This book argues that this powerful technique permits the social sciences to meet an explanation, in which one 'grows' the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors.

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

This paper uses autocatalytic networks to model discontinuous cultural transitions involving cross-domain transfer, using as an illustrative example, artworks inspired by the oldest-known uncontested example of figurative art: the carving of the Hohlenstein-Stadel Löwenmensch, or lion-human. Autocatalytic networks provide a general modeling setting in which nodes are not just passive transmitters of activation; they actively galvanize, or “catalyze” the synthesis of novel nodes from existing ones This makes them uniquely suited to model how new structure grows out of earlier (...) structure, i.e., cumulative, generative network growth. They have been used to model the origin and early evolution of biological life, and the emergence of cognitive structures capable of undergoing cultural evolution. We conducted a study in which six individual creators and one group generated music, prose, poetry, and visual art inspired by the Hohlenstein-Stadel Löwenmensch, and answered questions about the process. The data revealed four through-lines by which they expressed the Löwenmensch in an alternative art form: lion-human hybrid, subtracting from the whole to reveal the form within, deterioration, and waiting to be found with a story to tell. Autocatalytic networks were used to model how these four spontaneously derived through-lines form a cultural lineage from Löwenmensch to artist to audience. We used the resulting data from three creators to model the cross-domain transfer from inspirational source to creative product. These four spontaneously-generated threads of cultural continuity formed the backbone of this Löwenmensch-inspired cultural lineage, enabling culture to evolve even in the face of discontinuity at the level conventional categories or domains. We know of no other theory of cultural evolution that accommodates cross-domain transfer or other forms of discontinuity. The approach paves the way for a broad scientific framework for the origins of evolutionary processes. (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)

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)

Though rapid collection of big data is ubiquitous across domains, from industry settings to academic contexts, the ethics of big data collection and research are contested. A nexus of data ethics issues is the concept of creep, or repurposing of data for other applications or research beyond the conditions of original collection. Data creep has proven controversial and has prompted concerns about the scope of ethical oversight. Institutional review boards offer little guidance regarding big data, and problematic research can still (...) meet ethical standards. While ethics seem concrete through institutional deployment, I frame ethics as produced. Informed by my ethnographic research at a large public university in the U.S., I explore ethics through two models: ethics as institutional procedures and ethics as acts and intentions. The university where I conducted fieldwork is the development grounds for a predictive model that uses student data to anticipate academic success. While students consent to data collection, the circumstances of consent and the degree to which they are informed are not so apparent, as many data are a product of creep. Drawing from interviews and participant observation with administrators, data scientists, developers, and students, I examine data ethics, from a larger institutional model to everyday enactments related to data creep. After demonstrating the limits of such models, I propose a remodeling of ethics that draws on recent works on data, justice, and refusal to pose generative questions for rethinking ethics in institutional contexts. (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)

Although discriminative deep neural networks are currently dominant in cognitive modeling, we suggest that capacity-limited, generative models are a promising avenue for future work. Generative models tend to learn both local and global features of stimuli and, when properly constrained, can learn componential representations and response biases found in people's behaviors.

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

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine, a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and (...) can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models. We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. (shrink)

Making sense of modeling: beyond representation Content Type Journal Article Category Original paper in Philosophy of Science Pages 335-352 DOI 10.1007/s13194-011-0032-8 Authors Isabelle Peschard, Philosophy Department, San Francisco State University, 1600 Holloway Ave, San Francisco, CA 94132, USA Journal European Journal for Philosophy of Science Online ISSN 1879-4920 Print ISSN 1879-4912 Journal Volume Volume 1 Journal Issue Volume 1, Number 3.

