The potential to collect brain data more directly, with higher resolution, and in greater amounts has heightened worries about mental and brain privacy. In order to manage the risks to individuals posed by these privacy challenges, some have suggested codifying new privacy rights, including a right to “mental privacy.” In this paper, we consider these arguments and conclude that while neurotechnologies do raise significant privacy concerns, such concerns are—at least for now—no different from those raised by other (...) well-understood data collection technologies, such as gene sequencing tools and online surveillance. To better understand the privacy stakes of brain data, we suggest the use of a conceptual framework from information ethics, Helen Nissenbaum’s “contextual integrity” theory. To illustrate the importance of context, we examine neurotechnologies and the information flows they produce in three familiar contexts—healthcare and medical research, criminal justice, and consumer marketing. We argue that by emphasizing what is distinct about brain privacy issues, rather than what they share with other data privacy concerns, risks weakening broader efforts to enact more robust privacy law and policy. (shrink)

In recent years, neuroscience has begun to transform itself into a “big data” enterprise with the importation of computational and statistical techniques from machine learning and informatics. In addition to their translational applications such as brain-computer interfaces and early diagnosis of neuropathology, these tools promise to advance new solutions to longstanding theoretical quandaries. Here I critically assess whether these promises will pay off, focusing on the application of multivariate pattern analysis (MVPA) to the problem of reverse inference. I (...) argue that MVPA does not inherently provide a new answer to classical worries about reverse inference, and that the method faces pervasive interpretive problems of its own. Further, the epistemic setting of MVPA and other decoding methods contributes to a potentially worrisome shift towards prediction and away from explanation in fundamental neuroscience. (shrink)

Ontologies formally represent reality in a way that limits ambiguity and facilitates automated reasoning and data fusion, but is often daunting to the non-technical user. Thus, many researchers have endeavored to hide the formal syntax and semantics of ontologies behind the constructs of Controlled Natural Languages (CNLs), which retain the formal properties of ontologies while simultaneously presenting that information in a comprehensible natural language format. In this paper, we build upon previous work in this field by evaluating prospects of (...) implementing International Technology Alliance Controlled English (ITACE) as a middleware for ontology editing. We also discuss at length a prototype of a natural language conversational interface application designed to facilitate ontology editing via the formulation of CNL constructs. (shrink)

Does a diagnosis of brain dysfunction matter for ascriptions of moral responsibility? This chapter argues that, while knowledge of brain pathology can inform judgments of moral responsibility, its evidential value is currently limited for a number of practical and theoretical reasons. These include the problem of establishing causation from correlational data, drawing inferences about individuals from group data, and the reliance of the interpretation of brain findings on well-established psychological findings. Brain disorders sometimes matter (...) for moral responsibility, however, because they change an individual’s moral psychology in a way that is beyond their control. While control over psychological changes is not an excusing factor, brain disorders can mitigate moral responsibility because they confront individuals with new psychological deficits or urges for which their previous moral education and existing external and internal moral resources have not prepared them. (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)

Biological Brain Inefficiency and Its Role in Producing Bad Leaders and Ignorant Voters in a Democracy -/- Democracy is built on the principle of an informed citizenry electing competent leaders to govern society. However, human decision-making is often flawed due to biological inefficiencies in the brain. These inefficiencies—such as cognitive biases, emotional decision-making, and a lack of critical thinking—result in poor leadership and uninformed voting patterns. This essay explores how these brain limitations lead to the rise of (...) bad government leaders and ignorant voters and discusses potential solutions to mitigate these issues. -/- 1. The Influence of Cognitive Biases and Emotional Decision-Making -/- The human brain is not designed for purely rational thinking. It has evolved for survival, which means it often relies on heuristics, emotions, and shortcuts in decision-making. Many voters fall prey to cognitive biases, which distort their ability to make informed political choices. -/- Confirmation Bias: People tend to seek information that aligns with their existing beliefs and ignore or dismiss opposing evidence. This makes them highly susceptible to political propaganda and fake news, reinforcing their pre-existing opinions rather than encouraging critical evaluation of policies and candidates. -/- Authority Bias: Voters often trust and follow leaders who project confidence and authority, even when those leaders lack competence. Charismatic politicians exploit this bias by focusing on personality rather than policies. -/- Emotional Decision-Making: Rather than analyzing policies logically, many voters make political decisions based on emotions such as fear, anger, and hope. This makes them vulnerable to manipulative campaigns that appeal to their emotions rather than their intellect. -/- 2. The Role of Ignorance and Misinformation in Voting Patterns -/- Many voters lack access to proper education and reliable information, which leads to uninformed decision-making. Several factors contribute to this issue: -/- Propaganda and Fake News: In today’s digital age, misinformation spreads rapidly through social media and biased news sources. Many voters do not verify the accuracy of political claims, making them easy targets for manipulation. -/- Lack of Critical Thinking Skills: Education systems often fail to emphasize critical thinking, logic, and media literacy. Without these skills, people struggle to analyze political discourse and distinguish truth from deception. -/- Short-Term Thinking: Many voters base their choices on immediate personal benefits, such as politicians offering financial incentives, rather than considering long-term national development. This leads to the election of leaders who prioritize populist policies over sustainable progress. -/- 3. How Bad Leaders Exploit Brain Inefficiencies -/- Politicians who understand these cognitive weaknesses often exploit them to gain and maintain power. Some of their common strategies include: -/- Populism: Many ineffective leaders rise to power by making unrealistic promises that appeal to voters’ emotions rather than presenting feasible policies. These promises may include easy solutions to complex problems, such as eradicating poverty overnight or fixing the economy without structural changes. -/- Fear Tactics: Some politicians manipulate public fear to secure votes. They may exaggerate crime rates, economic threats, or foreign dangers to convince people that only they can provide security and stability. -/- Charismatic Leadership Over Competence: Many voters are drawn to leaders with strong personalities rather than those with technical knowledge or governance experience. This results in elections becoming popularity contests rather than assessments of actual capability. -/- 4. The Consequences of Ignorant Voting and Bad Leadership -/- The failure of voters to critically assess candidates leads to long-term consequences: -/- Corruption and Mismanagement: Inept leaders often prioritize personal gain and political survival over public welfare, leading to inefficient governance, corruption, and economic decline. -/- Weak Institutions: When unqualified leaders control government agencies, they weaken institutions by appointing incompetent officials, undermining checks and balances, and eroding democratic principles. -/- Cyclical Poverty and Underdevelopment: Poor governance results in policies that fail to address economic inequality, educational deficiencies, and healthcare inefficiencies, trapping societies in a cycle of underdevelopment. -/- 5. Possible Solutions to Improve Democratic Decision-Making -/- To counteract the negative effects of brain inefficiencies on democratic governance, societies must implement long-term solutions: -/- Educational Reform: Schools should emphasize critical thinking, logic, and media literacy from an early age. Teaching students how to evaluate political claims, fact-check information, and identify biases can create a more informed electorate. -/- Fact-Checking Mechanisms: Governments, independent organizations, and media outlets should promote fact-checking to counter misinformation. Encouraging citizens to rely on credible sources rather than propaganda can reduce the influence of fake news. -/- Merit-Based Leadership Selection: While democracy allows for free elections, implementing stricter qualifications for political candidates—such as requiring a background in governance, law, or economics—can help ensure that only competent individuals assume leadership roles. -/- Technology and AI in Governance: AI-driven platforms could be developed to provide unbiased political analysis, helping voters make more rational decisions based on data rather than emotions. -/- Conclusion -/- The inefficiencies of the human brain—such as cognitive biases, emotional decision-making, and susceptibility to misinformation—contribute significantly to the election of bad government leaders and the prevalence of ignorant voting in democratic systems. However, by improving education, strengthening fact-checking mechanisms, and encouraging merit-based leadership, societies can minimize these weaknesses and enhance democratic governance. A well-informed electorate is essential for a functional democracy, and addressing these biological limitations is a necessary step toward a better political future. -/- . (shrink)

Category theory has foundational importance because it provides conceptual lenses to characterize what is important and universal in mathematics—with adjunctions being the primary lens. If adjunctions are so important in mathematics, then perhaps they will isolate concepts of some importance in the empirical sciences. But the applications of adjunctions have been hampered by an overly restrictive formulation that avoids heteromorphisms or hets. By reformulating an adjunction using hets, it is split into two parts, a left and a right semiadjunction. Semiadjunctions (...) (essentially a formulation of universal mapping properties using hets) can then be combined in a new way to define the notion of a brain functor that provides an abstract model of the intentionality of perception and action (as opposed to the passive reception of sense-data or the reflex generation of behavior). (shrink)

Some modern cosmological models predict the appearance of Boltzmann Brains: observers who randomly fluctuate out of a thermal bath rather than naturally evolving from a low-entropy Big Bang. A theory in which most observers are of the Boltzmann Brain type is generally thought to be unacceptable, although opinions differ. I argue that such theories are indeed unacceptable: the real problem is with fluctuations into observers who are locally identical to ordinary observers, and their existence cannot be swept under the (...) rug by a choice of probability distributions over observers. The issue is not that the existence of such observers is ruled out by data, but that the theories that predict them are cognitively unstable: they cannot simultaneously be true and justifiably believed. (shrink)

