Processing of natural scenes in the human pulvinar

Domain: neuroscience
Published: 2025-12-16
Editorial outcome: major_revision

Abstract
Theories of high-level visual processing and object recognition have typically focused on ventral visual cortex, often overlooking potential contributions of subcortical structures. The pulvinar, via extensive connections with visual cortex, is well-positioned to play a role in high-level vision. Here, we investigated how the pulvinar represents visual information using a high-resolution 7T fMRI dataset of responses to tens of thousands of natural scenes. Encoding models targeting different stimulus features revealed a pulvinar region selective for bodies and faces presented in the contralateral visual hemifield. Complementary model-free analyses demonstrated that this region is co-active with body- and face-selective cortical areas during natural scene viewing. These findings challenge cortico-centric models of object vision by demonstrating that principles of cortical organization, including functional clustering and hierarchical organization, also manifest in subcortex. Moreover, they highlight the value of using naturalistic stimuli to probe visual function.

Introduction
Higher visual functions, particularly object and face recognition, are crucial for interacting with and making sense of the environment. A hallmark of primate vision is our ability to rapidly and effortlessly identify and categorize objects across variations in the retinal input1. This remarkable capacity relies on the brain’s ability to extract relevant visual features from complex visual input and integrate them into a coherent percept.

The hierarchical organization of the ventral visual cortex has long been recognized as a key factor in transforming retinal input into useful perceptual representations2. This hierarchy progresses from early visual cortex (V1, V2) responding to low-level features such as local contrast and orientation, through intermediate areas (V4) encoding more complex features such as textures and shapes, to anterior regions exhibiting categorical distinctions in their responsiveness to faces, bodies, and objects3–5. In addition to feedforward pathways giving rise to increasingly complex representations, recurrent and feedback connections are thought to help ensure robust recognition in the face of challenges during natural visual experience, such as partial occlusion or varying light conditions6,7. However, this cortico-centric view is incomplete. Recent evidence suggests that recurrent connectivity with the thalamus, particularly the pulvinar, may play a critical role in achieving robust perceptual and cognitive functions8–10. The pulvinar, with its extensive reciprocal connections to visual cortex, is well-positioned to influence visual processing at multiple levels. While much research has focused on the pulvinar’s involvement in early sensory processing11–13, visual attention14,15, and visuomotor behaviors16,17, its distinct anatomical connectivity with ventral visual cortex18 suggests a potential role in high-level object recognition processes.

Despite the presence of distinct anatomical connectivity between the pulvinar and cortical regions supporting high-level visual processes, our understanding of the pulvinar’s role in complex visual functions, such as object recognition, remains limited. Previous investigations of pulvinar function have predominantly employed simple stimuli, focusing on low-level visual features, such as orientation and image contrast11,19. Some evidence hints at the pulvinar’s involvement in higher-level visual representations: anatomical tracing in non-human primates reveals connections between the pulvinar and face-selective cortical regions20; electrophysiological recordings have identified pulvinar neurons sensitive to face-like shapes21 and facial emotion22; and fMRI studies in humans have identified functional substructure within the pulvinar with sensitivity to visual categories23,24. However, the specificity of these findings to object recognition processes remains unclear. For example, medial pulvinar damage can impair emotion recognition, while sparing other aspects of visual recognition25. Moreover, individual pulvinar regions receive convergent inputs from functionally diverse cortical regions, potentially resulting in broadly tuned representations. Finally, whereas prior work has demonstrated the existence of retinotopic maps in pulvinar for low-level visual features11,19,26, it is presently unknown whether such spatial coding is also present in areas of the pulvinar that may be responsive to higher-level visual features. Thus, how the pulvinar represents complex visual information, its functional topography, and its relationship to cortical processing of visually presented objects remains unclear.

To investigate the pulvinar’s role in object recognition and the relationship of the pulvinar to cortical areas, we leveraged the Natural Scenes Dataset (NSD; http://naturalscenesdataset.org), a 7T fMRI dataset consisting of responses to tens of thousands of natural scenes. NSD constitutes an especially promising opportunity to study the pulvinar, given the use of ultra-high magnetic field strength (7T) to improve signal-to-noise ratio and achieve high spatial resolution (1.8 mm). We first developed encoding models to characterize spatial coding and responsiveness to different visual features present within natural scenes, including low-level image content and selectivity to specific visual categories. We then used model-free correlation analyses to identify co-activation patterns between the pulvinar and visual cortex during the processing of natural scenes. These analyses revealed a pulvinar region selectively responsive to faces and bodies, distinct from portions of the pulvinar sensitive to low-level visual content. Notably, both regions exhibited retinotopic organization, suggesting a common organizing principle across different levels of visual processing. The body- and face-selective pulvinar regions also showed specific co-activation patterns with corresponding body- and face-selective cortical areas. Furthermore, there was some evidence that these clusters were embedded within a broader gradient of cortical correlations across the pulvinar. These findings position the pulvinar as an important structure in high-level vision, potentially interfacing with cortex at multiple levels of visual processing.

Results
Using pRF models to characterize representations of features in natural scenes
We first characterized the pulvinar’s response to different visual features within natural scenes by building and testing encoding models for the Natural Scenes Dataset (NSD). We evaluated a wide range of image features, including low-level properties, such as local contrast, and high-level content, such as bodies and faces. To predict voxel-wise responses, we coupled features extracted from the NSD images with a population receptive field (pRF) model27 (Fig. 1). This approach characterizes each voxel as being jointly tuned for a specific stimulus feature and an area of visual space. We tested models corresponding to seven different features prevalent in real-world scenes: local contrast, salience, faces, bodies, words, foreground, and background (Supplementary Fig. 1; see Methods for details about how each feature was computed). For each feature, we obtained a full set of best-fitting pRF parameters, including pRF position, size, and gain for every voxel within each participant. This model-based approach allowed us to assess multiple levels of visual processing within the pulvinar. Importantly, when applied in cortex, these pRF models revealed selective responses in the ventral visual stream regions tuned for particular visual categories, including the body-selective FBA and the face-selective FFA (Supplementary Fig. 6), thereby validating the approach before probing subcortex.

Distinct pulvinar regions responsive to low- and high-level features
Our analyses revealed distinct regions within the pulvinar responsive to different types of visual content within natural scenes. We observed a clear functional segregation between regions of the pulvinar that process low-level visual features and those responsive to high-level object categories (Fig. 2). The variance explained by the pRF models was relatively low in the pulvinar compared to cortex (Supplementary Fig. 6). This difference can be attributed to many factors that impair signal detection within the thalamus, including the relatively high distance from the radio frequency coils, the intermixing of cell bodies and white matter, the close proximity to ventricles, and other intrinsic differences in noise sources between midbrain and cortical structures28. Despite this, the observed patterns of activation were consistent across individuals, underscoring the reliability and functional relevance of these distinctions. Across subjects, low-level feature processing was predominantly localized to the inferior-lateral portions of the pulvinar and the neighboring lateral geniculate nucleus (LGN). The local contrast and image salience models yielded highly correlated variance-explained maps (r = 0.8; Supplementary Fig. 2). We found that these models best accounted for BOLD responses in the LGN (green outline) and inferior-lateral portions (light blue) of the pulvinar (Fig. 2A, B; Supplementary Fig. 3). This finding aligns with previous human neuroimaging studies demonstrating these regions’ sensitivity to basic visual patterns, such as flickering checkerboards26,29,30. In contrast, high-level feature processing was localized to medial and posterior regions of the pulvinar across subjects. The body and face pRF models best accounted for BOLD responses in these regions (Fig. 2A, B; Supplementary Fig. 3). The high correlation between face and body variance-explained maps (r = 0.93) may reflect the frequent co-occurrences of these features in natural scenes. The foreground pRF model showed a broader pattern of responsiveness, accounting for variance in both low-level and feature-responsive regions. Foreground maps were correlated with image contrast (r = 0.6) and salience (r = 0.65) as well as with face (r = 0.41) and body (r = 0.35) feature maps. As an important control comparison, not all features were linked to activations in the pulvinar. Background and word pRF models explained little variance in pulvinar activations (Supplementary Fig. 4), and we therefore do not examine these models further.

A winner-take-all analysis comparing the variance explained by different feature models further illustrates this feature segregation within the pulvinar. Activity in the inferior-lateral pulvinar was best explained by image contrast and salience, while activity in the posterior-medial pulvinar was best explained by bodies and faces (Fig. 2C). Notably, none of these models captured substantial variance in dorsal parts of the pulvinar associated with attentional filtering23, suggesting that object-recognition processes are specifically linked to the ventral pulvinar. The limited overlap between variance-explained maps for the contrast model versus the body and face models, combined with the lack of correlation in variance explained across models (Supplementary Fig. 2), demonstrates a clear functional substructure within the pulvinar, with anatomically distinct regions exhibiting selective responses to low- and high-level visual features.

