Causal Inference in Multisensory Heading Estimation

Winkel, Ksander N. de; Katliar, Mikhail; Bülthoff, HH

doi:10.1371/journal.pone.0169676

Cited by 44 publications

(83 citation statements)

References 60 publications

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“…Our orientation relative to gravity can therefore be considered analogous to the heading of horizontal linear accelerations. Given that this premise is accepted, the present findings are similar to those of two recent studies on estimation of heading of translational motions in the horizontal plane [28,29]. Findings of the earlier study favored FF -although evidence for CI was found for one participant [28].…”

Section: Discussionsupporting

confidence: 90%

What’s Up: an assessment of Causal Inference in the Perception of Verticality

Winkel

Katliar

Diers

et al. 2017

Preprint

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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 the upright is usually above the head. Results from a number of recent studies furthermore show that the weighting of the respective sensory signals is proportional to their reliability, consistent with a Bayesian interpretation of the idea of a vector sum (Forced Fusion, FF). However, findings from a study conducted in partial gravity suggest that the CNS may rely on a single sensory system (Cue Capture, CC), or choose to process sensory signals differently based on inferred signal causality (Causal Inference, CI). We developed a novel Alternative-Reality system to manipulate visual and physical tilt independently, and tasked participants (n=28) to indicate the perceived upright for various (in-)congruent combinations of visual-inertial stimuli. Overall, the data appear best explained by the FF model. However, an evaluation of individual data reveals considerable variability, favoring different models in about equal proportions of participants (FF, n=12; CI, n=7, CC, n=9). Given the observed variability, we conclude that the notion of a vector sum does not provide a comprehensive explanation of the perception of the upright.

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Section: Discussionsupporting

confidence: 90%

What’s Up: an assessment of Causal Inference in the Perception of Verticality

Winkel

Katliar

Diers

et al. 2017

Preprint

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“…This variability could be interpreted as a sign that individuals are wired differently with respect to processes governing the formation of perceptions. However, we believe this to be unlikely: a similar variability has been reported in recent studies on the applicability of CI for perception of heading [20,21] and verticality [22]. As in the present study, it was found that different models were preferred over CI in individual cases, although in these studies the FF model was preferred in the majority of cases.…”

Section: Multisensory Distance Estimationsupporting

confidence: 87%

“…This formulation reflects the Model Averaging CI strategy (e.g., [18,28,29]). It is similar to earlier work by our group on heading estimation [20,30,21] and the perception of verticality [22]. The probability of a common cause given the sensory estimates is .…”

Section: Causal Inference Modelsupporting

confidence: 85%

“…In previous work, we obtained evidence that the CNS performs CI in the estimation of heading of visual-inertial horizontal linear translations [20,21], and in the estimation of verticality [22]. Here, we extend the assessment of the applicability of the CI model from the qualitative domain of heading and orientation [23] to the quantitative domain of traveled distance.…”

Section: Introductionmentioning

confidence: 69%

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An assessment of Causal Inference in visual-inertial traveled distance estimation

Winkel

Diers

Lächele

et al. 2018

Preprint

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Recent work indicates that the central nervous system assesses the causality of visual and inertial information in the estimation of qualitative characteristics of self-motion and spatial orientation, and forms multisensory perceptions in accordance with the outcome of these assessments.Here, we extend the assessment of this Causal Inference (CI) strategy to the quantitative domain of traveled distance. We present a formal model of how stimuli result in sensory estimates, how percepts are constructed from sensory estimates, and how responses result from percepts. Starting with this formalization, we derived probabilistic formulations of CI and competing models for perception of traveled distance. In an experiment, participants (n=9) were seated in the Max Planck Cablerobot Simulator, and shown a photo-realistic virtual rendering of the simulator hall via a Head-Mounted Display. Using this setup, the participants were presented with various unisensory and (incongruent) multisensory visual-inertial horizontal linear surge motions, differing only in amplitude (i.e., traveled distance). Participants performed both a Magnitude Estimation and a Two-Interval Forced Choice task. Overall, model comparisons favor the CI model, but individual analysis shows a Cue Capture strategy is preferred in most individual cases. Parameter estimates indicate that visual and inertial sensory estimates follow a Stevens' power law with positive exponent, and that noise increases with physical distance in accordance with a Weber's law. Responses were found to be biased towards the mean stimulus distance, consistent with an interaction between percepts and prior knowledge in the formulation of responses. Magnitude estimate data further showed a regression to the mean effect. The experimental data did not provide unambiguous support for the CI model. However, model derivations and fit results demonstrate it can reproduce empirical findings, arguing in favor of the CI model. Moreover, the methods outlined in the present study demonstrate how different sources of distortion in responses may be disentangled by combining psychophysical tasks.

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“…() observed subjects integrating visual and inertial information in heading estimation even under large spatial discrepancies (forced fusion). In a later study (de Winkel et al ., ), they did find evidence for causal inference models, which, however, also depended in a complex way on the specifics of the motion profiles (acceleration, maximum velocity, duration) of the stimuli.…”

Section: Models For Psychophysical Estimation and Judgment Paradigms:mentioning

confidence: 94%

Formal models and quantitative measures of multisensory integration: a selective overview

Colonius

Diederich

2018

Eur J of Neuroscience

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Multisensory integration (MI) is defined as the neural process by which unisensory signals are combined to form a new product that is significantly different from the responses evoked by the modality-specific component stimuli. In recent years, MI research has seen exponential growth in the number of empirical and theoretical studies. This study presents a selective overview of formal modeling approaches to MI. Emphasis is on models and measures for behavioral paradigms, such as localization, judgment of temporal order or simultaneity, and reaction times, but some concepts for the modeling of single-cell spike rates are treated as well. We identify a number of essential concepts underlying most model classes, such as Bayesian causal inference, probability summation, coactivation, and time window of integration. Quantitative indexes for measuring and comparing the strength of MI across different paradigms are also discussed. Whereas progress over the last years is remarkable, we point out some strengths and weaknesses of the modeling approaches and discuss some obstacles toward a unified theory of MI.

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Causal Inference in Multisensory Heading Estimation

Cited by 44 publications

References 60 publications

What’s Up: an assessment of Causal Inference in the Perception of Verticality

What’s Up: an assessment of Causal Inference in the Perception of Verticality

An assessment of Causal Inference in visual-inertial traveled distance estimation

Formal models and quantitative measures of multisensory integration: a selective overview

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