• Yujie Hou, 
• Alessandra Pizzuti, 
• Kenneth Knoblauch, 
• Julien Vezoli, 
• Rainer Goebel, 
• Martin Vinck, 
• Henry Kennedy

x

Abstract

Citation: Hou Y, Pizzuti A, Knoblauch K, Vezoli J, Goebel R, Vinck M, et al. (2026) The primate claustrum as a hub for precision weighting of prediction errors across the cortical hierarchy. PLoS Biol 24(7): e3003906. https://doi.org/10.1371/journal.pbio.3003906

Published: July 16, 2026

Copyright: © 2026 Hou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: H.K. is supported by ERC-2023-Adv 101142153 PREDICTION (https://erc.europa.eu/); ANR-24-CE37-5022-CONNECTOME (https://anr.fr/); ANR-22-CE92-0010-Claustrumhub; LabEX CORTEX ANR-11 LABX-0042; and IRP Inserm 2021-Cortical_Connectome (https://www.inserm.fr/). M.V. is supported by ERC starting grant (850861) (https://erc.europa.eu/); DFG VI Grants (908/5-1 and 908/7-1; 505660261; 520285844; SPP LOOPS) (https://www.dfg.de/en); the NWO VIDI Grant (VI.Vidi.213.124)(https://www.nwo.nl/); and the Dutch Brain Interface Initiative (DBI2) of the Gravitation Program (024.005.022)(https://dbi2.nl/). J.V. is supported by ANR-25-CE37-7332-MACLAU. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: HK serves as an Academic Editor at the journal. The other authors have declared that no competing interests exist.

Abbreviations: AAV, adeno-associated virus; BU, bottom–up; EDR, exponential distance rule; fMRI, functional MRI; hdPC, hierarchical dendritic predictive coding; hPC, hierarchical predictive coding; NHPs, non-human primates; TD, top–down; VASO, vascular space occupancy

Introduction

The cerebral cortex comprises constellations of sensory, motor, and associative areas linked by dense ascending and descending pathways across a hierarchical architecture. Understanding how signals are exchanged across this hierarchy is a central challenge for theories of perception and cognition that remains incompletely understood. In this Essay, we propose a roadmap for investigating these cortical networks in the primate brain.

The relative merits of generative and discriminative theories of perception are still debated, reflecting their importance for understanding human cognition. We first review theories of cortical function and situate discriminative and generative models (Box 1) with respect to the hierarchical organization of cortex. This provides the basis for evaluating predictive processing accounts of cognition in humans while highlighting the need for detailed circuit descriptions in non-human primates (NHPs). We use hierarchical predictive coding (hPC; Box 1) as a heuristic framework for organizing circuit questions but remain agnostic as to whether cortical hierarchies ultimately implement classical residual-error coding, feature-selective sensory amplification, precision-weighted (Box 1) hybrid schemes, or different solutions in different circuits. Recent work has emphasized that neural responses often described as ‘prediction errors’ (Box 1) may be similar at the response level while reflecting distinct underlying computations [1]. Our roadmap is therefore aimed not at presupposing a single canonical algorithm, but at identifying the circuits and signals required to discriminate among competing predictive architectures.

In this Essay, we distinguish direct cortico–cortical pathways from cortico–claustral–cortical loops, which provide a major conduit for communication between areas (i.e., inter-areal). We propose that the claustrum, by virtue of its widespread cortical connectivity, implements precision control across the cortical hierarchy (Box 1). We also identify the critical gaps in knowledge that need to be addressed to move the field forward and outline how human and NHP studies can be integrated to better explain hPC in the human brain.

