Biology subjects

Nishida, S.

Publications and source records attributed to Nishida, S..

18 recordsLinked to original sources

bioRxiv2025

Human visual grouping based on within- and cross-area temporal correlations

Perceptual organization in the human visual system involves neural mechanisms that spatially group and segment image areas based on local feature similarities, such as the temporal correlation of luminance changes. Successful segmentation models in computer vision, including graph-based algorithms and vision transformer, leverage similarity computations across all elements in an image, suggest that effective similarity-based grouping should rely on a global computational process. However, whether human vision employs a similarly global computation remains unclear due to the absence of appropriate methods for manipulating similarity matrices across multiple elements within a stimulus. To investigate how "temporal similarity structures" influence human visual segmentation, we developed a stimulus generation algorithm based on Vision Transformer. This algorithm independently controls within-area and cross-area similarities by adjusting the temporal correlation of luminance, color, and spatial phase attributes. To assess human segmentation performance with these generated texture stimuli, participants completed a temporal two-alternative forced-choice task, identifying which of two intervals contained a segmentable texture. The results showed that segmentation performance is significantly influenced by the configuration of both within- and cross-correlation across the elements, regardless of attribute type. Furthermore, human performance is closely aligned with predictions from a graph-based computationalmodel, suggesting that human texture segmentation can be approximated by a global computational process that optimally integrates pairwise similarities across multiple elements. Author SummaryHow does the human visual system use temporal information to segment objects in a dynamic scene? When observing ever-changing environments, our brains must determine which regions belong to the same object and which are distinct. However, the mechanisms underlying this process remain poorly understood. In this study, we investigate how "temporal similarity structures"--patterns of correlation over time--affect visual segmentation. We developed a novel method for generating dynamic stimuli with precisely controlled temporal similarity and systematically tested how within-area and cross-area correlations influence segmentation. Participants performed a task in which they identified segmentable textures, and the results showed that segmentation performance improves when regions exhibit strong internal consistency but lower similarity with adjacent regions. Our findings revealed that human visual segmentation relies on a global computational mechanism that integrates temporal similarity cues to distinguish visual structures. Additionally, our stimulus generation framework provides a powerful tool for future research on perceptual organization and mid-level vision.

neuroscience↗

bioRxiv2024

Neural correlates of phenomenological attitude toward perceptual experience

Phenomenology is one of the most promising approaches to study conscious experience. It holds that a rigorous study of conscious experience requires a transition in the subject from the "natural attitude" (NA) to the "phenomenological attitude" (PA). NA describes our ordinary stance, in which our attention is directed at external objects and events. PA is a distinctive, reflective stance in which our attention is directed at our conscious experience itself. Despite its theoretical importance in philosophy and science of consciousness, the neural mechanisms underlying PA remain unknown. To clarify this point, we developed a novel behavioral task in which participants alternate between NA and PA in relation to their stimulus-evoked subjective experiences. Participants are presented with two sentences and requested to identify the one that best captures their experience. These sentences are designed to induce either NA or PA. We found that participants had lower error rates but slower reaction times in the PA condition compared to the NA condition, suggesting a difference beyond task difficulty. Using fMRI, we also found that multivoxel activation patterns in the premotor cortex, posterior parietal cortex, supplementary motor area, and cerebellum successfully classified the task conditions. Furthermore, the activation strength in these regions was lower in the PA condition, indicating that PA depends on neural processes that suppress action-related information. These findings provide the first evidence for the neural signature of PA, contributing to a better understanding of phenomenological method and its underlying neural mechanisms. Significance statementPhenomenology is one of the most promising approaches to study conscious experience. A key step is a transition from the natural attitude (NA)--where our attention is directed at external objects and events--to the phenomenological attitude (PA)--where our attention is directed toward our conscious experience itself. However, the neural processes underlying PA remain unclear. This study aimed to clarify this point by analyzing fMRI signals measured during a cognitive task that forced participants to repeatedly alternate between NA and PA. We observed that multiple action-related brain regions exhibited different activation patterns between NA and PA. Our findings provide the first neuroscientific evidence that illuminates the core process of the phenomenological method.

