Biology subjects

Shivkumar, S.

Publications and source records attributed to Shivkumar, S..

12 recordsLinked to original sources

bioRxiv2026

Implicit vs. explicit choice promotes the combination vs. selection of sensorimotor memories under contextual uncertainty

Skilled action requires expressing motor memories as appropriate for the current context, but context is often uncertain. Theoretical models make conflicting proposals about memory expression under contextual uncertainty, predicting belief-weighted combination of memories versus the selection of the most probable memory. We tested these predictions by training human participants to reach in two opposing force fields cued by the direction of a random dot motion stimulus whose coherence varied. When participants moved before reporting dot direction, adaptation scaled with coherence: low-reliability cues produced partial expression of both memories. Fitting Bayesian observer models to behavior favored belief-weighted memory combination. In contrast, when participants reported their choice before moving, adaptation was independent of coherence and model fits favored categorical memory selection. Thus, sensorimotor memories are expressed as either a probabilistic combination or categorical selection, depending on whether participants contextual inference remains implicit or is made explicit at the time of memory expression.

animal behavior and cognition↗

bioRxiv2025

Bayesian causal inference unifies perceptual and neuronal processing of center-surround motion in area MT

Center-surround interactions are a hallmark of visual processing and are especially prominent in area MT, where surround motion can either suppress or facilitate neuronal responses depending on context. However, existing mechanistic descriptions, including divisive normalization, do not explain the full diversity of these effects or their relationship to motion perception. Here, we show that both perceptual and neuronal center-surround phenomena can be understood as consequences of Bayesian causal inference over reference frames. Building on a normative model of motion perception, we derived predictions for the mean responses and variability of single MT neurons across the full fourdimensional space of center and surround directions and speeds. The model generates structured patterns of suppression, facilitation, and coordinate-frame selectivity that qualitatively match the diversity of center-surround effects reported in primate MT. Our results provide a unified computational account linking motion integration and segmentation in perception with contextual response modulation in MT, and yield testable predictions for how the visual system infers and represents reference frames.

neuroscience↗

bioRxiv2024

How to reward animals based on their subjective percepts: A Bayesian approach to online estimation of perceptual biases.

1.Elucidating the neural basis of perceptual biases, such as those produced by visual illusions, can provide powerful insights into the neural mechanisms of perceptual inference. However, studying the subjective percepts of animals poses a fundamental challenge: unlike human participants, animals cannot be verbally instructed to report what they see, hear, or feel. Instead, they must be trained to perform a task for reward, and researchers must infer from their responses what the animal perceived. However, animals responses are shaped by reward feedback, thus raising the major concern that the reward regimen may alter the animals decision strategy or even their intrinsic perceptual biases. Using simulations of a reinforcement learning agent, we demonstrate that conventional reward strategies fail to allow accurate estimation of perceptual biases. We developed a method that estimates perceptual bias during task performance and then computes the reward for each trial based on the evolving estimate of the animals perceptual bias. Our approach makes use of multiple stimulus contexts to dissociate perceptual biases from decision-related biases. Starting with an informative prior, our Bayesian method updates a posterior over the perceptual bias after each trial. The prior can be specified based on data from past sessions, thus reducing the variability of the online estimates and allowing it to converge to a stable estimate over a small number of trials. After validating our method on synthetic data, we apply it to estimate perceptual biases of monkeys in a motion direction discrimination task in which varying background optic flow induces robust perceptual biases. This method overcomes an important challenge to understanding the neural basis of subjective percepts.

neuroscience↗

bioRxiv2023

Hierarchical motion perception as causal inference

Since motion can only be defined relative to a reference frame, which reference frame guides perception? A century of psychophysical studies has produced conflicting evidence: retinotopic, egocentric, world-centric, or even object-centric. We introduce a hierarchical Bayesian model mapping retinal velocities to perceived velocities. Our model mirrors the structure in the world, in which visual elements move within causally connected reference frames. Friction renders velocities in these reference frames mostly stationary, formalized by an additional delta component (at zero) in the prior. Inverting this model automatically segments visual inputs into groups, groups into supergroups, etc. and "perceives" motion in the appropriate reference frame. Critical model predictions are supported by two new experiments, and fitting our model to the data allows us to infer the subjective set of reference frames used by individual observers. Our model provides a quantitative normative justification for key Gestalt principles providing inspiration for building better models of visual processing in general.

