Causal inference accounts for heading perception in the presence of object motion

Dokka, Kalpana; Park, Hyeshin; Jansen, M.; DeAngelis, Gregory C.; Angelaki, Dora E.

doi:10.1073/pnas.1820373116

Cited by 66 publications

(76 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, biases in heading perception are smaller than they would be if the object's motion were treated as a world-stationary landmark generating optic flow ( Raudies & Neumann, 2013 ). A causal inference model can account for biases in heading perception by predicting biases conditioned on whether an object is judged to be stationary ( Dokka et al, 2019 ). If an independently moving object is incorrectly judged to be stationary, its motion will be integrated with the optic flow produced by self-motion, generating biases in perceived heading.…”

Section: Discussionmentioning

confidence: 99%

“…This segmentation may occur in part automatically, as independently moving objects in the presence of optic flow appear to pop out to viewers (Rushton et al, 2007). Humans are more likely to detect independently moving objects that have larger speed mismatches with the inferred optic flow at the same location (Dokka, Park, Jansen, DeAngelis, & Angelaki, 2019;Royden & Connors, 2010;Royden, Parsons, & Travatello, 2016).…”

Section: Causal Inference and The Detection Of Moving Objectsmentioning

confidence: 99%

“…If the object is inferred to be moving independently, it will be segmented from the optic flow field and its motion will have little or no effect on perceived heading. Thus, causal inference can explain biases in heading perception that are greatest when the discrepancy between object motion and optic flow is in an intermediate range (Dokka et al, 2019).…”

Section: Causal Inference and The Detection Of Moving Objectsmentioning

confidence: 99%

See 2 more Smart Citations

Optic flow parsing in the macaque monkey

2020

Self Cite

View full text Add to dashboard Cite

During self-motion, an independently moving object generates retinal motion that is the vector sum of its world-relative motion and the optic flow caused by the observer's self-motion. A hypothesized mechanism for the computation of an object's world-relative motion is flow parsing, in which the optic flow field due to self-motion is globally subtracted from the retinal flow field. This subtraction generates a bias in perceived object direction (in retinal coordinates) away from the optic flow vector at the object's location. Despite psychophysical evidence for flow parsing in humans, the neural mechanisms underlying the process are unknown. To build the framework for investigation of the neural basis of flow parsing, we trained macaque monkeys to discriminate the direction of a moving object in the presence of optic flow simulating self-motion. Like humans, monkeys showed biases in object direction perception consistent with subtraction of background optic flow attributable to self-motion. The size of perceptual biases generally depended on the magnitude of the expected optic flow vector at the location of the object, which was contingent on object position and self-motion velocity. There was a modest effect of an object's depth on flow-parsing biases, which reached significance in only one of two subjects. Adding vestibular self-motion signals to optic flow facilitated flow parsing, increasing biases in direction perception. Our findings indicate that monkeys exhibit perceptual hallmarks of flow parsing, setting the stage for the examination of the neural mechanisms underlying this phenomenon.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Causal Inference and The Detection Of Moving Objectsmentioning

confidence: 99%

Section: Causal Inference and The Detection Of Moving Objectsmentioning

confidence: 99%

See 1 more Smart Citation

Optic flow parsing in the macaque monkey

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, if visual and vestibular heading cues are largely consistent, then it is likely that they have a common cause, and therefore information about heading provided by both cues should be integrated [8,3]. On the other hand, if a person is wearing a virtual reality headset but sitting still, then visual and vestibular cues would be inconsistent and the brain should segregate cue information [9]. The problem of inferring the cause of the cues is known as causal inference [10,11], and psychological evidence has shown that indeed the brain carries out such an operation [10,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

Emergence of opposite neurons in a firing-rate model of multisensory integration

Hy¹,

Zhang

Lee

2019

Preprint

View full text Add to dashboard Cite

Opposite neurons, found in macaque dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas, combine visual and vestibular cues of self-motion in opposite ways. A neural circuit recently proposed utilizes opposite neurons to perform causal inference and decide whether the visual and vestibular cues in MSTd and VIP should be integrated or segregated. However, it is unclear how these opposite connections can be formed with biologically realistic learning rules. We propose a network model capable of learning these opposite neurons, using Hebbian and Anti-Hebbian learning rules. The learned neurons are topographically organized and have von Mises-shaped feedforward connections, with tuning properties characteristic of opposite neurons. Our purpose is two-fold: on the one hand, we provide a circuit-level mechanism that explains the properties and formation of opposite neurons; on the other hand, we present a way to extend current theories of multisensory integration to account for appropriate segregation of sensory cues.

show abstract

“…Humans 6 integrate multisensory information in a near-optimal way according to Bayes' rule, and 7 it is desirable to understand how this is performed by underlying neural circuits. 8 9 The multisensory neurons in visual and vestibular brain areas, such as dorsal medial 10 superior temporal area (MSTd) and the ventral intraparietal (VIP) areas, can be 11 divided into two categories according to their tuning properties. One type of neurons is 12 called congruent neuron, as they prefer visual and vestibular cues of the same heading 13 directions ( Fig.…”

mentioning

confidence: 99%

Emergence of opposite neurons in a decentralized firing-rate model of multisensory integration

Niu

Chau

Lee

et al. 2019

Preprint

View full text Add to dashboard Cite

Multisensory integration areas such as dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas in macaques combine visual and vestibular cues to produce better estimates of self-motion. Congruent and opposite neurons, two types of neurons found in these areas, prefer congruent inputs and opposite inputs from the two modalities, respectively. A recently proposed computational model of congruent and opposite neurons reproduces their tuning properties and shows that congruent neurons optimally integrate information while opposite neurons compute disparity information. However, the connections in the network are fixed rather than learned, and in fact the connections of opposite neurons, as we will show, cannot arise from Hebbian learning rules. We therefore propose a new model of multisensory integration in which congruent neurons and opposite neurons emerge through Hebbian and anti-Hebbian learning rules, and show that these neurons exhibit experimentally observed tuning properties.

show abstract

Causal inference accounts for heading perception in the presence of object motion

Cited by 66 publications

References 66 publications

Optic flow parsing in the macaque monkey

Optic flow parsing in the macaque monkey

Emergence of opposite neurons in a firing-rate model of multisensory integration

Emergence of opposite neurons in a decentralized firing-rate model of multisensory integration

Contact Info

Product

Resources

About