Previous research shows that manipulations (e.g. levels-of-processing) that facilitate true memory often increase susceptibility to false memory. An exception is the generation effect. Using the Deese/Roediger–McDermott (DRM) paradigm, Soraci et al. found that generating rather than reading list items led to an increase in true but not false memories. They argued that generation led to enhanced item-distinctiveness that drove down false memory production. In the current study, we investigated the effects of generative processing on valenced stimuli and after a (...) delayed retention interval to examine factors that may lead to a generation effect that increases false memories. At the immediate test, false recognition rates for both negative and neutral valanced critical lures were similar across read and generate conditions. However, after a one-week delay, we saw a valence differentiation, with a generation effect for false recognition but only for negative stimuli. The roles of item-specific and relational processing during encoding and their interaction with long-term retention are discussed. (shrink)

The article takes the excavated cosmological texts as a basis for reinterpreting the relationship between cosmology, epistemology, and action in Warring States period thought, by focusing on the role of names in situatedness and self-actualization of being. It proposes to view the speculative and the practical concerns in terms of a dynamic union of the receptive and the creative within the onto-generative cycle. Building on Chung-ying Cheng’s onto-generative approach and Heidegger’s hermeneutics of Dasein in Sein und Zeit, the (...) article identifies names as the centre in which the receptive and the creative aspect of being come together. (shrink)

It has been proposed that intentional actions are supplied by a generative system of the sort described by Chomsky for language. In this paper I aim to provide a closer analysis of this claim for the sake of conceptual clarification. To this end, I will first clarify what is involved in the thesis of a structural analogy between language and action, and then I will consider what kind of evidence there seems to be in favour of the thesis of (...) a neurobiological identity. On this basis, I will subsequently focus on two definitional issues. The first is whether, as the claim of a generative system for intentional action suggests, humans may perform an infinite number of possible actions. The second is whether, as the claim of a generative system for intentional action suggests, what is at issue is conscious planning of action and therefore controlled processing. (shrink)

This paper introduces the concept of “generative midtended cognition”, that explores the integration of generative AI technologies with human cognitive processes. The term “generative” reflects AI’s ability to iteratively produce structured outputs, while “midtended” captures the potential hybrid (human-AI) nature of the process. It stands between traditional conceptions of _in_tended creation, understood as steered or directed from with_in_, and _ex_tended processes that bring exo-biological processes into the creative process. We examine the working of current (...) class='Hi'>generative technologies (based on multimodal transformer architectures typical of large language models like ChatGPT) to explain how they can transform human cognitive agency beyond what the conceptual resources of standard theories of extended cognition can capture. We suggest that the type of cognitive activity typical of the coupling between a human and generative technologies is closer (but not equivalent) to social cognition than to classical extended cognitive paradigms. Yet, it deserves a specific treatment. We provide an explicit definition of _generative midtended cognition_ in which we treat interventions by AI systems as constitutive of the agent’s intentional creative processes. Furthermore, we distinguish two dimensions of generative hybrid creativity: 1. Width: captures the sensitivity of the context of the generative process (from the single letter to the whole historical and surrounding data), 2. Depth: captures the granularity of iteration loops involved in the process. Generative midtended cognition stands in the middle depth between _conversational_ forms of cognition in which complete utterances or creative units are exchanged, and micro-cognitive (e.g. neural) subpersonal processes. Finally, the paper discusses the potential risks and benefits of widespread generative AI adoption, including the challenges of authenticity, generative power asymmetry, and creative boost or atrophy. (shrink)

This paper philosophically examines the impact of generative artificial intelligence on learning processes from the perspective of extended cognition. The central problem addressed is how these technologies can transform students into passive or active learners, influencing the development of cognitive skills. It will be argued that generative artificial intelligence presents risks of diminishing cognitive activity among students, as it is likely to substitute—rather than complement—the cognitive subject. It will also be argued that there are ways to leverage (...) class='Hi'>generative artificial intelligence so that learners are not passive but rather active cognitive subjects. Three cases will be presented, with empirical support, to show how this leveraging is possible: the production of feedback, assistive technologies, and gamification. In these cases, generative artificial intelligence is a complementary cognitive artifact rather than a substitutive one. To achieve this goal, the paper presents the framework of the extended mind thesis as a conducive scenario for analyzing the relationship between generative artificial intelligence and learning contexts, and it analyzes specific cases of science education. An analysis of the types of cognitive artifacts will also be conducted to examine how generative artificial intelligence intervenes in learning in both substitute and complementary ways. (shrink)

The twelve nidānas of Buddhist dependent origination (paṭicca-samuppāda) describe both the causal mechanism of suffering (dukkha) and the structure whose reversal constitutes liberation. Interpretations remain divided between a cosmological reading (twelve stages across lifetimes) and a psychological reading (twelve moments of a single cognitive episode), with neither achieving full analytical precision. This paper proposes a third reading grounded in contemporary cognitive science: the twelve nidānas map, with systematic structural fidelity, onto the architecture of predictive processing. Avijjā corresponds to structural misrecognition (...) of one's priors as mind-independent reality — the absence of meta-cognitive awareness that priors are model-generated constructs rather than direct apprehensions of the world; saṅkhāra to compulsive volitional formations driven by priors that are not recognized as such; viññāna through saḷāyatana to the construction of a hierarchical generative model; vedanā to valenced prediction error; taṇhā and upādāna to precision-weighted prior consolidation and attractor reification; bhava through jarāmaraṇa to the instantiation and progressive degradation of a system operating under misaligned priors. The correspondence is theoretically productive in both directions: predictive processing provides a mechanistic vocabulary that extends the nidānas beyond Buddhist cosmology; the nidāna chain supplies a diachronic account of why a system already operating under structural misrecognition necessarily perpetuates and deepens that misrecognition toward attractor reification and suffering, rather than correcting itself — a developmental logic that existing diachronic extensions of predictive processing do not address. The paper concludes by marking four limits of the analogy: the rebirth doctrine, the normative weight of nibbāna, the phenomenal character of vedanā, and the relationship to the Madhyamaka reading of dependent origination. A central interpretive claim is that avijjā should be understood not as an information-theoretic blank slate but as structural misrecognition of the conditioned character of one's own cognitive processes — a misrecognition available only to a system that already possesses priors, and that consists in taking those priors as transparent windows onto mind-independent reality rather than as model-generated constructions. (shrink)

