Choice-Related Activity during Visual Slant Discrimination in Macaque CIP but Not V3A

Elmore, L. Caitlin; Rosenberg, Ari; DeAngelis, Gregory C.; Angelaki, Dora E.

doi:10.1101/457531

Cited by 7 publications

(39 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Responses to stereoscopic, left eye perspective, and right eye perspective cues were simulated for each monkey based on recordings from neurons in the caudal intraparietal (CIP) area of the same animal (see Methods ). Area CIP neurons are implicated in 3D perception since they are selective for 3D surface orientation, and their activity functionally correlates with behavioral reports of 3D orientation [32, 36–40]. We tested three neural architectures for combining cue-isolated responses (Fig 7A), and decoded the resulting combined-cue representation to simulate perceptual data (Fig 7B).…”

Section: Resultsmentioning

confidence: 99%

“…At 77 cm and beyond, all dots were behind the plane of fixation. Presenting the stimuli at distances where the dots were entirely in front of, distributed about, or entirely behind the plane of fixation prevented the monkeys from relying on local absolute disparity cues to perform the task, ensuring that they judged the tilt of the plane [5, 40]. Under natural viewing conditions, changes in slant or distance affect the retinotopic area subtended by an object.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Optimized but not maximized cue integration for 3D visual perception

Chang

Kim

Thompson

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

36Reconstructing three-dimensional (3D) scenes from two-dimensional (2D) retinal images is an ill-37 posed problem. Despite this, our 3D perception of the world based on 2D retinal images is 38 seemingly accurate and precise. The integration of distinct visual cues is essential for robust 3D 39 perception in humans, but it is unclear if this mechanism is conserved in non-human primates, 40 and how the underlying neural architecture constrains 3D perception. Here we assess 3D 41 perception in macaque monkeys using a surface orientation discrimination task. We find that 42 perception is generally accurate, but precision depends on the spatial pose of the surface and 43 available cues. The results indicate that robust perception is achieved by dynamically reweighting 44 the integration of stereoscopic and perspective cues according to their pose-dependent 45 reliabilities. They further suggest that 3D perception is influenced by a prior for the 3D orientation 46 statistics of natural scenes. We compare the data to simulations based on the responses of 3D 47 orientation selective neurons. The results are explained by a model in which two independent 48 neuronal populations representing stereoscopic and perspective cues (with perspective signals 49 from the two eyes combined using nonlinear canonical computations) are optimally integrated 50 through linear summation. Perception of combined-cue stimuli is optimal given this architecture. 51However, an alternative architecture in which stereoscopic cues and perspective cues detected 52 by each eye are represented by three independent populations yields two times greater precision 53 than observed. This implies that, due to canonical computations, cue integration for 3D perception 54 is optimized but not maximized. 55 56 Author summary 57Our eyes only sense two-dimensional projections of the world (like a movie on a screen), yet we 58 perceive the world in three dimensions. To create reliable 3D percepts, the human visual system 59 integrates distinct visual signals according to their reliabilities, which depend on conditions such 60 as how far away an object is located and how it is oriented. Here we find that non-human primates 61 similarly integrate different 3D visual signals, and that their perception is influenced by the 3D 62 orientation statistics of natural scenes. Cue integration is thus a conserved mechanism for 63 creating robust 3D percepts by the primate brain. Using simulations of neural population activity, 64 based on neuronal recordings from the same animals, we show that some computations which 65 occur widely in the brain facilitate 3D perception, while others hinder perception. This work 66addresses key questions about how neural systems solve the difficult problem of generating 3D 67 percepts, identifies a plausible neural architecture for implementing robust 3D vision, and reveals 68 how neural computation can simultaneously optimize and curb perception. 69 were linearly summed [10]). However, an alternative architecture in which stereoscopic as we...

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Optimized but not maximized cue integration for 3D visual perception

Chang

Kim

Thompson

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…At 77 cm and beyond, all dots were behind the plane of fixation. The protocol was thus similar to previous studies which varied the stimulus distance while holding the fixation distance constant (Nguyenkim and DeAngelis, 2003;Hegdé and Van Essen, 2005;Ban and Welchman, 2015;Alizadeh et al, 2018;Elmore et al, 2019;Henderson et al, 2019). Others have yoked the stimulus and fixation distances (Banks et al, 2001;Hillis et al, 2004).…”

Section: Visual Stimulimentioning

confidence: 75%

“…Others have yoked the stimulus and fixation distances (Banks et al, 2001;Hillis et al, 2004). By presenting stimuli with the dots entirely in front of, distributed about, or entirely behind the plane of fixation, we prevented the monkeys from relying on local absolute disparity cues to perform the discrimination task (described below), ensuring that they judged the plane's 3D orientation (Elmore et al, 2019).…”

Section: Visual Stimulimentioning

confidence: 99%

“…Neurons in CIP are selective for 3D surface orientation (Taira et al, 2000;Rosenberg et al, 2013), and respond to both stereoscopic and perspective cues (Tsutsui et al, 2001;Rosenberg and Angelaki, 2014b). Furthermore, their inactivation impairs 3D perception (Tsutsui et al, 2001;Van Dromme et al, 2016), and their activity correlates with surface orientation reports (Elmore et al, 2019). We tested three architectures for combining cue-isolated responses to create a combined-cue representation (Fig.…”

Section: Neuronal Models Of 3d Visual Cue Integrationmentioning

confidence: 99%

See 1 more Smart Citation

Optimized but Not Maximized Cue Integration for 3D Visual Perception

Chang

Thompson

Doudlah

et al. 2019

eNeuro

Self Cite

View full text Add to dashboard Cite

Reconstructing three-dimensional (3D) scenes from two-dimensional (2D) retinal images is an ill-posed problem. Despite this, 3D perception of the world based on 2D retinal images is seemingly accurate and precise. The integration of distinct visual cues is essential for robust 3D perception in humans, but it is unclear whether this is true for non-human primates (NHPs). Here, we assessed 3D perception in macaque monkeys using a planar surface orientation discrimination task. Perception was accurate across a wide range of spatial poses (orientations and distances), but precision was highly dependent on the plane's pose. The monkeys achieved robust 3D perception by dynamically reweighting the integration of stereoscopic and perspective cues according to their pose-dependent reliabilities. Errors in performance could be explained by a prior resembling the 3D orientation statistics of natural scenes. We used neural network simulations based on 3D orientation-selective neurons recorded from the same monkeys to assess how neural computation might constrain perception. The perceptual data were consistent with a model in which the responses of two independent neuronal populations representing stereoscopic cues and perspective cues (with perspective signals from the two eyes combined using nonlinear canonical computations) were optimally integrated through linear summation. Perception of combined-cue stimuli was optimal given this architecture. However, an alternative architecture in which stereoscopic cues, left eye perspective cues, and right eye perspective cues were represented by three independent populations yielded two times greater precision than the monkeys. This result suggests that, due to canonical computations, cue integration for 3D perception is optimized but not maximized. Significance StatementOur eyes sense two-dimensional (2D) projections of the world, like a movie on a screen, but we perceive the world as three-dimensional (3D). Here, we show that non-human primates (NHPs), like humans, achieve more precise 3D vision by perceptually integrating distinct 3D cues. We also present evidence that perception is influenced by 3D natural scene statistics, and that priors over 3D orientation are subjectively encoded. Using simulations, we examine how neural computation can constrain 3D perception and estimate that perception is half as precise as theoretically possible. Our findings suggest that the concurrence of multiple canonical computations simultaneously optimizes and curbs 3D visual perception, and highlight that what constitutes optimal task performance depends on the underlying neural architecture.

show abstract