Organized spatial coding of low- and high-level visual features within the pulvinar
Having identified distinct pulvinar regions responsive to low- and high-level visual content in real-world scenes, we next examined their spatial coding. Each pRF feature model produced a set of parameter estimates characterizing voxel-wise spatial selectivity throughout the pulvinar (Supplementary Fig. 4). We focused on the image contrast and body pRF models, as these models explained the most variance in responses to the NSD stimuli within the lateral and medial pulvinar, respectively (Fig. 2C).

With respect to low-level features, the contrast model revealed a bilateral topographic organization of contralateral visual space within inferior-lateral portions of the pulvinar across subjects (Fig. 3B). The upper visual field (Fig. 3B, first row; red) was represented ventral-laterally, while the lower visual field (blue) was represented superior and medially. Between these vertical meridian representations, there was a qualitative gradual progression of preferred visual angle crossing the horizontal midline (green). Quantitatively, pRF angle estimates could be predicted by a linear gradient oriented in 3D largely along the ventral-lateral to dorsal-medial axis (left hemisphere: R2 = 0.16; right hemisphere: R2 = 0.16; Supplementary Fig. 8A–D). These representations were predominantly within central visual space (Fig. 3B, second row) and exhibited strong lateralization to the contralateral visual field (Fig. 3B, third row). At the group-average level, 73% of voxels having at least 0.1% variance explained had pRFs tuned within 3° eccentricity (range at individual subject level: 49–79%). pRF eccentricity estimates could be predicted by a simple model consisting of foveal center surrounded by more peripheral representations in 3D (left hemisphere: R2 = 0.26; right hemisphere: R2 = 0.36; Supplementary Fig. 8E). pRF organization was consistent across individual subjects (Fig. 3D) and aligns with prior electrophysiological work in non-human primates11,19 and fMRI studies in humans26,30 showing an inverted retinotopic map in this region of the pulvinar. These results demonstrate that the spatial coding of the ventral pulvinar can be effectively probed by modeling responses to low-level feature content in real-world scenes.

Surprisingly, responses to high-level visual content also exhibited clear topographic organization. The body pRF model revealed bilateral representations of contralateral visual space in pulvinar regions posterior and medial to the contrast model activations (Fig. 3C, first row). Upper visual field representations were localized to dorsal medial portions, while horizontal meridian and lower visual field representations were ventral and lateral. The medial pulvinar contained a central visual field representation surrounded by peripheral representations (Fig. 3C, second row). Body pRFs were generally lateralized to the contralateral visual hemifield (Fig. 3C, third row) and covered the extent of the visual field stimulated by the NSD images (Fig. 3E). At the group-average level, only 19% of voxels having at least 0.1% variance explained had pRFs tuned within 3° eccentricity (range at individual level: 17–57%). Similar to the contrast maps, body pRF angle estimates (left hemisphere: R2 = 0.07; right hemisphere: R2 = 0.03; Supplementary Fig. 8F–I) and eccentricity estimates (left hemisphere: R2 = 0.33; right hemisphere: R2 = 0.43; Supplementary Fig. 8J) could be partially predicted by simple low-dimensional spatial models. These results demonstrate that the processing of high-level categorical content in the pulvinar is anchored to spatial coding of sensory input.

Comparison of visual field representations derived from the contrast and body models revealed two distinct, mirror symmetric retinotopic zones. The organization of visual space along the polar angle dimension for the body model was inverted relative to that in the lateral pulvinar for the contrast model. The lower visual field representations of both models overlapped (Fig. 3B, C; Supplementary Fig. 5, dashed lines), whereas the upper visual field representation from the contrast model was located ventrolaterally and that of the body model dorsomedially. In the left hemisphere, this qualitative characterization of map orientations was reinforced by the quantitative analysis results (Supplementary Fig. 8). Together, these findings indicate that lateral and medial regions of the pulvinar each contain distinct retinotopic maps, and that their relative positioning suggests a continuity of retinotopic organization across the pulvinar. This organization parallels the layout of multiple retinotopic maps in visual cortex, where adjacent areas are arranged so that visual field representations progress smoothly from one map to the next.

Model-free analyses reveal similarity in representations between cortex and pulvinar
As a complement to our model-based analyses, we devised a model-free approach to relate cortical and subcortical representations of natural scenes. Prior research has shown that different regions of the pulvinar are anatomically connected31 and functionally coupled23 with different parts of visual cortex. The anatomical locations of the contrast- and body-selective subregions align with evidence for a gradient of pulvino-cortical connectivity26,31, suggesting that visually evoked activity in different parts of the pulvinar might resemble activity in functionally corresponding cortical regions. To test this hypothesis and the functional specificity of such connectivity, we conducted a correlational analysis between the pulvinar and cortex based on activations from the NSD dataset.

We analyzed single-trial BOLD response amplitudes in the pulvinar and cortex, derived using GLMsingle to capture the response of each voxel to individual image trials in NSD (Fig. 4A; see Methods for details). For each subject, we identified pulvinar voxels showing the highest variance explained by the contrast and body models. We then computed trial-by-trial correlations between the BOLD responses in these pulvinar voxels and all cortical voxels (Fig. 4B). This analysis revealed robust correlations between the pulvinar and extensive portions of the visual cortex for both the contrast-peak and body-peak pulvinar voxels, with weaker correlations in non-visual cortex (Fig. 4D). Contrast-peak voxels showed strong correlations with both early and anterior visual cortex, whereas body-peak voxels correlated more selectively with anterior visual cortex. Importantly, qualitatively similar maps are produced using different voxel seed selections (Supplementary Fig. 7), indicating the robustness of these findings. These findings demonstrate distinct patterns of cortical coupling for lateral and medial regions of the ventral pulvinar.

To isolate correlations driven by stimulus-evoked responses from other potential sources of co-activation (such as arousal and attention), we recomputed correlations between the pulvinar and cortex using responses to the same image but from different trials (Fig. 5A). This approach yielded more spatially localized correlations. For the contrast peak, cortical correlations were predominantly confined to posterior occipital cortex, aligning with the extent of early visual retinotopic maps (V1-hV4) identified from a separate localizer experiment (Fig. 5B; see Methods for details). Body peak co-activations were primarily constrained to extrastriate visual cortical areas responsive to faces and bodies, including EBA, FBA, and FFA (Fig. 5C). Notably, the body peak showed no correlation with early visual cortex or other category-selective extrastriate areas, such as place (e.g., TOS, PPA) and word areas (e.g., OWFA, VWFA). These findings demonstrate functionally specific coupling between pulvinar and cortex in response to real-world stimulation, reflecting an alignment of processing between the thalamus and cortex.

To contextualize these results with respect to the entire pulvinar, we performed a third analysis correlating responses from various cortical areas with responses from each voxel within the pulvinar (Fig. 6A). As in Fig. 5, this analysis was performed using responses to the same images from different trials (Fig. 5A). This analysis revealed that V1 correlations were strongest within the LGN and ventrolateral areas of the pulvinar (Fig. 6B), consistent with the greater anatomical connectivity of those subcortical regions with early visual cortex31. In contrast, FFA and FBA correlations were biased towards superior and medial aspects of pulvinar (Fig. 6B). Notably, voxels best explained by the contrast pRF model (Fig. 6B; purple circles) aligned with the peak V1 correlation, while those best explained by the face (yellow circles) or body (green circles) pRF models aligned with the peak FFA and FBA correlations.

Visualizing cortical correlation maps with half-max contours revealed a progression across the pulvinar from anterior-inferior-lateral to posterior-superior-medial as the cortical seed area shifted from V1 in the posterior visual cortex to hV4 anteriorly (Fig. 6C; see Methods for details). While all early visual cortical areas showed correlations with the LGN and ventrolateral pulvinar, the superior-medial extent of correlation increased systematically across successive cortical seeds. Because fMRI responses from nearby cortical ROIs are themselves correlated, we tested whether unique contributions from individual areas could be isolated using multiple regression (Supplementary Fig. 9). This analysis yielded more focal correlations in the pulvinar for V1, V2, V3, hV4, and FBA. Notably, while the main analysis showed both V1 and V2 correlating with anterior coronal slices overlapping the LGN, multiple regression isolated focal LGN correlations for V1, with V2 peaking instead in adjacent pulvinar. Similarly, whereas the main analysis showed widespread MT correlations, the multiple regression revealed a more focal ventral pulvinar region just medial to the V1/V2 peaks, consistent with the location of pulvinar subregion PIm, which connects exclusively with MT32. These patterns mirror prior fingerprint functional connectivity results23, suggesting that multivariate approaches such as regression or pattern analysis can better separate shared variance between nearby cortical areas. Overall, these findings reinforce the conclusion of structured subcortical–cortical correlations.