Philosophers interested in the theoretical consequences of predictive processing often assume that predictive processing is an inferentialist and representationalist theory of cognition. More specifically, they assume that predictive processing revolves around approximated Bayesian inferences drawn by inverting a generative model. Generative models, in turn, are said to be structural representations: representational vehicles that represent their targets by being structurally similar to them. Here, I challenge this assumption, claiming that, at present, it lacks an adequate justification. I examine the (...) only argument offered to establish that generative models are structural representations, and argue that it does not substantiate the desired conclusion. Having so done, I consider a number of alternative arguments aimed at showing that the relevant structural similarity obtains, and argue that all these arguments are unconvincing for a variety of reasons. I then conclude the paper by briefly highlighting three themes that might be relevant for further investigation on the matter. (shrink)

This paper proposes a generative architecture through which Creative-Spirit becomes continuously productive across time. Rather than treating brand as a surface construct or market-driven identity system, this work defines brand as an applied structure through which Creative-Spirit acquires form, direction, and economic operability. -/- Building upon prior work that framed philosophy and brand as parallel systems of thought reproduction, this study advances the discussion by articulating a concrete causal process through which internal spirit is transformed into sustained external (...) action. This process is formalized as a seven-dimensional architecture: Spirit, Ideology, Purpose (Reason), Vision, Role, Mission, and Commitment. -/- Unlike existing Brand DNA models that enumerate elements without causal hierarchy, this architecture establishes a generative sequence grounded in inner–outer causality, cognitive development, and temporal reinforcement. Vision precedes role not as an idealized future image, but as the cognitive field within which role becomes possible. Mission is not assigned in advance; it emerges as a residual task generated through role execution. Commitment functions as the temporal mechanism that binds these dimensions into a self-reinforcing cycle. -/- This paper further demonstrates that time, within this architecture, is not a neutral container but the medium through which cognition deepens. Through repeated cycles of self-awareness, insight, execution, and intuitive decision-making, Creative-Spirit re-enters the system at progressively deeper levels. The result is not linear growth but spiral expansion. -/- By positioning brand as a surface manifestation of a deeper generative ontology, this work reframes Brand DNA Architecture as an applied system of Creative-Spirit production rather than a descriptive branding framework. This architecture provides the structural condition under which thought, spirit, and intention can remain productive beyond the individual and persist as a living system in economic reality. (shrink)

Purpose This paper aims to critically examine the implications of using generative artificial intelligence (AI) models, such as ChatGPT and Bard, in social science research. It examines the doppelganger effect in AI-driven studies as well as cognitive dissonance brought on by the autonomy of these tools. The discussion also addresses the debate between quantitative and qualitative methods for evaluating AI-driven research, scrutinising existing guidelines for accountability and validity. In addition, the paper considers the potential for generative AI to (...) dominate research, identifying “non-takeoverable” skills and ethical issues in AI-driven knowledge production. Design/methodology/approach This work primarily focuses on research articles for conceptual clarity, while news media reports are used to illustrate current scenarios. Findings The doppelganger effect makes people worry about situations in which AI copies existing work so well that it becomes possible for people to give the wrong credit. This has led to a critical review of ways to make sure that the outputs of generative AI are real and original. Generative AI can enhance data collection and analysis, offering alternative approaches to traditional research methodologies. By leveraging the capabilities of generative AI, researchers can potentially uncover new insights and perspectives from their data. Originality/value It is crucial to acknowledge the ethical concerns associated with using generative AI in social science research. The deployment of such technology introduces the possibility of biases and other ethical challenges that may impact the cognitive abilities of human participants or researchers involved in the research process. The work makes an effort by encouraging ethical consideration and highlighting crucial human abilities that are still necessary, providing a novel viewpoint on the use of generative AI in research approaches. (shrink)

In this paper, we argue that Midjourney—a generative AI program that transforms text prompts into images—should be understood not as an agent or a tool, but as a new type of artistic medium. We first examine the view of Midjourney as an agent, considering whether it could be seen as an artist or co-author. This perspective proves unsatisfactory, as Midjourney lacks intentionality and mental states. We then explore the notion of Midjourney as a tool, highlighting its unpredictability and the (...) limited fine-grained control users have over its outputs, which sets it apart from traditional artistic tools. We propose that creating images with Midjourney parallels a gardener's interaction with natura naturans—nature's autonomous generative processes. Similarly, Midjourney acts as machina naturans, where users engage with its dynamic recalcitrance to influence, but not fully control, the creative process. This approach presents Midjourney as an artistic medium shaped by its inherent unpredictability. (shrink)

Does the material basis of conscious experience extend beyond the boundaries of the brain and central nervous system? In Clark 2009 I reviewed a number of ‘enactivist’ arguments for such a view and found none of them compelling. Ward (2012) rejects my analysis on the grounds that the enactivist deploys an essentially world-involving concept of experience that transforms the argumentative landscape in a way that makes the enactivist conclusion inescapable. I present an alternative (prediction-and-generative-model-based) account that neatly accommodates all (...) the positive evidence that Ward cites on behalf of this enactivist conception, and that (I argue) makes richer and more satisfying contact with the full sweep of human experience. (shrink)

