing field of infant neuroimaging has found that brain connectivity and functional activation at birth share many similarities with the mature brain. Inspired by machine learning, where deep neural networks also benefit from a 'helpless period' of pre-training, we propose that human infants are learning a foundation model: a set of fundamental representations that underpin later cognition with high performance and rapid generalisation.

Many human behavioral and brain imaging studies have used narratively structured stimuli (e.g., written, audio, or audiovisual stories) to better emulate real-world experience in the laboratory. However, narratives are a special class of real-world experience, largely defined by their causal connections across time. Much contemporary neuroscience research does not consider this key property. We review behavioral and neuroscientific work that speaks to how causal structure shapes comprehension of and memory for narratives. We further draw connections between this work and reinforcement learning, highlighting how narratives help link causes to outcomes in complex environments. By incorporating the plausibility of causal connections between classes of actions and outcomes, reinforcement learning models may become more ecologically valid, while simultaneously elucidating the value of narratives.

People regularly encounter various types of conflict. Here, we ask if, and, if so, how, different types of conflict, from lab-based Stroop conflicts to everyday-life self-control or moral conflicts, are related to one other. We present a framework that assumes that action-goal representations are hierarchically organized, ranging from concrete actions to abstract goals. The framework's key assumption is that conflicts involving more abstract goals (e.g., self-control/moral conflict) are embedded in a more complex action space; thus, to resolve such conflicts, people need to consider more associated goals and actions. We discuss how differences in complexity impact conflict resolution mechanisms and the costs/benefits of resolving conflicts. Altogether, we offer a new way to conceptualize and analyze conflict regulation across different domains.