This paper addresses a fundamental line of research in neuroscience: the identification of a putative neural processing core of the cerebral cortex, often claimed to be “canonical”. This “canonical” core would be shared by the entire cortex, and would explain why it is so powerful and diversified in tasks and functions, yet so uniform in architecture. The purpose of this paper is to analyze the search for canonical explanations over the past 40 years, discussing the theoretical frameworks informing this (...) research. It will highlight a bias that, in my opinion, has limited the success of this research project, that of overlooking the dimension of cortical development. The earliest explanation of the cerebral cortex as canonical was attempted by David Marr, deriving putative cortical circuits from general mathematical laws, loosely following a deductive-nomological account. Although Marr’s theory turned out to be incorrect, one of its merits was to have put the issue of cortical circuit development at the top of his agenda. This aspect has been largely neglected in much of the research on canonical models that has followed. Models proposed in the 1980s were conceived as mechanistic. They identified a small number of components that interacted as a basic circuit, with each component defined as a function. More recent models have been presented as idealized canonical computations, distinct from mechanistic explanations, due to the lack of identifiable cortical components. Currently, the entire enterprise of coming up with a single canonical explanation has been criticized as being misguided, and the premise of the uniformity of the cortex has been strongly challenged. This debate is analyzed here. The legacy of the canonical circuit concept is reflected in both positive and negative ways in recent large-scale brain projects, such as the Human Brain Project. One positive aspect is that these projects might achieve the aim of producing detailed simulations of cortical electrical activity, a negative one regards whether they will be able to find ways of simulating how circuits actually develop. (shrink)

Systems biologists often distance themselves from reductionist approaches and formulate their aim as understanding living systems “as a whole.” Yet, it is often unclear what kind of reductionism they have in mind, and in what sense their methodologies would offer a superior approach. To address these questions, we distinguish between two types of reductionism which we call “modular reductionism” and “bottom-up reductionism.” Much knowledge in molecular biology has been gained by decomposing living systems into functional modules or through detailed studies (...) of molecular processes. We ask whether systems biology provides novel ways to recompose these findings in the context of the system as a whole via computational simulations. As an example of computational integration of modules, we analyze the first whole-cell model of the bacterium M. genitalium. Secondly, we examine the attempt to recompose processes across different spatial scales via multi-scale cardiac models. Although these models rely on a number of idealizations and simplifying assumptions as well, we argue that they provide insight into the limitations of reductionist approaches. Whole-cell models can be used to discover properties arising at the interfaces of dynamically coupled processes within a biological system, thereby making more apparent what is lost through decomposition. Similarly, multi-scale modeling highlights the relevance of macroscale parameters and models and challenges the view that living systems can be understood “bottom-up.” Specifically, we point out that system-level properties constrain lower-scale processes. Thus, large-scale modeling reveals how living systems at the same time are more and less than the sum of the parts. (shrink)

The claim defended in the paper is that the mechanistic account of explanation can easily embrace idealization in big-scale brain simulations, and that only causally relevant detail should be present in explanatory models. The claim is illustrated with two methodologically different models: Blue Brain, used for particular simulations of the cortical column in hybrid models, and Eliasmith’s SPAUN model that is both biologically realistic and able to explain eight different tasks. By drawing on the mechanistic theory of computational explanation, (...) I argue that large-scale simulations require that the explanandum phenomenon is identified; otherwise, the explanatory value of such explanations is difficult to establish, and testing the model empirically by comparing its behavior with the explanandum remains practically impossible. The completeness of the explanation, and hence of the explanatory value of the explanatory model, is to be assessed vis-à-vis the explanandum phenomenon, which is not to be conflated with raw observational data and may be idealized. I argue that idealizations, which include building models of a single phenomenon displayed by multi-functional mechanisms, lumping together multiple factors in a single causal variable, simplifying the causal structure of the mechanisms, and multi-model integration, are indispensable for complex systems such as brains; otherwise, the model may be as complex as the explanandum phenomenon, which would make it prone to so-called Bonini paradox. I conclude by enumerating dimensions of empirical validation of explanatory models according to new mechanism, which are given in a form of a “checklist” for a modeler. (shrink)

Nick Bostrom’s recently patched ‘‘simulation argument’’ (Bostrom in Philos Q 53:243–255, 2003; Bos- trom and Kulczycki in Analysis 71:54–61, 2011) purports to demonstrate the probability that we ‘‘live’’ now in an ‘‘ancestor simulation’’—that is as a simulation of a period prior to that in which a civilization more advanced than our own—‘‘post-human’’—becomes able to simulate such a state of affairs as ours. As such simulations under consid- eration resemble ‘‘brains in vats’’ (BIVs) and may appear open to (...) similar objections, the paper begins by reviewing objections to BIV-type proposals, specifically those due a presumed mad envatter. In counter example, we explore the motivating rationale behind current work in the development of psychologically realistic social simula- tions. Further concerns about rendering human cognition in a computational medium are confronted through review of current dynamic systems models of cognitive agency. In these models, aspects of the human condition are repro- duced that may in other forms be considered incomputable, i.e., political voice, predictive planning, and consciousness. The paper then argues that simulations afford a unique potential to secure a post-human future, and may be nec- essary for a pre-post-human civilization like our own to achieve and to maintain a post-human situation. Long-s- tanding philosophical interest in tools of this nature for Aristotle’s ‘‘statesman’’ and more recently for E.O. Wilson in the 1990s is observed. Self-extinction-level threats from State and individual levels of organization are compared, and a likely dependence on large-scale psychologically realistic simulations to get past self-extinction-level threats is projected. In the end, Bostrom’s basic argument for the conviction that we exist now in a simulation is reaffirmed. (shrink)