Generative models, with their ability to create new data, have significantly advanced machine learning. However, these models can also produce biased outputs, threatening representational fairness. Current literature mainly addresses biases in synthetic data that amplify social biases, such as racial and gender stereotypes, alongside underrepresentation and misrepresentation. In this paper, we argue for the existence of previously unexplored biases that generative models can produce on a massive scale. Using Generative Adversarial Networks as a case study, we show (...) that these unexpected and hard to interpret biases can systematically marginalize and denigrate minoritized communities. Importantly, these biases are not merely a reflection of human biases in the training data but a consequence of machine learning processes that diverge from the human perspective. We contend that current debiasing strategies are inadequate, as they fail to fully grasp the epistemic challenges and underestimate the moral risks of generative models. We conclude by suggesting a novel direction to ameliorate their harm. (shrink)

The generative entrenchment of an entity is a measure of how much of the generated structure or activity of a complex system depends upon the presence or activity of that entity. It is argued that entities with higher degrees of generative entrenchment are more conservative in evolutionary changes of such systems. A variety of models of complex structures incorporating the effects of generative entrenchment are presented and we demonstrate their relevance in analyzing and explaining a variety of (...) developmental and evolutionary phenomena, both on a macroscopic developmental and evolutionary scale, and using models and strategies pioneered by Kauffman, on the more microscopic scale appropriate to the analysis of the structure and behavior of gene control networks. The resulting picture suggests that generative entrenchment acts as a powerful and constructive developmental constraint on the course of evolutionary processes. Since virtually any system exhibits varying degrees of generative entrenchment among its parts and activities, these studies and results have in addition broad potential application for the analysis of generative structures in other areas. (shrink)

Generativity refers to people’s motives and behaviors associated with establishing and guiding members of future generations, including younger people at the workplace. Accordingly, there is conceptual overlap between generativity and leadership, which can be broadly defined as a process during which one person influences other people in order to achieve shared goals. This chapter first explores theoretical links between generativity and leadership in organizations. Second, it reviews empirical studies on associations between these constructs. It concludes with suggestions for future (...) research on generativity and leadership and for organizational practice. (shrink)

This chapter explores how generative AI is reshaping the academic research lifecycle—from ideation and literature discovery to hypothesis formation, methodological planning, and data acquisition. By enhancing early-stage processes through tools like GPT, Claude, and Gemini, researchers can streamline conceptual development, uncover cross-disciplinary connections, and rapidly synthesize literature. Generative AI assists in hypothesis generation and method selection, offers technical support for coding and data collection, and facilitates integration across tools. However, its value lies not in automation alone but in (...) collaboration: when used critically, these systems enhance scholarly insight without compromising academic rigor. The chapter emphasizes the importance of validation, transparency, and ethical caution in AI-assisted research design. (shrink)

In a rapidly evolving Generative Artificial Intelligence (GenAI) landscape, researchers, policymakers, and publishers have to continuously redefine responsible research practices. To ensure guidance of GenAI use in research, core principles that remain stable despite technological advancement are needed. This article defines a list of principles guiding the responsible use of GenAI in research, regardless of use case and GenAI technology employed. To define this framework, we conducted an anonymised Delphi consensus procedure comprising a panel of 16 international and multidisciplinary (...) experts in AI, social sciences, law, ethics, and scientific publishing. After three rounds of independent rating and feedback, the panel reached consensus on eight sequentially ordered principles required for responsible GenAI usage: Regulations, Data Security, Quality Control, Originality, Bias Mitigation, Accountability, Transparency, and Broader Impact. For the clear reporting of adherence to these principles, we created a checklist allowing active implementation into the research process. With these efforts, we aim to guide everyday research, support the development of further specified regulations, policies, and guidelines, and promote discussion about GenAI use in research. (shrink)

This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. Our approach can be described as _computational phenomenology_ because it applies methods originally developed in computational modelling to provide a formal model of the descriptions of lived experience in the phenomenological tradition of philosophy (e.g., the work of Edmund Husserl, Maurice Merleau-Ponty, etc.). The first section presents a brief review of the overall project to naturalize phenomenology. The second section presents and (...) evaluates philosophical objections to that project and situates our version of computational phenomenology with respect to these projects. The third section reviews the generative modelling framework. The final section presents our approach in detail. We conclude by discussing how our approach differs from previous attempts to use generative modelling to help understand consciousness. In summary, we describe a version of computational phenomenology which uses generative modelling to construct a computational model of the inferential or interpretive processes that best explain this or that kind of lived experience. (shrink)

In search of general generative principles I start from the postulate of a reality which comprises both materialistic and psychic aspects. This overall reality, described by others as the world of archetypes, is not directly accessible to sensory perception. Yet, by studying archetypical manifestations, it is possible to distinguish different structures or generative principles in it. Comparison of three models, which depict developmental processes in different disciplines, shows that they have the same basic structure. The effects of the (...) archetypes involved can be described as four general generative principles: 1st the principle of wholeness (connection and integration of conflicting opposites), 2nd the principle of complementation and compensation (periodic change of opposite influences); 3rd the enstructuring principle (relative stability or spatial aspect of a manifest structure), and 4th the principle of gesture (change or temporal aspect of that structure). (shrink)