Methods
Participants
Eight participants (six females and two males; age range, 19–32 years) participated in the NSD study, which was approved by the Institutional Review Panel of the University of Minnesota. All participants provided informed consent and had normal or corrected-to-normal acuity. For detailed participant information, see ref. 56.

Dataset
The NSD comprises fMRI measurements from 8 participants viewing 9000–10,000 distinct natural color scenes (22,000–30,000 trials) over 30–40 scan sessions. Scanning was conducted using a 7T MRI scanner, with whole-brain gradient-echo EPI at 1.8-mm isotropic resolution and 1.6-s repetition time. Images were sourced from the Microsoft Common Objects in Context (COCO) database57, square cropped, and presented at 8.4°×8.4° visual angle. Stimulus images reproduced here (Figs. 1, 4, and 5, and Supplementary Fig. 1) are thus modifications of the original images used in compliance with COCO database’s Creative Commons 4.0 license (https://creativecommons.org/licenses/by/4.0/). A set of 1000 images was shared across participants, with the remaining images unique to each participant. Images were presented for 3 s each, with 1-s gaps between them.

Data preprocessing included temporal interpolation for slice time correction and spatial interpolation for head motion correction and compensation for spatial distortion. Single-trial beta weights, representing BOLD response amplitudes, were estimated using a general linear model. The study utilized “Version 3” trial response estimates56, which incorporate voxel-specific hemodynamic response functions, denoising using GLMdenoise 1.458, and ridge regression for trial responses estimation. For cortical surface data, we utilized the NSD-prepared subject-native surface data, registered and transferred to the fsaverage template using nearest neighbor interpolation. For subcortical data, we used the 1.0-mm volumetric preparation of the NSD data in subject-native space, mapped into the MNI template space using the provided T1-to-MNI transformation. To control for inter-session variability, all data were z-scored within each voxel (or vertex for surface preparations) on a session-by-session basis.

Regions of interest (ROIs)
Subcortical analyses focused on a manually defined region of the posterior thalamus that included the pulvinar, lateral geniculate nucleus (LGN) and superior colliculus (SC). This region was identified on each participant’s native-space T1-weighted anatomical image, using the contrast between gray and white matter (for more details, see ref. 26). To ensure consistency across subjects, each subject’s subcortical ROI was then transformed to 1mm MNI space using nearest-neighbor interpolation. A group average was constructed by selecting all voxels labeled as posterior thalamus in at least 3 out of 8 participants, then projected back to each participant’s native 0.8-mm anatomical space.

Specific structures within this ROI were identified based on previously reported group average functional data26. These data were warped from 1-mm MNI space to each participant’s native space. The ventral pulvinar was defined by voxels in vPul1 or vPul2 retinotopic maps. Dorsomedial and dorsolateral pulvinar were defined by correlations with the precuneus and retinotopic portions of the frontoparietal cortex (IPS1-5, SPL1, FEF, and IFS), respectively. The LGN and SC were defined by their respective retinotopic maps. ROIs were manually edited to exclude voxels outside the anatomical extent of each structure and transformed back to 1mm MNI space for group-average ROI construction.

Stimulus feature maps
Feature maps for image contrast, saliency, foreground, background, and specific image categories (faces, bodies, and words) were generated for fMRI analyses. Local image contrast maps were computed by converting images to greyscale, resizing to 800×800 pixels, squaring to approximate luminance response, and calculating local contrast within a 51×51 grid (grid element size: 0.168×0.168 degrees). The grid was padded with half a grid element on all sides to capture transitions between the image and the mean gray background (for more detail, see ref. 56). Saliency maps were generated using a pre-trained deep neural network model59 trained on the SALICON dataset, a fixation dataset based on the COCO image database60. As the NSD images also were sourced from the COCO database, this model was expected to generalize well to the NSD stimuli. Saliency maps were cropped and resampled to match the NSD stimuli from the COCO database56. All other feature maps comprised binary mask annotations. Face annotations were generated using the RetinaFace model (https://github.com/deepinsight/insightface/tree/master/RetinaFace), which identifies rectangular bounding boxes around faces. Word annotations were created using the EAST text detector (https://github.com/argman/EAST), which outputs bounding quadrangles or rectangles around text. Quadrangles were converted to enclosing rectangles for consistency with other annotations. Whole body annotations were sourced from the Microsoft COCO dataset (http://images.cocodataset.org/annotations/annotations_trainval2017.zip), using bounding boxes for human bodies (category ID 1) and animals (categories 16:25). Foreground annotations were derived from segmentations across all 80 object categories, and background annotations were defined as the inverse of the foreground annotations.

Population receptive field (pRF) analysis
A pRF encoding model with compressive spatial summation was employed to characterize spatially specific responses to stimulus feature maps within each subject’s subcortical ROI. The model was implemented using a modified version of AnalyzePRF (http://cvnlab.net/analyzePRF/), which estimates voxel-wise parameters for each feature map58, including spatial position, pRF size, gain, and variance explained. Separate models were fit for each feature map, with variance explained (R2) reflecting the percentage of variance in trial-averaged BOLD responses accounted for by the model. Only stimuli unique to each subject were included, excluding the common stimuli viewed by all participants. For non-contrast and non-saliency pRF models, a baseline term was added to account for responses to image regions covered by the annotated feature. For group-level analysis, voxel-wise parameter estimates were transformed to 1mm MNI space using linear interpolation, and median values then were calculated across participants for all parameters.

Winner-take-all pRF analysis
The performance of different features was evaluated with a winner-take-all analysis. Variance explained values for each feature model were averaged across participants in the shared 1mm MNI space (see above). Then, each voxel within the thalamus ROI with at least 0.2% variance explained by any feature was labeled to indicate which feature model explained the most variance.

pRF map characterization
To characterize the organization of retinotopic maps derived from the pRF analysis, follow-up analyses were carried out on the contrast and body pRF parameter estimates. To test whether pRF angle maps followed a consistent orientation, we extracted group-averaged pRF angle values that exceeded a 0.15% variance-explained criterion within the thalamus ROI. Voxel coordinates were then projected onto candidate map orientations, represented as oriented 3D unit vectors. Scalar projection values for each voxel were then correlated with the voxels’ pRF angle value (transformed to span 0° = lower vertical meridian to 180° = upper vertical meridian). A strong correlation coefficient indicated that voxel positions along the tested orientation predicted pRF angle values. This procedure was repeated across 60 candidate orientations evenly distributed in 3D space (azimuth values from 0 to 2π, elevation values from 0 to π), separately for each hemisphere. Resulting correlation coefficients were visualized on the surface of a unit sphere (Supplementary Fig. 8). To assess significance of the best-fit orientation, the analysis was repeated with shuffled voxel-to-pRF correspondences to generate a null distribution of peak correlation coefficients, against which the observed peak was compared.

To test whether pRF eccentricity values followed a systematic spatial arrangement, we fit anisotropic 3D Gaussian to the eccentricity parameter estimates. The model had six parameters: a 3D mean (x, y, z), a single standard deviation, a baseline term, and a gain term. Note that when the baseline term is high (e.g., 4° eccentricity) and the gain negative, the model predicts central visual field eccentricities near the Gaussian center and peripheral eccentricities further away. The model was fit using the nonlinear minimizer from SciPy61 with standard parameter settings and 1-R2 as the objective function, separately for the contrast and body maps in each hemisphere. Analyses were restricted to voxels within the thalamus ROI exceeding a 0.15% variance-explained threshold in the pRF analysis. For each fit, 50 random initializations of the Gaussian mean within the 3D bounding box were attempted, and the best-fitting model was retained.

Pulvino-cortical correlation analysis
Correlation analyses comprised two parts: (1) correlating single-voxel pulvinar responses with those across the entire cortical surface, and (2) correlating average responses for cortical ROIs with all pulvinar voxels. For the first analysis, we identified voxels with the highest variance explained for the contrast model (contrast peaks) and for the body model (body peaks) in each participant’s native space. Trial response estimates from these voxels were correlated with response estimates from every vertex on the fsaverage cortical surface. This process was repeated 1000 times for each participant with trials randomly resampled with replacement (i.e., bootstrapped). For visualization of results on the cortical surface (Figs. 4 and 5), the correlation coefficient, averaged across bootstrap replicates and participants, was shown only if at least 95% of the bootstrapped correlation coefficients exceeded a value of 0 in a majority of participants in the given vertex.

