Causal inference accounts for heading perception in the presence of object motion.

The brain infers our spatial orientation and properties of the world from ambiguous and noisy sensory cues. Judging self-motion (heading) in the presence of independently moving objects poses a challenging inference problem because the image motion of an object could be attributed to movement of the object, self-motion, or some combination of the two. We test whether perception of heading and object motion follows predictions of a normative causal inference framework. In a dual-report task, subjects indicated whether an object appeared stationary or moving in the virtual world, while simultaneously judging their heading. Consistent with causal inference predictions, the proportion of object stationarity reports, as well as the accuracy and precision of heading judgments, depended on the speed of object motion. Critically, biases in perceived heading declined when the object was perceived to be moving in the world. Our findings suggest that the brain interprets object motion and self-motion using a causal inference framework.

Bayesian comparison of explicit and implicit causal inference strategies in multisensory heading perception.

The precision of multisensory perception improves when cues arising from the same cause are integrated, such as visual and vestibular heading cues for an observer moving through a stationary environment. In order to determine how the cues should be processed, the brain must infer the causal relationship underlying the multisensory cues. In heading perception, however, it is unclear whether observers follow the Bayesian strategy, a simpler non-Bayesian heuristic, or even perform causal inference at all. We developed an efficient and robust computational framework to perform Bayesian model comparison of causal inference strategies, which incorporates a number of alternative assumptions about the observers. With this framework, we investigated whether human observers' performance in an explicit cause attribution and an implicit heading discrimination task can be modeled as a causal inference process. In the explicit causal inference task, all subjects accounted for cue disparity when reporting judgments of common cause, although not necessarily all in a Bayesian fashion. By contrast, but in agreement with previous findings, data from the heading discrimination task only could not rule out that several of the same observers were adopting a forced-fusion strategy, whereby cues are integrated regardless of disparity. Only when we combined evidence from both tasks we were able to rule out forced-fusion in the heading discrimination task. Crucially, findings were robust across a number of variants of models and analyses. Our results demonstrate that our proposed computational framework allows researchers to ask complex questions within a rigorous Bayesian framework that accounts for parameter and model uncertainty.

Multisensory Integration of Visual and Vestibular Signals Improves Heading Discrimination in the Presence of a Moving Object.

Humans and animals are fairly accurate in judging their direction of self-motion (i.e., heading) from optic flow when moving through a stationary environment. However, an object moving independently in the world alters the optic flow field and may bias heading perception if the visual system cannot dissociate object motion from self-motion. We investigated whether adding vestibular self-motion signals to optic flow enhances the accuracy of heading judgments in the presence of a moving object. Macaque monkeys were trained to report their heading (leftward or rightward relative to straight-forward) when self-motion was specified by vestibular, visual, or combined visual-vestibular signals, while viewing a display in which an object moved independently in the (virtual) world. The moving object induced significant biases in perceived heading when self-motion was signaled by either visual or vestibular cues alone. However, this bias was greatly reduced when visual and vestibular cues together signaled self-motion. In addition, multisensory heading discrimination thresholds measured in the presence of a moving object were largely consistent with the predictions of an optimal cue integration strategy. These findings demonstrate that multisensory cues facilitate the perceptual dissociation of self-motion and object motion, consistent with computational work that suggests that an appropriate decoding of multisensory visual-vestibular neurons can estimate heading while discounting the effects of object motion. SIGNIFICANCE STATEMENT: Objects that move independently in the world alter the optic flow field and can induce errors in perceiving the direction of self-motion (heading). We show that adding vestibular (inertial) self-motion signals to optic flow almost completely eliminates the errors in perceived heading induced by an independently moving object. Furthermore, this increased accuracy occurs without a substantial loss in the precision. Our results thus demonstrate that vestibular signals play a critical role in dissociating self-motion from object motion.

Multisensory self-motion compensation during object trajectory judgments.

Judging object trajectory during self-motion is a fundamental ability for mobile organisms interacting with their environment. This fundamental ability requires the nervous system to compensate for the visual consequences of self-motion in order to make accurate judgments, but the mechanisms of this compensation are poorly understood. We comprehensively examined both the accuracy and precision of observers' ability to judge object trajectory in the world when self-motion was defined by vestibular, visual, or combined visual-vestibular cues. Without decision feedback, subjects demonstrated no compensation for self-motion that was defined solely by vestibular cues, partial compensation (47%) for visually defined self-motion, and significantly greater compensation (58%) during combined visual-vestibular self-motion. With decision feedback, subjects learned to accurately judge object trajectory in the world, and this generalized to novel self-motion speeds. Across conditions, greater compensation for self-motion was associated with decreased precision of object trajectory judgments, indicating that self-motion compensation comes at the cost of reduced discriminability. Our findings suggest that the brain can flexibly represent object trajectory relative to either the observer or the world, but a world-centered representation comes at the cost of decreased precision due to the inclusion of noisy self-motion signals.

