One justification for preregistering research hypotheses, methods, and analyses is that it improves the transparent evaluation of the severity of hypothesis tests. In this article, I consider two cases in which preregistration does not improve this evaluation. First, I argue that, although preregistration may facilitate the transparent evaluation of severity in Mayo’s error statistical philosophy of science, it does not facilitate this evaluation in Popper’s theory-centric approach. To illustrate, I show that associated concerns about Type I error rate (...) inflation are only relevant in the error statistical approach and not in a theory-centric approach. Second, I argue that a test procedure that is preregistered but that also allows deviations in its implementation (i.e., “a plan, not a prison”) does not provide a more transparent evaluation of Mayoian severity than a non-preregistered procedure. In particular, I argue that sample-based validity-enhancing deviations cause an unknown inflation of the test procedure’s Type I error rate and, consequently, an unknown reduction in its capability to license inferences severely. I conclude that preregistration does not improve the transparent evaluation of severity (a) in Popper’s philosophy of science or (b) in Mayo’s approach when deviations are allowed. (shrink)

Gelman and Loken (2013, 2014) proposed that when researchers base their statistical analyses on the idiosyncratic characteristics of a specific sample (e.g., a nonlinear transformation of a variable because it is skewed), they open up alternative analysis paths in potential replications of their study that are based on different samples (i.e., no transformation of the variable because it is not skewed). These alternative analysis paths count as additional (multiple) tests and, consequently, they increase the probability of making a Type I (...) error during hypothesis testing. The present article considers this forking paths problem and evaluates four potential solutions that might be used in psychology and other fields: (a) adjusting the prespecified alpha level, (b) preregistration, (c) sensitivity analyses, and (d) abandoning the Neyman-Pearson approach. It is concluded that although preregistration and sensitivity analyses are effective solutions to p-hacking, they are ineffective against result-neutral forking paths, such as those caused by transforming data. Conversely, although adjusting the alpha level cannot address p-hacking, it can be effective for result-neutral forking paths. Finally, abandoning the Neyman-Pearson approach represents a further solution to the forking paths problem. (shrink)

Preregistration has been proposed as a useful method for making a publicly verifiable distinction between confirmatory hypothesis tests, which involve planned tests of ante hoc hypotheses, and exploratory hypothesis tests, which involve unplanned tests of post hoc hypotheses. This distinction is thought to be important because it has been proposed that confirmatory hypothesis tests provide more compelling results (less uncertain, less tentative, less open to bias) than exploratory hypothesis tests. In this article, we challenge this proposition and argue that (...) there are several advantages of exploratory hypothesis tests that can make their results more compelling than those of confirmatory hypothesis tests. We also consider some potential disadvantages of exploratory hypothesis tests and conclude that their advantages can outweigh the disadvantages. We conclude that exploratory hypothesis tests avoid researcher commitment and researcher prophecy biases, reduce the probability of data fraud, are more appropriate in the context of unplanned deviations, facilitate inference to the best explanation, and allow peer reviewers to make additional contributions at the data analysis stage. In contrast, confirmatory hypothesis tests may lead to an inappropriate level of confidence in research conclusions, less appropriate analyses in the context of unplanned deviations, and greater bias and errors in theoretical inferences. (shrink)

-/- For all that digital methods—including network visualization, text analysis, and others—have begun to show extensive promise in philosophical contexts, a tension remains between two uses of those tools that have often been taken to be incompatible, or at least to engage in a kind of trade-off: the discovery of new hypotheses and the testing of already-formulated positions. This paper presents this basic distinction, then explores ways to resolve this tension with the help of two interdisciplinary case studies, taken from (...) preregistration in contemporary science and the debate over whig history in the history of science. These case studies, the paper argues, refocus our attention from a mutually exclusive testing/discovery binary to the relationship between our background data or philosophical views and the empirical generalizations that we might draw from the data. Finally, it develops a set of three challenges for philosophers and corresponding avenues for future work that will, it is hoped, allow us to better justify our use of these methods. (shrink)