For the second analysis, we constructed a set of cortical ROIs covering most of the visual cortex. These included: (1) retinotopic areas (V1, V2, V3, and hV4) defined from the pRF localizer experiment included with NSD56, (2) additional retinotopic areas from the Wang probabilistic retinotopic atlas62, and (3) category-selective areas (OFA, FFA, aTL-faces, EBA, FBA, PPA, and VWFA) from the NSD functional category localizer experiment56. ROIs were pruned on an individual-participant basis to avoid overlap. Average responses were generated for each cortical ROI in fsaverage space and correlated with the trial response estimates from voxels within the subcortical ROI.

We performed analyses between the pulvinar and cortex using both same-trial and different-trial (for same image) pairings of response estimates. For same-trial correlations, each response in the subcortex was correlated with responses from the same trials in the cortex. For different-trial correlations, we leveraged that each participant viewed each distinct image three times. This allowed us to identify six possible pairs of image repeats, resulting in six response pairings for 10,000 images across different trials of the same image. We computed the correlation for each pairing, and averaged the resulting six correlation coefficients to derive a final correlation estimate between the pulvinar and cortex.

Programming languages and packages
Analyses were carried out and figures were produced using custom code written in MATLAB R2018a (data preprocessing, stimulus feature map generation, pRF analysis, correlation analysis, surface visualizations) or Python 3.13.9 (pRF map characterization, volume visualizations). The following packages were essential in this process, some of which were already mentioned in the text above: analyzePRF 1.2 (https://github.com/cvnlab/analyzePRF/), NumPy 2.2.4 (https://numpy.org/), SciPy 1.15.2 (https://scipy.org/), matplotlib 3.10.1 (https://matplotlib.org/), and nibabel 5.3.2 (https://nipy.org/nibabel/).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript by Guest et al. describes a study that re-analyzed a large 7T dataset (the \"Natural Scenes Dataset\", NSD, consisting of BOLD responses to thousands of images, for a small number of individuals) to investigate object selectivity in the human pulvinar and its connectivity to cortex. The experimental team is superb, including the NSD co-developers Allen & Kay as well as senior author Arcaro, who has already made ground-breaking contributions to our understanding of the human pulvinar.\n\nThe team employs a technically highly sophisticated approach to elucidate pulvinar selectivity, consisting of fitting population receptive field (pRF) models to the NSD data. Specifically, the authors defined several features (\"contrast\", \"salience\", \"foreground\", \"words\", \"faces\", \"bodies\" and \"background\") and then calculate feature maps for each image, which are then used to fit a pRF model for each voxel in the pulvinar. Results reveal selectivity of the ventral pulvinar to lower-level features (contrast, salience) as well as more dorsal selectivity for high-level objects such as bodies and faces. In addition, the study also shows a topographic organization in the pulvinar as well as object-specific connectivity between pulvinar regions selective for complex objects (here, faces and bodies) and corresponding cortical areas selective for these objects. These analyses are done well, and the results are compelling.\n\nMy concerns do not lie at all with the level of technical sophistication of the study. My enthusiasm for the present manuscript is rather limited by the incremental nature of the insights the study provides about pulvinar organization and selectivity, relative to the senior author's previous work and more recent papers in the literature. Specifically, Arcaro et al., J Neurosci, 2015, already showed visuotopic representations in the pulvinar, and Arcaro et al., Nat Comm, 2018, showed selectivity for faces and scenes in the human pulvinar and coupling with corresponding ventral stream regions. In addition Wen et al., J Neurosci, 2023, showed selectivity for animals and tools and corresponding connectivity to cortical areas in the human pulvinar. The contribution of the present study appears to lie in its clever re-analysis of the NSD scans, which is indeed quite elegant. Yet, I did not find that the study changed my understanding of selectivity and function of the human pulvinar relative to the literature. Whether the manuscript is judged suitable for Nature Communications depends on how much one weighs the beauty of the analyses vs. the advancement in understanding they provide. Reading the manuscript is a bit reminiscent of reading a novel, elegant proof of an already proved theorem.\n\nMinor:\nFig. 2 caption: \"magneta\"", "reviewer_2": "Guest et al develop a new analysis of an existing dataset which suggests that the pulvinar has a functional organization that mirrors the hierarchical organization of the ventral visual cortex. The analysis is clever and the findings offer a new perspective on the pulvinar and its functional role. I find the work elegant and important. I have a couple of suggestions to further improve the study.\n\n1. The computational analysis illustrated in Fig 1 leverages encoding models and fMRI responses to natural scenes. The goal is to determine whether a voxel is more selective for low-level image features such as spatial contrast, or for high-level features such as the presence of a face. I suggest you begin by applying this analysis to the ventral stream to validate that it identifies what it claims to identify, before focusing on the pulvinar. This will also provide us with a quantitative reference. The R-square values in the pulvinar seem very small (but locally consistent and with a plausible structure). Are they comparable to cortical values? If not, why not?\n2. Fig 3 is very cool and suggestive. Could you try to quantify the systematic nature of the data in summary statistics? It would be nice to have a single number that expresses how well the data respect angular (top-row) and radial (middle row) retinotopic organization, respectively. Same for the laterality.\n3. If I understand correctly, the maps in Fig 4C are each based on a single pulvinar voxel. Can you document the population RF for both voxels? It would be good to know to what degree the RFs overlap. Could you also make a supplementary figure that shows the same maps for some other voxels (maybe the rest of the top 5 and the bottom voxel)? This would reveal how stable these results are across voxels, and how strong the association with the contrast and body selectivity is.\n4. I have a similar request for Fig 5. Please provide some additional supplementary illustrations to document the stability and specificity of these patterns.\n5. For Fig 6, it would be better to follow the convention of Fig 2 and 3 and show three maps per cortical seed (1 anterior, 1 middle, 1 posterior).\n6. Can you expand the discussion of the functional role of these Pulvinar representations? It’s fine if you have to speculate. Just say so explicitly, so that the readers is aware of this.\n7. Could you discuss what a targeted follow-up experiment would look like? For example, if you were to conduct a decoding analysis, do you expect that face vs non-face decoding performance will differ substantially for the anterior vs posterior parts of Pulvinar?", "reviewer_3": "In this study, Guest et al. leveraged the Natural Scenes Dataset (NSD) to investigate the functional organization of the human pulvinar during naturalistic visual processing. Using pRF modeling, they identified two distinct groups of pulvinar voxels: one group best modulated by contrast and salience features, and another group best modulated by body and face features. Further cortical correlation analyses revealed that the contrast/salience group exhibited strong functional coupling with early visual areas, whereas the body/face group showed preferential coupling with higher-order ventral stream areas, including EBA and FFA. These findings suggest that the pulvinar mirrors the cortical visual hierarchy, with distinct subdivisions supporting low-level visual features and high-level categorical representations.\n\nIn general, I find the results clear, interesting, and the manuscript well written. However, my major concern relates to the claim of \"mirroring the hierarchical organization of ventral visual cortex,\" which I believe is not fully supported by the current evidence. High-level object vision encompasses not only face and body processing but also areas specialized for scene and inanimate object processing. However, the study does not appear to identify pulvinar voxels specialized for these additional categories, suggesting that the proposed \"mirroring\" of the ventral visual cortex may be incomplete.\n\nMoreover, the hierarchical analysis shows that the maximal superior-medial extent of correlation increases across successive visual cortical areas. However, given the high degree of correlation among early visual areas (V1, V2, V3, hV4), it remains unclear whether the current dataset possesses sufficient signal-to-noise ratio to reliably differentiate these areas. To strengthen this point, the authors could first apply their method solely within early visual areas to verify whether it can distinguish among V1, V2, V3, and hV4. Alternatively, they could isolate the unique contribution of each area by regressing out shared responses from other areas and then re-run the correlation analysis.\n\nAdditionally, hierarchical organization typically implies that information is processed in a stepwise fashion. However, it has been reported that face processing in the pulvinar can occur very rapidly, with latencies of less than 50 ms (Nguyen et al., 2013). It is difficult to reconcile such short processing latencies with the notion that facial information undergoes several hierarchical stages before reaching the pulvinar. This temporal constraint raises further questions about the appropriateness of the \"hierarchical mirroring\" interpretation.\n\nNevertheless, even if the pulvinar does not exhibit a clear hierarchical organization analogous to the ventral visual stream, it remains highly interesting that both high-level object information and low-level contrast information are co-represented within the same subcortical structure. This finding alone provides valuable insights into the functional diversity of the pulvinar.