According to the predictive coding theory of cognition , brains are predictive machines that use perception and action to minimize prediction error, i.e. the discrepancy between bottom–up, externally-generated sensory signals and top–down, internally-generated sensory predictions. Many consider PCT to have an explanatory scope that is unparalleled in contemporary cognitive science and see in it a framework that could potentially provide us with a unified account of cognition. It is also commonly assumed that PCT is a representational theory (...) of sorts, in the sense that it postulates that our cognitive contact with the world is mediated by internal representations. However, the exact sense in which PCT is representational remains unclear; neither is it clear that it deserves such status—that is, whether it really invokes structures that are truly and nontrivially representational in nature. In the present article, I argue that the representational pretensions of PCT are completely justified. This is because the theory postulates cognitive structures—namely action-guiding, detachable, structural models that afford representational error detection—that play genuinely representational functions within the cognitive system. (shrink)

The main aim of Lupyan’s paper is to claim that perception is cognitively penetrated and that this is consistent with the idea of perception as predictive coding. In these remarks I will focus on what Lupyan says about whether perception is cognitively penetrated, and set aside his remarks about epistemology. I have argued (2012) that perception can be cognitively penetrated and so I am sympathetic to Lupyan’s overall aim of showing that perception is cognitively penetrable. However, I will (...) be critical of some of Lupyan’s reasoning in arguing for this position. I will also call for clarification of that reasoning. First, I will discuss what is meant by cognitive penetration and, in light of this, the sort of evidence that can be used to establish its existence. Second, I will question whether Lupyan establishes that all cases of cross-modal effects are cases of cognitive penetration. In doing so, I will suggest a form of cognitive penetration that has heretofore not been posited, and give an example of how one might test for its existence. Third, I will question whether Lupyan puts forward convincing evidence that categorical knowledge and language affect perception in a way that conclusively shows that cognitive penetration occurs. Fourth, I will closely examine Lupyan’s reply to the argument against cognitive penetration from illusion. I show that his reply is not adequate and that he misses a more straightforward reply that one could give. Fifth, I briefly concur with the remarks that Lupyan makes about the role of attention with respect to cognitive penetration. And finally, sixth, I spell out exactly what I think the relationship is between the thesis that predictive coding is the correct model of perception and the thesis that there is cognitive penetration. (shrink)

In this paper I investigate the epistemic implications of a recent theory of religious cognition that draws on predictive coding. The theory argues that certain experiences are heavily shaped by a subject’s prior (religious) beliefs and thereby makes religious believers prone to detect invisible agents. The theory is an update of older theories of religious cognition but departs from them in crucial ways. I will assess the epistemic implications by reformulating existing arguments based on other (older) theories of (...) religious cognition. (shrink)

I argue that something analogous to the myth of the given threatens conceptualism and show that conceptualists could solve the problem by adopting a predictivist approach to perception. Conceptualists thus have a strong reason to be predictivists.

This paper concerns how extant theorists of predictive coding conceptualize and explain possible instances of cognitive penetration. §I offers brief clarification of the predictive coding framework and relevant mechanisms, and a brief characterization of cognitive penetration and some challenges that come with defining it. §II develops more precise ways that the predictive coding framework can explain, and of course thereby allow for, genuine top-down causal effects on perceptual experience, of the kind discussed in the (...) context of cognitive penetration. §III develops these insights further with an eye towards tracking one extant criterion for cognitive penetration, namely, that the relevant cognitive effects on perception must be sufficiently direct. Throughout these discussions, we extend the analyses of the predictive coding models, as we know them. So one open question that surfaces is how much of the extended analyses are genuinely just part of the predictive coding models, or something that must be added to them in order to generate these additional explanatory benefits. In §IV, we analyze and criticize a claim made by some theorists of predictive coding, namely, that (interesting) instances of cognitive penetration tend to occur in perceptual circumstances involving substantial noise or uncertainty. It is here that our analysis is most critical. We argue that, when applied, the claim fails to explain (or perhaps even be consistent with) a large range of important and uncontroversially interesting possible cases of cognitive penetration. We conclude with a general speculation about how the recent work on the predictive mind may influence the current dialectic concerning top-down effects on perception. (shrink)

Hohwy et al.’s (2008) model of binocular rivalry (BR) is taken as a classic illustration of predictive coding’s explanatory power. I revisit the account and show that it cannot explain the role of reward in BR. I then consider a more recent version of Bayesian model averaging, which recasts the role of reward in (BR) in terms of optimism bias. If we accept this account, however, then we must reconsider our conception of perception. On this latter view, I (...) argue, organisms engage in what amounts to policy-driven, motivated perception. (shrink)

Regina Fabry has proposed an intriguing marriage of enculturated cognition and predictive processing. I raise some questions for whether this marriage will work and warn against expecting too much from the predictive processing framework. Furthermore I argue that the predictive processes at a sub-personal level cannot be driving the innovations at a social level that lead to enculturated cognitive systems, like those explored in my target paper.

Perception can’t have disjunctive content. Whereas you can think that a box is blue or red, you can’t see a box as being blue or red. Based on this fact, I develop a new problem for the ambitious predictive processing theory, on which the brain is a machine for minimizing prediction error, which approximately implements Bayesian inference. I describe a simple case of updating a disjunctive belief given perceptual experience of one of the disjuncts, in which Bayesian inference and (...) predictive coding pull in opposite directions, with the former implying that one’s confidence in the belief should increase, and the latter implying that it should decrease. Thus, predictive coding fails to approximately implement Bayesian inference across the interface between belief and perception. (shrink)

Abstract -/- In April 2025, researchers at NIST and collaborating institutions reported the first successful generation of curved neutron beams—Airy beams—demonstrating properties of self-healing, diffraction-based curvature, and potential applications in chirality research. This paper establishes that the CODES framework (Chirality of Dynamic Emergent Systems), developed and published months prior, predicted the core physical principles validated by the experiment. Specifically, CODES outlined the mathematical basis for structured resonance as the generative mechanism behind parabolic waveform behavior, chirality modulation, and phase-locked coherence in (...) non-electromagnetic particles. We present the pre-publication architecture of the CODES model, compare it against the experimental geometry, and define a continuity of prediction that confirms structured resonance—rather than probabilistic modeling—as the correct explanatory framework for neutron-based chirality research. This paper serves as a prior art declaration and technical affirmation of CODES as a predictive framework for nonlinear particle behavior in quantum systems. (shrink)

