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)

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)

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)

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)

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)

We propose, following Clark, that generative models also play a central role in the perception and interpretation of linguistic signals. The data explanation approach provides a rationale for the role of prediction in language processing and unifies a number of phenomena, including multiple-cue integration, adaptation effects, and cortical responses to violations of linguistic expectations.

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)

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)

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)

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)

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.

ABSTRACTThis 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)

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)

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)

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)

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)

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)

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)

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)

Upshot: Thanks to the commentaries we have been able to further clarify the situation of generative first-person analysis in the general framework of neurophenomenology and more specifically of cardio-phenomenology as its extension and reformulation. We have also provided more detailed information about the way phenomenology as transcendental philosophy is genuinely operating as a practice in cardio-phenomenology and has a central function regarding the creation of categories and their suspensive questioning thanks to the epoché method. We have also drawn great (...) benefits from the questions about how micro-phenomenology allows a refinement of descriptive categories and the way new categories are generated, and we have been able to provide some answers about different scales of newness in the generative process. (shrink)

The generative grammar approach to language seeks a fully explicit account of the modular systems of knowledge (competence) that underlies the human language capacity. Similarly, the relevance-theoretic approach to pragmatics attempts an explicit characterisation of the sub-personal systems involved in utterance interpretation. As an on-line performance system, however, it is subject to certain additional constraints; this is demonstrated by the way in which matters of computational (processing effort) economy are currently employed in the two types of theory. A sub-module (...) of “discourse competence” is shown to be compatible with and complementary to the wider system of pragmatic processes. (shrink)

Mechanisms consist of component parts and processes organized in a specific way to produce changes that may give rise to one or more phenomena. I aim to examine the generative mechanism of generative material models in the production of new material models. A generative material model in biology is a living material model that is capable of generating new material models. I contend that generative mechanisms of a generative material model are not to be conflated (...) with biological mechanisms: the former is a mechanism manipulated and altered by the modeler while the latter is a naturally occurring mechanism. I examine the ways in which idealization may contribute to the generative mechanism in generating new model organisms. I articulate that the component parts and activities in a generative mechanism are to a large extent shaped by idealization in generative material modeling. Although biological mechanisms do have a role to play in the generation of model organisms, I propose that we shall view the generative mechanism as a hybrid of biological mechanisms and modeling activities. I shall argue that the notion of generative mechanisms as hybrid mechanisms is different from the extant conceptions of mechanisms in various aspects. (shrink)

Until now, the mass spread of fake news and its negative consequences have implied mainly textual content towards a loss of citizens' trust in institutions. Recently, a new type of machine learning framework has arisen, Generative Adversarial Networks (GANs) – a class of deep neural network models capable of creating multimedia content (photos, videos, audio) that simulate accurate content with extreme precision. While there are several areas of worthwhile application of GANs – e.g., in the field of audio-visual production, (...) human-computer interactions, satire, and artistic creativity – their deceptive uses, at least as currently foreseeable, are just as numerous and worrying. The main concern is linked to the so-called “deepfakes”, fake images or videos that simulate real events with extreme precision. When trained on a human face, GANs can make the face assume hyper-realistic movements, expressions and (verbal and non-verbal) communication abilities. This technology poses an urgent threat to the governance of democratic processes concerning the production of public opinions and political discourses, with significant potential for reality-altering and disinformation. After a short introduction of their current technical state-of-the-art, in this paper we want to enquire the GANs` socio-technical system alongside different and intertwined philosophical accounts. Firstly, we will argue about the conditions that make perceived a GANs-generated content as trustworthy, arguing also about the general effects GANs might have on the perceived trustworthiness of individuals. Thereafter, we will discuss about the inadequacy to approach GANs only as perception-altering technology. Against this backdrop, we will propose a theoretical turn that considers the human-machine relationships of trustworthiness as elements of a broader hybrid socio-technical systems. This turn come up with political repercussions that we will discuss in the last part of the paper. (shrink)

