Biology subjects

Henderson, M. M.

Publications and source records attributed to Henderson, M. M..

10 recordsLinked to original sources

bioRxiv2026

Diffusion-based stimulus optimization reveals functional organization across higher visual cortex

Characterizing the fine-grained functional organization of human higher visual cortex remains a central challenge, as traditional neuroimaging experiments constrain the diversity of stimuli that can be sampled. In prior work we addressed this challenge by developing a novel data-driven tool, termed "BrainDiVE" (Luo et al. 2023), which synthesizes naturalistic images predicted to strongly activate specific brain regions. BrainDiVE leverages pretrained image diffusion models guided by gradients from an image-computable fMRI encoding model. Here, we experimentally validated BrainDiVE by generating images predicted to maximally activate different functional regions of interest and then presenting them to new participants (n=12) in an fMRI study. The model-generated images elicited robust, spatially specific responses in the targeted brain regions, producing significantly greater category selectivity than natural images, validating the methods ability to capture generalizable neural tuning properties in human ventral visual cortex. We further showed that region-targeted images exaggerate specific sets of low-level and mid-level image statistics, suggesting that category-selective regions are tuned to continuous directions in feature space. Moreover, we demonstrated fine-grained experimental control by differentially activating two face-selective regions, the occipital face area (OFA) and fusiform face area (FFA), providing additional evidence that these regions encode distinct aspects of faces. Finally, we identify a posterior-to-anterior functional gradient within the occipital place area (OPA), suggesting topographic organization based on scene properties such as distance and indoor-outdoor location. These findings enhance our understanding of the representational structure of category-selective regions and introduce a new paradigm for probing neural selectivity in human visual cortex.

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bioRxiv2023

Categorization dynamically alters representations in human visual cortex

Everyday perceptual tasks require sensory stimuli to be dynamically encoded and analyzed according to changing behavioral goals. For example, when searching for an apple at the supermarket, one might first find the Granny Smith apples by separating all visible apples into the categories "green" and "non-green". However, suddenly remembering that your family actually likes Fuji apples would necessitate reconfiguring the boundary to separate "red" from "red-yellow" objects. This flexible processing enables identical sensory stimuli to elicit varied behaviors based on the current task context. While this phenomenon is ubiquitous in nature, little is known about the neural mechanisms that underlie such flexible computation. Traditionally, sensory regions have been viewed as mainly devoted to processing inputs, with limited involvement in adapting to varying task contexts. However, from the standpoint of efficient computation, it is plausible that sensory regions integrate inputs with current task goals, facilitating more effective information relay to higher-level cortical areas. Here we test this possibility by asking human participants to visually categorize novel shape stimuli based on different linear and non-linear boundaries. Using fMRI and multivariate analyses of retinotopically-defined visual areas, we found that shape representations in visual cortex became more distinct across relevant decision boundaries in a context-dependent manner, with the largest changes in discriminability observed for stimuli near the decision boundary. Importantly, these context-driven modulations were associated with improved categorization performance. Together, these findings demonstrate that codes in visual cortex are adaptively modulated to optimize object separability based on currently relevant decision boundaries.

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bioRxiv2023

Neural Selectivity for Real-World Object Size In Natural Images

1Real-world size is a functionally important high-level visual property of objects that supports interactions with our physical environment. Critically, real-world-size is robust over changes in visual appearance as projected onto our retinae such that large and small objects are correctly perceived to have different real-world sizes. To better understand the neural basis of this phenomenon, we examined whether the neural coding of real-world size holds for objects embedded in complex natural scene images, as well as whether real-world size effects are present for both inanimate and animate objects, whether low- and mid-level visual features can account for size selectivity, and whether neural size tuning is best described by a linear, logarithmic, or exponential neural coding function. To address these questions, we used a large-scale dataset of fMRI responses to natural images combined with per-voxel regression and contrasts. Importantly, the resultant pattern of size selectivity for objects embedded in natural scenes was aligned with prior results using isolated objects. Extending this finding, we also found that size coding exists for both animate and inanimate objects, that low-level visual features cannot account for neural size preferences, and size tuning functions have different shapes for large versus small preferring voxels. Together, these results indicate that real-world size is an ecologically significant dimension in the larger space of behaviorally-relevant cortical representations that support interactions with the world around us.

