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The topic of how humans navigate using vision has been studied for decades. Research has identified the emergent patterns of retinal optic flow from gaze behavior may play an essential role in human curvilinear locomotion. However, the link towards control has been poorly understood. Lately, it has been shown that human locomotor behavior is corrective, formed from intermittent decisions and responses. A simulated virtual reality experiment was conducted where fourteen participants drove through a texture-rich simplistic road environment with left and right curve bends. The goal was to investigate how human intermittent lateral control can be associated with the retinal optic flow-based cues and vehicular heading as sources of information. This work reconstructs dense retinal optic flow using a numerical estimation of optic flow with measured gaze behavior. By combining retinal optic flow with the drivable lane surface, a cross-correlational relation to intermittent steering behavior could be observed. In addition, a novel method of identifying constituent ballistic correction using particle swarm optimization was demonstrated to analyze the incremental correction-based behavior. Through time delay analysis, our results show a human response time of approximately 0.14 s for retinal optic flow-based cues and 0.44 s for heading-based cues, measured from stimulus onset to steering correction onset. These response times were further delayed by 0.17 s when the vehicle-fixed steering wheel was visibly removed. In contrast to classical continuous control strategies, our findings support and argue for the intermittency property in human neuromuscular control of muscle synergies, through the principle of satisficing behavior: to only actuate when there is a perceived need for it. This is aligned with the human sustained sensorimotor model, which uses readily available information and internal models to produce informed responses through evidence accumulation to initiate appropriate ballistic correction, even amidst another correction.

Hippocampal place cells are influenced by both self-motion (idiothetic) signals and external sensory landmarks as an animal navigates its environment. To continuously update a position signal on an internal ‘cognitive map’, the hippocampal system integrates self-motion signals over time, a process that relies on a finely calibrated path integration gain that relates movement in physical space to movement on the cognitive map. It is unclear whether idiothetic cues alone, such as optic flow, exert sufficient influence on the cognitive map to enable recalibration of path integration, or if polarizing position information provided by landmarks is essential for this recalibration. Here, we demonstrate both recalibration of path integration gain and systematic control of place fields by pure optic flow information in freely moving rats. These findings demonstrate that the brain continuously rebalances the influence of conflicting idiothetic cues to fine-tune the neural dynamics of path integration, and that this recalibration process does not require a top-down, unambiguous position signal from landmarks. Using a closed-loop virtual reality system, the authors show that optic flow cues can causally drive and recalibrate the hippocampal place cell system in the absence of an absolute spatial reference frame defined by external landmarks.

Animals flying within natural environments are constantly challenged with complex visual information. Therefore, it is necessary to understand the impact of the visual background on the motion detection system. Locusts possess a well-identified looming detection pathway, comprising the lobula giant movement detector (LGMD) and the descending contralateral movement detector (DCMD). The LGMD/DCMD pathway responds preferably to objects on a collision course, and the response of this pathway is affected by the background complexity. However, multiple other neurons are also responsive to looming stimuli. In this study, we presented looming stimuli against different visual backgrounds to a rigidly-tethered locust, and simultaneously recorded the neural activity with a multichannel electrode. We found that the number of spike-sorted units that responded to looms was not affected by the visual background. However, the peak times of these units were delayed, and the rise phase was shortened in the presence of a flow field background. Dynamic factor analysis (DFA) revealed that fewer types of common trends were present among the units responding to looming stimuli against the flow field background, and the response begin time was delayed among the common trends as well. These results suggest that background complexity affects the response of multiple motion-sensitive neurons, yet the animal is still capable of responding to potentially hazardous visual stimuli.

We examined the intricate mechanisms underlying visual processing of complex motion stimuli by measuring the detection sensitivity to contraction and expansion patterns and the discrimination sensitivity to the location of the center of motion (CoM) in various real and unreal optic flow stimuli. We conducted two experiments (N = 20 each) and compared responses to both \"real\" optic flow stimuli containing information about self-movement in a three-dimensional scene and \"unreal\" optic flow stimuli lacking such information. We found that detection sensitivity to contraction surpassed that to expansion patterns for unreal optic flow stimuli, whereas this trend was reversed for real optic flow stimuli. Furthermore, while discrimination sensitivity to the CoM location was not affected by stimulus duration for unreal optic flow stimuli, it showed a significant improvement when stimulus duration increased from 100 to 400 ms for real optic flow stimuli. These findings provide compelling evidence that the visual system employs distinct processing approaches for real versus unreal optic flow even when they are perfectly matched for two-dimensional global features and local motion signals. These differences reveal influences of self-movement in natural environments, enabling the visual system to uniquely process stimuli with significant survival implications.

Accurate estimation of heading direction from optic flow is a crucial aspect of human spatial perception. Previous psychophysical studies have shown that humans are typically biased in their heading estimates, but the reported results are inconsistent. While some studies found that humans generally underestimate heading direction (center bias), others observed the opposite, an overestimation of heading direction (peripheral bias). We conducted three psychophysical experiments showing that these conflicting findings may not reflect inherent differences in heading perception but can be attributed to the different sizes of the response range that participants were allowed to utilize when reporting their estimates. Notably, we show that participants' heading estimates monotonically scale with the size of the response range, leading to underestimation for small and overestimation for large response ranges. Additionally, neither the speed profile of the optic flow pattern nor the response method (mouse vs. keyboard) significantly affected participants' estimates. Furthermore, we introduce a Bayesian heading estimation model that can quantitatively account for participants' heading reports. The model assumes efficient sensory encoding of heading direction according to a prior inferred from human heading discrimination data. In addition, the model assumes a response mapping that linearly scales the perceptual estimate with a scaling factor that monotonically depends on the size of the response range. This simple perception-action model accurately predicts participants' estimates both in terms of mean and variance across all experimental conditions. Our findings underscore that human heading perception follows efficient Bayesian inference; differences in participants reported estimates can be parsimoniously explained as differences in mapping percept to probe response.