neuroscience↗

bioRxiv2024

Asymmetric representation of symmetric semantic information in the human brain

Specific pairs of semantic entities have symmetric relationships, such as word pairs with opposite meanings (e.g., "intelligent" and "stupid"; "human" and "mechanical"). Such semantic symmetry is a key feature of semantic information. However, the representation of symmetric semantic information in the brain is not yet understood. Additionally, it is unclear whether symmetric pairs of semantic information do have symmetric representations in the brain? We addressed this question in a data-driven manner by using the voxelwise modeling of movie-evoked cortical response measured by functional magnetic resonance imaging. In this modeling, response in each voxel was predicted from semantic labels designated for each movie scene. The semantic labels consisted of 30 different items, including 15 pairs of semantically symmetric items. Each item was manually evaluated using a 5-point scale. By localizing the semantic representation associated with each item based on the voxelwise accuracy of brain-response predictions, we found that semantic representations of symmetric item pairs are broadly distributed but with little overlap in the cortex. Additionally, the weight of voxelwise models revealed highly complex, various patterns of cortical representations for each item pair. These results suggest that symmetric semantic information is rather asymmetric and heterogeneous representations in the human brain. Significance statementThis study aimed to investigate if symmetric pairs of semantic information have symmetric representations in the human brain using statistical modeling of functional magnetic resonance imaging signals evoked by naturalistic movies. We built a model based on movie labeling of symmetric semantic items to quantify the cortical representations of symmetric semantic entities. These findings showed that symmetric pairs of semantic entities are represented in widespread cortical regions; however, still exhibit little overlap in localization and heterogeneous representations. These results offer significant insights into the cortical representations of semantic symmetry and advance the understanding of representational structure of semantic information in the human brain.

neuroscience↗

bioRxiv2023

Psychophysical measurement of perceived motion flow of naturalistic scenes

The neural and computational mechanisms underlying visual motion perception have been extensively investigated over several decades, but most studies have used simple artificial stimuli such as random-dot kinematograms. Thus, it remains difficult to predict how human observers perceive optical flows in complex natural scenes. Here, we report a novel method to measure, psychophysically, optical flows perceived by human observers watching naturalistic movies, and to reveal the characteristics of human motion perception via comparison of the measured perceived flow to the ground truths and model predictions. We selected movie clips from the MPI Sintel Flow Dataset, which contains open-source computer graphics animations with ground truths. To measure the perceived vectors at a spatiotemporal point, we flashed a small dot during presentation of a brief clip and asked the observers to adjust the speed and direction of a matching random-noise stimulus, to reproduce the vector at the flashed point. The proposed method adequately estimated perceived flow, and the estimated perceived vector also indicated flow illusions, i.e., consistent deviations from the ground truths, in various ways, depending on the stimulus patterns. Comparisons with the predictions of biologically motivated models and machine vision algorithms indicated that some flow illusions were attributable to lower-level factors such as spatiotemporal pooling and signal loss, but others reflected higher-level computations including coordinate transformations that cannot be precisely predicted by existing flow estimation models. Psychophysical measurement of the optical flows that humans perceive in realistic environments constitutes a promising paradigm for advancing our understanding of visual motion perception. Significance StatementThe basic approach to studying human vision is to analyze relationships among subjective perceptual experiences, responses of neural mechanisms, and predictions of computational models. Recent technical advances have enabled researchers to access large-scale neuronal responses and model predictions for complex sensory inputs. The data on human visual perception, however, cannot be easily scaled up. Accurate measurement of rich visual experiences when viewing natural scenes remains challenging. We thus devised a novel psychophysical method to measure optical motion flows perceived by humans. We successfully visualized human-perceived flows of complex naturalistic movies and show, for the first time, ways in which human-perceived naturalistic flows agree with, and deviate from, the physical ground truths and the predictions of various visual motion models.

neuroscience↗

bioRxiv2023

Behavioral and neural evidence for the underestimated attractiveness of faces synthesized using an artificial neural network

