A Bayesian framework for sensory adaptation

Range adaptive value representations in schizophrenia and major depression

Anhedonia and amotivation are core symptoms of schizophrenia (SCZ) and major depressive disorder (MDD). Reward processing involves constructing and contrasting the representations for expected value (EV) and outcome value (OV) of a given stimulus, a phenomenon termed range adaptation. Impaired range adaptation can lead to anhedonia and amotivation. This study aimed to examine range adaptation in SCZ patients and MDD patients. Fifty SCZ, 46 MDD patients and 56 controls completed the Effort-based Pleasure Experience Task to measure EV and OV adaptation. SCZ and MDD patients showed altered range adaptation, albeit in different patterns. SCZ patients exhibited over-adaptation to OV and reduced adaptation to EV. By contrast, MDD patients exhibited diminished OV adaptation but intact EV adaptation. Both OV and EV adaptation were correlated with anhedonia and amotivation in SCZ and MDD. Taken together, our findings suggest that range adaptation is altered in both SCZ and MDD patients. Associations of OV and EV adaptation with anhedonia and amotivation were consistently found in SCZ and MDD patients. Impaired range adaptation in SCZ and MDD patients may be putative neural mechanisms and potential intervention targets for anhedonia and amotivation.

10 years of Bayesian theories of autism: A comprehensive review

Ten years ago, Pellicano and Burr published one of the most influential articles in the study of autism spectrum disorders, linking them to aberrant Bayesian inference processes in the brain. In particular, they proposed that autistic individuals are less influenced by their brains' prior beliefs about the environment. In this systematic review, we investigate if this theory is supported by the experimental evidence. To that end, we collect all studies which included comparisons across diagnostic groups or autistic traits and categorise them based on the investigated priors. Our results are highly mixed, with a slight majority of studies finding no difference in the integration of Bayesian priors. We find that priors developed during the experiments exhibited reduced influences more frequently than priors acquired previously, with various studies providing evidence for learning differences between participant groups. Finally, we focus on the methodological and computational aspects of the included studies, showing low statistical power and often inconsistent approaches. Based on our findings, we propose guidelines for future research.

Does Amount of Information Support Aesthetic Values?

Obtaining information from the world is important for survival. The brain, therefore, has special mechanisms to extract as much information as possible from sensory stimuli. Hence, given its importance, the amount of available information may underlie aesthetic values. Such information-based aesthetic values would be significant because they would compete with others to drive decision-making. In this article, we ask, "What is the evidence that amount of information support aesthetic values?" An important concept in the measurement of informational volume is entropy. Research on aesthetic values has thus used Shannon entropy to evaluate the contribution of quantity of information. We review here the concepts of information and aesthetic values, and research on the visual and auditory systems to probe whether the brain uses entropy or other relevant measures, specially, Fisher information, in aesthetic decisions. We conclude that information measures contribute to these decisions in two ways: first, the absolute quantity of information can modulate aesthetic preferences for certain sensory patterns. However, the preference for volume of information is highly individualized, with information-measures competing with organizing principles, such as rhythm and symmetry. In addition, people tend to be resistant to too much entropy, but not necessarily, high amounts of Fisher information. We show that this resistance may stem in part from the distribution of amount of information in natural sensory stimuli. Second, the measurement of entropic-like quantities over time reveal that they can modulate aesthetic decisions by varying degrees of surprise given temporally integrated expectations. We propose that amount of information underpins complex aesthetic values, possibly informing the brain on the allocation of resources or the situational appropriateness of some cognitive models.

Suboptimality in perceptual decision making

Human perceptual decisions are often described as optimal. Critics of this view have argued that claims of optimality are overly flexible and lack explanatory power. Meanwhile, advocates for optimality have countered that such criticisms single out a few selected papers. To elucidate the issue of optimality in perceptual decision making, we review the extensive literature on suboptimal performance in perceptual tasks. We discuss eight different classes of suboptimal perceptual decisions, including improper placement, maintenance, and adjustment of perceptual criteria; inadequate tradeoff between speed and accuracy; inappropriate confidence ratings; misweightings in cue combination; and findings related to various perceptual illusions and biases. In addition, we discuss conceptual shortcomings of a focus on optimality, such as definitional difficulties and the limited value of optimality claims in and of themselves. We therefore advocate that the field drop its emphasis on whether observed behavior is optimal and instead concentrate on building and testing detailed observer models that explain behavior across a wide range of tasks. To facilitate this transition, we compile the proposed hypotheses regarding the origins of suboptimal perceptual decisions reviewed here. We argue that verifying, rejecting, and expanding these explanations for suboptimal behavior - rather than assessing optimality per se - should be among the major goals of the science of perceptual decision making.

