Processing Fluency and Predictive Processing: How the Predictive Mind Becomes Aware of its Cognitive Limitations

Most theories of psychopathology have focused on etiology at a specific level (e.g., genetic, neurobiological, psychological, or environmental) to explain specific symptoms or disorders. A few biopsychosocial theories have provided explanations that attempt to integrate different levels and disorders to some extent. However, these theories lack a framework in which different levels of analysis are integrated and thus do not explain the mechanism by which etiological factors interact and perturb neurobiology which in turn leads to psychopathology. We propose that predictive processing (PP), which originated in theoretical neurobiology literature, may provide a conceptually parsimonious and biologically plausible framework to achieve such integration. In PP, the human brain can be cast as implementing a generative model whose task is to minimize the surprise of sensory evidence by inferring its causes and actively controlling future sensory signals via action. This account offers a unifying model of perception, action, and emotion implicated in psychopathology. Furthermore, we show that PP can explain how different factors or levels result in psychopathology via updates of the generative model (the depth of the PP framework). Finally, we demonstrate the transdiagnostic appeal of PP by showing how perturbations within this framework can explain a broad range of psychopathology (the breadth of the PP framework), with a focus on bridging well-established psychosocial theories of psychopathology and PP. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Autism and intolerance of uncertainty: an ill-fitting pair.

Bargain effectiveness in differentiated store environments: The role of store affect, processing fluency, and store familiarity

Through the use of an experiment, this research investigates the value of active user control of brand exposure in a racing-style video game, and the moderating effect of brand familiarity. It is found that, for familiar brands only, giving the game player active control to select a branded race car or racetrack results in better recall of the brand compared to low control (passive exposure on a billboard). The effect of active control via brand choice was found for both visual and verbal brand exposures, indicating that the effects are independent of visual placement proximity within the game. The findings are discussed with respect to the limited-capacity model of attention and processing fluency theory, and a conceptual diagram of the relationship between active control, attention, processing fluency, and brand recall is presented.

The paper presents research on morality and logic as social-communicative phenomena attempting to explicate them basing on the Free Energy Principle (FEP), Predictive Pro­cessing (PP), and Active Inference (ACI). These frameworks, rooted in statistical ther­modynamics, computational neuroscience and theoretical biology, provide a thought-pro­voking viewpoint for understanding how humans and other living or artificial systems may navigate complex social, intellectual and moral environments by minimizing uncer­tainty and optimizing their models of the world. According to the view presented hereto, a society’s generative model is a distributed, emergent structure enacted through the pre­dictive activities of its agents’ brains. This model maintains non-equilibrium stability of the social whole by self-evidencing – agents are urged to act as to confirm the society’s existence as a coherent entity (e.g., through rituals, laws, or moral codes). This distri­buted generative model (DGM) induces some counter-utilitarian beliefs (e.g., loyalty over personal gain), which are embedded as priors to bias agents toward bond-main­taining behaviors, ensuring the system’s integrity over time. This process is both adaptive (evolutionary) and normative (cultural), aligning with FEP’s claim that systems persist by minimizing free energy across scales.

The informed sampler: A discriminative approach to Bayesian inference in generative computer vision models

The paper proposes a structural Bayesian network learning in a biological decision-theoretic intelligent agent model to solve a herding problem. The proposed structural learning methods show that an agent can update its world model by changing the structure of its Bayesian network with the data gathered by experience. The structural learning of the Bayesian network is accomplished by implementing a score based greedy search algorithm. The search algorithm is designed heuristically and exhaustively. A complexity analysis for the search algorithms is performed. Intelligent agent software, IntelliAgent, is written to simulate the herding problem with one sheep and one dog.

Connectome generative models, otherwise known as generative network models, provide insight into the wiring principles underpinning brain network organization. While these models can approximate numerous statistical properties of empirical networks, they typically fail to explicitly characterize an important contributor to brain organization-axonal growth. Emulating the chemoaffinity-guided axonal growth, we provide a novel generative model in which axons dynamically steer the direction of propagation based on distance-dependent chemoattractive forces acting on their growth cones. This simple dynamic growth mechanism, despite being solely geometry-dependent, is shown to generate axonal fiber bundles with brain-like geometry and features of complex network architecture consistent with the human brain, including lognormally distributed connectivity weights, scale-free nodal degrees, small-worldness, and modularity. We demonstrate that our model parameters can be fitted to individual connectomes, enabling connectome dimensionality reduction and comparison of parameters between groups. Our work offers an opportunity to bridge studies of axon guidance and connectome development, providing new avenues for understanding neural development from a computational perspective.

