26 papers · ranked by Valyu relevance
Daniel Williams
Clark has recently suggested that predictive processing advances a theory of neural function with the resources to put an ecumenical end to the “representation wars” of recent cognitive science. In this paper I defend and develop this suggestion. First, I broaden the representation wars to include three foundational…
Michelle L. Eisenberg, Jeffrey M. Zacks, Shaney Flores
The ability to predict what is going to happen in the near future is integral for daily functioning. Previous research suggests that predictability varies over time, with increases in prediction error at those moments that people perceive as boundaries between meaningful events. These moments also tend to be points of…
Ross Pain, Stephen Francis Mann
We propose a pluralist account of content for predictive processing systems. Our pluralism combines Millikan’s teleosemantics with existing structural resemblance accounts. The paper has two goals. First, we outline how a teleosemantic treatment of signal passing in predictive processing systems would work, and how it…
Anila M. D'Mello, Patric Bach, Philip R. Corlett, Liron Rozenkrantz
The world is a noisy place, and people must decide which inputs are meaningful, which are uninformative, and how to use incoming information to optimize their behavior in different contexts. It has been argued that rather than passively processing incoming information, we use past experience to form predictions about…
D. Eric Chamberlin
Eye Movement Desensitization and Reprocessing Therapy (EMDR) is an effective treatment for Post-traumatic Stress Disorder (PTSD). The Adaptive Information Processing Model (AIP) guides the development and practice of EMDR. The AIP postulates inadequately processed memory as the foundation of PTSD pathology. Predictive…
Alain de Cheveigné
Predictive coding is an influential concept in sensory and cognitive neuroscience. It is often understood as involving top-down prediction of bottom-up sensory patterns, but the term also applies to feed-forward predictive mechanisms, for example in the retina. Here, I discuss a recent model of low-level predictive…
Matthew Crippen, Jia Huang
Predictive processing (PP) accounts often characterize mental illness as maladaptive and epistemically distorting due to mismatches between brain-generated top-down models and bottom-up sensory inputs, with this review identifying exceptions. First, hypervigilance in trauma survivors with PTSD or depression may sustain…
Javier Ortiz-Tudela, Victoria I. Nicholls, Alex Clarke
Despite the recent popularity of predictive processing models of brain function, the term prediction is often instantiated very differently across studies. These differences in definition can substantially change the type of cognitive or neural operation hypothesised and thus have critical implications for the…
Markus Conci, Martina Zellin, Hermann J. Müller
Generating predictions for task-relevant goals is a fundamental requirement of human information processing, as it ensures adaptive success in our complex natural environment. Clark ([4]) proposed a model of hierarchical predictive processing, in which perception, attention, and learning are unified within a coherent…
Christopher J. Whyte, Amanda K. Robinson, Tijl Grootswagers, Hinze Hogendoorn + 1 more
Classic models of predictive coding propose that sensory systems use information retained from prior experience to predict current sensory input. Any mismatch between predicted and current input (prediction error) is then fed forward up the hierarchy leading to a revision of the prediction. We tested this hypothesis in…
Chiara Di Francescomarino, Chiara Ghidini, Fabrizio Maria Maggi, Williams Rizzi + 1 more
'Williams Rizzi' 'Cosimo Persia'] Abstract. A characteristic of existing predictive process monitoring techniques is to first construct a predictive model based on past process executions, and then use it to predict the future of new ongoing cases, without the possibility of updating it with new cases when they…
Chiara Di Francescomarino, Chiara Ghidini, Fabrizio Maria Maggi, Fredrik Milani
'Fredrik Milani'] Abstract. Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate in this domain in order to find, provided certain data, what can be predicted and what methods…
Dominic Alexander Neu, Johannes Lahann, Peter Fettke
Process mining enables the reconstruction and evaluation of business processes based on digital traces in IT systems. An increasingly important technique in this context is process prediction. Given a sequence of events of an ongoing trace, process prediction allows forecasting upcoming events or performance…
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…
Ario Santoso
Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of…
Daniel Calegari, Andrea Delgado
> Abstract. Process mining on business process execution data has focused primarily on orchestration-type processes performed in a single organization (intra-organizational). Collaborative (inter-organizational) processes, unlike those of orchestration type, expand several organizations (for example, in e-Government)…
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…
Authors not listed
Artificial intelligence (AI) is reshaping scientific research by accelerating discovery and enabling the analysis of complex data that traditional methods struggle to handle. This review examines over 310,000 journal articles and patents from the CAS Content Collection (2015–2025), with a focus on, biomedical research…
Simona Fioretto, Elio Masciari
The application of Predictive Process Monitoring (PPM) techniques is becoming increasingly widespread due to their capacity to provide organizations with accurate predictions regarding the future behavior of business processes, thereby facilitating more informed decisionmaking. A plethora of solutions have been…
Authors not listed
The global drive towards net-zero has accelerated the adoption of carbon fibre reinforced polymers (CFRP) for lightweight structures in various sectors such as aerospace, automotive, energy and biomedical. Mechanical machining of CFRP is often necessary to meet dimensional or assembly-related requirements. However…
M. I. Garrido, E. G. Rowe, Veronika Halász, J. B. Mattingley
Predictive coding posits that the human brain continually monitors the environment for regularities and detects inconsistencies. It is unclear, however, what effect attention has on expectation processes, as there have been relatively few studies and the results of these have yielded contradictory findings. Here, we…
Authors not listed
Kinetic modeling is essential for predicting changes in food quality during processing and storage. This study evaluates the application of physics-informed neural networks (PINN) for food kinetic modeling, integrating kinetic insights into neural network frameworks. Based on three case studies, namely seed drying…
Nihal Dadheech
In our research journey, we undertook a comprehensive exploration of protein-protein interaction (PPI) prediction, with a primary focus on unraveling the intricate web of interactions involving the SARS-CoV-2 virus. Our research endeavor encompassed a multi-faceted approach that seamlessly integrated data…
Prashanth Athri, Vidhya Murali, Pradyumna Y Muralidhar, Cassandra Königs + 4 more
- 1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India - 2. PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, Bengaluru, India - 3. Bioinformatics and Medical Informatics, Bielefeld University…
Tianfan Jin, Brett M Savoie
Contemporary machine learning algorithms have largely succeeded in automating the development of mathematical models from data. Although this is a striking accomplishment, it leaves unaddressed the multitude of scenarios, especially across the chemical sciences and engineering, where deductive, rather than inductive…
Authors not listed
Bioprocess engineering has incorporated effective AI applications in recent years that consist of traditional approaches to training models on relevant data to then analyze and predict new and unseen data. The missing component has been the ability to process mixed data from an assortment of dissimilar information…