This essay presents a point of view for looking at `free will', with the purpose of interpreting where exactly the freedom lies. For, freedom is what we mean by it. It compares the exercise of free will with the making of inferences, which usually is predominantly inductive in nature. The making of inference and the exercise of free will, both draw upon psychological resources that define our ‘selves’. I examine the constitution of the self of an individual, especially the involvement (...) of personal beliefs, personal memories, affects, emotions, and the hugely important psychological value-system, all of which distinguish the self of one individual from that of another. The foundational position that adopted in this essay is that all psychological processes are correlated with corresponding ones involving large scale neural aggregates in the brain, communicating with one another through wavelike modes of excitation and de-excitation. Of central relevance is the value-network around which the affect system is organized, the latter, in turn, being the axis around which is assembled the self, with all its emotional correlates. The self is a complex system. I include a brief outline of what complexity consists of. In reality all systems are complex, for complexity is ubiquitous, and certain parts of nature appear to us to be ‘simple’ only in certain specific contexts. It is in this background that the issue of determinism is viewed in this essay. Instead of looking at determinism as a grand principle entrenched in nature independent of our interpretation of it, I look at our ability to explain and to predict events and phenomena around us, which is made possible by the existence of causal links running in a complex course that set up correlations between diverse parts of nature, in this way putting the stamp of necessity on these events. However, the complexity of systems limits our ability to explain and to predict to within certain horizons defined by contexts. Our ability to explain and predict in matters relating to acts of free will is similarly limited by the operations of the self that remain hidden from our own awareness. The aspects of necessity and determinism appear to us in the form of reason and rationality that explain and predict only within a limited horizon, while the rest depends on the complex operation of self-linked psychological resources, where the latter appear as contingent in the context of the exercise of free will. The hallmark of complex systems is the existence of amplifying factors that operate as destabilizing ones, along with inhibiting or stabilizing factors as well that generally limit the destabilizing influences to local occurrences, while preserving the global integrity of a system. This complex interplay of destabilizing and stabilizing influences lead to the possibility of an enormous number of distinct modes of behavior that appear as emergent phenomena in complex systems. Looking at the particular case of the self of an individual that guide her actions and thoughts, it is the operation of our emotions, built around the psychological value-system, that provide for the amplifying and inhibiting factors mentioned above. The operation of these self-linked factors stamps our actions and thoughts as contingent ones that do not fit with our concepts of reason and rationality. And this is what provides the basis of our idea of free will. Free will is not ‘free’ in virtue of exemption from causal links running through all our self-based processes - ones that remain hidden from our awareness, but is free of what is perceived to be ‘reason and rationality’ based on knowledge and the common pool of beliefs and principles associated with a shared world-view. When we speak of the choice involved in an act of exercise of free will what we actually refer to is the commonly observed fact that various different individuals of a similar disposition respond differently when placed under similar circumstances. In other words, free will is ‘free’ precisely because it is not subject to constraints of a commonly accepted and shared set of principles, beliefs, and values. While it is possible that, in an exercise of free will, the self-linked psychological resources are brought into action when a number of alternatives are presented to the self by the operation of the ingredients of a commonly shared world-view, it seems likely that, that set of alternatives is not of primary relevance in the final response that the mind produces, since the latter is primarily a product of the operation of the self-based psychological resources. What is of greater relevance here is the operation of emotion-driven processes, guided by the psychological value-system based on the activity of the so-called reward-punishment network in the brain. These processes lead the individual to a response that appears to be free from the shackles of determinism precisely because their mechanisms, which are hidden from us, do not conform to commonly accepted and shared rules and principles. In contrast, inductive inference is a process that is based on the ‘cognitive face’ of self, where the self-based psychological resources play a supportive role to the commonly shared world-view of an individual. There is never any freedom from the all-pervading causal links representing correlations among all objects, entities, and events in nature. In the midst of all this, the closest thing to freedom that we can have in our life comes with self-examination and self-improvement. The possibility of self-examination appears in the form of specific conjunctions between our complex self-processes and the ceaseless changes of scenario in our external world. This actually makes the emergent phenomenon of self-examination a matter of chance, but one that keeps on appearing again and again in our life. Once realized, self examination creates possibilities that would not be there in the absence of it, and these possibilities include the enhancement of further self-enrichment and further diversity in the exercise of our free will. (shrink)

Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human (...) observers. To this end, we exploit a neural network metric in the output of a biologically inspired Self- Organizing Map Quantization Error (SOM-QE). Shape pairs with perfect geometry mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within and/or across the shapes in a given pair produce, as shown here, a longer choice response time (RT) for “yes” responses relative to symmetry. These data are consistently mirrored by the variations in the SOM-QE from unsupervised neural network analysis of the same stimulus images. The neural network metric is thus capable of detecting and scaling human symmetry uncertainty in response to patterns. Such capacity is tightly linked to the metric’s proven selectivity to local contrast and color variations in large and highly complex image data. (shrink)

The diversity in agronomic practices being used by sugarcane producers in Brazil determines differences in economic performance and cost structure. The purpose of this study is to evaluate the cost of six systems of agronomic practices using fixed or variable rates for soil amendment, fertilizer, and defensive applications and assess the profitability of these systems at three scales of sugarcane production. We then describe the data sample related to the 2019–2020 harvest season and collected from fifty-five sugarcane producers in the (...) central-south region of Brazil. Thereafter, using a quantitative approach, a cost analysis was performed, and the cumulative frequency of the net revenue for the three scales of production (small, medium, and large), was calculated using a Monte Carlo simulation. The cost analysis indicated that fertilizer had the highest cost considering the agronomic practices adopted at the three scales of production analyzed. The cumulative frequency analysis results from the Monte Carlo simulation showed the highest net revenue per hectare for medium sugarcane producers. In addition, the presence of economies of scale was not confirmed because the lowest cost was found in small-scale sugarcane producers and the highest net revenue was obtained by medium-scale sugarcane producers. (shrink)

Apart from what (little) OpenAI may be concealing from us, we all know (roughly) how ChatGPT works (its huge text database, its statistics, its vector representations, and their huge number of parameters, its next-word training, and so on). But none of us can say (hand on heart) that we are not surprised by what ChatGPT has proved to be able to do with these resources. This has even driven some of us to conclude that ChatGPT actually understands. It is not (...) true that it understands. But it is also not true that we understand how it can do what it can do. I will suggest some hunches about benign biases: convergent constraints that emerge at LLM scale that may be helping ChatGPT do so much better than we would have expected. These biases are inherent in the nature of language itself, at LLM scale, and they are closely linked to what it is that ChatGPT lacks, which is direct sensorimotor grounding to connect its words to their referents and its propositions to their meanings. These convergent biases are related to (1) the parasitism of indirect verbal grounding on direct sensorimotor grounding, (2) the circularity of verbal definition, (3) the mirroring of language production and comprehension, (4) iconicity in propositions at LLM scale, (5) computational counterparts of human categorical perception in category learning by neural nets, and perhaps also (6) a conjecture by Chomsky about the laws of thought. The exposition will be in the form of a dialogue with ChatGPT-4. (shrink)

The “Human Brain Project” (HBP) is a large-scale European neuroscience and information communication technology (ICT) project that has been a matter of heated controversy since its inception. With its aim to simulate the entire human brain with the help of supercomputing technologies, the HBP plans to fundamentally change neuroscientific research practice, medical diagnosis, and eventually the use of computers itself. Its controversial nature and its potential impacts render the HBP a subject of crucial importance for critical studies of (...) science and society. In this paper, we provide a critical exploratory analysis of the potential mid- to long-term impacts the HBP and its ICT infrastructure could be expected to have, provided its agenda will indeed be implemented and executed to a substantive degree. We analyse how the HBP aspires to change current neuroscientific practice, what impact its novel infrastructures could have on research culture, medical practice and the use of ICT, and how, given a certain degree of successful execution of the project’s aims, potential clinical and methodological applications could even transform society beyond scientific practice. Furthermore, we sketch the possibility that research such as that projected by the HBP may eventually transform our everyday world, even beyond the scope of the HBP’s explicit agenda, and beyond the isolated ‘application’ of some novel technological device. Finally, we point towards trajectories for further philosophical, historical and sociological research on the HBP that our exploratory analysis might help to inspire. Our analysis will yield important insights regardless of the actual success of the HBP. What we drive at, for the most part, is the broader dynamics of scientific and technological development of which the HBP agenda is merely one particularly striking exemplification. (shrink)