Theories and models of perception

An important clue to the function of the cortex in primates is its hierarchical organization. Generations of neuroanatomists have uncovered well-defined signatures of bottom–up (BU) and top–down (TD) connections (Box 1) linking hierarchically organized areas [2–4]. One view of brain function and perception is that the senses trigger sensory information flow and extraction in the BU pathways ascending the brain’s hierarchical levels [5]. Physiologists were quick to show that pathways ascending the hierarchy generate increasingly abstract representations of the world in higher areas, leading to the large range of receptive field properties observed at different levels of the hierarchy [6–13]. According to feedforward or discriminative theories of perception (Fig 1A), perception is enabled by the rapid analytical BU processing of features present in the sensory input generating increasingly complex representations of the sensorium (Fig 1B) [14]. However, the feedforward model largely ignores TD connections [15–19]. Recent anatomical studies have shown that TD feedback connections are twice as numerous as BU connections [20], and importantly, despite the ubiquity of collateralization of individual neuron projections in the cortex, individual inter-areal projection neurons virtually never possess collateral axons with TD and BU directed branches in macaques [21], although they do in mice [22]. In addition, TD pathways substantially contribute to perception [23] and can act on the earliest part of feedforward sensory response [24,25]. Furthermore, there is a long-standing recognition that prior knowledge of the world and contextual processing carried by TD pathways are necessary to make sense of incoming information. According to Helmholtz, virtually all perception involves some degree of internal reconstruction where perceptual inference (Box 1) enables the brain to build generative models that in turn enable model-based perception [26] (Fig 1A). Perceptual inference is thought to require signals from the senses ascending the BU pathways to be integrated with internal expectations and priors (Box 1) according to an as-yet undetermined process [27,28] (Fig 1A, lower). This integration solicits a dynamic information exchange between distributed neuronal assemblies across brain areas, with distinct populations of BU and TD neurons that reside in and project to specific cortical laminae. Fig 1A suggests that discriminative theories of perception in the empiricist tradition and generative theories proposing perception via synthesis are composed of symmetrical opposite features (e.g., direct perception versus perception as inference, etc.).

Nearly all large-scale models of cortical function refer explicitly to cortical hierarchy. Numerous tract tracing (Box 1) experiments conducted between 1970 and 1990 showed that the laminar patterns of the origin and termination of inter-areal pathways stemming from the primary cortical areas to the association cortices allowed pathways to be categorized as feedforward (i.e., BU) and feedback (i.e., TD). Pairwise comparisons of these connectivity patterns made it possible to quantify the structural hierarchical organization of the cortex for model species including mouse and macaque [2,21,29,30]. These connectivity hierarchies correspond to hierarchies of transcription-defined cell types (Box 1) [22,31], as well as to electrophysiologically defined functional hierarchies [30,32].

Both discriminative and generative models of perception accommodate the cortical hierarchical organization with increases in the receptive field complexity and size while ascending the cortical hierarchy (Fig 1B). In discriminative models, BU signals are driving while the TD are conceived to be uniquely modulatory [33–35]. This contrasts with generative models, where the BU connections are also conceived to be driving and convey prediction errors, but the TD connections predictions (Box 1) are thought to be both driving (for the suppression of prediction errors via a subtractive effect) and modulatory (for the precision via synaptic gain control) [36,37]. While these models have distinct historical roots, a number of researchers argue both from computational resources and high-level vision perspectives that it might be necessary to consider hybrid models of direct perception versus perception as inference, where distinct computations are needed for different tasks [1,38–40]. In this Essay, we describe a comprehensive strategy to generate the data required to sufficiently constrain future models of perception whilst remaining agnostic to the possibility of hybrid models.

The past 20 years have seen a rapid development of predictive processing and active inference frameworks (Box 1), of which hPC is one influential formulation. hPC postulates that the hierarchical levels of the cortex are in the business of a dynamic updating of a statistical model of the causes of the sensory manifold [36,37,41–44]. According to hPC, the empirically observed TD signals of expectation or prediction [19] and modulatory precision-weighting of the ascending prediction error interact via an as yet undetermined circuit [45]. In the exchange of information between hierarchical levels, the (precision-weighted) prediction error has classically been conceived as the residue of an unresolved prediction and hypothesized to be a principal ascending signal in some hierarchical predictive-coding models, although recent work has questioned whether ascending sensory pathways encode residual differences per se, or instead convey sensory representations where the gain is modulated by TD predictions [1] (Box 2). While, ultimately, knowledge of the world is involved in TD prediction, in generative models, it is the multiple reiterations of prediction between individual areas across the hierarchical levels in conjunction with the aforementioned TD attention-like precision signals [46] that leads to a process of error minimization. Although the hypothesis that ascending pathways predominantly convey prediction errors has been questioned [47,48], it remains unresolved whether these signals encode residual differences, amplified sensory features, or more general surprise-related computations [1,48–51]. While non-invasive neuroimaging demonstrates TD predictions, evidence at the neuronal and mesoscopic scale is crucially still lacking [19]. This is particularly the case in primates, partly due to only very sparse knowledge concerning BU and TD pathway convergence and the lack of appropriate molecular tools [48].

