Causal inference predicts the transition from integration to segmentation in motion perception.

Motion provides a powerful sensory cue for segmenting a visual scene into objects and inferring the causal relationships between objects. Fundamental mechanisms involved in this process are the integration and segmentation of local motion signals. However, the computations that govern whether local motion signals are perceptually integrated or segmented remain unclear.

How to reward animals based on their subjective percepts: A Bayesian approach to online estimation of perceptual biases.

Elucidating the neural basis of perceptual biases, such as those produced by visual illusions, can provide powerful insights into the neural mechanisms of perceptual inference. However, studying the subjective percepts of animals poses a fundamental challenge: unlike human participants, animals cannot be verbally instructed to report what they see, hear, or feel. Instead, they must be trained to perform a task for reward, and researchers must infer from their responses what the animal perceived.

How does the primate brain combine generative and discriminative computations in vision?

Vision is widely understood as an inference problem. However, two contrasting conceptions of the inference process have each been influential in research on biological vision as well as the engineering of machine vision. The first emphasizes bottom-up signal flow, describing vision as a largely feedforward, discriminative inference process that filters and transforms the visual information to remove irrelevant variation and represent behaviorally relevant information in a format suitable for downstream functions of cognition and behavioral control.

Bayesian encoding and decoding as distinct perspectives on neural coding.

The Bayesian brain hypothesis is one of the most influential ideas in neuroscience. However, unstated differences in how Bayesian ideas are operationalized make it difficult to draw general conclusions about how Bayesian computations map onto neural circuits. Here, we identify one such unstated difference: some theories ask how neural circuits could recover information about the world from sensory neural activity (Bayesian decoding), whereas others ask how neural circuits could implement inference in an internal model (Bayesian encoding).

Pattern completion and disruption characterize contextual modulation in the visual cortex.

Vision is fundamentally context-dependent, with neuronal responses influenced not just by local features but also by surrounding contextual information. In the visual cortex, studies using simple grating stimuli indicate that congruent stimuli - where the center and surround share the same orientation - are more inhibitory than when orientations are orthogonal, potentially serving redundancy reduction and predictive coding. Understanding these center-surround interactions in relation to natural image statistics is challenging due to the high dimensionality of the stimulus space, yet crucial for deciphering the neuronal code of real-world sensory processing.

Weak evidence for neural correlates of task-switching in macaque V1.

A central goal of systems neuroscience is to understand how populations of sensory neurons encode and relay information to the rest of the brain. Three key quantities of interest are ) how mean neural activity depends on the stimulus (sensitivity), ) how neural activity (co)varies around the mean (noise correlations), and ) how predictive these variations are of the subject's behavior (choice probability). Previous empirical work suggests that both choice probability and noise correlations are affected by task training, with decision-related information fed back to sensory areas and aligned to neural sensitivity on a task-by-task basis.

Aberrant causal inference and presence of a compensatory mechanism in autism spectrum disorder.

Autism spectrum disorder (ASD) is characterized by a panoply of social, communicative, and sensory anomalies. As such, a central goal of computational psychiatry is to ascribe the heterogenous phenotypes observed in ASD to a limited set of canonical computations that may have gone awry in the disorder. Here, we posit causal inference - the process of inferring a causal structure linking sensory signals to hidden world causes - as one such computation.

Task-induced neural covariability as a signature of approximate Bayesian learning and inference.

Perception is often characterized computationally as an inference process in which uncertain or ambiguous sensory inputs are combined with prior expectations. Although behavioral studies have shown that observers can change their prior expectations in the context of a task, robust neural signatures of task-specific priors have been elusive. Here, we analytically derive such signatures under the general assumption that the responses of sensory neurons encode posterior beliefs that combine sensory inputs with task-specific expectations.

A confirmation bias in perceptual decision-making due to hierarchical approximate inference.

Making good decisions requires updating beliefs according to new evidence. This is a dynamical process that is prone to biases: in some cases, beliefs become entrenched and resistant to new evidence (leading to primacy effects), while in other cases, beliefs fade over time and rely primarily on later evidence (leading to recency effects). How and why either type of bias dominates in a given context is an important open question.

Stimulus-dependent relationships between behavioral choice and sensory neural responses.

Understanding perceptual decision-making requires linking sensory neural responses to behavioral choices. In two-choice tasks, activity-choice covariations are commonly quantified with a single measure of choice probability (CP), without characterizing their changes across stimulus levels. We provide theoretical conditions for stimulus dependencies of activity-choice covariations.

Utica Shale Play Oil and Gas Brines: Geochemistry and Factors Influencing Wastewater Management.

