15 papers · ranked by Valyu relevance
Giuseppe Notaro, Wieske van Zoest, David Melcher, Uri Hasson
A core question underlying neurobiological and computational models of behavior is how individuals learn environmental statistics and use them for making predictions. Treatment of this issue largely relies on reactive paradigms, where inferences about predictive processes are derived by modeling responses to stimuli…
Olgerta Asko, Vegard A. Volehaugen, Anaïs Llorens, Ingrid Funderud + 9 more
Humans extract regularities from the environment to form expectations that guide perception and optimize behavior. Although the prefrontal cortex (PFC) is central to this process, the relative contributions of orbitofrontal (OFC) and lateral PFC (LPFC) remain unclear. Here, we show that the brain tracks sound…
Buse M. Urgen, Hilal I. Basturk, Huseyin Boyaci
The effects of prior knowledge, predictions, and expectations on sensory and decision-making processes have been extensively studied. Yet, the neural mechanisms underlying those effects are still unclear. Here, we propose a recurrent neuronal model and test its predictions on behavioral and neuroimaging data from the…
Carla den Ouden, Andong Zhou, Vinay Mepani, Gyula Kovács + 2 more
Humans and other animals can learn and exploit repeating patterns that occur within their environments. These learned patterns can be used to form expectations about future sensory events. Several influential predictive coding models have been proposed to explain how learned expectations influence the activity of…
Antonino Greco, Clara Rastelli, Leonardo Bonetti, Christoph Braun + 1 more
A longstanding question in cognitive science is whether the human brain learns sensory regularities that are irrelevant to ongoing behavior, a phenomenon known as incidental associative learning. Here, we provide evidence at the single subject level that humans indeed acquired such incidental associations and reveal…
Vanessa Ferdinand, Amy Yu, Sarah Marzen
Organisms can solve complex tasks despite having limited cognitive resources when those resources are used optimally. Doing so optimally makes an organism “resource-rational”. In this paper, we show for the first time that humans are resource-rational at prediction. In a novel sequence learning experiment, participants…
Beren Millidge, Mufeng Tang, Mahyar Osanlouy, Nicol S. Harper + 1 more
One of the key problems the brain faces is inferring the state of the world from a sequence of dynamically changing stimuli, and it is not yet clear how the sensory system achieves this task. A well-established computational framework for describing perceptual processes in the brain is provided by the theory of…
Euan Prentis, Akram Bakkour
Predicting how our actions will affect future events is essential for effective behavior. However, learning predictive relationships is not trivial in a multidimensional world where numerous causes bring any one event about. Here we examine (1) how these multidimensional dynamics may distort predictive learning, and…
Maxime Maheu, Florent Meyniel, Stanislas Dehaene
Detecting and learning temporal regularities is essential to accurately predict the future. A long-standing debate in cognitive science concerns the existence of a dissociation, in humans, between two systems, one for handling statistical regularities governing the probabilities of individual items and their…
Pedro L. Cobos, Miguel A. Vadillo, David Luque, Mike E. Le Pelley
Previous studies have provided evidence that selective attention tends to prioritize the processing of stimuli that are good predictors of upcoming events over nonpredictive stimuli. In the present study we explored whether the mechanism responsible for this effect critically reflects the influence of prior experience…
Tarini Naravane, Ilias Tagkopoulos
The future of personalized health relies on knowledge of dietary composition. The current analytical methods are impractical to scale up, and the computational methods are inadequate. We propose machine learning models to predict the nutritional profiles of cooked foods given the raw food composition and cooking…
Karolina Horváth, Bianka Brezóczki, Adrienn Holczer, Teodóra Vékony + 1 more
Adaptive behavior relies on a dynamic balance between flexible, goal-directed control and efficient, automatic processes. This equilibrium is often disrupted in Attention-Deficit/Hyperactivity Disorder (ADHD), a condition understood to exist along a continuum of traits in the general population. While ADHD is…
Niloufar Razmi, Xufeng Caesar Dai, Leah Bakst, Matthew R. Nassar
People rapidly recalibrate their expectations about the world in the face of surprising observations. This recalibration should depend on the temporal structure of the environment, however how people should and do learn temporal structures remains unknown. To examine this gap, we developed a Bayesian model that infers…
Danilo Bzdok, Denis Engemann, Olivier Grisel, Gaël Varoquaux + 1 more
In the 20^th^ century many advances in biological knowledge and evidence-based medicine were supported by p-values and accompanying methods. In the beginning 21^st^ century, ambitions towards precision medicine put a premium on detailed predictions for single individuals. The shift causes tension between traditional…
Daniele Pessina, Tony Tian, Oliver Watson, Jerry Y. Heng + 1 more
Modelling complex crystallisation processes remains challenging due to limited experimental datasets, high measurement noise, and the need for generalisability across varying operating conditions. Neural Ordinary Differential Equations (NODEs) and transfer learning (TL) offer promising tools to overcome these…