Theories of hPC have widespread psychological and philosophical implications for perception [50], cognition [72], language [73,74], episodic memory [75], and consciousness [76,77]. The corollary of this broad reach is that hPC as a theory of brain function brings together not only neuroanatomy, neurophysiology, and neurodevelopment, but also psychophysics, computational neuroscience, psychology, and philosophy of mind. In light of the widespread impact of these theories, especially related to high-level human cognition and, in order to better comprehend the importance of hPC for understanding the relation of the mind to the world [43], we argue that it is necessary to investigate prediction error minimization in parallel studies in humans and in NHP, where invasive techniques will provide insight on cellular mechanisms in the animal model, and the human imaging studies will reveal their relevance to perceptual mechanisms and their dysfunction in the human brain. Elsewhere, we have described the severe limitations of the mouse model for understanding primate cortical function [78–81]. However, projected experimental investigations in the more reductionist mouse model will provide additional crucial mechanistic insight that will complement, stimulate, and enrich the investigations that we discuss here [82].

Communication across the cortex

There are three main channels through which cortical areas in primates can exchange signals: cortico–thalamo–cortical loops [83]; direct cortico–cortical pathways [2,20]; and cortico–claustral–cortical loops [84]. While projections across the cortex via the thalamus are largely restricted to neighboring areas [85], cortico–cortical projections provide direct links between any two-thirds of cortical areas [20], and projections via the claustrum can directly influence virtually any cortical area [84]. To study these projections, tract tracing is used to define a weight index (FLNe) of connection strength in terms of the relative proportion of neurons participating in a particular cortical pathway. In addition to the high density of inter-areal communication via the claustrum, the weights of inter-areal interactions via the claustrum is significantly higher than those from inter-areal cortical connections. This is because direct cortico–cortical pathways exhibit an exponential decline in connection weight [86], which is not the case of projections via the claustrum hub [84] (Fig 2A and 2B).

Inspirationally, Crick and Koch [87] envisaged a special role for the claustrum in the inclusive nature of consciousness that allows you, “when holding a rose, to smell its fragrance and see its red petals while feeling its textured stem with your fingers.” These authors reasoned that binding information across modalities would require widespread claustral–cortical connectivity. While such connectivity was largely confirmed by numerous anatomical tracing investigations in rodents, these studies also claimed that cortico-claustral pathways were topographically organized [88]. Likewise, physiological experiments exploring sensory responses in claustrum described them as localized with largely unimodal responses and therefore seemingly at odds with the binding function, previously proposed by Crick and Koch [87,89–92]. Might the apparent lack of multimodal sensory integration be due to an absence of TD engagement [93–95]? Evidence that this might be the case comes from recent electrophysiological findings in mice suggesting functional convergence from widespread cortical sources onto single claustral neurons, establishing networks leading to multimodal sensory responses [96]. These findings suggest that a reappraisal of claustral function in higher cognitive processes including aspects of consciousness may be warranted [97].

Higher cortical association areas via claustral pathways to early sensory areas in macaques [84] can exert TD cognitive control [89]. In macaques, recent findings show that claustrum and insula share connectomic and transcriptomic features [84], and functional MRI (fMRI) studies suggest a possible involvement of insula in precision weighting of prediction errors in visual cortex [98]. We argue for a special role of the claustral-insula complex in long-distance interactions in the cortex, suggesting that it likely complements and prolongs direct inter-areal pathways in relaying TD predictions (Fig 2A–2C).

The claustrum receives input from ascending projections that control brain-state transitions in the wake–sleep cycle [99,100]. Activation of the claustrum leads to feedforward inhibition in the cortex via claustral projections onto inhibitory cortical cells [101], provoking a transient down state followed by a widespread synchronized down-to-up state transition across cortical areas, typical of activity patterns linked to slow-wave sleep [99]. Sharp wave ripples in the claustrum of rodents and its putative homolog in reptiles have a causal role in relaying and generating cortical state transitions found in slow-wave sleep [99,100]. The involvement of claustrum in state transitions in sleep, however, does not preclude an involvement in wake states, especially given that significantly higher levels of claustral neural activity are reported in wake versus sleep states [102,103].

Brain-state transitions are tightly correlated with specific behavioral repertoires [104–106], depend on the coordinated fluctuation of single-cell properties, and engage key brain functions across neocortex and hippocampus [107]. Rapid state transitions and their spatial distribution according to behavioral needs is a major and, as yet poorly understood, feature of the brain. A working hypothesis is that the spatial embedding of the claustrum and its intrinsic circuits [108] give it a privileged role in orchestrating rapid changes of brain state [109], promoting robust representation of sensory stimuli [110], and influencing TD signals [93]. This issue can be addressed in NHP using a combination of multi-site, high-yield, high-density electrophysiological recording of identified neurons in neocortex and claustrum during brain-state transitions in macaques engaged in cognitive tasks (see section on Roadmap for coordinated parallel investigations of NHP electrophysiology and human neuroimaging).