animal behavior and cognition↗

bioRxiv2023

Flexible Perception of Tactile Motion in Multiple Reference Frames

AO_SCPLOWBSTRACTC_SCPLOWLeading models of somatosensory processing posit that integration of tactile and proprioceptive cues is essential for touch perception. Yet, such integration is not universally beneficial. While posture-dependent remapping of tactile signals is critical for guiding hand actions, discrimination of certain tactile features is enhanced when touch is represented independently of hand posture. How the brain flexibly controls the integration of tactile and proprioceptive cues based on task demands remains unclear, as most studies have only examined touch perception within a single reference frame. Here, we studied how reference frame demands shape touch perception in humans making motion judgements on a finger, while varying hand posture. Participants were cued to report motion direction relative to the finger or the sternum (i.e., the bodys midline). We found that tactile and proprioceptive cues are integrated in a task-specific manner, with hand posture biasing judgments only in the Sternum-centric task. Further, we observe that reaction times are faster for Sternum-centric judgments, with accumulation-to-bound models indicating that this advantage is driven by lower decision thresholds. Finally, we developed a Bayesian computational framework that formalizes how tactile motion signals are transformed across hand postures, advancing our understanding of how the brain maps sensory information from skin-to body-centered coordinates. Together, these findings demonstrate that reference frame task demands shape both the transformation of tactile information and speed of perceptual decisions, within a Bayesian computational framework for task-dependent coordinate transformations.

neuroscience↗

bioRxiv2023

Ouvrai: Opening access to remote VR studies of human behavioral neuroscience

Modern virtual reality (VR) devices offer 6 degree-of-freedom kinematic data with high spatial and tem-poral resolution, making them powerful tools for research on sensorimotor and cognitive functions. We introduce Ouvrai, an open-source solution that facilitates the design and execution of remote VR studies, capitalizing on the surge in VR headset ownership. This tool allows researchers to develop sophisticated experiments using cutting-edge web technologies like the WebXR Device API for browser-based VR, with-out compromising on experimental design. Ouvrais features include easy installation, intuitive JavaScript templates, a component library managing front- and back-end processes, and a streamlined workflow. It also integrates APIs for Firebase, Prolific, and Amazon Mechanical Turk and provides data processing utilities for analysis. Unlike other tools, Ouvrai remains free, with researchers managing their web hosting and cloud database via personal Firebase accounts. Through three distinct motor learning experiments, we confirm Ouvrais efficiency and viability for conducting remote VR studies.

neuroscience↗

bioRxiv2022

Task-uninformative visual stimuli improve auditory spatial discrimination: an elusive effect with an ambiguous contribution of relative reliability

The brain combines information from multiple sensory modalities to interpret the environment. These processes, collectively known as multisensory integration, have been modeled as Bayesian causal inference, which proposes that perception involves the combination of information from different sensory modalities based on their reliability and their likelihood of stemming from the same causes in the outside world. Bayesian causal inference has explained a variety of multisensory effects in simple tasks but is largely untested in complex sensory scenes where multisensory integration can provide the most benefit. Recently, we presented data challenging the ideal Bayesian model from a new auditory spatial discrimination task in which spatially aligned visual stimuli improve performance despite providing no information about the correct response. Here, we tested the hypothesis that, despite deviating from the ideal observer, the influence of task-uninformative stimuli was still dependent on the reliabilities of auditory and visual cues. We reasoned shorter stimulus durations should lead to less reliable auditory spatial encoding, and hence stronger effects of more reliable visual cues, which are easily localized even at short durations. While our results replicated the effect from our previous study across a wide range of stimulus durations, we did not find a significant increase in effect size with shorter stimuli, leaving our principal question not fully answered.

neuroscience↗

bioRxiv2022

Inferring sources of suboptimality in perceptual decision making using a causal inference task

Perceptual decision-making has been extensively modeled using the ideal observer framework. However, a range of deviations from optimality demand an extension of this framework to characterize the different sources of suboptimality. Prior work has mostly formalized these sources by adding biases and variability in the context of specific process models but are hard to generalize to more complex tasks. Here, we formalize suboptimalities as part of the brains probabilistic model of the task. Data from a traditional binary discrimination task cannot separate between different kinds of biases, or between sensory noise and approximate computations. We showed that this was possible using a recently developed causal inference task in which observers discriminated auditory cues in the presence of choice-uninformative visual cues. An extension of the task with different stimulus durations provided evidence for an increase in the precision of the computations with stimulus duration, separate from a decrease in observation noise.

