Biology subjects

DeAngelis, G. C.

Publications and source records attributed to DeAngelis, G. C..

15 recordsLinked to original sources

bioRxiv2026

Reassessing Choice Probability: What 59 Macaque Studies Tell Us About Decision-Related Activity in Visual Cortex

Choice probability (CP) quantifies the trial-by-trial covariation between a sensory neurons response and perceptual reports. Despite extensive interest in CP as a window into how perception is linked to the activity of sensory neurons, the usefulness of CP remains debated. On one hand, reported CP magnitudes vary widely across seemingly equivalent studies, questioning its utility as a metric. On the other hand, the absence of clear patterns in CP variability has made it difficult to use it to adjudicate between competing models of visual perception. Here, we performed a meta-analysis of 150 CP estimates from 59 macaque neurophysiology studies to identify factors that systematically influence CP. We confirmed the positive relationship between CP and neuronal sensitivity both across and within individual studies. When controlled for sensitivity, we found remarkable consistency in CP across varying tasks and brain regions with two notable exceptions. First, CPs were higher in tasks involving bistable percepts, reinforcing the link between CP magnitude and subjective perception. Second, CPs were smaller in area V1, supporting prior suggestions about V1s special role in visual processing. We further found a significant effect of stimulus duration on CP, providing evidence against strictly feedforward models and favoring models with substantial feedback and recurrent processing. Finally, we offer recommendations for future studies to enhance the cross-study comparability and theoretical utility of choice signals as the field transitions to large-scale population recordings. More broadly, our findings demonstrate the benefits of meta-studies that expose patterns across many different tasks and animals - yielding insights that complement large-scale population recordings.

neuroscience↗

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↗

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

Transformations of sensory information in the brain reflect a changing definition of optimality

Neurons throughout the brain modulate their firing rate lawfully in response to changes in sensory input. Theories of neural computation posit that these modulations reflect the outcome of a constrained optimization: neurons aim to efficiently and robustly represent sensory information under resource limitations. Our understanding of how this optimization varies across the brain, however, is still in its infancy. Here, we show that neural responses transform along the dorsal stream of the visual system in a manner consistent with a transition from optimizing for information preservation to optimizing for perceptual discrimination. Focusing on binocular disparity - the slight differences in how objects project to the two eyes - we re-analyze measurements from neurons characterizing tuning curves in macaque monkey brain regions V1, V2, and MT, and compare these to measurements of the natural visual statistics of binocular disparity. The changes in tuning curve characteristics are computationally consistent with a shift in optimization goals from maximizing the information encoded about naturally occurring binocular disparities to maximizing the ability to support fine disparity discrimination. We find that a change towards tuning curves preferring larger disparities is a key driver of this shift. These results provide new insight into previously-identified differences between disparity-selective regions of cortex and suggest these differences play an important role in supporting visually-guided behavior. Our findings support a key re-framing of optimal coding in regions of the brain that contain sensory information, emphasizing the need to consider not just information preservation and neural resources, but also relevance to behavior. SignificanceA major role of the brain is to transform information from the sensory organs into signals that can be used to guide behavior. Neural activity is noisy and can consume large amount of energy, so sensory neurons must optimize their information processing so as to limit energy consumption while maintaining key behaviorally-relevant information. In this report, we re-examine classically-defined brain areas in the visual processing hierarchy, and ask whether neurons in these areas vary lawfully in how they represent sensory information. Our results suggest that neurons in these brain areas shift from being an optimal conduit of sensory information to optimally supporting perceptual discrimination during natural tasks.

neuroscience↗

bioRxiv2023

Causal inference during closed-loop navigation: parsing of self- and object-motion