Psychology endeavors to develop theories of human capacities and behaviors on the basis of a variety of methodologies and dependent measures. We argue that one of the most divisive factors in psychological science is whether researchers choose to use computational modeling of theories (over and above data) during the scientific-inference process. Modeling is undervalued yet holds promise for advancing psychological science. The inherent demands of computational modeling guide us toward better science by forcing us to conceptually analyze, specify, and formalize (...) intuitions that otherwise remain unexamined—what we dub open theory. Constraining our inference process through modeling enables us to build explanatory and predictive theories. Here, we present scientific inference in psychology as a path function in which each step shapes the next. Computational modeling can constrain these steps, thus advancing scientific inference over and above the stewardship of experimental practice (e.g., preregistration). If psychology continues to eschew computational modeling, we predict more replicability crises and persistent failure at coherent theory building. This is because without formal modeling we lack open and transparent theorizing. We also explain how to formalize, specify, and implement a computational model, emphasizing that the advantages of modeling can be achieved by anyone with benefit to all. (shrink)

Introduced is a single, pre-registered coherence ratio τ = S/(D+ε) and we test its falsifiable predictions across language models and EEG, running all eligible public datasets and benchmarks under a frozen configuration. We specify hard failure modes (ICC, ∆AUC/∆R2, equivalence via TOST) and include an exploratory sleep add-on (REM “corridor”) with separate inclusion criteria. Importantly, null and equivalence results are reported as central outcomes, not secondary omissions, underscoring the falsifiability of the framework. This paper reports the full protocol, preregistration, (...) results, null distributions, and implications for falsifiability in cognitive and symbolic systems. Stage-1 denotes the first preregistered baseline test of ENT, not universality. -/- Emergent Necessity Theory (ENT) is a structural framework that proposes coherence in complex systems emerges as a necessity once containment forces (synchrony) and dispersive forces (entropy) reach a measurable balance. Its central object is the coherence ratio τ = S/(D+ε), a domain-agnostic, falsifiable metric. (shrink)

MST v4 treats “simulation” as a testable architecture claim. We preregistered endpoints, (i) periodicity scans (0.01–200 Hz), (ii) lossless compression-delta (ON vs matched OFF), (iii) mutual information (with spacelike variant), and required compiler-randomization survival and cross-site sign-stability for any effect. Two lanes were executed: photonic W-state optics (1550 nm, SNSPDs) and a v4-W virtual audit (logical-qubit simulations) to validate analysis against false positives. -/- Results: after full max-T correction and compiler rotation, no MI windows survived in optics; the audit layer (...) returned clean nulls, and injected artefacts were eliminated by the guards. Under explicit assumptions, findings tighten bounds on detectable renderer-like artefacts without adjudicating the metaphysical Simulation Argument. -/- Data, code, and signed manifests are open; replication is invited (nulls refine bounds; positives must survive our guards). © 2025 Augusto Bartolomeu. All rights reserved. (shrink)

This article advances an operative form of phenomenological realism grounded in the axiom that “what has not shown itself is not yet.” Truth is treated not as a static property but as an ontological event of donation, articulated through a hinge between Realism of the Ontological Event (ROE) and Epistemic Inferentialism (EI), which integrates the Sellarsian critique of the “Myth of the Given” without collapsing into coherentism. Truth persists through modalities of trace: the reinstantiable R-trace of replicable laboratory phenomena and (...) the archival A-trace of singular events, both possessing equal ontological dignity. Applying this framework to quantum physics, I argue for ontological neutrality until donation: interpretations gain commitment only when they generate new forms of appearing. To make this operative, I propose an L1–L4 hierarchy with thresholds involving replication, inter-instrumental convergence, counterfactual controls, statistical preregistration, open traceability, and temporal durability. This approach reframes truth across philosophical and scientific practice. (shrink)

This encyclopedia entry examines the epistemology of replicability, arguing that replicability, the ability of scientific results to be obtained again under similar conditions, is a central marker of the reliability, objectivity, and trustworthiness of scientific results. The entry clarifies the concept by distinguishing different forms of replication (direct, partial, and conceptual) and situates the recent Replicability Crisis within a broader epistemological framework. It identifies key factors undermining replicability, including low statistical power, misuse of methodological flexibility, failures of representativeness, and weak (...) or poorly specified theories. While acknowledging debates about whether replicability should function as a universal epistemic standard across disciplines, the entry defends it as an indispensable goal of empirical science. Finally, it surveys proposed proposals aimed at promoting replicability, including preregistration, use of well-powered designs and representative samples, and theory formalization, and outlines strategies for selecting and conducting replication studies, concluding that replicability remains essential for scientific self-correction and the production of reliable results. (shrink)