This book represents a unique contribution to the field of clinical ethics consultation. What might seem at first glance to be an anthology, that is, a collection of independent essays, is actually more akin to a conversation, a shared engagement, a mutual undertaking. At the center of this conversation is a steadfastness, abiding and serious in its orientation – exemplified in these voices and contributions collected from colleagues – to explore, identify, and examine the actual conduct of individuals who engage (...) in ethics consultation practice. Although there is some helpful resemblance to an anthology, for example in the variety of ways these essays describe and depict a complex array of different standpoints regarding the practical and conceptual commitments in this growing field, more important to the Zadeh Project is the deliberate focus on explicating and probing the ways these commitments influence how a particular individual acting as an “ethics consultant” might understand and interpret the roles and prevalent expectations represented by those differing standpoints. In that light, and perhaps more urgently, this book is motivated by a mutual recognition that part and parcel with the responsibility of doing clinical ethics is an ongoing and clear need to describe and consider what clinical ethics consultants actually encounter when actively working with physicians, nurses, social workers, and other healthcare providers as well as with patients, families, and others who care about patients. As a contribution to that necessity of being accountable, this Project and the resulting book represent much more than merely collecting together the thoughts and perspectives of a group of colleagues from within a common field: this book is an attempt to display, to model through written text, a set of practices and what is at stake in these practices. (shrink)

There is a broad consensus about the fundamental role of thehippocampal system (hippocampus and its adjacent areas) in theencoding and retrieval of episodic memories. This paper presents afunctional model of this system. Although memory is not asingle-unit cognitive function, we took the view that the wholesystem of the smooth, interrelated memory processes may have acommon basis. That is why we follow the Ockham's razor principleand minimize the size or complexity of our model assumption set.The fundamental assumption is the requirement of (...) solving the socalled ``homunculus fallacy'', which addresses the issue ofinterpreting the input. Generative autoassociators seem to offer aresolution of the paradox. Learning to represent and to recallinformation, in these generative networks, imply maximization ofinformation transfer, sparse representation and noveltyrecognition. A connectionist architecture, which integrates theseaspects as model constraints, is derived. Numerical studiesdemonstrate the novelty recognition and noise filtering propertiesof the architecture. Finally, we conclude that the derivedconnectionist architecture can be related to the neurobiologicalsubstrate. (shrink)

Shanker & King's (S&K's) proposal is consistent with a Vygotskian model of development which assumes that cognition is first social and visible, and only later internalized and invisible. Rather than slipping into positing “epistemic operators” like understand or intend as generative of behavior during language learning or theory of mind tasks, this approach profits from keeping its focus on charting the ontogeny of embodied interactions.

We review state‐of‐the‐art computational methods for constructing, from image data, generative statistical models of cellular and nuclear shapes and the arrangement of subcellular structures and proteins within them. These automated approaches allow consistent analysis of images of cells for the purposes of learning the range of possible phenotypes, discriminating between them, and informing further investigation. Such models can also provide realistic geometry and initial protein locations to simulations in order to better understand cellular and subcellular processes. To determine the (...) structures of cellular components and how proteins and other molecules are distributed among them, the generative modeling approach described here can be coupled with high throughput imaging technology to infer and represent subcellular organization from data with few a priori assumptions. We also discuss potential improvements to these methods and future directions for research. (shrink)

We consider human performance on an optimal stopping problem where people are presented with a list of numbers independently chosen from a uniform distribution. People are told how many numbers are in the list, and how they were chosen. People are then shown the numbers one at a time, and are instructed to choose the maximum, subject to the constraint that they must choose a number at the time it is presented, and any choice below the maximum is incorrect. We (...) present empirical evidence that suggests people use threshold-based models to make decisions, choosing the first currently maximal number that exceeds a fixed threshold for that position in the list. We then develop a hierarchical generative account of this model family, and use Bayesian methods to learn about the parameters of the generative process, making inferences about the threshold decision models people use. We discuss the interesting aspects of human performance on the task, including the lack of learning, and the presence of large individual differences, and consider the possibility of extending the modeling framework to account for individual differences. We also use the modeling results to discuss the merits of hierarchical, generative and Bayesian models of cognitive processes more generally. (shrink)

I argue that there are no plausible non-representational explanations of episodes of hallucination. To make the discussion more specific, I focus on visual hallucinations in Charles Bonnet syndrome. I claim that the character of such hallucinatory experiences cannot be explained away non-representationally, for they cannot be taken as simple failures of cognizing or as failures of contact with external reality—such failures being the only genuinely non-representational explanations of hallucinations and cognitive errors in general. I briefly introduce a recent computational model (...) of hallucination, which relies on generative models in the brain, and argue that the model is a prime example of a representational explanation referring to representational mechanisms. The notion of the representational mechanism is elucidated, and it is argued that hallucinations—and other kinds of representations—cannot be exorcised from the cognitive sciences. (shrink)

