Biology subjects

Bedi, S.

Publications and source records attributed to Bedi, S..

12 recordsLinked to original sources

bioRxiv2026

Goal-dependent resource-rational compression of attribute differences explains nonlinearities in multi-attribute decision making

Why do multi-attribute choices so often depart from classical weighted-additive decision rules? Rather than attributing such deviations solely to biases or heuristics, we propose a resource-rational account in which value differences are encoded via capacity-limited information channels. Under resource-rational compression, these difference representations are systematically distorted, such that behavior deviates from weighted-additive predictions because value differences are not represented veridically. This theoretical account makes testable predictions about power-law relationships between true and internally represented differences. The amount of power-law-like compression is determined by information-processing capacity, emergent long-tailed prior distributions over attribute differences, and, in choice contexts, goal-dependent subjective weights that govern the allocation of limited capacity across attribute channels. We test and find support for these predictions in an attribute difference-estimation task and by reanalyzing existing food- and social-choice datasets. These results provide converging evidence for a normative, information-theoretic account of systematic nonlinearities in multi-attribute decision making. Together they show how goals can interact with cognitive capacity and priors to shape representational precision in ways that may facilitate or impair decision making.

neuroscience↗

bioRxiv2026

Multi-stage efficient coding of perception and value in goal-directed behavior

To act effectively, the brain must transform information through a chain of processing stages, from sensing the environment, to evaluating options, to selecting actions. Because neural resources are limited, each stage should represent information efficiently. Yet efficient coding has been studied almost exclusively in perception, and always one stage at a time. Whether perception and valuation are each governed by their own efficient code, and how these codes interact along the pathway from sensation to action, remains an open question. We developed a formal framework, comparing models in which efficient coding and Bayesian decoding shape perception only, valuation only, or both. To tease apart contributions from each stage, we designed an experiment that independently varied how stimuli map onto values. In a preregistered study, behavior was best explained by efficient coding operating at both stages, with each stage tracking its own objective prior. Crucially, changing the value distribution reversed the classic repulsion biases seen in orientation perception, revealing separable efficient codes in perception and valuation. The two stages also operated on different timescales: perceptual representations reflected stable, long-term environmental structure, while value representations updated rapidly with changing context. Together, these findings show that separable efficient codes at successive processing stages combine to shape behavior. This principle likely extends well beyond perception and valuation: whenever behavior depends on abstract, constructed representations rather than raw sensory signals, the brain may solve the efficiency problem anew at each stage.

neuroscience↗

bioRxiv2025

Separable neurocomputational mechanisms underlying multisensory learning

Efficient control of behavior requires multisensory learning from information distributed across senses. However, most neurocomputational studies have focused on unisensory signals. Here, we identify distinct but interacting neurocomputational mechanisms that support learning of multisensory associations. We designed a task in which behaviorally relevant information was available only from combinations of visual cues with either auditory or tactile cues. In 58 participants undergoing fMRI, we dissociated three processes: multisensory statistical learning (SL), modeled as stimulus-locked Shannon surprise; reinforcement learning (RL), modeled as feedback-locked signed reward prediction errors (RPEs); and feedback-locked unsigned RPEs (uRPEs), reflecting surprise about reward outcomes. Behaviorally, response times scaled with Shannon surprise (SL) while accuracy improved with feedback (RL). Model-based fMRI revealed dissociable but complementary networks: RPEs engaged ventral striatum, vmPFC, and left angular gyrus; surprise recruited bilateral angular gyrus, dlPFC, and precuneus; and uRPEs involved insula, dorsomedial prefrontal, and lateral frontoparietal cortices. Several of these regions extend beyond canonical learning circuits and may contribute to learning when behavior depends on information distributed across sensory modalities. All three networks were modality-general, showing comparable strength for audiovisual and visuotactile learning. Notably, left angular gyrus tracked both Shannon surprise and RPE, suggesting its potential role for integrating structural and value information. These findings indicate that the brain engages distinct but complementary systems for structure-based, reward-based, and outcomesurprise computations. By combining behavioral modeling and fMRI with a novel task design, we provide a framework for dissecting the neurocomputational architecture of multisensory learning.

