I am grateful to the authors of the commentary articles for identifying productive points of pressure for any neurocomputational account of syntax: whether semantic interpretation can proceed without a full syntactic derivation; how dynamical motifs are selected and coordinated; whether proposed mechanisms generalize across languages and modalities; how evolution may have repurposed older memory circuitry; and how neural dynamics distinguish types and tokens. Here, I clarify that ROSE is an architecture for implementing hierarchical syntactic computation, bringing with it no commitment that its full code is obligatory for every act of meaning construction. The commentaries suggest a number of compelling concrete extensions: task-dependent gating of S/E; factorized R/O subspaces; representation-relative rather than language-specific complexity measures; comparative tests of neural reuse; and occurrence-sensitive phase or state-space addresses. These thoughtful extensions sharpen the central aim of ROSE to formulate multiscale, falsifiable links between formal properties of language and neural dynamics.
In a previous discussion paper, we conducted a systematic review of 13 implicit memory studies that reported fMRI activity in the hippocampus. It was determined that each of these studies suffered from at least one of the following confounds that could produce hippocampal activity: explicit memory contamination, imbalanced attentional states, imbalanced stimuli between conditions, or differential novelty. Thus, we concluded that implicit memory is not associated with the hippocampus. Six commentaries on that discussion paper were received from Hannula (2024), Henke and Ruch (2024), Rosenthal (2024), Spaak (2024), Thakral et al. (2024), and Züst (2024). In this response, we address several issues to clarify our theoretical and experimental framework and maintain that when confounds are excluded, there is no evidence for hippocampal involvement during implicit memory. This discourse highlights the need for conceptual clarity, methodological consistency, and the critical distinction between neural engagement and behavioral expression.
Slotnick (2026) provides a large number of simulations to demonstrate that statistical power in fMRI can be improved by including the sample size N when calculating an appropriate cluster extent threshold for thresholding statistical maps. I argue that the problems acknowledged by Slotnick can instead be solved using threshold free cluster enhancement (TFCE) and a permutation test, which together apply a large number of cluster forming thresholds and implicitly model the sample size as well as the spatial autocorrelation. Furthermore, I briefly mention some other approaches for increasing statistical power in fMRI.
fMRI research is highly prolific but raises multiple concerns. Many competing statistical methods and respective packages are available using different assumptions, none of which applies equally well to all settings. However, the most fundamental concerns are not about the statistical machinery, but about issues of reproducibility, utility, and even construct validity. One can probe how much the field would benefit by statistical refinements, the conduct of larger studies and/or improved reproducibility practices. Alternatively, maybe fMRI research should largely be abandoned with focus shifting toward developing imaging methods with construct validity for granular neuronal activity and higher potential for clinical utility.
Sustained focus is essential for effective goal-directed behavior. Yet, as sustained attention tasks drag on, the occurrence of mind wandering increases. Recent studies suggest that such increases in mind wandering correspond with increases in response time variability and declines in accuracy with greater time-on-task. Relatively little is known about how large-scale brain dynamics unfold over similar timescales. EEG microstates offer a way to characterize these dynamics by capturing brief, quasi-stable topographical patterns that index distinct large-scale neural configurations. Prior work has shown that microstate C corresponds with episodes of mind wandering, whereas microstate E corresponds with task-focused periods. The present study asked whether the prominence of these microstates may systematically shift with greater time-on-task. Thirty-four adults completed a 45-min Sustained Attention to Response Task, while EEG was recorded and canonical microstates were extracted. In line with established behavioral findings, self-reported mind wandering and performance indices suggested poorer task-focus with longer time-on-task. Critically, microstate metrics revealed a gradual increase in the prominence of microstate C (greater time coverage and occurrence) over the course of the task and a corresponding decrease in the prominence of microstate E (shorter duration). These results indicate that EEG microstate dynamics are sensitive to time-on-task related changes in sustained attention and track a shift from externally oriented task focus toward internally oriented, mind wandering states.
In our target article, we proposed a hub-and-processors model of technological cognition. The twelve commentaries that followed offer a rare opportunity to refine, extend and test that framework against new perspectives. We integrate their contributions into a revised synthesis. The parietal sites now gain sharper anatomical definition: core substrates of technical reasoning can be more clearly distinguished from adjacent regions supporting manipulation knowledge and mental-to-digital goal-directed conceptual transformations. The inferior frontal gyrus, in turn, emerges as a dual-function node - one that routes information across hubs while simultaneously meeting the planning and control demands of tool-related behavior. Beyond cortical organization, the commentaries push the framework toward white-matter connectivity, reward and motivational circuits, as well as affordance-based theoretical accounts spanning the physical, digital and symbolic domains. They also broaden the model's translational scope to aging, neurodegeneration, digital inclusion, and neurorehabilitation, and open new lines of inquiry into temporal dynamics, expertise, and cognitive extension. The cumulative result is a strengthened case for a cognitive neuroscience of technology: a mechanistic, translational and lifespan-oriented program aimed at understanding how the brain supports technology use, acquires technological skills and is, over time, reshaped by sustained engagement with technological artifacts.
