Computational Mechanisms for Perceptual Stability using Disparity and Motion Parallax

Learning to segment self-generated from externally caused optic flow through sensorimotor mismatch circuits

Efficient sensory detection requires the capacity to ignore task-irrelevant information, for example when optic flow patterns created by egomotion need to be disentangled from object perception. To investigate how this is achieved in the visual system, predictive coding with sensorimotor mismatch detection is an attractive starting point. Indeed, experimental evidence for sensorimotor mismatch signals in early visual areas exists, but it is not understood how they are integrated into cortical networks that perform input segmentation and categorization. Our model advances a biologically plausible solution by extending predictive coding models with the ability to distinguish self-generated from externally caused optic flow. We first show that a simple three neuron circuit produces experience-dependent sensorimotor mismatch responses, in agreement with calcium imaging data from mice. This microcircuit is then integrated into a neural network with two generative streams. The motor-to-visual stream consists of parallel microcircuits between motor and visual areas and learns to spatially predict optic flow resulting from self-motion. The second stream bidirectionally connects a motion-selective higher visual area (mHVA) to V1, assigning a crucial role to the abundant feedback connections to V1: the maintenance of a generative model of externally caused optic flow. In the model, area mHVA learns to segment moving objects from the background, and facilitates object categorization. Based on shared neurocomputational principles across species, the model also maps onto primate visual cortex. Our work extends Hebbian predictive coding to sensorimotor settings, in which the agent actively moves - and learns to predict the consequences of its own movements.

Temporal stability of human heading perception

Humans are capable of accurately judging their heading from optic flow during straight forward self-motion. Despite the global coherence in the optic flow field, however, visual clutter and other naturalistic conditions create constant flux on the eye. This presents a problem that must be overcome to accurately perceive heading from optic flow-the visual system must maintain sensitivity to optic flow variations that correspond with actual changes in self-motion and disregard those that do not. One solution could involve integrating optic flow over time to stabilize heading signals while suppressing transient fluctuations. Stability, however, may come at the cost of sluggishness. Here, we investigate the stability of human heading perception when subjects judge their heading after the simulated direction of self-motion changes. We found that the initial heading exerted an attractive influence on judgments of the final heading. Consistent with an evolving heading representation, bias toward the initial heading increased with the size of the heading change and as the viewing duration of the optic flow consistent with the final heading decreased. Introducing periods of sensory dropout (blackouts) later in the trial increased bias whereas an earlier one did not. Simulations of a neural model, the Competitive Dynamics Model, demonstrates that a mechanism that produces an evolving heading signal through recurrent competitive interactions largely captures the human data. Our findings characterize how the visual system balances stability in heading perception with sensitivity to change and support the hypothesis that heading perception evolves over time.

The accuracy of object motion perception during locomotion

Human observers are capable of perceiving the motion of moving objects relative to the stationary world, even while undergoing self-motion. Perceiving world-relative object motion is complicated because the local optical motion of objects is influenced by both observer and object motion, and reflects object motion in observer coordinates. It has been proposed that observers recover world-relative object motion using global optic flow to factor out the influence of self-motion. However, object-motion judgments during simulated self-motion are biased, as if the visual system cannot completely compensate for the influence of self-motion. Recently, Xie et al. demonstrated that humans are capable of accurately judging world-relative object motion when self-motion is real, actively generated by walking, and accompanied by optic flow. However, the conditions used in that study differ from those found in the real world in that the moving object was a small dot with negligible optical expansion that moved at a fixed speed in retinal (rather than world) coordinates and was only visible for 500 ms. The present study investigated the accuracy of object motion perception under more ecologically valid conditions. Subjects judged the trajectory of an object that moved through a virtual environment viewed through a head-mounted display. Judgments exhibited bias in the case of simulated self-motion but were accurate when self-motion was real, actively generated, and accompanied by optic flow. The findings are largely consistent with the conclusions of Xie et al. and demonstrate that observers are capable of accurately perceiving world-relative object motion under ecologically valid conditions.