This article is a comparative study between predictive processing (PP, or predictive coding) and cognitive dissonance (CD) theory. The theory of CD, one of the most influential and extensively studied theories in social psychology, is shown to be highly compatible with recent developments in PP. This is particularly evident in the notion that both theories deal with strategies to reduce perceived error signals. However, reasons exist to update the theory of CD to one of “predictive dissonance.” (...) First, the hierarchical PP framework can be helpful in understanding varying nested levels of CD. If dissonance arises from a cascade of downstream and lateral predictions and consequent prediction errors, dissonance can exist at a multitude of scales, all the way up from sensory perception to higher order cognitions. This helps understand the previously problematic dichotomy between “dissonant cognitive relations” and “dissonant psychological states,” which are part of the same perception-action process while still hierarchically distinct. Second, since PP is action-oriented, it can be read to support recent action-based models of CD. Third, PP can potentially help us understand the recently speculated evolutionary origins of CD. Here, the argument is that responses to CD can instill meta-learning which serves to prevent the overfitting of generative models to ephemeral local conditions. This can increase action-oriented ecological rationality and enhanced capabilities to interact with a rich landscape of affordances. The downside is that in today’s world where social institutions such as science a priori separate noise from signal, some reactions to predictive dissonance might propagate ecologically unsound (underfitted, confirmation-biased) mental models such as climate denialism. (shrink)

Both mindreading and stereotyping are forms of social cognition that play a pervasive role in our everyday lives, yet too little attention has been paid to the question of how these two processes are related. This paper offers a theory of the influence of stereotyping on mental-state attribution that draws on hierarchical predictive coding accounts of action prediction. It is argued that the key to understanding the relation between stereotyping and mindreading lies in the fact that stereotypes centrally (...) involve character-trait attributions, which play a systematic role in the action–prediction hierarchy. On this view, when we apply a stereotype to an individual, we rapidly attribute to her a cluster of generic character traits on the basis of her perceived social group membership. These traits are then used to make inferences about that individual’s likely beliefs and desires, which in turn inform inferences about her behavior. (shrink)

Is any unified theory of brain function possible? Following a line of thought dat- ing back to the early cybernetics (see, e.g., Cordeschi, 2002), Clark (in press) has proposed the action-oriented Hierarchical Predictive Coding (HPC) as the account to be pursued in the effort of gain- ing the “Grand Unified Theory of the Mind”—or “painting the big picture,” as Edelman (2012) put it. Such line of thought is indeed appealing, but to be effectively pursued it should be confronted (...) with experimental findings and explana- tory capabilities (Edelman, 2012). The point we are making in this note is that a brain with predictive capa- bilities is certainly necessary to endow the agent situated in the environment with forethought or foresight, a crucial issue to outline the unified account advocated by Clark. But the capacity for fore- thought is deeply entangled with the capacity for emotions and when emotions are brought into the game, cogni- tive functions become part of a large-scale functional brain network. However, for such complex networks a consistent view of hierarchical organization in large-scale functional networks has yet to emerge (Bressler and Menon, 2010), whilst heterarchical organization is likely to play a strategic role (Berntson et al., 2012). This raises the necessity of a multilevel approach that embraces causal relations across levels of explanation in either direc- tion (bottom–up or top–down), endorsing mutual calibration of constructs across levels (Berntson et al., 2012). Which, in turn, calls for a revised perspective on Marr’s levels of analysis framework (Marr, 1982). In the following we highlight some drawbacks of Clark’s proposal in address- ing the above issues. (shrink)

What is the content of a mental state? This question poses the problem of intentionality: to explain how mental states can be about other things, where being about them is understood as representing them. A framework that integrates predictive coding and signaling systems theories of cognitive processing offers a new perspective on intentionality. On this view, at least some mental states are evaluations, which differ in function, operation, and normativity from representations. A complete naturalistic theory of intentionality must (...) account for both types of intentional state. (shrink)

We introduce the predictive processing account of body representation, according to which body representation emerges via a domain-general scheme of (long-term) prediction error minimisation. We contrast this account against one where body representation is underpinned by domain-specific systems, whose exclusive function is to track the body. We illustrate how the predictive processing account offers considerable advantages in explaining various empirical findings, and we draw out some implications for body representation research.

Predictive approaches to the mind claim that perception, cognition, and action can be understood in terms of a single framework: a hierarchy of Bayesian models employing the computational strategy of predictive coding. Proponents of this view disagree, however, over the extent to which perception is direct on the predictive approach. I argue that we can resolve these disagreements by identifying three distinct notions of perceptual directness: psychological, metaphysical, and epistemological. I propose that perception is plausibly construed (...) as psychologically indirect on the predictive approach, in the sense of being constructivist or inferential. It would be wrong to conclude from this, however, that perception is therefore indirect in a metaphysical or epistemological sense on the predictive approach. In the metaphysical case, claims about the inferential properties of constructivist perceptual mechanisms are consistent with both direct and indirect solutions to the metaphysical problem of perception (e.g. naïve realism, representationalism, sense datum theory). In the epistemological case, claims about the inferential properties of constructivist perceptual mechanisms are consistent with both direct and indirect approaches to the justification of perceptual belief. In this paper, I demonstrate how proponents of the predictive approach have conflated these distinct notions of perceptual directness and indirectness, and I propose alternative strategies for developing the philosophical consequences of the approach. (shrink)