Spontaneous recovery of motion and face aftereffects

The ability of the visual system to rapidly adjust to changing environmental conditions is one of its key characteristics. Environmental changes can occur over a variety of timescales, however, and it remains unknown how the visual system adapts to these. Does a single mechanism control adaptation across all timescales, or is adaptation subserved by multiple mechanisms, each of which is tuned to its preferred duration? To address this question, we conducted three experiments in which subjects viewed motion (Exp. 1 and 2) or faces (Exp. 3) in a sequence designed to produce opposing aftereffects. A first adapter was presented for a relatively long duration, while a second one was presented only long enough to extinguish the effects of the initial adapter. Continued measurement of aftereffects revealed a spontaneous recovery of adaptation caused by the initial, longer-lasting adapter in all three experiments. This pattern of results suggests that adaptation in the visual system generally reflects a combination of multiple temporally-tuned mechanisms.

The dynamic range of human lightness perception

Natural viewing challenges the visual system with images that have a dynamic range of light intensity (luminance) that can approach 1,000,000:1 and that often exceeds 10,000:1 [1, 2]. The range of perceived surface reflectance (lightness), however, can be well approximated by the Munsell matte neutral scale (N 2.0/ to N 9.5/), consisting of surfaces whose reflectance varies by about 30:1. Thus, the visual system must map a large range of surface luminance onto a much smaller range of surface lightness. We measured this mapping in images with a dynamic range close to that of natural images. We studied simple images that lacked segmentation cues that would indicate multiple regions of illumination. We found a remarkable degree of compression: at a single image location, a stimulus luminance range of 5,905:1 can be mapped onto an extended lightness scale that has a reflectance range of 100:1. We characterized how the luminance-to-lightness mapping changes with stimulus context. Our data rule out theories that predict perceived lightness from luminance ratios or Weber contrast. A mechanistic model connects our data to theories of adaptation and provides insight about how the underlying visual response varies with context.

Computational characterization of visually induced auditory spatial adaptation

Recent research investigating the principles governing human perception has provided increasing evidence for probabilistic inference in human perception. For example, human auditory and visual localization judgments closely resemble that of a Bayesian causal inference observer, where the underlying causal structure of the stimuli are inferred based on both the available sensory evidence and prior knowledge. However, most previous studies have focused on characterization of perceptual inference within a static environment, and therefore, little is known about how this inference process changes when observers are exposed to a new environment. In this study we aimed to computationally characterize the change in auditory spatial perception induced by repeated auditory–visual spatial conflict, known as the ventriloquist aftereffect. In theory, this change could reflect a shift in the auditory sensory representations (i.e., shift in auditory likelihood distribution), a decrease in the precision of the auditory estimates (i.e., increase in spread of likelihood distribution), a shift in the auditory bias (i.e., shift in prior distribution), or an increase/decrease in strength of the auditory bias (i.e., the spread of prior distribution), or a combination of these. By quantitatively estimating the parameters of the perceptual process for each individual observer using a Bayesian causal inference model, we found that the shift in the perceived locations after exposure was associated with a shift in the mean of the auditory likelihood functions in the direction of the experienced visual offset. The results suggest that repeated exposure to a fixed auditory–visual discrepancy is attributed by the nervous system to sensory representation error and as a result, the sensory map of space is recalibrated to correct the error.