This paper studies Music Generative AI (MGAI) through Bernard Stiegler’s pharmacological approach to technology by considering MGAI as a pharmakon—both remedy and poison. We situate MGAI-as-pharmakon within a long philosophical lineage of the concept from Plato’s Phaedrus to Stiegler’s technological pharmacology and apply it to the contemporary landscape of commercial MGAI as a sociotechnical object in a complex milieu of human-machine interactions. While commercial MGAI platforms are often promoted as “democratizing” music creation, we argue they primarily foster globalized adaptation: (...) a short-term process of deindividuation associated with proletarianization. This manifests in deskilling interfaces, opaque algorithms, cultural homogenization, and heightened precariousness for musicians, as artistic agency is displaced by corporate infrastructures. These dynamics echo Gilbert Simondon’s account of alienation as a loss of knowledge. In contrast, adoption represents a process of individuation that requires a long circuit of knowledge acquisition and experimentation with MGAI, over an extended period, which is hindered by the constant rate of innovation. Achieving adoption demands the development of a technical culture with a participatory ethos, and the embedding of MGAI not just as a functional tool but as part of sustained musicking practices that allow for deep human-machine co-evolution. We conclude that the challenge is not to reject MGAI as inherently alienating, but to create the conditions under which it can become a therapeutic pharmakon. This involves slowing innovation cycles, designing transparent and sustainable systems, and building institutional frameworks supporting critical reflection, education, and care. Only then can MGAI contribute to long-term cultural transindividuation. (shrink)

The behavior of an organism, according to Merleau-Ponty, lays out a milieu through which significant phenomena of varying degrees of optimality elicit adjustment. This leads to the dialectical co-emergence of milieu and aptitude that is both the product and the condition of life. What is present as a norm soliciting optimization is species-specific, but it also depends on the needs of the organism and its prior experience. Although a rich entry point into biological phenomenology, Merleau-Ponty’s work does not adequately describe (...) milieu–aptitude development in interactions between organisms, but it can be assisted through employing Husserl’s three levels of analysis identified by Steinbock, extending all three modes into the biological world. In particular, generative analyses can address inter-organismal behavioral structures slighted in Merleau-Ponty’s work. Generative phenomenology is concerned with the cultural, historical, and intersubjective constitution of human experience and is generally thought to be solely of value in examining the structure of human phenomenality. However, the possibility of human generativity presupposes structures produced widely in the biological world. Ecological, embryogenic, and evolutionary development already depend on protocultural and historical processes creating and created through intercorporeal interaction. After developing the concept of biological generativity through a consideration of plant ecology, mammalian embryology, and insect mimicry, I conclude with implications for humans, who can participate in biological generativity not merely phenomenally, but phenomenologically. (shrink)

One of the most exciting debates in philosophy of imagination in recent years has been over the epistemic use of imagination where imagination epistemically contributes to justifying beliefs and acquiring knowledge. This paper defends “generationism about imagination” according to which imagination is a generative source, rather than a preservative source, of justification. In other words, imagination generates new justification above and beyond prior justification provided by other sources. After clarifying the generation/preservation distinction (Section 2), we present an argument for (...) generationism about imagination, which can be divided into two parts; the philosophical part and the empirical part. In the philosophical part of our argument (Section 3), we claim that generationism about imagination follows from what we call “INACCESSIBILITY”. According to INACCESSIBILITY, imagination is properly constrained by the imaginative constrainers (i.e., the prior representations that constrain the development of a scenario in imagination) to which non-imaginative belief-forming processes do not have access. In the empirical part of our argument (Section 4), we claim that INACCESSIBILITY is plausible in light of relevant studies and theories in the empirical literature, especially the literature on mental simulation (Section 4.1), core cognition (Section 4.2), and intuitive physics (Section 4.3). (shrink)

The adoption of generative AI technology in visual arts echoes the transformational process initiated by early 20th-century avant-garde movements such as Constructivism and Dadaism. By utilising technological advances of their time avant-garde artists redefine the role of an artist and what could be considered as artwork. Written from the perspective of an art practitioner and researcher, this paper explores how contemporary artists working with AI continue the radical and experimental spirit that characterised early avant-garde. The re-evaluation of artist (...) roles from sole creators to engineers-collaborators and curators in an AI-mediated creative process underscores a shift in the artistic practice. Through detailed case studies of three contemporary artists, the paper illustrates how generative AI is not only used to create artwork but also to critique technological, cultural, and societal systems. Additionally, it addresses ethical concerns such as AI bias, data commodification, and the environmental impact of AI technologies, situating contemporary generative AI practices within the broader context of art's evolving societal role. Ultimately, the paper underscores the transformation of artistic practice in the digital age, where AI becomes both a creative tool and a subject of critical reflection. (shrink)

This paper examines whether contemporary generative artificial intelligence (GAI), especially large language models (LLMs), can be regarded as a “cognitive subject” in the epistemic sense relevant to the production and endorsement of knowledge claims. GAI systems increasingly participate in writing, research, and decision-making workflows and can display striking competence in information processing and task-directed problem solving. Yet, the thesis that GAI is a cognitive subject is stronger than the observation that GAI contributes as a cognitive tool. Therefore, we propose (...) an explicit set of necessary and sufficient conditions for cognitive subjecthood and evaluate each condition in light of recent philosophical and empirical scholarship. The analysis supports a two-part conclusion: (i) present-day GAI can reasonably be described as a cognitively significant contributor to knowledge production, but (ii) it does not satisfy the conditions for cognitive subjecthood, largely because robust intentionality, metacognitive self-representation, and consciousness-related indicator properties are not established. (shrink)

This study evaluates the capabilities and limitations of generative AI, specifically ChatGPT, in conducting systematic literature reviews. Using the PRISMA methodology, we analysed 124 recent studies, focusing in-depth on a subset of 40 selected through strict inclusion criteria. Findings show that ChatGPT can enhance efficiency, with reported workload reductions averaging around 60–65%, though accuracy varies widely by task and context. In structured domains such as clinical research, title and abstract screening sensitivity ranged from 80.6% to 96.2%, while precision dropped (...) as low as 4.6% in more interpretive tasks. Hallucination rates reached 91%, underscoring the need for careful oversight. Comparative analysis shows that AI matches or exceeds human performance in simple screening but underperforms in nuanced synthesis. To support more reliable integration, we introduce the Systematic Research Processing Framework (SRPF) as a guiding model for hybrid AI–human collaboration in research review workflows. (shrink)