\n\nFinally, although this is a minor point, I would also like to raise a small suggestion. I appreciate the elegant use of pRF modeling with different feature maps to estimate each voxel’s pRF location and feature preference. However, I wonder whether the authors could also explore distinguishing these voxel groups based purely on their response profiles, for example by applying k-means clustering or other unsupervised clustering methods.\n\nWhile the authors identify pulvinar regions tuned to body and face features, it remains unclear how selective these representations truly are. For instance, are these voxels exclusively driven by body and face stimuli, or do they also respond to other categories? Including an analysis of the category selectivity of these voxels would not only strengthen the conclusions but also provide readers with a deeper understanding of the functional specificity within the pulvinar.\n\n(Remarks on code availability)\nThe code I read is mainly focused on plotting the results, but I've been struggling to find the actual code for pRF estimation. Unfortunately, the pRF estimation code seems to be missing"}, "author_response": "We would like to sincerely thank the reviewers for their helpful feedback on our manuscript. We\nbelieve that the revised manuscript is significantly improved over the original and addresses the\nreviewers’ major concerns. For convenience, we have reproduced the reviewers’ comments\nbelow in bold and our replies are immediately below each comment in roman.\n\nReviewer #1:\n[...]\nWe appreciate this thoughtful comment, which has motivated us to clarify the distinct\ncontributions of our work beyond prior studies of the human pulvinar in the revised text. While\nbuilding upon foundational work (Arcaro et al., 2015, 2018; Wen et al., 2023), our study provides\nseveral novel contributions that substantially advance our understanding of how subcortical\nstructures participate in high-level vision:\n- Retinotopic organization of high-level visual representations in the pulvinar. We show\nthat the posterior–medial, face/body–selective pulvinar exhibits its own stimulus-\ndependent retinotopic map. Prior work suggested that retinotopic coding was restricted\nto a small portion of the pulvinar sensitive to low-level features (Arcaro et al., 2015) and\nwas gated by spatial attention (Fischer & Whitney, 2012, Nat Comm.). Our findings\nreveal that this map is mirror-symmetric to the low-level feature map in ventral–lateral\npulvinar and sits largely outside previously mapped retinotopic zones, extending\nretinotopy from low-level to high-level content in human pulvinar. Beyond revealing\nretinotopic coding for high-level visual information, we demonstrate that retinotopy\nserves as a common organizing principle for both low- and high-level visual coding in the\npulvinar. This finding parallels ongoing debates about the role of retinotopy in organizing\nhigh-level visual cortex (e.g., Arcaro & Livingstone, 2021 & Op de Beeck et al. 2019) and\nestablishes the pulvinar as following similar organizational principles across processing\nhierarchies. Characterization of the retinotopic maps has also been improved throughout\nthe text and supplementary materials thanks to suggestions from the reviewers.\n- Robust category selectivity in the pulvinar resolved through high-resolution imaging.\nUsing tens of thousands of natural images and high-resolution data, we demonstrate\nconsistent face and body selectivity across individuals, with spatially organized,\nfunctionally specific responses rather than diffuse activation. Previous studies provided\nonly suggestive evidence for category selectivity in the pulvinar for bodies and faces.\nArcaro et al. (2018) showed face preferences based solely on face vs. scene contrasts,\na limitation explicitly noted by reviewers of that work. Wen et al. (2023) found face-\nselective activations in only one of two experiments, raising questions about the\nreliability and specificity of these responses. Critically, we show these responses are\nspatially organized and functionally specific rather than the diffuse, non-selective\nactivation pattern that might be expected given the pulvinar's broad anatomical cortical\nconnectivity. (Benevento & Rezak 1976).\n- Stimulus-driven pulvino-cortical coupling during active vision. Our different-trials\ncorrelation analysis represents a methodological advance that isolates stimulus-driven\nco-activation from shared noise or non-visual factors. This approach reveals that\npulvinar-cortical correlations during natural scene viewing are far more spatially specific\nthan resting-state patterns, with face/body-selective pulvinar regions showing selective\ncoupling only with corresponding cortical areas (FFA, EBA, FBA) and not with other\ncategory-selective regions (places, words). This specificity demonstrates that the\npulvinar actively participates in category-specific visual processing rather than merely\nproviding diffuse modulatory input. Our prior work (Arcaro et al. 2018) demonstrated\npulvino-cortical functional coupling based on resting-state data in the absence of visual\ninput.\n- Pulvinar function during naturalistic visual processing. Previous pulvinar studies have\nused highly controlled, artificial stimuli that engage isolated components of the visual\nsystem. Our use of tens of thousands of natural scenes addresses a critical gap: how\nthe pulvinar functions during ecologically valid vision when multiple visual processes are\nsimultaneously engaged. Given the pulvinar's diffuse cortical projections, naturalistic\nstimulation could have resulted in non-specific, \"washed out\" responses. Instead, we find\nthat the pulvinar maintains functional specificity even during complex natural vision, with\ndistinct regions processing different stimulus features in an organized manner. This\ndemonstrates that the pulvinar's role in vision extends beyond laboratory conditions to\nnatural visual behavior.\nMinor:\nFig. 2 caption: \"magneta\"\nFixed.\n\nReviewer #2:\n[...]\n1. The computational analysis illustrated in Fig 1 leverages encoding models and fMRI responses to natural scenes. The goal is to determine whether a voxel is more selective for low-level image features such as spatial contrast, or for high-level features such as the presence of a face. I suggest you begin by applying this analysis to the ventral stream to validate that it identifies what it claims to identify, before focusing on the pulvinar. This will also provide us with a quantitative reference. The R-square values in the pulvinar seem very small (but locally consistent and with a plausible structure). Are they comparable to cortical values? If not, why not?\nThis is a great suggestion. We concur that it would be valuable for readers to evaluate our\nmethods in the context of visual cortex, whose organization and selectivity are better\ncharacterized. We have added a supplementary cortical figure mirroring Figure 2’s pulvinar R2\nmaps. The cortical R2 maps align with known selectivity: the contrast model performs best in\nearly visual cortex, and the face and body models perform best in face- and body-selective\nregions in ventral visual cortex, including the FFA and FBA. We have modified the Results on\npage 3 to point to the supplementary cortical figure:\nImportantly, when applied in cortex, these pRF models revealed selective responses in the ventral visual\nstream regions tuned for particular visual categories, including the body-selective FBA and the face-\nselective FFA (Supplementary Figure 6), thereby validating the approach before using the approach to\nprobe subcortex.\nThe R2 values in the pulvinar are much smaller than in cortex, typically by more than an order of\nmagnitude. We believe there are several factors limit signal quality in the pulvinar, including\ngreater distance from the RF coils, the presence of ventricles and vasculature surrounding the\npulvinar, and the intermixing of gray and white matter within the pulvinar (as opposed to discrete\nwhite matter tracts in cortex). To address the robustness of our results, we have added a new\nSupplementary Figure 3 that illustrates the spatial consistency of contrast, salience, face, and\nbody models across all subjects. We discuss these differences in signal quality between the\nthalamus and cortex on page 4:\nThe variance explained by the pRF models was relatively low in the pulvinar compared to cortex\n(Supplementary Figure 6). This difference can be attributed to many factors that impair signal detection\nwithin the thalamus, including the relatively high distance from the radiofrequency coils, the intermixing\nof cell bodies and white matter, the close proximity to ventricles, and other intrinsic differences in noise\nsources between midbrain and cortical structures (Barry et al., 2013). Despite this, the observed patterns\nof activation were consistent across individuals, underscoring the reliability and functional relevance of\nthese distinctions.\n2. Fig 3 is very cool and suggestive. Could you try to quantify the systematic nature of the data in summary statistics? It would be nice to have a single number that expresses how well the data respect angular (top-row) and radial (middle row) retinotopic organization, respectively. Same for the laterality.\nThanks for this suggestion. We have added summary statistics to further characterize the\nretinotopic maps to the main results text, supported by the new Supplementary Figure 8. For\nangle maps, we tested whether the organization of pRF angle estimates could be predicted by\nan oriented linear gradient in 3D space, that is, a linear progression of pRF angle along a\nparticular 3D orientation. Full details of this analysis are provided in the revised Methods and\nare shown in Supplementary Figure 8. This analysis yielded evidence of organized angle maps\nfor both the contrast and body pRF models.\nFor eccentricity estimates, we used nonlinear optimization to fit a 3D isometric Gaussian to the\neccentricity pRF parameter estimates. This model performed well (in terms of variance\nexplained) for both the contrast and body results. The fitted parameters can be interpreted as:\n(1) the 3D mean vector capturing the location of peak foveal representations, and (2) the\nstandard deviation parameter in voxel units, indicating how quickly preferred eccentricity\nincreases with distance from the foveal peak.