Predictive processing theories are increasingly popular in philosophy of mind; such process theories often gain support from the Free Energy Principle (FEP)—a nor- mative principle for adaptive self-organized systems. Yet there is a current and much discussed debate about conflicting philosophical interpretations of FEP, e.g., repre- sentational versus non-representational. Here we argue that these different interpre- tations depend on implicit assumptions about what qualifies (or fails to qualify) as representational. We deploy the Free Energy Principle (FEP) instrumentally to dis- tinguish (...) four main notions of representation, which focus on organizational, struc- tural, content-related and functional aspects, respectively. The various ways that these different aspects matter in arriving at representational or non-representational interpretations of the Free Energy Principle are discussed. We also discuss how the Free Energy Principle may be seen as a unified view where terms that tradition- ally belong to different ontologies—e.g., notions of model and expectation versus notions of autopoiesis and synchronization—can be harmonized. However, rather than attempting to settle the representationalist versus non-representationalist debate and reveal something about what representations are simpliciter, this paper demon- strates how the Free Energy Principle may be used to reveal something about those partaking in the debate; namely, what our hidden assumptions about what represen- tations are—assumptions that act as sometimes antithetical starting points in this persistent philosophical debate. (shrink)

Unsupervised learning algorithms are widely used for many important statistical tasks with numerous applications in science and industry. Yet despite their prevalence, they have attracted remarkably little philosophical scrutiny to date. This stands in stark contrast to supervised and reinforcement learning algorithms, which have been widely studied and critically evaluated, often with an emphasis on ethical concerns. In this article, I analyze three canonical unsupervised learning problems: clustering, abstraction, and generative modeling. I argue that these methods raise unique (...) epistemological and ontological questions, providing data-driven tools for discovering natural kinds and distinguishing essence from contingency. This analysis goes some way toward filling the lacuna in contemporary philosophical discourse on unsupervised learning, as well as bringing conceptual unity to a heterogeneous field more often described by what it isnot(i.e., supervised or reinforcement learning) than by what itis. I submit that unsupervised learning is not just a legitimate subject of philosophical inquiry but perhaps the most fundamental branch of all AI. However, an uncritical overreliance on unsupervised methods poses major epistemic and ethical risks. I conclude by advocating for a pragmatic, error-statistical approach that embraces the opportunities and mitigates the challenges posed by this powerful class of algorithms. (shrink)

Any approach to the study of managerial situations undertaken without reflection on the underpinning philosophy is flawed because it limits our ability to question the validity of the knowledge claimed in the analysis. The paper considers this issue and presents a philosophical reflection on the use of a systems approach to the modelling of human enterprises. It draws on insights from systems thinking, cognitive science, autopoiesis, communication theory and non-linear dynamics. These are interpreted within the context of social systems as (...) networks of conversations that are generated in language. It is written to invite an exchange of ideas concerning the role of the generative dialogue. (shrink)

Verbs such as enjoy in the student enjoyed the book exhibit logical metonymy: enjoy is interpreted as enjoy reading. Theoreticalwork [Computational Linguistics 17 (4) (1991) 409; The Generative Lexicon, MIT Press, Cambridge, MA, 1995] predicts that this interpretation can be influenced by intra‐sentential context, e.g., by the subject of enjoy. In this article, we test this prediction using a completion experiment and find that the interpretation of a metonymic verb is influenced by the semantic role of its subject. We (...) present a Bayesian model that accounts for the interpretation of logical metonymy and achieves a good fit on our experimental data. We show that the parameters of the model can be estimated from completion data or from corpus data. (shrink)

The explanatory power of structures in analytical sociologists’ agent-based models brings into question methodological individualism. We defend that from an explanatory point of view, the syntactic properties of models require semantic conditions of interpretation drawn from a conceptual research framework; in such a framework, social/relational structures have only partial, explanatory power ; and taking the explanation further through generative mechanism modeling necessitates calling upon methodological individualism’s generic framework of interpretation that relies on social actors’ rational capacity. According to (...) this interpretive framework, forces in action in society are governed by the subjective meaning of/the reasons for individual actions. (shrink)