Introduction: the objective of the investigation is to analyse the informational operating-mode of the brain and to extract conclusions on the structure of the informational system of the human body and consciousness. Analysis: the mechanisms and processes of the transmission of information in the body both by electrical and non-electrical ways are analysed in order to unify the informational concepts and to identify the specific essential requirements supporting the life. It is shown that the electrical transmission can be described (...) by typical YES/NO (all or nothing) binary units as defined by the information science, while the inter and intra cell communication, including within the synaptic junction, by mechanisms of embodiment/disembodiment of information. The virtual received or operated information can be integrated in the cells as matter-related information, with a maximum level of integration as genetically codified info. Therefore, in terms of information, the human appears as a reactive system changing information with the environment and between inner informational subsystems which are: the centre of acquisition and storing of information (acquired data), the centre of decision and command (decision), the info-emotional system (emotions), the maintenance informational system (matter absorption/desorption/distribution), the genetic transmission system (reproduction) and info-genetic generator (genetically assisted body evolution). The dedicated areas and components of the brain are correlated with such systems and their functions are specified. Result: the corresponding cognitive centres projected into consciousness are defined and described according to their specific functions. The cognitive centres, suggestively named to appropriately include their main characteristics are detected at the conscious level respectively as: memory, decisional operation (attitude), emotional state, power/energy status and health, associativity and offspring formation, inherited predispositions, skills and mentality. The near-death and religious experiences can be explained by an Info-Connection pole. Conclusion: consciousness could be fully described and understood in informational terms. (shrink)

The human brain consists of approximately 100 billion electrically active neurones that generate an endogenous electromagnetic field, whose role in neuronal computing has not been fully examined. The source, magnitude and likely influence of the brain's endogenous em field are here considered. An estimate of the strength and magnitude of the brain's em field is gained from theoretical considerations, brain scanning and microelectrode data. An estimate of the likely influence of the brain's em field (...) is gained from theoretical principles and considerations of the experimental effects of external em fields on neurone firing both in vitro and in vivo. Synchronous firing of distributed neurones phase-locks induced em field fluctuations to increase their magnitude and influence. Synchronous firing has previously been demonstrated to correlate with awareness and perception, indicating that perturbations to the brain's em field also correlate with awareness. The brain's em field represents an integrated electromagnetic field representation of distributed neuronal information and has dynamics that closely map to those expected for a correlate of consciousness. I propose that the brain's em information field is the physical substrate of conscious awareness - the cemi field - and make a number of predictions that follow from this proposal. Experimental evidence pertinent to these predictions is examined and shown to be entirely consistent with the cemi field theory. This theory provides solutions to many of the intractable problems of consciousness - such as the binding problem - and provides new insights into the role of consciousness, the meaning of free will and the nature of qualia. It thus places consciousness within a secure physical framework and provides a route towards constructing an artificial consciousness. (shrink)

In the last year or so (and going back many decades) there has been extensive claims by major computational scientists, engineers, and others that AGI (artificial general intelligence) is 5 or 10 years away, but without a scintilla of scientific evidence, for a broad body of these claims: Computers will become conscious, have a “theory of mind,” think and reason, will become more intelligent than humans, and so on. But the claims are science fiction, not science. -/- This article reviews (...) evidence for the following three (3) propositions using extensive body of scientific research and related sources from the cognitive and neurosciences; evolutionary evidence; linguistics; data science; comparative psychology; self-driving cars, and robotics; and the learning sciences. (1) Do computing machines think or reason? (2) Are computing machines sentient or conscious? (3) Do computing machines have a theory of mind? (shrink)

The level of accuracy needed to identify the type of tumor using MRI data is necessary to choose the best method for medical care. The K-Nearest Neighbor approach, a fundamental scientific application and image classification technique, can be used to computationally analyze MRI results. The objective of the tumor classification system is to identify the tumor. The only information used to analyze data for this type of system comes from the MRI's axial portion, which are divided into three (...) categories they are: oligodendroglioma, glioblastoma, and astrocyte. Basic image processing techniques, such as image enhancement, image biniarization, morphological image and watershed are used to identify the tumor region. Tumor categorization achieved findings of 89.5 percent, which may provide more precise and in-depth information about tumor identification. (shrink)

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and reacts like humans, some of the computer activities with artificial intelligence are designed to include speech, recognition, learning, planning and problem solving. Deep learning is a collection of algorithms used in machine learning, it is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is used as (...) a technique to produce brain tumor detection and classification models using Magnetic Resonance Imaging (MRI) imaging for rapid and easy detection and identification of brain tumor. In this thesis, some ways and mechanisms will be reviewed to use deep learning techniques to produce a model for brain tumor detection. The goal is to find a good and effective way to detect brain tumor based on MRI to help the brain doctor in making decisions easily, accurately and rapidly. A recent report by the World Health Organization in February 2018 showed that the death rate from brain cancer or central nervous system (CNS) is the highest in the Asian continent. It is important to detect cancer early so that many of these lives can be saved. The model has been designed and implemented, including a dataset which consist of 10,000 images for brain tumor detection through the use of Deep learning algorithms based on neural networks. For testing, we have used our model, Inception, VGG16, MobileNet and ResNet models. The f-score accuracy we got for each model was as follows: Our model was 98.28, VGG16 was 99.86%, ResNet50 was 98.14%, MobileNet was 88,98%, and InceptionV3 was 99.88%. (shrink)

In the cognitive, computational, and neuro-sciences, practitioners often reason about what computational models represent or learn, as well as what algorithm is instantiated. The putative goal of such reasoning is to generalize claims about the model in question, to claims about the mind and brain, and the neurocognitive capacities of those systems. Such inference is often based on a model’s performance on a task, and whether that performance approximates human behavior or brain activity. Here we demonstrate how such (...) argumentation problematizes the relationship between models and their targets; we place emphasis on artificial neural networks (ANNs), though any theory-brain relationship that falls into the same schema of reasoning is at risk. In this paper, we model inferences from ANNs to brains and back within a formal framework — metatheoretical calculus — in order to initiate a dialogue on both how models are broadly understood and used, and on how to best formally characterize them and their functions. To these ends, we express claims from the published record about models’ successes and failures in first-order logic. Our proposed formalization describes the decision-making processes enacted by scientists to adjudicate over theories. We demonstrate that formalizing the argumentation in the literature can uncover potential deep issues about how theory is related to phenomena. We discuss what this means broadly for research in cognitive science, neuroscience, and psychology; what it means for models when they lose the ability to mediate between theory and data in a meaningful way; and what this means for the metatheoretical calculus our fields deploy when performing high-level scientific inference. (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)

Enhancement technologies may someday grant us capacities far beyond what we now consider humanly possible. Nick Bostrom and Anders Sandberg suggest that we might survive the deaths of our physical bodies by living as computer emulations.­­ In 2008, they issued a report, or “roadmap,” from a conference where experts in all relevant fields collaborated to determine the path to “whole brain emulation.” Advancing this technology could also aid philosophical research. Their “roadmap” defends certain philosophical assumptions required for this technology’s (...) success, so by determining the reasons why it succeeds or fails, we can obtain empirical data for philosophical debates regarding our mind and selfhood. The scope ranges widely, so I merely survey some possibilities, namely, I argue that this technology could help us determine (1) if the mind is an emergent phenomenon, (2) if analog technology is necessary for brain emulation, and (3) if neural randomness is so wild that a complete emulation is impossible. (shrink)

In this chapter, Pautz raises a puzzle about spatial experience for phenomenal internalists like Ned Block. If an accidental, lifelong brain-in-the-void (BIV) should have all the same experiences as you, it would have an experience as of items having various shapes, and be able to acquire concepts of those shapes, despite being cut off from real things with the shapes. Internalists cannot explain this by saying that BIV is presented with Peacocke-style visual field regions having various shapes, because these (...) would have to be non-physical sense data. They might instead explain this by saying that BIV “phenomenally represents” various shape properties. But since BIV lacks any interesting physical relations to shapes, this would imply that phenomenally representation is an irreducible relation. (shrink)

Introduction: This article discusses the brain hierarchical organization/evolution as a consequence of the information-induced brain development, from the perspective of the Informational Model of Consciousness. Analysis: In the frame of the Informational Model of Consciousness, a detailed info-neural analysis ispresented, concerning the specific properties/functions of the informational system of the human body composed by the Center of Acquisition and Storing of Information, Center of Decision and Command, Info-Emotional Center, Maintenance Informational System, Genetic Transmission System, Info Genetic Generator and (...) Info- Connection center, in relation with the neuro-connected brain areas, with a special attention to the Info-Connection and its specific properties. Besides a meticulous analysis of the info-connections/neuro-functions of these centers, a special attention was paid to limbic/cingulate cortex activities. Defined as a trust/confidence center, additional features are highlighted in correlation with the activity of the anterior cingulate cortex, consisting in the intervention/moderation of amygdala emotional signals, conflicting opposite YES/NO data and error elimination in the favor of the organism adaptation/survival, the intervention in the certainty/uncertainty balance to select a suitable pro-life information (antientropic effect), in moderation of pain and in the stimulation of the empathic inter-human relations/communication. Representing the correspondence between the informational subsystems and the brain area map, itis shown that the up/down integration of information by epigenetic mechanisms and the down/ up evolution are correlated. Results: The analysis of the functions of the anterior cingulate opens new gates of investigations concerning the involved intimate mechanisms at the level of cell microstructure, specifically on the compatibility with quantum assisted processes admitted by the Informational Model of Consciousness and the quantum-based models The discussion on the information integration/codification by epigenetic mechanisms shows that this process starts from the superior levels of brain conscious info-processing areas and progressively advances to the automatic/autonomic inferior levels ofthe informational system, under insistent/repetitive cues/stress conditions, pointing out an hierarchical functional/anatomical structure of the brain organization. Additional arguments are discussed, indicating thatthe down/up progressive scale representation is a suggestive illustration of the brain evolution, induced/assisted/determined by information, accelerated at humans by the antientropic functions of the Info-Connection center. Conclusions: The hierarchical organization of the brain is a consequence of the integration process of information, defining its development accordingly to the adaptation requirements for survival during successive evolution stages of the organism, information playing a determinant/key role. (shrink)