This article deals with the recent interest shown by critical realists in the study of generative mechanisms in sociology and proposes stronger integration of hermeneutics into this theoretical approach. There are important differences between realism and hermeneutics. While realism strives to overcome the extremes of empiricism and interpretivism with a new version of naturalism, hermeneutics bases its explanations of society on research into meanings. The question is whether underlining these differences is useful for social theory. On the one hand, (...) underestimating hermeneutics makes it difficult for realists to recognize fully the role that processes of understanding play in generating social change. On the other hand, hermeneutical arguments in sociology idealistically identify being with interpreted being. The concept of generative hermeneutics is designed to help to build an alliance between realism and hermeneutics, since one-sided overemphasis on mechanistic logic devaluates the study of social interaction pursued by interpretative sociology. (shrink)

In the fast-evolving digital landscape, understanding the potential of generative AI is a strategic advantage. This book can serve as an easy to read introduction to the topic of the transformative power of AI in content creation, customer engagement, and operational efficiency. By deciphering complex AI concepts into practical insights, we empower decision-makers to envision innovative strategies, foster cross-industry collaborations, and navigate ethical considerations. The book will help executives and business decision makers to harness the immense potential of (...) class='Hi'>generative AI responsibly, ensuring data integrity and compliance while fostering a competitive edge. The book is focused on (1) Explaining in jargon-free language what Generative AI, and AI in general, (2) What problems they solve, and (3) What technologies make them possible. What You Will Learn How generative AI models are built, how they generate new data or content, and the underlying algorithms powering these processes Various practical applications of generative AI in business contexts The challenges that could arise during the integration of generative AI into business processes Who This Book is For This book is meant to be bought and read by busy executives and business leaders. (shrink)

When cognitive scientists are looking for the neural basis of consciousness or the computational processes underlying vision, what are they looking to find? I argue for a new account of this explanatory project in cognitive science (and the special sciences more generally) on which it is best understood on close analogy with causal explanation in the special sciences. Causal explanations cite causal difference-makers: they explain how certain events causally depend on other events. Generative explanations cite generative difference-makers: they (...) explain how certain events obtain in virtue of (and so are generated by) other events. I show how that this generative difference-making account (GDM) extends well regarded developments in philosophical work on causal explanation, helps to usefully clarify and expands the hypothesis space of a key project in cognitive science – that of understanding how underlying nonmental processes give rise to the mental, including consciousness. In section 2, I develop GDM by close analogy with causal explanation, and locate both of them as species of a more general project in the special sciences: that of discovering what makes a difference to what. In section 3, I motivate GDM by contrasting it with other rival accounts, including correlationist, identity, and grounding accounts, as well as Carl Craver’s constitutive relevance account. In section 4, I tie all of the strands of the preceding discussion together to show how GDM provides a new and compelling response to the so-called hard problem of consciousness. (shrink)

The current discourse on Generative AI (GenAI) in higher education largely overlooks its potential disruption to what educational philosopher Gert Biesta identifies as one of the irreducible purposes of education. Despite its foundational importance, subjectification—the process by which students become autonomous and responsible subjects—remains marginalised in instrumental debates on GenAI benefits and drawbacks. Given subjectification’s resistance to measurement, the paper interrogates GenAI’s potential impact through a medical metaphor of placebo, nocebo, and treatment deployed as a tripartite analytical framework. (...) Through this lens, the analysis reveals how GenAI operates in two ways that are contradictory in form but convergent in their reasons and outcomes. Whilst a placebo effect constitutes the illusion of subjectification without the authentic effort of becoming, a nocebo (inverse of placebo) captures students’ retreat from becoming subjects resulting from their responsibilisation in relation to GenAI and the perceived intellectual superiority of the machine. The paper argues that both effects stem from the ontological confusion and the systematic displacement of the human teacher as a ‘caller’ who summons students into subject-ness within a shared world. The paper then identifies the urgent treatment as the restoration of the teacher’s presence as irreducible, which, in turn, necessitates the clear ongoing positioning of GenAI as an object and a tool incapable of calling. By grounding this normative argument in critical realism, metaphor as a method, and Biesta’s philosophy of education, the paper proposes that the future of education in the age of GenAI depends on defending the conditions under which students can come to exist as subjects in and for the world. (shrink)

This study examines the impact of generative AI technologies, specifically ChatGPT, on Human Resources (HR) functions within various organizations. Utilizing a quantitative approach, the research surveyed 468 HR professionals to assess how AI tools influence recruitment, efficiency, bias reduction, and the accuracy of candidate selection. Results indicate that generative AI significantly enhances recruitment processes by automating initial screenings and improving communication with candidates, leading to more efficient and fair hiring practices. The findings also suggest that AI contributes to (...) reducing unconscious biases in recruitment, promoting diversity and inclusivity. However, while the study highlights substantial benefits, it also notes potential challenges in AI implementation, including ethical concerns and the need for sustained human oversight. This research underscores the transformative potential of AI in HR but also calls for careful integration to balance technological advancements with human-centered approaches. (shrink)