We are thankful for the thoughtful commentaries of our colleagues. In our discussion article, we argued for a course correction to how the field approaches the organization of visual function in occipitotemporal cortex (OTC) - one that moves us away from the dominant framework of category-selectivity towards a more ethological one centered on behavioral relevance. Some of the commentaries raised important issues regarding how our proposed framework does (or does not) contrast with category-selectivity. Other commentaries made helpful suggestions for how behavioral relevance can be studied. As a whole, the commentaries show that much work remains to be done, both theoretically and empirically, in developing a framework centered on behavioral relevance.
Many procedures to correct for multiple comparisons in functional magnetic resonance imaging (fMRI) analysis require a minimum cluster-extent threshold; however, sample size (N) is often not modeled. In this study, a series of simulations was conducted where N was varied to determine whether this parameter affected cluster threshold. The primary hypothesis was that modeling N in the simulations would reduce cluster thresholds. A secondary hypothesis was that this cluster size reduction was due to between-subject variability, which was tested by eliminating the corresponding standard error term. Acquisition volume parameters were fixed, while key parameters were varied to reflect reasonable ranges: N (10, 20, or 30), corrected p-value (.05, .01, or .001), individual-voxel p-value (.01, .005, or .001), FWHM (3, 5, or 7 mm), and voxel resolution (2 or 3 mm). Each simulation consisted of 100 iterations repeated 100 times, with a total of 4,860,000 iterations and 66,420,000 simulated subjects. There was a significant effect of condition with clusters approximately 18% smaller with versus without N modeled and a significant increase in cluster thresholds for larger sample sizes. Bayesian analysis provided very strong support for the secondary hypothesis. These simulation results were replicated in a real fMRI data set. The present findings indicate that sample size should be incorporated into all methods to provide the most accurate thresholds possible and reduce type II error. A broader range of topics is discussed including balancing type I and type II error, and the assumption that non-task fMRI activity reflects null data is questioned.
A hallmark of human intelligence is the ability to infer abstract rules from limited experience and apply these rules to unfamiliar situations. This capacity is widely studied in the visual domain using the Raven's Progressive Matrices. Recent advances in deep learning have led to multiple artificial neural network models matching or even surpassing human performance. However, while humans can identify and express the rule underlying these tasks with little to no exposure, contemporary neural networks often rely on massive pattern-based training and cannot express or extrapolate the rule inferred from the task. Furthermore, most Raven's Progressive Matrices or Raven-like tasks used to train neural networks consist only of symbolic challenges, whereas humans can flexibly solve both symbolic and perceptual challenges. In this work, we present an algorithmic approach to rule detection and application using feature detection, affine transformation estimation and search. We applied our model to a simplified Raven's Progressives Matrices task, previously designed for behavioral testing and neuroimaging in humans. The model exhibited one-shot inference and achieved near human-level performance in the symbolic reasoning condition of the simplified task. Furthermore, the model can express the relationships discovered and generate multi-step predictions in accordance with the underlying rule. Finally, the model can handle perceptual challenges containing continuous patterns. We discuss our results and their relevance to studying abstract reasoning in humans, as well as their implications for improving intelligent machines.
This commentary emphasizes the critical role of white matter tracts in technological cognition, complementing Federico et al.'s (2025) hub-and-processor model focused on cortical regions. Diffusion tensor imaging highlights white matter pathways as essential for integrating visuospatial, semantic, and motor information, underpinning complex tool use and technological interaction. The disconnectionist perspective reveals that cognitive deficits often arise from disrupted white matter connectivity. Additionally, the emerging concept of "digital dementia" underscores the impact of excessive digital exposure on brain networks. Integrating white matter connectivity advances clinical understanding and informs rehabilitation strategies for technological cognition deficits.
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.
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.
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.
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.
With recent developments in artificial intelligence (AI), there is great interest in how mechanisms of human cognitive processing may be instantiated in those models and how those models may help us better understand human cognitive and neural processes. Recent research suggests predictive coding theories and associated generative models may help explain the processes of visual perception and language production, while newer AI models include mechanisms akin to human memory and attention. This special issue of Cognitive Neuroscience: Current Debates, Research & Reports presents 16 new papers that highlight important topics and present exciting new data, models, and controversies. The articles include a new discussion paper by Parr, Pezzulo, and Friston exploring how transformer architectures utilize non-Markovian generative models and how an attention-like process is critical for processing complex sequential data. This is followed by seven insightful commentaries and a reply from the authors. A discussion paper on a new neurocomputational model of syntax is provided by Murphy, in which predictive processes are integrated in a multi-level, hierarchical syntax architecture. This is followed by five commentaries suggesting important evolutionary and developmental perspectives and ways to explore and test the model. Finally, an empirical article by Bastug, Roeber, and Schröger on auditory perception presents new evidence suggesting that distracting information requires less cognitive processing when it is predictable. The topics of this special issue are evolving rapidly and promise to be at the heart of future developments in artificial learning systems and theories of the brain mechanisms that mediate cognitive processes.