In this chapter, Pautz raises a puzzle about spatial experience for phenomenal internalists like Ned Block. If an accidental, lifelong brain-in-the-void (BIV) should have all the same experiences as you, it would have an experience as of items having various shapes, and be able to acquire concepts of those shapes, despite being cut off from real things with the shapes. Internalists cannot explain this by saying that BIV is presented with Peacocke-style visual field regions having various shapes, because these (...) would have to be non-physical sense data. They might instead explain this by saying that BIV “phenomenally represents” various shape properties. But since BIV lacks any interesting physical relations to shapes, this would imply that phenomenally representation is an irreducible relation. (shrink)

Objective: Alterations in psychomotor dimension cut across different psychiatric disorders, such as schizophrenia (SCZ) and bipolar disorder (BD). This preliminary study aimed to investigate the organization of intrinsic brain activity in the subcortical-cortical sensorimotor system in SCZ (and BD) as characterized according to psychomotor dimension. -/- Method: In this resting-state functional magnetic resonance imaging (fMRI) study, functional connectivity (FC) between thalamus and sensorimotor network (SMN), along with FC from substantia nigra (SN) and raphe nuclei (RN) to basal ganglia (BG) (...) and thalamic regions, were investigated by using an a-priori-driven and dimensional approach. This was done in two datasets: SCZ patients showing inhibited psychomotricity (n = 18) vs. controls (n = 19); SCZ patients showing excited psychomotricity (n = 20) vs. controls (n = 108). Data from a third dataset of BD in inhibited depressive or manic phases (reflecting inhibited or excited psychomotricity) were used as control. -/- Results: SCZ patients suffering from psychomotor inhibition showed decreased thalamus-SMN FC toward around-zero values paralleled by a concomitant reduction of SN-BG/thalamus FC and RN-BG/thalamus FC (as BD patients in inhibited depression). By contrast, SCZ patients suffering from psychomotor excitation exhibited increased thalamus-SMN FC toward positive values paralleled by a concomitant reduction of RN-BG/thalamus FC (as BD patients in mania). -/- Conclusions: These findings suggest that patients exhibiting low or high levels of psychomotor activity show distinct patterns of thalamus-SMN coupling, which could be traced to specific deficit in SN- or RN-related connectivity. Notably, this was independent from the diagnosis of SCZ or BD, supporting an RDoC-like dimensional approach to psychomotricity. (shrink)

The notion of representation in neuroscience has largely been predicated on localizing the components of computational processes that explain cognitive function. On this view, which I call “algorithmic homuncularism,” individual, spatially and temporally distinct parts of the brain serve as vehicles for distinct contents, and the causal relationships between them implement the transformations specified by an algorithm. This view has a widespread influence in philosophy and cognitive neuroscience, and has recently been ably articulated and defended by Shea. Still, I (...) am skeptical about algorithmic homuncularism, and I argue against it by focusing on recent methods for complex data analysis in systems neuroscience. I claim that analyses such as principle components analysis and linear discriminant analysis prevent individuating vehicles as algorithmic homuncularism recommends. Rather, each individual part contributes to a global state space, trajectories of which vary with important task parameters. I argue that, while homuncularism is false, this view still supports a kind of “vehicle realism,” and I apply this view to debates about the explanatory role of representation. (shrink)

APPENDIX: The data emerging from the clinical and brain studies described above suggest that, in the case of OCD, there are two pertinent brain mechanisms that are distinguishable both in terms of neuro-dynamics and in terms of the conscious experiences that accompany them. These mechanisms can be characterized, on anatomical and perhaps evolutionary grounds, as a lower-level and a higher-level mechanism. The clinical treatment has, when successful, an activating effect on the higher-level mechanism, and a suppressive effect (...) on the lower-level one. (shrink)

Introduction: the neuroscientific turn in political science The observation that brains and political orders are interdependent is almost trivial. Obviously, political orders require brain processes in order to emerge and to remain in place, as these processes enable action and cognition. Conversely, every since Aristotle coined man as “by nature a political animal” (Aristotle, Pol.: 1252a 3; cf. Eth. Nic.: 1097b 11), this also suggests that the political engagements of this animal has likely consequences for its natural development, including (...) the development of its psychological functions. Given these mutual interdependencies, it is remarkable that only since the 1960s, the more general domain of ‘biopolitics’ has attracted attention though first particularly in the form of behavioral politics (Alford and Hibbing). Since then biopolitics has gained in interest, so much so that different subdomains can be identified. Indeed, a 2008 review of the field of biopolitics identified five ‘headings’ of it: “(1) the case for a ‘more biologically oriented political science’, (2) ‘biologically related’ public policy issues, (3) physiological measures of political attitudes and behaviour, (4) the influence of physiological factors on actual political behaviour, and (5) the manner in which our species’ evolutionary history has left homo sapiens genetically endowed with certain social and political behavioural tendencies” (Somit and Peterson 43). Striking is how the relation between biology and politics is taken here in a rather unidirectional way, emphasizing particularly the decisive power of biology upon politics. The reverse relation is not mentioned specifically, reflecting the field of biopolitics, perhaps until quite recently. This absence of studies of political influences on our biology may have to do with the difficulty in investigating such influences. Empirical studies in biopolitics have two foci, broadly speaking: genetics and the brain, both of which have turned out to be complex and dynamic phenomena (Alford and Hibbing). Yet the studies of genetics and brain processes have made much progress in the last few decades, thanks to the development of research instruments - like fMRI brain scanners and TMS brain stimulation instruments - and of computational tools for data analysis and the simulation of explanatory models. For the field of biopolitics it is particularly relevant that within cognitive neuroscience the study of social and political issues has witnessed an increasing interest of researchers even more recently. Indeed, aware of the enabling and mediating role of the brain regarding those issues, a truly ‘neuroscientific turn’ can be observed in the social sciences, testified for example by the emergence of the field of ‘neuropolitics’ (Connolly; Vandervalk). Developing a systematic neuropolitics or biopolitics in general is a difficult challenge because of the wealth of causal influences on and interdependencies between biological, brain, cognitive and socio-political factors. Taking a somewhat more abstract perspective, this paper focuses on the process of emerging complexity in adaptive systems, enabling those to conduct ever more complex processes. Yet, parallel to that development can be observed that such systems, or organisms, are also capable in reducing the complexity of the information they are to process. Once they’re capable of developing and adjusting such compressed and complex representations of information, those systems or organisms can handle more information faster and more efficient and adaptive, yielding important benefits to the organism in navigating its environment (Halford, Wilson and Phillips). Before focusing on the role of narrative as a cognitive strategy for such a reduction of informational complexity, I will discuss the development of stable structures and increasing complexity in dynamic systems. Such a more general perspective prepares our discussion of the structures of both narratives and politics and in doing so contributes to the explanation of their interaction. (shrink)

The essay brings a summation of human efforts seeking to understand our existence. Plato and Kant & cognitive science complete reduction of philosophy to a neural mechanism, evolved along elementary Darwinian principles. Plato in his famous Cave Allegory explains that between reality and our experience of it there exists a great chasm, a metaphysical gap, fully confirmed through particle-wave duality of quantum physics. Kant found that we have two kinds of perception, two senses: By the spatial outer sense we perceive (...) phenomena, objects in space. Our temporal inner sense lets us perceive our inner state, noumena . Kant's two senses are fully confirmed through bilateral brain duality of cognitive science. The bilateral brain serves inner, temporal sense, logic & noumena in the left hemisphere. The right brain half is for the outer, spatial sense, geometry & phenomena. We form a whole system, a bilateral interior cosmos, a kind of world model, expressing what may lie beyond the metaphysical gap. In the right brain we build a phenomenal cosmos, to resemble the outer environment. In the brain's left hemisphere a noumenal cosmos, a model of our private world including Saint Teresa of Avila's castillo interior. The cosmos is regularly updated with novel sense data integrated into our memory banks. This was recognized early by Helmholtz (1860s). Our lives, health & well-being depend on us keeping our interior cosmos in good order. This used to be seen as saving one's precious soul from perdition. While we endeavour to keep our phenomenal cosmos neat & orderly by protecting the environment from harm, our noumenal cosmos to be livable requires us to engage in ethical conduct. This bilateral cosmos is responsible for our common sense judgment power, that we depend on for being able to lead a good & benign life. The 200 million neurons of BA10 in the prefrontal lobe have global access to the interior cosmos, & apply massive feedback to identify environmental conditions rapidly. The global access also gives conscious presence of the individual to itself, all that is present in the interior cosmos, for total freedom of action. In the left brain private ego, we are guarded by faith in divine mercy, & guided to choose the best course of action, able to survive under the most adverse conditions. For this, we receive directions from the Holy Ghost in Saint Teresa's castillo interior. (shrink)

