Predictive Processing Research Articles

How does it end? Endpoints of boundaries lead to completion in macro-events.

While watching someone kicking a ball, missing moments of ball contact can be incorrectly identified as seen if the event is continued in a causal manner (i.e., the ball flying off). Does event completion also occur for events of a larger scale such as having breakfast (macro-event), which consists of multiple sub-steps like toasting bread (micro-event)? We conducted two experiments to measure event completion in macro-events presenting portions of multiple micro-events. In Experiment 1, video summaries were formed with or without event boundary information where a macro-end was either present or absent. Macro-end signified an overarching goal achievement that signaled the completion of previous tasks (such as leaving the kitchen with a full breakfast plate). More completion occurred for summaries with event boundary information and macro-ends. In Experiment 2, we tested two alternative hypotheses to explore the underlying process by showing the beginnings or ends of a micro-event. While the predictive processing hypothesis suggests that event completion is based more on predicting the future states of the event based on beginning information, the backward inferences hypothesis suggests that event completion relies more on deductions formed after the fact based on event endings. Results of Experiment 2 suggest that the ends of event boundaries lead to more event completion, possibly due to their role in forming causal connectivity. These results help to further understand event completion on a macro level.

How does depressive cognition develop? A state-dependent network model of predictive processing.

Depression is vastly heterogeneous in its symptoms, neuroimaging data, and treatment responses. As such, describing how it develops at the network level has been notoriously difficult. In an attempt to overcome this issue, a theoretical "negative prediction mechanism" is proposed. Here, eight key brain regions are connected in a transient, state-dependent, core network of pathological communication that could facilitate the development of depressive cognition. In the context of predictive processing, it is suggested that this mechanism is activated as a response to negative/adverse stimuli in the external and/or internal environment that exceed a vulnerable individual's capacity for cognitive appraisal. Specifically, repeated activation across this network is proposed to update individual's brain so that it increasingly predicts and reinforces negative experiences over time-pushing an individual at-risk for or suffering from depression deeper into mental illness. Within this, the negative prediction mechanism is poised to explain various aspects of prognostic outcome, describing how depression might ebb and flow over multiple timescales in a dynamically changing, complex environment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

EchoPT: A Pretrained Transformer Architecture that Predicts 2D In-Air Sonar Images for Mobile Robotics

The predictive brain hypothesis suggests that perception can be interpreted as the process of minimizing the error between predicted perception tokens generated via an internal world model and actual sensory input tokens. When implementing working examples of this hypothesis in the context of in-air sonar, significant difficulties arise due to the sparse nature of the reflection model that governs ultrasonic sensing. Despite these challenges, creating consistent world models using sonar data is crucial for implementing predictive processing of ultrasound data in robotics. In an effort to enable robust robot behavior using ultrasound as the sole exteroceptive sensor modality, this paper introduces EchoPT (Echo-Predicting Pretrained Transformer), a pretrained transformer architecture designed to predict 2D sonar images from previous sensory data and robot ego-motion information. We detail the transformer architecture that drives EchoPT and compare the performance of our model to several state-of-the-art techniques. In addition to presenting and evaluating our EchoPT model, we demonstrate the effectiveness of this predictive perception approach in two robotic tasks.

Diverse frontoparietal connectivity supports semantic prediction and integration in sentence comprehension.