Recent findings in mouse show that the influence of the claustrum on the cortex is layer- and cell-type dependent [111]. These electrophysiological findings support the proposal that claustrum is involved in salience processing (i.e., directing attention to relevant, that is, salient, events) [112–114]. The interaction between salience and attentional mechanisms is mediated via precision weighting [115]. The cortex-wide connectivity of claustrum is complemented by a spatially extensive intra-claustral inhibition [108,116]. Hence, salience processing can be achieved by the cortico–claustral–cortical loops acting as a winner-take-all circuit [117], where the sensory stimulus of the wining claustral–cortical loop is the one that is selectively attended to [118,119] or becomes conscious [87]. This functional model is compatible with the evidence that the claustrum has a role in ensuring cognitive control [120,121].

Inter-regional long-range connections in the primate cortex fulfill a specific role in terms of the complexity of the structural network and their functional dynamics [122–124], and have been explored in large-scale dynamic models of cortical function [125,126]. Inter-regional connectivity in the cortex obeys an exponential distance rule (EDR), where connection weight declines approximately exponentially with distance [86]. The evolutionary expansion of the primate brain could contribute to making the critical task of the long-range pathways more fragile [80]. These structural limitations on long-range cortical interactions do not apply to cortico–claustral–cortical loops, as shown by our comparison of the intra-cortical and cortico–claustral connectivity in macaque (Fig 2B) [84]. Furthermore, this study of whole-brain connectivity and single-cell spatial transcriptomics (Box 1) of macaque claustrum revealed a regionalization with preferential cortical projections and differential composition of transcriptome-defined glutaminergic cell types, some of which may be macaque specific. There is also considerable evidence of the importance of the thalamus in precision weighting [127]. Importantly, the patterns of thalamic connectivity of the claustral sub-regions mirror those of the related cortical regions, allowing the ascending thalamic pathways to both structures to optimize precision weighting [84]. The rich thalamic connectivity of the macaque claustrum could imply that this structure has a specialized role in precision weighting.

The non-compliance of macaque claustral connectivity with the EDR may be highly relevant to recent findings of high-level prediction errors in early visual cortex. Multi-unit recordings showed that in macaque area V1, the primary visual area of the cortex receiving the bulk of the ascending visual information, natural stimuli with low predictability induced late-onset beta synchronization, reflecting cortical feedback [128]. Similar findings were found in humans, where fMRI results suggest that predictions are computed at higher cortical levels and consequently sent down the cortical hierarchy reaching area V1 [129,130]. Multi-site recordings in claustrum and cortex, paying particular attention to cortical area, layer, and cell type [111], and exploiting current understanding of macaque claustral cell types and regionalization [84], may help clarify the role of claustrum in broadcasting predictions across the cortical hierarchy.

A precision-weighting hub model of macaque claustral function

Our proposition that the claustrum serves as a precision-weighting hub builds on findings of Mathur and colleagues [120] suggesting that the claustrum is a task-dependent hub whereby the frontal cortex dynamically instantiates task-relevant cortical networks in support of cognitive control. This view extends earlier conceptions of the claustrum as a conductor of cortical binding, an integrator of synchrony, and a mediator of attentional selection [87,118,131]. These formulations suggest a predictive processing framework, in which attentional selection is often understood as the context-sensitive precision weighting of prediction error signals, and cognitive control may depend on the allocation of precision over task-relevant representations and policies. Our proposal is further motivated by recent single-unit recordings in humans during aversive learning, showing that claustrum neurons track model-derived uncertainty and prediction error, two variables well suited to support precision weighting [132]. Here, we describe two aspects of the anatomical organization of the macaque claustrum that suggest differential modulation of the gain of prediction-error signals via precision weighting across the entire cortical hierarchy.

Convergence of BU and TD signals at laminar scale in the cortical hierarchy

In a recent review of inter-areal processing, we argued [93] that the understanding and modelling of the interaction of BU and TD streams need to be better constrained and informed by a greatly improved understanding of the hierarchical circuits that are involved in their integration (Fig 4). There are fundamental differences between BU and TD connectomics: they constitute strictly separate populations, supportive of organized inter-areal interactions likely to subserve the integration of priors and sensory evidence; BU and TD form multiple pathways, originating from different laminar compartments (Fig 4A); and single brain areas project to multiple hierarchical levels (Fig 4B upper panel), requiring a revision of the serial processing scheme of traditional hPC models [37,153] (Fig 4B lower panel). This latter point suggests that, in order for interaction of BU and TD signals to occur, it is necessary for activity to be expressed, assembled, and integrated over a large, distributed recurrent network.