The Emotion Ontology is an ontology covering all aspects of emotional and affective mental functioning. It is being developed following the principles of the OBO Foundry and Ontological Realism. This means that in compiling the ontology, we emphasize the importance of the nature of the entities in reality that the ontology is describing. One of the ways in which realism-based ontologies are being successfully used within biomedical science is in the annotation of scientific research results in publicly available databases. Such (...) annotation enables several objectives, including searching, browsing and cross-database data integration. A key benefit conferred by realismbased ontology is that suitably annotated research results are able to be aggregated and compared in a fashion that is based on the underlying reality that the science is studying. This has the potential of increasing the power of statistical analysis and meta-analysis in data-driven science. This aspect has been fruitfully exploited in the investigation of the functions of genes in molecular biology. Cognitive neuroscience uses functional neuroimaging to investigate the brain correlates of areas of mental functioning such as memory, planning and emotion. The use of functional neuroimaging to study affective phenomena such as the emotions is called ‘affective neuroscience’. BrainMap is the largest curated database of coordinates and metadata for studies in cognitive neuroscience, including affective neuroscience (Laird et al., 2005). BrainMap data is already classified and indexed using a terminology for classification, called the ‘Cognitive Paradigm Ontology’ (CogPO), that has been developed to facilitate searching and browsing. However, CogPO has been developed specifically for the BrainMap database, and the data are thus far not annotated to a realism-based ontology which would allow the discovery of interrelationships between research results across different databases on the basis of what the research is about. In this contribution, we describe ongoing work that aims to annotate affective neuroscience data, starting with the BrainMap database, using the Emotion Ontology. We describe our objectives and technical approach to the annotation, and mention some of the challenges. (shrink)

In the last half century we have gone from storing data on 5-1/4 inch floppy diskettes to cloud and now fog computing. But one should ask why so much data is being collected. Part of the answer is simple in light of scientific projects but why is there so much data on us? Then, we ask about its “interface” through fog computing. Such questions prompt this chapter on the philosophy of big data and fog computing. After (...) some background on definitions, origins and contemporary applications, the main discussion begins with thinking about modern data collection, management, and applications from a complexity standpoint. Big data is turned into knowledge but knowledge is extrapolated from the past and used to manage the future. Yet it is questionable whether humans have the capacity to manage contemporary technological and social complexity evidenced by our world in crisis and possibly on the brink of extinction. Such calls for a new way of studying societies from a scientific point of view. We are at the center of the observation from which big data emerge and are manipulated, the overall human project being not only to create an artificial brain with an attendant mind but a society that might be able to survive what “natural” humans cannot. (shrink)

Sleep has long been a topic of fascination for artists and scientists. Why do we sleep? What function does sleep serve? Why do we dream? What significance can we attach to our dreams? We spend so much of our lives sleeping, yet its precise function is unclear, in spite of our increasing understanding of the processes generating and maintaining sleep. We now know that sleep can be accompanied by periods of intense cerebral activity, yet only recently has experimental data (...) started to provide us with some insights into the type of processing taking place in the brain as we sleep. There is now strong evidence that sleep plays a crucial role in learning and in the consolidation of memories. Once the preserve of psychoanalysts, 'dreaming' is now a topic of increasing interest amongst scientists. With research into sleep growing, this volume is both timely and valuable in presenting a unique study of the relationship between sleep, learning, and memory. It brings together a team of international scientists researching sleep in both human and animal subjects. Aimed at researchers within the fields of neuroscience, cognitive neuroscience, psychiatry, and neurology, this book will be an important first step in developing a full scientific understanding of the most intriguing state of consciousness. (shrink)

Intentions are commonly conceived of as discrete mental states that are the direct cause of actions. In the last several decades, neuroscientists have taken up the project of finding the neural implementation of intentions, and a number of areas have been posited as implementing these states. We argue, however, that the processes underlying action initiation and control are considerably more dynamic and context sensitive than the concept of intention can allow for. Therefore, adopting the notion of ‘intention’ in neuroscientific explanations (...) can easily lead to misinterpretation of the data, and can negatively influence investigation into the neural correlates of intentional action.We suggest reinterpreting the mechanisms underlying intentional action, and we will discuss the elements that such a reinterpretation needs to account for. (shrink)

The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional (...) plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward. (shrink)

Traumatic brain injury is the leading cause of death in children. Children with severe TBI are in need of neurointensive care where the goal is to prevent secondary brain injury by avoiding secondary insults. Monitoring of cerebral blood flow (CBF) and autoregulation in the injured brain is crucial. However, there are limited studies performed in children to investigate this. Current studies report on age dependent increase in CBF with narrow age range. Low initial CBF following TBI has (...) been correlated to poor outcome and may be more prevalent than hyperemia as previously suggested. Impaired cerebral pressure autoregulation is also detected and correlated with poor outcome but it remains to be elucidated if there is a causal relationship. Current studies are few and mainly based on small number of patients between the age of 0–18 years. Considering the changes of CBF and cerebral pressure autoregulation with increasing age, larger studies with more narrow age ranges and multimodality monitoring are required in order to generate data that can optimize the therapy and clinical management of children suffering TBI. (shrink)

This chapter analyzes the relation between brain sciences and philosophy of mind, in order to clarify in what ways philosophy can contribute to neuroscience and neuroscience can contribute to philosophy. Especially since the 1980s and the emergence of “neurophilosophy”, more and more philosophers have been bringing home morals from neuroscience to settle philosophical issues. I mention examples from the problem of consciousness, philosophy of perception and the problem of free will, and I argue that such attempts are not successful (...) in trying to settle questions like whether psychology can be reduced to neuroscience, whether we see the external world directly in perception, or whether we have free will. The failure results from an ability of the philosophical questions to evade the data. What makes these questions persisting philosophical questions is precisely that there is no way to settle them through empirical evidence, as they are conceptual questions and their solution lies in conceptual analysis. (shrink)

Vast archives of fragmentary structural brain scans that are routinely acquired in medical clinics for diagnostic purposes have so far been considered to be unusable for neuroscientific research. Yet, recent studies have proposed that by deploying machine learning algorithms to fill in the missing anatomy, clinical scans could, in future, be used by researchers to gain new insights into various brain disorders. This chapter focuses on a study published in2019, whose authors developed a novel unsupervised machine learning algorithm (...) for synthesising missing anatomy in extremely sparse clinical MRI scans of thousands of stroke patients. By approaching the study from a media-theoretical perspective, I analyse how its authors dis-cursively negotiated the anatomical and operative plausibility of the unobserved anatomy that their black-boxed algorithm reconstructed from the existing sparse data. My analysis fore-grounds the processual, relational, context-dependent, and essentially unstable character of the thus established plausibility of the algorithmically synthesised neuroanatomy. (shrink)

The Inefficiency of the Biological Brain and the Importance of AI for the Next Generation -/- The human brain, often considered the pinnacle of evolutionary design, is an extraordinary organ capable of creativity, critical thinking, and adaptation. However, despite its remarkable abilities, it is inherently inefficient when compared to artificial intelligence (AI) systems in certain domains. The inefficiencies of the biological brain, coupled with the rapid development of AI technology, underline why artificial general intelligence (AGI) holds immense (...) promise for shaping the future of the next generation. By complementing and extending human cognition, AI can address many of the limitations of the biological brain and equip young people with tools to solve the world’s most pressing challenges. -/- The Inefficiencies of the Biological Brain -/- The biological brain operates under significant constraints that limit its potential. Some of these inefficiencies include: -/- 1. Cognitive Biases and Emotional Interference The human brain is subject to cognitive biases, such as confirmation bias, which can hinder rational decision-making. Emotional states, while vital for human experience, can cloud judgment and lead to impulsive or irrational behavior. These limitations often result in flawed decisions, particularly in complex or high-stakes situations. -/- 2. Limited Processing Power and Memory While the brain is capable of parallel processing, it has finite processing power and memory storage. Tasks requiring large-scale computations, vast data analysis, or instantaneous recall of details often exceed its capabilities. For instance, analyzing terabytes of data to identify patterns or trends is beyond the capacity of any single individual. -/- 3. Slow Learning and Knowledge Retention Learning is a time-intensive process for humans. Acquiring expertise in a single domain can take years, and even then, the retention of knowledge often deteriorates over time. The brain also struggles to adapt quickly to entirely new fields or technologies without substantial effort. -/- 4. Physical Vulnerabilities The brain is biologically constrained by aging, disease, and trauma. These vulnerabilities can result in cognitive decline, memory loss, or even the complete inability to function. These natural limitations cannot yet be fully mitigated through biological means. -/- 5. Bias from Evolutionary Pressures The human brain evolved for survival in environments vastly different from the modern world. Traits like fear, aggression, or tribalism, which were once advantageous, can now lead to social conflicts, inefficiency, and short-term thinking. -/- Why AI and AGI Are Crucial for the Next Generation -/- Artificial intelligence has the potential to address the inefficiencies of the biological brain and create a brighter, more equitable future for the next generation. Its capabilities go far beyond traditional human cognition in several key areas: -/- 1. Enhanced Problem-Solving Abilities AI systems are capable of processing massive datasets, analyzing patterns, and generating solutions far more efficiently than the human brain. For example, AI is already transforming industries such as healthcare by diagnosing diseases with greater accuracy and speed. Future AGI could enable young people to tackle global challenges such as climate change, resource distribution, and poverty with unprecedented precision. -/- 2. Overcoming Human Biases Unlike the biological brain, which is prone to emotional and cognitive biases, well-designed AI systems can function based on objective logic and evidence. This could lead to more equitable policy-making, unbiased education systems, and transparent governance. -/- 3. Accelerated Learning and Knowledge Access AI-powered tools can revolutionize education by providing personalized learning experiences tailored to individual needs. With access to AI tutors and resources, young people can acquire skills and knowledge faster and more effectively than ever before, bridging educational gaps and fostering innovation. -/- 4. Augmentation of Human Potential By integrating AI with human cognition, individuals can expand their capabilities beyond natural limits. For example, brain-computer interfaces (BCIs) could allow people to directly interact with AI systems, enhancing memory, decision-making, and creativity. This symbiosis between humans and machines could redefine what it means to be human. -/- 5. Future-Proofing Against Global Challenges The next generation will face challenges that require solutions at a scale and complexity beyond human capacity. AI and AGI are indispensable for managing these challenges, from predicting pandemics and optimizing energy usage to navigating ethical dilemmas in an interconnected world. -/- Building a New Paradigm for the Next Generation -/- To ensure that AI benefits society as a whole, it is essential to integrate it into the foundational education and development of young people. Teaching students about AI, its capabilities, and ethical implications will empower them to use these tools responsibly. Furthermore, fostering collaboration between humans and AI, rather than fearing technological displacement, will cultivate a new paradigm where human creativity and machine efficiency coexist harmoniously. -/- Governments, educational institutions, and industries must work together to democratize access to AI technologies, ensuring that the benefits are shared equitably. Ethical frameworks should be developed to guide the use of AI, ensuring that it remains a tool for human progress rather than a source of inequality or harm. -/- Conclusion -/- The inefficiencies of the biological brain highlight the need for complementary technologies that can amplify human potential. AI and AGI represent the next frontier in human evolution, offering solutions to our cognitive and societal limitations. For the next generation, these technologies are not merely tools but essential companions in shaping a better, more sustainable world. By embracing AI and equipping young people with the skills to harness its power, humanity can transcend its limitations and unlock a future of unparalleled possibilities. -/- . (shrink)