A key computation in building adaptive internal models of the external world is to ascribe sensory signals to their likely cause(s), a process of Bayesian Causal Inference (CI). CI is well studied within the framework of two-alternative forced-choice tasks, but less well understood within the cadre of naturalistic action-perception loops. Here, we examine the process of disambiguating retinal motion caused by self- and/or object-motion during closed-loop navigation. First, we derive a normative account specifying how observers ought to intercept hidden and moving targets given their belief over (i) whether retinal motion was caused by the target moving, and (ii) if so, with what velocity. Next, in line with the modeling results, we show that humans report targets as stationary and steer toward their initial rather than final position more often when they are themselves moving, suggesting a misattribution of object-motion to the self. Further, we predict that observers should misattribute retinal motion more often: (i) during passive rather than active self-motion (given the lack of an efference copy informing self-motion estimates in the former), and (ii) when targets are presented eccentrically rather than centrally (given that lateral self-motion flow vectors are larger at eccentric locations during forward self-motion). Results confirm both of these predictions. Lastly, analysis of eye-movements show that, while initial saccades toward targets are largely accurate regardless of the self-motion condition, subsequent gaze pursuit was modulated by target velocity during object-only motion, but not during concurrent object- and self-motion. These results demonstrate CI within action-perception loops, and suggest a protracted temporal unfolding of the computations characterizing CI.

neuroscience↗

bioRxiv2021

Speed tuning in head coordinates as an alternative explanation of depth selectivity from motion parallax in area MT

There are two distinct sources of retinal image motion: motion of objects in the world and movement of the observer. In cases where an object moves in a scene and the eyes also move, a coordinate transformation that involves smooth eye movements and retinal motion will be needed in order to estimate object motion in world coordinates. More recently, interactions between retinal and eye velocity signals have also been suggested to generate depth selectivity from motion parallax (MP) in the macaque middle temporal (MT) area. We explored whether the nature of the interaction between eye and retinal velocities in MT neurons favors one of these two possibilities or a mixture of both. We analyzed responses of MT neurons to retinal and eye velocities in a viewing context in which the observer translates laterally while maintaining visual fixation on a world-fixed target. In this scenario, the depth of an object can be inferred from the ratio between retinal velocity and eye velocity, according to the motion-pursuit law. Previous studies have shown that MT responses to retinal motion are gain-modulated by the direction of eye movement, suggesting a potential mechanism for depth tuning from MP. However, our analysis of the joint tuning profile for retinal and eye velocities reveals that some MT neurons show a partial coordinate transformation toward head coordinates. We formalized a series of computational models to predict neural spike trains as well as selectivity for depth, and we used factorial model comparisons to quantify the relative importance of each model component. Our findings for many MT neurons reveal that the data are equally well explained by gain modulation or a partial coordinate transformation toward head coordinates, although some responses can only be well fit by the coordinate transform model. Our results highlight the potential role of MT neurons in representing multiple higher-level sensory variables, including depth from MP and object motion in the world.

neuroscience↗

bioRxiv2019

Tracking the mind’s eye: Primate gaze behavior during virtual visuomotor navigation reflects belief dynamics

To take the best actions, we often need to maintain and update beliefs about variables that cannot be directly observed. To understand the principles underlying such belief updates, we need tools to uncover subjects belief dynamics from natural behaviour. We tested whether eye movements could be used to infer subjects beliefs about latent variables using a naturalistic, visuomotor navigation task. We observed eye movements that appeared to continuously track the goal location even when no visible target was present there. Accurate goal-tracking was associated with improved task performance, and inhibiting eye movements in humans impaired navigation precision. By using passive stimulus playback and manipulating stimulus reliability, we show that subjects eye movements are likely voluntary, rather than reflexive. These results suggest that gaze dynamics play a key role in action-selection during challenging visuomotor behaviours, and may possibly serve as a window into the subjects dynamically evolving internal beliefs.