Hence, future anatomical tracing in NHP will need to investigate, for the first time, the weighted layer-to-layer connections between cortical areas. This will provide critical data for developing computational models of the circuit on how multiple hierarchical levels interact with each other and integrate BU and TD information, supporting cell-type layer-specific TD and BU processes.

One major difficulty with the active inference model is the glaring lack of detail concerning hierarchical circuits (Fig 4A) that ensure direct interaction between BU and TD pathways. Hence, the current understanding of anatomical cortical circuits lags behind the sophistication of the theoretical framework of active inference [93]. Existing accounts of the circuits of hierarchy as described in Fig 4 are relatively crude, being entirely based on laminar patterns of connectivity and mesoscale activity measures [32,93,155].

Moreover, the generation of prediction error and its modulation by precision requires an interaction between BU and TD pathways. Presently, we lack empirical evidence concerning the circuits implementing such interactions. TD signals from supragranular layers may largely but not exclusively target upper layers and, in particular, layer 1 (L1) [156]. The laminar targets of the more extensive L6 TD signals are even less certain, but at least over long distances they appear to project to L2/3 and L1 [20,156–160]. One hypothesis [45,161] is that L6 neurons project TD to L6 neurons at lower levels. Such L6 > L6 TD signals have been reported [162]. Within the same cortical column, L6 neurons project to L4 and L2/3 [154]. Shipp [45,163] speculates that such local L6 > L2/3 connections would enable the L6 TD pathway to participate in generating prediction error in L2/3. However, another possibility that needs to be considered is that the TD signal into upstream L6 is relayed locally to L4, a connection which is substantially stronger than the local L6 > L3 signal [154] (Fig 4C). Hence, there are three routes by which TD excitatory influence can interact with BU signals located in the granular and supragranular layers, one direct and two indirect routes; these three routes may employ distinct cell types and could subserve distinct functions. Alternatively, TD pathways may interact with BU signals via the local inhibitory microcircuit of the upstream triggering disinhibitory and inhibitory processes underlying respectively prediction error and precision-weighting of prediction error [75,164]. Note that the TD route via L6 and targeting L4 corresponds to that proposed by Rao and Ballard in their original description of hPC [36].

L1 is a major target of TD pathways [62], and there is now strong evidence that the TD projection to L1 can serve to control BU–TD interactions via contacts upon the apical tufts in L1 belonging to the pyramidal neurons located in L5 and L2/3 [65]. In line with these findings is the recent characterization in mouse of an excitatory cell type called Baz1a in L2/3 [165], with a demonstrable capability to integrate TD and BU activity. Baz1a generates prediction errors in area V1 in mice [166]. L2/3 in primates is known to house five distinct glutamatergic cell types [167–170], two more than in mouse, suggesting an enriched interaction of TD and BU influence in NHP and human cortex. Interestingly, two of the cell types described in human L2/3 are homologous to cells in mouse L5/6 [167]. Could such cells constitute the targets for the within-column L6 neuron projections? To summarize, we lack a clear understanding in primate of how the circuits of the more substantial L6 TD signals differ from those of the L3 TD signals, nor is it clear if L6 signals to L4 and L3 have distinct roles in terms of integration of TD signals. Such issues will be an important focus for future research.

The literature on cortico–claustral–cortical loops reveals only a few studies using anterograde tracers to examine the projections of claustrum to the cortex in NHP. However, given the global similarity of cortical connectivity in macaque to that observed in cat, we can consider the numerous studies in cat on claustral projections to the cortex. Three studies using autoradiography have shown claustral projections to all layers of cat visual cortex with notably higher densities in L4 and L6 [91,171,172]. These findings suggest that the claustral input to sensory cortex is a major driving input, although a study using biotinylated dextrin amine found claustral projections concentrated in supragranular layers [133]. These findings are intriguing because they suggest that, at least in cat, the cortico–claustral–cortical loop could provide a direct interaction between BU and TD signals, which is not the case for direct cortico–cortical pathways.

Evidence of TD prediction and BU prediction-error signals

Testing different models of perceptual inference requires physiological investigations in the awake brain combined with precise psychophysical control. Here, the major challenge is to distinguish experimentally between BU and TD processes in vivo. An additional challenge is to determine the computational content of putative mismatch signals, since similar response profiles can arise from distinct mechanisms [1]. While BU information is relatively easily manipulated via external stimuli, TD information is generated internally and can only be indirectly manipulated.