neuroscience↗

bioRxiv2025

Acute stress reduces risk-aversion by changing magnitude perception

Stress is thought to impair financial decision-making by influencing the willingness to take risks. This effect is commonly attributed to stress-related changes in affective evaluation of rewards and their associated uncertainty. However, existing research has yielded inconsistent findings, and the psychological and neural mechanisms at play remain poorly understood. Here we show that acute psychosocial stress can affect financial risk-taking by altering basic perception of payoff magnitudes, independent of subjective valuation processes. Psychophysical and fMRI modelling reveals that stress reduces risk-aversion without changing choice consistency. A Bayesian perceptual model attributes this effect to an upward shift in prior beliefs about payoff magnitude: Under stress, participants choices are guided by relatively more optimistic beliefs about the risky options. A convergent pattern emerges in parietal neural representations of the payoff magnitudes, which show a systematic upward shift in neural coding as a consequence of the stress manipulation. These findings provide converging neural and behavioral evidence that stress influences financial risk-taking by changing neurocomputational processes related to magnitude perception. This has significant implications for psychological and neuroscience theories of stress and risky behavior, with potential applications to policy and clinical interventions.

neuroscience↗

bioRxiv2025

Distributed range adaptation in human parietal encoding of numbers

The brain's representations are encoded in the collective activity of neural populations, whose non-monotonic tuning properties define the population code. Adaptive behavior calls for flexible retuning of this code as stimuli statistics change across contexts. Yet how such adaptation occurs is unknown. Using fMRI during a numerosity-estimation task with variable number ranges, we show that number representations in human parietal cortex dynamically recalibrate to context, enabling context-sensitive behavioral accuracy. The tuning properties of neural populations collectively shift and rescale with the range. This distributed range adaptation achieves efficient coding in real time: neural precision scales with the range and predicts corresponding changes in behavioral precision. Individuals with stronger neural adaptation show larger behavioral adjustments. These findings extend static sensory efficient coding to adaptive representations of abstract magnitudes. Such flexible population tuning may constitute a canonical mechanism of encoding networks that enables the brain to sustain precise, adaptive behavior.

neuroscience↗

bioRxiv2025

Probability weighting arises from boundary repulsions of cognitive noise

In both risky choice and perception, people overweight small and underweight large probabilities. While prospect theory models this with a probability weighting function, and Bayesian noisy coding models attribute it to specific encoding functions or priors, we propose a more general account: Probability distortions arise from cognitive noise being repelled by the natural boundaries of probability (0,1). This boundary repulsion occurs in any encoding-decoding system that efficiently encodes, or Bayesian-decodes, bounded quantities, independent of specific priors or encoding functions. Our theory predicts: new, experimentally-induced boundaries should cause additional distortions; increasing cognitive noise should amplify distortions; and boundaries should reduce behavioral variability near them. We confirmed all predictions in three pre-registered experiments spanning risky choice and probability perception. Our findings further suggest that these changes originate largely during decoding. Our work provides a unified explanation for distorted and variable probability judgments, reframing them as consequences of bounded, noisy cognitive inference. SignificanceThe origin of probability weighting--a central feature of decision-making under risk--remains a longstanding puzzle. Does it arise from processes unique to risk, or does it reflect a more general cognitive mechanism? Here, we show that the classic probability weighting pattern is not domain-specific but instead emerges from a general property of noisy inference over bounded quantities, such as probabilities. Our account formalizes how resource-rational encoding and Bayesian optimal decoding naturally lead to interactions between cognitive noise and the 0-1 bounds of probability, giving rise to systematic distortions. Using pre-registered experimental manipulations across both risky lottery valuation and probability perception, we demonstrate that distortions in probability weighting and estimation are not fixed, intrinsic features, but rather predictable consequences of the interaction between noise and boundaries. This provides a mechanistic account of probability weighting and suggests a unifying explanation for its emergence across different cognitive domains. Similar mechanisms should extend to other naturally or contextually bounded quantities.

neuroscience↗

bioRxiv2025

Growing Minds, Integrating Senses: Neural and Computational Insights into Age-related Changes in Audio-Visual and Tactile-Visual Learning in Children

Multisensory processing and learning shape cognitive and language development, influencing how we perceive and interact with the world from an early age. While multisensory processes mature into adolescence, it remains poorly understood how age influences multisensory associative learning. This study investigated age-related effects on multisensory processing and learning during audio-visual and tactile-visual learning in 67 children (5.7-13 years) by integrating behavioural and neuroimaging data with computational methods. A reinforcement-learning drift diffusion model revealed that older children processed information faster and made more efficient decisions on multisensory associations. These age-related increases coincided with higher activity in brain regions associated with cognitive control, multisensory integration, and memory retrieval, specifically during audio-visual learning. Notably, the bilateral anterior insula exhibited heightened activation in response to lower reward prediction errors, indicative of increased sensitivity to negative feedback with development. Finally, reward prediction errors modulated activation in reward processing and cognitive control regions, with this modulation remaining modality-independent and largely stable across age. In conclusion, while children employ similar learning strategies, older children make decisions more efficiently and engage neural resources more strongly. Our findings reflect ongoing maturation of neural networks supporting multisensory learning in middle childhood, enabling more adaptive learning in later childhood. Highlights- faster information processing in older children during a multisensory learning task - increasing brain activation with age in visual, parietal, and frontal regions - reward prediction error processing are independent of age and modality - heightened response to negative reward prediction errors with increasing age

neuroscience↗

bioRxiv2024

Illuminating the Potential: Light Harvesting and Semiconducting Properties of Bacterial Microcompartment Shell Proteins