The established definition of replication in terms of the conditions of causality, similarity and information transfer is very broad. We draw inspiration from the literature on self-reproducing automata to strengthen the notion of information transfer in replication processes. To the triple conditions of causality, similarity and information transfer, we add a fourth condition that defines a “generative replicator” as a conditional generative mechanism, which can turn input signals from an environment into developmental instructions. Generative replication must have (...) the potential to enhance complexity, which in turn requires that developmental instructions are part of the information that is transmitted in replication. Demonstrating the usefulness of the generative replicator concept in the social domain, we identify social generative replicators that satisfy all of the four proposed conditions. (shrink)

Words come into existence through a number of distinct processes including naming, semantic shifts, morphological productivity, and compounding. In accounting for the instantiation and individuation of word-types, two diachronic proposals termed Originalism and History are considered, which view word-types as emerging through a tokening act after which they are subsequently distinguished from others on the basis of having a unique event-like origin. In the following paper I elucidate two central tenets of Originalism and History, which I name essentialism and propagation. (...) Next, I demonstrate that each suffer considerable challenges from evidence pertaining to the systematic generativity of words grounded in universal features of human lexical cognition. A third diachronic theory named Originalism-Plus-Transfer (OPT) is then outlined. I argue that this more nuanced version of Originalism, in its present form, still lacks a thorough explanation of pivotal features of word generativity, word instantiation, and word individuation. A number of ‘synchronic’ constraints on word-hood—phonological, phonotactic, morpho-syntactic, and semantic—are put forward to ground the possible space for word generation. I conclude by exploring the viability of supplementing OPT with synchronic constraints and propose that a union between the two has the potential to provide OPT with greater empirical strength and predictive power. The upshot of this analysis is that there exists great programmatic value for diachronic theories in drawing on synchronic data and that the latter must feature as central to the philosophy of words if such work is to anticipate achieving explanatory adequacy. (shrink)

A successful vision system must solve the problem of deriving geometrical information about three-dimensional objects from two-dimensional photometric input. The human visual system solves this problem with remarkable efficiency, and one challenge in vision research is to understand howneural representations of objects are formed and what visual information is used to form these representations. Ideal observer analysis has demonstrated the advantages of studying vision from the perspective of explicit generative models and a specified visual task, which divides the causes (...) of image variations into the separate categories of signal and noise. Classification image techniques estimate the visual information used in a task from the properties of “noise” images that interact most strongly with the task. Both ideal observer analysis and classification image techniques rely on the assumption of a generative model. We show here how the ability of the classification image approach to understand how an observer uses visual information can be improved by matching the type and dimensionality of the model to that of the neural representation or internal template being studied. Because image variation in real world object tasks can arise from both geometrical shape and photometric (illumination or material) changes, a realistic image generation process should model geometry as well as intensity. A simple example is used to demonstrate what we refer to as a “classification object” approach to studying three-dimensional object representations. (shrink)

Chung-ying Cheng has been systematically expounding, expanding, and extending the insights and parameters of Western hermeneutics, producing a new understanding of Chinese philosophy by way of an onto-generative hermeneutics that unravels not only the epistemological workings of the ineluctable human process of interpreting and understanding, but also encapsulates the ontological conditions of which the process is an integral expression. His work functions as the bedrock of a philosophy of culture; the practical expression of Cheng's onto-generative hermeneutics, (...) construed as a valid and consistent theory of culture, dismisses the ideality of meaning by subjecting all cultural realities to constant reinterpretation, based on a non-foundationalist conception of culture, while squarely rooted in the ontological source of creativity. (shrink)

Theories of autobiographical memory emphasise effortful, generative search processes in memory retrieval. However recent research suggests that memories are often retrieved directly, without effortful search. We investigated whether direct and generative retrieval differed in the characteristics of memories recalled, or only in terms of retrieval latency. Participants recalled autobiographical memories in response to cue words. For each memory, they reported whether it was retrieved directly or generatively, rated its visuo-spatial perspective, and judged its accompanying recollective experience. Our results (...) indicated that direct retrieval was commonly reported and was faster than generative retrieval, replicating recent findings. The characteristics of directly retrieved memories differed from generatively retrieved memories: directly retrieved memories had higher field perspective ratings and lower observer perspective ratings. However, retrieval mode did not influence recollective experience. We discuss our findings in terms of cue generation and content construction, and the implication for reconstructive models of autobiographical memory. (shrink)