The need for a cell-type account of BU and TD signals and circuits

Many uncertainties concerning the circuitry underlying prediction, prediction error, and precision stem from the current inability of classical investigations to disentangle the BU and TD circuits in the dual counter-stream architecture and their integration into the local circuit [93]. However new tools are at hand. One that should enable major progress in deciphering neuronal circuits is transcriptome-based cell-type classification, which will lead to better understanding of the particular roles of different circuit components. With respect to the canonical circuits of hPC, high-throughput transcriptome-based methods for cell-type identification in rodents [236] have been shown to scale to primates [31,168,170,237]. Defining cell types by their transcriptomic profiles enables the identification of cell-type specific short gene-regulatory enhancer sequences that can be used to develop adeno-associated virus (AAV)-based vectors for cell-type selective genetic manipulation, thereby extending to primates certain viral tools that have proved uniquely productive in the mouse [238]. Recent work using these methods has identified a wide variety of ‘new’ excitatory projection neuron types, enabling finer classification of the population of cortico–cortical, or intratelencephalic (IT) neurons, subsets of which give rise to BU and TD projections [168,237,239–241]. This work has revealed an evolutionary expansion of the diversity of IT types in L3 of primate cortex [167–170] that warrants intensive further investigation.

A cell-type account of hierarchical anatomy and physiology in NHP is now made possible by the recent description of the cell-type organization of the macaque cortex via spatial transcriptomics [31]. A critical element will be to perform genomic analysis of the BU and TD projection cell types and the circuits that they form in extrastriate visual cortex of NHP to uncover their functional diversity. This will lead, in turn, to the development of enhancer-AAV tools for not only mapping but experimentally manipulating the projection cell types responsible for canonical pathways of hPC. In this manner, future electrophysiology recordings will be able to identify the functional properties of TD and BU pathways. Here, this approach is greatly facilitated by the recent description of cell-type-specific and primate-selective enhancers in macaques [242].

Roadmap for coordinated parallel investigations of NHP electrophysiology and human neuroimaging

An overarching objective of neuroscience is to understand hierarchical processing in the human brain, which in the emerging field of computational psychiatry is expected to be key to understanding numerous brain disorders [243]. hPC theories hold strong explanatory power and have been proposed to constitute a potential ‘paradigm shift’ [244], as witnessed by their successful application in mouse models. However, accumulating evidence demonstrates numerous primate-specific features during development that determine the unique cognitive architecture of the primate cortex [79,245]. These primate-specific features explain why mice are often inadequate models for human neuropsychiatric diseases [78]. Even structural properties alone constitute a barrier for extrapolation: the mouse cortex exhibits a graph density of 97%, far exceeding that of NHP and human cortex [80,246], which implies the presence of numerous inter-areal connections in mice that have no primate homologs. One example is the projection from the anterior cingulate cortex to area V1 in mouse, which conveys TD spatial and stimulus-specific predictions [184], yet is absent in NHP. Similarly, the aforementioned primate-specific expansion of glutamatergic cell types in L2/3 [167,168] reflect unique developmental processes [79] that further differentiate primate cortical computations from those of rodents.

Having established NHP as the appropriate model for probing primate cortical mechanisms, the literature reviewed in the previous section nevertheless remains fragmented and isolated, lacking a common framework for a mechanistic understanding of TD and BU processes across scales. To overcome this limitation, we propose a coordinated parallel investigation is needed that integrates NHP electrophysiology and human neuroimaging, capitalizing on the strengths of each modality while compensating for their intrinsic limitations. Motivated by the goal of understanding primate cortical computation, this combined approach could be used to test hypotheses about TD and BU processes within the hPC framework, targeting cortico–cortical and cortico–claustral laminar pathways both anatomically (e.g., cell types) and functionally (e.g., relevance for perceptual inference, frequency-specific contributions).

