Abstract
The precision of multisensory heading perception improves when visual and vestibular cues arising from the same cause, namely motion of the observer through a stationary environment, are integrated. Thus, 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 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.
Footnotes
Luigi Acerbi, Center for Neural Science, New York University, New York, NY; Kalpana Dokka and Dora E. Angelaki, Baylor College of Medicine, Houston, TX; Wei Ji Ma, Center for Neural Science and Department of Psychology, New York University, New York, NY.
This work was supported by award number R01EY020958 from the National Eye Institute, and award number W911NF-12–1-0262 from the Army Research Office to Wei Ji Ma. Kalpana Dokka was supported by National Institute of Deafness and Communications Disorders Grant R03 DC013987. Dora E. Angelaki was supported by National Institute of Health Grant R01 DC007620.
3 URL: https://github.com/lacerbi/gofit.