Biology subjects

Anzai, A.

Publications and source records attributed to Anzai, A..

4 recordsLinked to original sources

bioRxiv2024

Viewing geometry drives flexible perception of object motion and depth

1Vision is an active process. We move our eyes and head to acquire useful information and to track objects of interest. While these movements are essential for many behaviors, they greatly complicate the analysis of retinal image motion--the image motion of an object reflects both how that object moves in the world and how the eye moves relative to the scene. Our brain must account for the visual consequences of self-motion to accurately perceive the 3D layout and motion of objects in the scene. Traditionally, compensation for eye movements (e.g., smooth pursuit) has been modeled as a simple vector subtraction process. While these models are effective for pure eye rotations and 2D scenes, we show that they fail to apply to more natural viewing geometries involving combinations of eye rotation and translation. We develop theoretical predictions for how perception of object motion and depth should depend on the observers inferred viewing geometry. Through psychophysical experiments, we demonstrate novel perceptual biases that manifest when different viewing geometries are simulated by optic flow, in the absence of physical eye movements. Remarkably, these biases occur automatically, without training or feedback, and are well predicted by our theoretical framework. A neural network model trained to perform the same tasks exhibits neural response patterns similar to those observed in macaque area MT, suggesting a possible neural basis for these adaptive computations. Our findings demonstrate that the visual system automatically infers viewing geometry from optic flow and flexibly attributes components of image motion to either self-motion or depth structure according to the inferred geometry. Our findings unify previously separate bodies of work by showing that the visual consequences of self-motion play a crucial role in computing object motion and depth, thus enabling the visual system to adaptively perceive a dynamic 3D environment.

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↗

bioRxiv2024

A Neural Mechanism for Optic Flow Parsing in Macaque Visual Cortex

For the brain to compute object motion in the world during self-motion, it must discount the global patterns of image motion (optic flow) caused by self-motion. Optic flow parsing is a proposed visual mechanism for computing object motion in the world, and studies in both humans and monkeys have demonstrated perceptual biases consistent with the operation of a flow parsing mechanism. However, the neural basis of flow parsing remains unknown. We demonstrate, at both the individual unit and population levels, that neural activity in macaque area MT is biased by peripheral optic flow in a manner that can at least partially account for perceptual biases induced by flow parsing. These effects cannot be explained by conventional surround suppression mechanisms or choice-related activity, and have a substantial neural latency. Together, our findings establish the first neural basis for the computation of scene-relative object motion based on flow parsing.

neuroscience↗

bioRxiv2019

Identifying the most influential features of neural population responses for information encoding and behavior

Identifying the features of population responses that are relevant to the amount of information encoded by neuronal populations is a crucial step toward understanding the neural code. Statistical features such as tuning properties, individual and shared response variability, and global activity modulations could all affect the amount of information encoded and modulate behavioral performance. We show that two features in particular affect information: the modulation of population responses across conditions and the projection of the inverse population variability along the modulation axis. We demonstrate that fluctuations of these two quantities are correlated with fluctuations of behavioral performance in various tasks and brain regions. In contrast, fluctuations in mean correlations among neurons and global activity have negligible or inconsistent effects on the amount of information encoded and behavioral performance. Our results are consistent with predictions of a model that optimally decodes population responses, which suggests that in our behavioral tasks the readout of information is near-optimal.

neuroscience↗