Modeling dynamic networks has attracted much interest in recent years, which helps understand networks’ behavior. Many works have been dedicated to modeling discrete-time networks, but less work is done for continuous-time networks. Point processes as powerful tools for modeling discrete events in continuous time have been widely used for modeling events over networks and their dynamics. These models have solid mathematical assumptions, making them interpretable but decreasing their generalizability for different datasets. Hence, neural point processes were (...) introduced that don’t have strong assumptions on generative functions. However, these models can be impractical in the case of a large number of event types. This research presents a comparative study of different point process models for continuous-time networks. Furthermore, a previously introduced neural point process model is applied for modeling network interactions. In this work, network clustering is used for specifying interaction types. These methods are compared using different synthetic and real-world datasets, and their efficiency is evaluated on these datasets. The experiments represent that each model is appropriate for a group of datasets. In addition, the effect of clustering on results is discussed, and experiments for different clusters are presented. (shrink)

Selectional preferences have a long history in both generative and computational linguistics. However, since the publication of Resnik's dissertation in 1993, a new approach has surfaced in the computational linguistics community. This new line of research combines knowledge represented in a pre‐defined semantic class hierarchy with statistical tools including information theory, statistical modeling, and Bayesian inference. These tools are used to learn selectional preferences from examples in a corpus. Instead of simple sets of semantic classes, selectional preferences are (...) viewed as probability distributions over various entities. We survey research that extends Resnik's initial work, discuss the strengths and weaknesses of each approach, and show how they together form a cohesive line of research. (shrink)

Artificial intelligence is man's attempt to use software to emulate the intelligence of human beings. But the complexity of the human neurological system formed in the course of evolution is impossible to replicate: "Human languages and societies are complex systems, indeed complex systems of many complex systems," so much so that their mathematical modeling is impossible. Barry Smith, philosopher and professor at the University at Buffalo. shows no uncertainty about this. His latest book written with Jobst Landgrebe, a mathematician (...) and entrepreneur in the AI field, leaves no doubt – starting with the title: Why machines will never rule the world (Routledge 2023). The book was presented in Turin during an evening organized by the Fondazione CRT and the Bruno Leoni Institute, in which Smith was interviewed based on questions developed by generative Ai. We have accepted the challenge to conduct an interview parallel to the one done with GPT4. (shrink)

Online educational technologies offer opportunities for providing individualized feedback and detailed profiles of students' skills. Yet many technologies for mathematics education assess students based only on the correctness of either their final answers or responses to individual steps. In contrast, examining the choices students make for how to solve the equation and the ways in which they might answer incorrectly offers the opportunity to obtain a more nuanced perspective of their algebra skills. To automatically make sense of step‐by‐step solutions, we (...) propose a Bayesian inverse planning model for equation solving that computes an assessment of a learner's skills based on her pattern of errors in individual steps and her choices about what sequence of problem‐solving steps to take. Bayesian inverse planning builds on existing machine learning tools to create a generative model relating (mis)‐understandings to equation solving choices. Two behavioral experiments demonstrate that the model can interpret people's equation solving and that its assessments are consistent with those of experienced teachers. A third experiment uses this model to tailor guidance for learners based on individual differences in misunderstandings, closing the loop between assessing understanding, and using that assessment within an educational technology. Finally, because the bottleneck in applying inverse planning to a new domain is in creating the model of possible student misunderstandings, we show how to combine inverse planning with an existing production rule model to make inferences about student misunderstandings of fraction arithmetic. (shrink)

Historically, the dynamic between philosophy of artificial intelligence and its practical application has been essential for the development of both, and thus the encounter between theory of AI and architectural/urban theory should be a site of considerable productivity. However, in many ways, it is not. This is due to two primary factors, one arising from each side of this encounter. First, legacies of overly-anthropomorphic models of AI permeate design discourses, where issues of how well AI can be constrained to social (...) issues of philosophy of mind exclude more foundational and cross-cultural questions about artificial intelligence as material process in the physical world that could inform new philosophical insights. Concurrently, the mobilization of investment in “Smart Cities” discourses dominates the space of application of AI at urban scale in ways that prematurely fixes solutions in the skeuomorphic image of architectural and urban conventions. To break this impasse, we held a series of think-tanks, workshops, and design charettes that brought together leading figures in philosophy of artificial intelligence, urban design, and commercial AI platforms. The goal was to re-think from first principles how alternative philosophical models of AI as a distributed, discontinuous, landscape-scale technology in a direct encounter with the applications in contexts of ecological sensing, automation’s impact on urban form, and issues of algorithmic governance. The design brief for this research establishes a generative framework for conceiving how embedded machine sensing and intelligence is already changing urban form and could change it in the future. The framework focuses on issues of urban zoning and architectural programming, data modeling and governance, platform cognition and design, and how these inform shifts in dynamics of public and private institutions. (shrink)