The pace of technological change continues to outstrip the evolution of democratic institutions, creating an urgent need for innovative approaches to democratic reform. However, the experimentation bottleneck - characterized by slow speed, high costs, limited scalability, and ethical risks - has long hindered progress in democracy research. This paper proposes a novel solution: employing generative artificial intelligence (GenAI) to create synthetic data through the simulation of digital homunculi, GenAI-powered entities designed to mimic human behavior in social contexts. By enabling (...) rapid, low-risk experimentation with alternative institutional designs, this approach could significantly accelerate democratic innovation. I examine the potential of GenAI-assisted research to mitigate current limitations in democratic experimentation, including the ability to simulate large-scale societal interactions and test complex institutional mechanisms. While acknowledging potential risks such as algorithmic bias, reproducibility challenges, and AI alignment issues, I argue that the benefits of synthetic data are likely to outweigh their drawbacks if implemented with proper caution. To address existing challenges, I propose a range of technical, methodological, and institutional adaptations. The paper concludes with a call for interdisciplinary collaboration in the development and implementation of GenAI-assisted methods in democracy research, highlighting their potential to bridge the gap between democratic theory and practice in an era of rapid technological change. (shrink)

Computer simulations constitute a significant scientific tool for promoting scientific understanding of natural phenomena and dynamic processes. Substantial leaps in computational force and software engineering methodologies now allow the design and development of large-scale biological models, which – when combined with advanced graphics tools – may produce realistic biological scenarios, that reveal new scientific explanations and knowledge about real life phenomena. A state-of-the-art simulation system termed Reactive Animation (RA) will serve as a study case to examine the (...) contemporary philosophical debate on the scientific value of simulations, as we demonstrate its ability to form a scientific explanation of natural phenomena and to generate new emergent behaviors, making possible a prediction or hypothesis about the equivalent real-life phenomena. (shrink)

There are many social psychological theories regarding the nature of prejudice, but only one major theory of prejudice reduction: under the right circumstances, prejudice between groups will be reduced with increased contact. On the one hand, the contact hypothesis has a range of empirical support and has been a major force in social change. On the other hand, there are practical and ethical obstacles to any large-scale controlled test of the hypothesis in which relevant variables can be manipulated. (...) Here we construct a spatialized model that tests the core hypothesis in a large array of game-theoretic agents. Robust results offer a new kind of support for the contact hypothesis: results in simulation do accord with a hypothesis of reduced prejudice with increased contact. The spatialized game-theoretic model also suggests a deeper explanation for at least some of the social psychological phenomena at issue. (shrink)

In contemporary human brain mapping, it is commonly assumed that the “mind is what the brain does”. Based on that assumption, task-based imaging studies of the last three decades measured differences in brain activity that are thought to reflect the exercise of human mental capacities (e.g., perception, attention, memory). With the advancement of resting state studies, tractography and graph theory in the last decade, however, it became possible to study human brain connectivity without relying on cognitive tasks or constructs. It (...) therefore is currently an open question whether the assumption that “the mind is what the brain does” is an indispensable working hypothesis in human brain mapping. This paper argues that the hypothesis is, in fact, dispensable. If it is dropped, researchers can “meet the brain on its own terms” by searching for new, more adequate concepts to describe human brain organization. Neuroscientists can establish such concepts by conducting exploratory experiments that do not test particular cognitive hypotheses. The paper provides a systematic account of exploratory neuroscientific research that would allow researchers to form new concepts and formulate general principles of brain connectivity, and to combine connectivity studies with manipulation methods to identify neural entities in the brain. These research strategies would be most fruitful if applied to the mesoscopic scale of neuronal assemblies, since the organizational principles at this scale are currently largely unknown. This could help researchers to link microscopic and macroscopic evidence to provide a more comprehensive understanding of the human brain. The paper concludes by comparing this account of exploratory neuroscientific experiments to recent proposals for large-scale, discovery-based studies of human brain connectivity. (shrink)

Computer simulations of the formation and evolution of large-scale structure in the universe are integral to the enterprise of modern cosmology. Establishing the reliability of these simulations has been extremely challenging, primarily because of epistemic opacity. In this setting, robustness analysis defined by requiring converging outputs from a diverse ensemble of simulations is insufficient to determine simulation validity. We propose an alternative path of structured code validation that applies eliminative reasoning to isolate and reduce possible sources of (...) error, a potential path that is already being explored by some cosmologists. (shrink)

Abstract: A corn crop typically refers to a large-scale cultivation of corn (also known as maize) for commercial purposes such as food production, animal feed, and industrial uses. Corn is one of the most widely grown crops in the world, and it is a major staple food for many cultures. Corn crops are grown in various regions of the world with different climates, soil types, and farming practices. In the United States, for example, the Midwest is known as (...) the "Corn Belt" due to its extensive corn production. Corn crops can be grown using a variety of methods, including conventional tillage, no-till farming, and the use of genetically modified crops. Due to its importance, there is a need for discovering the corn diseases and treating them. The aim of this study is to propose a deep learning model for the classification of corn leaf diseases based on Convolutional Neural Network. A model for classifying maize diseases was developed using a dataset for classifying Corn diseases that contains 4 classes of disease. The dataset was collected from Keggel website. The proposed model was trained, validated, and tested. The F1-score (99.83%), Recall (99.83%), precision (99.83%), Accuracy (99.83%). (shrink)

In the current chapter, I examined the relationship between the cerebellum, emotion, and morality with evidence from large-scale neuroimaging data analysis. Although the aforementioned relationship has not been well studied in neuroscience, recent studies have shown that the cerebellum is closely associated with emotional and social processes at the neural level. Also, debates in the field of moral philosophy, psychology, and neuroscience have supported the importance of emotion in moral functioning. Thus, I explored the potentially important but (...) less-studies topic with NeuroSynth, a tool for large-scale brain image analysis, while addressing issues associated with reverse inference. The result from analysis demonstrated that brain regions in the cerebellum, the right Crus I and Crus II in particular, were specifically associated with morality in general. I discussed the potential implications of the finding based on clinical and functional neuroimaging studies of the cerebellum, emotional functioning, and neural networks for diverse psychological processes. (shrink)

The emergence of Large Language Models (LLMs) and advancements in Artificial Intelligence (AI) offer an opportunity for computational social science research at scale. Building upon prior explorations of LLM agent design, our work introduces a simulated agent society where complex social relationships dynamically form and evolve over time. Agents are imbued with psychological drives and placed in a sandbox survival environment. We conduct an evaluation of the agent society through the lens of Thomas Hobbes's seminal Social Contract Theory (...) (SCT). We analyze whether, as the theory postulates, agents seek to escape a brutish "state of nature" by surrendering rights to an absolute sovereign in exchange for order and security. Our experiments unveil an alignment: Initially, agents engage in unrestrained conflict, mirroring Hobbes's depiction of the state of nature. However, as the simulation progresses, social contracts emerge, leading to the authorization of an absolute sovereign and the establishment of a peaceful commonwealth founded on mutual cooperation. This congruence between our LLM agent society's evolutionary trajectory and Hobbes's theoretical account indicates LLMs' capability to model intricate social dynamics and potentially replicate forces that shape human societies. By enabling such insights into group behavior and emergent societal phenomena, LLM-driven multi-agent simulations, while unable to simulate all the nuances of human behavior, may hold potential for advancing our understanding of social structures, group dynamics, and complex human systems. (shrink)