The Utica and Marcellus Shale Plays in the Appalachian Basin are the fourth and first largest natural gas producing plays in the United States, respectively. Hydrocarbon production generates large volumes of brine ("produced water") that must be disposed of, treated, or reused. Though Marcellus brines have been studied extensively, there are few studies from the Utica Shale Play.

Full Water-Cycle Monitoring in an Urban Catchment Reveals Unexpected Water Transfers (Detroit MI, USA).

A goal in urban water management is to reduce the volume of stormwater runoff in urban systems and the effect of combined sewer overflows into receiving waters. Effective management of stormwater runoff in urban systems requires an accounting of various components of the urban water balance. To that end, precipitation, evapotranspiration, sewer flow, and groundwater in a 3.

Task-uninformative visual stimuli improve auditory spatial discrimination in humans but not the ideal observer.

In order to survive and function in the world, we must understand the content of our environment. This requires us to gather and parse complex, sometimes conflicting, information. Yet, the brain is capable of translating sensory stimuli from disparate modalities into a cohesive and accurate percept with little conscious effort.

Differentiating between Models of Perceptual Decision Making Using Pupil Size Inferred Confidence.

During perceptual decisions, subjects often rely more strongly on early, rather than late, sensory evidence, even in tasks when both are equally informative about the correct decision. This early psychophysical weighting has been explained by an integration-to-bound decision process, in which the stimulus is ignored after the accumulated evidence reaches a certain bound, or confidence level. Here, we derive predictions about how the average temporal weighting of the evidence depends on a subject's decision confidence in this model.

Feedback determines the structure of correlated variability in primary visual cortex.

The variable responses of sensory neurons tend to be weakly correlated (spike-count correlation, r). This is widely thought to reflect noise in shared afferents, in which case r can limit the reliability of sensory coding. However, it could also be due to feedback from higher-order brain regions.

Characterizing and interpreting the influence of internal variables on sensory activity.

The concept of a tuning curve has been central for our understanding of how the responses of cortical neurons depend on external stimuli. Here, we describe how the influence of unobserved internal variables on sensory responses, in particular correlated neural variability, can be understood in a similar framework. We suggest that this will lead to deeper insights into the relationship between stimulus, sensory responses, and behavior.

A neural basis for the spatial suppression of visual motion perception.

In theory, sensory perception should be more accurate when more neurons contribute to the representation of a stimulus. However, psychophysical experiments that use larger stimuli to activate larger pools of neurons sometimes report impoverished perceptual performance. To determine the neural mechanisms underlying these paradoxical findings, we trained monkeys to discriminate the direction of motion of visual stimuli that varied in size across trials, while simultaneously recording from populations of motion-sensitive neurons in cortical area MT.

Perceptual Decision-Making as Probabilistic Inference by Neural Sampling.

We address two main challenges facing systems neuroscience today: understanding the nature and function of cortical feedback between sensory areas and of correlated variability. Starting from the old idea of perception as probabilistic inference, we show how to use knowledge of the psychophysical task to make testable predictions for the influence of feedback signals on early sensory representations. Applying our framework to a two-alternative forced choice task paradigm, we can explain multiple empirical findings that have been hard to account for by the traditional feedforward model of sensory processing, including the task dependence of neural response correlations and the diverging time courses of choice probabilities and psychophysical kernels.

Biomechanics of walking in adolescents with progressive pseudorheumatoid arthropathy of childhood leads to physical activity recommendations as therapeutic focus.

Background: Progressive pseudorheumatoid arthropathy of childhood is a rare disease with an estimated prevalence of approximately 1/1,000,000. The disease manifests around the age of three to eight years and progresses with symptoms of early fatigue, muscle weakness, joint swelling and stiffness. The resulting functional limitations are often described as having a waddling gait.

A Modality-Specific Feedforward Component of Choice-Related Activity in MT.

The activity of individual sensory neurons can be predictive of an animal's choices. These decision signals arise from network properties dependent on feedforward and feedback inputs; however, the relative contributions of these inputs are poorly understood. We determined the role of feedforward pathways to decision signals in MT by recording neuronal activity while monkeys performed motion and depth tasks.

Inferring decoding strategies from choice probabilities in the presence of correlated variability.

The activity of cortical neurons in sensory areas covaries with perceptual decisions, a relationship that is often quantified by choice probabilities. Although choice probabilities have been measured extensively, their interpretation has remained fraught with difficulty. We derive the mathematical relationship between choice probabilities, read-out weights and correlated variability in the standard neural decision-making model.