Neural bases of self- and object-motion in a naturalistic vision.
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ABSTRACT:
SUBMITTER: Pitzalis S
PROVIDER: S-EPMC7267932 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Human observers can rapidly perceive complex real-world scenes. Grouping visual elements into meaningful units is an integral part of this process. Yet, so far, the neural underpinnings of perceptual grouping have only been studied with simple lab stimuli. We here uncover the neural mechanisms of one important perceptual grouping cue, local parallelism. Using a new, image-computable algorithm for detecting local symmetry in line drawings and photographs, we manipulated the local parallelism content of real-world scenes. We decoded scene categories from patterns of brain activity obtained via functional magnetic resonance imaging (fMRI) in 38 human observers while they viewed the manipulated scenes. Decoding was significantly more accurate for scenes containing strong local parallelism compared to weak local parallelism in the parahippocampal place area (PPA), indicating a central role of parallelism in scene perception. To investigate the origin of the parallelism signal we performed a model-based fMRI analysis of the public BOLD5000 dataset, looking for voxels whose activation time course matches that of the locally parallel content of the 4916 photographs viewed by the participants in the experiment. We found a strong relationship with average local symmetry in visual areas V1-4, PPA, and retrosplenial cortex (RSC). Notably, the parallelism-related signal peaked first in V4, suggesting V4 as the site for extracting paralleism from the visual input. We conclude that local parallelism is a perceptual grouping cue that influences neuronal activity throughout the visual hierarchy, presumably starting at V4. Parallelism plays a key role in the representation of scene categories in PPA.
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Project description:With every glimpse of our eyes, we sample only a small and incomplete fragment of the visual world, which needs to be contextualized and integrated into a coherent scene representation. Here we show that the visual system achieves this contextualization by exploiting spatial schemata, that is our knowledge about the composition of natural scenes. We measured fMRI and EEG responses to incomplete scene fragments and used representational similarity analysis to reconstruct their cortical representations in space and time. We observed a sorting of representations according to the fragments' place within the scene schema, which occurred during perceptual analysis in the occipital place area and within the first 200 ms of vision. This schema-based coding operates flexibly across visual features (as measured by a deep neural network model) and different types of environments (indoor and outdoor scenes). This flexibility highlights the mechanism's ability to efficiently organize incoming information under dynamic real-world conditions.
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Project description:Judging object trajectory during self-motion is a fundamental ability for mobile organisms interacting with their environment. This fundamental ability requires the nervous system to compensate for the visual consequences of self-motion in order to make accurate judgments, but the mechanisms of this compensation are poorly understood. We comprehensively examined both the accuracy and precision of observers' ability to judge object trajectory in the world when self-motion was defined by vestibular, visual, or combined visual-vestibular cues. Without decision feedback, subjects demonstrated no compensation for self-motion that was defined solely by vestibular cues, partial compensation (47%) for visually defined self-motion, and significantly greater compensation (58%) during combined visual-vestibular self-motion. With decision feedback, subjects learned to accurately judge object trajectory in the world, and this generalized to novel self-motion speeds. Across conditions, greater compensation for self-motion was associated with decreased precision of object trajectory judgments, indicating that self-motion compensation comes at the cost of reduced discriminability. Our findings suggest that the brain can flexibly represent object trajectory relative to either the observer or the world, but a world-centered representation comes at the cost of decreased precision due to the inclusion of noisy self-motion signals.
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Project description:PurposeEfficacy of current visual prostheses in object recognition is limited. Among various limitations to be addressed, such as low resolution and low dynamic range, here we focus on reducing the impact of background clutter on object recognition. We have proposed the use of motion parallax via head-mounted camera lateral scanning and computationally stabilizing the object of interest (OI) to support neural background decluttering. Simulations in head-mounted displays (HMD), mimicking the proposed effect, were used to test object recognition in normally sighted subjects.MethodsImages (24° field of view) were captured from multiple viewpoints and presented at a low resolution (20 × 20). All viewpoints were centered on the OI. Experimental conditions (2 × 3) included clutter (with or without) × head scanning (single viewpoint, 9 coherent viewpoints corresponding to subjects' head positions, and 9 randomly associated viewpoints). Subjects used lateral head movements to view OIs in the HMD. Each object was displayed only once for each subject.ResultsThe median recognition rate without clutter was 40% for all head scanning conditions. Performance with synthetic background clutter dropped to 10% in the static condition, but it was improved to 20% with the coherent and random head scanning (corrected P = 0.005 and P = 0.049, respectively).ConclusionsBackground decluttering using motion parallax cues but not the coherent multiple views of the OI improved object recognition in low-resolution images. The improvement did not fully eliminate the impact of background.Translational relevanceMotion parallax is an effective but incomplete decluttering solution for object recognition with visual prostheses.
