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)

Social scientists associate agent-based simulation (ABS) models with three ideas about explanation: they provide generative explanations, they are models of mechanisms, and they implement methodological individualism. In light of a philosophical account of explanation, we show that these ideas are not necessarily related and offer an account of the explanatory import of ABS models. We also argue that their bottom-up research strategy should be distinguished from methodological individualism.

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)

Generative AI appears to threaten argument creativity. Because of its capacity to generate coherent texts, individuals are likely to integrate its ideas, and not their own, into arguments, thereby reducing their creative contribution. This article argues that this view is mistaken—it rests on a misunderstanding of the nature of creativity. Within arguments, creative and critical thinking cannot be separated. Because creativity is enmeshed with skills such as analysis and evaluation, the use of generative AI in the construction of (...) arguments, especially in the role as universal audience, has the potential to heighten, not diminish argument creativity. (shrink)

This paper explores the implications of the psychology of constructive memory for philosophical theories of the metaphysics of memory and for a central question in the epistemology of memory. I first develop a general interpretation of the psychology of constructive memory. I then argue, on the basis of this interpretation, for an updated version of Martin and Deutscher's influential causal theory of memory. I conclude by sketching the implications of this updated theory for the question of memory 's status as (...) a generative epistemic source. (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)

This Perspective traces the evolution of certain central notions in the theory of Generative Grammar (GG). The founding documents of the field suggested a relation between the grammar, construed as recursively enumerating an infinite set of sentences, and the idealized native speaker that was essentially equivalent to the relation between a formal language (a set of well-formed formulas) and an automaton that recognizes strings as belonging to the language or not. But this early view was later abandoned, when the (...) focus of the field shifted to the grammar's strong generative capacity as recursive generation of hierarchically structured objects as opposed to strings. The grammar is now no longer seen as specifying a set of well-formed expressions and in fact necessarily constructs expressions of any degree of intuitive “acceptability.” The field of GG, however, has not sufficiently acknowledged the significance of this shift in perspective, as evidenced by the fact that (informal and experimentally-controlled) observations about string acceptability continue to be treated as bona fide data and generalizations for the theory of GG. The focus on strong generative capacity, it is argued, requires a new discussion of what constitutes valid empirical evidence for GG beyond observations pertaining to weak generation. (shrink)

Generative AI programs can produce high-quality written and visual content that may be used for good or ill. We argue that a credit–blame asymmetry arises for assigning responsibility for these outputs and discuss urgent ethical and policy implications focused on large-scale language models.

This paper has two motivations. First, I want to delineate structurally the dimensions of phenomenological method: not merely the static and genetic methods, but along with them I want to introduce the new ideas of generativity and generative method (Section 2). Second, because these dimensions cannot merely be treated structurally, I want to examine their dynamic interrelation, that is, the system of motivations obtaining between them. I will do this by elaborating the phenomenological concept of "leading clue" (Section 3). (...) Finally, I will conclude by addressing the relation of phenomenology to generativity (Section 4). (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)

Although Hurricane Katrina precipitated considerable reflection across various media, a practice crucial to our capacities to apprehend and interpret the disaster has not yet been analyzed as such. I call this practice generative assembly. I don’t mean the events of emergency and political gathering that took place in response to the massive storm and fatal, preventable levee failures—although I will propose connections between different forms of assembly. Instead I mean a kind of documentary practice. That practice, which can be (...) undertaken individually or in collaboration, and sometimes at anonymous remove, involves the work of assembling records and signifiers related to the disaster into particular kinds of media artifacts. Those artifacts— which take shape in formats as varied as comics, photobooks, paintings, films, exhibitions, and multithousand-item online archives—characteristically maintain the active appearance and interactive potential of selection and arrangement. In exhibiting such qualities, or so I will argue, these assembly-based artifacts support various articulable and often highly effective kinds of generativity. And thus, I will further suggest, these artifacts can constitute especially powerful means of intervening in prevailing conditions of representation and remembrance around events of environmental and social violence. Crucially, those material legacies of generative assembly can also fall short, fail, and deceive, and this is especially true of those assemblies that take digital forms. It is my contention, however, that we would be misguided if we thus entirely dismissed such assemblies or if we refused their potential for dissident reuse or generative reassembly. (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)

