Search Results (276)

The nature of the psychedelic experience, and the distinctive phenomenological features it generates, leaves it susceptible to an unresolved epistemological tension between first-person phenomenological reports and claims about the fundamental nature of consciousness and reality. This tension is examined through the concept of ontological tourism: the practice of treating phenomenological data as sufficient grounds for ontological conclusions without adequate engagement with alternative mechanistic explanations or the philosophical requirements that such inferences demand. This practice, absent adequate epistemic scaffolding, poses a genuine threat to scientific integrity and clinical ethics. At the same time, reflexively dismissing phenomenological data as mere pharmacological artifacts represents an equally impoverished position. The tourist analogy cuts both ways: the tourist may be an unreliable cartographer, but the tourist has been somewhere. An alternative stance is proposed, one that takes subjective experience seriously as a form of evidence without overclaiming its metaphysical implications or dismissing its epistemic weight, along with a framework for achieving it. Ontological tourism risks bypassing the explanatory work that makes psychedelic phenomenology valuable to consciousness science. Full article

The mathematization of science is undergoing a structural transformation driven by the rise of computation and data-intensive methods. While classical mathematization relied largely on explicitly formulated laws and formal structures, contemporary scientific practice increasingly encounters mathematical objects that arise from dynamical and algorithmic processes. This paper introduces the notion of computationally emergent structures to describe objects, representations, or effective functional spaces that are generated and stabilized through the interaction of parameterized models, optimization dynamics, and data. We propose a minimal formal schema in which such structures can be understood as stabilized outcomes of learning dynamics. In overparameterized regimes, this schema clarifies how optimization procedures may select particular solutions through implicit biases or variational tendencies that are not specified a priori. framework brings together implicit regularization, kernel regimes, and stability phenomena in modern learning systems, while distinguishing between established formal results and broader epistemological interpretation. It suggests that contemporary learning systems provide a privileged setting in which geometry, dynamics, and data jointly contribute to the production of effective mathematical structure. This perspective identifies a shift from representation to dynamical emergence and extends the study of mathematization toward an analysis of structure formation grounded in computation. Full article

We present a unifying thesis which posits that the biological resolution of uncertainty is a fundamental adaptation to entropy’s negative impact on the highly ordered molecular structures required to maintain the living state. These molecular biological adaptations are highly conserved and play a critical role in the survival of the earliest multicellular organisms and the vertebrates thereafter. The imperative to reduce cognitive uncertainty is effected through the dopaminergic Medial Forebrain Bundle (MFB) Reward Prediction Error (RPE) mechanism, or its homologous equivalents, to compute a biological valuation of information. This hypothesis is supported by the central role of the MFB seeking system as the neural substrate of exploratory behavior that leads to the reduction of uncertainty when information is apprehended and cognitively assimilated. We define the human experience of intellectual beauty as the subjective emotional reward that is activated by the MFB seeking system. Accordingly, humans experience intellectual beauty when a high-entropy state of confusion is suddenly resolved into a low-entropy state of insight. In humans, the neuroanatomical substrates of inductive reasoning, inquiry, and the intellectual beauty to which they lead are present at birth. What develops postnatally is synaptic plasticity in the connections among these neurons that is activated in the loving didactic relationship that is established between mother and child during infancy. This dynamic is critically dependent on observational learning on the part of the child. It is supported by the joyful engagement and emotional support of the mother. This provides a paradigm of joy in learning that we propose is the developmental origin of intellectual beauty. This is the reinforcement that maintains inquiring behavior in the search for information that is needed to resist the adverse effects of entropy on life. This paper traces the continuous thread of uncertainty resolution from its phylogenetic origins in associative learning to the intuitive science of early childhood, and ultimately to the highest levels of human inquiry in science, as well as literary, musical and visual arts. The intuitive scientific method gives rise to the collective intelligence of groups, an evolved trait that likely contributed to the success of our hominin ancestors. At the societal level, this collective intelligence scales into the institutional working of markets, driving the macroeconomic price discovery of new information to counter entropy. Importantly, we compare the cost of information across the disparate domains of pharmaceutical drug discovery and the contemporary art market to demonstrate that the imperative to reduce uncertainty manifests as a universal, falsifiable mechanism for the “price discovery” of information. Full article

