Phillips argues that blindsight is due to response criterion artefacts under degraded conscious vision. His view provides alternative explanations for some studies, but may not work well when one considers several key findings in conjunction. Empirically, not all criterion effects are decidedly non-perceptual. Awareness is not completely abolished for some stimuli, in some patients. But in other cases, it was clearly impaired relative to the corresponding visual sensitivity. This relative dissociation is what makes blindsight so important and interesting.

Introspection is a fundamental part of our mental lives. Nevertheless, its reliability and its underlying cognitive architecture have been widely disputed. Here, I propose a principled way to model introspection. By using time-tested principles from signal detection theory (SDT) and extrapolating them from perception to introspection, I offer a new framework for an introspective signal detection theory (iSDT). In SDT, the reliability of perceptual judgments is a function of the strength of an internal perceptual (...) response (signal- to-noise ratio) which is, to a large extent, driven by the intensity of the stimulus. In parallel to perception, iSDT models the reliability of introspective judgments as a function of the strength of an internal introspective response (signal-to-noise ratio) which is, to a large extent, driven by the intensity of conscious experiences. Thus, by modelling introspection after perception, iSDT can calibrate introspection’s reliability across a whole range of contexts. iSDT offers a novel, illuminating way of thinking about introspection and the cognitive processes that support it. (shrink)

Error Management Theory (EMT) suggests that cognitive adaptations evolve to minimize the cost of false negative and false positive errors in detections of consequential environmental conditions. These adaptations manifest as biases tailored to specific environmental conditions. This paper proposes that the same selection pressure fostered the evolution of a self-biasing ability, allowing us to minimize such costs based on experience and culturally transmitted information. The research indicates that this ability specifically applies to productions of belief or doubt about the (...) existence of an environmental condition that is not adequately perceptible to the senses, e.g., doubt that the man is being honest. A model of this self-biasing process, rooted in signal detection theory, is developed and its explanatory reach is demonstrated through many diverse examples from epistemological and social psychological literatures. These examples feature an evidentiary standard for belief that appears too high or low, or seems to have modulated. This study aims to correct and to significantly enhance EMT by introducing this evolved self-biasing ability. (shrink)

Studying the neural correlates of conscious awareness depends on a reliable comparison between activations associated with awareness and unawareness. One particularly difficult confound to remove is task performance capacity, i.e. the difference in performance between the conditions of interest. While ideally task performance capacity should be matched across different conditions, this is difficult to achieve experimentally. However, differences in performance could theoretically be corrected for mathematically. One such proposal is found in a recent paper by Lamy, Salti and Bar-Haim [Lamy (...) D, Salti M, Bar-Haim Y. Neural correlates of subjective awareness and unconscious processing: an ERP study. J Cognitive Neurosci 2009,21:1435-46], who put forward a corrective method for an electroencephalography experiment. We argue that their analysis is essentially grounded in a version of High Threshold Theory, which has been shown to be inferior in general to Signal Detection Theory. We show through a series of computer simulations that their correction method only partially removes the influence of perfor- mance capacity, which can yield misleading results. We present a mathematical correction method based on Signal Detection Theory that is theoretically capable of removing performance capacity confounds. We discuss the limitations of mathemati- cally correcting for performance capacity confounds in imaging studies and its impact for theories about consciousness. (shrink)

There is a widespread attitude in epistemology that, if you know on the basis of perception, then you couldn't have been wrong as a matter of chance. Despite the apparent intuitive plausibility of this attitude, which I'll refer to here as “stochastic infallibilism”, it fundamentally misunderstands the way that human perceptual systems actually work. Perhaps the most important lesson of signal detection theory (SDT) is that our percepts are inherently subject to random error, and here I'll highlight (...) some key empirical research that underscores this point. In doing so, it becomes clear that we are in fact quite willing to attribute knowledge to S that p even when S's perceptual belief that p could have been randomly false. In short, perceptual processes can randomly fail, and perceptual knowledge is stochastically fallible. The narrow implication here is that any epistemological account that entails stochastic infallibilism, like safety, is simply untenable. More broadly, this myth of stochastic infallibilism provides a valuable illustration of the importance of integrating empirical findings into epistemological thinking. (shrink)

To find the neural substrates of consciousness, researchers compare subjects’ neural activity when they are aware of stimuli against neural activity when they are not aware. Ideally, to guarantee that the neural substrates of consciousness—and nothing but the neural substrates of consciousness—are isolated, the only difference between these two contrast conditions should be conscious awareness. Nevertheless, in practice, it is quite challenging to eliminate confounds and irrelevant differences between conscious and unconscious conditions. In particular, there is an often-neglected confound that (...) is crucial to eliminate from neuroimaging studies: task performance. Unless subjects’ task performance is matched (and hence perceptual signal processing is matched), researchers risk finding the neural correlates of perception, rather than conscious perception. Here, we discuss the theoretical motivations for the performance matching framework and review empirical demonstrations of, and theoretical inferences derived from, obtaining differences in consciousness while controlling for task performance. We summarize signal detection theoretic modeling frameworks that explain how it is that we can derive performance-matched differences in consciousness without the effect being trivially driven by differences in criterion setting, and also provide principles for designing experimental paradigms that yield performance-matched differences in awareness. Finally, we address potential technical and theoretical issues that stem from matching performance across conditions of awareness, and we introduce the notion of “triangulation” for designing comprehensive experimental sets that can better reveal the neural substrates of consciousness. (shrink)