It is possible to learn multiple layers of non-linear features by backpropagating error derivatives through a feedforward neural network. This is a very effective learning procedure when there is a huge amount of labeled training data, but for many learning tasks very few labeled examples are available. In an effort to overcome the need for labeled data, several different generative models were developed that learned interesting features by modeling the higher order statistical structure of a set of input (...) vectors. One of these generative models, the restricted Boltzmann machine (RBM), has no connections between its hidden units and this makes perceptual inference and learning much simpler. More significantly, after a layer of hidden features has been learned, the activities of these features can be used as training data for another RBM. By applying this idea recursively, it is possible to learn a deep hierarchy of progressively more complicated features without requiring any labeled data. This deep hierarchy can then be treated as a feedforward neural network which can be discriminatively fine-tuned using backpropagation. Using a stack of RBMs to initialize the weights of a feedforward neural network allows backpropagation to work effectively in much deeper networks and it leads to much better generalization. A stack of RBMs can also be used to initialize a deep Boltzmann machine that has many hidden layers. Combining this initialization method with a new method for fine-tuning the weights finally leads to the first efficient way of training Boltzmann machines with many hidden layers and millions of weights. (shrink)

Tschacher and Haken have recently applied a systems-based approach to modeling psychotherapy process in terms of potentially beneficial tendencies toward deterministic as well as chaotic forms of change in the client’s behavioral, cognitive and affective experience during the course of therapy. A chaotic change process refers to a greater exploration of the states that a client can be in, and it may have a potential positive role to play in their development. A distinction is made between on the one (...) hand, specific instances of instability which are due to techniques employed by the therapist, and on the other, a more general instability which is due to the therapeutic relationship, and a key, necessary result of a successful therapeutic alliance. Drawing on Friston’s systems-based model of free energy minimization and predictive coding, it is proposed here that the increase in the instability of a client’s functioning due to therapy can be conceptualized as a reduction in the precisions with which the client’s prior beliefs about themselves and their world, are held. It is shown how a good therapeutic alliance results in the emergence of a new hierarchical level in the client’s generative model of themselves and their relationship with the world. The emergence of this new level of functioning permits the reduction of the precisions of the client’s priors, which allows the client to ‘open up’: to experience thoughts, emotions and experiences they did not have before. It is proposed that this process is a necessary precursor to change due to psychotherapy. A good consilience can be found between this approach to understanding the role of the therapeutic alliance, and the role of epistemic trust in psychotherapy as described by Fonagy and Allison. It is suggested that beneficial forms of instability in clients are an underappreciated influence on psychotherapy process, and thoughts about the implications, as well as situations in which instability may not be beneficial for therapy, are considered. (shrink)

We present an algebraic account of the Tongan kinship terminology (TKT) that provides an insightful journey into the fabric of Tongan culture. We begin with the ethnographic account of a social event. The account provides us with the activities of that day and the centrality of kin relations in the event, but it does not inform us of the conceptual system that the participants bring with them. Rather, it is a slice in time of an ongoing dynamic process that links (...) behavior with a conceptual system of kin relations and vice versa. To understand this interplay, we need an account of the underlying conceptual system that is being activated during the event. Thus, we introduce a formal, algebraically based account of TKT. This account brings to the fore the underlying logic of TKT and allows us to distinguish between features of the kinship system that arise from the logic of TKT as a generative structure and features that must have arisen through cultural intervention. (shrink)