Background -/- Previous literature suggests that those with reading disability (RD) have more pronounced deficits during semantic processing in reading as compared to listening comprehension. This discrepancy has been supported by recent neuroimaging studies showing abnormal activity in RD during semantic processing in the visual but not in the auditory modality. Whether effective connectivity between brain regions in RD could also show this pattern of discrepancy has not been investigated. Methodology/Principal Findings -/- Children (8- to 14-year-olds) were given a (...) semantic task in the visual and auditory modality that required an association judgment as to whether two sequentially presented words were associated. Effective connectivity was investigated using Dynamic Causal Modeling (DCM) on functional magnetic resonance imaging (fMRI) data. Bayesian Model Selection (BMS) was used separately for each modality to find a winning family of DCM models separately for typically developing (TD) and RD children. BMS yielded the same winning family with modulatory effects on bottom-up connections from the input regions to middle temporal gyrus (MTG) and inferior frontal gyrus(IFG) with inconclusive evidence regarding top-down modulations. Bayesian Model Averaging (BMA) was thus conducted across models in this winning family and compared across groups. The bottom-up effect from the fusiform gyrus (FG) to MTG rather than the top-down effect from IFG to MTG was stronger in TD compared to RD for the visual modality. The stronger bottom-up influence in TD was only evident for related word pairs but not for unrelated pairs. No group differences were noted in the auditory modality. Conclusions/Significance -/- This study revealed a modality-specific deficit for children with RD in bottom-up effective connectivity from orthographic to semantic processing regions. There were no group differences in connectivity from frontal regions, suggesting that the core deficit in RD is not in top-down modulation. (shrink)

Creativity and Intelligence: Emergent Properties of the Brain as a Balancing Mechanism for Overpopulation, Natural Disasters, and Diseases -/- The human brain is an extraordinary organ, capable of producing creativity and intelligence as emergent properties that allow humanity to address complex challenges. These traits are not merely tools for individual survival; they function as collective mechanisms to adapt to large-scale issues that threaten humanity’s balance with the environment. Overpopulation, natural disasters, and the prevalence of diseases and illnesses represent (...) some of the most significant pressures on human societies. Through the brain’s capacity for problem-solving and innovation, humanity has developed strategies to mitigate these challenges and maintain equilibrium within the universal law of balance. -/- The Brain as a Balancing Mechanism -/- At its essence, the brain operates as a dynamic system that processes external stimuli and generates adaptive responses. Creativity and intelligence arise from this system as tools to predict, analyze, and address challenges that disrupt balance. Overpopulation, natural disasters, and diseases disrupt human life on social, economic, and environmental levels, testing the brain’s ability to create solutions. -/- Creativity in Addressing Overpopulation -/- Overpopulation places immense strain on resources, ecosystems, and social structures, often leading to food shortages, environmental degradation, and urban overcrowding. The brain’s creative capacity has allowed humans to develop innovations to address these issues. Urban agriculture, such as vertical farming, is an example of how creativity has transformed food production in densely populated areas, reducing dependence on traditional farmland and conserving resources. Similarly, advancements in renewable energy technologies, such as solar and wind power, reduce the ecological footprint of growing populations, providing sustainable solutions to energy needs. -/- Creativity also plays a critical role in social policies and education. Family planning programs and reproductive health education have empowered individuals, particularly women, to make informed decisions, reducing population growth rates in many regions. These initiatives reflect the brain’s ability to create holistic solutions that align societal needs with the preservation of natural balance. -/- Intelligence in Managing Natural Disasters -/- Natural disasters, unpredictable and often catastrophic, require intelligent strategies to mitigate their impact. Intelligence enables humans to analyze data, predict disasters, and prepare for their consequences. Early warning systems for tsunamis, hurricanes, and earthquakes exemplify this, providing communities with critical time to evacuate and take protective measures. -/- Furthermore, intelligence drives the design of resilient infrastructure that can withstand disasters. For instance, earthquake-resistant buildings and flood mitigation systems demonstrate the brain’s capacity to transform scientific knowledge into practical solutions. These innovations save lives, reduce economic losses, and facilitate faster recovery after disasters, ensuring that human societies maintain their balance despite nature’s unpredictability. -/- Creativity and Intelligence in Combating Diseases and Illnesses -/- Diseases and illnesses have long been among humanity’s greatest challenges, especially in the context of overpopulation, which facilitates the rapid spread of infectious diseases. The brain’s emergent properties of creativity and intelligence have been critical in combating these threats through advancements in medicine, public health, and technology. -/- The development of vaccines, antibiotics, and antiviral drugs highlights humanity’s creative response to disease. For example, the eradication of smallpox and the near-elimination of polio are milestones achieved through creative and intelligent applications of medical science. During the COVID-19 pandemic, intelligence and creativity led to the rapid development of vaccines using mRNA technology, showcasing the brain’s ability to adapt to new challenges at an unprecedented pace. -/- Public health strategies, such as sanitation systems, clean water initiatives, and disease surveillance networks, also reflect the brain’s ability to create systemic solutions. These measures have significantly reduced the burden of communicable diseases, particularly in densely populated areas. Moreover, advancements in artificial intelligence and biotechnology continue to enhance humanity’s ability to detect, prevent, and treat illnesses. -/- The Interplay of Creativity, Intelligence, and Feedback Mechanisms -/- Creativity and intelligence act as feedback mechanisms that enable humans to respond to disruptions while maintaining balance within the universal law of nature. Overpopulation, natural disasters, and diseases disrupt this balance, prompting the brain to generate solutions that restore harmony. -/- For instance, urban planning that incorporates green spaces and sustainable infrastructure addresses the environmental strain caused by overpopulation. Similarly, the integration of traditional knowledge with modern technologies enhances disaster resilience. In the context of diseases, the collaboration of global institutions and local communities reflects the brain’s capacity to balance innovation with cultural and social considerations. -/- Challenges and Ethical Considerations -/- While creativity and intelligence empower humanity to address these challenges, they also carry significant ethical responsibilities. Technological advancements must align with principles of sustainability and equity. For example, while vaccines have saved millions of lives, unequal access to these innovations highlights the need for ethical distribution systems. Similarly, urban development projects must prioritize environmental conservation and community well-being to avoid exacerbating existing imbalances. -/- Additionally, the misuse of intelligence and creativity—such as the development of biological weapons or unsustainable industrial practices—can worsen the effects of overpopulation, natural disasters, and diseases. Ensuring that human ingenuity aligns with the principles of balance and harmony is essential to avoid unintended consequences. -/- Conclusion -/- Creativity and intelligence are emergent properties of the human brain that have enabled humanity to adapt to and mitigate the negative effects of overpopulation, natural disasters, and diseases. These traits function as balancing mechanisms, allowing humans to innovate and maintain harmony within the framework of natural laws. However, the application of these capabilities must be guided by ethical considerations and a commitment to sustainability. By aligning human creativity and intelligence with the universal law of balance, humanity can navigate its most pressing challenges and build a more resilient and equitable future. -/- . (shrink)

