scholarly journals Human visual motion perception shows hallmarks of Bayesian structural inference

2020 ◽  
Author(s):  
Sichao Yang ◽  
Johannes Bill ◽  
Jan Drugowitsch ◽  
Samuel J. Gershman
Keyword(s):  
Motion Perception ◽  
Visual Motion ◽  
Choice Model ◽  
Relevant Information ◽  
Ideal Observer ◽  
Neural Mechanisms ◽  
Structural Inference ◽  
Visual Motion Perception ◽  
Decision Confidence ◽  
Motion Structure

AbstractMotion relations in visual scenes carry an abundance of behaviorally relevant information, but little is known about the computations underlying the identification of visual motion structure by humans. We addressed this gap in two psychophysics experiments and found that participants identified hierarchically organized motion relations in close correspondence with Bayesian structural inference. We demonstrate that, for our tasks, a choice model based on the Bayesian ideal observer can accurately match many facets of human structural inference, including task performance, perceptual error patterns, single-trial responses, participant-specific differences, and subjective decision confidence, particularly when motion scenes are ambiguous. Our work can guide future neuroscience experiments to reveal the neural mechanisms underlying higher-level visual motion perception.

scholarly journals Human visual motion perception shows hallmarks of Bayesian structural inference

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sichao Yang ◽  
Johannes Bill ◽  
Jan Drugowitsch ◽  
Samuel J. Gershman
Keyword(s):  
Motion Perception ◽  
Visual Motion ◽  
Probabilistic Reasoning ◽  
Choice Model ◽  
Human Motion ◽  
Object Motion ◽  
Ideal Observer ◽  
Structure Identification ◽  
Structural Inference ◽  
Visual Motion Perception

AbstractMotion relations in visual scenes carry an abundance of behaviorally relevant information, but little is known about how humans identify the structure underlying a scene’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’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—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.

Neural mechanisms underlying sound-induced visual motion perception: An fMRI study

2017 ◽  
Vol 178 ◽  
pp. 66-72
Author(s):  
Souta Hidaka ◽  
Satomi Higuchi ◽  
Wataru Teramoto ◽  
Yoichi Sugita
Keyword(s):  
Motion Perception ◽  
Visual Motion ◽  
Neural Mechanisms ◽  
Visual Motion Perception ◽  
Fmri Study

scholarly journals Hierarchical structure is employed by humans during visual motion perception

2020 ◽  
Vol 117 (39) ◽  
pp. 24581-24589
Author(s):  
Johannes Bill ◽  
Hrag Pailian ◽  
Samuel J. Gershman ◽  
Jan Drugowitsch
Keyword(s):  
Visual System ◽  
Motion Perception ◽  
Visual Motion ◽  
Dynamic Scenes ◽  
Tight Control ◽  
Complex Dynamic ◽  
Visual Motion Perception ◽  
Fine Grained ◽  
Motion Structure ◽  
Human Participants

In the real world, complex dynamic scenes often arise from the composition of simpler parts. The visual system exploits this structure by hierarchically decomposing dynamic scenes: When we see a person walking on a train or an animal running in a herd, we recognize the individual’s movement as nested within a reference frame that is, itself, moving. Despite its ubiquity, surprisingly little is understood about the computations underlying hierarchical motion perception. To address this gap, we developed a class of stimuli that grant tight control over statistical relations among object velocities in dynamic scenes. We first demonstrate that structured motion stimuli benefit human multiple object tracking performance. Computational analysis revealed that the performance gain is best explained by human participants making use of motion relations during tracking. A second experiment, using a motion prediction task, reinforced this conclusion and provided fine-grained information about how the visual system flexibly exploits motion structure.

scholarly journals Hierarchical structure is employed by humans during visual motion perception

2019 ◽  
Author(s):  
Johannes Bill ◽  
Hrag Pailian ◽  
Samuel J Gershman ◽  
Jan Drugowitsch
Keyword(s):  
Visual System ◽  
Motion Perception ◽  
Visual Motion ◽  
Dynamic Scenes ◽  
Tight Control ◽  
Complex Dynamic ◽  
Visual Motion Perception ◽  
Fine Grained ◽  
Motion Structure ◽  
Human Participants

AbstractIn the real world, complex dynamic scenes often arise from the composition of simpler parts. The visual system exploits this structure by hierarchically decomposing dynamic scenes: when we see a person walking on a train or an animal running in a herd, we recognize the individual’s movement as nested within a reference frame that is itself moving. Despite its ubiquity, surprisingly little is understood about the computations underlying hierarchical motion perception. To address this gap, we developed a novel class of stimuli that grant tight control over statistical relations among object velocities in dynamic scenes. We first demonstrate that structured motion stimuli benefit human multiple object tracking performance. Computational analysis revealed that the performance gain is best explained by human participants making use of motion relations during tracking. A second experiment, using a motion prediction task, reinforced this conclusion and provided fine-grained information about how the visual system flexibly exploits motion structure.

Role of the Vermal Cerebellum in Visually Guided Eye Movements and Visual Motion Perception

2019 ◽  
Vol 5 (1) ◽  
pp. 247-268 ◽  
Author(s):  
Peter Thier ◽  
Akshay Markanday
Keyword(s):  
Eye Movements ◽  
Motion Perception ◽  
Visual Motion ◽  
Cerebellar Nuclei ◽  
Visually Guided ◽  
Past Performance ◽  
Visual Motion Perception ◽  
Oculomotor Vermis ◽  
Retinal Information

The cerebellar cortex is a crystal-like structure consisting of an almost endless repetition of a canonical microcircuit that applies the same computational principle to different inputs. The output of this transformation is broadcasted to extracerebellar structures by way of the deep cerebellar nuclei. Visually guided eye movements are accommodated by different parts of the cerebellum. This review primarily discusses the role of the oculomotor part of the vermal cerebellum [the oculomotor vermis (OMV)] in the control of visually guided saccades and smooth-pursuit eye movements. Both types of eye movements require the mapping of retinal information onto motor vectors, a transformation that is optimized by the OMV, considering information on past performance. Unlike the role of the OMV in the guidance of eye movements, the contribution of the adjoining vermal cortex to visual motion perception is nonmotor and involves a cerebellar influence on information processing in the cerebral cortex.

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