Biomaterials with self-assembly, genetic tunability, self-healing, and biocompatibility hold promise for next-generation electronics. This study explores disc-like shell proteins from bacterial microcompartments as photovoltaic materials. These self-assembling proteins exhibit semiconducting properties, including a low work function and non-linear I-V behavior. Notably, under UV light, they generate significant photocurrent without external voltage, demonstrating efficient electron transfer. Electron conduction occurs via tunneling, enabled by distinct electron-rich surface regions. High responsivity and quantum efficiency outperform prior protein-based systems, emphasizing their potential in photocurrent and light-harvesting applications. Genetic mutations revealed a proton-coupled electron transfer (PCET) mechanism, where tyrosine residues strategically positioned near proton abstraction sites enhance photocurrent generation. This study highlights the innovative capabilities of shell proteins for energy-efficient devices and demonstrates how genetic engineering can further optimize biomaterial performance, paving the way for sustainable electronic applications.

bioengineering↗

bioRxiv2023

Spatial Transcriptomics Arena (STAr): an Integrated Platform for Spatial Transcriptomics Methodology Research

The emerging field of spatially resolved transcriptomics (SRT) has revolutionized biomedical research. SRT quantifies expression levels at different spatial locations, providing a new and powerful tool to interrogate novel biological insights. An essential question in the analysis of SRT data is to identify spatially variable (SV) genes; the expression levels of such genes have spatial variation across different tissues. SV genes usually play an important role in underlying biological mechanisms and tissue heterogeneity. Currently, several computational methods have been developed to detect such genes; however, there is a lack of unbiased assessment of these approaches to guide researchers in selecting the appropriate methods for their specific biomedical applications. In addition, it is difficult for researchers to implement different existing methods for either biological study or methodology development. Furthermore, currently available public SRT datasets are scattered across different websites and preprocessed in different ways, posing additional obstacles for quantitative researchers developing computational methods for SRT data analysis. To address these challenges, we designed Spatial Transcriptomics Arena (STAr), an open platform comprising 193 curated datasets from seven technologies, seven statistical methods, and analysis results. This resource allows users to retrieve high-quality datasets, apply or develop spatial gene detection methods, as well as browse and compare spatial gene analysis results. It also enables researchers to comprehensively evaluate SRT methodology research in both simulated and real datasets. Altogether, STAr is an integrated research resource intended to promote reproducible research and accelerate rigorous methodology development, which can eventually lead to an improved understanding of biological processes and diseases. STAr can be accessed at https://lce.biohpc.swmed.edu/star/.

genomics↗

bioRxiv2021

Similarities Between Bacterial GAD and Human GAD65:Implications in Gut Mediated Autoimmune Type 1 Diabetes

A variety of islet autoantibodies (AAbs) can predict and possibly dictate eventual type 1 diabetes (T1D) diagnosis. Upwards of 75% of those with T1D are positive for AAbs against glutamic acid decarboxylase (GAD65), a producer of gamma-aminobutyric acid (GABA) in human pancreatic beta cells. Interestingly, bacterial populations within the human gut also express GAD65 and produce GABA. Evidence suggests that dysbiosis of the microbiome may correlate with T1D pathogenesis and physiology. Therefore, autoimmune linkages between the gut microbiome and islets susceptible to autoimmune attack need to be further elucidated. Utilizing silico analyses, we show here that 25 GAD sequences from different human gut bacterial sources show sequence and motif similarities to human beta cell GAD65. Our motif analyses determined that a majority of gut GAD sequences contain the pyroxical dependent decarboxylase domain of human GAD65 which is important for its enzymatic activity. Additionally, we showed overlap with known human GAD65 T-cell receptor epitopes which may implicate the immune destruction of beta cells. Thus, we propose a physiological hypothesis in which changes in the gut microbiome in those with T1D result in a release of bacterial GAD, thus causing miseducation of the host immune system. Due to the notable similarities, we found between humans and bacterial GAD, these deputized immune cells may then go on to target human beta cells leading to the development of T1D.

genomics↗

bioRxiv2020

A Glu-Glu-Tyr sequence in the cytoplasmic tail of the M2 protein renders IAV susceptible to restriction of HA-M2 association in primary human macrophages