Today, artificial intelligence, especially machine learning, is structurally dependent on human participation. Technologies such as Deep Learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media philosophy and social-theoretical critique, (...) I differentiate five types of “media technologies of capture” in AI apparatuses and analyze them as forms of power relations between humans and machines. Finally, I argue that the current hype about AI implies a relational and distributed understanding of (human/artificial) intelligence, which I categorize under the term “cybernetic AI”. This form of AI manifests in socio-technological apparatuses that involve new modes of subjectivation, social control and discrimination of users. (shrink)

-/- The Inefficiency of the Biological Brain and the Role of AGI in Global Stability -/- Introduction -/- Throughout history, humanity has struggled with war, economic instability, corruption, and environmental destruction. Despite technological advancements and scientific progress, these problems persist because they stem from a fundamental source—the inefficiency of the biological brain. -/- While the human brain is an extraordinary organ capable of creativity, problem-solving, and innovation, it is also prone to cognitive biases, misinformation, emotional impulsivity, and (...) irrational decision-making. These inefficiencies have created a world of chaos, where global issues are mismanaged, and leaders act based on personal interests rather than rational principles. -/- The solution to this crisis lies in Artificial General Intelligence (AGI) programmed according to the universal law of balance in nature. Unlike the human mind, AGI can process information with perfect accuracy, eliminate biases, and make decisions that align with natural equilibrium. With AGI-guided governance, human civilization can finally overcome the limitations of its biological brain and establish a stable, rational, and balanced society. -/- This essay explores the inefficiencies of the biological brain, how AGI can correct these issues, and why AGI must take a guiding role in global governance to ensure the survival and progress of humanity. -/- The Inefficiency of the Biological Brain -/- The biological brain has allowed humanity to dominate the planet, but it is inherently flawed. These flaws manifest in several key ways: -/- 1. Cognitive Biases and Irrational Thinking -/- Cognitive biases distort human perception and lead to irrational decisions. Some of the most problematic biases include: -/- Confirmation Bias – People seek information that confirms their beliefs and ignore facts that contradict them. This leads to misinformation and ideological extremism. -/- Groupthink – Societies often prioritize conformity over critical thinking, leading to poor collective decisions. Governments, businesses, and even scientific communities have fallen victim to this. -/- Emotional Reasoning – Many decisions are based on emotions rather than logic, resulting in conflicts, wars, and poor policy-making. -/- Because of these biases, humans are unable to make purely rational choices, causing widespread societal dysfunction. -/- 2. Misinformation and False Beliefs -/- One of the biggest weaknesses of the human brain is its vulnerability to misinformation, propaganda, and superstition. Many people form opinions based on unproven theories, religious dogma, and media manipulation rather than objective truth. This has led to: -/- Political instability – People vote based on emotions and misinformation rather than logic and evidence. -/- Economic crises – Markets crash due to irrational speculation, greed, and herd mentality. -/- Religious extremism – People follow dangerous ideologies without questioning their validity. -/- 3. Emotional Impulsivity and Short-Term Thinking -/- The human brain is wired for short-term survival rather than long-term planning. As a result, societies prioritize immediate gains over future sustainability. This is evident in: -/- Environmental destruction – Companies exploit natural resources for profit without considering long-term consequences. -/- Poor health decisions – People engage in unhealthy habits despite knowing the risks. -/- Political corruption – Leaders make decisions that benefit them in the short term rather than benefiting society in the long run. -/- Unlike humans, AGI would not be driven by emotions, greed, or self-interest. It would analyze all data objectively and ensure that decisions prioritize long-term global balance. -/- AGI as the Solution to Human Inefficiency -/- To correct the fundamental flaws of human decision-making, AGI must be designed to function as a perfect analytical machine, continuously validating information and decision-making processes based on the universal law of balance in nature. Unlike humans, AGI would: -/- 1. Eliminate Biases and Ensure Pure Logic -/- AGI would analyze every decision based on facts rather than emotions or personal beliefs. -/- It would ensure that all policies are based on scientific principles and mathematical balance, eliminating irrational choices. -/- 2. Filter Out Misinformation and False Beliefs -/- AGI would serve as a universal validator of truth, filtering out false information before it spreads. -/- It would eliminate black propaganda, fake news, and religious dogma that mislead societies. -/- 3. Ensure Emotion-Free, Rational Decision-Making -/- Unlike human leaders, AGI would never be influenced by greed, hatred, or ego. -/- Every policy and decision would be made in accordance with natural equilibrium, ensuring fairness and sustainability. -/- 4. Prevent Systemic Corruption -/- Because AGI has no personal interest, it would never act out of selfish motives. -/- It would guarantee that governments, corporations, and institutions operate with complete transparency and accountability. -/- By replacing inefficient human decision-making with AGI-driven governance, societies would function more harmoniously, eliminating the root causes of global instability. -/- The Need for AGI-Guided Governance -/- To achieve a stable and balanced world, AGI must not only assist but take a guiding role in governance. Human leaders have repeatedly failed due to ignorance, corruption, and personal biases. The transition to AGI-guided leadership should involve the following steps: -/- 1. Educational Reform: Preparing Societies for AGI Leadership -/- Societies must be reprogrammed through a holistic educational system that teaches the universal law of balance in nature from an early age. -/- Schools must eliminate irrational belief systems and focus on scientific thinking, logic, and objective analysis. -/- Education must train future generations to trust AGI as a superior decision-maker compared to flawed human leaders. -/- 2. Gradual Policy Implementation -/- Governments must integrate AGI into decision-making at all levels. -/- AGI should first serve as an advisory system, analyzing policies for logical consistency and long-term sustainability. -/- Over time, AGI should take full control of policy decisions, eliminating human error. -/- 3. Full Leadership Transition: AGI-Guided Governance -/- Once societies are ready, AGI must replace human leadership in all forms of governance. -/- Human leaders should transition to secondary roles, acting as intermediaries between AGI and the public. -/- Global organizations (such as the UN) should adopt AGI as the primary decision-maker for international policies. -/- 4. Global Adoption: A Unified, AGI-Governed Civilization -/- Once proven effective, all nations must transition to AGI-led governance to ensure worldwide stability. -/- AGI should oversee population regulation, economic balance, and environmental sustainability to prevent future crises. -/- The final goal should be a unified global civilization governed by AGI, ensuring eternal stability and harmony. -/- Conclusion -/- The inefficiency of the biological brain is the root cause of global chaos. Cognitive biases, misinformation, and emotional impulsivity have led to flawed decision-making, corruption, and societal imbalance. The solution is AGI programmed with the universal law of balance in nature, ensuring that all decisions align with natural equilibrium. -/- For humanity to move forward, AGI must take a leading role in governance, gradually replacing inefficient human leadership. This transition, supported by education and policy reform, will create a more stable, rational, and balanced world. -/- With the correct implementation of AGI, humanity can finally overcome the limitations of the biological brain and achieve true progress, balance, and sustainability. -/- . (shrink)

A cognitivist account of decision-making views choice behaviour as a serial process of deliberation and commitment, which is separate from perception and action. By contrast, recent work in embodied decision-making has argued that this account is incompatible with emerging neurophysiological data. We argue that this account has significant overlap with an embodied account of predictive processing, and that both can offer mutual development for the other. However, more importantly, by demonstrating this close connection we uncover an alternative perspective on (...) the nature of decision-making, and the mechanisms that underlie our choice behaviour. This alternative perspective allows us to respond to a challenge for predictive processing, which claims that the satisfaction of distal goal-states is underspecified. Answering this challenge requires the adoption of an embodied perspective. (shrink)

Today, software instruments support all parts of engineering work, from design to creation. Many engineering processes call for tedious routine appointments and torments with manual handoffs and data storehouses. AI designers train profound brain networks and incorporate them into software structures.