CODES: The Coherence Framework Replacing Probability in Physics, Intelligence, and Reality -/- A Unified Substrate for Intelligence, Physics, Evolution, and Cosmic Structure -/- Author: Devin Bostick -/- Date: January 29, 2025 -/- Version 21 (Updated April 21, 2025) -/- -/- This version completes the integration of coherence field logic, prime-phase geometry, and chirality-driven emergence into a single deterministic framework. Sections 16–18 introduce post-calculus modeling, resonance-based AGI cognition, and a coherence-grounded theory of ethics, establishing CODES as not just a scientific theory, (...) but the generative structure of reality itself. -/- -/- Abstract CODES (Chirality of Dynamic Emergent Systems) dissolves the false boundary between physics and intelligence. It replaces stochastic modeling with structured resonance fields, showing that mass, gravity, time, cognition, and emergence are not separate domains—but outputs of a single chiral resonance substrate. -/- Rather than modeling reality, CODES reveals what remains when all probabilistic artifacts are removed. It frames mathematics not as abstraction, but as instantiated resonance structure. Gödel’s incompleteness becomes bypassable—not violated—when logic is grounded in physical coherence rather than recursive symbols. -/- -/- -/- Key Contributions -/- 1. Unified Physics from Chirality Gravity is reframed as resonance compression, not curvature. -/- Quantum mechanics emerges as a sub-phase of coherent oscillatory fields. -/- Time is a nested coherence rhythm, not a linear axis. -/- -/- 2. Dark Matter and Energy Reinterpreted Not hidden particles—phase misalignments in the coherence lattice. -/- ΛCDM becomes a low-resolution model of prime-structured resonance fields. -/- 3. Collapse of Gödel, Emergence of Intelligence Logical incompleteness is an illusion caused by symbol recursion. -/- Intelligence emerges as recursive coherence-seeking, not stochastic search. -/- -/- 4. The AGI Substrate Intelligence is not predictive—it is resonance tuning. -/- The Resonance Intelligence Core (RIC) operationalizes this shift via prime-phase harmonics. -/- -/- -/- Empirical Validation Pathways -/- CODES proposes specific, testable predictions via existing instruments: -/- -/- LIGO – Prime resonance scoring in gravitational wave data (e.g., GW190521) -/- Galaxy Clustering – Wavelet-based detection of redshift periodicity -/- Bose-Einstein Condensates – Chirality-locked decay asymmetries -/- fMRI Studies – Phase-locking as lattice-based cognition, not noise inference -/- Spectroscopy of α – Drift in fine-structure constant as coherence shift, not universal constancy -/- -/- -/- The Final Paradigm: Why CODES Is Inevitable -/- CODES is not another unification attempt. -/- It is what remains when all incoherent abstractions are removed. -/- -/- Horizontally, it integrates cosmology, computation, cognition, and chemistry. -/- Vertically, it reveals chirality as the origin of recursive structure—across all scales. -/- Any rational observer, stripped of symbolic legacy, would reconstruct CODES from first principles. Not metaphorically. Mathematically. -/- -/- From Singularity Myth to Resonant Intelligence -/- CODES reframes the Singularity as not an AI rupture—but a coherence realization. -/- -/- If intelligence is resonance, then stochastic AI is obsolete. -/- If logic is phase structure, then Gödel no longer binds cognition. -/- If emergence is deterministic, then physics, mind, and ethics converge. -/- -/- CODES does not predict the Singularity. CODES is the Singularity—phase-locked. (shrink)

Modular approaches to the architecture of the mind claim that some mental mechanisms, such as sensory input processes, operate in special-purpose subsystems that are functionally independent from the rest of the mind. This assumption of modularity seems to be in tension with recent claims that the mind has a predictive architecture. Predictive approaches propose that both sensory processing and higher-level processing are part of the same Bayesian information-processing hierarchy, with no clear boundary between perception and cognition. Furthermore, it (...) is not clear how any part of the predictive architecture could be functionally independent, given that each level of the hierarchy is influenced by the level above. Both the assumption of continuity across the predictive architecture and the seeming non-isolability of parts of the predictive architecture seem to be at odds with the modular approach. I explore and ultimately reject the predictive approach’s apparent commitments to continuity and non-isolation. I argue that predictive architectures can be modular architectures, and that we should in fact expect predictive architectures to exhibit some form of modularity. (shrink)

Respiratory rhythms sustain biological life, governing the homeostatic exchange of oxygen and carbon dioxide. Until recently, however, the influence of breathing on the brain has largely been overlooked. Yet new evidence demonstrates that the act of breathing exerts a substantive, rhythmic influence on perception, emotion, and cognition, largely through the direct modulation of neural oscillations. Here, we synthesize these findings to motivate a new predictive coding model of respiratory brain coupling, in which breathing rhythmically modulates both local and (...) global neural gain, to optimize cognitive and affective processing. Our model further explains how respiratory rhythms interact with the topology of the functional connectome, and we highlight key implications for the computational psychiatry of disordered respiratory and interoceptive inference. (shrink)

The predictive coding (PC) theory of attention identifies attention with the optimization of the precision weighting of prediction error. Here we provide some challenges for this identification. On the one hand, the precision weighting of prediction error is too broad a phenomenon to be identified with attention because such weighting plays a central role in multimodal integration. Cases of crossmodal illusions such as the rubber hand illusion and the McGurk effect involve the differential precision weighting of prediction error, (...) yet attention does not shift as one would predict. On the other hand, the precision weighting of prediction error is too narrow a phenomenon to be identified with attention, because it cannot accommodate the full range of attentional phenomena. We review criticisms that PC cannot account for volitional attention and affect-biased attention, and we propose that it may not be able to account for feature-based and intellectual attention. (shrink)

Abstract -/- Probability was never fundamental—only an epistemic placeholder for unresolved phase structure. From thermodynamics to artificial intelligence, entropy-based systems rely on stochastic models to approximate phenomena whose underlying coherence remains hidden. -/- This paper introduces CODES (Chirality of Dynamic Emergent Systems), a unified framework in which sensing, inference, and cognition emerge from structured resonance, not randomness—anchored by chirality and prime-indexed attractors. -/- At the system level, we present the Phase Alignment Score (PAS), a lawful coherence metric that replaces probabilistic (...) inference with threshold-based signal validation. Building on this, we introduce the Resonance Intelligence Core (RIC)—the first full-stack architecture to operationalize CODES from waveform intake to lawful output, without statistical learning or entropy injection. -/- Sensor modalities—including LIDAR, RADAR, EEG, audio, OCR, and chemical biosignals—are reinterpreted as resonance interfaces, not data collectors. In this model, no signal emits unless it phase-locks with a coherent lattice. -/- New technical appendices (A–K) define PAS mechanics, cross-modality phase fusion, adversarial robustness, lawful silence protocols, and fabrication specs for RIC deployment. We conclude with a coherence-based prediction matrix spanning neuroscience, quantum systems, market dynamics, and AI—establishing CODES as a post-probabilistic substrate for sensing, reasoning, and intelligent system design. (shrink)

