Topic: uncertainty

Researchers and practitioners will discuss mathematical foundations of data science, including high-dimensional geometry, dimensionality reduction, scalable algorithms, uncertainty and machine learning. The conference takes place in person at the Salt Palace Convention Center on 16–20 November 2026. Early rates run through 19 October; abstract and travel-support deadlines have passed. Registration also covers the co-located SIAM Imaging Science and Data Mining conferences.

Develop statistical methods that integrate eBird observations with passive acoustic monitoring to improve estimates of bird populations, distribution and change. Work with the Cornell Lab of Ornithology on uncertainty, detection bias and ecological data integration. Two years of funding support consecutive annual appointments, with possible extensions. The Ithaca role offers a hybrid schedule. Submit a cover letter, CV and three referee contacts; review starts 5 October and continues until a suitable candidate is identified.

Stuart Firestein examines why productive science depends on ignorance, unsuccessful experiments and uncertainty as well as established knowledge. The conversation considers research at the boundary of what is understood, the role of conjecture and testing, and the absence of a guaranteed method for making discoveries. It also asks how public understanding and science education can better represent the process of investigation.

Humans and other animals approach reward and avoid punishment and pay attention to cues predicting these events. Such motivated behavior thus appears to be guided by value, which directs behavior towards or away from positively or negatively valenced outcomes. Moreover, it is facilitated by (top-down) salience, which enhances attention to behaviorally relevant learned cues predicting the occurrence of valenced outcomes. Using human neuroimaging, we recently separated value (ventral striatum, posterior ventromedial prefrontal cortex) from salience (anterior ventromedial cortex, occipital cortex

David Spiegelhalter discusses how to reason about chance, incomplete knowledge, risk and luck. Drawing on his book The Art of Uncertainty, the conversation moves between everyday surprises and consequential historical decisions. It examines why uncertainty can be personal and why disagreement can help people reason more carefully.

Neural computation is inextricably bound to decisions that must be made under time pressure and uncertainty. At the level of neural circuits, single neurons need to decide whether to spike. On longer timescales, the component circuitry needs to decide whether to reconfigure to store memories and adapt to novel situations. In this talk I will focus on two fun ideas in each of these contexts by showing how nonlinearities in neural components naturally form excitable switches that enable reliable decisions to be made in fluctuating environments. I will also issue propaganda that the kind of high

There has been increasing interest in the neurocomputational mechanisms underlying psychotic disorders in recent years. One promising approach is based on the theoretical framework of predictive processing, which proposes that inferences regarding the state of the world are made by combining prior beliefs with sensory signals. Delusions and hallucinations are the core symptoms of psychosis and often co-occur. Yet, different predictive-processing alterations have been proposed for these two symptom dimensions, according to which the relative weighting of prior beliefs in perceptual inference is

Efficient navigation requires animals to track their position, velocity and heading direction (HD). Some animals’ behavior suggests that they also track uncertainties about these navigational variables, and make strategic use of these uncertainties, in line with a Bayesian computation. Ring-attractor networks have been proposed to estimate and track these navigational variables, for instance in the HD system of the fruit fly Drosophila. However, such networks are not designed to incorporate a notion of uncertainty, and therefore seem unsuited to implement dynamic Bayesian inference. Here, we c

To localize ourselves in an environment for spatial navigation, we rely on vision and self-motion inputs, which only provide noisy and partial information. It is unknown how the resulting uncertainty affects navigation behavior and neural representations. Here we show that spatial uncertainty underlies key effects of environmental geometry on navigation behavior and grid field deformations. We develop an ideal observer model, which continually updates probabilistic beliefs about its allocentric location by optimally combining noisy egocentric visual and self-motion inputs via Bayesian filterin

In an ever-changing environment, uncertainty is omnipresent. To deal with this, organisms have evolved mechanisms that allow them to take advantage of environmental regularities in order to make decisions robustly and adjust their behavior efficiently, thus maximizing their chances of survival. In this talk, I will present behavioral evidence that animals perform model-based state inference to predict environmental state changes and adjust their behavior rapidly, rather than slowly updating choice values. This model-based inference process can be described using Bayesian change-point models. F

Optimally interacting with a changeable and uncertain world requires estimating and representing uncertainty. Psychiatric and neurodevelopmental conditions such as anxiety and autism are characterized by an altered response to uncertainty. I will review the evidence for these phenomena from computational modelling, and outline the planned experiments from our lab to add further weight to these ideas. If time allows, I will present results from a control sample in a novel task interrogating a particular type of uncertainty and their associated transdiagnostic psychiatric traits.

Join us for a conversation on the science of uncertainty, mind wandering and creativity!

Uncertainty plays a critical role in reinforcement learning and decision making. However, exactly how subjective uncertainty influences behaviour remains unclear. Multi-armed bandits are a useful framework to gain more insight into this. Paired with computational tools such as Kalman filters, they allow us to closely characterize the interplay between trial-by-trial value, uncertainty, learning, and choice. In this talk, I will present recent research where we also measured participants visual fixations on the options in a multi-armed bandit task. The estimated value of each option, and the un

Exploratory behaviors are crucial to the adaptation of organisms to uncertain environments. Human neuroscience studies have implicated the intraparietal sulcus (IPS), anterior cingulate cortex (ACC), and medial prefrontal cortex (mPFC) in exploratory behaviors. However, the roles of these regions in signaling specific aspects of exploratory behaviors remain unclear. Based on earlier studies, we classified exploratory behaviors into two types driven by different motives: internal and external explorations. Internal exploration involves encountering options with uncertain outcomes and seeking in

This study explores the decision-making processes involved in trajectory extrapolation tasks. Vertical paths were disturbed in the horizontal direction by noise generated by Random Walks (RDW) or Independent and Identically Distributed (IID) noise. In the Experiment, participants (n = 28) extrapolated if a moving point would end right or left of the screen’s center. We modeled path extrapolation using a three-parameter Random Sampling Model (RSM), and a Multi-Layer Perceptron (MLP). Statistically, for RDW paths, the optimal extrapolation strategy is to use the last visible position, whereas fo

The brain's ability to make economically efficient decisions plays a crucial role in processes like foraging and risk management. Given the inherent uncertainty about reward outcomes and world's latent states, the brain needs to construct distributional beliefs to enhance its performance in the decision-making process. It is not yet fully understood how the brain makes economic decisions in the presence of uncertainty, for instance, in a two-armed bandit task where arms have different reward distributions. Different frameworks have been suggested for representing probability distributions in t

Noisy and ambiguous environments require using the arising uncertainty strategically for efficient behavior. Recent behavioral evidence confirms such strategic use during spatial navigation. Despite this, leading neural models for navigation ignore such uncertainty. I will focus on two examples for revising these models. First, I will show that common ring attractor architectures are able to track uncertainty in heading direction only if they are tuned to feature slow dynamics. Second, I will show that approximate computations that only partially track uncertainty are sufficient to perform cl