It is a well-established finding that more informative optic flow (e.g., faster, denser, or presented over a larger portion of the visual field) yields decreased variability in heading judgements. Current models of heading perception further predict faster processing under such circumstances, which has, however, not been supported empirically so far. In this study, we validate a novel continuous psychophysics paradigm by replicating the effect of the speed and density of optic flow on variability in performance, and we investigate how these manipulations affect the temporal dynamics. To this end, we tested 30 participants in a continuous psychophysics paradigm administered in Virtual Reality. We immersed them in a simple virtual environment where they experienced four 90-second blocks of optic flow where their linear heading direction (no simulated rotation) at any given moment was determined by a random walk. We asked them to continuously indicate with a joystick the direction in which they perceived themselves to be moving. In each of the four blocks they experienced a different combination of simulated self-motion speeds (SLOW and FAST) and density of optic flow (SPARSE and DENSE). Using a Cross-Correlogram Analysis, we determined that participants reacted faster and displayed lower variability in their performance in the FAST and DENSE conditions than in the SLOW and SPARSE conditions, respectively. Using a Kalman Filter-based analysis approach, we found a similar pattern, where the fitted perceptual noise parameters were higher for SLOW and SPARSE. While replicating previous results on variability, we show that more informative optic flow can speed up heading judgements, while at the same time validating a continuous psychophysics as an efficient method for studying heading perception.

We move our eyes and head to sample the visual environment. While these movements are essential for survival, they greatly complicate the analysis of retinal image motion. Our brain must account for the visual consequences of self-motion to perceive the 3D layout and motion of objects in a scene. We show that traditional models of visual compensation for eye movements fail when the eye both translates and rotates, and we propose a theory that computes both motion and depth in more natural viewing geometries. Consistent with our theoretical predictions, humans exhibit distinct perceptual biases when different viewing geometries are simulated by optic flow, and these biases occur without training or feedback. A neural network model trained to perform the same tasks suggests that viewing geometry modulates the joint tuning of neurons for retinal and eye velocity to mediate these adaptive computations. Our findings unify previously separate bodies of work by demonstrating that the brain adaptively perceives the dynamic 3D environment according to viewing geometry inferred from optic flow. People typically perceive the motion and depth of objects correctly even during walking or running, when visual inputs change markedly. The authors show that this accurate perception is achieved by inferring the observer’s viewing geometry from optic flow.

We examined the effect of the smoothness of motion on vection strength. The smoothness of stimulus motion was modulated by varying the number of frames comprising the movement. In this study, a horizontal grating translated through 360° of phase in 1 s divided into steps of 3, 4, 6, 12, 20, 30, or 60 frames. We hypothesized that smoother motion should induce stronger vection because the smoother stimulus is more natural and contains more motion energy. We examined this effect of frame number on vection for both downward (Experiment 1) and expanding (Experiment 2) optical flow. The results clearly showed that vection strength increased with increasing frame rate, however, the rates of increase in the vection strength with frame rate are not constant, but rapidly increase in the low frame-rate range and appear to asymptote in the high range. The strength estimates saturated at lower frame rates for expanding flow than for downward flow. This might be related to the fact that to process expanding flow it is necessary to integrate motion signals across the visual field. We conclude that the smoothness of the motion stimulus highly affects vection induction.

There is a critical need to understand how aging visual systems contribute to age-related increases in vehicle accidents. We investigated the potential contribution of age-related detriments in steering based on optic flow, a source of information known to play a role in navigation control. Seventeen younger adults (mean age: 21.1 years) and thirteen older adults (mean age: 57.3 years) performed a virtual reality steering task. The virtual environment depicted movement at 19 m/s along a winding road. Participants were tasked with maintaining a central lane position while experiencing eight repetitions of each combination of optic flow density (low, medium, high), turn radius (35, 55, 75 m), and turn direction (left, right), presented in random order. All participants cut corners, but did so less on turns with rotational flow from distant landmarks and without proximal optic flow. We found no evidence of an interaction between age and optic flow density, although older adults cut corners more on all turns. An exploratory gaze analysis revealed no age-related differences in gaze behavior. The lack of age-related differences in steering or gaze behavior as a function of optic flow implies that processing of naturalistic optic flow stimuli when steering may be preserved with age.

The visual system plays an integral role in maintaining quiet stance. When visual feedback is amplified by increasing the gain of optic flow, individuals develop a tighter control of upright stance. The pattern of optic flow can also vary depending on the eccentricity of gaze, where looking to the side or down can increase the proportion of lamellar, compared to radial optic flow. Further, previous work has shown differences between visual motion perception when exposed to varying types of optic flow. It currently remains unknown how the type of optic flow contributes to postural control while under the influence of modified gain. Therefore, this study aimed to better understand how the gain of radial and lamellar optic flow, manipulated by changing head orientation, contributes to balance control during quiet stance among healthy adults. Participants were recruited to stand quietly with feet together on a foam pad placed over a force plate while wearing a virtual reality head-mounted display Three head orientations (forward, 45° left, 45° down) were used to expose participants to primarily radial (forward) or lamellar (side or down) optic flow. For each head orientation, participants completed 3 trials, where the gain of optic flow was amplified to either 1x, 4x, or 16x normal optic flow. Overall, an increase in optic flow gain decreased amplitude and increased frequencies of balance measures. Some mediolateral amplitude measures of balance were also greatest when looking to the side; however, the effect of optic flow gain on center of pressure and head displacement were similar across head orientations.