Morphospaces are theoretical tools to explore the morphological organization of living and fossil organisms. They have been used mostly by the paleontological community in an effort to get the most out of one of the only pieces of evidence that fossil material usually provide: the morphology of hard parts. The expectation with the establishment of theoretical morphospaces is that, by abstracting and modeling the fundamental parts of form, the multiple processes that generate the phenotypes of embryonic and adult structures (...) will be better understood. In this essay, we suggest that ontogenetic trajectories can be used as the generative functions that build morphospaces, and propose approaches to build theoretical models for the establishment of left–right asymmetries during vertebrate heart embryogenesis. BioEssays 26:405–412, 2004. © 2004 Wiley Periodicals, Inc. (shrink)

The ambition and proposal for data modeling of myths presented in this paper is to link contemporary technical affordances to some canonical projects developed in structural anthropology. To articulate the theoretical promise and innovation of this proposal, we present a discrete-event system specification modeling and simulation approach in order to perform a generative analysis and a dynamic visualization of selected narratives, aimed at validating and revitalizing the transformational and morphodynamic theory and methodology proposed by Claude Lévi-Strauss in (...) his structural analysis of myth. After an analysis of Lévi-Strauss’s transformational methodology, we describe in detail how discrete-event system specification models are implemented and developed in the framework of a DEVSimPy software environment. The validation of the method involves a discrete-event system specification simulation based on the extension of discrete-event system specification models dedicated to provide a dynamic Google Earth visualization of the selected myth. Future work around the discrete-event system specification formalism in anthropology is described as well as future applications regarding the impact of computational models to new contemporary anthropological domains. (shrink)

Extensive literatures exist on the epistemology of testimony, memory, and perception, but for the most part these literatures do not systematically consider the extent of the analogies between the three epistemic sources. A number of the same problems reappear in all three literatures, however. Dealing simultaneously with all three sources and making a careful accounting of the analogies and disanalogies between them should therefore avoid unnecessary duplication of effort. Other than limits on the scope of which memorially- and testimonially-based beliefs (...) should be included in the Parity Thesis, I argue that most of the disanalogies that different philosophers have proffered between the sources do not mark distinctions among the universes of possible testimonially-, memorially-, and perceptually-based beliefs regarding the explanation of those beliefs' epistemic status. I first criticize the suggestion that perception is a generative epistemic source, while testimony and memory are not; I propose and defend counterexamples in which testimony and memory produce new beliefs. Next, I criticize a variety of distinctions that have been drawn between testimony and perception, taken chiefly from the reductionist-antireductionist literature on testimony. I criticize the suggestion that the conceptualization of content and the transparency of experience affect the epistemologies of testimony and perception in different ways. Regarding memory and testimony, I advocate modeling testimony on the legal relationship of a principal and an agent, arguing that law's apparatus used to analyze such situations suggests that using others' epistemic services in testimony will supply the same epistemic benefits and burdens as if we had performed those epistemic tasks personally and then relied only on memory. I apply this analysis to the transmission of defeaters in testimony. I argue that memory does feature the epistemic equivalent of a perceptual image and that both perceptually- and memorially-based beliefs can concern either the past or the present. Finally, I construct a set of six transformations that turn individual possible instances of perceptually-, memorially-, or testimonially-based beliefs into individual possible instances of the other two types of beliefs without changing the structure of those beliefs' epistemologies. (shrink)

The ability to convey our thoughts using an infinite number of linguistic expressions is one of the hallmarks of human language. Understanding the nature of the psychological mechanisms and representations that give rise to this unique productivity is a fundamental goal for the cognitive sciences. A long-standing hypothesis is that single words and rules form the basic building blocks of linguistic productivity, with multiword sequences being treated as units only in peripheral cases such as idioms. The new millennium, however, has (...) seen a shift toward construing multiword linguistic units not as linguistic rarities, but as important building blocks for language acquisition and processing. This shift—which originated within theoretical approaches that emphasize language learning and use—has far-reaching implications for theories of language representation, processing, and acquisition. Incorporating multiword units as integral building blocks blurs the distinction between grammar and lexicon; calls for models of production and comprehension that can accommodate and give rise to the effect of multiword information on processing; and highlights the importance of such units to learning. In this special topic, we bring together cutting-edge work on multiword sequences in theoretical linguistics, first-language acquisition, psycholinguistics, computational modeling, and second-language learning to present a comprehensive overview of the prominence and importance of such units in language, their possible role in explaining differences between first- and second-language learning, and the challenges the combined findings pose for theories of language. (shrink)