The protocol dynamically adjusts transmission power based on node depth and residual energy, reducing communication overhead and prolonging network lifetime. The proposed methodology employs a multi-step approach, starting with the initialization phase, where nodes calculate their depth and energy levels. Following this, a depth-based clustering mechanism organizes nodes into clusters, allowing more efficient data aggregation. The routing process then prioritizes nodes with higher energy levels, reducing premature node failure. A novel energy-aware transmission algorithm ensures that data packets are transmitted over (...) the most energy-efficient paths, thus extending the overall network longevity. Simulation results demonstrate that the proposed protocol achieves significant improvements in energy consumption, data transmission reliability, and network lifetime compared to traditional methods. The conclusion discusses the potential future enhancements, including adaptive algorithms that can further reduce energy consumption in large-scale UWSNs. (shrink)

: In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume of data (...) generated by modern applications. By leveraging algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), the proposed method efficiently distributes data across multiple paths, ensuring balanced network utilization. The combination of these algorithms with multipath routing significantly reduces congestion and improves overall network performance. Simulations conducted on various network scenarios demonstrate the effectiveness of this approach, showcasing improvements in data throughput, reduced packet loss, and enhanced quality of service (QoS). (shrink)

The routing process then prioritizes nodes with higher energy levels, reducing premature node failure. A novel energy-aware transmission algorithm ensures that data packets are transmitted over the most energy-efficient paths, thus extending the overall network longevity. Simulation results demonstrate that the proposed protocol achieves significant improvements in energy consumption, data transmission reliability, and network lifetime compared to traditional methods. The conclusion discusses the potential future enhancements, including adaptive algorithms that can further reduce energy consumption in large-scale UWSNs.

Abstract. Death seems to be a permanent event, but there is no actual proof of its irreversibility. Here we list all known ways to resurrect the dead that do not contradict our current scientific understanding of the world. While no method is currently possible, many of those listed here may become feasible with future technological development, and it may even be possible to act now to increase their probability. The most well-known such approach to technological resurrection is cryonics. Another method (...) is indirect mind uploading, or digital immortality, namely the preservation of data about a person to allow for future reconstruction by powerful AI. More speculative ways to immortality include combinations of future superintelligence on a galactic scale, which could use simulation to resurrect all possible people, and new physical laws, which may include time-travel or obtaining information from the past. Acausal trade with parallel worlds could help combine random resurrection and reconstruction based on known data without loss of share of worlds where I exist (known as existence measure). Quantum immortality could help to increase the probability of success for cryonics and digital immortality. There many possible approaches to technological resurrection and thus if large-scale future technological development occurs, some form of resurrection is inevitable. (shrink)

Advancements in cloud technologies have increased the infrastructural needs of data centers due to storage needs and processing of extensive dimensional data. Many service providers envisage anomaly detection criteria to guarantee availability to avoid breakdowns and complexities caused due to large-scale operations. The streaming log data generated is associated with multi-dimensional complexity and thus poses a considerable challenge to detect the anomalies or unusual occurrences in the data. In this research, a hybrid model is proposed that is motivated (...) by deep belief criteria and meta-heuristics. Using Search-and-Rescue—BrainStorm Optimization (SAR-BSO), a hybrid feature selection (FS) and deep belief network classifier is used to localize and detect anomalies for streaming data logs. The significant contribution of the research lies in FS, which is carried out using SAR-BSO which increases the detection power of the model as it selects the most significant variables by minimizing redundant features. The evaluation of accuracy is efficiently improved when compared with the predictable methods, such as Extract Local Outlier Factor (ELOF), Track-plus, Hybrid Distributed Batch Stream (HDBS), IForestASD, DBN, BSO-based Feature Selection with DBN, Genetic Algorithm-Deep Belief Network (GA-DBN), Mutual Information-Deep Belief Network (MI-DBN), information entropy-Deep Belief Network(I + DBN), Flat Field-Deep Belief Network (FF + DBN), African Vulture Optimization Algorithm-Deep Belief Network(AVOA + DBN), Gorilla Troop Optimizer-Deep Belief Network(GTO-DBN), and SARO-based Feature Selection with DBN. Further, the accurate detection of the anomalies in the data stream is established by the Deep Belief Neural Network (DBN) classifier. The model’s efficacy is determined using Apache, Hadoop, HDFS, Spark, and Linux datasets and evaluated against existing similar models. The model efficiency is provided using multiple evaluation metrics and is found effective. From the experimentation, the accuracy of the proposed model is found to be 93.3, 95.4, 93.6, 94.2, and 93.5% respectively for the dataset such as Apache, Hadoop, HDFS, spark, and Linux. This enhancement in accuracy is due to the selection of optimal features by the proposed SAR-BSO algorithm. (shrink)

Psychopathy is a personality disorder characterized by callous antisocial behavior and criminal recidivism. Here we examine whether psychopathy is associated with alterations in functional connectivity in three large-scale cortical networks. Using fMRI in 142 adult male prison inmates, we computed resting-state functional connectivity using seeds from the default mode network, frontoparietal network, and cingulo-opercular network. To determine the specificity of our findings to these cortical networks, we also calculated functional connectivity using seeds from two comparison primary sensory networks: (...) visual and auditory networks. Regression analyses related network connectivity to overall psychopathy scores and to subscores for the “factors” and “facets” of psychopathy: Factor 1, interpersonal/affective traits; Factor 2, lifestyle/antisocial traits; Facet 1, interpersonal; Facet 2, affective; Facet 3, lifestyle; Facet 4, antisocial. Overall psychopathy severity was associated with reduced functional connectivity between lateral parietal cortex and dorsal anterior cingulate cortex. The two factor scores exhibited contrasting relationships with functional connectivity: Factor 1 scores were associated with reduced functional connectivity in the three cortical networks, whereas Factor 2 scores were associated with heightened connectivity in the same networks. This dissociation was evident particularly in the functional connectivity between anterior insula and dorsal anterior cingulate cortex. The facet scores also demonstrated distinct patterns of connectivity. We found no associations between psychopathy scores and functional connectivity within visual or auditory networks. These findings provide novel evidence on the neural correlates of psychopathy and suggest that connectivity between cortical association hubs, such as the dorsal anterior cingulate cortex, may be a neurobiological marker of the disorder. (shrink)