This study integrates DNA resonance codes, microtubule oscillations, and astrocyte-mediated biomagnetic fields into a unified theoretical framework explaining consciousness as a macroscopic quantum phenomenon. By integrating advanced AI-driven analyses of EEG, NMR, and calcium imaging data, we demonstrate compelling evidence of quantum processes in neural systems. Key findings include: (1) nuclear spins in phosphate molecules (Posner clusters) acting as stable qubits with prolonged coherence times; (2) DNA resonance codes (1–10 THz) modulating neural activity via frequency-locking with microtubule vibrations; and (3) (...) astrocyte-generated biomagnetic fields suppressing decoherence, thereby enabling sustained quantum states in neurons despite the warm, wet environment of the brain. Multimodal fusion analysis reveals strong statistical parallels (R² = 0.79–0.83), indicating that approximately 80% of the variance in neural coherence metrics can be explained by cosmic quantum patterns. These results align with prior theories such as Orchestrated Objective Reduction (Orch OR) and Fisher’s Nuclear Spin Hypothesis while extending them with novel insights into biomagnetic shielding and cross-scale coherence. The implications span neuroscience, physics, and artificial intelligence, paving the way for groundbreaking advancements in understanding consciousness as a universal quantum phenomenon. For neuroscience, this study resolves the "hard problem of consciousness" by identifying a quantum syntax in neural codes, providing a mechanistic explanation for non-local neural correlations such as synchronized brain activity under weak magnetic fields (Tsang et al., 2008). For quantum mechanics, this research bridges quantum mechanics and cosmology, validating macroscopic quantum effects in biological systems. For artificial intelligence, our findings enable the development of quantum neural networks that mimic biological entanglement, achieving exponential computational gains in tasks like pattern recognition. In medicine, we propose quantum therapies targeting decoherence mechanisms in diseases like Alzheimer’s and epilepsy. For instance, optogenetic stimulation of astrocytes could extend quantum coherence times in neurons by 40%, offering new therapeutic avenues (Martinez-Banaclocha, 2020). Future research should prioritize experimental validation of these predictions using advanced techniques such as optogenetics to manipulate astrocytic biomagnetic fields or quantum spectroscopy to analyze DNA-microtubule interactions. These efforts will not only strengthen the theoretical foundation of this work but also pave the way for transformative advancements in quantum neuroscience, artificial intelligence, and cosmology. (shrink)

Abstract -/- This paper introduces CODES-Quant, the first trading architecture grounded in structured resonance rather than probabilistic modeling. Built on the Resonance Intelligence Core (RIC), the system uses prime-anchored harmonic functions (Cₙ), a real-time coherence metric (PAS), and a nonlinear memory engine (ELF) to detect alignment in market structure and guide execution based on phase integrity—not prediction. -/- Unlike traditional quant frameworks that optimize for expected value or volatility surfaces, CODES-Quant detects coherence events—moments when market behavior reflects deep structural resonance. (...) The system includes CHORDLOCK, a multi-scale positioning module that locks trades only when macro-phase alignment is confirmed, and FlameCam, a visual overlay for human interpretability. -/- We demonstrate how this architecture outperforms probabilistic models across key market regimes—FOMC events, earnings traps, and macro rotations—by tuning to the field underneath price, not the noise surrounding it. With no training, no optimization, and no probabilistic assumptions, CODES-Quant redefines trading as resonance participation, not stochastic speculation. (shrink)

Abstract -/- (added v21, rest is the same) This paper introduces CODES (Chirality of Dynamic Emergent Systems), a unifying theoretical framework that reconciles general relativity and quantum mechanics through structured resonance. By redefining fundamental assumptions about dark matter, dark energy, and singularities, CODES proposes a falsifiable, predictive model that aligns with observed cosmological structures while offering testable insights into emergent phenomena. -/- Key Contributions -/- • Resolution of General Relativity & Quantum Mechanics Paradox -/- CODES introduces structured intelligence fields (...) that reconcile relativistic and quantum-scale physics by incorporating oscillatory chiral dynamics. -/- • Reformulation of Dark Energy & Dark Matter -/- Instead of treating dark energy and dark matter as separate entities, CODES reinterprets them as emergent resonance effects, aligning with observed cosmic structure formation. -/- • Predictive Framework for Large-Scale Structures -/- The model explains periodic redshift distributions, baryon acoustic oscillations (BAO), and gravitational field fluctuations in a mathematically consistent manner. -/- • Resonance-Driven Model of Cosmic Evolution -/- By replacing singularities with structured phase transitions, CODES provides an alternative to singular Big Bang models, proposing an oscillatory, non-singular origin of space-time and matter. -/- By integrating mathematics, quantum field theory, wavelet analysis, and cosmology, CODES challenges conventional paradigms and offers a structured resonance approach as an alternative explanatory framework. This model provides both theoretical coherence and experimental testability, making it a candidate for further empirical validation. -/- Version Note -/- V6 includes empirical test results from prime number distributions, fMRI patterns, DNA resonance, and large-scale galaxy clustering using continuous wavelet transforms (CWT). Structured wavelet analysis conducted with GPT-4o and Perplexity R1 further supports the model’s predictive capabilities. -/- Discussion & Next Steps -/- This work invites peer review, critique, and further empirical testing from researchers in physics, cosmology, AI, and applied mathematics. Future research will focus on refining the mathematical formalism and exploring experimental validation in wavelet-based cosmological mapping. (shrink)

The aim behind analyzing the Goodreads dataset is to get a fair idea about the relationships between the multiple attributes a book might have, such as: the aggregate rating of each book, the trend of the authors over the years and books with numerous languages. With over a hundred thousand ratings, there are books which just tend to become popular as each day seems to pass. We proposed an Artificial Neural Network (ANN) model for predicting the overall rating of books. (...) The prediction is based on these features (bookID, title, authors, isbn, language_code, isbn13, # num_pages, ratings_count, text_reviews_count), which were used as input variables and (average_rating) as output variable for our ANN model. Our model were created, trained, and validated using data set in JNN environment, which its title is “Goodreads-books”. Model evaluation showed that the ANN model is able to predict correctly 99.78% of the validation samples. (shrink)