To experimentally manipulate TD–BU interactions, it will be necessary deploy not one but a battery of visual paradigms designed to reduce retinal input and induce internally generated activations under different TD conditions. A review of paradigms previously used in either NHP or humans reveals numerous paradigms covering a broad range of TD effects targeting early visual cortex, notably including area V1. Examples include: motion-specific illusions such as apparent [247,248] and bistable motion [176,249], which are hypothesized to involve TD influences originating from motion-sensitive areas, such as the middle temporal area; illusory contours such as the Kanizsa illusion [175], carried by TD input from the secondary visual area, area V2 (in NHP) or lateral-occipital area (in humans); long-range color filling-in [250–252] carried by TD projections from ventral and dorsal pathways; and finally occlusion paradigms [179,180] that convey contextual TD signals from V2/V3 (second and third visual areas of the cortex) and higher areas. All TD conditions will therefore need to be balanced in luminance and background color, paired with corresponding control conditions, and presented in identical retinotopic locations. For example, both the apparent motion and Kanizsa contours could be presented on a uniform gray background and be displayed in identical locations of the visual field. This would ensure that differences between conditions can be directly attributed to TD manipulation rather than inadvertent engagement of distinct neuronal populations. Such a coordinated design would impose cross-species constraints (Fig 5). The retinotopic location of NHP electrode implantations could be used to define the visual field, where all stimuli are presented in both species. To maximize feasibility across a large task battery for NHP training, all paradigms would need to rely on passive fixation rather than explicit behavioral reports, and adaptation effects minimized using simple detection tasks (saccades in NHP; button presses in humans).

For the proposed investigation, the human cohort should follow a deep-sampling strategy [253], acquiring extensive data from a small number of individuals (5–10), mirroring the NHP approach with two or three animals. To resolve issues concerning laminar-specific cortico–cortical and cortico–claustral connectivity, ultra-high-field (≥7T) fMRI would be an optimal technique, offering sub-millimeter resolution capable of resolving mesoscopic structures such as layers and columns [254–261]. Despite multiple layer-fMRI studies probing TD effects in area V1 (e.g., [175,262]), findings vary across studies, partly due to differences in acquisition modality (e.g., gradient echo, spin echo, vascular space occupancy; VASO) and contrast mechanism (blood-oxygen level dependent; BOLD, versus cerebral blood volume; CBV,-sensitive fMRI). VASO-based acquisition should therefore be favored for CBV-sensitive fMRI due to its higher specificity with respect to the microvasculature and its relative robustness against oxygenation-related vascular changes and large draining vein effects that can mislocalize neuronal activation [263–266].

When targeting the claustrum non-invasively with fMRI in humans, ultra-high spatial resolution provides a key advantage by improving the separation of signals from the claustrum and the adjacent insular cortex. Owing to its thin geometry and close proximity to surrounding structures (e.g., the insula), studies using conventional resolution (3–1.5 mm isotropic) are highly susceptible to partial volume effects and have therefore often relied on modelling or deconvolution approaches to disentangle claustral signals from those of neighboring regions [267]. By contrast, we advocate adopting sub-millimeter acquisitions (0.8 mm isotropic) at ≥ 7T to directly mitigate these limitations by reducing partial volume, thereby enabling a more faithful isolation of claustral signals. Similar acquisition techniques at 7T have been recently used for imaging visual features of the claustrum [268].

Moreover, a complementary source of signal mixing arises from the vasculature, particularly venous contributions that can dominate the fMRI signal. For instance, shared draining veins between the claustrum and the insular cortex can blur the measured signal, leading to spatial mislocalization of the underlying neuronal activity [249]. To address this, we propose leveraging recent advances in high-resolution vascular imaging, achieving up to 0.35 mm isotropic resolution, to explicitly resolve the vascular architecture of the claustrum and its neighboring regions [269]. This approach will enable a more informed interpretation of functional signals by improving the characterization of neurovascular coupling and enhancing the spatial specificity of inferred neuronal sources in this underexplored structure.

Inter-subject variability constitutes an additional source of inconsistency. A deep-sampling design could be used to address both challenges by holding acquisition parameters and subjects constant across all tasks; thus, observed laminar differences can more confidently be attributed to differences in TD modulation. A similar strategy was used by Bergmann and colleagues [262], who employed two paradigms (visual imagery and contour illusions) and revealed distinct area V1 laminar profiles: deep layer involvement for imagery and superficial layer involvement for contour perception. This approach could be extended by including more paradigms (3–4) while maintaining feasibility within 5–6 scanning sessions per individual (~10–12 hours).

Perception arises from distributed cortico–cortical and cortico–subcortical interactions, particularly when TD signals dominate under reduced BU input. This complexity motivates our suggestion for a parallel NHP and human approach. Human layer-fMRI, with its advantageous extensive brain coverage, can identify the main laminar correlates of perception across this extended network. The findings from such studies would then subsequently guide targeted NHP electrophysiology, which provides the temporal precision and anatomical specificity needed to dissect the underlying circuitry.