Current development in a deep neural network has given an opportunity to a novel framework for the reconstruction of a holographic image and a phase recovery method with real-time performance. There are many deep learning-based techniques that have been proposed for the holographic image reconstruction, but these deep learning-based methods can still lack in performance, time complexity, accuracy, and real-time performance. Due to iterative calculation, the generation of a CGH requires a long computation time. A novel deep generative adversarial (...) network holography framework is proposed for hologram reconstruction. This novel framework consists of two phases. In phase one, we used the Fresnel-based method to make the dataset. In the second phase, we trained the raw input image and holographic label image data from phase one acquired images. Our method has the capability of the noniterative process of computer-generated holograms. The experimental results have demonstrated that the proposed method outperforms the existing methods. (shrink)

Homology continues to be a concept of central importance in the study of phylogenetic relations, but its relation to ontogenetic processes remains problematical. A definition of homology in terms of equivalent morphogenetic processes is defined and applied to the comparative study of tetrapod limbs. This allows for a consistent treatment of relations of similarity and difference of appendage structure in vertebrates, and the distinction between fishes fins and tetrapod limbs in terms of the concept of equivalence is described. The role (...) of genes can also be clarified in this context, in particular the influence of the Hox 4 complex in determining digit character and the homeotic transformations that arise from changes in their expression patterns. It is argued that these observations are not compatible with the notion of homology between individual digits (I, II, III, etc.) across the tetrapods, and that homology cannot be consistently identified with gene action. The relations between homology and the properties of the morphogenetic limb field are discussed. (shrink)

Predictive Processing (PP) is an increasingly influential neurocognitive-computational framework. PP research has so far focused predominantly on lower level perceptual, motor, and various psychological phenomena. But PP seems to face a “scale-up challenge”: How can it be extended to conceptual thought, language, and other higher cognitive competencies? Compositionality, arguably a central feature of conceptual thought, cannot easily be accounted for in PP because it is not couched in terms of classical symbol processing. I argue, using the example of language, that (...) there is no strong reason to think that PP cannot be scaled up to higher cognition. I suggest that the tacitly assumed common-sense conception of language as Generative Grammar (“folk linguistics”) and its notion of composition leads to the scale-up concerns. Fodor’s Language of Thought Hypothesis (LOTH) plays the role of a cognitive computational paradigm for folk linguistics. Therefore, we do not take LOTH as facing problems with higher cognition, at least with regard to compositionality. But PP can plausibly play the role of a cognitive-computational paradigm for an alternative conception of language, namely Construction Grammar. If Construction Grammar is a plausible alternative to folk linguistics, then PP is not in a worse position than LOTH. (shrink)

Name der Zeitschrift: Cognitive Linguistics Jahrgang: 26 Heft: 4 Seiten: 613-632.

Generative art theory involves any artistic practice where an artist uses external systems (rules, algorithms, organic processes) to complete an artwork. Commonly utilized in video game design, gen...

This paper analyzes an explicit instantiation of the program of “neurophenomenology” in a neuroscientific protocol. Neurophenomenology takes seriously the importance of linking the scientific study of consciousness to the careful examination of experience with a specific first-person methodology. My first claim is that such strategy is a fruitful heuristic because it produces new data and illuminates their relation to subjective experience. My second claim is that the approach could open the door to a natural account of the structure of human (...) experience as it is mobilized in itself in such methodology. In this view, generative passages define the type of circulation which explicitly roots the active and disciplined insight the subject has about his/her experience in a biological emergent process. (shrink)

Inspired by, and in close relation with, the contributions of this special issue, Kuperberg elegantly links event comprehension, production, and learning. She proposes an overarching hierarchical generative framework of processing events enabling us to make sense of the world around us and to interact with it in a competent manner.