One determining characteristic of contemporary sociopolitical systems is their power over increasingly large and diverse populations. This raises questions about power relations between heterogeneous individuals and increasingly dominant and homogenizing system objectives. This article crosses epistemic boundaries by integrating computer engineering and a historicalphilosophical approach making the general organization of individuals within large-scale systems and corresponding individual homogenization intelligible. From a versatile archeological-genealogical perspective, an analysis of computer and social architectures is conducted that reinterprets Foucault’s disciplines and (...) political anatomy to establish the notion of politics for a purely technical system. This permits an understanding of system organization as modern technology with application to technical and social systems alike. Connecting to Heidegger’s notions of the enframing (Gestell) and a more primal truth (anfänglicheren Wahrheit), the recognition of politics in differently developing systems then challenges the immutability of contemporary organization. Following this critique of modernity and within the conceptualization of system organization, Derrida’s democracy to come (à venir) is then reformulated more abstractly as organizations to come. Through the integration of the discussed concepts, the framework of Large-Scale Systems Composed of Homogeneous Individuals (LSSCHI) is proposed, problematizing the relationships between individuals, structure, activity, and power within large-scale systems. The LSSCHI framework highlights the conflict of homogenizing system-level objectives and individual heterogeneity, and outlines power relations and mechanisms of control shared across different social and technical systems. (shrink)

Nineteenth-century optical toys that showcase illusions of motion such as the phenakistoscope, zoetrope, and praxinoscope, have enjoyed active “afterlives” in the twentieth and twenty-first centuries. Contemporary incarnations of the zoetrope are frequently found in the realms of fine art and advertising, and they are often much larger than their nineteenth-century counterparts. This article argues that modern-day optical toys are able to conjure feelings of wonder and spectacle equivalent to their nineteenth-century antecedents because of their adjustment in scale. Exploring a (...) range of contemporary philosophical toys found in arts, entertainment, and advertising contexts, the article discusses various technical adjustments made to successfully “scale up” optical toys, including the replacement of hand-spun mechanisms with larger sources of motion and the use of various means such as architectural features and stroboscopic lights to replace traditional shutter mechanisms such as the zoetrope’s dark slots. Critical consideration of scale as a central feature of these installations reconfigures the relationship between audience and device. Large-scale adaptations of optical toys revise the traditional conception of the user, who is able to tactilely manipulate and interact with the apparatus, instead positing a viewer who has less control over the illusion’s operation and is instead a captive audience surrounded by the animation. It is primarily through their adaptation of scale that contemporary zoetropes successfully elicit wonder as scientific spectacles from their audiences today. (shrink)

Like most bioethical discussion, examination of human biobanks has been largely framed in terms of research subjects’ rights, principally informed consent, with some gestures toward public benefits. However, informed consent is for the competent, rights-bearing individual: focussing on the individual, it thus neglects social, economic and even political matters; focussing on the competent rights-bearer, it does not serve situations where consent is plainly inappropriate (eg, the young child) or where coercion can obviously be justified (the criminal). Using the British experience (...) of large-scale biobanking, I argue that the focus on consenting individuals distorts our ways of thinking about biobanks and has serious practical ramifications. This becomes clear if we contrast the case of adult biobanks intended for medical research with two other forms of biobanking. Thus child cohort studies – vital for sound scientific investigation of the interplay of genetics and environment in health – have been very badly funded next to adult studies. On the other hand, forensic databases have attracted massive investment, but little debate – partly owing to a sense that here, at least, is a case where consent is not relevant. Contrasting these central types of biobanking, I will suggest that there are powerful factors at work in limiting ‘ethics’ to individual rights. Projects of this size should direct our attention to more overtly political questions concerning priority setting and organisation of medical research. (shrink)

Large-scale Social Limitation (hereinafter referred to as PSBB) is one form of concern. The government and local governments are Pendemic throughout Indonesia and the world, namely Pandemic Corona Virus Disease (Covid-19). Bogor Regency, which is one of the buffer cities of the Republic of Indonesia, is an area that is quite vulnerable in spreading the Corona virus. Why? because many DKI Jakarta employees / laborers live in Bogor Regency, whose mobility is very high. With the birth of Regent (...) Regulation No. 16 of 2020 concerning Implementation of Large- Scale Social Restrictions in Handling COVID-19 in order to conserve the use of Covid 19. The purpose of this study is to analyze the Implementation of Large Scale Social Limitation Policies in the Government of Bogor Regency. This research method uses Qualitative Methods with Literature Study research methods. (Huberman, Miles, 1994). The technique of inviting data is by searching Scientific Journals, Online Mass Media, Legislation and Books. The technique to determine Online Media by Pusposive, while for data analysis using Nvivo 12 Plus. Test the validity of the data by testing the data source. The results of the study show that the implementation of the Large-Scale Social Limitation Policy in the Bogor District Government has not yet proceeded, starting from the clash of authority between the Central Government and the Regional Government so that it cannot make improvements, and many more are in accordance with the provisions of Covid-19 and its distribution. Social assistance to the community. (shrink)

Some consequentialists argue that ordinary individuals are obligated to act in specific, concrete ways to address large-scale harms. For example, they argue that we should each refrain from meat-eating and avoid buying sweatshop-made clothing. The case they advance for such prescriptions can seem intuitive and compelling: by acting in those ways, a person might help prevent serious harms from being produced at little or no personal cost, and so one should act in those ways. But I argue that (...) such reasoning often relies on an overly simplistic assessment of the costs and benefits of those prescriptions, one that misconstrues or neglects important issues. Indeed, a closer look at those costs and benefits reveals just how little we often know about a number of real-world matters that bear on the expected consequences of the prescribed individual actions. Our predicament is one of radical uncertainty: we currently lack a sound basis for concluding that the relevant actions are more likely to do good than to backfire. I distinguish this empirically grounded objection from others, which are not convincing—including one based on the mere conceivability of the actions in question backfiring and another based on the supposition that such actions cannot make a difference in addressing the massive problems at issue. The upshot is that, at least for now, consequentialist arguments for many specific individual actions aimed at addressing large-scale harms are inconclusive. (shrink)

Solar radiation management (SRM) may help to reduce the negative outcomes of climate change by minimising or reversing global warming. However, many express the worry that SRM may pose a moral hazard, i.e., that information about SRM may lead to a reduction in climate change mitigation efforts. In this paper, we report a large-scale preregistered, money-incentivised, online experiment with a representative US sample (N = 2284). We compare actual behaviour (donations to climate change charities and clicks on climate (...) change petition links) as well as stated preferences (support for a carbon tax and self-reported intentions to reduce emissions) between participants who receive information about SRM with two control groups (a salience control that includes information about climate change generally and a content control that includes information about a different topic). Behavioural choices are made with an earned real-money endowment, and stated preference responses are incentivised via the Bayesian Truth Serum. We fail to find a significant impact of receiving information about SRM and, based on equivalence tests, we provide evidence in favour of the absence of a meaningfully large effect. Our results thus provide evidence for the claim that there is no detectable moral hazard with respect to SRM. [Open access]. (shrink)

In the biomedical context, policy makers face a large amount of potentially discordant evidence from different sources. This prompts the question of how this evidence should be aggregated in the interests of best-informed policy recommendations. The starting point of our discussion is Hunter and Williams’ recent work on an automated aggregation method for medical evidence. Our negative claim is that it is far from clear what the relevant criteria for evaluating an evidence aggregator of this sort are. What is (...) the appropriate balance between explicitly coded algorithms and implicit reasoning involved, for instance, in the packaging of input evidence? In short: What is the optimal degree of ‘automation’? On the positive side: We propose the ability to perform an adequate robustness analysis as the focal criterion, primarily because it directs efforts to what is most important, namely, the structure of the algorithm and the appropriate extent of automation. Moreover, where there are resource constraints on the aggregation process, one must also consider what balance between volume of evidence and accuracy in the treatment of individual evidence best facilitates inference. There is no prerogative to aggregate the total evidence available if this would in fact reduce overall accuracy. (shrink)