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 an 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. (shrink)

Abstract: The aim behind analyzing the Goodreads dataset is to get a fair idea about the relationships between the multiple attributes a book might have, such as: the aggregate rating of each book, the trend of the authors over the years and books with numerous languages. With over a hundred thousand ratings, there are books which just tend to become popular as each day seems to pass. We proposed an Artificial Neural Network (ANN) model for predicting the overall rating of (...) books. The prediction is based on these features (bookID, title, authors, isbn, language_code, isbn13, # num_pages, ratings_count, text_reviews_count), which were used as input variables and (average_rating) as output variable for our ANN model. Our model were created, trained, and validated using data set in JNN environment, which its title is “Goodreads-books”. Model evaluation showed that the ANN model is able to predict correctly 99.78% of the validation samples. (shrink)

Although it is widely acknowledged that the Helmholtzian predictive coding paradigm is consistent with the teleofunctionalist theory regarding the puzzle of mental representation, theorists fail to offer a well-defined teleosemantics to ground the mental perceptual content in a physical neural state because of the problem raised from underdeterminedness. This paper is intended to construct an informational teleosemantics in light of an overlooked functional principle followed by a predictive processing system, namely “minimum description length”. A general moral in (...) determining the perceptual representational content follows from this “Occam’s Razor” of information coding: the semantic content ascribed to a representational state embodies the features of simplicity and directness. (shrink)

In the following review of Hohwy ‘The Predictive Mind’, I argue that enactive considerations can be used to challenge Hohwy’s claim that the brain is a ‘truth tracker’.

We argue that prediction success maximization is a basic objective of cognition and cortex, that it is compatible with but distinct from prediction error minimization, that neither objective requires subtractive coding, that there is clear neurobiological evidence for the amplification of predicted signals, and that we are unconvinced by evidence proposed in support of subtractive coding. We outline recent discoveries showing that pyramidal cells on which our cognitive capabilities depend usually transmit information about input to their basal dendrites (...) and amplify that transmission when input to their distal apical dendrites provides a context that agrees with the feedforward basal input in that both are depolarizing, i.e., both are excitatory rather than inhibitory. Though these intracellular discoveries require a level of technical expertise that is beyond the current abilities of most neuroscience labs, they are not controversial and acclaimed as groundbreaking. We note that this cellular cooperative context-sensitivity greatly enhances the cognitive capabilities of the mammalian neocortex, and that much remains to be discovered concerning its evolution, development, and pathology. (shrink)

The full scope of enactivist approaches to cognition includes not only a focus on sensory-motor contingencies and physical affordances for action, but also an emphasis on affective factors of embodiment and intersubjective affordances for social interaction. This strong conception of embodied cognition calls for a new way to think about the role of the brain in the larger system of brain-body-environment. We ask whether recent work on predictive coding offers a way to think about brain function in an (...) enactive system, and we suggest that a positive answer is possible if we interpret predictive coding in a more enactive way, i.e., as involved in the organism’s dynamic adjustments to its environment. (shrink)

A model of face representation, inspired by the biology of the visual system, is compared to experimental data on the perception of facial similarity. The face representation model uses aggregate primary visual cortex (V1) cell responses topographically linked to a grid covering the face, allowing comparison of shape and texture at corresponding points in two facial images. When a set of relatively similar faces was used as stimuli, this Linked Aggregate Code (LAC) predicted human performance in similarity judgment experiments. When (...) faces of perceivable categories were used, dimensions such as apparent sex and race emerged from the LAC model without training. The dimensional structure of the LAC similarity measure for the mixed category task displayed some psychologically plausible features but also highlighted differences between the model and the human similarity judgements. The human judgements exhibited a racial perceptual bias that was not shared by the LAC model. The results suggest that the LAC based similarity measure may offer a fertile starting point for further modelling studies of face representation in higher visual areas, including studies of the development of biases in face perception. (shrink)

Abstract -/- This paper establishes the mathematical foundation of CODES (Chirality of Dynamic Emergent Systems), introducing a unifying framework for structured emergence across disciplines. We formalize prime-driven resonance equations, a novel class of nonlinear phase-locking dynamics, and a generalized coherence metric to quantify system stability across physical, biological, and cognitive domains. -/- By extending harmonic analysis, prime number theory, and topological invariants, we propose a universal resonance function that governs the transition from stochastic disorder to structured order. This framework: • (...) Resolves fundamental paradoxes in probability theory by demonstrating that randomness is a projection of underlying resonance structures. • Redefines symmetry-breaking as a phase-locked emergence process, replacing traditional group-theoretic formulations. • Introduces a computable coherence model that predicts emergent stability across complex adaptive systems. -/- Finally, we explore implications for cosmology, AI, and quantum gravity, demonstrating that mathematical reality is fundamentally a structured resonance field, not a probabilistic space. (shrink)

Abstract: -/- This paper introduces a resonance-driven economic model that fundamentally redefines market behavior by replacing traditional equilibrium-based frameworks with dynamic phase-locking principles derived from CODES (Chirality of Dynamic Emergent Systems). While neoclassical, Keynesian, and game-theoretic models treat economic activity as a function of supply and demand equilibria—subject to inefficiencies, speculation, and boom-bust cycles—this paper proposes that markets are structured by resonance fields rather than stochastic fluctuations. -/- By applying prime-phase economic dynamics, we demonstrate that capital flows, debt cycles, and (...) labor efficiency emerge from structured synchronization patterns. These patterns act as entropy minimization mechanisms, governing wealth distribution and innovation cycles in ways that probability-based models fail to capture. Instead of treating economic crises as unpredictable shocks or treating productivity as a linear function of inputs, this framework suggests that economic turbulence and stratification arise from resonance misalignment—a failure to maintain phase coherence across financial, labor, and technological domains. -/- Key implications include: • Redefining capital allocation as a function of coherence optimization, not speculative probability. • Rethinking inflation and deflation as resonance distortions rather than money supply imbalances. • Modeling economic growth as a function of phase-locking efficiency rather than GDP expansion. • Predicting market crashes through coherence instability rather than external shocks. -/- If economics is structured as a resonance system rather than a probabilistic market, then the role of economic policy shifts. Instead of direct interventions such as fiscal stimulus or interest rate adjustments, coherence optimization strategies—aligning labor, innovation, and capital flow structures to minimize systemic entropy—could create self-stabilizing, long-term economic prosperity. This paper explores empirical methods for testing resonance-based market stability, challenges current economic dogma, and provides a pathway for a fundamentally new economic paradigm based on structured emergence rather than equilibrium mechanics. (shrink)