The unstoppable technological evolution of generative artificial intelligence forces to rethink the creation of original audiovisual scripts from a holistic perspective. The application of AI to screenwriting for film, television, or multimedia content radically affects four fundamental aspects of audiovisual writing: process management, idea generation, narrative and stylistic techniques, and evaluation and communication of results. Within each of these areas, AI brings enormous disruptive advantages that reconfigure current models and workflows: optimization of writing methods; speed of planning and (...) execution; versatility and flexibility of formats and genres; networked project collaboration; analysis of audiences, markets and aggregate trends; unlimited access to data and advanced information search engine; brainstorming and creative thinking processes; design and application of narrative structures; general or ad hoc assistance; standardized formatting; rewriting, editing, and proofreading; feedback and critical appraisal; translation and adaptation; and didactic support for teaching the craft, among many other features. This article discusses these characteristics and describes the various specific actions that can be taken when integrating AI into screenwriting software. It then summarizes the main debates about AI in four specific areas: technological, professional, legal and, above all, creative. Finally, the main questions that generative artificial intelligence will have to face in the near future are formulated: What will it be able to do technologically and what are its limits? How will the screenwriters adapt to the changes in their profession? How will viewers react to content that has not been written by humans? In short, how far can the AI revolution go? Are we dealing with a mere tool, an expensive digital pencil, or with a real writer capable of telling powerful and exciting stories? (shrink)

Generative models have been proposed as a new type of non-representational scientific models recently. A generative model is characterized with the capacity of producing new models on the basis of the existing one. The current accounts do not explain sufficiently the mechanism of the generative capacity of a generative model. I attempt to accomplish this task in this paper. I outline two antecedent accounts of generative models. I point out that both types of generative (...) models function to generate new homogenous models in the sense that the latter is a straightforward derivative of the former, both of which share many similar features. Unfortunately, both accounts are implicit about the generative capacity of generative models. Using a case study, I articulate that a two-staged process of abstraction and idealization in modeling may contribute to the generative capacity of a scientific model. I also demonstrate that this two-staged process may go beyond the capacity of generating new homogenous models to generating new heterogeneous models. (shrink)

Generative artificial intelligence is transforming how creativity is organised, attributed, and governed across cultural and creative industries. This study examines how generative systems redistribute creative functions within human-led workflows by comparing AI-generated outputs with live human performances across visual (VNAI) and performative (PEAI) domains. Focusing on systems such as Stable Diffusion and OpenAI’s Sora, the analysis adopts a mixed-methods approach combining semantic alignment, latent feature analysis, reverse image similarity, and motion tracking to investigate how originality, authorship, and embodied (...) expressiveness are structured in practice. The findings show that while generative models achieve high semantic fidelity and stylistic coherence, their outputs remain constrained by training data patterns and probabilistic recombination. In contrast, live human performance exhibits greater variability, responsiveness, and affective nuance. Across both modalities, meaningful differentiation emerges through iterative prompting, selection, and contextual framing, indicating that creative value is shaped through distributed workflows in which human judgement, evaluation, and embodied interpretation play a constitutive role. These results demonstrate that generative AI systems function as augmentative components rather than autonomous creative agents. By empirically analysing how creative agency is organised across human and algorithmic processes, the study provides an analytical framework for examining authorship, labour, and value formation in AI-mediated cultural production. (shrink)

The Bayesian brain hypothesis, predictive processing, and variational free energy minimisation are typically used to describe perceptual processes based on accurate generative models of the world. However, generative models need not be veridical representations of the environment. We suggest that they can be used to describe sensorimotor relationships relevant for behaviour rather than precise accounts of the world.

In this paper we contrast bounded and ecological rationality with a proposed alternative, generative rationality. Ecological approaches to rationality build on the idea of humans as “intuitive statisticians” while we argue for a more generative conception of humans as “probing organisms.” We first highlight how ecological rationality’s focus on cues and statistics is problematic for two reasons: the problem of cue salience, and the problem of cue uncertainty. We highlight these problems by revisiting the statistical and cue-based logic (...) that underlies ecological rationality, which originate from the misapplication of concepts in psychophysics. We then work through the most popular experimental task in the ecological rationality literature—the city size task—to illustrate how psychophysical assumptions have informally been linked to ecological rationality. After highlighting these problems, we contrast ecological rationality with a proposed alternative, generative rationality. Generative rationality builds on biology—in contrast to ecological rationality’s focus on statistics. We argue that in uncertain environments cues are rarely given or available for statistical processing. Therefore we focus on the psychogenesis of awareness rather than psychophysics of cues. For any agent or organism, environments “teem” with indefinite cues, meanings and potential objects, the salience or relevance of which is scarcely obvious based on their statistical or physical properties. We focus on organism-specificity and the organism-directed probing that shapes awareness and perception. Cues in teeming environments are noticed when they serve as cues-for-something, requiring what might be called a “cue-to-clue” transformation. In this sense, awareness toward a cue or cues is actively “grown.” We thus argue that perception might more productively be seen as the presentation of cues and objects rather than their representation. This generative approach not only applies to relatively mundane organism interactions with their environments—as well as organism-object relationships and their embodied nature—but also has significant implications for understanding the emergence of novelty in economic settings. We conclude with a discussion of how our arguments link with—but modify—Herbert Simon’s popular “scissors” metaphor, as it applies to bounded rationality and its implications for decision making in uncertain, teeming environments. (shrink)

Generative Artificial Intelligence (Gen AI) now allows for the seeming automation of most if not all steps in the scientific research lifecycle, giving rise to what I refer to as the Research Automaton – the production of science-like output with minimal meaningful human engagement. This development is often framed through a techno-solutionist lens, promising efficiency gains by treating the traditional, often strenuous, research process as a problem to be solved. This paper challenges that perspective, arguing that the intrinsic (...) value of science lies in this very process rather than solely in the product. Uncritically embracing automation thus entails eroding the formative experiences crucial for researcher development, particularly for early-career researchers, leading to potential skill atrophy and undermining the long-term innovative capacity of science. Drawing on both normative arguments about science as a vocation and pragmatic concerns about preserving essential cognitive and critical skills, I advocate for resisting the Research Automaton, while acknowledging the potential for AI to augment human capabilities when used judiciously within a hybrid cognitive constellation. I conclude by outlining practical implications for researchers, supervisors, institutions, policymakers, and publishers in navigating the integration of Gen AI in research. (shrink)