The advent of Generative AI, particularly through Large Language Models (LLMs) like ChatGPT and its successors, marks a paradigm shift in the AI landscape. Advanced LLMs exhibit multimodality, handling diverse data formats, thereby broadening their application scope. However, the complexity and emergent autonomy of these models introduce challenges in predictability and legal compliance. This paper analyses the legal and regulatory implications of Generative AI and LLMs in the European Union context, focusing on liability, privacy, intellectual property, and cybersecurity. (...) It examines the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA), in addressing the challenges posed by Generative AI in general and LLMs in particular. The paper identifies potential gaps and shortcomings in the EU legislative framework and proposes recommendations to ensure the safe and compliant deployment of generative models. (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)

Zero-shot learning is dedicated to solving the classification problem of unseen categories, while generalized zero-shot learning aims to classify the samples selected from both seen classes and unseen classes, in which “seen” and “unseen” classes indicate whether they can be used in the training process, and if so, they indicate seen classes, and vice versa. Nowadays, with the promotion of deep learning technology, the performance of zero-shot learning has been greatly improved. Generalized zero-shot learning is a challenging topic that has (...) promising prospects in many realistic scenarios. Although the zero-shot learning task has made gratifying progress, there is still a strong deviation between seen classes and unseen classes in the existing methods. Recent methods focus on learning a unified semantic-aligned visual representation to transfer knowledge between two domains, while ignoring the intrinsic characteristics of visual features which are discriminative enough to be classified by itself. To solve the above problems, we propose a novel model that uses the discriminative information of visual features to optimize the generative module, in which the generative module is a dual generation network framework composed of conditional VAE and improved WGAN. Specifically, the model uses the discrimination information of visual features, according to the relevant semantic embedding, synthesizes the visual features of unseen categories by using the learned generator, and then trains the final softmax classifier by using the generated visual features, thus realizing the recognition of unseen categories. In addition, this paper also analyzes the effect of the additional classifiers with different structures on the transmission of discriminative information. We have conducted a lot of experiments on six commonly used benchmark datasets. The experimental results show that our model outperforms several state-of-the-art methods for both traditional as well as generalized zero-shot learning. (shrink)

Generative artificial intelligence (GenAI) promises to revolutionize data-driven fields (Milmo 2023). Building on decades of large language modeling (LLM) (Toner 2023), GenAI can collect, harmonize...

A probabilistic Chomsky–Schützenberger hierarchy of grammars is introduced and studied, with the aim of understanding the expressive power of generative models. We offer characterizations of the distributions definable at each level of the hierarchy, including probabilistic regular, context-free, (linear) indexed, context-sensitive, and unrestricted grammars, each corresponding to familiar probabilistic machine classes. Special attention is given to distributions on (unary notations for) positive integers. Unlike in the classical case where the "semi-linear" languages all collapse into the regular languages, using analytic (...) tools adapted from the classical setting we show there is no collapse in the probabilistic hierarchy: more distributions become definable at each level. We also address related issues such as closure under probabilistic conditioning. (shrink)

There is a history of thinking that photographs provide a special kind of access to the objects depicted in them, beyond the access that would be provided by a painting or drawing. What is included in the photograph does not depend on the photographer’s beliefs about what is in front of the camera. This feature leads Kendall Walton to argue that photographs literally allow us to see the objects which appear in them. Current generative algorithms produce images in response (...) to users’ text prompts. Depending on the parameters, the output can resemble specific people or things which are named in the prompt. This resemblance does not depend on the user’s beliefs, so generated images are in this sense like photographs. Given this parallel, how should we think about AI-generated images? (shrink)

Peter Ludlow presents the first book on the philosophy of generative linguistics, including both Chomsky's government and binding theory and his minimalist ...

