It has been shown that the Central Nervous System (CNS) integrates visual and inertial information in heading estimation for congruent multisensory stimuli and stimuli with small discrepancies. Multisensory information should, however, only be integrated when the cues are redundant. Here, we investigated how the CNS constructs an estimate of heading for combinations of visual and inertial heading stimuli with a wide range of discrepancies. Participants were presented with 2s visual-only and inertial-only motion stimuli, and combinations thereof. Discrepancies between visual and inertial heading ranging between 0-90° were introduced for the combined stimuli. In the unisensory conditions, it was found that visual heading was generally biased towards the fore-aft axis, while inertial heading was biased away from the fore-aft axis. For multisensory stimuli, it was found that five out of nine participants integrated visual and inertial heading information regardless of the size of the discrepancy; for one participant, the data were best described by a model that explicitly performs causal inference. For the remaining three participants the evidence could not readily distinguish between these models. The finding that multisensory information is integrated is in line with earlier findings, but the finding that even large discrepancies are generally disregarded is surprising. Possibly, people are insensitive to discrepancies in visual-inertial heading angle because such discrepancies are only encountered in artificial environments, making a neural mechanism to account for them otiose. An alternative explanation is that detection of a discrepancy may depend on stimulus duration, where sensitivity to detect discrepancies differs between people.

A large body of research shows that the Central Nervous System (CNS) integrates multisensory information. However, this strategy should only apply to multisensory signals that have a common cause; independent signals should be segregated. Causal Inference (CI) models account for this notion. Surprisingly, previous findings suggested that visual and inertial cues on heading of self-motion are integrated regardless of discrepancy. We hypothesized that CI does occur, but that characteristics of the motion profiles affect multisensory processing. Participants estimated heading of visual-inertial motion stimuli with several different motion profiles and a range of intersensory discrepancies. The results support the hypothesis that judgments of signal causality are included in the heading estimation process. Moreover, the data suggest a decreasing tolerance for discrepancies and an increasing reliance on visual cues for longer duration motions.

In the present study, we investigated whether the perception of heading of linear self-motion can be explained by Maximum Likelihood Integration (MLI) of visual and non-visual sensory cues. MLI predicts smaller variance for multisensory judgments compared to unisensory judgments. Nine participants were exposed to visual, inertial, or visual-inertial motion conditions in a moving base simulator, capable of accelerating along a horizontal linear track with variable heading. Visual random-dot motion stimuli were projected on a display with a 40° horizontal × 32° vertical field of view (FoV). All motion profiles consisted of a raised cosine bell in velocity. Stimulus heading was varied between 0 and 20°. After each stimulus, participants indicated whether perceived self-motion was straight-ahead or not. We fitted cumulative normal distribution functions to the data as a psychometric model and compared this model to a nested model in which the slope of the multisensory condition was subject to the MLI hypothesis. Based on likelihood ratio tests, the MLI model had to be rejected. It seems that the imprecise inertial estimate was weighed relatively more than the precise visual estimate, compared to the MLI predictions. Possibly, this can be attributed to low realism of the visual stimulus. The present results concur with other findings of overweighing of inertial cues in synthetic environments.

The perceptual upright is thought to be constructed by the central nervous system (CNS) as a vector sum; by combining estimates on the upright provided by the visual system and the body’s inertial sensors with prior knowledge that upright is usually above the head. Recent findings furthermore show that the weighting of the respective sensory signals is proportional to their reliability, consistent with a Bayesian interpretation of a vector sum (Forced Fusion, FF). However, violations of FF have also been reported, suggesting that the CNS may rely on a single sensory system (Cue Capture, CC), or choose to process sensory signals based on inferred signal causality (Causal Inference, CI). We developed a novel alternative-reality system to manipulate visual and physical tilt independently. We tasked participants (n = 36) to indicate the perceived upright for various (in-)congruent combinations of visual-inertial stimuli, and compared models based on their agreement with the data. The results favor the CI model over FF, although this effect became unambiguous only for large discrepancies (±60°). We conclude that the notion of a vector sum does not provide a comprehensive explanation of the perception of the upright, and that CI offers a better alternative.