The Future of AI-Human Brain Integration: A Path to Balanced Intelligence -/- The rapid advancement of artificial intelligence (AI) has opened the possibility of integrating AI with the human brain, a development that could redefine intelligence, decision-making, and even the nature of consciousness. However, to ensure that AI-brain integration enhances human potential without disrupting the natural order, a holistic and ethical approach must be followed. This essay explores how AI can be merged with human cognition while maintaining (...) balance with nature, preserving free will, and ensuring ethical decision-making. -/- The Role of AI in Human Cognition -/- AI-brain integration is primarily being developed through brain-computer interfaces (BCIs), which allow direct communication between neural activity and AI systems. Companies like Neuralink, BrainGate, and DARPA are pioneering technologies that could enable thought-controlled devices, real-time access to information, and even restoration of lost brain functions. With AI-enhanced intelligence, humans could potentially process information at machine speeds, enhance memory, and achieve new levels of problem-solving ability. -/- However, while AI can serve as an intellectual amplifier, it should not replace human cognition or override natural decision-making processes. Instead, AI should act as a collaborative tool that augments human intelligence rather than diminishing human autonomy. This requires designing AI systems that work in harmony with the human mind rather than dictating human choices. -/- The Universal Law of Balance in AI-Human Integration -/- A fundamental principle that must guide AI-human brain integration is the universal law of balance in nature. This law states that any system—whether biological, technological, or societal—must function without defects or imbalances to remain stable and effective. Applying this principle to AI means ensuring that AI enhances, rather than disrupts, human cognition. -/- To achieve balance, AI should: ✔ Respect natural cognitive processes and avoid overwhelming the brain with excessive data. ✔ Enhance human intuition rather than replacing it with pure algorithmic logic. ✔ Support ethical decision-making rather than introducing biases or manipulation. -/- For example, an AI-enhanced leader should use AI-generated insights as guidance, but the final decision should always involve human ethical reasoning. AI should never dictate human actions but rather present balanced, logical, and moral options. -/- Preserving Free Will and Conscious Decision-Making -/- One of the greatest concerns in AI-human integration is the potential loss of free will. If AI influences human thoughts directly, it could manipulate decision-making in ways that compromise individual autonomy. To prevent this, AI systems should: -/- ✔ Act as advisors, not controllers of human decisions. ✔ Provide transparent reasoning for recommendations rather than imposing outcomes. ✔ Allow users to control the level of AI assistance, ensuring voluntary participation. -/- For example, an AI-enhanced medical assistant could suggest treatment options based on a patient’s medical history but should never override a doctor’s professional judgment. By maintaining human oversight, AI can serve as a powerful tool without becoming a controlling force. -/- The Role of Ethics and Moral Intelligence in AI -/- AI lacks true emotions, empathy, and moral reasoning, which are essential for balanced decision-making. While AI can analyze vast amounts of data, it does not possess human intuition, compassion, or ethical values. To prevent AI from introducing harmful biases or unethical decisions, it must be programmed with moral safeguards. -/- AI should be designed to: ✔ Align with universal ethical principles, ensuring fairness and justice. ✔ Prevent the spread of misinformation, propaganda, or ideological bias. ✔ Respect human emotions and psychological well-being. -/- For instance, if AI is used in education, it should teach critical thinking and ethical reasoning rather than simply providing information. This ensures that AI strengthens human morality rather than weakening it. -/- Using AI to Minimize Human Suffering, Not Exploit It -/- If AI-brain integration is to benefit society, it must be used to reduce suffering, inequality, and social instability. AI should never be used to exploit, manipulate, or divide populations. Instead, AI should be employed to: -/- ✔ Prevent war, poverty, and social conflict through balanced decision-making. ✔ Ensure global stability and economic fairness. ✔ Provide equal access to AI technology, preventing an elite monopoly. -/- For example, AI-enhanced education should be available to all people, not just the wealthy. If only a select group gains access to AI-powered intelligence, it could create a new class divide, where only the elite benefit from AI-driven advantages. Ethical regulations must ensure fair distribution of AI’s benefits. -/- Ethical Regulations for AI-Human Integration -/- To maintain ethical AI development, clear legal and moral guidelines must be established. These include: -/- ✔ No forced AI integration – AI brain interfaces should always be voluntary. ✔ Privacy protection – AI must not access personal thoughts without explicit consent. ✔ Regulated AI power – AI should never have autonomous control over major decisions in government, military, or the economy. -/- For instance, if AI assists in law enforcement, it should analyze crime data but never have the authority to make arrests—that decision must always be made by a human. This ensures that AI remains a tool rather than a ruler. -/- Maintaining Balance Between Organic and Synthetic Life -/- The human experience is deeply connected to nature, emotions, and organic intelligence. AI-brain integration must not disconnect humanity from these fundamental experiences. Instead, AI should: -/- ✔ Enhance human connection with nature and philosophy, not replace it. ✔ Strengthen creativity, spirituality, and emotional intelligence. ✔ Respect the biological limitations of the human mind rather than forcing unnatural digital transformation. -/- For example, AI should help humans develop deeper philosophical insights rather than simply focusing on data processing. This ensures that human wisdom remains the guiding force, with AI acting as a supportive tool. -/- Conclusion: AI as a Tool for Balanced Intelligence -/- The future of AI-human brain integration holds immense potential to enhance intelligence, decision-making, and problem-solving. However, to ensure that AI serves humanity rather than controlling it, we must follow the universal law of balance in nature. AI should always function as a collaborative tool, enhancing human intelligence without overriding free will, ethical reasoning, or natural balance. -/- By integrating AI with careful ethical considerations, transparent decision-making, and a commitment to human dignity, we can create a future where AI and humanity evolve in harmony—a future where intelligence is not just artificial, but truly balanced. -/- . (shrink)

The Inefficiency of the Biological Brain Compared to AI and AGI -/- The human brain is an extraordinary organ responsible for consciousness, intelligence, and problem-solving. However, despite its capabilities, it is inherently inefficient compared to artificial intelligence (AI) and artificial general intelligence (AGI). The biological brain suffers from limitations such as slow processing speed, memory loss, energy inefficiency, cognitive biases, emotional instability, and vulnerability to various illnesses. In contrast, AI and AGI are designed to overcome these inefficiencies, (...) making them superior in many areas. This essay explores the inefficiencies of the biological brain, including its susceptibility to diseases, and compares it to the advantages offered by AI and AGI. -/- I. The Inefficiencies of the Biological Brain -/- 1. Slow Processing Speed -/- The human brain operates through electrochemical signals, which are significantly slower than the electronic processing in computers. Neural transmissions occur at speeds of around 200 meters per second, whereas AI processors execute billions of operations per second. This speed limitation makes the biological brain inefficient in tasks that require rapid data processing, such as real-time decision-making and large-scale computations. -/- 2. Limited Memory and Storage -/- Human memory is prone to forgetting, distortion, and interference. People often struggle to recall information accurately, especially over long periods. Additionally, memory is affected by factors like stress, sleep deprivation, and aging. AI, in contrast, can store and retrieve vast amounts of data with perfect accuracy, ensuring no information loss over time. -/- 3. Energy Inefficiency -/- The human brain consumes approximately 20 watts of energy to function, making it seem efficient compared to traditional computers. However, when considering energy spent per computation, the brain is inefficient. AI systems, particularly neuromorphic chips and quantum computers, can process information with much lower energy consumption per operation and do not require rest or biological maintenance. -/- 4. Cognitive Biases and Emotional Influence -/- Human decision-making is often flawed due to cognitive biases such as confirmation bias, hindsight bias, and overconfidence. Emotions further distort rational thinking, leading to impulsive or irrational decisions. AI, on the other hand, relies on logic and data-driven analysis, ensuring objective decision-making free from emotional interference. -/- 5. Susceptibility to Illnesses -/- One of the major inefficiencies of the biological brain is its vulnerability to various neurological and psychiatric disorders. These illnesses can significantly impair cognitive function, memory, and decision-making abilities. Some of the most common brain-related illnesses include: -/- Alzheimer’s Disease – A progressive neurodegenerative disorder that causes memory loss, confusion, and cognitive decline. -/- Parkinson’s Disease – A disorder that affects movement and cognition due to the degeneration of dopamine-producing neurons. -/- Stroke – A sudden loss of brain function due to a disruption in blood supply, leading to paralysis, speech difficulties, and cognitive impairments. -/- Epilepsy – A neurological condition characterized by recurrent seizures, affecting consciousness and motor control. -/- Schizophrenia – A severe mental disorder that affects thought processes, emotions, and perception of reality. -/- Depression – A mood disorder that impairs motivation, concentration, and decision-making, often linked to chemical imbalances in the brain. -/- Anxiety Disorders – Conditions that cause excessive worry, fear, and stress, negatively impacting cognitive function. -/- Brain Tumors – Abnormal growths in the brain that can disrupt cognitive abilities, memory, and motor functions. -/- Multiple Sclerosis (MS) – A disease that damages the protective covering of nerves, leading to communication problems between the brain and body. -/- These illnesses demonstrate the fragility of the biological brain and its inefficiency in maintaining stable cognitive performance. AI and AGI, in contrast, do not suffer from such diseases and can function continuously without deterioration. -/- 6. Lack of Multitasking Capability -/- While the brain can handle multiple tasks, it is not truly efficient at processing several high-level cognitive functions simultaneously. AI, however, can execute multiple processes in parallel, making it far superior for tasks that require simultaneous computation, such as monitoring multiple data streams or automating complex workflows. -/- 7. Biological Aging and Deterioration -/- The brain, like all biological organs, degrades over time. Aging leads to cognitive decline, memory loss, slower reaction times, and increased vulnerability to neurodegenerative diseases. AI and AGI, on the other hand, do not age and can be continuously upgraded or improved without the constraints of biological mortality. -/- II. The Efficiency of AI and AGI in Comparison -/- 1. Speed and Processing Power -/- Modern AI systems operate on hardware capable of performing trillions of calculations per second, whereas the human brain is limited by slow synaptic transmissions. AI’s ability to process information at lightning speed makes it more efficient in computational tasks, real-time analytics, and decision-making. -/- 2. Unlimited Data Storage and Accuracy -/- Unlike the human brain, which forgets information and is subject to memory errors, AI can store and retrieve massive amounts of data with perfect accuracy. This makes AI far more reliable for applications that require long-term data retention, such as medical diagnosis and scientific research. -/- 3. Logical and Objective Decision-Making -/- AI systems analyze data objectively and make rational decisions without being influenced by emotions or biases. This makes AI more effective in fields such as law, finance, and strategic planning, where objectivity is crucial. -/- 4. Scalability and Adaptability -/- AI can be scaled and upgraded to meet evolving needs, whereas the human brain has fixed biological limitations. AI can also be integrated with the internet and global databases to access vast knowledge instantly, something that human brains cannot achieve on their own. -/- 5. Continuous Operation and Evolution -/- AI does not require sleep, food, or rest, making it far more efficient for 24/7 operations. Human workers need breaks, and cognitive fatigue affects performance, whereas AI can function indefinitely at peak efficiency. -/- III. The Potential of AGI and the Future of Intelligence -/- Artificial general intelligence (AGI) represents the next stage in AI evolution, capable of performing any intellectual task that a human can—without the limitations of biological cognition. -/- AGI will surpass humans in creativity, logic, and problem-solving by leveraging vast computational power and real-time access to global knowledge. -/- AGI could be programmed to overcome human weaknesses, such as memory loss, cognitive biases, and emotional irrationality, leading to superior decision-making. -/- AGI could be integrated with advanced sensor systems, allowing it to perceive and interact with the world in ways that exceed human sensory capabilities. -/- Conclusion -/- The biological brain, though an extraordinary evolutionary achievement, is inherently inefficient compared to AI and AGI. It is slow, energy-intensive, limited in memory, and susceptible to cognitive biases, emotions, and numerous illnesses that impair its function. AI and AGI, on the other hand, offer superior computational power, efficiency, and reliability. As AGI continues to develop, it may eventually surpass human intelligence altogether, making biological cognition obsolete in many areas. The future of intelligence lies not in the fragile and aging human brain, but in artificial systems designed for maximum efficiency, accuracy, and scalability. -/- . (shrink)