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 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.

Discourses on Responsible Innovation and Responsible Research and Innovation, in short RI, have revolved around but not elaborated on the notion of critique. In this article, generative critique is introduced to RI as a practice that sits in-between adversarial armchair critique and co-opted, uncritical service. How to position oneself and be positioned on this spectrum has puzzled humanities scholars and social scientists who engage in interdisciplinary collaborations with scientists, engineers, and other professionals. Recently, generative critique has been presented (...) as a solution to the puzzle in interdisciplinary collaborations on neuroscientific experiments. Generative critique seeks to create connections across disciplines that help remake seemingly stable objects in moments when taken-for-granted ways of seeing and approaching objects are unsettled. In order to translate generative critique from the neurosciences to RI, socio-technical integration research is proposed as a practice of generative critique in interdisciplinary RI collaborations. These collaborations aim to account for societal aspects in research and technology development. For this purpose, a variety of approaches have been developed, including STIR and video-reflexive ethnography. STIR and VRE resemble each other but diverge on affective, collaborative, and temporal dimensions. Their juxtaposition serves to develop suggestions for how STIR could be modified on these dimensions to better enact generative critique in interdisciplinary RI collaborations. In this way, the article contributes to ongoing discussions in RI and in the engaged programme in science and technology studies more broadly on the dynamics of positioning in collaborative work. (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)

Since their public launch, a little over a year ago, large language models (LLMs) have inspired a flurry of analysis about what their implications might be for medical ethics, and for society more broadly. 1 Much of the recent debate has moved beyond categorical evaluations of the permissibility or impermissibility of LLM use in different general contexts (eg, at work or school), to more fine-grained discussions of the criteria that should govern their appropriate use in specific domains or towards certain (...) ends. 2 With each passing week, it seems more and more inevitable that LLMs will be a pervasive feature of many, if not most, of our lives. It would not be possible—and would not be desirable—to prohibit them across the board. We need to learn how to live with LLMs; to identify and mitigate the risks they pose to us, to our fellow creatures, and the... (shrink)

Social scientists associate agent-based simulation (ABS) models with three ideas about explanation: they provide generative explanations, they are models of mechanisms, and they implement methodological individualism. In light of a philosophical account of explanation, we show that these ideas are not necessarily related and offer an account of the explanatory import of ABS models. We also argue that their bottom-up research strategy should be distinguished from methodological individualism.

Written by two of the leading figures in the field, this is a lucid and systematic introduction to semantics as applied to transformational grammars of the ...

This paper presents the treatment of anaphora in generative grammar. After a broad review of of the different theories covered under the term, it provides an overview of the history of the treatment of anaphora in generative grammar. It then discusses Ariel’s theory of accessibility and shows how it can account for the choice of some of the anaphoric expressions provided in the text. In a final section, the paper presents the author’s recent work on the choice between (...) verbal anaphors. It is shown that accessibility can account for some aspects of the choice but is not sufficient. Further constraints on the choice are proposed in terms of the notion of Question Under Discussion, for which experimental evidence is adduced. (shrink)

A key property of relational representations is their generativity: From partial descriptions of relations between entities, additional inferences can be drawn about other entities. A major theoretical challenge is to demonstrate how the capacity to make generative inferences could arise as a result of learning relations from non-relational inputs. In the present paper, we show that a bottom-up model of relation learning, initially developed to discriminate between positive and negative examples of comparative relations, can be extended to make (...) class='Hi'>generative inferences. The model is able to make quasi-deductive transitive inferences and to qualitatively account for human responses to generative questions such as “What is an animal that is smaller than a dog?” These results provide evidence that relational models based on bottom-up learning mechanisms are capable of supporting generative inferences. (shrink)

OpenAI’s Chat Generative Pre-training Transformer (ChatGPT), Google’s Bard and other generative artificial intelligence (GenAI) technologies can greatly enhance the capability of healthcare profess...