Recent work by Kahan et al. (2017) on the psychology of motivated numeracy in the context of intracultural disagreement suggests that people are less likely to employ their capabilities when the evidence runs contrary to their political ideology. This research has so far been carried out primarily in the USA regarding the liberal–conservative divide over gun control regulation. In this paper, we present the results of a modified replication that included an active reasoning intervention with Western European participants regarding both (...) the hierarchy–egalitarianism and individualism–collectivism divides over immigration policy (n = 746; considerably less than the preregistration sample size). We reproduce the motivated numeracy effect, though we do not find evidence of increased polarization of high-numeracy participants. (shrink)

This paper defines an empirical standard for testing the Structured Resonance Dynamics (SRD) corpus. Six gates map SRD invariants to measurable observables under deterministic replay with fixed-point Q32 numerics. Proof bundles (header.json, steps.jsonl, graph.json) are hashed (SHA256) and Ed25519-signed to ensure bit-identical reproduction. The protocol provides preregistration schemas, regression targets (R² ≥ 0.95), confidence-interval routines, and an explicit kill-switch table that suspends claims upon replicated failure. Gates cover drift-volume scaling, an E_eff-based floor postulate, chirality collapse, fine-structure stability, cosmological lensing (...) parity, and computational parity via RIC replay. The goal is to move SRD from theoretical closure to externally auditable empirical law via independent laboratory and observatory execution. (shrink)

In light of the recent credibility crisis in psychology, this paper argues for a greater emphasis on theorizing in scientific research. Although reliable experimental evidence, preregistration, methodological rigor, and new computational frameworks for modeling are important, scientific progress also relies on properly functioning theories. However, the current understanding of the role of theorizing in psychology is lacking, which may lead to future crises. Theories should not be viewed as mere speculations or simple inductive generalizations. To address this issue, the (...) author introduces a framework called “cognitive metascience,” which studies the processes and results of evaluating scientific practice. This study should proceed both qualitatively, as in traditional science and technology studies and cognitive science, and quantitatively, by analyzing scientific discourse using language technology. By analyzing theories as cognitive artifacts that support cognitive tasks, this paper aims to shed more light on their nature. This perspective reveals that multiple distinct theories serve entirely different roles, and studying these roles, along with their epistemic vices and virtues, can provide insight into how theorizing should proceed. The author urges a change in research culture to appreciate the variety of distinct theories and to systematically advance scientific progress. . (shrink)

It might seem obvious that the scientific process should not be biased. We strive for reliable inference, and systematically skewing the results of inquiry apparently conflicts with this. Publication bias—which involves only publishing certain types of results—seems particularly troubling and has been blamed for the replication crisis. While we ultimately agree, there are considerable nuances to take into account. Using a Bayesian model of scientific reasoning we show that a scientist who is aware of publication bias can (theoretically) interpret the (...) published literature so as to avoid acquiring biased beliefs. Moreover, in some highly specific circumstances she might prefer not to bother with policies designed to mitigate or reduce the presence of publication bias—it would impose a cost in time or effort that she would not see any benefit in paying. However, we also argue that science as a social endeavour is made worse off by publication bias. This is because the social benefits of science are largely secured via go-between agents, various non-experts who nonetheless need to make use of or convey the results of scientific inquiry if its fruits are to be enjoyed by society at large. These are unlikely to be well-informed enough to account for publication bias appropriately. As such, we conclude, the costs of having to implement policies like mandatory pre-registration are worth imposing on scientists, even if they would perhaps not view these costs as worth paying for their own sake. The benefits are reaped by the go-between agents, and we argue that their perspective is quite properly favoured when deciding how to govern scientific institutions. (shrink)

This work introduces the Consciousness Curvature Framework (CCFy), a simulation-based theoretical model that explores curvature-regulated dynamics as an alternative to noise-driven variability in abstract cognitive systems. The framework conceptualizes conscious regulation as a dynamic state space in which rigidity, flexibility, and transitions correspond to geometric properties. The study was preregistered prior to data generation, specifying the full simulation design, parameter structure, and qualitative evaluation criteria. CCFy is presented as an open, interdisciplinary framework bridging philosophy of mind, cognitive dynamics, and computational (...) modeling. (shrink)