Statistics of optical coherence tomography data from human retina

Optical coherence tomography (OCT) has recently become one of the primary methods for noninvasive probing of the human retina. The pseudoimage formed by OCT (the so-called B-scan) varies probabilistically across pixels due to complexities in the measurement technique. Hence, sensitive automatic procedures of diagnosis using OCT may exploit statistical analysis of the spatial distribution of reflectance. In this paper, we perform a statistical study of retinal OCT data. We find that the stretched exponential probability density function can model well the distribution of intensities in OCT pseudoimages. Moreover, we show a small, but significant correlation between neighbor pixels when measuring OCT intensities with pixels of about 5 microm. We then develop a simple joint probability model for the OCT data consistent with known retinal features. This model fits well the stretched exponential distribution of intensities and their spatial correlation. In normal retinas, fit parameters of this model are relatively constant along retinal layers, but varies across layers. However, in retinas with diabetic retinopathy, large spikes of parameter modulation interrupt the constancy within layers, exactly where pathologies are visible. We argue that these results give hope for improvement in statistical pathology-detection methods even when the disease is in its early stages.

Visual motion aftereffects arise from a cascade of two isomorphic adaptation mechanisms

Prolonged exposure to a moving stimulus can substantially alter the perceived velocity (both speed and direction) of subsequently presented stimuli. Here, we show that these changes can be parsimoniously explained with a model that combines the effects of two isomorphic adaptation mechanisms, one nondirectional and one directional. Each produces a pattern of velocity biases that serves as an observable "signature" of the corresponding mechanism. The net effect on perceived velocity is a superposition of these two signatures. By examining human velocity judgments in the context of different adaptor velocities, we are able to separate these two signatures. The model fits the data well, successfully predicts subjects' behavior in an additional experiment using a nondirectional adaptor, and is in agreement with a variety of previous experimental results. As such, the model provides a unifying explanation for the diversity of motion aftereffects.

Speed adaptation as Kalman filtering

If the purpose of adaptation is to fit sensory systems to different environments, it may implement an optimization of the system. What the optimum is depends on the statistics of these environments. Therefore, the system should update its parameters as the environment changes. A Kalman-filtering strategy performs such an update optimally by combining current estimations of the environment with those from the past. We investigate whether the visual system uses such a strategy for speed adaptation. We performed a matching-speed experiment to evaluate the time course of adaptation to an abrupt velocity change. Experimental results are in agreement with Kalman-modeling predictions for speed adaptation. When subjects adapt to a low speed and it suddenly increases, the time course of adaptation presents two phases, namely, a rapid decrease of perceived speed followed by a slower phase. In contrast, when speed changes from fast to slow, adaptation presents a single phase. In the Kalman-model simulations, this asymmetry is due to the prevalence of low speeds in natural images. However, this asymmetry disappears both experimentally and in simulations when the adapting stimulus is noisy. In both transitions, adaptation now occurs in a single phase. Finally, the model also predicts the change in sensitivity to speed discrimination produced by the adaptation.

Properties of stimulus-dependent synchrony in retinal ganglion cells

Neighboring retinal ganglion cells often spike synchronously, but the possible function and mechanism of this synchrony is unclear. Recently, the strength of the fast correlation between ON-OFF directionally selective cells of the rabbit retina was shown to be stimulus dependent. Here, we extend that study, investigating stimulus-dependent correlation among multiple ganglion-cell classes, using multi-electrode recordings. Our results generalized those for directionally selective cells. All cell pairs exhibiting significant spike synchrony did it for an extended edge but rarely for full-field stimuli. The strength of this synchrony did not depend on the amplitude of the response and correlations could be present even when the cells' receptive fields did not overlap. In addition, correlations tended to be orientation selective in a manner predictable by the relative positions of the receptive fields. Finally, extended edges and full-field stimuli produced significantly greater and smaller correlations than predicted by chance respectively. We propose an amacrine-network model for the enhancement and depression of correlation. Such an apparently purposeful control of correlation adds evidence for retinal synchrony playing a functional role in vision.

The relation between color discrimination and color constancy: when is optimal adaptation task dependent?

Color vision supports two distinct visual functions: discrimination and constancy. Discrimination requires that the visual response to distinct objects within a scene be different. Constancy requires that the visual response to any object be the same across scenes. Across changes in scene, adaptation can improve discrimination by optimizing the use of the available response range. Similarly, adaptation can improve constancy by stabilizing the visual response to any fixed object across changes in illumination. Can common mechanisms of adaptation achieve these two goals simultaneously? We develop a theoretical framework for answering this question and present several example calculations. In the examples studied, the answer is largely yes when the change of scene consists of a change in illumination and considerably less so when the change of scene consists of a change in the statistical ensemble of surface reflectances in the environment.