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)

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)

Humanistic management and mindfulness practices can potentiate one another to foster an inclusive society. By moving beyond a limited instrumental understanding of mindfulness practice to a generative mindfulness that incorporates a recognition of the rich nature of the human mind, awareness of cultural practices, and deeply rooted ethical foundations, managers can create organizational cultures that honor the sacred in every human being. A set of interviews with noted consultants and researchers on mindfulness and leadership suggests convergence on this perspective, (...) as does the experience of a university administrator in developing an anti-racist agenda for a large U.S. university. This article is based on both the personal experience and scholarly research of its authors and suggests ways that humanistic management education can contribute to creating inclusive workplaces through incorporating generative mindfulness in executive development as well as undergraduate programs. (shrink)

The new wave of ‘foundation models’—general-purpose generative AI models, for production of text (e.g., ChatGPT) or images (e.g., MidJourney)—represent a dramatic advance in the state of the art for AI. But their use also introduces a range of new risks, which has prompted an ongoing conversation about possible regulatory mechanisms. Here we propose a specific principle that should be incorporated into legislation: that any organization developing a foundation model intended for public use must demonstrate a reliable detection mechanism for (...) the content it generates, as a condition of its public release. The detection mechanism should be made publicly available in a tool that allows users to query, for an arbitrary item of content, whether the item was generated (wholly or partly) by the model. In this paper, we argue that this requirement is technically feasible and would play an important role in reducing certain risks from new AI models in many domains. We also outline a number of options for the tool’s design, and summarize a number of points where further input from policymakers and researchers would be required. (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)

Mesoudi et al.'s new synthesis for cultural evolution closely parallels the evolutionary synthesis of Neo-Darwinism. It too draws inspiration from population genetics, recruits other fields, and, unfortunately, also ignores development. Enculturation involves many serially acquired skills and dependencies that allow us to build a rich cumulative culture. The newer synthesis, evolutionary developmental biology, provides a key tool, generative entrenchment, to analyze them. (Published Online November 9 2006).

Traditional scholarship seems not to pay sufficient attention to the fact that Daxue 《大學》 has established a system of ethical and political philosophy on the basis of the idea of xin 心 (heart-mind) whereas the Zhongyong 《中庸》 has argued for the participation of the human person in the creativities of heaven and earth based on the onto-generative nature (xing 性) of the human person. How to explain this fact and interrelate and integrate these two systems become both a historical (...) challenge and a theoretical issue in the understanding of the Confucian philosophy of humanity as we find in the Lunyu 《論語》. (shrink)

We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed in this (...) manner takes the form of a directed weighted graph, whose nodes are recursively (hierarchically) defined patterns over the elements of the input stream. We evaluated the model in seventeen experiments, grouped into five studies, which examined, respectively, (a) the generative ability of grammar learned from a corpus of natural language, (b) the characteristics of the learned representation, (c) sequence segmentation and chunking, (d) artificial grammar learning, and (e) certain types of structure dependence. The model's performance largely vindicates our design choices, suggesting that progress in modeling language acquisition can be made on a broad front—ranging from issues of generativity to the replication of human experimental findings—by bringing biological and computational considerations, as well as lessons from prior efforts, to bear on the modeling approach. (shrink)

This article seeks to disclose the sense(s) of translation by attending to it in the phenomenological key with Edmund Husserl’s The Origin of Geometry. I suggest that the text could be approached through the prism of generative phenomenology, and I investigate it from two different perspectives: xenological and Derridean. Both perspectives are employed toward the same objective: to demonstrate the theoretical relevance of Husserl’s text to the understanding of a complex social phenomenon such as translation. The emphasis on sociality, (...) historicity, and tradition, as these themes transpire in The Origin, brings about a new understanding of translation as a phenomenon of the liminal in-between, with its basic structure defined as the encounter with the alien. Derrida gives this emphasis much attention in his commentary on The Origin. In this commentary, he focuses on the constitution of a communal history, which is not possible without the iteration of the origin, the basic means of which is communication on the general level, while on the level of different socio-cultural worlds translation comes up as the most optimal mode for the encounter with the alien. The encounter takes place in the liminal in-between which points to repeatability as the original sense of translation inscribed in the acts of binding and approximation. (shrink)

