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Abstract:
A large body of research shows that the Central Nervous System (CNS) integrates multisensory information in a fashion consistent with Bayesian Inference. However, this strategy should only apply when multisensory signals have a common cause; when signals have independent causes, they should be segregated. We recently developed a Causal Inference (CI) model that can account for this notion in multisensory heading estimation (De Winkel, Katliar, and Bülthoff, 2015). In this particular study, participants were presented with visual-inertial horizontal motion stimuli with various headings and a wide range of discrepancies. Surprisingly, the data suggested that multisensory signals were always integrated–regardless of the discrepancy. In the present work, we hypothesized that the CNS accumulates evidence on signal causality over time. In other words, signals will be segregated when a common cause is unlikely, and integrated otherwise. To test this hypothesis, we expanded the experimental paradigm of the previous study by increasing both the incidence of stimuli with large discrepancies and the range of motion durations. The results reflect CI for the majority of our participants. For some participants, discrepant stimuli were more likely to be integrated for short, and segregated for longer motion durations. We conclude that the CNS includes judgments of signal causality in the heading estimation process. This result may have been occluded in previous research by a relatively low incidence of stimuli with large discrepancies. Moreover, we present evidence that CI is likely to result from an accumulation of evidence over time on signal causality.