Predictive processing in parietal, temporal, frontal, and sensory cortex allows us to anticipate future meanings to maximize the efficiency of language comprehension, with the temporoparietal junction (TPJ) and inferior frontal gyrus (IFG) thought to be situated towards the top of a predictive hierarchy. Although the regions underpinning this fundamental brain function are well-documented, it remains unclear how they interact to achieve efficient comprehension. To this end we recorded functional magnetic resonance imaging (fMRI) in 22 participants (11 males) while they comprehended sentences presented part-by-part, in which we manipulated the constraint provided by sentential contexts on upcoming semantic information. Using this paradigm, we examined the connectivity patterns of bilateral TPJ and IFG during anticipatory phases (i.e., before the onset of targets) and integration phases (i.e., after the onset of targets). When upcoming semantic content was highly predictable in strong-constraint contexts, both left TPJ and bilateral IFG showed stronger visual coupling, while right TPJ showed stronger connectivity with regions within control, default mode, and visual networks, including IFG, parahippocampal gyrus, posterior cingulate, and fusiform gyrus. These connectivity patterns were weaker when predicted semantic content appeared, in line with predictive coding theory. Conversely, for less predictable content, these connectivity patterns were stronger during the integration phase. Overall, these results suggest that both top-down semantic prediction and bottom-up integration during predictive processing are supported by flexible coupling of frontoparietal regions with control, memory, and sensory systems.Significance Statement Recent work has revealed the neural basis of predictive language comprehension. However, it remains unclear how brain regions change their connectivity in a dynamic fashion to support comprehension in highly predictive and less predictive contexts. Here, we show that stronger frontoparietal connectivity with cognitive control, memory, and sensory areas supports top-down prediction generation in strong-constraint contexts; these connectivity patterns are reduced when the anticipated information appears. This pattern is reversed when upcoming sensory input is unpredictable; connectivity is stronger after word inputs have been presented, allowing semantic integration with preceding low-constraint context. Our findings suggest that both top-down semantic prediction and bottom-up semantic integration in language comprehension rely upon diverse functional coupling of higher-order frontoparietal regions with other brain systems.

The Role Of Machine Learning In Transforming Healthcare: A Systematic Review

This systematic review explores the transformative role of machine learning (ML) in healthcare, focusing on its applications in disease diagnosis, personalized medicine, healthcare operations, and patient monitoring. By analyzing 167 peer-reviewed studies published between 2015 and 2024, the review highlights how ML algorithms have significantly improved early disease detection, treatment personalization, and operational efficiencies in healthcare settings. Findings reveal that ML-powered diagnostic tools, especially deep learning models, demonstrate accuracy levels comparable to human experts in areas like cancer detection. Additionally, ML models tailored for personalized medicine have shown promise in optimizing treatment protocols based on genetic profiles, reducing adverse reactions and improving patient outcomes. In healthcare operations, ML applications have streamlined hospital resource management, enhanced workflow efficiency, and reduced administrative burdens through predictive analytics and natural language processing (NLP). Moreover, ML-driven remote patient monitoring systems have enabled proactive interventions for chronic disease management. Despite these advancements, challenges related to data privacy, algorithmic transparency, and regulatory compliance persist. Addressing these barriers is critical for realizing the full potential of ML in healthcare, ensuring ethical deployment and equitable access. The review concludes by emphasizing the need for robust regulatory frameworks and further research to enhance the integration of ML technologies into clinical practice.

Fermatean Fuzzy TOPSIS Method and Its Application in Ranking Business Intelligence-Based Strategies in Smart City Context

With the expansion of smart cities, the use of business intelligence (BI) has emerged as a crucial tool for resource optimization, increasing efficiency, and improving the citizens’ quality of life. BI enables companies to make better strategic decisions by analyzing vast amounts of urban data, helping them remain competitive in the dynamic smart city environment. This study utilizes content analysis and the Fermatean Fuzzy TOPSIS (FF-TOPSIS) method to rank the strategies based on business intelligence in the context of smart city. Initially, relevant criteria were identified through content analysis, and subsequently, five strategies were developed and ranked based on these criteria. The results revealed that the "Development of IOT-enabled smart networks (S2)" ranked highest, as it plays a significant role in optimizing resource management and enhancing urban service performance, thereby contributing greatly to the advancement of smart cities. "Process automation and the deployment of robotic systems (S5)" ranked second, as it enhances efficiency and reduces human errors. "Cloud platform integration for seamless access to data and services (S3)" also proved to be of considerable importance, ranking third, as it provides seamless access to data and services. " Artificial intelligence deployment for predictive analytics and process optimization (S4)" ranked fourth and was vital for predictive analytics and process optimization, while " Big data analytics for smart decision-making (S1)"—despite its importance—ranked fifth. Urban managers should prioritize the development of IOT networks to fully leverage their potential for resource management and efficiency gains. Following this, attention to process automation and AI integration can significantly enhance the quality of life for citizens and reduce urban costs.

