Cognitive Neuroscience最新文献

Alzheimer's disease and related dementias (ADRDs) are a pressing public health concern worldwide, and there is a need for innovative strategies to understand and address its impact. The integrated neurocognitive model introduced by Federico et al. (2025) integrates neural systems with everyday technology use and offers valuable implications for ADRD prevention, detection, and care. Future validation of the model could clarify mechanisms of resilience for late life cognitive health and inform clinical detection and intervention.

Federico et al. (2025) propose a comprehensive neurocognitive model of technological cognition, i.e. the brain's capability to generate and interact with technology. We situate their framework within the Affordance Competition Hypothesis, extending the notion of affordances from physical tools to technological devices. We highlight how specific aspects of technological cognition may rely on the interactions between parietal and temporal networks together with their interplay with the social brain. Understanding how these networks interact offers a promising path to explain how technological cognition emerges depending on the characteristics of the adopted tool and of the context in which it is used.

The tenuicortical supramarginal gyrus, i.e., area PFt, as a node controlling tool use, multisensory integration, or even some aspects of language, can be considered a hub for causal/technical reasoning and, putatively, the control of technological artifacts. By analogy to its involvement in motor-to-mechanical goal-directed physical transformations associated with complex tool use, we suggest that it should also be invoked in more complex mental-to-digital goal-directed conceptual transformations supported by digital devices. Conversely, a simple 'outsourcing' of mental functions to a handy digital tool - by clicking or touch-selecting for a desired outcome - will not suffice to its engagement.

A wealth of studies report evidence that occipitotemporal cortex tessellates into 'category-selective' brain regions that are apparently specialized for representing ecologically important visual stimuli like faces, bodies, scenes, and tools. Here, we argue that while valuable insights have been gained through the lens of category-selectivity, a more complete view of visual function in occipitotemporal cortex requires centering the behavioral relevance of visual properties in real-world environments rather than stimulus category. Focusing on behavioral relevance challenges a simple mapping between stimulus and visual function in occipitotemporal cortex because the environmental properties relevant to a behavior are visually diverse and how a given property is represented is modulated by our goals. Grounding our thinking in behavioral relevance rather than category-selectivity raises a host of theoretical and empirical issues that we discuss while providing proposals for how existing tools can be harnessed in this light to better understand visual function in occipitotemporal cortex.

Understanding how the human brain generates, utilizes, and adapts to technology is one of our most urgent scientific questions today. Recent advances in cognitive neuroscience reveal a complex neurocognitive structure that underpins human interaction with technology. Here, we propose an integrated framework that considers the interplay of causal reasoning, semantic cognition, visuospatial skills, sensorimotor knowledge, and social learning in shaping our technological abilities. Drawing on neuroimaging, lesion studies, and evolutionary evidence, we identify key brain regions that act as specialized processors and integrative hubs within a distributed network supporting 'technological cognition.' We argue that different categories of technologies - mechanical versus digital - activate separate neural subsystems, reflecting their diverse cognitive demands. Ultimately, we situate technological cognition within the broader concepts of embodied cognition and extended mind theories, suggesting that technology can expand human mental capacities and actively influence the structure and functioning of the mind itself. This framework advocates for an interdisciplinary approach to deepen our understanding of how technology influences and integrates with human cognition.

Murphy's discussion (2025) of his recent ROSE model includes explicit linking hypotheses connecting computational, algorithmic, and implementational levels in the study of language and its neurobiological basis. Here, I argue that establishing the neural basis of the abstract principles underlying natural language syntax will require new data from sign languages, tactile sign languages, as well as typologically diverse spoken languages. The assumption of modality-independent processes for structure building lies at the heart of ROSE, but the proposed correlates for hierarchical and sequential operations must be subjected to empirical test across languages and modalities in the future.

This paper asks what predictive processing models of brain function can learn from the success of transformer architectures. We suggest that the reason transformer architectures have been successful is that they implicitly commit to a non-Markovian generative model - in which we need memory to contextualize our current observations and make predictions about the future. Interestingly, both the notions of working memory in cognitive science and transformer architectures rely heavily upon the concept of attention. We will argue that the move beyond Markov is crucial in the construction of generative models capable of dealing with much of the sequential data - and certainly language - that our brains contend with. We characterize two broad approaches to this problem - deep temporal hierarchies and autoregressive models - with transformers being an example of the latter. Our key conclusions are that transformers benefit heavily from their use of embedding spaces that place strong metric priors on an implicit latent variable and utilize this metric to direct a form of attention that highlights the most relevant, and not only the most recent, previous elements in a sequence to help predict the next.

ROSE is a rare example of a neurocomputational model of language that attempts, and partly manages, to align a formal theory of syntax and parsing with an oscillations-based 'neural code' that could implement the required operations. ROSE successfully reconciles hierarchical and predictive syntactic processing, but I argue that models of language in the brain should make room for the possibility that meaning may also be derived in the absence of any syntactic computation, be it hierarchical or predictive.