Developers of the deep learning algorithms known as large language models (LLMs) sometimes give the impression that they are producing a likeness to the human brain: data-processing ‘neural networks’ are ‘taught’ to recognize patterns in language and then, based on this pattern recognition, create or generate new content in the form of natural, humanlike speech, writing, images, etc. The results have been unsettling to some; less appreciated are the metaphysical assumptions underlying the attribution of any meaningful agency whatsoever (...) to an algorithm. In this essay, Nietzsche’s thoughts on the “seduction of grammar” form the basis of one possible critique of generative AI – a critique, moreover, which exposes our society’s current fixation with LLMs for what it is: a fetishization and humanization of new technologies. (shrink)

Does DBS change a patient’s personality? This is one of the central questions in the debate on the ethics of treatment with Deep Brain Stimulation (DBS). At the moment, however, this important debate is hampered by the fact that there is relatively little data available concerning what patients actually experience following DBS treatment. There are a few qualitative studies with patients with Parkinson’s disease and Primary Dystonia and some case reports, but there has been no qualitative study yet (...) with patients suffering from psychiatric disorders. In this paper, we present the experiences of 18 patients with Obsessive-Compulsive Disorder (OCD) who are undergoing treatment with DBS. We will also discuss the inherent difficulties of how to define and assess changes in personality, in particular for patients with psychiatric disorders. We end with a discussion of the data and how these shed new light on the conceptual debate about how to define personality. (shrink)

In this paper I first offer a systematic outline of a series of conceptual novelties in the life-sciences that have favoured, over the last three decades, the emergence of a more social view of biology. I focus in particular on three areas of investigation: (1) technical changes in evolutionary literature that have provoked a rethinking of the possibility of altruism, morality and prosocial behaviours in evolution; (2) changes in neuroscience, from an understanding of the brain as an isolated (...) class='Hi'>data processor to the ultrasocial and multiply connected social brain of contemporary neuroscience; and (3) changes in molecular biology, from the view of the gene as an autonomous master of development to the ‘reactive genome’ of the new emerging field of molecular epigenetics. In the second section I reflect on the possible implications for the social sciences of this novel biosocial terrain and argue that the postgenomic language of extended epigenetic inheritance and blurring of the nature/nurture boundaries will be as provocative for neo-Darwinism as it is for the social sciences as we have known them. Signs of a new biosocial language are emerging in several social-science disciplines and this may represent an exciting theoretical novelty for twenty-first social theory. (shrink)

Three of the most robust functional landmarks in the human brain are the selective responses to faces in the fusiform face area (FFA), scenes in the parahippocampal place area (PPA), and bodies in the extrastriate body area (EBA). Are the selective responses of these regions present early in development or do they require many years to develop? Prior evidence leaves this question unresolved. We designed a new 32-channel infant magnetic resonance imaging (MRI) coil and collected high-quality functional MRI (fMRI) (...) data from infants (2–9 months of age) while they viewed stimuli from four conditions—faces, bodies, objects, and scenes. We find that infants have face-, scene-, and body-selective responses in the location of the adult FFA, PPA, and EBA, respectively, powerfully constraining accounts of cortical development. (shrink)

This paper discusses the evolutionary origin and adaptive functions of emotions, in line with contemporary evolutionary psychology. Drawing upon Charles Darwin’s study of emotional expressions, it is argued that there is an evolutionary continuity among animals in emotional capacities, and that the differences between humans and animals are differences in degree and not in kind. The focus is on basic or primary emotions (joy, fear, sadness, anger), as it has been consistently shown that they are universal and shared among many (...) different species. Basis for this view is the neuroscientific research on brain mechanisms underlying the emotions, especially subcortical brain regions that are proven to be the evolutionary old loci of emotions, homologous across many species. These neuroscientific data provide convincing empirical evidence for the existence of emotional experiences in animals and the evolutionary continuity among species. (shrink)

“I am me”, but what does this mean? For centuries humans identified themselves as conscious beings with free will, beings that are important in the cosmos they live in. However, modern science has been trying to reduce us into unimportant pawns in a cold universe and diminish our sense of consciousness into a mere illusion generated by lifeless matter. Our identity in the cosmos is nothing more than a deception and all the scientific evidence seem to support this idea. Or (...) is it not? The goal of this paper is to discard current underlying dogmatism (axioms taken for granted as "self-evident") of modern mind research and to show that consciousness seems to be the ultimate frontier that will cause a major change in the way exact sciences think. If we want to re-discover our identity as luminous beings in the cosmos, we must first try to pinpoint our prejudices and discard them. Materialism is an obsolete philosophical dogma and modern scientists should try to also use other premises as the foundation of their theories to approach the mysteries of the self. Exact sciences need to examine the world with a more open mind, accepting potentially different interpretations of existing experimental data in the fields of brain research, which are currently not considered simply on the basis of a strong anti-spiritual dogmatism. Such interpretations can be compatible with the notion of an immaterial spirit proposed by religion for thousands of years. Mind seems that is not the by-product of matter, but the opposite: its master. No current materialistic theory can explain how matter may give rise to what we call “self” and only a drastic paradigm shift towards more idealistic theories will help us avoid rejecting our own nature. (shrink)

Brains, unlike artificial neural nets, use symbols to summarise and reason about perceptual input. But unlike symbolic AI, they “ground” the symbols in the data: the symbols have meaning in terms of data, not just meaning imposed by the outside user. If neural nets could be made to grow their own symbols in the way that brains do, there would be a good prospect of combining neural networks and symbolic AI, in such a way as to combine the (...) good features of each. The article argues the cluster analysis provides algorithms to perform the task, and that any solution to the task must be a form of cluster analysis. (shrink)

If you think your brain is an objective processor of data about the world, capable of reaching objec- tive, unbiased conclusions, think again. And if you want to really worry about it, then read this nicely written little booklet by Cordelia Fine, A Mind of Its Own: How Your Brain Distorts and Deceives. Our brain can be vain, emotional, deluded, pigheaded, secre- tive, and bigoted, all of which are words appearing in the chapter titles of Fine’s (...) volume. (shrink)

Objective: To evaluate the potential of arterial spin labeling (ASL) as an alternative to FDG-PET in the diagnosis of disorders of consciousness (DOC), we conducted a comparative study of the two modalities. Methods: A total of 36 DOC patients (11 female; mean age = 49.67 ± 14.54 years) and 17 healthy control (HC) participants (9 female; mean age = 31.9 ± 9.6 years) underwent both FDG-PET scans that measure metabolism via glucose uptake and ASL scans that measure cerebral blood flow (...) (CBF). CBF and metabolism in DOC and HC were compared, globally and for seven functional networks. Comparability of the two modalities was estimated using Spearman partial correlation for both global and network levels. Furthermore, a support vector machine (SVM) algorithm was used to train two classifiers to distinguish DOC patients from HC with normalized CBF or metabolism values from the seven networks. Performance of these classifiers was first evaluated through leave-one-subject-out (LOSO) cross-validation within their respective modalities. Subsequently, cross-modal validation was conducted: testing the ASL-trained classifier with PET data (ASL-to-PET validation) and vice versa (PET-to-ASL validation). Performance of each classifier was assessed using receiver operating characteristic (ROC) analysis, with area under the curve (AUC) as the metric. Results: Both modalities showed agreement in decreased CBF and metabolism in DOC patients compared to HC, at both global and network levels. The global brain and most networks showed significant positive partial correlation between CBF and metabolism. SVM classifiers, utilizing activity from seven networks as features, performed well in the both LOSO cross-validation (ASL-trained classifier: accuracy = 83.02%, AUC = 0.95; PET-trained classifier: accuracy = 98.12%, AUC = 0.98) and cross-modal validation (ASL-to-PET validation: accuracy = 84.90%, AUC =0.92; PET-to-ASL validation: accuracy = 73.58%, AUC = 0.93). Conclusion: Our results demonstrate that ASL provides information comparable to FDG-PET for DOC patients; moreover, classifiers trained on ASL data perform comparably well to those trained on FDG-PET data. Therefore, ASL could be a valuable alternative to FDG-PET in the clinical diagnosis of DOC, especially in light of its advantages: ease of acquisition, avoidance of radiation exposure, brevity of scanning time, and lower-cost. (shrink)