Despite recent advantages in artificial intelligence (AI), the potential human aversion to AI has not been dispelled yet. If such aversion degrades the human preference to AI-synthesized visual information, the preference should be reduced solely by the human belief that the information is synthesized by AI, independently of its appearance. To test this hypothesis, this study designed a task paradigm in which naive participants rated the attractiveness of various faces synthesized using an artificial neural network, under the fake instruction that half of the faces were synthetic and the other half were real. This design allowed evaluating the effect of participants beliefs on their attractiveness ratings separately from the effect of facial appearance. In addition, to investigate the neural substrates of the belief effect, brain responses to faces were collected using fMRI during this task. It is found that participants ratings declined when the faces were believed to be synthetic. Furthermore, the belief changed the responsiveness of fMRI signals to facial attractiveness in the right fusiform cortex. These behavioral and neural findings support the notion that the human preference to visual information becomes lower solely due to the beliefs that the information is synthesized by AI.

neuroscience↗

bioRxiv2022

Glucosinolate catabolism maintains glucosinolate profiles and transport in sulfur-starved Arabidopsis

Glucosinolates (GSL) are sulfur (S)-rich specialized metabolites produced by plants of the Brassicales order. Our previous study found that in Arabidopsis seedlings, S deficiency (-S) promoted GSL catabolism by activating two {beta}-glucosidases (BGLU), BGLU28 and BGLU30. The induced GSL catabolism was a survival strategy for seedlings grown under -S, because S released from GSL was reincorporated into primary S metabolites which are essential for plant growth. However, as GSL profile in plants vary among growth stages and organs, we set out to test a potential contribution of BGLU28/30-dependent GSL catabolism at the reproductive growth stage. Thus, in this study, we assessed growth, metabolic, and transcriptional phenotypes of mature bglu28/30 double mutants grown under different S conditions. Our results showed that compared to wild-type plants grown under -S, mature bglu28/30 mutants displayed impaired growth and accumulated increased levels of GSL in their mature seeds, siliques, flowers, and rosette leaves of before bolting plants. In contrast, the levels of primary S-containing metabolites, glutathione and cysteine, were decreased in mature seeds. Furthermore, the transport of GSL from rosette leaves to the reproductive organs was stimulated in the bglu28/30 mutants under -S. Transcriptome analysis revealed that genes related to other biological processes, such as phytohormone signaling and plant response to heat, responded differentially to -S in the bglu28/30 mutants. Altogether, these findings broadened our understanding of the roles of BGLU28/30-dependent GSL catabolism in plant adaptation to nutrient stress. One-sentence summaryDisruption of glucosinolate catabolic genes, BGLU28 and BGLU30, in sulfur-starved mature Arabidopsis impaired growth, affected glucosinolate distribution, and altered transcriptional profiles.

plant biology↗

bioRxiv2022

GGSDT: A unified signal detection framework for confidence data analysis

Human decision behavior entails a graded awareness of its certainty, known as a feeling of confidence. Until now, considerable interest has been paid to behavioral and computational dissociations of decision and confidence, which has raised an urgent need for measurement frameworks that can quantify the efficiency of confidence rating relative to decision accuracy (metacognitive efficiency). As a unique addition to such frameworks, we have developed a new signal detection theory paradigm utilizing the generalized gaussian distribution (GGSDT). This framework evaluates the observers internal standard deviation ratio and metacognitive efficiency through the scale and shape parameters respectively. The shape parameter quantifies the kurtosis of internal distributions and can practically be understood in reference to the proportion of the gaussian ideal observers confidence being disrupted with random guessing (metacognitive lapse rate). This interpretation holds largely irrespective of the contaminating effects of decision accuracy or operating characteristic asymmetry. Thus, the GGSDT enables hitherto unexplored research protocols (e.g., direct comparison of yes/no versus forced-choice metacognitive efficiency), expected to find applications in various fields of behavioral science. This paper provides a detailed walkthrough of the GGSDT analysis with an accompanying R package (ggsdt).

neuroscience↗

bioRxiv2022

Do training with blurred images make convolutional neural networks closer to humans concerning object recognition performance and internal representations?