animal behavior and cognition↗

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↗

bioRxiv2019

Motion perception in the common marmoset

Visual motion processing is a well-established model system for studying neural population codes in primates. The common marmoset, a small new world primate, offers unparalleled opportunities to probe these population codes in key motion processing areas, such as cortical areas MT and MST, because these areas are accessible for imaging and recording at the cortical surface. However, little is currently known about the perceptual abilities of the marmoset. Here, we introduce a paradigm for studying motion perception in the marmoset and compare their psychophysical performance to human observers. We trained two marmosets to perform a motion estimation task in which they provided an analog report of their perceived direction of motion with an eye movement to a ring that surrounded the motion stimulus. Marmosets and humans exhibited similar trade-offs in speed vs. accuracy: errors were larger and reaction times were longer as the strength of the motion signal was reduced. Reverse correlation on the temporal fluctuations in motion direction revealed that both species exhibited short integration windows, however, marmosets had substantially less non-decision time than humans. Our results provide the first quantification of motion perception in the marmoset and demonstrate several advantages to using analog estimation tasks.

neuroscience↗

bioRxiv2018

Choice-Related Activity during Visual Slant Discrimination in Macaque CIP but Not V3A

Creating three-dimensional (3D) representations of the world from two-dimensional retinal images is fundamental to many visual guided behaviors including reaching and grasping. A critical component of this process is determining the 3D orientation of objects. Previous studies have shown that neurons in the caudal intraparietal area (CIP) of the macaque monkey represent 3D planar surface orientation (i.e., slant and tilt). Here we compare the responses of neurons in areas V3A (which is implicated in 3D visual processing and which precedes CIP in the visual hierarchy) and CIP to 3D oriented planar surfaces. We then examine whether activity in these areas correlates with perception during a fine slant discrimination task in which monkeys report if the top of a surface is slanted towards or away from them. Although we find that V3A and CIP neurons show similar sensitivity to planar surface orientation, significant choice-related activity during the slant discrimination task is rare in V3A but prominent in CIP. These results implicate both V3A and CIP in the representation of 3D surface orientation, and suggest a functional dissociation between the areas based on slant-related decision signals.\n\nSignificance StatementSurface orientation perception is fundamental to visually guided behaviors such as reaching, grasping, and navigation. Previous studies implicate the caudal intraparietal area (CIP) in the representation of 3D surface orientation. Here we show that responses to 3D oriented planar surfaces are similar in CIP and V3A, which precedes CIP in the cortical hierarchy. However, we also find a qualitative distinction between the two areas: only CIP neurons show robust choice-related activity during a fine visual orientation discrimination task.

neuroscience↗

bioRxiv2017

Speed tuning in the macaque posterior parietal cortex

Neurons in the macaque posterior parietal cortex are known to encode the direction of self-motion. But do they also encode ones speed? To test this, we performed neural recordings from area 7a while monkeys were passively translated or rotated at various speeds. Visual stimuli were delivered as optic flow fields and vestibular stimuli were generated by a motion platform. Under both conditions, the responses of a fraction of neurons scaled linearly with self-motion speed, and speed-selective neurons were not localized to specific layers or columns. We analyzed ensembles of simultaneously recorded neurons and found that the precision of speed representation was sufficient to support path integration over modest distances. Our findings describe a multisensory neural code for linear and angular self-motion speed in the posterior parietal cortex of the macaque brain, and suggest a potential role for this representation.

neuroscience↗

bioRxiv2017

A Dynamic Bayesian Observer Model Reveals Origins of Bias in Visual Path Integration

Path integration is a navigation strategy by which animals track their position by integrating their self-motion velocity over time. To identify the computational origins of bias in visual path integration, we asked human subjects to navigate in a virtual environment using optic flow, and found that they generally travelled beyond the goal location. Such a behaviour could stem from leaky integration of unbiased self-motion velocity estimates, or from a prior expectation favouring slower speeds that causes underestimation of velocity. We tested both alternatives using a probabilistic framework that maximizes expected reward, and found that subjects biases were better explained by a slow-speed prior than imperfect integration. When subjects integrate paths over long periods, this framework intriguingly predicts a distance-dependent bias reversal due to build-up of uncertainty, which we also confirmed experimentally. These results suggest that visual path integration performance is limited largely by biases in processing optic flow rather than by suboptimal signal integration.

neuroscience↗