Abstract: For the last two decades, land and land-related problems are more complicated ever before. Especially the proliferation of large-scale rural land investments and the vulnerability of the local communities in land abundant developing countries instigated researchers, human right activists, and international and regional organizations to proposed governance guidelines, principles, and codes of conduct for large-scale land investments. To identify policy flaws on the protection of local community rights under the governance process of large-scale (...) rural land investment in Amhara National Regional State Ethiopia the commonalities of those international and regional accepted governance guiding lines and principles are taken as a point of reference to examine the land governance policies of the region through the approach of document analysis. The result shows all legislation with regard to the governance of large-scale rural land investment is flawed throughout the land policy reform processes and identified five ways in which the de jure land-related rights of the local communities are compromised and exacerbated the vulnerability of their livelihoods. (shrink)

What is it for a sound or gesture to have a meaning, and how does it come to have one? In this paper, a range of simulations are used to extend the tradition of theories of meaning as use. The authors work throughout with large spatialized arrays of sessile individuals in an environment of wandering food sources and predators. Individuals gain points by feeding and lose points when they are hit by a predator and are not hiding. They can (...) also make sounds heard by immediate neighbours in the array, and can respond to sounds from immediate neighbours. No inherent meaning for these sounds is built into the simulation; under what circumstances they are sent, if any, and what the response to them is, if any, vary initially with the strategies randomized across the array. These sounds do take on a specific function for communities of individuals, however, with any of three forms of strategy change: direct imitation of strategies of successful neighbours, a localized genetic algorithm in which strategies are ‘crossed’ with those of successful neighbours, and neural net training on the behaviour of successful neighbours. Starting from an array randomized across a large number of strategies, and using any of these modes of strategy change, communities of ‘communicators’ emerge. Within these evolving communities the sounds heard from immediate neighbours, initially arbitrary across the array, come to be used for very specific communicative functions. ‘Communicators’ make a particular sound on feeding and respond to that same sound from neighbours by opening their mouths; they make a different sound when hit with a predator and respond to that sound by hiding. Robustly and persistently, even in simple computer models of communities of self-interested agents, something suggestively like signalling emerges and spreads. Keywords: meaning, communication, genetic algorithms, neural networks. (shrink)

Physics has been slowly and reluctantly beginning to address the role and fundamental basis of the ‘observer’ which has until now also been considered metaphysical and beyond the mandate empirical rigor. It is suggested that the fundamental premise of the currently dominant view of ‘Cognitive Theory’ - “Mind Equals Brain” is erroneous; and the associated belief that the ‘Planck scale, ‘the so-called basement level of reality’, as an appropriate arena from which to model psycho-physical bridging is also in error. (...) In this paper we delineate a simple, inexpensive experimental design to ‘crack the so-called cosmic egg’ thereby opening the door to largescale extra dimensions (LSXD) tantamount to the regime of the unified field and thus awareness. The methodology surmounts the quantum uncertainty principle in a manner violating Quantum Electrodynamics, (QED), a cornerstone of modern theoretical physics, by spectrographic analysis of newly theorized Tight-Bound State (TBS) Bohr orbits in ‘continuous-state’ transition frequencies of atomic hydrogen. If one wonders why QED violation in the spectra of atomic hydrogen relates to solving the mind-body (observer) problem; consider this a 1st wrench in a forthcoming tool box of Unified Field Mechanics, UF that will soon enough in retrospect cause the current tools of Classical and Quantum Mechanics to appear as stone axes. Max Planck is credited as the founder of quantum mechanics with his 1900 quantum hypothesis that energy is radiated and absorbed discretely by the formulation, E = hv. Empirically implementing this next paradigm shift utilizing parameters of the long sought associated ‘new physics’ of the 3rd regime (classical-quantum- unified) allows access to LSXD of space; thus pragmatically opening the domain of mental action for the 1st time in history. This rendering constitutes a massive paradigm shift to Unified Field Theory creating a challenge for both the writer and the reader! (shrink)

An orbital simulation program is described that uses a geometrical approach to modeling gravitational and atomic orbits at the Planck scale. Orbiting objects A, B, C... are sub-divided into points, each point representing 1 unit of Planck mass, for example, a 1kg satellite would divide into 1kg/Planck mass = 45940509 points. Each point in object A then forms a rotating orbital pair with every corresponding point in objects B, C... resulting in a universe-wide, n-body network of rotating point-to-point (...) orbital pairs. Each orbital pair rotates 1 unit of Planck length per unit of Planck time at velocity c in hypersphere space co-ordinates, the results are then summed and averaged to give the new co-ordinates, the program then repeats. When these rotations are mapped over time on a 2-D plane (representing 3D space), objects A, B, C... appear to be orbiting each other. The basic simulation uses the fine structure constant alpha as an orbital constant to simulate gravitational orbit parameters. As each orbital comprises only 2 points, 1 at each orbital pole, information regarding the objects A, B, C... ; momentum, size, center of mass, barycenter etc ... is not required, instead only the start (x, y, z) co-ordinates of each point are defined. Each point, by having a mass of Planck mass, is itself a construct of multiple particles, and so we can also form individual particle to particle orbital pairs. The simulation uses only geometry, no dimensioned constants (G, h, c ...) are required, although the results can be measured in Planck units for comparison. Points are physically linked together by a unit of momentum (a graviton), the rationale for this is described in a subsequent article on atomic orbitals. (shrink)

One could argue that the use, extraction, and development of natural resources for human purposes, i.e. resource exploitation, constitutes a form of instrumentalization of the ecosystems from which these resources are derived. Moreover, that such instrumentalization may be carried out in a way that has adverse social and environmental impacts. Given that a number of ecosystems are indispensable for the satisfaction of human interests and needs, their instrumentalization may nevertheless be justified. In this context, if the amount and rate of (...) instrumentalization of ecosystems leads to their depletion, people whose well-being depends on the ecosystems’ existence may be profoundly harmed. Those instrumentalization practices and actions that lead to states of depletion are generally considered as harmful, but only to those whose interests and needs are frustrated as a result of such practices and actions. In this paper, I argue that the way in which ecosystems are instrumentalized, and not just the quantity and rate, may also cause a philosophically relevant form of harm, and not necessarily to humans: A given ecosystem may be meaningfully harmed not only when it is depleted, but also when its functioning is altered in such a way that it cannot retain core capacities it had before it was instrumentalized. (shrink)