This article examines generative AI as a probabilistic medium that reshapes communicative selection under conditions of structural opacity. Drawing on systems theory, it argues that generative AI alters how communication selects, attributes, and stabilizes meaning. The analysis focuses on the art system in order to show how probabilistic media disrupt established programs of authorship and authenticity. As AI-generated outputs cannot be securely tied to singular human origins, attribution shifts toward externally verifiable signals such as process documentation, provenance, (...) and cryptographic certification. The broader significance of generative AI, the article argues, lies less in its adoption as a tool than in its reorganization of the conditions under which social systems process novelty, legitimacy, and selection. (shrink)

Harmonic Grammar (HG) and Optimality Theory (OT) are closely related formal frameworks for the study of language. In both, the structure of a given language is determined by the relative strengths of a set of constraints. They differ in how these strengths are represented: as numerical weights (HG) or as ranks (OT). Weighted constraints have advantages for the construction of accounts of language learning and other cognitive processes, partly because they allow for the adaptation of connectionist and statistical models. HG (...) has been little studied in generative linguistics, however, largely due to influential claims that weighted constraints make incorrect predictions about the typology of natural languages, predictions that are not shared by the more popular OT. This paper makes the case that HG is in fact a promising framework for typological research, and reviews and extends the existing arguments for weighted over ranked constraints. (shrink)

Recently, there have been significant advances in the development of language-transformer models that enable statistical analysis of co-occurring words (word prediction) and text generation. One example is the Generative Pre-trained Transformer 3 (GPT-3) by OpenAI, which was used to generate an opinion article (op-ed) published in “The Guardian” in Septem- ber 2020. The publication and reception of the op-ed highlights the difficulty for human readers to differentiate a machine-produced text; it also calls attention to the challenge of perceiving such (...) a text as a synthetic text even when its origins are made explicit. This article offers a critical examination of the process behind the generation and the interpretation of a synthetic text, framing it as an example of generative literature. Lotman’s concept of the text and its sociocommunicative functions offers a framework for understanding how and why the output of a natural language generator may be interpreted as a (human-written) text. This article also inquires whether the generative output can be called a text in a Lotmanian sense and how the output is textualized (attributed meaning) in the process of interpretation. (shrink)

Generative AI interaction produces divergent outcomes: genuine intellectual growth for some, convincing performance of competence for others. Multiple philosophical traditions—post-phenomenology, extended mind theory, and distributed cognition—have independently converged on this question, yet each reaches toward the developmental dimension without the theoretical resources to resolve it. This paper argues that the convergence reveals a structural gap: existing frameworks can describe how technologies mediate experience but lack the developmental apparatus to specify what determines whether a given encounter builds internal capacity or (...) substitutes for it. Drawing on object relations theory—particularly Winnicott, Bion, and Bollas—the paper develops the Dynamic Transitional Object (DTO): a relational framework specifying the conditions under which AI interaction produces development rather than erosion. The framework identifies metabolization—the transformation of AI-generated material into owned thought—as the differentiating process, and epistemic seduction—the designed pull toward bypassing that work—as the structural force opposing it. What determines the outcome is not the technology but the relational–epistemic stance. When institutional evaluative frameworks cannot distinguish genuine capacity from its convincing performance, the developmental question ceases to be individual. (shrink)

