Cognitive Neuroscience最新文献

The provocative review by Richie et al. (this issue) provides a platform for reflection on developing new experimental designs and data analysis methods. Here I offer support for their ideas, and add some additional considerations related to: (1) environmental image statistics, (2) multisensory experimentation, (3) embracing non-linearities in brain-body function and tackling data with non-linear analysis approaches; (4) perturbing mature cortical networks with Focused Ultrasound (FUS) or Transcranial Magnetic Stimulation (TMS) guided by functional magnetic resonance imaging (fMRI) activation; and (5) considering spatial scales and aberrant scaffolding in human development.

Decades of work demonstrate that the ventral temporal cortex (VTC) comprises category selective regions. Ritchie et al. urge a shift in perspective: new research should be grounded in behavioral relevance, not category selectivity. Here, we outline how leveraging, not shifting away from category selectivity, expands our understanding of brain function, complex cognition, and development. Further, while we agree that naturalistic paradigms will accelerate progress in this field, given category selectivity is central to VTC's information processing, we suggest future work to examine information transfer from VTC initial object recognition computation to other cortices for facilitating complex human behavior.

Ritchie and colleagues propose that the functional organization of higher visual cortex is best understood through the lens of behavioral relevance, advocating for a shift away from theories that center around category selectivity. Building on this, I suggest the statistical structure of visual inputs acts as an additional critical constraint on visual cortex, and that a complete understanding of visual system organization must account for input statistics and how they interact with behavioral relevance. I discuss this using cortical food selectivity as a case study, and additionally describe how deep neural networks can provide new avenues for testing these theories.

Ritchie et al. (this issue) argue that a deeper understanding of occipitotemporal cortex (OTC) requires shifting emphasis from category selectivity to behavioral relevance. They suggest that focusing on categories such as faces, bodies, or scenes is too narrow and overlooks how OTC supports flexible, goal-directed behavior. We agree that linking neural representations to behavior is essential but caution against treating category selectivity and behavioral relevance as opposing views. Category selectivity provides valuable insight into how cortical representations are organized to support behavior, and recent advances in computational modeling, particularly with deep neural networks, offer a powerful framework for probing this relationship.

When using novel tools with low semantic content, the left inferior-frontal-gyrus (IFG) plays a role. We argue that this activation is not purely specific to novel tool use but rather represents part of a cross-domain cognitive network supporting sequential planning processes. The IFG does not only manage information flow between distributed areas but functionally contributes by maintaining focus on the intended target state and supporting the processing, monitoring, and adjustment of steps needed to achieve that goal. These cognitive functions are particularly important when compensation for reduced tool-related semantic knowledge is needed during the usage of novel tools and technologies.

Ritchie et al. argue that the traditional framework of category selectivity has limited value for understanding the organization of the ventral visual stream and propose shifting focus toward behavioral relevance and examining vision under naturalistic task conditions. While I agree with many of their points, I expand on their discussion of category selectivity, as well as the drivers of ventral stream organization, from a nonhuman primate perspective.

The category selectivity model has shaped our understanding of the organization of object-related information in the occipitotemporal visual cortex (OTC). Ritchie et al. propose that OTC represents objects depending on the properties that are behaviorally relevant in a specific task/context, rather than by encoding the invariant visual properties to determine category membership. We consider this proposal in the context of recent developments that have extended the function of vision (and OTC) beyond object recognition, to include a representation of how objects relate to each other, a key piece of information for planning and acting toward behavioral goals.

How is the visual cortex organized? Ritchie et al. (this issue) argue for moving beyond category-selective accounts toward an emphasis on complex, behaviorally relevant functions-a perspective we fully endorse. Extending this view, we emphasize that human behaviors are diverse and differentially prioritized. Among these, language emerges as a uniquely critical domain. Converging evidence from developmental and cognitive neuroscience demonstrates that language exerts a powerful influence on visual processing. These findings underscore the need to incorporate language-alongside other high-priority behaviors-into frameworks seeking to elucidate the organizational principles of the human visual cortex.

Ritchie et al. (this issue) urge a shift from stimulus categories to behavioral relevance as the organizing principle of occipitotemporal cortex. I argue that realizing this vision requires a formal taxonomy of natural behavior: an ontology that maps how humans actually act in real environments. Only then can we discover which behaviors consistently recruit categorization as a subroutine of adaptive visual processing. Naturalistic datasets that annotate tasks, gaze, and movement provide the empirical backbone for this taxonomy, transforming category-selectivity from a starting assumption into a data-driven outcome of ethological neuroscience.

Federico et al. present an interesting framework for technological cognition distinguishing mechanical and digital technologies within a distributed brain network. We build on this contribution by emphasizing two key issues for neuropsychology. First, greater weight on semantic processing may not suffice for efficient digital tool use: selection mechanisms are crucial for translating abstract goals into concrete action sequences. Second, digital technologies must be considered in terms of what they offer (functional opportunities) and what they demand (user skills). These distinctions clarify pathways for assessment, rehabilitation, and inclusion, and highlight open questions essential to advancing digital neuropsychology.