AO_SCPLOWBSTRACTC_SCPLOWIt is suggested that experiences of perceiving blurry images in addition to sharp images contribute to the development of robust human visual processing. To computationally investigate the effect of exposure to blurry images, we trained Convolutional Neural Networks (CNNs) on ImageNet object recognition with a variety of combinations of sharp and blurry images. In agreement with related studies, mixed training on sharp and blurred images (B+S) makes the CNNs close to humans with respect to robust object recognition against a change in image blur. B+S training also reduces the texture bias of CNN in recognition of shape-texture-cue-conflict images, but the effect is not strong enough to achieve a strong shape bias comparable to what humans show. Other tests also suggest that B+S training is not sufficient to produce robust human-like object recognition based on global con-figurational features. We also show using representational similarity analysis and zero-shot transfer learning that B+S-Net does not acquire blur-robust object recognition through separate specialized sub-networks, each for sharp and blurry images, but through a single network analyzing common image features. However, blur training alone does not automatically create a mechanism like the human brain where subband information is integrated into a common representation. Our analyses suggest that experience with blurred images helps the human brain develop neural networks that robustly recognize the surrounding world, but it is not powerful enough to fill a large gap between humans and CNNs.

neuroscience↗

bioRxiv2022

Decoding Individual Differences in Mental Information from Human Brain Response Predicted by Convolutional Neural Networks

Recent advantages in brain decoding with functional magnetic resonance imaging (fMRI) have enabled us to estimate individual differences in mental information from brain responses to natural stimuli. However, the physical constraints and costs of fMRI measurements prevent brain decoding from achieving real-world applications. We address this issue by building a novel framework to decode individual differences in mental information under natural situations from brain responses predicted using convolutional neural networks (CNNs). Once the CNN-based prediction model is trained using measured response, mental information can be decoded from the predicted responses of individual brains with no need for additional fMRI measurements. The model was found to capture individual-difference patterns consistent with conventional decoding using measured responses in 81/87 items to be decoded. Our framework has great potential to decode personal mental information with low dependence on fMRI measurements, which could help substantially expand the applicability of brain decoding in daily life.

neuroscience↗

bioRxiv2021

T cell-intrinsic vitamin A metabolism and its signaling are targets for memory T cell-based cancer immunotherapy

Memory T cells play an essential role in infectious and tumor immunity. Vitamin A metabolites such as retinoic acid are immune modulators, but the role of vitamin A metabolism in memory T- cell differentiation is unclear. In this study, we identified retinol dehydrogenase 10 (Rdh10), which metabolizes vitamin A to retinal (RAL), as a key molecule for regulating T cell differentiation. T cell-specific Rdh10 deficiency enhanced memory T-cell formation through blocking RAL production in infection model. Epigenetic profiling revealed that retinoic acid receptor (RAR) signaling activated by vitamin A metabolites induced comprehensive epigenetic repression of memory T cell-associated genes, including TCF7, thereby promoting effector T-cell differentiation. Importantly, memory T cells generated by Rdh10 deficiency and blocking RAR signaling elicited potent anti-tumor responses in adoptive T-cell transfer setting. Thus, T cell differentiation is regulated by vitamin A metabolism and its signaling, which should be novel targets for memory T cell-based cancer immunotherapy.

immunology↗

bioRxiv2021

Short 5'UTR enables optimal translation of plant virus tricistronic RNA via leaky scanning

Regardless of the general model of translation in eukaryotic cells, a number of studies suggested that many of mRNAs encode multiple proteins. Leaky scanning, which supplies ribosomes to downstream open reading frames (ORFs) by read-through of upstream ORFs, is the most major regulatory mechanism to translate polycistronic mRNAs. However, the general regulatory factors controlling leaky scanning and their biological relevance have rarely been elucidated, with exceptions such as the Kozak sequence. Here, we have analyzed the strategy of a plant RNA virus to translate three movement proteins from a single RNA molecule through leaky scanning. The in planta and in vitro results indicate that significantly shorter 5' UTR of the most upstream ORF promotes leaky scanning, potentially finetuning the translation efficiency of the three proteins in a single RNA molecule to optimize viral propagation. Moreover, in plant endogenous mRNAs, we found that shorter UTRs were more frequently observed in uORFs of polycistronic mRNAs. We propose that the promotion of leaky scanning induced by a short 5' UTR (LISH), together with the Kozak sequence, is a conserved gene regulation mechanism not only in viruses but also in eukaryotes.