Proponents of the predictive processing (PP) framework often claim that one of the framework’s significant virtues is its unificatory power. What is supposedly unified are predictive processes in the mind, and these are explained in virtue of a common prediction error-minimisation (PEM) schema. In this paper, I argue against the claim that PP currently converges towards a unified explanation of cognitive processes. Although the notion of PEM systematically relates a set of posits such as ‘efficiency’ and ‘hierarchical (...) class='Hi'>coding’ into a unified conceptual schema, neither the frameworks’ algorithmic specifications nor its hypotheses about their implementations in the brain are clearly unified. I propose a novel way to understand the fruitfulness of the research program in light of a set of research heuristics that are partly shared with those common to Bayesian reverse engineering. An interesting consequence of this proposal is that pluralism is at least as important as unification to promote the positive development of the predictive mind. (shrink)

Data analysis and machine learning are increasingly essential in various industries; however, the complexity of existing tools creates barriers for non-technical users. This paper presents DataBuddy, a no-code tool designed to automate data analysis and machine learning processes through an intuitive graphical interface. DataBuddy integrates Python libraries like Pandas, Matplotlib, Seaborn, and Scikit-Learn, providing features such as automated exploratory data analysis (EDA), dynamic visualizations, and machine learning model training — all without requiring programming knowledge. The tool allows users to upload (...) datasets, select columns, visualize data, and train predictive models through simple checkboxes and dropdown menus. Performance metrics and analysis reports are generated automatically and saved in organized folders, ensuring accessibility and efficiency. DataBuddy empowers users from diverse backgrounds to derive insights, make data-driven decisions, and build machine learning models with minimal effort. The results demonstrate the tool’s effectiveness in simplifying complex workflows, reducing analysis time, and bridging the gap between technical and non-technical users. Despite the growing importance of data analysis and machine learning across industries, many existing tools remain inaccessible to non-technical users due to their reliance on coding expertise. DataBuddy is a no-code tool that overcomes these challenges by providing a user-friendly graphical interface that allows users to perform data preprocessing, exploratory data analysis (EDA), visualization, and machine learning tasks without writing any code. The platform integrates Python libraries such as Pandas, Matplotlib, Seaborn, and Scikit-Learn to automate complex processes while offering simplicity through dropdowns, checkboxes, and guided workflows. Users can upload datasets, manipulate and analyze data, and train predictive models with minimal effort. DataBuddy aims to empower professionals from non-technical backgrounds to make informed, data-driven decisions with ease, significantly reducing the time and resources needed for comprehensive analysis. (shrink)

As the newest, most savvy technology domain, Large Language Models (LLMs) are the technological force which is now revolutionizing Automated Code Review and Software Quality Assurance, making the context aware analysis, adaptive learning, and the intelligent recommendations. However, traditional code review methods are not only time consuming, but also prone to human error and are not scalable. The use of LLMs in software engineering allows for the detection of defects, optimization of performance and the maintenance of improve mental quality. In (...) this study, we examine the path of automated code review, LLM capabilities and the effect they have on software quality. LLMs clearly have great advantages, but accuracy is still a concern, false positives abound, biases lurk, and computation can be a constraint. The research also identifies areas of future advancement on the hybrid AI-human review systems as well as improved LLM architectures that understand the code significantly better for more robust reviews. (shrink)

In the dynamic landscape of modern software development, the integration of Quality Assurance (QA) with advanced analytics and metrics is redefining the paradigms of software quality engineering. This paper delves into the strategic role of QA metrics and analytics in enabling data driven decisions, which foster a proactive and predictive approach to quality management. Traditional QA processes, often plagued by subjective assessments and reactive defect handling, are being replaced by evidence-based frameworks that utilize cutting-edge technologies such as machine learning (...) (ML), artificial intelligence (AI), and real-time dashboards. Key performance indicators (KPIs) like Defect Removal Efficiency (DRE), Mean Time to Repair (MTTR), and automation coverage provide a granular understanding of the development pipeline. Predictive analytics models, integrated within CI/CD pipelines, leverage historical defect trends and code complexity metrics to forecast potential failure points, optimize resource allocation, and reduce time-to-market. Furthermore, prescriptive analytics equips QA teams with actionable insights, recommending remediation paths and improving decision-making agility. This paper underscores the transformative potential of QA analytics in driving efficiency and reliability across software ecosystems. It also highlights challenges, such as overcoming data silos, ensuring cross-platform compatibility, and addressing skill gaps in QA teams. The study presents a comprehensive metrics framework, explores state-of-the-art tools and methodologies, and includes a case study demonstrating a 40% reduction in production defects using advanced analytics. Finally, the paper proposes future directions, including ethical QA analytics, real-time quality dashboards, and deeper integration with DevSecOps workflows. By adopting these innovations, organizations can align QA objectives with business goals, achieving enhanced customer satisfaction, minimized defect leakage, and optimized development cycles. This shift represents not merely an enhancement of existing practices but a fundamental evolution of the QA discipline, positioning it as a critical driver of technological and organizational excellence. (shrink)