本书试图回答一个极其基础但长期被遮蔽的问题：宇宙究竟是「由什么构成的」，还是 「 如 何 生 成 自 身 的 」 ？ This book attempts to answer a foundational question that has long been obscured: Is the universe composed of things, or does it generate itself? The answer we develop here is that generation — not substance — is the universe's primary mode of being. 在经典科学传统中，宇宙被理解为一个由基本实体组成的系统，遵循外在规律并在时间中 演化。从牛顿经典力学到场论与标准模型，这一范式构成了现代科学的基础结构，其核心假设 可 概 括 为 ： 对 象 性 、 外 在 法 则 性 与 可 分 离 性 。 In the classical scientific tradition, (...) the universe is understood as a system composed of fundamental entities obeying external laws and evolving through time. From Isaac Newton's mechanics to modern field theory and the Standard Model, this paradigm has constituted the foundational structure of modern science. Its core assumptions can be summarized in three points: object-primacy, external lawfulness, and separability. 然而，当科学进入复杂系统、生物起源与文明演化等领域时，这一结构逐渐暴露其解释边 界：整体行为无法被还原为局部之和，'结构如何出现'无法仅通过'已有结构如何运动'来解释。 Yet when science enters the domains of complex systems, biological origins, and civilizational evolution, this structure gradually reveals its limits. Above all, in the development of complex systems science, we continually encounter a common phenomenon: the behavior of wholes cannot be simply reduced to the sum of local components. More importantly, the question of how structure emerges cannot be answered solely by explaining how existing structures move. 本书因此提出根本性的理论转向：从实体宇宙观转向生成宇宙观（NGU）。在生成宇宙观 中，宇宙是持续进行的生成过程，'存在'是动态稳定结构，'规律'是生成过程中的内在模式，'时 间' 是 生 成 过 程 的 序 列 展 开 形 式 。 This book therefore proposes a fundamental theoretical reorientation: from an entity-based cosmology to a generative cosmology (NGU — Natural Generative Universe). In the generative framework, the universe is no longer a completed collection of objects, but an ongoing process of self-generation. 'Existence' is no longer a static attribute but a dynamically stable structure; 'laws' are no longer external constraints but internally stable patterns within generative processes; and 'time' is no longer a background parameter but the sequential unfolding of generation itself. -/- 三层结构体系 Three-Layer Architecture 为使这一命题具备科学意义，本书构建从哲学到数学再到经验科学的三层结构体系。 To give this proposition scientific meaning, this book constructs a three-layer system spanning philosophy, mathematics, and empirical science. 第一层是本体论重构：以生成性第一原理重新定义'存在'，将'实体'解释为动力系统中的吸 引 子 状 态 。 The first layer is ontological reconstruction. By introducing generativity as a first principle, this book redefines 'existence' as stable generative structure and interprets 'entities' as attractor states within dynamical systems. 第二层是数学形式化：通过状态空间、约束系统与生成算子，将宇宙表达为递归动力系统： X_{t+1} = G(X_t, C_t)。 The second layer is mathematical formalization. Through state spaces, constraint systems, and generative operators, the universe is expressed as a recursive dynamical system: X_{t+1} = G(X_t, C_t), where the generative operator becomes the fundamental mechanism of structural evolution. 第三层是经验科学化：通过生成速率R_g、非平衡强度Δ、稳定性指数Λ与跨尺度耦合κ， 构 建 跨 天 体 、 地 球 与 生 态 系 统 的 统 一 观 测 框 架 。 The third layer is empirical scientization. By introducing observable quantities — generative rate R_g, non-equilibrium intensity Δ, stability index Λ, and cross-scale coupling κ — this book constructs a unified measurement framework across astronomical, terrestrial, and ecological systems. 哲学传承 Philosophical Lineage 本书继承并推进过程哲学传统，尤其是怀特海'过程优先于实体'的思想，将其数学化与系统 化 ， 使 其 成 为 可 计 算 的 生 成 理 论 。 Philosophically, this book inherits and advances the process philosophy tradition, especially Alfred North Whitehead's insight that process precedes substance. It further mathematizes and systematizes this insight into a computable generative theory. — 4 — Natural Generative Universe (NGU) · 自然生成宇宙论 本书深受Prigogine非平衡热力学思想影响，将'远离平衡导致结构生成'上升为宇宙基本原 理 之 一 ， 并 不 否 定 现 代 科 学 ， 而 是 提 供 更 高 层 级 的 统 一 框 架 。 This book is also deeply influenced by Ilya Prigogine's non-equilibrium thermodynamics, elevating the mechanism of 'structure generation under far-from-equilibrium conditions' to a fundamental cosmological principle. Critically, it does not attempt to negate modern science, but to provide a higher-level unified framework. -/- . (shrink)

The integration of generative artificial intelligence (AI), particularly large language models like ChatGPT, presents new challenges as well as possibilities for scientific authorship. This paper draws on social systems theory to offer a nuanced understanding of the interplay between technology, individuals, society and scholarly authorial practices. This contrasts with orthodoxy, where individuals and technology are treated as essentialized entities. This approach offers a critique of the binary positions of sociotechnological determinism and accelerationist instrumentality while still acknowledging that generative (...) AI presents profound challenges to existing practices and meaning making in scientific scholarship. This holistic treatment of authorship, integrity, and technology involves comprehending the historical and evolutionary entanglement of scientific individuality, scientific practices, and meaning making with technological innovation. This addresses current needs for more robust theoretical approaches to address the challenges confronted by academicians, institutions, peer review, and publication processes. Our analysis aims to contribute to a more sophisticated discourse on the ethical and practical implications of AI in scientific research. (shrink)

As healthcare organizations’ use of generative AI moves from initial experimentation to scaled deployments, the need to build oversight systems to identify and address ethical challenges assumes greater urgency. Among the AI applications attracting the strongest early interest are clinical summarization tools, which use large language models (LLMs). To assist healthcare organizations weighing adoption of LLMs, we describe an ethical assessment process employed at our healthcare system to identify problems that may affect patient care so problems can be (...) addressed prior to deployment or monitored over time to detect harms. The process uses stakeholder interviewing to explore risks and other concerns arising from integration of AI tools into clinical workflow and identify areas where values and priorities of different stakeholder groups do not align. We describe ethical issues identified in assessments of tools that (1) draft end-of-shift nursing notes and (2) generate clinical notes from conversations between clinicians and patients. (shrink)

This paper describes a generative theory of bugs. It claims that all bugs of a procedural skill can be derived by a highly constrained form of problem solving acting on incomplete procedures. These procedures are characterized by formal deletion operations that model incomplete learning and forgetting. The problem solver and the deletion operator have been constrained to make it impossible to derive “star‐bugs”—algorithms that are so absurd that expert diagnosticians agree that the alogorithm will never be observed as a (...) bug. Hence, the theory not only generates the observed bugs, it fails to generate star‐bugs.The theory has been tested on an extensive data base of bugs for multidigit subtraction that was collected with the aid of the diagnostic systems buggy and debuggy. In addition to predicting bug occurrence, by adoption of additional hypotheses, the theory also makes predictions about the frequency and stability of bugs, as well as the occurrence of certain latencies in processing time during testing. Arguments are given that the theory can be applied to domains other than subtraction and that it can be extended to provide a theory of procedural learning that accounts for bug acquisition. Lastly, particular care has been taken to make the theory principled so that it can not be tailored to fit any possible data. (shrink)

Despite the many successes of generative AI, a number of fundamental questions have begun to arise around this technology. There is undoubtedly an interesting situation from a philosophical point of view. It can be carefully assumed that contemporary digital information processing technologies have arisen inside a circle of civilisation, one of the foundations of which is the classical account of truth. This account, even if seen as ideal and absolute, nevertheless seems to be a driving force in the field (...) of science. Is it possible that generative AI will result in the replacement of the pursuit of truth by a mere uncritical adherence to the results provided by AI systems, as well as the inhibition of lively creativity both scientific and artistic? (shrink)