Event Abstract Back to Event Structure learning in human sequential decision-making Humans daily perform sequential decision-making under uncertainty to choose products, services, careers, and jobs. Studies of sequential decision-making in humans frequently find suboptimal performance relative to an ideal actor that knows the task that generates reward in the environment. This has led to conclusions about how we explore new courses of actions and exploit what we have learned. We argue, however, that humans have uncertainty about both the task and environmental structure, and that task and structure learning can potentially explain much better how people schedule actions, including behaviors previously deemed sub-optimal. We illustrate the task structure learning problem with an important special case that controls optimal exploration/exploitation. In particular, we formulate the structure learning problem using mixtures of two reward models'two-arm and one-arm bandit models'and solve the optimal action selection using Bayesian Reinforcement Learning. These two reward models represent extremes in both the exploration'exploitation tradeoff and computational difficulty'one model needs to balance exploration'exploitation and use long future horizons to compute actions, while the other needs no look-ahead and the action selection is greedy. In simulations, we show that optimal learning with uncertainty about the task structure can produce a range of qualitative behaviors deemed suboptimal in previous studies on sequential binary choice. In our experiments, each of 16 subjects (8 females) ran on 32 bandit tasks, a block of 16 in a two-arm bandits and a block of 16 one-arm bandits. Within blocks, the presentation order was randomized, and the order of the one-arm bandits was randomized across subjects. On average, each task required 48 choices. For two-arm bandits, the subjects made 1194 choices across the 16 tasks, and 925 for the one-arm bandits. Our results show that humans rapidly learn and exploit new reward structure'human behavior tracks the behavior of our structure learning model but is not explained by models that assume the task is known. Other kinds of reward structure learning may account for a broad variety of human decision-making performance. In particular, allowing dependence between the probability of reward at a site and previous actions can produce large changes in decision-making behavior. For instance, in a "foraging" model where reward is collected from a site and probabilistically replenished, optimal strategies will produce choice sequences that alternate between reward sites. Thus, uncertainty about the independence of reward on previous actions can produce a continuum of behavior, from maximization to probability matching. Instead of explaining behavior in terms of the idiosyncrasies of a learning rule, structure learning constitutes a fully rational response to uncertainty about the causal structure of rewards in the environment. Our hope is that, by expanding the range of normative hypotheses for human decision-making, we can begin to develop more principled accounts of human sequential decision-making behavior. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). Structure learning in human sequential decision-making. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.238 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 03 Feb 2009; Published Online: 03 Feb 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

How are new Bayesian hypotheses generated within the framework of predictive processing? This explanatory framework purports to provide a unified, systematic explanation of cognition by appealing to Bayes rule and hierarchical Bayesian machinery alone. Given that the generation of new hypotheses is fundamental to Bayesian inference, the predictive processing framework faces an important challenge in this regard. By examining several cognitive-level and neurobiological architecture-inspired models of hypothesis generation, we argue that there is an essential difference between the two types of models. Cognitive-level models do not specify how they can be implemented in brains and include structures and assumptions that are external to the predictive processing framework. By contrast, neurobiological architecture-inspired models, which aim to better resemble brain processes, fail to explain important capacities of cognition, such as categorization and few-shot learning. The "scaling-up" challenge for proponents of predictive processing is to explain the relationship between these two types of models using only the theoretical and conceptual machinery of Bayesian inference.