Korea Atomic Energy Research Institute (KAERI) has been carrying out a scaled-down moderator test program to simulate the CANDU-6 moderator circulation phenomena during steady state operation and accident conditions. The Moderator Circulation Test (MCT) facility was designed and constructed as 1/4 scale of the prototype of CANDU-6 reactor. In the present work spatial distribution of two-dimensional velocity in the MCT facility under isothermal condition was measured using planar Particle Image Velocimetry (PIV) system which consists of double pulsed laser, synchronizer, (...) and high speed camera. TSI’s Insight TM 4G software was used to perform PIV image capturing, PIV calculation and post processing. 10 μm sized silver-coated hollow sphere particles were used as flow tracer particles. Multiple experiments were conducted to cover large area of the MCT facility with limited field of view of a single camera. Instantaneous and averaged velocity field were analyzed for each spatial position and flow rate. This research is capable of offering validation data for self-reliant CFD tools to predict moderator subcooling margin in CANDU-6 reactor. (shrink)

A crucial question for artificial cognition systems is what meaning is and how it arises. In pursuit of that question, this paper extends earlier work in which we show that emergence of simple signaling in biologically inspired models using arrays of locally interactive agents. Communities of "communicators" develop in an environment of wandering food sources and predators using any of a variety of mechanisms: imitation of successful neighbors, localized genetic algorithms and partial neural net training on successful neighbors. Here (...) we focus on environmental variability, comparing results for environments with (a) constant resources, (b) random resources, and (c) cycles of "boom and bust." In both simple and complex models across all three mechanisms of strategy change, the emergence of communication is strongly favored by cycles of "boom and bust." These results are particularly intriguing given the importance of environmental variability in fields as diverse as psychology, ecology and cultural anthropology. (shrink)

In this chapter, we discuss a selection of current views of the neural correlates of consciousness (NCC). We focus on the different predictions they make, in particular with respect to the role of prefrontal cortex (PFC) during visual experiences, which is an area of critical interest and some source of contention. Our discussion of these views focuses on the level of functional anatomy, rather than at the neuronal circuitry level. We take this approach because we currently understand more about (...) experimental evidence at this coarse level and because these results are appropriate for arbitrating between current theoretical frameworks. We discuss the Two-Visual-Systems Hypothesis (Milner & Goodale 1995; 2006), Local Recurrency (Lamme 2010; Lamme 2006), Higher Order (Lau 2008; Lau & Rosenthal 2011) and Global Workspace theories (Baars 1997; Baars 2005; Dehaene & Naccache 2001; Dehaene 2014). Despite the apparent stark differences between conscious and unconscious perceptual processing, available evidence suggests that their neural substrates must be largely shared. This indicates that the difference in neural activity between conscious and unconscious perceptual processing is likely to be subtle and highly specialized. We argue that current experimental evidence about the involvement of specific activity in prefrontal cortex supports the higher order neural theory of consciousness. In consequence, imaging techniques that focus only on marked differences between conscious and unconscious level of activity are likely to be insensitive to the relevant neural activity patterns that underlie conscious experiences. Finally, it follows from the evidence we discuss that the functional advantages of conscious over unconscious perceptual processing may be more limited than commonly thought. (shrink)

A recent evolution of computer simulations has led to the emergence of complex computer simulations. In particular, the need to formalize composite objects (those objects that are composed of other objects) has led to what the author suggests calling pluriformalizations, i.e. formalizations that are based on distinct sub-models which are expressed in a variety of heterogeneous symbolic languages. With the help of four case-studies, he shows that such pluriformalizations enable to formalize distinctly but simultaneously either different aspects or different parts (...) or different scales of the same object or system. From an epistemological standpoint, he suggests that this kind of computer-aided complex formalization of composite objects renew the traditional relations between computer simulations, theoretical models and operational models. (shrink)

We investigate the application of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model for forecasting air pollution levels based on historical data. Our experimental setup uses real-world air quality datasets from multiple regions, containing measurements of pollutants like PM2.5, PM10, CO, NO2, and SO2, alongside meteorological data such as temperature, humidity, and wind speed. The models are trained, validated, and tested using a split dataset, and their accuracy is evaluated using performance metrics like (...) Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared. (shrink)

(Draft of Feb 2023, see upcoming issue for Chalmers' reply) In Reality+: Virtual Worlds and the Problems of Philosophy, David Chalmers argues, among other things, that: if we are living in a full-scale simulation, we would still enjoy broad swathes of knowledge about non-psychological entities, such as atoms and shrubs; and, our lives might still be deeply meaningful. Chalmers views these claims as at least weakly connected: The former claim helps forestall a concern that if objects in the (...) simulation are not genuine (and so not knowable), then life in the simulation is illusory and therefore, not as valuable as a non-simulated life. Taking up these questions, I argue that in general, the value of social knowledge for a meaningful life dramatically swamps the value of non-social knowledge for a meaningful life. Along the way, I propose a non-additive model of the meaningfulness of life, according to which the overall effect of some potential contributor of value to a life depends in part on what is already in a life. One upshot is that the vindication of non-social knowledge, absent a correlative vindication of social knowledge, contributes either not at all or scarcely at all to the claim that our lives in the simulation might be deeply meaningful. This is so even though the vindication of non-social knowledge does forestall the concern that in the simulation, our lives might be wholly meaningless. (shrink)

The notion of Simulative Reasoning in the study of propositional attitudes within Artificial Intelligence (AI) is strongly related to the Simulation Theory of mental ascription in Philosophy. Roughly speaking, when an AI system engages in Simulative Reasoning about a target agent, it reasons with that agent’s beliefs as temporary hypotheses of its own, thereby coming to conclusions about what the agent might conclude or might have concluded. The contrast is with non-simulative meta-reasoning, where the AI system reasons within a (...) detailed theory about the agent’s (conjectured) reasoning acts. The motive within AI for preferring Simulative Reasoning is that it is more convenient and efficient, because of a simplification of the representations and reasoning processes. The chapter discusses this advantage in detail. It also sketches the use of Simulative Reasoning in an AI natural language processing system, ATT-Meta, that is currently being implemented. This system is directed at the understanding of propositional attitude reports. In ATT-Meta, Simulative Reasoning is yoked to a somewhat independent set of ideas about how attitude reports should be treated. Central here are the claims that (a) speakers often employ commonsense (and largely metaphorical) models of mind in describing agents’ attitudes, (b) the listener accordingly needs often to reason within the terms of such models, rather than on the basis of any objectively justifiable characterization of the mind, and (c) the commonsense models filter the suggestions that Simulative Reasoning comes up with concerning target agents’ reasoning conclusions. There is a yet tighter connection between the commonsense models and the Simulative Reasoning. It turns out that Simulative Reasoning can be rationally reconstructed in terms of a more general type of reasoning about the possibly-counterfactual “world” that the target agent believes in, together with an assumption that that agent has a faithful representation of the world. In the ATT-Meta approach, the reasoner adopts that assumption when it views the target agent through a particular commonsense model (called IDEAS-AS-MODELS). (shrink)