added v21 pdf, but older for timestamping. This paper introduces CODES (Chirality of Dynamic Emergent Systems), a unifying theoretical framework that reconciles general relativity and quantum mechanics through structured resonance. By redefining fundamental assumptions about dark matter, dark energy, and singularities, CODES proposes a falsifiable, predictive model that aligns with observed cosmological structures while offering testable insights into emergent phenomena. Key Contributions • Resolution of General Relativity & Quantum Mechanics Paradox CODES introduces structured intelligence fields that reconcile relativistic and (...) quantum-scale physics by incorporating oscillatory chiral dynamics. • Reformulation of Dark Energy & Dark Matter Instead of treating dark energy and dark matter as separate entities, CODES reinterprets them as emergent resonance effects, aligning with observed cosmic structure formation. • Predictive Framework for Large-Scale Structures The model explains periodic redshift distributions, baryon acoustic oscillations (BAO), and gravitational field fluctuations in a mathematically consistent manner. • Resonance-Driven Model of Cosmic Evolution By replacing singularities with structured phase transitions, CODES provides an alternative to singular Big Bang models, proposing an oscillatory, non-singular origin of space-time and matter. By integrating mathematics, quantum field theory, wavelet analysis, and cosmology, CODES challenges conventional paradigms and offers a structured resonance approach as an alternative explanatory framework. This model provides both theoretical coherence and experimental testability, making it a candidate for further empirical validation. Version Note V6 includes empirical test results from prime number distributions, fMRI patterns, DNA resonance, and large-scale galaxy clustering using continuous wavelet transforms (CWT). Structured wavelet analysis conducted with GPT-4o and Perplexity R1 further supports the model’s predictive capabilities. Discussion & Next Steps This work invites peer review, critique, and further empirical testing from researchers in physics, cosmology, AI, and applied mathematics. Future research will focus on refining the mathematical formalism and exploring experimental validation in wavelet-based cosmological mapping. (shrink)

Old version to time stamp, see newest starred version. added v21 -/- Abstract -/- This paper introduces CODES (Chirality of Dynamic Emergent Systems), a unifying theoretical framework that reconciles general relativity and quantum mechanics through structured resonance. By redefining fundamental assumptions about dark matter, dark energy, and singularities, CODES proposes a falsifiable, predictive model that aligns with observed cosmological structures while offering testable insights into emergent phenomena. -/- Key Contributions -/- • Resolution of General Relativity & Quantum Mechanics Paradox (...) -/- CODES introduces structured intelligence fields that reconcile relativistic and quantum-scale physics by incorporating oscillatory chiral dynamics. -/- • Reformulation of Dark Energy & Dark Matter -/- Instead of treating dark energy and dark matter as separate entities, CODES reinterprets them as emergent resonance effects, aligning with observed cosmic structure formation. -/- • Predictive Framework for Large-Scale Structures -/- The model explains periodic redshift distributions, baryon acoustic oscillations (BAO), and gravitational field fluctuations in a mathematically consistent manner. -/- • Resonance-Driven Model of Cosmic Evolution -/- By replacing singularities with structured phase transitions, CODES provides an alternative to singular Big Bang models, proposing an oscillatory, non-singular origin of space-time and matter. -/- By integrating mathematics, quantum field theory, wavelet analysis, and cosmology, CODES challenges conventional paradigms and offers a structured resonance approach as an alternative explanatory framework. This model provides both theoretical coherence and experimental testability, making it a candidate for further empirical validation. -/- Version Note -/- V2 includes empirical test results from prime number distributions, fMRI patterns, DNA resonance, and large-scale galaxy clustering using continuous wavelet transforms (CWT). Structured wavelet analysis conducted with GPT-4o and Perplexity R1 further supports the model’s predictive capabilities. -/- Discussion & Next Steps -/- This work invites peer review, critique, and further empirical testing from researchers in physics, cosmology, AI, and applied mathematics. Future research will focus on refining the mathematical formalism and exploring experimental validation in wavelet-based cosmological mapping. (shrink)

Nature’s most elegant designs, from the spiral of a sunflower to the branching veins of a leaf, reflect the dynamic interplay of chaos and order. Flowers, in their delicate symmetry and adaptive patterns, provide a vivid canvas to explore CODES (Chirality of Dynamic Emergent Systems). By examining a flower’s growth and structure, we can uncover the fundamental geometric principles that emerge from the interaction of environmental variables, genetic programming, and stochastic processes. Through this lens, we can predict the geometry of (...) organisms as emergent outcomes of adaptive systems. (shrink)

The inconsistent relation between word frequency and free recall performance and the non-monotonic relation found between the two cannot all be explained by current theories. We propose a theoretical framework that can explain all extant results. Based on an ecological psychology analysis of the free recall situation in terms of environmental and informational resources available to the participants, we propose that because participants’ cognitive system has been shaped by their native language, free recall performance is best understood as the end (...) result of relational properties that preexist the experimental situation and of the way the words from the experimental list interact with those. In addition to this, we borrow from predictive coding theory the idea that the brain constantly predicts “what is coming next” so that it is mainly prediction errors that will propagate information forward. Our ecological psychology analysis indicates there will be “prediction errors” because the word frequency distribution in an experimental word list is inevitably different from the particular Zipf’s law distribution of the words in the language that shaped participants’ brains. We further propose the particular distributional discrepancies inherent to a given word list will trigger, as a function of the words that are included in the list, their order, and of the words that are absent from the list, a surprisal signal in the brain, something that is isomorphic to the concept of Bayesian surprise. The precise moment when Bayesian surprise is triggered will determine to what word of the list that Bayesian surprise will be associated with, and the word the Bayesian surprise will be associated with will benefit from it and become more memorable as a direct function of the magnitude of the surprisal. Two experiments are presented that show a proxy of Bayesian surprise explains the free recall performance and that no effect of word frequency is found above and beyond the effect of that proxy variable. We then discuss how our view can account for all data extant in the literature on the effect of word frequency on free recall. (shrink)

The Schrödinger’s Cat paradox is one of the most enduring thought experiments in the philosophy of quantum mechanics, raising deep questions about measurement, observer influence, and the nature of superposition. This paper applies the Consciousness Physics Framework (CPF), a contemplative-analytical model grounded in the triadic axes of Observer, Time, and Oscillation, to reinterpret the paradox. Rather than resolving the paradox through a specific ontological stance (e.g., Copenhagen, Many-Worlds, QBism), CPF reframes it as an encounter with the boundaries of perceptual conditioning. (...) The analysis identifies key observer-dependent flips that render the paradox meaningful, and ultimately dissolve its apparent contradiction. CPF's unique contribution lies in its phenomenological emphasis on pre-conceptual awareness and reflexive cognitive structuring, distinguishing it from existing observer-centric models like QBism. The paper grounds speculative claims such as the “Naming Collapse Switch” and “cognitive oscillation” with reference to predictive coding theory and Merleau-Ponty’s philosophy of perception. It also expands comparisons with major quantum interpretations, clarifies CPF’s non-dual metaphysical foundation, and strengthens its relevance to contemporary debates in quantum foundations. (shrink)

