Causal Inference in Multisensory Heading Estimation.
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ABSTRACT:
SUBMITTER: de Winkel KN
PROVIDER: S-EPMC5218471 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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.
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Project description: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.
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Project description:Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events. Here we use multisensory cue combination to study causal inference in perception. We formulate an ideal-observer model that infers whether two sensory cues originate from the same location and that also estimates their location(s). This model accurately predicts the nonlinear integration of cues by human subjects in two auditory-visual localization tasks. The results show that indeed humans can efficiently infer the causal structure as well as the location of causes. By combining insights from the study of causal inference with the ideal-observer approach to sensory cue combination, we show that the capacity to infer causal structure is not limited to conscious, high-level cognition; it is also performed continually and effortlessly in perception.
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Project description:Sitting in a static railway carriage can produce illusory self-motion if the train on an adjoining track moves off. While our visual system registers motion, vestibular signals indicate that we are stationary. The brain is faced with a difficult challenge: is there a single cause of sensations (I am moving) or two causes (I am static, another train is moving)? If a single cause, integrating signals produces a more precise estimate of self-motion, but if not, one cue should be ignored. In many cases, this process of causal inference works without error, but how does the brain achieve it? Electrophysiological recordings show that the macaque medial superior temporal area contains many neurons that encode combinations of vestibular and visual motion cues. Some respond best to vestibular and visual motion in the same direction ("congruent" neurons), while others prefer opposing directions ("opposite" neurons). Congruent neurons could underlie cue integration, but the function of opposite neurons remains a puzzle. Here, we seek to explain this computational arrangement by training a neural network model to solve causal inference for motion estimation. Like biological systems, the model develops congruent and opposite units and recapitulates known behavioral and neurophysiological observations. We show that all units (both congruent and opposite) contribute to motion estimation. Importantly, however, it is the balance between their activity that distinguishes whether visual and vestibular cues should be integrated or separated. This explains the computational purpose of puzzling neural representations and shows how a relatively simple feedforward network can solve causal inference.
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Project description:In daily life, our brain needs to eliminate irrelevant signals and integrate relevant signals to facilitate natural interactions with the surrounding. Previous study focused on paradigms without effect of dominant laterality and found that human observers process multisensory signals consistent with Bayesian causal inference (BCI). However, most human activities are of bilateral interaction involved in processing of interhemispheric sensory signals. It remains unclear whether the BCI framework also fits to such activities. Here, we presented a bilateral hand-matching task to understand the causal structure of interhemispheric sensory signals. In this task, participants were asked to match ipsilateral visual or proprioceptive cues with the contralateral hand. Our results suggest that interhemispheric causal inference is most derived from the BCI framework. The interhemispheric perceptual bias may vary strategy models to estimate the contralateral multisensory signals. The findings help to understand how the brain processes the uncertainty information coming from interhemispheric sensory signals.
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Project description:To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the "causal inference problem." Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI), and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation). At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion). Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world.
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Project description:To accurately represent the environment, our brains must integrate sensory signals from a common source while segregating those from independent sources. A reasonable strategy for performing this task is to restrict integration to cues that coincide in space and time. However, because multisensory signals are subject to differential transmission and processing delays, the brain must retain a degree of tolerance for temporal discrepancies. Recent research suggests that the width of this 'temporal binding window' can be reduced through perceptual learning, however, little is known about the mechanisms underlying these experience-dependent effects. Here, in separate experiments, we measure the temporal and spatial binding windows of human participants before and after training on an audiovisual temporal discrimination task. We show that training leads to two distinct effects on multisensory integration in the form of (i) a specific narrowing of the temporal binding window that does not transfer to spatial binding and (ii) a general reduction in the magnitude of crossmodal interactions across all spatiotemporal disparities. These effects arise naturally from a Bayesian model of causal inference in which learning improves the precision of audiovisual timing estimation, whilst concomitantly decreasing the prior expectation that stimuli emanate from a common source.
| S-EPMC4835789 | biostudies-literature
Project description:Self-motion perception (e.g., when walking/driving) relies on the integration of multiple sensory cues including visual, vestibular, and proprioceptive signals. Changes in the efficacy of multisensory integration have been observed in older adults (OA), which can sometimes lead to errors in perceptual judgments and have been associated with functional declines such as increased falls risk. The objectives of this study were to determine whether passive, visual-vestibular self-motion heading perception could be improved by providing feedback during multisensory training, and whether training-related effects might be more apparent in OAs vs. younger adults (YA). We also investigated the extent to which training might transfer to improved standing-balance. OAs and YAs were passively translated and asked to judge their direction of heading relative to straight-ahead (left/right). Each participant completed three conditions: (1) vestibular-only (passive physical motion in the dark), (2) visual-only (cloud-of-dots display), and (3) bimodal (congruent vestibular and visual stimulation). Measures of heading precision and bias were obtained for each condition. Over the course of 3 days, participants were asked to make bimodal heading judgments and were provided with feedback ("correct"/"incorrect") on 900 training trials. Post-training, participants' biases, and precision in all three sensory conditions (vestibular, visual, bimodal), and their standing-balance performance, were assessed. Results demonstrated improved overall precision (i.e., reduced JNDs) in heading perception after training. Pre- vs. post-training difference scores showed that improvements in JNDs were only found in the visual-only condition. Particularly notable is that 27% of OAs initially could not discriminate their heading at all in the visual-only condition pre-training, but subsequently obtained thresholds in the visual-only condition post-training that were similar to those of the other participants. While OAs seemed to show optimal integration pre- and post-training (i.e., did not show significant differences between predicted and observed JNDs), YAs only showed optimal integration post-training. There were no significant effects of training for bimodal or vestibular-only heading estimates, nor standing-balance performance. These results indicate that it may be possible to improve unimodal (visual) heading perception using a multisensory (visual-vestibular) training paradigm. The results may also help to inform interventions targeting tasks for which effective self-motion perception is important.
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Project description:Transforming the barrage of sensory signals into a coherent multisensory percept relies on solving the binding problem - deciding whether signals come from a common cause and should be integrated or, instead, segregated. Human observers typically arbitrate between integration and segregation consistent with Bayesian Causal Inference, but the neural mechanisms remain poorly understood. Here, we presented people with audiovisual sequences that varied in the number of flashes and beeps, then combined Bayesian modelling and EEG representational similarity analyses. Our data suggest that the brain initially represents the number of flashes and beeps independently. Later, it computes their numbers by averaging the forced-fusion and segregation estimates weighted by the probabilities of common and independent cause models (i.e. model averaging). Crucially, prestimulus oscillatory alpha power and phase correlate with observers' prior beliefs about the world's causal structure that guide their arbitration between sensory integration and segregation.
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Project description: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.
| S-EPMC6500172 | biostudies-literature