Model‐Driven Manufacturing of High‐Energy‐Density Batteries: A Review

AbstractThe rapid advancement in energy storage technologies, particularly high‐energy density batteries, is pivotal for diverse applications ranging from portable electronics to electric vehicles and grid storage. This review paper provides a comprehensive analysis of the recent progress in model‐driven manufacturing approaches for high‐energy‐density batteries, highlighting the integration of computational models and simulations with experimental manufacturing processes to optimize performance, reliability, safety, and cost‐effectiveness. We systematically examine various modeling techniques, including electrochemical, thermal, and mechanical models, and their roles in elucidating the complex interplay of materials, design, and manufacturing parameters. The review also discusses the challenges and opportunities in scaling up these model‐driven approaches, addressing key issues such as model validation, parameter sensitivity, and the integration of machine learning and artificial intelligence for predictive modeling, process optimization, and quality assurance. By synthesizing current research findings and industry practices, this paper aims to outline a roadmap for future developments in model‐driven manufacturing of high‐energy density batteries, emphasizing the need for interdisciplinary collaboration and innovation to meet the increasing demands for energy storage solutions.

High-level visual prediction errors in early visual cortex.

Perception is shaped by both incoming sensory input and expectations derived from our prior knowledge. Numerous studies have shown stronger neural activity for surprising inputs, suggestive of predictive processing. However, it is largely unclear what predictions are made across the cortical hierarchy, and therefore what kind of surprise drives this up-regulation of activity. Here, we leveraged fMRI in human volunteers and deep neural network (DNN) models to arbitrate between 2 hypotheses: prediction errors may signal a local mismatch between input and expectation at each level of the cortical hierarchy, or prediction errors may be computed at higher levels and the resulting surprise signal is broadcast to earlier areas in the cortical hierarchy. Our results align with the latter hypothesis. Prediction errors in both low- and high-level visual cortex responded to high-level, but not low-level, visual surprise. This scaling with high-level surprise in early visual cortex strongly diverged from feedforward tuning. Combined, our results suggest that high-level predictions constrain sensory processing in earlier areas, thereby aiding perceptual inference.

Predictive learning shapes the representational geometry of the human brain

Predictive coding theories propose that the brain constantly updates internal models to minimize prediction errors and optimize sensory processing. However, the neural mechanisms that link prediction error encoding and optimization of sensory representations remain unclear. Here, we provide evidence how predictive learning shapes the representational geometry of the human brain. We recorded magnetoencephalography (MEG) in humans listening to acoustic sequences with different levels of regularity. We found that the brain aligns its representational geometry to match the statistical structure of the sensory inputs, by clustering temporally contiguous and predictable stimuli. Crucially, the magnitude of this representational shift correlates with the synergistic encoding of prediction errors in a network of high-level and sensory areas. Our findings suggest that, in response to the statistical regularities of the environment, large-scale neural interactions engaged in predictive processing modulate the representational content of sensory areas to enhance sensory processing.