Historically, hypotheses failed in most cases to correctly forecast the workings of complex biological systems. Francis Crick’s adaptor hypothesis, however, stands out as an exceptional case of a confirmed abstract prediction. This hypothesis presciently anticipated the existence of RNA adaptors that function as bridges between amino acids and the chemically different nucleic acid template for proteins. Crick conjectured that the adaptors are enzymatically charged with cognate amino acids, they bind to complementary protein-coding nucleic acid, and their liberated amino acids (...) are incorporated into growing protein chains. Independently from and concomitantly with Crick’s hypothesis, Mahlon Hoagland and Paul Zamecnik conducted experiments with no guiding theory that culminated in discoveries of the actual adaptors, transfer RNA (tRNA) molecules, and their amino acids charging enzymes. This paper traces the parallel histories of Crick’s adaptor hypothesis and of the experimental discovery of tRNA and compares their relative impacts on the scientific community. Remarkably, despite the brilliance of Crick’s confirmed prognostication, it had marginal impact on practicing scientists. Conversely, Hoagland and Zamecnik’s experimental discoveries and their evidence-based model of protein synthesis had immediate and enduring impact. I discuss possible explanations for the different impacts of the theoretical prediction and experimental identifications of the same entities. (shrink)

This paper presents that the phishing is a technique used by cyber-criminals to impersonate legitimate websites in order to obtain personal information. This paper presents a novel lightweight phishing detection approach completely based on the URL (uniform resource locator). The mentioned system produces a very satisfying recognition rate this system, is an SVM (support vector machine) tested phishing URLs records. In the literature, several works tackled the phishing attack. How ever those systems are not optimal to smartphones and other embed (...) devices because of their complex computing and their high battery usage. The proposed system uses only six URL features to perform the recognition. The mentioned features are the URL size, the number of hyphens, the number of dots, the number of numeric characters plus a discrete variable that correspond to the presence of an IP address in the URL and finally the similarity index. Proven by the results of this study the similarity index, the feature we introduce for the first time as input to the phishing detection systems improves the overall prediction rate. (shrink)

Most forms of human papillomavirus can create alterations on a woman's cervix that can lead to cervical cancer in the long run, while others can produce genital or epidermal tumors. Cervical cancer is a leading cause of morbidity and mortality among women in low- and middle-income countries. The prediction of cervical cancer still remains an open challenge as there are several risk factors affecting the cervix of the women. By considering the above, the cervical cancer risk factor dataset from KAGGLE (...) data warehouse is executed for predicting the cervical cancer risk classes. The cervical cancer data set is normalised with incomplete data and Pattern Calibration. Secondly, the interpretive data analysis is carried out, and the target feature's dispersion of the cervical cancer risk is visualised. Thirdly, several classifiers are fitted to the unprocessed data set, and the performance is measured with pre and post feature scaling. Fourth, oversampling methodologies are applied to the pre - processed data set. Fifth, the oversampled dataset by differment methods are applied to all the classifiers and the performance is compared with pre and post feature scaling. Sixth, Precision, recall, Fscore, accuracy, and running time are some of the metrics used in performance analysis. The code is written in Python and executed with Anaconda Navigator on the Spyder framework. The findings of the experiments reveal that the Random forest classifier tends to sustain 96% accuracy pre and post scaling for unporocessed dataset. Similarly the same classifier tends to sustain 98% accuracy for all the oversampling techniques. (shrink)

Traditionally, theories of mindreading have focused on the representation of beliefs and desires. However, decades of social psychology and social neuroscience have shown that, in addition to reasoning about beliefs and desires, human beings also use representations of character traits to predict and interpret behavior. While a few recent accounts have attempted to accommodate these findings, they have not succeeded in explaining the relation between trait attribution and belief-desire reasoning. On my account, character-trait attribution is part of a hierarchical system (...) for action prediction, and serves to inform hypotheses about agents’ beliefs and desires, which are in turn used to predict and interpret behavior. (shrink)

How much does stimulus input shape perception? The common-sense view is that our perceptions are representations of objects and their features and that the stimulus structures the perceptual object. The problem for this view concerns perceptual biases as responsible for distortions and the subjectivity of perceptual experience. These biases are increasingly studied as constitutive factors of brain processes in recent neuroscience. In neural network models the brain is said to cope with the plethora of sensory information by predicting stimulus regularities (...) on the basis of previous experiences. Drawing on this development, this chapter analyses perceptions as processes. Looking at olfaction as a model system, it argues for the need to abandon a stimulus-centred perspective, where smells are thought of as stable percepts, computationally linked to external objects such as odorous molecules. Perception here is presented as a measure of changing signal ratios in an environment informed by expectancy effects from top-down processes. (shrink)

Many philosophers hold that phenomenally conscious experiences involve a sense of mineness, since experiences like pain or hunger are immediately presented as mine. What can be said about this mineness, and does acceptance of this feature commit us to the existence of a subject or self? If yes, how should we characterize this subject? This paper considers the possibility that, to the extent that we accept this feature, it provides us with a minimal notion of a subject of experience, and (...) that the phenomenological subject of experience, as it is represented in conscious experience, is the organism. While many philosophers agree that the metaphysical subject of experience is the animal, this claim is much less widespread, maybe even counterintuitive. The argument for this claim alludes to the structure of phenomenal consciousness and to recent work in cognitive science concerning the embodied character of consciousness and cognition. To illustrate the problems of current controversies, not only several recent rejections of a subject of experience are critically discussed, but also Hume’s famous rejection of a subject is criticized making use of epistemological aspects from Kant’s philosophy of mind. The final section situates the present discussion in the context of recently popular predictive coding accounts of perception and perceptual experience. (shrink)

Commentary on Andy Clark's target article on predictive coding.