plant biology↗

bioRxiv2020

Behavioral correlates of cortical semantic representations modeled by word vectors

The quantitative modeling of semantic representations in the brain plays a key role in understanding the neural basis of semantic processing. Previous studies have demonstrated that word vectors, which were originally developed for use in the field of natural language processing, provide a powerful tool for such quantitative modeling. However, whether semantic representations revealed by the word vector-based models actually capture our perception of semantic information remains unclear, as there has been no study explicitly examining the behavioral correlates of the modeled semantic representations. To address this issue, we compared the semantic structure of nouns and adjectives estimated from word vector-based brain models with that evaluated from human behavior. The brain models were constructed using voxelwise modeling to predict the functional magnetic resonance imaging (fMRI) response to natural movies from semantic contents in each movie scene through a word vector space. The semantic dissimilarity of word representations was then evaluated using the brain models. Meanwhile, data on human behavior reflecting the perception of semantic dissimilarity between words were collected in psychological experiments. We found a significant correlation between brain model- and behavior-derived semantic dissimilarities of words. This finding suggests that semantic representations in the brain modeled via word vectors appropriately capture our perception of word meanings. Author summeryWord vectors, which have been originally developed in the field of engineering (natural language processing), have been extensively leveraged in neuroscience studies to model semantic representations in the human brain. These studies have attempted to model brain semantic representations by associating them with the meanings of thousands of words via a word vector space. However, there has been no study explicitly examining whether the modeled semantic representations actually capture our perception of semantic information. To address this issue, we compared the semantic representational structure of words estimated from word vector-based brain models with that evaluated from behavioral data in psychological experiments. The results revealed a significant correlation between these model- and behavior-derived semantic representational structures of words. This indicates that the brain semantic representations modeled using word vectors actually reflect the human perception of word meanings. Our findings contribute to the establishment of word vector-based brain modeling as a useful tool in studying human semantic processing.

neuroscience↗

bioRxiv2020

Deep Texture Representations as a Universal Encoder for Pan-cancer Histology

Cancer histological images contain rich biological and clinical information, but quantitative representation can be problematic and has prevented direct comparison and accumulation of large-scale datasets. Here we show that deep texture representations (DTRs) produced by a bilinear Convolutional Neural Network, express cancer morphology well in an unsupervised manner, and work as a universal encoder for cancer histology. DTRs are useful for content-based image retrieval, enabling quick retrieval of histologically similar images from optimally area selected datasets of 7,175 cases from The Cancer Genome Atlas. Via comprehensive comparison with driver and clinically actionable gene mutations, we have successfully predicted 309 combinations of genomic features and cancer types from hematoxylin and eosin-stained images at high accuracy (AUC > 0.70 and q < 0.02). With its mounting capabilities on accessible devices such as smartphones, DTR-based encoding for cancer histology has a potentially strong impact on global equalization for cancer diagnosis and targeted therapies.

cancer biology↗

bioRxiv2020

Reduced intra- and inter-individual diversity of semantic representations in the brains of schizophrenia patients

Schizophrenia patients often manifest semantic processing deficits. It has been proposed that these deficits stem from disorganized semantic representations in the brain. However, no study has yet examined the neural correlates of semantic disorganization by directly evaluating semantic representations in the brain. We used voxelwise modeling on functional magnetic resonance imaging signals to evaluate the semantic representations associated with several thousand words in individual patient brains. We then compared the structural properties of semantic representations to those in healthy controls. The variability of semantic representations was smaller both within individual patients and across patients compared to controls. Surrogate data analysis suggests that the observed reduction in representational variability is associated with disorganization of categorical information. To our knowledge, these findings provide the first evidence for sematic disorganization in schizophrenia at the level of brain representations.

neuroscience↗

bioRxiv2020

Expert programmers have fine-tuned cortical representations of source code

Expertise enables humans to achieve outstanding performance on domain-specific tasks, and programming is no exception. Many have shown that expert programmers exhibit remarkable differences from novices in behavioral performance, knowledge structure, and selective attention. However, the underlying differences in the brain are still unclear. We here address this issue by associating the cortical representation of source code with individual programming expertise using a data-driven decoding approach. This approach enabled us to identify seven brain regions, widely distributed in the frontal, parietal, and temporal cortices, that have a tight relationship with programming expertise. In these brain regions, functional categories of source code could be decoded from brain activity and the decoding accuracies were significantly correlated with individual behavioral performances on source-code categorization. Our results suggest that programming expertise is built up on fine-tuned cortical representations specialized for the domain of programming.