Distributed encoding of curvilinear self-motion across spiral optic flow patterns

Self-motion along linear paths without eye movements creates optic flow that radiates from the direction of travel (heading). Optic flow-sensitive neurons in primate brain area MSTd have been linked to linear heading perception, but the neural basis of more general curvilinear self-motion perception is unknown. The optic flow in this case is more complex and depends on the gaze direction and curvature of the path. We investigated the extent to which signals decoded from a neural model of MSTd predict the observer's curvilinear self-motion. Specifically, we considered the contributions of MSTd-like units that were tuned to radial, spiral, and concentric optic flow patterns in "spiral space". Self-motion estimates decoded from units tuned to the full set of spiral space patterns were substantially more accurate and precise than those decoded from units tuned to radial expansion. Decoding only from units tuned to spiral subtypes closely approximated the performance of the full model. Only the full decoding model could account for human judgments when path curvature and gaze covaried in self-motion stimuli. The most predictive units exhibited bias in center-of-motion tuning toward the periphery, consistent with neurophysiology and prior modeling. Together, findings support a distributed encoding of curvilinear self-motion across spiral space.

Estimating curvilinear self-motion from optic flow with a biologically inspired neural system

Optic flow provides rich information about world-relative self-motion and is used by many animals to guide movement. For example, self-motion along linear, straight paths without eye movements, generates optic flow that radiates from a singularity that specifies the direction of travel (heading). Many neural models of optic flow processing contain heading detectors that are tuned to the position of the singularity, the design of which is informed by brain area MSTd of primate visual cortex that has been linked to heading perception. Such biologically inspired models could be useful for efficient self-motion estimation in robots, but existing systems are tailored to the limited scenario of linear self-motion and neglect sensitivity to self-motion along more natural curvilinear paths. The observer in this case experiences more complex motion patterns, the appearance of which depends on the radius of the curved path (path curvature) and the direction of gaze. Indeed, MSTd neurons have been shown to exhibit tuning to optic flow patterns other than radial expansion, a property that is rarely captured in neural models. We investigated in a computational model whether a population of MSTd-like sensors tuned to radial, spiral, ground, and other optic flow patterns could support the accurate estimation of parameters describing both linear and curvilinear self-motion. We used deep learning to decode self-motion parameters from the signals produced by the diverse population of MSTd-like units. We demonstrate that this system is capable of accurately estimating curvilinear path curvature, clockwise/counterclockwise sign, and gaze direction relative to the path tangent in both synthetic and naturalistic videos of simulated self-motion. Estimates remained stable over time while rapidly adapting to dynamic changes in the observer's curvilinear self-motion. Our results show that coupled biologically inspired and artificial neural network systems hold promise as a solution for robust vision-based self-motion estimation in robots.

A neural mechanism for detecting object motion during self-motion

Detection of objects that move in a scene is a fundamental computation performed by the visual system. This computation is greatly complicated by observer motion, which causes most objects to move across the retinal image. How the visual system detects scene-relative object motion during self-motion is poorly understood. Human behavioral studies suggest that the visual system may identify local conflicts between motion parallax and binocular disparity cues to depth and may use these signals to detect moving objects. We describe a novel mechanism for performing this computation based on neurons in macaque middle temporal (MT) area with incongruent depth tuning for binocular disparity and motion parallax cues. Neurons with incongruent tuning respond selectively to scene-relative object motion, and their responses are predictive of perceptual decisions when animals are trained to detect a moving object during self-motion. This finding establishes a novel functional role for neurons with incongruent tuning for multiple depth cues.

ARTFLOW: A Fast, Biologically Inspired Neural Network that Learns Optic Flow Templates for Self-Motion Estimation

Most algorithms for steering, obstacle avoidance, and moving object detection rely on accurate self-motion estimation, a problem animals solve in real time as they navigate through diverse environments. One biological solution leverages optic flow, the changing pattern of motion experienced on the eye during self-motion. Here I present ARTFLOW, a biologically inspired neural network that learns patterns in optic flow to encode the observer’s self-motion. The network combines the fuzzy ART unsupervised learning algorithm with a hierarchical architecture based on the primate visual system. This design affords fast, local feature learning across parallel modules in each network layer. Simulations show that the network is capable of learning stable patterns from optic flow simulating self-motion through environments of varying complexity with only one epoch of training. ARTFLOW trains substantially faster and yields self-motion estimates that are far more accurate than a comparable network that relies on Hebbian learning. I show how ARTFLOW serves as a generative model to predict the optic flow that corresponds to neural activations distributed across the network.