We review the hypothesis that the brain uses a generative model to explain the causes of sensory inputs, using prediction schemes that operate based upon assimilation of time-series sensory data. We put this hypothesis in the context of psychopathology, in particular, schizophrenia's positive symptoms. Building upon work of Helmholtz and upon theories in computational cognitive processing, we hypothesize that delusions in schizophrenia can be explained in terms of false inference. An impairment in inferring appropriate information from the sensory input (...) reflects upon the ability to assess the environment and predict outcomes. Although the inference mechanism likely involves both conscious and unconscious processes, we hypothesize that the trigger of delusions may lie within the unconscious neural pathways. A collection of computational predictive codes have been proposed for modeling perception. We discuss two examples, which may be eligible as substrates for intuitive coding. We argue that failure of the psychotic patient to choose the correct computational scheme, or the optimal range of parameters, may readily lead to an altered reconstruction of the object and false inference, feeding into the delusion mechanism. We finally propose using these models in conjunction with cognitive and imaging data, in order to obtain more testable predictions. (shrink)

In this paper, representational structures of arithmetical thinking, encoded in human minds, are described. On the basis of empirical research, it is possible to distinguish four types of mental number lines: the shortest mental number line, summation mental number lines, point-place mental number lines and mental lines of exact numbers. These structures may be treated as generative mechanisms of forming arithmetical representations underlying our numerical acts of reference towards cardinalities, ordinals and magnitudes. In the paper, the theoretical framework for (...) a formal model of mental arithmetical representations is constructed. Many competitive conceptions of the mental system responsible for our arithmetical thinking may be unified within the presented framework. The paradigm underlying our research may be interpreted philosophically as a neo-Kantian approach to modeling the mind’s representational structures. (shrink)

This study presents a probabilistic model of melody perception, which infers the key of a melody and also judges the probability of the melody itself. The model uses Bayesian reasoning: For any “surface” pattern and underlying “structure,” we can infer the structure maximizing P(structure|surface) based on knowledge of P(surface, structure). The probability of the surface can then be calculated as ∑ P(surface, structure), summed over all structures. In this case, the surface is a pattern of notes; the structure is a (...) key. A generative model is proposed, based on three principles: (a) melodies tend to remain within a narrow pitch range; (b) note‐to‐note intervals within a melody tend to be small; and (c) notes tend to conform to a distribution (or key profile) that depends on the key. The model is tested in three ways. First, it is tested on its ability to identify the keys of a set of folksong melodies. Second, it is tested on a melodic expectation task in which it must judge the probability of different notes occurring given a prior context; these judgments are compared with perception data from a melodic expectation experiment. Finally, the model is tested on its ability to detect incorrect notes in melodies by assigning them lower probabilities than the original versions. (shrink)

This article traces my attempts to come to grips with the problem of change. Systems science deals with general principles, but, as with science in general, is wedded to mechanistic models. Natural systems are not machines, are generative, and can change unpredictably. An example is given showing that explicit dynamical models are subverted by the present moment, which is non-existent in them. This moment can be modeled by a compositional hierarchy, but no change happens therein. Subsumptive hierarchies can serve (...) as metaperspectives on change, modeling stages of development. The Second Law of thermodynamics provides the motivation for development, which can be modeled generally using thermodynamic and information theoretic (infodynamic) concepts. Development is progressive change, constitutive of different systems, and goes from vagueness toward increasingly more definite embodiment, ending up in senescence, which derives from information overload. While becoming increasingly burdened with information, the present moment, as such, still evades this developmental systematicity. Internalist thinking has emerged as an attempt to further come to grips with it. (shrink)

Mise en abyme is a term from literary theory denoting a work that doubles itself within itself, for example a story placed within a story or a play within a play. Proliferating in experimental fiction in midcentury France, this technique had a strong impact on contemporary literary theory, but also, as this book project argues, on post-Heideggerian and post-structuralist philosophy. The Little Crystalline Seed focuses on how three of these thinkers invoke the concept of mise en abyme in order to (...) establish ontologies that deviate from that of Heidegger. Iddo Dickmann demonstrates how the concept served in modeling Derrida's logic of supplementarity, Blanchot's philosophy of "ambiguity" and mechanism of Désouvrement, and Deleuze's philosophy of difference, time and repetition. Exploring the interpretative and generative potential of the mise en abyme for continental thought, Dickmann illustrates points of resonance between various philosophical topics such as aesthetics, ethics, time, logic, mirroring, play, and signification. (shrink)

