@article {5309,
	title = {Visual motion perception as online hierarchical inference},
	journal = {Nature Communications},
	volume = {13},
	year = {2022},
	month = {12/2022},
	abstract = {<p>Identifying the structure of motion relations in the environment is critical for navigation, tracking, prediction, and pursuit. Yet, little is known about the mental and neural computations that allow the visual system to infer this structure online from a volatile stream of visual information. We propose online hierarchical Bayesian inference as a principled solution for how the brain might solve this complex perceptual task. We derive an online Expectation-Maximization algorithm that explains human percepts qualitatively and quantitatively for a diverse set of stimuli, covering classical psychophysics experiments, ambiguous motion scenes, and illusory motion displays. We thereby identify normative explanations for the origin of human motion structure perception and make testable predictions for future psychophysics experiments. The proposed online hierarchical inference model furthermore affords a neural network implementation which shares properties with motion-sensitive cortical areas and motivates targeted experiments to reveal the neural representations of latent structure.</p>
},
	doi = {10.1038/s41467-022-34805-5},
	url = {https://www.nature.com/articles/s41467-022-34805-5},
	author = {Bill, Johannes and Gershman, Samuel J. and Drugowitsch, Jan}
}
@article {4824,
	title = {Human visual motion perception shows hallmarks of Bayesian structural inference},
	journal = {Scientific Reports},
	volume = {11},
	year = {2021},
	month = {02/2021},
	abstract = {<p>Motion relations in visual scenes carry an abundance of behaviorally relevant information, but little is known about how humans identify the structure underlying a scene{\textquoteright}s motion in the first place. We studied the computations governing human motion structure identification in two psychophysics experiments and found that perception of motion relations showed hallmarks of Bayesian structural inference. At the heart of our research lies a tractable task design that enabled us to reveal the signatures of probabilistic reasoning about latent structure. We found that a choice model based on the task{\textquoteright}s Bayesian ideal observer accurately matched many facets of human structural inference, including task performance, perceptual error patterns, single-trial responses, participant-specific differences, and subjective decision confidence{\textemdash}especially, when motion scenes were ambiguous and when object motion was hierarchically nested within other moving reference frames. Our work can guide future neuroscience experiments to reveal the neural mechanisms underlying higher-level visual motion perception.</p>
},
	doi = {10.1038/s41598-021-82175-7},
	url = {http://www.nature.com/articles/s41598-021-82175-7},
	author = {Yang, Sichao and Bill, Johannes and Drugowitsch, Jan and Samuel J Gershman}
}
@article {4807,
	title = {Hierarchical structure is employed by humans during visual motion perception},
	journal = {Proceedings of the National Academy of Sciences},
	volume = {117},
	year = {2020},
	month = {09/2022},
	pages = {24581 - 24589},
	issn = {0027-8424},
	doi = {10.1073/pnas.2008961117},
	url = {http://www.pnas.org/lookup/doi/10.1073/pnas.2008961117},
	author = {Bill, Johannes and Pailian, Hrag and Samuel J Gershman and Drugowitsch, Jan}
}