Recent work in cognitive and computational neuroscience depicts the human brain as a complex, multi-layer prediction engine. This family of models has had great success in accounting for a wide variety of phenomena involving perception, action, and attention. But despite their clear promise as accounts of the neurocomputational origins of perceptual experience, they have not yet been leveraged so as to shed light on the so-called “hard problem” of consciousness—the problem of explaining why and how the world is subjectively experienced (...) at all, and why those experiences seem just the way they do. To address this issue, I motivate and defend a picture of conscious experience as flowing from “generative entanglements” that mix predictions about the world, the body, and our own reactive dispositions. (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)

Mitte der fünfziger Jahre entsteht mit den Arbeiten der amerikanischen Linguisten Zellig S. Harris und Noam Chomsky die Theorie der generativen (Transformations-)Grammatiken a) Chomskys Grammatikmodell in den "Aspects" ... b) Entwicklung der Theorie ... nach 1965 ...

The revolution of artificial intelligence (AI), particularly generative AI, and its implications for human–robot interaction (HRI) opened up the debate on crucial regulatory, business, societal, and ethical considerations. This paper explores essential issues from the anthropomorphic perspective, examining the complex interplay between humans and AI models in societal and corporate contexts. We provided a comprehensive review of existing literature on HRI, with a special emphasis on the impact of generative models such as ChatGPT. The scientometric study posits that (...) due to their advanced linguistic capabilities and ability to mimic human-like behavior, generative AIs like ChatGPT will continue to grow in popularity in pair with human rational empathy, tendency for personification and their advanced linguistic capabilities and ability to mimic human-like behavior. As they blur the boundaries between humans and robots, these models introduce fresh moral and philosophical dilemmas. Our research aims to extrapolate key trends and unique factors in HRI and to elucidate the technical aspects of generative AI that enhance its effectiveness in this field compared to traditional rule-based AI systems. We further discuss the challenges and limitations of applying generative AI in HRI, providing a future research agenda for AI optimization in diverse applications, including education, entertainment, and healthcare. (shrink)

Linguistics as a science has rapidly changed during the course of a relatively short period. The mathematical foundations of the science, however, present a different story below the surface. In this paper, I argue that due to the former, the seismic shifts in theory over the past 80 years opens linguistics up to the problem of pessimistic meta-induction or radical theory change. I further argue that, due to the latter, one current solution to this problem in the philosophy of science, (...) namely structural realism :403–424, 1998; French in Proc Aristot Soc 106:167–185, 2006), should be viewed as especially enticing for linguists, as their field is a largely structural enterprise. I discuss particular historical instances of theory change in generative syntax before investigating two views on the nature of structural properties and eventually proposing an approach in terms of invariance :162–186, 2015) as a grounding for structural realism in the history and philosophy of linguistics. (shrink)

In recent decades, scientists and philosophers have developed several naturalistic theories of consciousness, in which they try to work out some theoretical foundations for a satisfactory solution to the problem of voluntary acts, in particular the genesis of voluntary bodily movements. From the author’s point of view, depending on which concept of consciousness scientists rely on in their empirical studies of voluntary movements, volition can be understood either as a generative act or as a perceptual act. The first part (...) of the article shows that nowadays there are two competing philosophical and scientific models of volition: the generative model (dualistic and casualistic types) and the perceptual model (strong and weak types). The second part of the article deals with some experimental data from cognitive psychology and neuroscience related to the study of voluntary movements and concludes that they are in favor of the perceptive model of volition. (shrink)