Space and time in visual context

No sensory stimulus is an island unto itself; rather, it can only properly be interpreted in light of the stimuli that surround it in space and time. This can result in entertaining illusions and puzzling results in psychological and neurophysiological experiments. We concentrate on perhaps the best studied test case, namely orientation or tilt, which gives rise to the notorious tilt illusion and the adaptation tilt after-effect. We review the empirical literature and discuss the computational and statistical ideas that are battling to explain these conundrums, and thereby gain favour as more general accounts of cortical processing.

Ganglion cell densities in normal and dark-reared turtle retinas

In dark-reared, neonatal turtle retinas, ganglion cell receptive fields and dendritic trees grow faster than normal. As a result, their areas may become, on average, up to twice as large as in control retinas. This raises the question of whether the coverage factor of dark-reared ganglion cells is larger than normal. Alternatively, dark rearing may lead to smaller-than-normal cell densities by accelerating apoptosis. To test these alternatives, we investigated the effect of light deprivation on densities and soma sizes of turtle retinal ganglion cells. For this purpose, we marked these cells using retrograde labeling of fixed turtle retinas with DiI (1,1'-dioctadecyl-3,3,3',3'-tetramethylindocarbocyanine perchlorate). Control turtles were maintained in a regular 12-h light/dark cycle from hatching until 4 weeks of age, whereas dark-reared turtles were maintained in total darkness for the same period. Ganglion cells in the control and dark-reared retinas were found to be similar in density and soma sizes. These results show that the mean coverage factor of turtle dark-reared ganglion cells is larger than normal.

An internal model of head kinematics predicts the influence of head orientation on reflexive eye movements

Our sense of self-motion and self-orientation results from combining information from different sources. We hypothesize that the central nervous system (CNS) uses internal models of the laws of physics to merge cues provided by different sensory systems. Different models that include internal models have been proposed; we focus herein on that referred to as the sensory weighting model. For simplicity, we isolate the portion of the sensory weighting model that estimates head angular velocity: it includes an inverse internal model of head kinematics and an 'idiotropic' vector aligned with the main body axis. Following a post-rotatory tilt in the dark, which is a rapid tilt following a constant-velocity rotation about an earth-vertical axis, the inverse internal model is applied to conflicting vestibular signals. Consequently, the CNS computes an inaccurate estimate of head angular velocity that shifts toward alignment with an estimate of gravity. Since reflexive eye movements known as vestibulo-ocular reflexes (VOR) compensate for this estimate of head angular velocity, the model predicts that the VOR rotation axis shifts toward alignment with this estimate of gravity and that the VOR time constant depends on final head orientation. These predictions are consistent with experimental data.

Power spectra and distribution of contrasts of natural images from different habitats

Some theories for visual receptive fields postulate that they depend on the image statistics of the natural habitat. Consequently, different habitats may lead to different receptive fields. We thus decided to study how some of the most relevant statistics vary across habitats. In particular, atmospheric and underwater habitats were compared. For these habitats, we looked at two measures of the power spectrum and one of the distributions of contrasts. From power spectra, we analyzed the log-log slope of the fall and the degree of isotropy. From the distribution of contrasts, we analyzed the fall in a semi-log scale. Past studies found that the spatial power spectra of natural atmospheric images fall linearly in logarithmic axes with a slope of about -2 and that their distribution of contrasts shows an approximate linear fall in semi-logarithmic axes. Here, we show that the power spectrum of underwater images have statistically significantly steeper slopes ( approximately -2.5 in log-log axes) than atmospheric images. The vast majority of power spectra are non-isotropic, but their degree of anisotropy is extremely low, especially in atmospheric images. There are also statistical differences across habitats for the distribution of contrasts, with it falling faster for underwater images than for atmospheric ones. We will argue that these differences are due to the optical properties of water and that the differences have relevance for theories of visual receptive fields. These theories would predict larger receptive fields for aquatic animals compared to land animals.