How the prospect of a clinical trial impacts decision-making for predictive genetic testing in ALS

Objective: Genetic testing practices are rapidly evolving for people living with, or at-risk for, amyotrophic lateral sclerosis (ALS), due to emerging genotype-driven therapies. This study explored how individuals at-risk for familial ALS (fALS) perceive the opportunity to participate in a clinical trial, and to better understand how that may influence the decision-making process for predictive genetic testing. Methods: This study used both quantitative and qualitative data analyses. Data were collected through an online questionnaire, followed by semi-structured interviews conducted with twelve (n = 12) individuals at-risk for either SOD1- or C9orf72-ALS who had predictive testing prior to study participation. Interview data were analyzed using reflexive thematic analysis. Results: Three overarching themes were conceptualized from the data: i) the psychosocial impact of fALS; ii) perspectives of at-risk individuals on research involvement; and iii) predictive genetic counseling and testing considerations. These results contribute perspectives of the lived experience to inform predictive genetic counseling and testing practices for individuals at-risk for fALS. Conclusion: Individuals at-risk for fALS view potential participation in a presymptomatic clinical trial as an actionable measure that may increase their desire for predictive genetic testing. Genetic counseling was identified as a critical component of the predictive testing process given the life-changing implications associated with a positive result. Increased access to predictive genetic counseling, and in a timely manner, is a significant need in the ALS population given potential access to gene-specific therapies in the presymptomatic stage.

Consumers' Expectations of Liking, Emotional, Conceptual and Sustainability Characteristics of Dairy, Plant‐Based and Sustainable Yoghurts

ABSTRACTAn online survey of consumers' liking, emotional, conceptual and sustainability expectations in response to traditional, plant‐based (PB), and sustainable yoghurt product names was conducted with 1206 New Zealanders. Results showed that consumers' sensory expectations are associated with hedonic and non‐sensory expectations. Consumers' sensory, emotional, conceptual and sustainability ideations and the impact of these variables on expected liking parallel the findings from recent tests of tasted yoghurts. Per cognitive theories of predictive processing, the results support the hypothesis that judgments of sensory expectations may provide important insights into likely product perceptions. The results also support the notion that surveys of consumer product expectations can be a tool to obtain consumer insights about new products, including PB and sustainable alternatives. Since sensory expectations were associated with non‐sensory expectations, modifying the sensory attributes of PB or other innovative yoghurt products may enhance consumers' conceptualizations of these products and improve their market acceptance.

Emotional stimuli boost incidental learning through predictive processing.

Extracting regularities and probabilities from the environment is a fundamental and critical ability in an ever-changing surrounding. Previous findings showed that people are highly efficient in learning these regularities and that emotional stimuli are better learned than neutral ones. Yet, the generality and the underlying mechanism of this benefit are poorly understood. Here, participants viewed a stream of images with negative and neutral valence. Unbeknownst, the items recurred in regularity as triplets. Then, to assess learning, a surprised familiarity test was conducted. The results of Experiment 1, using two sets of stimuli, found better statistical learning for negative triplets than for neutral triplets. Experiment 2 revealed similar benefits even when only a single negative item was in the triplet at the second or third position, suggesting the advantage is not cumulative. We speculated that the predictability of the negative items is driving the effect. Consequently, Experiment 3 confirmed that the memory for neutral items preceding negative items was better than for neutral items preceding neutral items. Together, these findings provide novel insights into the mechanism of how the learning of incidental temporal associations is influenced by negative stimuli and the role of predictability in the negative valence benefit.

Longitudinal relationship between adolescent emotional self-regulation and prosocial behavior toward powerful people: Disentangling between-person differences from within-person effects.

This study investigated the longitudinal effects between adolescent emotional self-regulation and prosocial behavior toward powerful people (i.e., actions aimed at benefiting or supporting individuals who hold significant influence or authority within a group or society), based on the perspective of positive socialization cycle. We recruited 543 Chinese adolescents (284 girls, M age at Time 1 = 11.27 years) and collected three waves of data over 3 years, each approximately 1 year apart (n Time 2 = 449, n Time 3 = 417). Traditional cross-lagged panel model was conducted to test the longitudinal relationship between emotional self-regulation and prosocial behavior toward powerful people. Then, a random-intercept cross-lagged panel model was performed to disentangle the between-person differences from the within-person predictive processes. The results showed that, emotional self-regulation in the previous year was positively associated with prosocial behavior toward powerful people in the next year, whereas earlier prosocial behavior toward powerful people was not predictive of subsequent emotional self-regulation. Findings advocate the domain-specific nature of self-regulation and prosocial behavior and deepen our understanding of the relationship between the two from the culture-specific perspective. This study also highlights the value of emotional self-regulation intervention as a viable way to promote adolescent prosocial development. Theoretical and empirical implications are discussed.