Contemporary cognitive science is predominantly grounded in physicalist assumptions that locate cognition and consciousness in neural and computational processes. While these approaches have yielded significant empirical advances, they often struggle to account for the qualitative and first-person structure of experience. This paper proposes an ethno-phenomenological perspective by engaging non-Western epistemic practices through the lens of embodied cognition and ontological pluralism. Rather than treating non-Western phenomenology as external to scientific inquiry, we analyze how it can inform cognitive dynamism and ontological enactment through sophisticated epistemic practices. Particularly, we analyze how by actively manipulating cognitive and informational structures, Śaiva-Śākta phenomenology may dynamically affect embodied subjectivity. From this perspective, certain methods like visualization and ritual enactment can be understood as systematic interventions that modulate attentional, mnemonic, and perceptual processes, thereby reconfiguring the experiential architecture of the self and world. We further examine how processes such as constructive imagination function as mechanisms of cognitive reorganization, enabling practitioners to inhabit multiple ontological configurations of subject–object relations. Drawing on predictive processing and enactive cognitive science, the paper argues that Śaiva-Śākta sādhanā may operationalize a form of embodied epistemology in which ontology is not merely represented but enacted. This supports a model of ontological pluralism in which divergent experiential worlds correspond to stabilized modes of cognition rather than competing metaphysical hypotheses. Within this framework, sādhanā is considered a methodological method for investigating consciousness beyond reductionist neurophysiological accounts, offering structured access to non-ordinary modes of experience. Conclusively, the paper proposes that certain non-Western traditions may provide not only philosophical insights but also alternative phenomenological pathways for exploring the informational dynamics of embodied cognition, thereby expanding the methodological and ontological horizons of cognitive science and philosophy of mind. Full article

The traditional paradigm of natural science treats the laws of nature as eternal and immutable. This review examines a powerful alternative tradition that views these laws as historically evolving and constructed entities, tracing this shift from ancient roots to evolutionary epistemology, radical constructivism and physics. Specifically, it provides a chronological analysis of how ideas about the variability of laws developed from ancient Greek philosophy through Enlightenment thinkers to contemporary physicists like Ilya Prigogine and Lee Smolin. We address the resulting methodological crisis—where different branches of science optimize their own laws and isolate from one another—by proposing a strict hierarchical framework. Under this method, invariant basic concepts are strictly separated from flexible models. Crucially, the Entropic Measure of Time (EMT) is presented as the central operational tool. By defining time through entropy production, EMT enables the deductive derivation of physical laws from specific models, restoring a unified, cohesive structure to modern science and offering a robust strategy to counteract the fragmentation of scientific disciplines. Full article

The development of artificial intelligence has proceeded within a disciplinary culture whose positivist epistemological foundations generate a constitutive blind spot: the systematic exclusion of tacit, embodied, relational and contextually situated knowledge from what counts as knowledge at all. This article argues that this exclusion is not a technical limitation, but a structural condition of AI systems as currently built, with serious consequences for individuals, institutions and the social order. The method is interdisciplinary and critically synthetic, integrating the anthropology of science and technology, the philosophy of language and the literary-philosophical tradition of techno-critique. Three original contributions are advanced. First, the Faustian framework is reformulated as a collective rather than individual pact: the AI contract implicates a civilisation. Second, the prevalent reductionist critique is refined: the error is not quantification of incommensurable goods but the enforcement of total orderings upon value landscapes that admit only partial orderings. Third, the stochastic parrot objection is reconciled with the Faustian analysis through the concept of institutional amplification: AI’s danger resides not in its intelligence but in the authority conferred upon its incomprehension. The article concludes that Mephistopheles, not Faustus, is the more precise figure for AI itself, and asks whether the defunding of humanities disciplines, those best placed to navigate the moral challenges AI presents, constitutes the most consequential characteristic deletion of all. Full article

This systematic review follows the PRISMA 2020 guidelines to provide an analysis of the mechanisms of stigmergy and self-organization in swarm robotics. The purpose of this review is to conduct a bibliometric, thematic, and epistemological analysis. Journal articles addressing stigmergy, self-organization, and swarm robotics were included, whereas duplicate, irrelevant, and methodologically insufficient studies were excluded. The scientific databases searched were IEEE Xplore, ACM Digital Library, ScienceDirect, Springer Nature, MDPI, and Wiley Online Library from June 2025 to April 2026. Three reviewers independently screened studies using predefined criteria; no formal risk-of-bias assessment or meta-analysis was performed. In total, 338 scientific works were analyzed, representing a wide range of different approaches and applications in stigmergy and self-organization in swarm robotics. The results were synthesized through four complementary analytical axes. The review highlights the significance of stigmergy and self-organization principles in providing robustness, scalability, and adaptability in swarm robots, and shows the increasing popularity of hybrid solutions based on swarm optimization, distributed learning, and adaptive control. Key limitations include the fragmentation of methodologies, the lack of benchmarking, the underrepresentation of computational and physical perspectives, and challenges in multi-scale modeling. The review provides an integrated conceptual framework and identifies future research directions. This work was supported by FAPERJ (grants 201.013/2022 and 200.434/2026) and registered with the Open Science Framework. Full article