neuroscience↗

bioRxiv2019

Visual discrimination of optical material properties: a large-scale study

Complex visual processing involved in perceiving the object materials can be better elucidated by taking a variety of research approaches. Sharing stimulus and response data is an effective strategy to make the results of different studies directly comparable and can assist researchers with different backgrounds to jump into the field. Here, we constructed a database containing several sets of material images annotated with visual discrimination performance. We created the material images using physically-based computer graphics techniques and conducted psychophysical experiments with them in both laboratory and crowdsourcing settings. The observers task was to discriminate materials on one of six dimensions (gloss contrast, gloss distinctness-of-image, translucent vs. opaque, metal vs. plastic, metal vs. glass, and glossy vs. painted). The illumination consistency and object geometry were also varied. We used a non-verbal procedure (an oddity task) applicable for diverse use-cases such as cross-cultural, cross-species, clinical, or developmental studies. Results showed that the material discrimination depended on the illuminations and geometries and that the ability to discriminate the spatial consistency of specular highlights in glossiness perception showed larger individual differences than in other tasks. In addition, analysis of visual features showed that the parameters of higher-order color texture statistics can partially, but not completely, explain task performance. The results obtained through crowdsourcing were highly correlated with those obtained in the laboratory, suggesting that our database can be used even when the experimental conditions are not strictly controlled in the laboratory. Several projects using our dataset are underway.

neuroscience↗

bioRxiv2019

Haptic metameric textures

Humans sense spatial patterns through their eyes and hands. Past studies have revealed differences (as well as similarities) between vison and touch in texture processing (e.g., eye is good at detecting texture boundaries, while hand can discriminate subtle texture differences), but the underlying computational differences remains poorly understood. Here we transcribed various textures as surface relief patterns by 3D-printing, and analyzed the tactile discrimination performance regarding the sensitivity to image statistics. The results suggest that touch is sensitive to texture differences in lower-order statistics (e.g., statistics of local amplitude spectrum), while may not to those in the higher-order statistics (e.g., joint statistics of local orientations). In agreement with this, we found that pairs of synthesized textures differing only in higher-order statistics were nearly indiscriminable (metameric) by touch, while easily discriminable by vision. Our findings show that touch and vision sense spatial information using different and complementary computational strategies.

neuroscience↗

bioRxiv2017

A common neural network among state, trait, and pathological anxiety from whole-brain functional connectivity

Anxiety is one of the most common mental states of humans. Although it drives us to avoid frightening situations and to achieve our goals, it may also impose significant suffering and burden if it becomes extreme. Because we experience anxiety in a variety of forms, previous studies investigated neural substrates of anxiety in a variety of ways. These studies revealed that individuals with high state, trait, or pathological anxiety showed altered neural substrates. However, no studies have directly investigated whether the different dimensions of anxiety share a common neural substrate, despite its theoretical and practical importance. Here, we investigated a neural network of anxiety shared by different dimensions of anxiety in a unified analytical framework using functional magnetic resonance imaging (fMRI). We analyzed different datasets in a single scale, which was defined by an anxiety-related neural network derived from whole brain. Through the fMRI task for provoking anxiety, we found a common neural network of state anxiety across participants (1,638 trials obtained from 10 participants). Then, using the resting-state fMRI in combination with the participants trait anxiety scale (879 participants from the Human Connectome Project), we demonstrated that trait anxiety also shared the same neural network as state anxiety. Furthermore, the common neural network between state and trait anxiety could detect patients with obsessive-compulsive disorder, which is characterized by pathological anxiety-driven behaviors (174 participants from multi-site datasets). Our findings provide direct evidence that different dimensions of anxiety are not completely independent but have a substantial biological inter-relationship. Our results also provide a biologically defined dimension of anxiety, which may promote further investigation of various human characteristics, including psychiatric disorders, from the perspective of anxiety.

neuroscience↗