An expanding repertoire of circuit mechanisms for visual prediction errors

Cortical responses to stimuli vary dependingon context and expectation. Adding insight into this process, Furutachi et al. recently demonstrated that higher-order thalamic input to visual cortex cooperates with interneurons to augment responses to unexpected stimuli, consistent with a body of literature implicating top-down modulation and local inhibition in predictive processing.

Fundamental study on the construction of anti-wear drug delivery system through the design of titanium surface morphology

The durability of dental implant carrier coatings is of paramount importance for the expeditious and predictable osseointegration process. The present work is based on a bionic micro/nano hierarchy structure, which consists of titanium surface microstructures and their internal TiO2 nanotubes (TNTs) with drug-carrying capacity. This effectively increases the wear resistance of the drug-carrying coating on the titanium surface. In comparison to untextured samples, the wear volume and wear depth of the optimal texture group are markedly diminished, resulting in a significant enhancement of wear resistance. This improvement was primarily attributed to the smaller contact area of the microstructure. Concurrently, the microstructure serves to safeguard the TNTs from damage during friction. The hydrophilic biomimetic anti-wear micro/nano hierarchies demonstrated the capacity to promote MC3T3-E1 cell adhesion and proliferation, while also exhibiting no cytotoxic effects. Moreover, the micro/nano hierarchical structure can be directly applied to the surface of commercialized implants. In simulated clinical conditions, the implant was inserted into a fresh Bama porcine mandible, and the structure of the drug-loading coatings remained intact. This structure enhances the abrasion resistance of the drug coating while minimizing alterations to the original treatment process of the implant, which is of great significance in the clinical application of implant-loaded drug delivery.

Explaining the impact of design choices on model quality in predictive process monitoring

Explaining the impact of design choices on model quality in predictive process monitoring

RDoC Framework Through the Lens of Predictive Processing: Focusing on Cognitive Systems Domain.

In response to shortcomings of the current classification system in translating discoveries from basic science to clinical applications, NIMH offers a new framework for studying mental health disorders called Research Domain Criteria (RDoC). This framework holds a multidimensional outlook on psychopathologies focusing on functional domains of behavior and their implementing neural circuits. In parallel, the Predictive Processing (PP) framework stands as a leading theory of human brain function, offering a unified explanation for various types of information processing in the brain. While both frameworks share an interest in studying psychopathologies based on pathophysiology, their integration still needs to be explored. Here, we argued in favor of the explanatory power of PP to be a groundwork for the RDoC matrix in validating its constructs and creating testable hypotheses about mechanistic interactions between molecular biomarkers and clinical traits. Together, predictive processing may serve as a foundation for achieving the goals of the RDoC framework.

Neural encoding of melodic expectations in music across EEG frequency bands.

