Asymmetries around the visual field: From retina to cortex to behavior

Visual performance varies around the visual field. It is best near the fovea compared to the periphery, and at iso-eccentric locations it is best on the horizontal, intermediate on the lower, and poorest on the upper meridian. The fovea-to-periphery performance decline is linked to the decreases in cone density, retinal ganglion cell (RGC) density, and V1 cortical magnification factor (CMF) as eccentricity increases. The origins of polar angle asymmetries are not well understood. Optical quality and cone density vary across the retina, but recent computational modeling has shown that these factors can only account for a small percentage of behavior. Here, we investigate how visual processing beyond the cone photon absorptions contributes to polar angle asymmetries in performance. First, we quantify the extent of asymmetries in cone density, midget RGC density, and V1 CMF. We find that both polar angle asymmetries and eccentricity gradients increase from cones to mRGCs, and from mRGCs to cortex. Second, we extend our previously published computational observer model to quantify the contribution of phototransduction by the cones and spatial filtering by mRGCs to behavioral asymmetries. Starting with photons emitted by a visual display, the model simulates the effect of human optics, cone isomerizations, phototransduction, and mRGC spatial filtering. The model performs a forced choice orientation discrimination task on mRGC responses using a linear support vector machine classifier. The model shows that asymmetries in a decision maker’s performance across polar angle are greater when assessing the photocurrents than when assessing isomerizations and are greater still when assessing mRGC signals. Nonetheless, the polar angle asymmetries of the mRGC outputs are still considerably smaller than those observed from human performance. We conclude that cone isomerizations, phototransduction, and the spatial filtering properties of mRGCs contribute to polar angle performance differences, but that a full account of these differences will entail additional contribution from cortical representations.

Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes

Efficient processing of sensory data requires adapting the neuronal encoding strategy to the statistics of natural stimuli. Previously, in Hermundstad et al., 2014, we showed that local multipoint correlation patterns that are most variable in natural images are also the most perceptually salient for human observers, in a way that is compatible with the efficient coding principle. Understanding the neuronal mechanisms underlying such adaptation to image statistics will require performing invasive experiments that are impossible in humans. Therefore, it is important to understand whether a similar phenomenon can be detected in animal species that allow for powerful experimental manipulations, such as rodents. Here we selected four image statistics (from single- to four-point correlations) and trained four groups of rats to discriminate between white noise patterns and binary textures containing variable intensity levels of one of such statistics. We interpreted the resulting psychometric data with an ideal observer model, finding a sharp decrease in sensitivity from two- to four-point correlations and a further decrease from four- to three-point. This ranking fully reproduces the trend we previously observed in humans, thus extending a direct demonstration of efficient coding to a species where neuronal and developmental processes can be interrogated and causally manipulated.

Effect of spatiotemporally changing environment on serial dependence in ensemble representations

The recent visual past has a strong impact on our current perception. Recent studies of serial dependence in perception show that low-level adaptation repels our current perception away from previous stimuli whereas post-perceptual decision attracts perceptual report toward the immediate past. In their studies, these repulsive and attractive biases were observed with different task demands perturbing ongoing sequential process. Therefore, it is unclear whether the opposite biases arise naturally in navigating complex real-life environments. Here we only manipulated the environmental statistics to characterize how serially dependent perceptual decisions unfold in spatiotemporally changing visual environments. During sequential mean orientation adjustment task on the array of Gabor patches, we found that the repulsion effect dominated only when ensemble variance increased across consecutive trials whereas the attraction effect prevailed when ensemble variance decreased or remained the same. The observed attractive bias by high-to-low-variance stimuli and repulsive bias by low-to-high-variance stimuli were reinforced by the repeated exposure to the low and the high ensemble variance, respectively. Further, this variance-dependent differential pattern of serial dependence in ensemble representation remained the same regardless of whether observers had a prior knowledge of environmental statistics or not. We used a Bayesian observer model constrained by visual adaptation to provide a unifying account of both attractive and repulsive bias in perception. Our results establish that the temporal integration and segregation of visual information is flexibly adjusted through variance adaptation.