The proliferation of generative artificial intelligence (GenAI) has precipitated an urgent, global reassessment of how higher education evaluates critical thinking, creative agency, and academic integrity. However, scholarly and institutional responses remain fragmented across cultural contexts, impeding the development of robust, flexible, and discipline-adaptable assessment frameworks. Responding to the imperative to move beyond the traditional standardized assessment paradigm, this study conducts a comparative discourse analysis of 5368 academic articles in Anglophone/Western scholarly discourse (Web of Science, WoS) and Chinese (CNKI). Using LDA topic modeling and Word2Vec-enhanced semantic analysis, the study identifies two divergent orientations: an Anglophone/Western discourse that frames AI as an instrument for cognitive augmentation, efficiency optimization, and functional human–AI collaboration; and a Chinese discourse that emphasizes epistemic sovereignty, the reconstruction of creative subjectivity, and systemic institutional rebuilding against technological alienation. These pathways are mapped onto a tripartite framework of Tools, Creative Subjectivity, and Organizational Ecosystems. The findings demonstrate that AI integration is culturally embedded rather than technically determined, carrying profound implications for assessment validity, academic integrity policy, and equitable access to AI-enhanced learning. The study synthesizes these insights into a culturally responsive assessment framework that redirects evaluation from standardized, product-centric outputs toward process-oriented, transparent, and ethically governed human–AI co-authorship. By centering critical autonomy, AI literacy, and epistemological diversity, this framework offers actionable strategies for inclusive assessment redesign, institutional policy development, and sustainable competency cultivation in the GenAI era. Full article

The rapid expansion of artificial intelligence in the educational field has configured a broad, dynamic, and constantly evolving research domain. Nevertheless, there remains a need to systematically analyze the evolution of its pedagogical approaches and to identify the conceptual dimensions that structure recent scientific production. For this purpose, a systematic literature review was conducted following the PRISMA protocol, based on searches in Web of Science and Scopus. The final corpus consisted of 235 articles, analyzed using bibliometric and semantic techniques in R, including bibliometrix, tidyverse, and ggplot2, complemented by co-occurrence maps developed with VOSviewer. The thematic classification was carried out through an inductive analysis based on clusters and emerging patterns. The results reveal a progressive transition from technocentric approaches toward more complex and integrative pedagogical perspectives. The semantic analysis made it possible to identify four structuring dimensions of the field: critical, ethical, literacy-oriented, and humanistic. Recent literature also shows a growing emphasis on teacher education, academic integrity, and cognitive coexistence between humans and intelligent systems. These findings indicate that artificial intelligence not only introduces technological innovations but is also reconfiguring the epistemological and pedagogical foundations of contemporary education, demanding conceptual frameworks capable of articulating its ethical, cognitive, and formative implications. Full article

Scientific theories survive on institutional fitness, not empirical merit alone. Under Soviet Stalinism, Vygotsky and Luria’s cultural-historical psychology was suppressed while Leontiev’s Activity Theory flourished because it aligned with Marxist-Pavlovian materialism. A game-theoretic framework formalizes this dynamic through three coupled mechanisms: a researcher utility function (U_r = αT + βR − γC), a state utility function (U_s(e) = δI(e) − εD(e) − κ(e)), and a replicator dynamic for institutional selection. Under sufficiently high punishment coefficients, the unique Nash equilibrium is aligned with the ideologically safe theory regardless of empirical truth, and the replicator dynamics drive empirically stronger theories to extinction in the institutional population. Classical findings on conformity and obedience from Sherif, Asch, Festinger, Schachter, and Milgram supply the foundations for the model’s parameters. This pattern—termed here as epistemological selection pressure—explains the Vygotsky case. Because the model assumes severe punishment, active enforcement, complete information, and a binary choice, it applies most directly to authoritarian science; contemporary liberal institutions correspond to the low-punishment regime in which the same model predicts that empirical merit can prevail, so the mechanism is expected to recur only in attenuated form within specific high-pressure domains where scientific truth and institutional power remain entangled. Full article

This contribution examines the role of artificial intelligence technologies in the co-construction of social reality, with specific attention to AI-generated data as emergent agents of knowledge production. Building on perspectives from science and technology studies and recent debates on algomorphic sociology, the contribution conceptualizes generative AI systems not as research instruments, but as active participants in epistemic processes. The analysis argues that AI-generated data exhibit a performative character: they do not simply represent social phenomena but actively contribute to their stabilization, classification, and circulation. This performativity fosters a shift from researcher-centered interpretation toward hybrid configurations in which meaning emerges through human–machine assemblages. Through a theoretical synthesis of recent methodological and epistemological reflections, the contribution highlights a transition from anthropocentric models of knowledge production to post-anthropocentric, relational frameworks in which agency, cognition, and sense-making are distributed across sociotechnical networks. The contribution concludes by outlining the implications of this shift for the future of digital social research and also for reflexivity, methodological design, and the ethics of social research, advocating a critical and adaptive stance toward AI as a co-producer of knowledge rather than a subordinate analytical tool. Full article