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bioRxiv2022

A texture statistics encoding model reveals hierarchical feature selectivity across human visual cortex

Mid-level visual features, such as contour and texture, provide a computational link between low- and high-level visual representations. While the detailed nature of mid-level representations in the brain is not yet fully understood, past work has suggested that a texture statistics model (P-S model; Portilla and Simoncelli, 2000) is a candidate for predicting neural responses in areas V1-V4 as well as human behavioral data. However, it is not currently known how well this model accounts for the responses of higher visual cortex regions to natural scene images. To examine this, we constructed single voxel encoding models based on P-S statistics and fit the models to fMRI data from human subjects (male and female) from the Natural Scenes Dataset (Allen et al., 2021). We demonstrate that the texture statistics encoding model can predict the held-out responses of individual voxels in early retinotopic areas as well as higher-level category-selective areas. The ability of the model to reliably predict signal in higher visual cortex voxels suggests that the representation of texture statistics features is widespread throughout the brain, potentially playing a role in higher-order processes like object recognition. Furthermore, we use variance partitioning analyses to identify which features are most uniquely predictive of brain responses, and show that the contributions of higher-order texture features increases from early areas to higher areas on the ventral and lateral surface of the brain. These results provide a key step forward in characterizing how mid-level feature representations emerge hierarchically across the visual system. Significance StatementIntermediate visual features, like texture, play an important role in cortical computations and may contribute to tasks like object and scene recognition. Here, we used a texture model proposed in past work to construct encoding models that predict the responses of neural populations in human visual cortex (measured with fMRI) to natural scene stimuli. We show that responses of neural populations at multiple levels of the visual system can be predicted by this model, and that the model is able to reveal an increase in the complexity of feature representations from early retinotopic cortex to higher areas of ventral and lateral visual cortex. These results support the idea that texture-like representations may play a broad underlying role in visual processing.

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bioRxiv2022

Low-level tuning biases in higher visual cortex reflect the semantic informativeness of visual features

Representations of visual and semantic information can overlap in human visual cortex, with the same neural populations exhibiting sensitivity to low-level features (orientation, spatial frequency, retinotopic position), and high-level semantic categories (faces, scenes). It has been hypothesized that this relationship between low-level visual and high-level category neural selectivity reflects natural scene statistics, such that neurons in a given category-selective region are tuned for low-level features or spatial positions that are diagnostic of the regions preferred category. To address the generality of this "natural scene statistics" hypothesis, as well as how well it can account for responses to complex naturalistic images across visual cortex, we performed two complementary analyses. First, across a large set of rich natural scene images, we demonstrated reliable associations between low-level (Gabor) features and high-level semantic dimensions (indoor-outdoor, animacy, real-world size), with these relationships varying spatially across the visual field. Second, we used a large-scale fMRI dataset (the Natural Scenes Dataset) and a voxelwise forward encoding model to estimate the feature and spatial selectivity of neural populations throughout visual cortex. We found that voxels in category-selective visual regions exhibit systematic biases in their feature and spatial selectivity which are consistent with their hypothesized roles in category processing. We further showed that these low-level tuning biases are largely independent of viewed image category. Together, our results are consistent with a framework in which low-level feature selectivity contributes to the computation of high-level semantic category information in the brain.

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bioRxiv2022

Why is human vision so poor in early development? The impact of initial sensitivity to low spatial frequencies on visual category learning

Humans are born with very low contrast sensitivity, meaning that developing infants experience the world "in a blur". Is this solely a byproduct of maturational processes or is there some functional advantage for beginning life with poor vision? We explore whether reduced visual acuity as a consequence of low contrast sensitivity facilitates the acquisition of basic-level visual categories and, if so, whether this advantage also enhances subordinate-level category learning as visual acuity improves. Using convolutional neural networks (CNNs) and the ecoset dataset to simulate basic-level category learning, we manipulated model training curricula along three dimensions: presence of blurred inputs early in training, rate of blur removal over time, and grayscale versus color inputs. We found that a training regimen where blur starts high and is gradually reduced over time - as in human development - improves basic-level categorization performance relative to a regimen in which non-blurred inputs are used throughout. However, this pattern was observed only when grayscale images were used (analogous to the low sensitivity to color infants experience during early development). Importantly, the observed improvements in basic-level performance generalized to subordinate-level categorization as well: when models were fine-tuned on a dataset including subordinate-level categories (ImageNet), we found that models initially trained with blurred inputs showed a greater performance benefit than models trained solely on non-blurred inputs. Consistent with several other recent studies, we conclude that poor visual acuity in human newborns confers multiple advantages, including, as demonstrated here, more rapid and accurate acquisition of visual object categories at multiple hierarchical levels. Author SummaryWhy do humans start life with extremely poor vision? The common evolutionary story is that head size is small to accommodate the development of human-level intelligence. However, there is growing evidence that beginning life in a premature state confers short-term advantages. The "starting small" principle states that learning can be facilitated by restricted or impoverished inputs that reduce the learning complexity. We suggest that blurred vision during early development biases learning toward shape features, which organize objects naturally into "basic-level" categories that are the foundation of human cognition (e.g., birds, cars, etc.). Such categories are defined by their visual similarity in global object shape. Since blurring restricts visual inputs to emphasize shape information, it should enhance the learning of basic-level categories. We trained artificial neural-network models on basic-level categorization using either blurred or non-blurred color or grayscale images and found that models trained with blurred images outperformed models trained with non-blurred images, but only for grayscale. These same models performed better in subsequent "subordinate-level" tasks that required discriminating between objects within a basic-level category. Our simulations provide evidence that initially poor vision in infants has an important functional role in organizing knowledge about complex environments.