Robust Control Optimization Based on Actuator Fault and Sensor Fault Compensation for Mini Motion Package Electro-Hydraulic Actuator

In recent years, electro-hydraulic systems have been widely used in many industries and have attracted research attention because of their outstanding characteristics such as power, accuracy, efficiency, and ease of maintenance. However, such systems face serious problems caused simultaneously by disturbances, internal leakage fault, sensor fault, and dynamic uncertain equation components, which make the system unstable and unsafe. Therefore, in this paper, we focus on the estimation of system fault and uncertainties with the aid of advanced fault compensation techniques. First, we design a sliding mode observer using the Lyapunov algorithm to estimate actuator faults that produce not only internal leakage fault but also disturbances or unknown input uncertainties. These faults occur under the effect of payload variations and unknown friction nonlinearities. Second, Lyapunov analysis-based unknown input observer model is designed to estimate sensor faults arising from sensor noises and faults. Third, to minimize the estimated faults, a combination of actuator and sensor compensation fault is proposed, in which the compensation process is performed due to the difference between the output signal and its estimation. Finally, the numerical simulations are performed to demonstrate the effectiveness of the proposed method obtained under various faulty scenarios. The simulation results show that the efficiency of the proposed solution is better than the traditional PID controller and the sensor fault compensation method, despite the influence of noises.

A simple modification to the inference process of Murray’s (2020) MIR improves the model’s performance

Murray (2020) recently introduced a novel computational lightness model, Markov Illuminance and Reflectance (MIR), a Bayesian observer model that represents input information and prior assumption with conditional random field (CRF) and that can account for many lightness illusions and phenomena. In the original MIR’s inference process, however, it did not utilize all the links in its CRF. Thus, this letter reports that a simple modification to the original MIR’s inference process improves its performance. MIR is a highly extensible model, so I recommend future research use the proposed version to attain further sophistication.

Analyzing the misperception of exponential growth in graphs.

Exponential growth is frequently underestimated, an error that can have a heavy social cost in the context of epidemics. To clarify its origins, we measured the human capacity to extrapolate linear and exponential trends in scatterplots. Four factors were manipulated: the function underlying the data (linear or exponential), the response modality (pointing or venturing a number), the scale on the y axis (linear or logarithmic), and the amount of noise in the data. While linear extrapolation was precise and largely unbiased, we observed a consistent underestimation of noisy exponential growth, present for both pointing and numerical responses. A biased ideal-observer model could explain these data as an occasional misperception of noisy exponential graphs as quadratic curves. Importantly, this underestimation bias was mitigated by participants’ math knowledge, by using a logarithmic scale, and by presenting a noiseless exponential curve rather than a noisy data plot, thus suggesting concrete avenues for interventions.

Prior expectations in visual speed perception predict encoding characteristics of neurons in area MT

Bayesian inference provides an elegant theoretical framework for understanding the characteristic biases and discrimination thresholds in visual speed perception. However, the framework is difficult to validate due to its flexibility and the fact that suitable constraints on the structure of the sensory uncertainty have been missing. Here, we demonstrate that a Bayesian observer model constrained by efficient coding not only well fits extensive psychophysical data of human visual speed perception but also provides an accurate quantitative account of the tuning characteristics of neurons known for representing visual speed. Specifically, we found that the population coding accuracy for visual speed in area MT ("neural prior") is precisely predicted by the power-law, slow-speed prior extracted from fitting the Bayesian model to the psychophysical data ("behavioral prior"), to the point that they are indistinguishable in a model cross-validation comparison. Our results demonstrate a quantitative validation of the Bayesian observer model constrained by efficient coding at both the behavioral and neural levels.

Immersion in Modified Reality: Sensitivity to the Slope (α) of the Amplitude Spectrum is Dependent on the α of Recently Viewed Environments

Scenes contain many statistical regularities that, if accounted for by the visual system, could greatly benefit visual processing. One such statistic to consider is the orientation-averaged slope (α) of the amplitude spectrum of natural scenes. Human observers are differently sensitive to αs, and they may utilize this statistic when processing natural scenes. Here, we explore whether discrimination sensitivity to α is associated with the recently viewed environment. Observers were immersed, using a Head-Mounted Display, in an environment that was either unaltered or had its average α steepened or shallowed. Discrimination thresholds were affected by the average shift in α: a steeper environment decreased thresholds for very steep reference αs while a shallower environment decreased thresholds for shallow values. We modelled these data with a Bayesian observer model and explored how different prior shapes may influence the ability of the model to fit observer thresholds. We explore three potential prior shapes: unimodal, bimodal and trimodal modified-PERT distributions and found the bimodal prior to best-capture observer thresholds for all experimental conditions. Notably, the prior modes' position was shifted following adaptation, which suggests that a priori expectations for α are sufficiently malleable to account for changes in the average α of the recently viewed scenes.