Influenza A virus (IAV) assembly at the plasma membrane is orchestrated by at least five viral components including hemagglutinin (HA), neuraminidase (NA), matrix (M1), the ion channel M2, and viral ribonucleoprotein (vRNP) complexes although particle formation itself requires only HA and/or NA. While these five viral components are expressed efficiently in primary human monocyte-derived macrophages (MDM) upon IAV infection, this cell type does not support efficient HA-M2 association and IAV particle assembly at the plasma membrane. The defects in HA-M2 association and particle assembly are specific to MDM and not observed in a monocytic cell line differentiated into macrophage-like cells. Notably, both these defects can be reversed upon disruption of the actin cytoskeleton. In the current study, we sought to examine whether M2 contributes to particle assembly in MDM and to identify a viral determinant involved in the MDM-specific and actin-dependent suppression of IAV assembly. An analysis using correlative fluorescence and scanning electron microscopy showed that an M2-deficient virus fails to form budding structures at the cell surface even after F-actin is disrupted, indicating that M2 is essential for virus particle formation at the MDM surface. Notably, proximity ligation analysis revealed that single amino acid substitution in a Glu-Glu-Tyr sequence (residues 74-76) in the M2 cytoplasmic tail allows HA-M2 association to occur efficiently even in MDM with intact actin cytoskeleton. This phenotype did not correlate with known phenotypes of the M2 substitution mutants regarding M1 interaction or vRNP packaging in epithelial cells. Overall, our study identifies a viral determinant for susceptibility to cytoskeleton-dependent regulation in MDM and hence, sheds light on the molecular mechanism behind the MDM-specific restriction of IAV assembly. ImportanceNon-permissive cell types that are unable to support viral replication serve as important tools for identification of host factors that either block viral replication (restriction factors) or support viral replication in permissive cell types (co-factors). We previously identified the MDM as a cell type that is non-permissive to IAV assembly, likely due to a block in HA-M2 association. In the current study, we determined that the IAV M2 protein is necessary for virus particle formation in MDM but also renders the virus susceptible to the MDM-specific suppression of virus assembly. We identified a specific amino acid motif in the M2 cytoplasmic tail, disruption of which allows M2 to associate with HA even in MDM. Our findings strongly support the possibility that the MDM-specific defect in HA-M2 association is due to the presence of a restriction factor(s) in MDM, which likely interacts directly with the M2 cytoplasmic domain, rather than indirectly through other internal viral components, and thereby prevents M2 from associating with HA.

microbiology↗

bioRxiv2017

A Cell-Type-Specific Defect in Assembly of Influenza A Virus Particles in Primary Human Macrophages

The primary target of Influenza A virus (IAV) is epithelial cells in the respiratory tract. In contrast to epithelial cells, productive infection of most IAV strains is either blocked or highly inefficient in macrophages. The exact nature of the defect in IAV replication in human macrophages remains unknown. In this study, we showed that primary human monocyte-derived macrophages (MDM) are inefficient in IAV release even when compared to a monocytic cell line differentiated to macrophage-like cells, despite comparable levels of expression of viral glycoproteins at the plasma membrane. Correlative fluorescence scanning electron microscopy revealed that formation of budding structures at the cell surface is inefficient in MDM even though clustering of a viral glycoprotein, hemagglutinin (HA), is observed, suggesting that IAV particle assembly is blocked in human MDM. Using an in situ proximity ligation assay, we further determined that association between HA and the viral ion channel protein M2 is defective at the plasma membrane of MDM. In contrast, HA and another glycoprotein neuraminidase (NA) associate with each other on the MDM surface efficiently. Notably, the defect in association between HA and M2 in MDM was reversed upon inhibition of actin polymerization by cytochalasin D. Altogether, these results suggest that HA-M2 association on the plasma membrane is a discrete step in the IAV assembly process, which is separable from the association between HA and NA and susceptible to suppression by actin cytoskeleton. Overall, our study revealed the presence of a cell-type-specific mechanism negatively regulating IAV assembly at the plasma membrane.\n\nImportanceIdentification of host cell determinants promoting or suppressing replication of many viruses has been aided by analyses of host cells that impose inherent blocks on viral replication. In this study, we show that primary human MDM are not permissive to IAV replication due to a defect at the virus particle formation step. This defect is specific to primary human macrophages, since a human monocytic cell line differentiated to macrophage-like cells supports IAV particle formation. We further identified association between two viral transmembrane proteins, HA and M2, on the cell surface as a discrete assembly step, which is defective in MDM. Defective HA-M2 association in MDM is rescued by disruption of the actin cytoskeleton, revealing a previously unknown, negative role for actin polymerization, which is generally thought to play positive roles in IAV assembly. Overall, our study uncovered a host-mediated restriction of association between viral transmembrane components during IAV assembly.

microbiology↗