24 papers · ranked by Valyu relevance
Erik Stänicke, Bendik Sparre Hovet, Line Indrevoll Stänicke, David Papo
The recent paradigm shift within cognitive neuroscience toward predictive processing appears to align with many psychoanalytic conceptualizations of the mind. In this article, we argue that several psychoanalytic concepts, such as projection, transference, wish-fulfillment, and perceptual identity, are particularly…
Carlos M. Gómez, Antonio Arjona, Francisco J. Ruíz-Martínez, Manuel Muñoz-Caracuel + 2 more
Predictive coding is a theory that tries to account for how the brain processes in an anticipatory manner the expected stimuli, and reorganizes the underlying neural networks as a consequence of the outcome of predictions: Correct or incorrect. EEG has the advantage of making a continuous and almost instantaneous…
David J. Harris, Callum A. O’Malley, Damian Beck, Stephan Zahno + 1 more
Background This scoping review explored the application of predictive processing in sport and exercise science, examining how cognitive and computational models have been used to understand perceptual-motor skills, decision-making, and physical performance. The review provides a tutorial introduction to key concepts in…
Tomoya Nakai, Tatsuya Daikoku, Yohei Oseki
Humans use various sequential signals, such as language, music, and mathematics, to convey complex information and facilitate communication. Previous research has identified two fundamental frameworks underlying human sequential signal processing: a structural framework, emphasizing rule-based hierarchical organization…
Idei, Hayato, Miyake Tamon, Ogata + 3 more
The rapid aging of societies is intensifying demand for autonomous care robots; however, most existing systems are task-specific and rely on handcrafted preprocessing, limiting their ability to generalize across diverse scenarios. A prevailing theory in cognitive neuroscience proposes that the human brain operates…
Stritzel, Oliver, Hühnerbein, Nick + 12 more
In recent years, Predictive Process Mining (PPM) techniques based on artificial neural networks have evolved as a method for monitoring the future behavior of unfolding business processes and predicting Key Performance Indicators (KPIs). However, many PPM approaches often lack reproducibility, transparency in decision…
Ashena Gorgan Mohammadi, Manu Srinath Halvagal, Friedemann Zenke
Tracking prey or recognizing a lurking predator is as crucial for survival as anticipating their actions. To guide behavior, the brain must extract information about object identities and their dynamics from entangled sensory inputs. How it accomplishes this feat remains an open question. Predictive coding theories…
Simone Gastaldon, Flavia Gheller, Noemi Bonfiglio, Davide Brotto + 5 more
This study provides the first neurophysiological evidence of how cochlear implant (CI) input affects predictive processing during audiovisual language comprehension in deaf individuals. Using EEG, we compared 18 CI users with 18 normal-hearing (NH) controls during sentence comprehension where final word predictability…
Wieger H. Scheurer, Micha Heilbron
Prediction is foundational to theories of perceptual processing and learning, in both neuroscience and AI. However, it remains unclear whether prediction occurs routinely during naturalistic perception, and at what level of abstraction the brain predicts. Here, we address both questions by analysing 7T fMRI recordings…
Romain Brette
Anticipation is a hallmark of all living phenomena, from bacteria to humans. If I notice a cloudy sky, I may take an umbrella to avoid getting wet. In cyanobacteria, which are photosynthetic bacteria, the chromosome decompacts shortly before dawn thanks to circadian rhythms, so that gene expression is favored when it…
Jisk J. Groot, Jeffrey D. Schall, Jacob A. Westerberg
Visual behavior depends on the ability to prioritize relevant sensory information while filtering out distractions. Predictable sensory contexts enable more efficient behavior by altering sensory processing. Using laminar neurophysiology in macaque visual cortex measuring population spiking during a feature-based…
Thomas Holstein, Jean-Christophe Sarrazin, Bruno Berberian, Andrea Desantis
Voluntary action shapes our perception through sensory predictions of its consequences. These predictions are thought to inhibit perceptual processing of predicted outcomes, leading to sensory attenuation. However, some studies have reported findings that contrast with this effect, suggesting that the influence of…
Pasquale Ardimento, Mario Luca Bernardi, Marta Cimitile, Samuele Latorre
This paper presents a reproducible and process-aware pipeline for predictive monitoring of clinical pathways. The approach integrates data lifting, temporal reconstruction, event log construction, prefix-based representations, and predictive modeling to support continuous reasoning on partially observed patient…
Jisk J. Groot, Jeffrey D. Schall, Jacob A. Westerberg
Visual behavior depends on the ability to prioritize relevant sensory information while filtering out distractions. Predictable sensory contexts enable more efficient behavior by altering sensory processing. Using laminar neurophysiology in macaque visual cortex measuring population spiking during a feature-based…
Francisco Gutiérrez-Blanco, Sergio Gaspar, Ana F. Palenciano, Carlos González-García + 1 more
Perceptual expectations facilitate perception of expected information by shaping neural coding. However, how the cognitive demands of different tasks may modulate this effect remains unclear. Here, we investigated whether and how expectations derived from sex/gender stereotypes impact behavioral and neural face…
Zan Li, Kyongmin Yeo, Wesley D. Gifford, Lara Marcuse + 2 more
Forecasting epileptic seizures from multivariate EEG signals represents a critical challenge in healthcare time series prediction, requiring high sensitivity, low false alarm rates, and subject-specific adaptability. We present STAN, an Adversarial Spatio-Temporal Attention Network that jointly models spatial brain…
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Accurately predicting chemical reaction yields in silico is a long-standing goal in organic chemistry that, if achieved, would revolutionize synthesis design, op-timization, and discovery. The vast reaction data within scientific literature rep-resents a rich resource for training predictive machine learning models…
Dimitris Bertsimas, Yu Ma, Kimberly Villalobos Carballo, Gagan Singh + 4 more
Problem definition: Capacity-constrained hospitals lack automated systems to harness the growing volume of heterogeneous clinical and operational data—ranging from vital signs and lab results to scheduling and patient flow metrics—to effectively forecast critical events. Early identification of patients at risk for…
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This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Sandro Claudio Lera, Shahrokh Firouzi, Jonathan Habshush, Robert Mahari
Legal disputes unfold through sequences of filings in which parties update their positions and may settle at any stage. Most computational studies of legal prediction, however, focus on adjudicated outcomes and treat cases as static objects observed only at the end of litigation. Here we develop a temporally structured…
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Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…
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Predicting solution conformation and aggregation of conjugated polymers remains a bottleneck for translating solution processing into controlled film microstructure and for closing the loop in self-driving laboratories. We construct a cleaned, machine-readable dataset of 256 entries that links polymer size…
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Background: Pharmaceutical batch production faces significant scheduling challenges due to operational uncertainties including equipment failures, yield variability, and demand fluctuations. While scheduling heuristics are widely used in practice, their comparative performance under varying uncertainty conditions…
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Accurate extrapolation in data-scarce scientific systems remains a central challenge for machine intelligence. In microbial bioprocessing, kinetic parameters change non-monotonically with reactor volume due to interacting hydrodynamic, oxygen-transfer, and mixing effects, rendering classical empirical scaling laws…