TY - JOUR
T1 - Bayesian comparison of explicit and implicit causal inference strategies in multisensory heading perception
AU - Acerbi, Luigi
AU - Dokka, Kalpana
AU - Angelaki, Dora E.
AU - Ma, Wei Ji
N1 - Funding Information:
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 WJM. KD was supported by National Institute of Deafness and Communications Disorders Grant R03 DC013987. DEA was supported by National Institute of Health Grant R01 DC007620. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank Bas van Opheusden and Shan Shen for useful discussions about absolute goodness of fit. This work has utilized the NYU IT High Performance Computing resources and services.
Publisher Copyright:
© 2018 Acerbi et al. http://creativecommons.org/licenses/by/4.0/.
PY - 2018/7
Y1 - 2018/7
N2 - 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.
AB - 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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U2 - 10.1371/journal.pcbi.1006110
DO - 10.1371/journal.pcbi.1006110
M3 - Article
C2 - 30052625
AN - SCOPUS:85050993902
SN - 1553-734X
VL - 14
JO - PLoS computational biology
JF - PLoS computational biology
IS - 7
M1 - e1006110
ER -