The brain represents the state of the world around us based upon a stream of ambiguous sensory data. This requires the integration of different sources of information, including information received from different sense modalities, and information drawn from past experience. Bayesian theories of perception provide an approach to characterising how systematic differences in the probabilistic integration of information in the brain may underlie the differences that occur between individuals in their perceptual experience. The present thesis explores how perceptual, motor and social aspects of autism spectrum disorder (ASD) may emerge from variation in the neurocognitive processes described by Bayesian theories. A focus is on the predictive processing model of brain function, which links Bayesian theories of perception to neural mechanisms. The thesis includes empirical studies that examine perceptual, sensorimotor and cognitive aspects of ASD. ASD and nonclinical autistic traits are first examined in the context of body perception; concerning, specifically, how the brain represents the state of the limbs based on visual, tactile and proprioceptive information, and how this sensory information is integrated with prior expectations regarding the body. These processes are investigated using a multisensory perceptual illusion: the rubber hand illusion. ASD is associated with a largely typical perceptual experience of this illusion, indicating intact multisensory integration in the context of body representation. A reduced influence of the illusion on motor function, however, supports a difference in ASD in the integration of expectations for limb position (influenced by the illusion) with conflicting sensory (proprioceptive) signals. The integration of incoming information with existing expectations about the environment is also examined in the context of statistical learning. A computerised task is developed to assess the integration of iterative feedback with prior information as participants predict the location of a noisy set of visual markers. The hypothesis tested is that features of ASD are associated with a persistently higher weighting of incoming information in driving inference, at the expense of expectations developed from recent experience; in this more cognitive context, these data provide evidence against this hypothesis, suggesting that a more context-dependent atypicality in information processing in ASD is more likely than that initially hypothesised. The thesis also develops theoretical and philosophically-relevant treatments of how the symptoms of ASD may emerge from differences in brain function characterised within a Bayesian framework. This includes drawing on recent theoretical developments regarding the role of volatility processing and action in maintaining optimal inference on the environment. In addition, models of Bayesian inference in the brain can be extended to the social domain. For instance, the process of representing the mental states of other people (i.e., theory of mind) can be cast in terms of implicit inference on the external causes of sensory signals. In light of these expanded models of predictive processing, the differences in information processing hypothesised to occur in ASD have implications not just for perception, but also for motor behaviours, social cognition and social interaction. The overall view that emerges is that diverse aspects of ASD may be captured in terms of how incoming sensory signals are integrated, in a probabilistic manner, with the brain’s hierarchical and multimodal model of its environment. A promising direction for this field is in developing this idea in the context of action (i.e., active inference) and more recent models of how the brain estimates the optimal weighting of sensory information. This area of research has the potential to provide a nuanced perspective on the neurocognitive basis of ASD and the relationship between sensory mechanisms and autistic behaviours.

Children Devise and Selectively Use Tools to Offload Cognition

Image steganography is an essential technique for concealing information by embedding secret information within images to make it undetectable. In recent years, with the rapid development and popularization of text-to-image generation models, many generated images have been disseminated through the Internet, thus making generated images ideal covers for steganography. Given that the distribution of generated images is more easily modeled than natural images, steganographic methods based on generated images exhibit higher security. Nevertheless, these methods typically require white-box access to the generative model, while contemporary popular generative models are black-box models. We observed that slight modifications in the input parameters of black-box image generative models result in subtle differences between generated images, offering new camouflage advantages for image steganography. Based on this observation, we propose an image steganography method based on the fluctuation of generative models. This approach leverages the fluctuation of image generative models, disguising stego images to appear as if they were generated by the parameter fluctuations of the generative model. Experimental results show that our proposed method outperforms baseline methods when facing steganalysis attacks, significantly enhancing steganographic security without compromising image quality.

Climate-responsive urban planning through generative models: Sensitivity analysis of urban planning and design parameters for urban heat island in Singapore's residential settlements

Background/Objectives: The integration of Artificial Intelligence (AI) and Machine Learning (ML) in pharmaceutical research and development is transforming the industry by improving efficiency and effectiveness across drug discovery, development, and healthcare delivery. This review explores the diverse applications of AI and ML, emphasizing their role in predictive modeling, drug repurposing, lead optimization, and clinical trials. Additionally, the review highlights AI’s contributions to regulatory compliance, pharmacovigilance, and personalized medicine while addressing ethical and regulatory considerations. Methods: A comprehensive literature review was conducted to assess the impact of AI and ML in various pharmaceutical domains. Research articles, case studies, and industry reports were analyzed to examine AI-driven advancements in predictive modeling, computational chemistry, clinical trials, drug safety, and supply chain management. Results: AI and ML have demonstrated significant advancements in pharmaceutical research, including improved target identification, accelerated drug discovery through generative models, and enhanced structure-based drug design via molecular docking and QSAR modeling. In clinical trials, AI streamlines patient recruitment, predicts trial outcomes, and enables real-time monitoring. AI-driven predictive maintenance, process optimization, and inventory management have enhanced efficiency in pharmaceutical manufacturing and supply chains. Furthermore, AI has revolutionized personalized medicine by enabling precise treatment strategies through genomic data analysis, biomarker discovery, and AI-driven diagnostics. Conclusions: AI and ML are reshaping pharmaceutical research, offering innovative solutions across drug discovery, regulatory compliance, and patient care. The integration of AI enhances treatment outcomes and operational efficiencies while raising ethical and regulatory challenges that require transparent, accountable applications. Future advancements in AI will rely on collaborative efforts to ensure its responsible implementation, ultimately driving the continued transformation of the pharmaceutical sector.