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Project description:PurposeLeading causes of irreversible blindness such as age-related macular degeneration (AMD) and glaucoma can, respectively, lead to central or peripheral vision loss. The ability of sufferers to process visual motion information can be impacted even during early stages of eye disease. We used head-mounted display virtual reality as a tool to better understand how vision changes caused by eye diseases directly affect the processing of visual information critical for self-motion perception.MethodsParticipants with intermediate AMD or early manifest glaucoma with near-normal visual acuities and visual fields were recruited for this study. We examined their experiences of self-motion in depth (linear vection), spatial presence, and cybersickness when viewing radially expanding patterns of optic flow simulating different speeds of self-motion in depth. Viewing was performed with the head stationary (passive condition) or while making lateral-sway head movements (active conditions).ResultsParticipants with AMD (i.e., central visual field loss) were found to have greater vection strength and spatial presence, compared to participants with normal visual fields. However, participants with glaucoma (i.e., peripheral visual field loss) were found to have lower vection strength and spatial presence, compared to participants with normal visual fields. Both AMD and glaucoma groups reported reduced severity in cybersickness compared to healthy normals.ConclusionsThese findings strongly support the view that perceived self-motion is differentially influenced by peripheral versus central vision loss, and that patients with different visual field defects are oppositely biased when processing visual cues to self-motion perception.
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Project description:All animals must transform ambiguous sensory data into successful behavior. This requires sensory representations that accurately reflect the statistics of natural stimuli and behavior. Multiple studies show that visual motion processing is tuned for accuracy under naturalistic conditions, but the sensorimotor circuits extracting these cues and implementing motion-guided behavior remain unclear. Here we show that the larval zebrafish retina extracts a diversity of naturalistic motion cues, and the retinorecipient pretectum organizes these cues around the elements of behavior. We find that higher-order motion stimuli, gliders, induce optomotor behavior matching expectations from natural scene analyses. We then image activity of retinal ganglion cell terminals and pretectal neurons. The retina exhibits direction-selective responses across glider stimuli, and anatomically clustered pretectal neurons respond with magnitudes matching behavior. Peripheral computations thus reflect natural input statistics, whereas central brain activity precisely codes information needed for behavior. This general principle could organize sensorimotor transformations across animal species.
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Project description:Many locomotor tasks involve interactions with moving objects. When observer (i.e., self-)motion is accompanied by object motion, the optic flow field includes a component due to self-motion and a component due to object motion. For moving observers to perceive the movement of other objects relative to the stationary environment, the visual system could recover the object-motion component - that is, it could factor out the influence of self-motion. In principle, this could be achieved using visual self-motion information, non-visual self-motion information, or a combination of both. In this study, we report evidence that visual information about the speed (experiment 1) and direction (experiment 2) of self-motion plays a role in recovering the object-motion component even when non-visual self-motion information is also available. However, the magnitude of the effect was less than one would expect if subjects relied entirely on visual self-motion information. Taken together with previous studies, we conclude that when self-motion is real and actively generated, both visual and non-visual self-motion information contribute to the perception of object motion. We also consider the possible role of this process in visually guided interception and avoidance of moving objects.
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Project description:During active tactile exploration, the dynamic patterns of touch are transduced to electrical signals and transformed by the brain into a mental representation of the object under investigation. This transformation from sensation to perception is thought to be a major function of the mammalian cortex. In primary somatosensory cortex (S1) of mice, layer 5 (L5) pyramidal neurons are major outputs to downstream areas that influence perception, decision-making, and motor control. We investigated self-motion and touch representations in L5 of S1 with juxtacellular loose-seal patch recordings of optogenetically identified excitatory neurons. We found that during rhythmic whisker movement, 54 of 115 active neurons (47%) represented self-motion. This population was significantly more modulated by whisker angle than by phase. Upon active touch, a distinct pattern of activity was evoked across L5, which represented the whisker angle at the time of touch. Object location was decodable with submillimeter precision from the touch-evoked spike counts of a randomly sampled handful of these neurons. These representations of whisker angle during self-motion and touch were independent, both in the selection of which neurons were active and in the angle-tuning preference of coactive neurons. Thus, the output of S1 transiently shifts from a representation of self-motion to an independent representation of explored object location during active touch.
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