\nWe note that both modeling approaches are simplified. The angle analysis ignores the\npossibility of non-linear maps or multiple maps within the pulvinar (which have been reported in\nanimal physiology experiments), and it is sensitive to outliers and to the R2 threshold used to\nsubset the data. Similarly, the Gaussian eccentricity model provides only a coarse summary of\ncentral-peripheral organization rather than capturing all possible complexities (e.g.,\nmagnification function). Nevertheless, as a supplementary analysis, we believe these analyses\nprovide useful quantitative confirmation of a systematic progression of visual angle within the\npulvinar, while making minimal assumptions about the exact structure of these maps.\n3. If I understand correctly, the maps in Fig 4C are each based on a single pulvinar voxel. Can you document the population RF for both voxels? It would be good to know to what degree the RFs overlap. Could you also make a supplementary figure that shows the same maps for some other voxels (maybe the rest of the top 5 and the bottom voxel)? This would reveal how stable these results are across voxels, and how strong the association with the contrast and body selectivity is.\nThanks for this helpful suggestion. Figure 4C was based on an average of the correlation maps\nacross all eight subjects, generated from each subject’s peak seed voxels. We have revised Fig.\n4 to include an additional panel showing the RFs of the voxels in question.\nWe also added a new supplementary figure (Supplementary Figure 7) that extends this analysis\nacross the top 20 voxels for contrast and face models, as well as the bottom 20 voxels for each.\nThese additions provide direct insight into the stability and strength of associations: results from\ntop-performing contrast voxels are similar, and likewise for top-performing body voxels, whereas\nresults for the worst voxels show little or no structure and produce inconsistent maps not\nreflective of visual processing.\n4. I have a similar request for Fig 5. Please provide some additional supplementary\nillustrations to document the stability and specificity of these patterns.\nDone. The supplementary figure added to address the stability of Fig. 4 also shows analogous\nresults for Fig. 5. As in the results shown in Fig. 4, results among top-performing voxels are\ncomparable and stable.\n5. For Fig 6, it would be better to follow the convention of Fig 2 and 3 and show three\nmaps per cortical seed (1 anterior, 1 middle, 1 posterior).\nFig. 6B was revised to show three coronal slices matching those used in Figs. 2 and 3 using the\nsame layout.\n6. Can you expand the discussion of the functional role of these Pulvinar\nrepresentations? It’s fine if you have to speculate. Just say so explicitly, so that the\nreaders is aware of this.\nWe thank the reviewer for this suggestion to expand on the functional implications of our\nfindings. While the pulvinar has traditionally been viewed as supporting visuospatial attention\nand dorsal stream processing, our identification of a distinct posterior medial region selective for\nfaces and bodies points to a broader role in high-level vision. We speculate that this face/body-\nselective pulvinar region may act as an integration hub for ventral stream processing,\ncoordinating category-specific information across distributed cortical areas through predictive\nlearning mechanisms (O'Reilly et al., 2021). More broadly, given the early establishment of\nretinotopically organized pulvino-cortical connectivity patterns observed in human neonates\n(Ayzenberg et al., 2025), we propose that the pulvinar may play an instructive role in scaffolding\nthe development of cortical face and body processing areas. This developmental contribution\naligns with longstanding theories that early perceptual abilities rely on subcortical circuits (e.g.,\nJohnson 2005; Cortes et al., 2024), suggesting that the pulvinar provides crucial developmental\nsignals that guide the initial organization of ventral stream category selectivity and may explain\nhow advanced visual recognition abilities emerge before cortical circuitry fully matures. The\nrevised text can be found on pages 11–13, with the additions highlighted below:\npg 12\nOur results suggest that the pulvinar may play a more integral role in object recognition than previously\nthought, potentially contributing to the rapid and efficient processing of socially relevant stimuli like\nfaces and bodies (Pessoa & Adolphs, 2010). Converging with recent work showing face responses in the\nsuperior colliculus (Yu et al., 2024), our results indicate a broader role for subcortex in both low- and\nhigh-level vision.\npg 12\nThese results also align with proposals that retinotopic organization shapes development of visual cortex,\npotentially serving as a scaffold for the emergence of complex functional specializations (Arcaro &\nLivingstone, 2021). Pulvinar connectivity with cortex emerges early in gestation (Shatz & Rakic, 1981),\nand is hierarchically and retinotopically organized at birth (Ayzenberg et al., 2025), preceding the\ndevelopment of functional clusters in cortex such as face- (Livingstone et al., 2017) and word-selective\nregions (Dehaene-Lambertz et al., 2018). This developmental timeline suggests that the pulvinar may\nplay a critical role in shaping the ventral visual cortical hierarchy (Bourne & Rosa, 2006),\npotentially providing error-driven signals that support the emergence of categorical representations (O’Reilly et al.,\n2021).\npg 13\nHow does the pulvinar integrate with the established cortical architecture of object vision to support\nrecognition? The pulvinar is an 'associative' thalamic nucleus with limited internal connectivity (Imura &\nRockland, 2006), forming input and output relationships primarily with cerebral cortex. Prior studies have\nrevealed a gradient of anatomical (Adams et al., 2000; Baleydier & Morel, 1992; Webster et al., 1993) and\nfunctional (Arcaro et al., 2018) connectivity across the pulvinar, progressing from posterior to anterior\nvisual cortex. Rather than constituting a distinct, parallel processing pathway, the pulvinar's\ntopographically organized, but overlapping, cortical connection zones enable indirect transcortical\ncommunication (Shipp 2003), effectively integrating with the ventral visual cortex at multiple hierarchical\nstages. Converging with these prior studies, our analysis of processing during natural scene viewing\nrevealed a gradient of functional coactivation between the pulvinar and cortex that recapitulates the\nhierarchical organization of the ventral visual pathway. This hierarchical organization of thalamo-cortical\nconnections suggests that the pulvinar functions as an integral part of the ventral visual cortical pathway.\npg 13\nThe connection between the pulvinar and object vision suggested by our results can be considered in\nlight of theories that the pulvinar contributes to “fast detection” circuits that support rapid, evolutionarily\nimportant visual behaviors (Soares et al., 2017). In macaques, some pulvinar neurons sensitive to face-\nlike features respond with latencies shorter than 50 ms (Nguyen et al., 2012), much earlier than typical\nresponses in IT cortex. Such rapid responses could arise within the pulvinar or reflect input from the\nsuperior colliculus (Berman & Wurtz, 2010), where similarly fast face-selective responses have been\nobserved (Yu et al., 2024). Importantly, not all pulvinar neurons respond this quickly, and population-level\nface responses accumulate more gradually (Nguyen et al., 2012). Rather than being inconsistent with a\nrole in ventral visual stream processing, these temporal differences may reflect complementary functions\nwith fast pulvinar responses providing early predictive signals that help shape slower, more detailed\ncortical analysis (Bar, 2003; O’Reilly et al., 2021).\n7. Could you discuss what a targeted follow-up experiment would look like? For example,\nif you were to conduct a decoding analysis, do you expect that face vs non-face\ndecoding performance will differ substantially for the anterior vs posterior parts of\nPulvinar?\nThis is a great suggestion for a direct experimental test of our functional organization findings.\nWe predict that face versus non-face decoding performance should be substantially higher in\nthe posterior medial pulvinar (where we observe face/body selectivity) compared to the anterior\nlateral pulvinar (where we observe contrast/salience selectivity). Specifically, posterior regions\nshould show decoding accuracies approaching those of cortical face-selective areas, while\nanterior regions should demonstrate poor face/non-face discrimination but strong performance\nfor low-level visual features such as contrast or spatial frequency.\nMore broadly, our results set the stage for developmental experiments that could test whether\nface and body selectivity emerges earlier in the pulvinar than in cortex, which would support the\npulvinar's proposed instructive role in guiding cortical organization. Additionally, our findings of\noverlapping face and body representations suggest the pulvinar may serve as an integration\nhub, which could be tested through representational similarity analyses to determine whether\npulvinar responses reflect weighted averages of nearby cortical representations. Such\nintegration could be particularly important for learning statistical regularities in natural visual\nenvironments, such as the spatial co-occurrence of faces and bodies that macaque IT neurons\ndemonstrate sensitivity to (Arcaro et al., 2020), suggesting the pulvinar may play a key role in\nextracting and representing contextual associations during natural vision. Relevant portions of\nthe modified text on pg 14 are copied below:\nOur findings also generate specific experimental predictions about the pulvinar's functional organization\nand development. Decoding analyses should reveal stronger face-versus-non-face classification in the\nposterior medial pulvinar compared to anterior lateral regions, with posterior responses approaching\nthose of cortical face-selective areas and anterior response better suited to low-level feature\ndiscrimination. Developmental studies could test whether face and body selectivity emerges earlier in the\npulvinar than in cortex, consistent with its early forming connectivity to cortical targets in neonates\n(Ayzenberg et al., 2025) and supporting a role in scaffolding cortical organization. The overlapping\nrepresentations of faces and bodies further suggest that the pulvinar may serve as an integration\nhub, a possibility that could be evaluated with representational similarity analyses to determine whether\npulvinar responses reflect weighted averages of nearby cortical representations.\n\nReviewer #3:\n[...]