Context: The use of first-person micro-phenomenological interviews and their productive interaction with third-person physiological data is a challenging and pressing issue in order to offer an effective and fruitful application of Varela’s neurophenomenological hypothesis. Problem: We aim at offering a generative method of analysis of first-person micro-phenomenological interviews using third-person physiological data. Our challenge is to describe this generative first-person analysis with the third-person physiological framework rather than put Varela’s hypothesis into practice in a generative way (as (...) we did in another paper. Method: The present contribution is a first pioneering study as far as the exposition of such an interactive generative methodology is concerned. It is also a new issue insofar as it deals with a case study, surprise in depression, that has not been thoroughly dealt with so far, either in philosophy or in psychopathology. Results: We show that the analysis of first-person data is an intrinsic generative one, insofar as new refined categories and multifarious circular micro- and macro-processes were discovered in the very process of analyzing. They provide the initial structural generic third-person description of surprise inherited both from philosophical phenomenological a priori categories and from the experimental startle setting with a refined micro-segmentation of the dynamic of the experience. Implications: Our article could be of interest to neurophenomenologists looking for an effective application and to researchers in quest of a method of analysis of first-person data. The present limitations are due to the still preliminary data-results we need to complete. Constructivist content: The article is directly linked to Varela’s neurophenomenological program and aims at extending and reforming it with a cardio-phenomenological approach. Keywords: First-person micro-phenomenological interviews, surprise, generative analysis of first-person data, depression, cardio-phenomenology, generative categories. (shrink)

As if 2020 were not a peculiar enough year, its fifth month has seen the relatively quiet publication of a preprint describing the most powerful Natural Language Processing (NLP) system to date, GPT-3 (Generative Pre-trained Transformer-3), by Silicon Valley research firm OpenAI. Though the software implementation of GPT-3 is still in its initial Beta release phase, and its full capabilities are still unknown as of the time of this writing, it has been shown that this Artificial Intelligence can comprehend (...) prompts in natural language, on virtually any topic, and generate relevant, original text content that is indistinguishable from human writing. Moreover, access to these capabilities, in a limited yet worrisome enough extent, is available to the general public as of the time of this writing. This paper presents select examples of original content generated by the author using GPT-3. These examples illustrate some of the capabilities of GPT-3 in comprehending prompts in natural language and generating convincing content in response. We use these examples to raise specific, fundamental questions pertaining to the intellectual property of this content and the potential use of GPT-3 to facilitate plagiarism. Our goal is to instigate not just a sense of urgency, but of a present tardiness on the part of the academic community in addressing these questions. (shrink)

While improvised behavior is so much a part of human existence as to be one of its fundamental realities, in order to avoid the risk of defining the act of improvising too broadly, my focus here will be upon one of the activities most explicitly centered around improvisation – that is, upon jazz. My contribution, as Wittgenstein would say, has a »grammatical« design to it: it proposes to clarify the significance of the term »improvisation.« The task of clarifying the cases (...) in which one may legitimately speak of improvisation consists first of all in reflecting upon the conditions that make the practice possible. This does not consist of calling forth mysterious, esoteric processes that take place in the unconscious, or in the minds of musicians, but rather in paying attention to the criteria that are satisfied when one ascribes to an act the concept of improvisation. In the second part of my contribution, I reflect upon the logic that governs the construction of an improvised performance. As I argue, in playing upon that which has already emerged in the music, in discovering the future as they go on (as a consequence of what they do), jazz players call to mind the angel in the famous painting by Klee that Walter Benjamin analyzed in his Theses on the History of Philosophy: while pulled towards the future, its eyes are turned back towards the past. (shrink)

This paper develops a practice-based Theory of Generative Interactions across diversity that builds on empirical findings and conceptual frameworks from multiple fields of study. This transdisciplinary review draws on the disciplines of sociology, social psychology, organization studies, and communications. The Theory of Generative Interactions suggests that in order to facilitate inclusion, multiple types of exclusionary dynamics must be overcome through adaptive cognitive processing and skill development, and engagement in positive interactions must occur in order to facilitate inclusion that (...) is created and sustained by contextually relevant sets of organizational practices. The organizational practices provide the following conditions for generative interactions: pursuing an important, shared organizational purpose, mixing diverse members frequently over protracted periods of time, enabling differing groups to have equal standing and insider status in contributing to success, and providing collaborative interdependence, interpersonal comfort, and self-efficacy. These interactions are generative in that they help to challenge the guiding assumptions of the organizational culture, reconsider taken-for-granted aspects, and raise fundamental questions about organizations. We assert that such interactions, properly structured, can help organizations more fully address all stakeholders in creating value ethically, and ultimately creating equity for individuals and groups in the organization. (shrink)