Cariño (care) should be central to equity-centered transformation in science, technology, engineering, and mathematics (STEM) higher education. Yet, relational leadership practices that prioritize culturally grounded care—such as cariño—are often absent in STEM initiatives, leaving unexamined how Women of Color (WOC) enact these practices to advance equity for historically marginalized students. Employing a qualitative methodology grounded in Chicana Feminist Epistemology, in-depth interviews were conducted with five WOC leading a multi-institutional, federally funded STEM initiative. Analysis revealed four interrelated dimensions of what we are calling “Cariño Competence”: (1) relational attunement grounded in moral obligation, (2) protective action when project systems fail students, (3) boundary-setting as care and resistance to extractive labor, and (4) community-sustained resilience through networks of WOC leaders. The findings offer a data-driven theorization of Cariño Competence, capturing how WOC operationalize culturally grounded care as a strategic, protective, and resistive praxis. By centering students as the moral and epistemic anchor of leadership decisions, this study demonstrates how relational, culturally sustaining practices can humanize bureaucratic systems, buffer harm, and advance systemic transformation in STEM higher education. These insights contribute to scholarship on culturally responsive leadership and provide a practical framework for advancing equity, inclusion, and empowerment in higher education contexts. Full article

Spaces labeled as ‘extreme’ have long fueled the imagination of those who sought to explore, conquer, and represent them. Framed in colonial narratives as terra incognita or finis terrae, these regions generated diverse forms of textual and visual knowledge while simultaneously arousing curiosity and fear. The polar regions, Amazonian forests, and the seas and mountains of Patagonia played a central role in shaping the epistemologies of modern science and art in South America, functioning as laboratories in which ways of seeing were tested and transformed through processes of encounter. In this paper, we move beyond approaches that treat extreme spaces as fixed geographical entities defined solely by climatic severity or environmental hostility. Drawing on a pedagogical experiment inspired by Warburgian image-based research practices, we argue that extreme spaces are better understood as relational constructs, co-produced through multispecies and intercultural encounters. Through a comparative analysis of European iconographies across South America, we identify two coexisting clusters of meaning: one organised around abundance, intercultural cooperation, and extractivism; the other around scarcity, resistance, and environmental imposition. By tracing the circulation and mobilization of these meanings across different environments, we propose an epistemology of extremes, suggesting a mode of knowledge production that classifies spaces through the lens of radical otherness. Full article

Disinformation has become one of the main contemporary social and political concerns. However, both public and academic debates continue to exhibit an epistemological confusion between disinformation—characterized by a deliberate intention to deceive—and the errors or deficiencies arising from journalistic practice. The aim of this study is to conceptually define these errors under the phenomenon of poor journalism and to propose a taxonomy that allows it to be examined as distinct from disinformation. To this end, a qualitative integrative systematic review was conducted, based on the inductive analysis of peer-reviewed academic publications in Spanish and English, indexed in Scopus, Web of Science, and EBSCO Host. The analysis identifies two main analytical dimensions: deficient practices and structural causes. The findings show that poor journalism does not stem from a deliberate intention to deceive, but rather from structural factors, commercial logics, and corporate interests within the media ecosystem. This phenomenon is intensified by a circular logic in which the same causes that generate it also reinforce it. This study helps to clarify a relevant conceptual gap by offering a definition and a taxonomy that may be used in future research and media literacy initiatives. Full article

Artificial intelligence (AI) systems and tools are increasingly reshaping educational practices. This study examines perspectives shared in student-focused online communities on AI’s impact on education, comparing those of computer science (CS) and business students through an analysis of Reddit posts. Using natural language processing (NLP), sentiment analysis, and Latent Dirichlet Allocation (LDA) topic modeling, we analyzed 1108 posts collected from six subreddits. Results reveal distinct thematic focuses: CS students emphasize technical aspects, including programming efficiency, coding assistance, and concerns about job displacement, while business students focus on decision-making enhancement, financial analysis applications, and operational efficiency. Sentiment analysis indicates that the Business/Finance-oriented corpus is slightly more positive than the CS-oriented corpus (51.9% vs. 50.1% positive). The CS-oriented corpus also contains a higher proportion of negative posts (36.0% vs. 33.2%). These differences reflect discipline-specific epistemological frameworks shaping AI perception. The findings provide educators with guidelines for developing tailored AI integration strategies that address discipline-specific concerns and opportunities. This study contributes to understanding how academic background influences perceptions of AI in education, offering insights for curriculum design and policy development. Full article