\nThank you for this thoughtful feedback, which has helped us clarify the scope and framing of our\nhierarchical claims. We agree with the reviewer that high-level object vision encompasses more\nthan face and body processing, and we acknowledge that our analyses did not exhaustively\nprobe all category domains represented in ventral visual cortex. Although we modeled word and\nscene features in our natural scene analyses, we did not observe clear category-selective\nresponses for these domains in the pulvinar. It is possible that the background and word models\nwere not sufficiently sensitive to detect such selectivity; however, with the same models we did\nobserve scene- and word-selectivity in ventral visual cortex (new Supplementary Fig. 6). These\nresults suggest that our approach is capable of detecting such selectivity when present, but that\nthe pulvinar may not exhibit the same breadth of category coverage as cortex.\nOur original emphasis was on demonstrating a progression from low-to-high level visual\nfeatures, rather than demonstrating comprehensive coverage of all ventral stream categories.\nSpecifically, we identified a gradient from low-level visual features (contrast, luminance) in\nanterior lateral pulvinar, through intermediate processing features (foreground segmentation)\nwith connectivity to mid-level cortical areas (hV4), to high-level face and body representations in\nposterior medial pulvinar. The main correlation analysis, however, had limited ability to fully\nseparate the contributions of nearby early visual areas (V1, V3, hV4), which are themselves\nhighly correlated. To address this concern, we performed a multiple regression analysis based\non reviewer feedback that better isolated unique contributions of individual cortical regions (new\nSupplementary Figure 9). This approach revealed more focal patterns of pulvinar correlations,\nwith V1, V2, and hV4 showing distinct peaks, thereby providing additional evidence that the\npulvinar contains spatially segregated representations aligned with multiple stages of the\ncortical hierarchy. That said, we acknowledge that our results stop show of a fully resolved\nhierarchical progression across multiple intermediate stages, particularly given the inherent\nchallenges of separating highly correlated cortical areas with fMRI. The revised presentation of\nthe correlation analysis on pg 11 Is copied below:\nVisualizing cortical correlation maps with half-max contours revealed a progression across the pulvinar\nfrom anterior-inferior-lateral to posterior-superior-medial as the cortical seed area shifted from V1 in the\nposterior visual cortex to hV4 anteriorly (Figure 6C; see Methods for details). While all early visual cortical\nareas showed correlations with the LGN and ventrolateral pulvinar, the superior-medial extent of\ncorrelation increased systematically across successive cortical seeds. Because fMRI responses from\nnearby cortical ROIs are themselves correlated, we tested whether unique contributions from individual\nareas could be isolated using multiple regression (Supplementary Figure 9). This analysis yielded more\nfocal correlations in the pulvinar for V1, V2, V3, hV4, and FBA. Notably, while the main analysis showed\nboth V1 and V2 correlating with anterior coronal slices overlapping the LGN, multiple regression isolated\nfocal LGN correlations for V1, with V2 peaking instead in adjacent pulvinar. Similarly, whereas the main\nanalysis showed widespread MT correlations, the multiple regression revealed a more focal ventral\npulvinar region just medial to the V1/V2 peaks, consistent with the location of pulvinar subregion PIm,\nwhich connects exclusively with MT (Gutierrez et al., 2000). These patterns mirror prior “fingerprint”\nfunctional connectivity results (Arcaro et al., 2018), suggesting that multivariate approaches such as\nregression or pattern analysis can better separate shared variance between nearby cortical areas.\nOverall, these findings reinforce the conclusion of structured subcortical–cortical correlations.\nIn response to the reviewer's concern about the term \"mirroring,\" we have revised the\nmanuscript to remove this language, as it may imply more comprehensive correspondence than\nour data support. Instead, we emphasize that the pulvinar follows hierarchical organization\nprinciples like those found in ventral visual cortex, specifically within the face and body\nprocessing pathway. We believe this framing more accurately reflects our findings while\nacknowledging the limits of categorical coverage in the present study and sets the stage for\nfuture work to test whether similar organization principles extend to other visual domains in the\npulvinar. Below, associated edits to the text are copied for convenience:\npg 2-3\n[OLD] Furthermore, these distinct functional clusters were embedded within a broader gradient of cortical\ncorrelations across the pulvinar, mirroring the hierarchical organization of ventral visual cortex.\n[NEW] Furthermore, there was some evidence that these clusters were embedded within a broader\ngradient of cortical correlations across the pulvinar.\npg 11\nOur analysis of BOLD responses to tens of thousands of natural scenes revealed a complex functional\narchitecture within the pulvinar. Using pRF models, we identified distinct regions responsive to low-level\nvisual features and high-level object categories, particularly faces and bodies that each contained orderly\nretinotopic maps and together formed a continuous organization across the pulvinar. Correlational\nanalyses further demonstrated functional coupling between pulvinar subregions and cortical areas\nselective for low- and high-level visual features, matching the distinctions revealed by the pRF analysis.\nTogether, these findings suggest that the pulvinar interacts with cortex at multiple levels of cortical\nprocessing, and that the pulvinar may play a more integral role in visual processing than previously\nappreciated.\n[...]\nThank you for pointing out this issue. We tested several strategies to further examine whether\ndifferential correlations between the pulvinar and early visual areas could be resolved. First, we\nimplemented the reviewer’s suggestion to regress out shared responses from other areas\nbefore computing the correlations. Specifically, we repeated the cortical-to-subcortical\ncorrelation analyses for each cortical seed area after first regressing out the contributions of the\nother 13 cortical seed areas from the subcortical voxel responses (multivariate semi-partial\ncorrelation). This procedure produced extremely low correlation values overall compared to the\noriginal analysis, consistent with the reviewer’s concern that early visual areas are too highly\ncorrelated to cleanly isolate their contributions with this method. However, this approach is\noverly conservative, as it effectively discards meaningful shared variance, which likely\nunderestimates the true strength of pulvinar-cortical relationships.\nTo address this limitation, we also tested a multiple regression framework that models\nsubcortical responses as a linear combination of cortical seed areas. Unlike the semi-partial\ncorrelation approach, this method preserves shared variance while allowing the unique\ncontributions of each cortical areas to be estimated simultaneously. As shown in Supplementary\nFigure 9, this analysis revealed more focal patterns of pulvinar correlations, with V1, V2, hV4,\nand MT showing distinct peaks, thereby providing additional evidence that the\npulvinar contains\nspatially segregated representations aligned with multiple stages of the\ncortical hierarchy.\n[...]\nThank you for this excellent point, which raises important considerations about temporal\ndynamics in face processing. Indeed, there is clear evidence for rapid subcortical responses to\nfaces that are difficult to reconcile with a purely stepwise cortical hierarchy. Recent work has\ndemonstrated rapid face responses in the superior colliculus with latencies under 100ms (Yu et\nal., 2024), and as the reviewer notes, similar responses have been reported in the pulvinar\n(Nguyen et al., 2013). These findings suggest that subcortical pathways can provide rapid\naccess to face information, bypassing the slower progression through central cortex.\nThe Nguyen et al. results also point to the intriguing possibility of integration between fast and\nslow processing streams. They reported that while some pulvinar neurons responded rapidly,\nstimulus information and discrimination improved over later time periods. This suggests that\ninitial rapid signals could be complemented by slower, cortex-linked processing. We do note\nimportant caveats: many of these recordings were from ventral lateral pulvinar, responses were\nstronger to face-like images than to natural face photos, and face versus non-face\ndiscrimination was not statistically reliable despite numerical differences.\nWe speculate that rapid subcortical responses may reflect a \"reverse hierarchy\" mechanism, in\nwhich early signals from pulvinar or superior colliculus provide feedback that primes ventral\nvisual cortex and shapes slower recognition processes (Kveraga et al., 2007).\nNeuroanatomically, both feedforward hierarchical pathways and recurrent alternative routes\nexist (Kravitz et al., 2013), suggesting that parallel processing streams, rather than a single\nsequential hierarchy underlie face and object recognition.\nWhile our fMRI data primarily address the spatial organization of pulvinar selectivity,\ntemporal\ndynamics will be needed to distinguish between these processing models. In response to this\nimportant point, we have expanded the Discussion on pg 13 to better situate our current findings\nwithin the extensive network of pathways that support face and body processing, emphasizing\nthat spatial organization principles can coexist with rapid, non-hierarchical processing\nmechanisms that operate through parallel subcortical routes. The relevant portion of the revised\nDiscussion is copied below:\nThe connection between the pulvinar and object vision suggested by our results can be considered in\nlight of theories that the pulvinar contributes to “fast detection” circuits that support rapid, evolutionarily\nimportant visual behaviors (Soares et al., 2017). In macaques, some pulvinar neurons sensitive to face-\nlike features respond with latencies shorter than 50 ms (Nguyen et al., 2012), much earlier than typical\nresponses in IT cortex. Such rapid responses could arise within the pulvinar or reflect input from the\nsuperior colliculus (Berman & Wurtz, 2010), where similarly fast face-selective responses have been\nobserved (Yu et al., 2024). Importantly, not all pulvinar neurons respond this quickly, and population-level\nface responses accumulate more gradually (Nguyen et al., 2012). Rather than being inconsistent with a\nrole in ventral visual stream processing, these temporal differences may reflect complementary functions\nwith fast pulvinar responses providing early predictive signals that help shape slower, more detailed\ncortical analysis (Bar, 2003; O’Reilly et al., 2021).