animal behavior and cognition↗

bioRxiv2020

Bayesian Encoding and Decoding as Distinct Perspectives on Neural Coding

One of the most influential, and controversial, ideas in neuroscience has been to understand the brain in terms of Bayesian computations. Unstated differences in how Bayesian ideas are operationalized across different models make it difficult to ascertain both which empirical data support which models, and how Bayesian computations might be implemented by neural circuits. In this paper, we make one such difference explicit by identifying two distinct philosophies that underlie existing neural models of Bayesian inference: one in which the brain recovers experimenter-defined structures in the world from sensory neural activity (Decoding), and another in which the brain represents latent quantities in an internal model that explains its inputs (Encoding). These philosophies require profoundly different assumptions about the nature of inference in the brain, and lead to different interpretations of empirical data. Here, we characterize and contrast both philosophies in terms of motivations, empirical support, and relationship to neural data. We also show that this implicit difference in philosophy underlies some of the debate on whether neural activity is better described as a sampling-based, or a parametric, distributional code. Using a simple model of primary visual cortex as an example, we show mathematically that it is possible that the very same neural activity can be described as probabilistic inference by neural sampling in the Encoding framework while also forming a linear probabilistic population code (PPC) in the Decoding framework. This demonstrates that certain families of Encoding and Decoding models are compatible with each other rather than competing explanations of data. In sum, Bayesian Encoding and Bayesian Decoding are distinct, non-exclusive philosophies, and appreciating their similarities and differences will help organize future work and allow for stronger empirical tests about the nature of inference in the brain.

neuroscience↗

bioRxiv2020

Effects of auditory reliability and ambiguous visual stimuli on auditory spatial discrimination

ABSTRACTThe brain combines information from multiple sensory modalities to interpret the environment. Multisensory integration is often modeled by ideal Bayesian causal inference, a model proposing that perceptual decisions arise from a statistical weighting of information from each sensory modality based on its reliability and relevance to the observer’s task. However, ideal Bayesian causal inference fails to describe human behavior in a simultaneous auditory spatial discrimination task in which spatially aligned visual stimuli improve performance despite providing no information about the correct response. This work tests the hypothesis that humans weight auditory and visual information in this task based on their relative reliabilities, even though the visual stimuli are task-uninformative, carrying no information about the correct response, and should be given zero weight. Listeners perform an auditory spatial discrimination task with relative reliabilities modulated by the stimulus durations. By comparing conditions in which task-uninformative visual stimuli are spatially aligned with auditory stimuli or centrally located (control condition), listeners are shown to have a larger multisensory effect when their auditory thresholds are worse. Even in cases in which visual stimuli are not task-informative, the brain combines sensory information that is scene-relevant, especially when the task is difficult due to unreliable auditory information.Competing Interest StatementThe authors have declared no competing interest.View Full Text

animal behavior and cognition↗

bioRxiv2019

Improvement in auditory spatial discrimination from ambiguous visual stimuli is not explained by ideal observer causal inference

In order to survive and function in the world, we must understand the content of our environment. This requires us to gather and parse complex, sometimes conflicting, information. Yet, the brain is capable of translating sensory stimuli from disparate modalities into a cohesive and accurate percept with little conscious effort. Previous studies of multisensory integration have suggested that the brains integration of cues is well-approximated by an ideal observer implementing Bayesian causal inference. However, behavioral data from tasks that include only one stimulus in each modality fail to capture what is in nature a complex process. Here we employed an auditory spatial discrimination task in which listeners were asked to determine on which side they heard one of two concurrently presented sounds. We compared two visual conditions in which task-uninformative shapes were presented in the center of the screen, or spatially aligned with the auditory stimuli. We found that performance on the auditory task improved when the visual stimuli were spatially aligned with the auditory stimuli--even though the shapes provided no information about which side the auditory target was on. We also demonstrate that a model of a Bayesian ideal observer performing causal inference cannot explain this improvement, demonstrating that humans deviate systematically from the ideal observer model.

animal behavior and cognition↗

bioRxiv2017

Modeling the Role of Striatum in Stochastic Multi Context Tasks

Decision making tasks in changing environments with probabilistic reward schemes present various challenges to the agents performing the task. These agents must use the experience gained in the past trials to characterize the environment which guides their actions. We present two models to predict an agents behavior in these tasks - a theoretical model which defines a Bayes optimal solution to the problem under realistic task conditions. The second is a computational model of the basal ganglia which presents a neural mechanism to solve the same. Both the models are shown to reproduce results in behavioral experiments and are compared to each other. This comparison allows us to characterize the theoretical model as a bound on the neural model and the neural model as a biologically plausible implementation of the theoretical model. Furthermore, we predict the performance of the agents in various stochastic regimes which could be tested in future studies.

neuroscience↗