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bioRxiv2022

Food for thought: selectivity for food in human ventral visual cortex

Visual cortex contains regions of selectivity for domains of ecological importance. Food is an evolutionarily critical category whose visual heterogeneity may make the identification of selectivity more challenging. We investigate neural responsiveness to food using natural images combined with large-scale human fMRI. Leveraging the improved sensitivity of modern designs and statistical analyses, we identify two food-selective regions in the ventral visual cortex. Our results are robust across 8 subjects from the Natural Scenes Dataset (NSD), multiple independent image sets and multiple analysis methods. We then test our findings of food selectivity in an fMRI "localizer" using grayscale food images. These independent results confirm the existence of food selectivity in ventral visual cortex and help illuminate why earlier studies may have failed to do so. Our identification of food-selective regions stands alongside prior findings of functional selectivity and adds to our understanding of the organization of knowledge within the human visual system.

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bioRxiv2021

Flexible utilization of spatial- and motor-based codes for the storage of visuo-spatial information

Working memory provides flexible storage of information in service of upcoming behavioral goals. Some models propose specific fixed loci and mechanisms for the storage of visual information in working memory, such as sustained spiking in parietal and prefrontal cortex during working memory maintenance. An alternative view is that information can be remembered in a flexible format that best suits current behavioral goals. For example, remembered visual information might be stored in sensory areas for easier comparison to future sensory inputs, or might be re-coded into a more abstract action-oriented format and stored in motor areas. Here, we tested this hypothesis using a visuo-spatial working memory task where the required behavioral response was either known or unknown during the memory delay period. Using fMRI and multivariate decoding, we found that there was less information about remembered spatial position in early visual and parietal regions when the required response was known versus unknown. Further, a representation of the planned motor action emerged in primary somatosensory, primary motor, and premotor cortex during the same task condition where spatial information was reduced in early visual cortex. These results suggest that the neural networks supporting working memory can be strategically reconfigured depending on specific behavioral requirements during a canonical visual working memory paradigm.

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bioRxiv2020

Biased orientation representations can be explained by experience with non-uniform training set statistics

Visual acuity is better for vertical and horizontal compared to other orientations. This cross-species phenomenon is often explained by "efficient coding", whereby more neurons show sharper tuning for the orientations most common in natural vision. However, it is unclear if experience alone can account for such biases. Here, we measured orientation representations in a convolutional neural network, VGG-16, trained on modified versions of ImageNet (rotated by 0{degrees}, 22.5{degrees}, or 45{degrees} counter-clockwise of upright). Discriminability for each model was highest near the orientations that were most common in the networks training set. Furthermore, there was an over-representation of narrowly tuned units selective for the most common orientations. These effects emerged in middle layers and increased with depth in the network. Biases emerged early in training, consistent with the possibility that non-uniform representations may play a functional role in the networks task performance. Together, our results suggest that biased orientation representations can emerge through experience with a non-uniform distribution of orientations, supporting the efficient coding hypothesis.

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bioRxiv2018

Human frontoparietal cortex represents behaviorally-relevant target status during invariant object recognition

Searching for items that are useful given current goals, or \"target\" recognition, requires an observer to generalize across identity-preserving transformations such as viewpoint changes, as well as to incorporate contextual information. While past work has found target recognition signals in areas of ventral visual cortex, it is not clear whether these signals support performance on demanding tasks that require invariant, flexible search. Here, we used a task that required subjects to match novel object stimuli based on invariant features (identity and viewpoint). Based on multivariate fMRI analyses, the data suggest that the multiple-demand (MD) network, including sub-regions of parietal and frontal cortex, encodes invariant representations of an objects status as a target. Furthermore, target information in MD regions, but not early or ventral visual cortex, was higher on correct compared to incorrect trials, suggesting a strong link between MD target signals and behavior.

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