Uncertainty

…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.

…faces problems of interpretability, uncertainty and connections between molecular and system-level behaviour. Applied mathematicians, learning researchers and synthetic biologists will explore statistical methods for complex biological data, models that expose mechanisms, stochastic descriptions of noisy systems, multiscale modelling from molecules to populations and robust optimization for design under uncertainty. The workshop aims to develop mathematical foundations for more depend

…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.

…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.

When we feel confident in a perceptual decision, that decision is more likely to be correct. How does the brain assess the quality of perceptual decisions? Here, we test two connected hypotheses. First, that perceptual confidence relates to the structure of population activity in the sensory cortex. And second, that this relation differs from the one between sensory activity and perceptual decisions. To this end, we studied neural population activity in the primary visual cortex of macaque monkeys performing a task that reveals the animal's subjective sense of perceptual confidence. Specifical

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

…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.

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

…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

…on the brain's representation of uncertainty. Expected utility provides a normative theory for explaining decision outcomes under uncertainty, though its limitations have prompted the exploration of other preference measures. Specifically, we do not yet fully understand which preference measure the brain uses for probabilistic decision making, and how dopaminergic system might serve to implement this measure. Here we suggest a new approach to find this preference measure and show how it can be computed within a population of dopami

…the outcomes of our actions and the uncertainty associated with them over both long and short timescales. This allows identification of the underlying regularities in the world that can be used to guide behavior. But how neural circuits support the integration of experience is poorly understood. Neuromodulators are ideal candidates for tracking such variables as they can signal over multiple timescales from milliseconds to seconds. Specifically, acetylcholine has frequently been pro

…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

…to hidden-state changes under varying uncertainty levels. The goal is to detect transitions between `safe' and `unsafe' states. In the

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

…designed to incorporate a notion of uncertainty, and therefore seem unsuited to implement dynamic Bayesian inference. Here, we c

…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

Across species, survival depends on harvesting essential rewards such as food in the face of potential, occasionally catastrophic, dangers (such as serious injuries). This presents the challenge as to how to remain as efficient as possible when it comes to reward acquisition, whilst avoiding accrual of damage, especially during early exploration of new environments. This safety-efficiency dilemma is a special case of the exploration-exploitation dilemma, which typically collapses punishment and reward into a single scalar signal, whereby early losses can be overcome by later gains. However, th

…coding is corrupted by noise and uncertainty, which in turn results in different magnitudes of error in the signals that are received by downstream regions. Therefore, even in the same task and given a fixed distribution of input signals, the performance of a neural code is characterized by a distribution of errors. Different task demands might necessitate distributions with different properties; for example, a task that requires avoiding failure at all costs might necessitate coding schemes that minim

Neural representations of statistical probability and uncertainty are critical for brain function and behavior (Ma and Jazayeri 2014), and it is particularly important that the brain learn temporal relationships since they can infer causation and allow predictions about future events. Influential models assume this information is available in the brain, but many details are speculative and direct evidence remains elusive. Multiple labs (Bear, Berry, Buonomano, Buschman, Carandini, Fiser, Gavornik, Hawkins, Keller, Mrsic-Flogel, Hussain Shuler, Shouval) have demonstrated that primary visual c

Understanding how the brain represents, updates, and accommodates uncertainty is a key challenge for computational neuroscience. Neuromodulators such as acetylcholine (ACh) have been suggested as playing important roles over multiple timescales, regulating excitability and plasticity to mediate various effects of uncertainty on inference and learning. While these influences are consistent with ACh's long-standing association with general functions of attention and arousal, recent studies using novel techniques to measure and manipulate this system with increasing exactitude have revealed intri

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

…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.