\n[...]\nWe appreciate the reviewer’s point and agree that the co-representation of both high-level\nobject information and low-level contrast information within the pulvinar represents a significant\nfinding in its own right. This functional diversity suggests the pulvinar serves as a unique\nintegration hub, simultaneously processing information across multiple levels of the visual\nhierarchy (e.g., Cortes et al. 2024). Such co-representation may be particularly important for\ncoordinating rapid subcortical processing pathways with slower cortical hierarchical processing,\nas discussed in our response above. Importantly, the spatial separation of these different\nfeature representations within the pulvinar, combined with their distinct cortical connectivity\npatterns, indicates that the pulvinar maintains functional specificity while still enabling cross-\nlevel integration.\nAs mentioned above, we have refined our interpretation throughout the manuscript to\nemphasize this important result of multi-level feature representation within a single subcortical\nstructure, rather than making strong claims about a “mirrored” hierarchical organization that our\ndata cannot conclusively determine. This reframing better captures the pulvinar's role as a\nfunctionally diverse structure that participates in visual processing through multiple, potentially\nparallel pathways rather than through a single sequential hierarchy\n[...]\nWe thank the reviewer for this valuable suggestion! Similar data-driven approaches have\nalready been employed successfully in the past in the pulvinar, so we believe it would work here\nas well. We performed some preliminary testing of the suggested strategy of k-means clustering\non the NSD data to test this. The NSD beta responses were analyzed separately for each\nhemisphere. We first applied PCA to perform dimensionality reduction across trials from ~30k\n(different for some subjects due to attrition) to 200 dimensions. We then tried to identify k-\nmeans clusters with several different candidate values of k.\nA figure illustrating the results for k=20 is included below. Each column corresponds to one of\nthe coronal slices shown in Fig. 3, and each row corresponds to a subject. Left and right\nhemisphere results are displayed together, thought clustering was performed separately for\neach hemisphere, so cluster labels are not matched across hemispheres. Each cluster label\nwas assigned a random color for visualization.\nAs can be seen, we sometimes observed clusters that clearly matched the overall pulvinar\norganization suggested by our pRF results. For example, in the second and third coronal\nsections, several subjects had a large ventral pulvinar cluster and another more superior and\nmedial cluster (marked with black arrows) that aligned with the general patterning of contrast\nand body peaks in the group-average results. Note that the colors are arbitrary and do not\ncorrespond across subjects or hemispheres. These results suggest that purely data-driven\napproaches could be useful in describing the functional organization of the pulvinar.\n[...]\nWe agree that this is an important issue in characterizing the specificity of responses within the\npulvinar. Our existing analyses provide several lines of evidence for category selectivity within\nthe pulvinar. Our pRF modeling approach directly tested multiple visual categories, including\nwords, foreground (predominantly comprising general objects), and background (scenes),\nfinding that these features explained little variance in the face/body-selective posterior medial\npulvinar (Supplementary Figure 4). This is bolstered by Supplementary Figure 1, which shows\nthat few voxels were well explained by the body/face pRF models while also being well\nexplained by the contrast pRF model, suggesting a selective representation. Most importantly,\nour different-trials correlation analysis demonstrates that face/body-selective pulvinar regions\nshow selective coupling only with corresponding cortical areas (FFA, EBA, FBA) and notably do\nnot correlate with other category-selective regions such as place areas (TOS, PPA) or word\nareas (OWFA, VWFA) (Figure 5C).\nTogether, the lack of responsiveness to other categories, the limited overlap with contrast\ntuning, and the selective cortical coupling patterns all support the conclusion that these pulvinar\nresponses are genuinely face- and body-selective rather than broadly responsive to visual input.\nWe have revised the text to point this out on pg 4:\nThe limited overlap between variance-explained maps for the contrast model versus the body and face\nmodels, combined the lack of a correlation across voxels between models (Supplementary Figure 2),\ndemonstrates a clear functional substructure within the pulvinar, with anatomically distinct regions\nexhibiting selective responses to low- and high-level visual features.\n[...]\nOur apologies for the incomplete code! The code repository now contains the pRF fitting scripts\nand functions so that readers can more easily understand how to reproduce our analyses.\nGiven the scope and complexity of the data and analysis, this is not a single “turnkey” script but\nit should now be possible for a motivated user to reproduce the pRF fitting procedure."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I commend the authors for the care with which they have prepared their response. The meticulousness and technical sophistication of the study is remarkable, and it solidifies a number of previous, more tentative findings in the literature regarding pulvinar selectivity and the pulvinar's connections to cortex. I now think the manuscript is suitable for Nature Communications (with one minor edit, see my comment below), and I think the paper will become a reference for the latest and greatest in pulvinar visual selectivity.\n\nOne comment regarding the abstract: In several place, the abstract discusses the pulvinar playing a \"role\", even a \"prominent role\" in \"visual recognition processes\". However, that language goes a bit too far: No evidence is provided that the pulvinar \"plays a role\" in visual recognition -- e.g., by showing that recognition abilities are impaired when activity in the pulvinar is manipulated, or at least by showing a correlation between measures of selectivity in the pulvinar and behavior. All that the study shows is that there are certain patterns of stimulus selectivity in the pulvinar. Whether these selectivities play a functional role in object recognition remains to be shown. It's an interesting question that I hope the authors will tackle in due time.", "reviewer_2": "The authors have carefully revised their manuscript and addressed all my concerns.", "reviewer_3": "All of my previous concerns have been adequately addressed.\n\nNow they provide PRF model fitting code."}, "author_response": "We would like to sincerely thank the reviewers for all of their feedback through the revisions\nprocess at Nature Communications. Below, we have copied reviewer feedback verbatim and\nresponded to each item:\n\nReviewer #1 (Remarks to the Author):\n[...]\nThanks for this important point! We have modified the abstract to remove language\nsuggesting that our data directly support a “role” for the pulvinar in visual tasks. For\nreference, the original and modified abstracts are copied below:\nOriginal: The hierarchical organization of the ventral visual cortex has been the focus of\ntheories and computational models characterizing high-level visual processing and object\nrecognition, often overlooking potential contributions of subcortical structures. The pulvinar,\nthrough its extensive reciprocal connections with ventral visual cortex, is well-positioned to\nplay a prominent role in high-level visual recognition processes. Here, we investigated\nwhether the pulvinar plays such a role using a high-resolution 7T fMRI dataset of responses\nto tens of thousands of natural scenes. Encoding models testing the representation of\ndifferent stimulus features revealed a pulvinar region selective for bodies and faces\npresented within the contralateral visual hemifield. Model-free analyses demonstrated that\nthis region is predominantly co-active with body- and face-selective cortical areas during\nnatural scene viewing. These findings challenge cortico-centric models of object vision and\nimplicate a role of the pulvinar in high-level vision. More broadly, these results demonstrate\nthat principles of cortical organization, including functional clustering and hierarchical\norganization, also manifest in subcortex, and highlight the value of using naturalistic stimuli\nto probe visual function.\nModified: Theories of high-level visual processing and object recognition have typically\nfocused on ventral visual cortex, often overlooking potential contributions of subcortical\nstructures. The pulvinar, via extensive connections with visual cortex, is well-positioned to\nplay a role in high-level vision. Here, we investigated how the pulvinar represents visual\ninformation using a high-resolution 7T fMRI dataset of responses to tens of thousands of\nnatural scenes. Encoding models targeting different stimulus features revealed a pulvinar\nregion selective for bodies and faces presented in the contralateral visual hemifield.\nComplementary model-free analyses demonstrated that this region is co-active with body-\nand face-selective cortical areas during natural scene viewing. These findings challenge\ncortico-centric models of object vision by demonstrating that principles of cortical\norganization, including functional clustering and hierarchical organization, also manifest in\nsubcortex. Moreover, they highlight the value of using naturalistic stimuli to probe visual\nfunction.\n\nReviewer #2 (Remarks to the Author):\nThe authors have carefully revised their manuscript and addressed all my concerns.\n\nReviewer #3 (Remarks to the Author):\nAll of my previous concerns have been adequately addressed.\n\nReviewer #3 (Remarks on code availability):\nNow they provide PRF model fitting code."}]

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