The human brain tracks regularities in the environment and extrapolates these to predict future events. Prior work on music cognition suggests that low-frequency (1-8Hz) brain activity encodes melodic predictions beyond the stimulus acoustics. Building on this work, we aimed to disentangle the frequency-specific neural dynamics linked to melodic prediction uncertainty (modelled as entropy) and prediction error (modelled as surprisal) for temporal (note onset) and content (note pitch) information. By using multivariate temporal response function (TRF) models, we re-analysed the electroencephalogram (EEG) from 20 subjects (10 musicians) who listened to Western tonal music. Our results show that melodic expectation metrics improve the EEG reconstruction accuracy in all frequency bands below the gamma range (< 30 Hz). Crucially, we found that entropy contributed more strongly to the reconstruction accuracy enhancement compared to surprisal in all frequency bands. Additionally, we found that the encoding of temporal, but not content, information metrics was not limited to low frequencies, rather it extended to higher frequencies (> 8Hz). An analysis of the TRF weights revealed that the temporal predictability of a note (entropy of note onset) may be encoded in the delta- (1-4Hz) and beta-band (12-30 Hz) brain activity prior to the stimulus, suggesting that these frequency bands associate with temporal predictions. Strikingly, we also revealed that melodic expectations selectively enhanced EEG reconstruction accuracy in the beta band for musicians, and in the alpha band (8-12 Hz) for non-musicians, suggesting that musical expertise influences the neural dynamics underlying predictive processing in music cognition.

Analytical and Finite Element Analysis of the Rolling Force for the Three-Roller Cylindrical Bending Process.

In the roll bending process, the rolling force acting on the roller shafts is one of the most important parameters since, on the one hand, it determines the process settings including the pre-loading, and, on the other hand, its distribution and size may affect the integrity of both the bending system and the final product. In this study, the three-roller bending process was modeled using a two-dimensional plane-strain finite element method, and the rolling force was determined as a function of plate thickness, upper roller diameter, and yield strength for various API steel grades. Based on the numerical simulation results, a critical bending angle of 41° was identified and the rolling systems were divided into two categories, of less than or equal to, and greater than 41°, and an analytical model for predicting the maximum rolling force was developed for each category. To determine the optimal pre-tensioning force, two optimization formulations were proposed by minimizing the maximum equivalent stress and the absolute maximum displacement. The rolling forces predicted by the analytical models were found to be in good agreement with the numerical simulation results, with relative errors generally less than 10%. The predictive analytical models developed in this study capture well the complex deformation behavior that occurs during the roll bending process of steel plates, providing guidelines and predictions for industrial applications of this process.

A Comparative Analysis of Machine Learning Algorithms for Big Data Applications in Predictive Analytics

As the volume and complexity of data continue to grow, predictive analytics has emerged as a vital tool for extracting actionable insights from big data, driving decision-making across various domains such as healthcare, finance, and e-commerce. However, selecting an appropriate machine learning algorithm for predictive analytics applications is challenging due to differences in algorithmic performance, computational requirements, and scalability, especially in the context of big data. This paper provides a comprehensive comparative analysis of popular machine learning algorithms utilized in predictive analytics, specifically focusing on their effectiveness and feasibility in big data environments. The study categorizes algorithms based on learning types—supervised, unsupervised, and reinforcement learning—and evaluates their performance across multiple dimensions: prediction accuracy, computational efficiency, scalability, and suitability for real-time analytics. Through a detailed analysis of algorithms, including linear regression, decision trees, support vector machines, neural networks, and clustering techniques, we assess each method’s strengths and limitations in handling large datasets. Additionally, the study introduces a series of metrics, such as accuracy, F1-score, and training time, as benchmarks for assessing the algorithms’ predictive capabilities and computational viability. A hypothetical case study demonstrates the application of these algorithms on a sample big data set, providing insights into their real-world performance across different predictive analytics scenarios. Visual data representations, including comparative tables and performance graphs, offer a clearer perspective on the trade-offs among algorithm choices. The findings highlight that while certain algorithms like random forests and neural networks achieve higher accuracy in prediction tasks, they may also require substantial computational resources, posing limitations for real-time processing in big data applications. This paper concludes with recommendations for selecting machine learning algorithms based on specific predictive analytics objectives, data characteristics, and processing requirements. Furthermore, it discusses the challenges associated with implementing these algorithms in big data contexts and explores potential advancements, such as the integration of deep learning and the use of distributed computing, as promising directions for enhancing predictive analytics performance in future applications.