Finally, NHP recordings would need to ultimately build on the viral tracing techniques previously mentioned in order to express light-sensitive opsins in specific populations of BU and TD neurons across multiple levels of the visual hierarchy. This will enable opto-tagging of projection neurons by the precise identification of single and multi-unit light-induced electrophysiological responses from high-density laminar recordings. The exploitation of enhancer–AAV tools developed for tracing the microcircuits of prediction could be used to enable recording from identified BU- and TD-projecting neurons, as well as helping to identify BU and TD inputs to neurons. Based on the circuits described in NHPs, it might then be possible to build generative (e.g., dynamic, causal) models of the laminar pattern of neural activity, generating the fMRI signals to test the models in humans [270].

Our argument is that clarification of where the signals and circuits of TD and BU converge in the human visual cortex requires a concerted mesoscale approach that exploits molecularly defined cell types to trace circuits and dynamics in NHP aligned with ultra-high field fMRI in humans. In turn, resolving the where and how will, we believe, lead to a more profound understanding of why BU and TD convergence is such a crucial aspect of human brain function, potentially providing the key to understanding the relationship between mind and brain. However, we need to guard against the notion that classical hPC will necessarily provide an inclusive and conclusive account of the mind–world relationship. The known neurobiology of the cortex leads us to anticipate multiple, possibly circuit-specific, levels of prediction signals: weak (imprecise) prediction signals could correspond to a model-based knowledge-sparse implementation of hPC, in contrast to strong (precise) prediction signals, as suggested in a discussion between Anderson, Chemero, and Clark [43]. Furthermore, a range of TD prediction signals could also intermesh with other proposed models, including mixed generative-feedforward models [40], predictive routing [191], and hdPC [199], leading to a varying hPC phenomena across sensory systems [271] and species [272]. Finally, significant progress in primate hPC in terms of circuits and signals is likely to further emphasize the synergy between neurobiology and artificial intelligence, with important consequences for both domains [74].

Conclusions

In this Essay, we investigated theories and models of perception and explored the empirical need for detailed descriptions of the circuits and signals supporting prediction, mismatch sensitivity, and precision-like control. We anticipate that this highly fertile area of empirical research may substantially refine current predictive processing theories. For example, long-term priors in area V1 are more strongly tuned to high-level features originating from upper reaches of the cortical hierarchy [128–130], suggesting that feature-specific predictive processing is a general feature of the cortex [183]. Interestingly, similar TD long-distance predictions identified in rodents have been shown to trigger prediction errors in primary areas [273,274]. These observations underline the values of cross-species comparisons, which can provide a deeper insight into the nature of the prediction signal and are expected to lead to a reappraisal of perceptual theories and models.

The precision-weighting hub model of claustrum that we propose shows that defined laminar projections of the cortex to claustrum could convey TD prediction and precision signals. The proposal that claustrum has a central role in precision unifies numerous properties of the claustrum, including orchestration of brain states and cognitive control. While predictive processing frameworks have been highly successful in formalizing brain function in terms of prediction error and precision, the biological implementation of precision remains unresolved. In contrast to the extensive empirical evidence for prediction-error signals, direct evidence for precision as a distinct neural quantity, particularly within TD cortical pathways, is comparatively sparse. We therefore propose a theoretical framework in which precision-like control might not be a characteristic property of the inter-areal network, but instead involve specialized circuit mechanisms involving thalamo–cortical and thalamo–claustral–cortical networks (Fig 3C). In this framework, thalamic inputs to cortex serve to calibrate the gain- and state-dependence of domain-specific processing streams, while the thalamo–claustral inputs to cortex implement spatially structured, inhibitory filtering that resolves competition between cortical representations and orchestrates their broadcast across multiple hierarchical levels. This division of labor provides a biologically grounded account of precision that extends beyond TD pathways alone and generates testable predictions linking thalamo–claustral–cortical dynamics to the flexible coordination of large-scale cortical activity under conditions of uncertainty, distraction, and learning.

An important implication of the framework proposed here is that the circuits supporting predictive processing and precision weighting might differ substantially across species. In particular, emerging evidence suggests that reciprocal interactions between claustrum and thalamus might be more developed in primates than in rodents. If confirmed, such differences would imply that rodents and primates might not share the same large-scale architectures for the regulation of cortical gain, salience, or precision. In rodents, thalamic influences on claustrum could operate predominantly in a unidirectional manner, whereas in primates reciprocal thalamo–claustral interactions might support a more elaborate coordination of distributed cortical processing. More generally, these considerations reinforce the need for direct comparative investigation of rodent and primate circuits, and caution against assuming that computational motifs inferred in rodents can be transferred unchanged to the primate brain.

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