Introduction. Observing the production and consumer area of modern cinematography makes it possible to draw conclusions about the value dominants of modern society, to conduct its sociocultural analysis. Cinema, both auteur and mass, is a way of reflecting and modeling the society spiritual state and its analysis makes it possible to draw quite serious and justified philosophical and social conclusions. The film in a philosophical sense is ontologized by the dominant intention of the era. Such a dominant intention of (...) modernity is hedonism. Pleasure, enjoyment, happiness, according to the humanities, is the basic values of the 21st century society. Cinema is the most comprehensive reaction to the needs of hedonism, since hedonistic prerequisites are to some degree laid down in its technical (movement, photographic phenomenology) and social nature (art and entertainment). In this regard, it seems relevant to conduct a philosophical analysis of the ontological level of modern cinema. The aim of the study is to conduct a philosophical analysis of the ontological level of modern cinema; identify ontological models, variations and combinations of which provide its artistic diversity. Methods: the authors use the general scientific methods of analysis and synthesis, the phenomenological method, since they identify the sensory intentions that underlie modern cinema. The authors also use the hermeneutic method, since they reveal the hidden meanings of the film, determined by the time and conditions of its creation, as well as the semiotic approach, which makes it possible to analyze the structural-semiotic whole of the film. The scientific novelty of the study lies in the fact that the authors analyze films from philosophical aspects, and not just cultural studies, as is customary. In modern cinema, the authors look for a reflection of the “social soul”, identify those intentions that underlie modern cinema, both auteur and mass. This makes it possible to deduce a typology of film models that have not previously been applied to the analysis of the film process. Results. During the analysis, the authors developed a hedonistic-ontological typology of cinematic models, identified five generative models of cinema, four of which have a hedonistic orientation: an alimentary model that pleases the recipient by intensively stimulating his perceptual sphere; a parkour model that gives pleasure to light and unhindered movement, minimizing the severity and resistance of the environment; an arcade model that provides the viewer the pleasure of victory through virtual immersion in the ups and downs of the “action film”; a detective model that gives the viewer pleasure through the removal of cognitive uncertainty. The fifth model, tentatively called existential, should lead the viewer into a complex philosophical state of being-with-self-self, an experience of one’s own fulfillment, associated with the experience of suffering. However, due to the hedonistic orientations of modern culture, this model is not relevant in the first decades of the 21st century. Conclusions. Philosophical analysis of modern cinema has revealed that at the ontological level it is based on the philosophy of hedonism. Modern cinema delivers the following types of pleasures to the recipient: enjoyment of the flesh of the world, enjoyment of bodily freedom in motion, enjoyment of peace, cognitive enjoyment. (shrink)

Missing from Quartz & Sejnowski's (Q&S's) unique and valuable effort to relate cognitive development to neural constructivism is an examination of the global emergent properties of adding new neural circuits. Such emergent properties can be studied with computational models. Modeling with generative connectionist networks shows that synaptogenic mechanisms can account for progressive increases in children's representational power.

Strengths, Opportunities, Aspirations, and Results is a strengths-based framework for strategic thinking, planning, conversations, and leading that focuses on strengths, opportunities, aspirations, and results. The SOAR framework leverages and integrates Appreciative Inquiry to create a transformation process through generative questions and positive framing. While SOAR has been used by practitioners since 2000 as a framework for generating positive organizational change, its use in empirical research has been limited by the absence of reliable and valid measures. We report on the (...) reliability, construct validity, and measurement invariance of the SOAR Scale, a 12-item self-report survey organized into four first-order factors. Data from a sample of 285 U.S. professionals were analyzed in Mplus using confirmatory factor analysis and exploratory structural equation modeling. The Four-Factor first-order exploratory structure equation modeling had the best model fit. Measurement invariance tests found the scalar invariance of the SOAR Scale across gender and education groups. Implications are discussed for using the SOAR Scale to build resilience at the individual, the team, and the organizational levels. (shrink)