Neural structural representations are cerebral map- or model-like structures that structurally resemble what they represent. These representations are absolutely central to the “cognitive neuroscience revolution”, as they are the only type of representation compatible with the revolutionaries’ mechanistic commitments. Crucially, however, these very same commitments entail that structural representations can be observed in the swirl of neuronal activity. Here, I argue that no structural representations have been observed being present in our neuronal activity, no matter the spatiotemporal scale (...) of observation. My argument begins by introducing the “cognitive neuroscience revolution” (Sect. 1 ) and sketching a prominent, widely adopted account of structural representations (Sect. 2 ). Then, I will consult various reports that describe our neuronal activity at various spatiotemporal scales, arguing that none of them reports the presence of structural representations (Sect. 3 ). After having deflected certain intuitive objections to my analysis (Sect. 4 ), I will conclude that, in the absence of neural structural representations, representationalism and mechanism can’t go together, and so the “cognitive neuroscience revolution” is forced to abandon one of its commitments (Sect. 5 ). (shrink)

Mental simulation refers to the imagination of alternative, counterfactual realities. This chapter provides an overview of research on simulations of the past— retrospective simulation—and simulations of the future— prospective simulation. Two major themes run throughout. The first is that both retrospective and prospective thinking are inextricably linked, relying on a mixture of episodic and semantic memories that share common neural substrates. The second is that retrospective and prospective simulation present trade-offs for the individual. On the (...) one hand, they are functional, identifying causal inferences and potential obstacles that prepare us to try harder and perform better in the future. On the other hand, they sometimes produce bias—retrospective thinking can enhance hindsight bias, whereas prospective thinking can evoke biased predictions about the likelihood of certain events as well as how one is going to feel about those events in the future. Fortunately, however, these biases can be diminished with debiasing techniques. (shrink)

The so-called Baldwin Effect generally says how learning, as a form of ontogenetic adaptation, can influence the process of phylogenetic adaptation, or evolution. This idea has also been taken into computation in which evolution and learning are used as computational metaphors, including evolving neural networks. This paper presents a technique called evolving self-taught neural networks – neural networks that can teach themselves without external supervision or reward. The self-taught neural network is intrinsically motivated. Moreover, the self-taught (...) neural network is the product of the interplay between evolution and learning. We simulate a multi-agent system in which neural networks are used to control autonomous agents. These agents have to forage for resources and compete for their own survival. Experimental results show that the interaction between evolution and the ability to teach oneself in self-taught neural networks outperform evolution and self-teaching alone. More specifically, the emergence of an intelligent foraging strategy is also demonstrated through that interaction. Indications for future work on evolving neural networks are also presented. (shrink)

The article examines the impact of the virtual public sphere on how urban spaces are experienced and conceived in our data-driven society. It places particular emphasis on urban scale digital twins, which are virtual replicas of cities that are used to simulate environments and develop scenarios in response to policy problems. The article also investigates the shift from the technical to the socio-technical perspective within the field of smart cities. Despite the aspirations of urban scale digital twins to (...) enhance the participation of citizens in the decision-making processes relayed to urban planning strategies, the fact that they are based on a limited set of variables and processes makes them problematic. The article aims to shed light on the tension between the real and the ideal at stake during this process of abstracting sets of variables and processes in the case of urban scale digital twins. (shrink)

While Thomas Kuhn's theory of scientific revolutions does not specifically deal with validation, the validation of simulations can be related in various ways to Kuhn's theory: 1) Computer simulations are sometimes depicted as located between experiments and theoretical reasoning, thus potentially blurring the line between theory and empirical research. Does this require a new kind of research logic that is different from the classical paradigm which clearly distinguishes between theory and empirical observation? I argue that this is not the case. (...) 2) Another typical feature of computer simulations is their being ``motley'' (Winsberg 2003) with respect to the various premises that enter into simulations. A possible consequence is that in case of failure it can become difficult to tell which of the premises is to blame. Could this issue be understood as fostering Kuhn's mild relativism with respect to theory choice? I argue that there is no need to worry about relativism with respect to computer simulations, in particular. 3) The field of social simulations, in particular, still lacks a common understanding concerning the requirements of empirical validation of simulations. Does this mean that social simulations are still in a pre-scientific state in the sense of Kuhn? My conclusion is that despite ongoing efforts to promote quality standards in this field, lack of proper validation is still a problem of many published simulation studies and that, at least large parts of social simulations must be considered as pre-scientific. (shrink)

I will begin by proposing a taxonomy of taxonomic positions regarding the mind–brain: localism, globalism, revisionism, and contextualism, and will go on to focus on the last position. Although some versions of contextualism have been defended by various researchers, they largely limit themselves to a version of neural contextualism: different brain regions perform different functions in different neural contexts. I will defend what I call “environmental-etiological contextualism,” according to which the psychological functions carried out by various neural (...) regions can only be identified and individuated against an environmental context or with reference to a causal history. While this idea may seem innocuous enough, it has important implications for a structure-to-function mapping in the mind and brain sciences. It entails that the same neural structures can subserve different psychological functions in different contexts, leading to crosscutting psycho-neural mappings. I will try to illustrate how this can occur with reference to recent research on episodic memory. (shrink)

There is conflicting experimental evidence about whether the “stakes” or importance of being wrong affect judgments about whether a subject knows a proposition. To date, judgments about stakes effects on knowledge have been investigated using binary paradigms: responses to “low” stakes cases are compared with responses to “high stakes” cases. However, stakes or importance are not binary properties—they are scalar: whether a situation is “high” or “low” stakes is a matter of degree. So far, no experimental work has investigated the (...) scalar nature of stakes effects on knowledge: do stakes effects increase as the stakes get higher? Do stakes effects only appear once a certain threshold of stakes has been crossed? Does the effect plateau at a certain point? To address these questions, we conducted experiments that probe for the scalarity of stakes effects using several experimental approaches. We found evidence of scalar stakes effects using an “evidence seeking” experimental design, but no evidence of scalar effects using a traditional “evidence-fixed” experimental design. In addition, using the evidence-seeking design, we uncovered a large, but previously unnoticed framing effect on whether participants are skeptical about whether someone can know something, no matter how much evidence they have. The rate of skeptical responses and the rate at which participants were willing to attribute “lazy knowledge”—that someone can know something without having to check—were themselves subject to a stakes effect: participants were more skeptical when the stakes were higher, and more prone to attribute lazy knowledge when the stakes were lower. We argue that the novel skeptical stakes effect provides resources to respond to criticisms of the evidence-seeking approach that argue that it does not target knowledge. (shrink)

How do conventions of communication emerge? How do sounds or gestures take on a semantic meaning, and how do pragmatic conventions emerge regarding the passing of adequate, reliable, and relevant information? My colleagues and I have attempted in earlier work to extend spatialized game theory to questions of semantics. Agent-based simulations indicate that simple signaling systems emerge fairly naturally on the basis of individual information maximization in environments of wandering food sources and predators. Simple signaling emerges by means of any (...) of various forms of updating on the behavior of immediate neighbors: imitation, localized genetic algorithms, and partial training in neural nets. Here the goal is to apply similar techniques to questions of pragmatics. The motivating idea is the same: the idea that important aspects of pragmatics, like important aspects of semantics, may fall out as a natural results of information maximization in informational networks. The attempt below is to simulate fundamental elements of the Gricean picture: in particular, to show within networks of very simple agents the emergence of behavior in accord with the Gricean maxims. What these simulations suggest is that important features of pragmatics, like important aspects of semantics, don't have to be added in a theory of informational networks. They come for free. (shrink)