Aberrant causal inference and presence of a compensatory mechanism in autism spectrum disorder.

Autism spectrum disorder (ASD) is characterized by a panoply of social, communicative, and sensory anomalies. As such, a central goal of computational psychiatry is to ascribe the heterogenous phenotypes observed in ASD to a limited set of canonical computations that may have gone awry in the disorder. Here, we posit causal inference - the process of inferring a causal structure linking sensory signals to hidden world causes - as one such computation. We show that audio-visual integration is intact in ASD and in line with optimal models of cue combination, yet multisensory behavior is anomalous in ASD because this group operates under an internal model favoring integration (vs. segregation). Paradoxically, during explicit reports of common cause across spatial or temporal disparities, individuals with ASD were less and not more likely to report common cause, particularly at small cue disparities. Formal model fitting revealed differences in both the prior probability for common cause (p-common) and choice biases, which are dissociable in implicit but not explicit causal inference tasks. Together, this pattern of results suggests (i) different internal models in attributing world causes to sensory signals in ASD relative to neurotypical individuals given identical sensory cues, and (ii) the presence of an explicit compensatory mechanism in ASD, with these individuals putatively having learned to compensate for their bias to integrate in explicit reports.

Self versus environment motion in postural control.

To stabilize our position in space we use visual information as well as non-visual physical motion cues. However, visual cues can be ambiguous: visually perceived motion may be caused by self-movement, movement of the environment, or both. The nervous system must combine the ambiguous visual cues with noisy physical motion cues to resolve this ambiguity and control our body posture. Here we have developed a Bayesian model that formalizes how the nervous system could solve this problem. In this model, the nervous system combines the sensory cues to estimate the movement of the body. We analytically demonstrate that, as long as visual stimulation is fast in comparison to the uncertainty in our perception of body movement, the optimal strategy is to weight visually perceived movement velocities proportional to a power law. We find that this model accounts for the nonlinear influence of experimentally induced visual motion on human postural behavior both in our data and in previously published results.

Estimating distance during self-motion: a role for visual-vestibular interactions.

A fundamental challenge for the visual system is to extract the 3D spatial structure of the environment. When an observer translates without moving the eyes, the retinal speed of a stationary object is related to its distance by a scale factor that depends on the velocity of the observer's self-motion. Here, we aim to test whether the brain uses vestibular cues to self-motion to estimate distance to stationary surfaces in the environment. This relationship was systematically probed using a two-alternative forced-choice task in which distance perceived from monocular image motion during passive body translation was compared to distance perceived from binocular disparity while subjects were stationary. We show that perceived distance from motion depended on both observer velocity and retinal speed. For a given head speed, slower retinal speeds led to the perception of farther distances. Likewise, for a given retinal speed, slower head speeds led to the perception of nearer distances. However, these relationships were weak in some subjects and absent in others, and distance estimated from self-motion and retinal image motion was substantially compressed relative to distance estimated from binocular disparity. Overall, our findings suggest that the combination of retinal image motion and vestibular signals related to head velocity can provide a rudimentary capacity for distance estimation.

Influence of visual scene velocity on segmental kinematics during stance.

We investigated how the velocity of anterior-posterior movement of a visual surround affected segmental kinematics during stance. Ten healthy young adults were exposed to sinusoidal oscillation of an immersive virtual scene at five peak velocities ranging from 1.2 to 188 cm/s at each of four frequencies: 0.05, 0.1, 0.2 and 0.55 Hz. Root mean square (RMS) values of head, trunk, thigh and shank angular displacements were calculated. RMS values of head-neck, hip, knee and ankle joint angles were also calculated. RMS values of head, trunk, thigh and shank displacements exhibited significant increases at a scene velocity of 188 cm/s when compared with lower scene velocities. RMS values of hip, knee and ankle joint angles exhibited significant increases at scene velocities of 125 and 188 cm/s when compared with lower scene velocities. These results suggest that visual cues continued to drive postural adjustments even during high velocity movement of the virtual scene. Significant increases in the RMS values of the lower-limb joint angles suggest that as visually-induced postural instability increased, the body was primarily controlled as a multi-segmental structure instead of a single-link inverted pendulum, with the knee playing a key role in postural stabilization.