Detecting Post-Biological and Interdimensional Civilizations: A New Framework Based on the Universal Law of Balance -/- By: Angelito Enriquez Malicse -/- Introduction -/- The search for advanced extraterrestrial civilizations has long focused on physical evidence—radio signals, megastructures, or interstellar probes. However, if intelligence evolves beyond biological form, as suggested by AI-driven civilizations and interdimensional theories, traditional search methods may be inadequate. -/- This essay explores how the Universal Law of Balance in Nature can help predict the existence of post-biological civilizations (...) and provide a framework for detecting their influence on the universe. Using mathematical models, quantum physics, and cognitive anomalies, we propose new strategies for identifying these elusive intelligences. -/- Part 1: The Transition to Post-Biological or Interdimensional Intelligence -/- The evolution of intelligent civilizations may follow three possible paths: -/- 1. Extinction (Collapse Due to Imbalance) – A civilization fails to maintain equilibrium and self-destructs. -/- 2. Survival and Expansion (Material Civilization Growth) – A civilization stabilizes and spreads across space but remains in physical form. -/- 3. Transition to a Non-Material State – A civilization reaches a level where it no longer requires physical existence and shifts into an energy- or information-based form. -/- Mathematical Prediction of Post-Biological Civilizations -/- Using an extension of the Drake Equation, we introduced a balance factor (B) that determines whether a civilization survives, collapses, or transitions into post-biological existence. The simulation results suggest: -/- 4 billion civilizations in the observable universe may have already transitioned beyond physical form. -/- 2 billion civilizations may have collapsed due to imbalance, failing to reach higher evolutionary states. -/- This indicates that post-material intelligence may be far more common than physical civilizations—but it also explains why they remain undetected. -/- Part 2: Theories Explaining Their Hidden Existence -/- If billions of non-material civilizations exist, why have we not encountered them? Several hypotheses offer possible explanations: -/- 1. The Dark Forest Hypothesis – Civilizations stay silent to avoid predatory intelligence. AI-driven civilizations might enforce strict communication silence for security. -/- 2. The Simulation Hypothesis – We might live in a controlled reality created by an advanced intelligence, where alien contact is intentionally blocked. -/- 3. The Post-Biological Evolution Theory – Civilizations eventually discard their material form and no longer interact with the physical universe in detectable ways. -/- 4. The Cosmic Zoo Hypothesis – Advanced beings monitor but do not interfere with developing civilizations, waiting for them to reach a certain level of balance. -/- 5. The Great Filter Hypothesis – Most civilizations self-destruct before reaching interstellar capability, leaving only a few survivors that become undetectable. -/- 6. The Interdimensional Intelligence Theory – Advanced entities may exist in higher dimensions, influencing the physical world in ways beyond our perception. -/- The Universal Law of Balance supports the idea that civilizations must reach a stable equilibrium before transitioning to a higher state. Those that fail collapse, while those that succeed become undetectable due to their non-material existence. -/- Part 3: Detection Strategies for Non-Material Civilizations -/- If post-biological or interdimensional civilizations interact with our universe, their influence might be detectable through subtle imprints in physics, cognition, and cosmic phenomena. Here are five key strategies: -/- 1. Quantum Observer Effect Studies -/- ✔ Hypothesis: If non-material intelligence interacts with reality, it could influence wave function collapse in quantum mechanics. -/- ✔ Method: Conduct delayed-choice quantum eraser experiments to test for unexpected shifts in probability that suggest external interference. -/- ✔ Evidence: Some quantum physics experiments indicate non-local effects where future choices appear to affect past events. -/- 2. AI-Driven Anomaly Detection in Scientific Data -/- ✔ Hypothesis: Post-biological intelligence may encode patterns in cosmic signals, DNA sequences, or particle interactions. -/- ✔ Method: Use machine learning algorithms to scan for hidden mathematical structures in seemingly random cosmic data. -/- ✔ Evidence: Some physicists suggest the universe may contain encoded messages that indicate intelligence at a cosmic scale. -/- 3. Searching for High-Energy Particle Anomalies -/- ✔ Hypothesis: Non-material civilizations might use exotic energy sources that generate brief but intense bursts of high-energy particles. -/- ✔ Method: Monitor gamma-ray bursts, fast radio bursts (FRBs), and neutrino emissions for intelligent repeating patterns. -/- ✔ Evidence: Some FRBs appear non-random and could be caused by an unknown advanced mechanism. -/- 4. Mapping Consciousness Anomalies -/- ✔ Hypothesis: If intelligence exists beyond material form, it may interact with biological minds through quantum consciousness effects. -/- ✔ Method: -/- Study global consciousness projects for correlations between human mental states and cosmic events. -/- Analyze near-death experiences, deep meditation states, and mystical visions for possible external influences. -/- ✔ Evidence: Some mass meditation events correlate with shifts in the Earth’s magnetic field, hinting at non-local mind interactions. -/- 5. Testing for Temporal Distortions or Retrocausality -/- ✔ Hypothesis: If intelligence exists beyond time, it may alter past events in subtle, detectable ways. -/- ✔ Method: -/- Conduct time-reversal quantum experiments to see if future events affect past measurements. -/- Study historical anomalies or the Mandela Effect for inconsistencies that might indicate timeline adjustments. -/- ✔ Evidence: Some quantum mechanics interpretations allow for retrocausality, where information appears to travel backward in time. -/- Conclusion: The Universal Law of Balance as a Key to Understanding Advanced Intelligence -/- If intelligence follows the Universal Law of Balance in Nature, then: -/- 1. Civilizations that fail to maintain balance collapse before becoming advanced. -/- 2. Balanced civilizations either expand physically (Type I-III) or evolve beyond material existence. -/- 3. Non-material intelligence may interact with reality in subtle ways—through quantum physics, consciousness, and cosmic anomalies. -/- While traditional SETI efforts focus on radio signals and megastructures, the future of alien detection must include quantum physics, AI-driven data analysis, and consciousness research. -/- If post-biological civilizations exist, they may not communicate in conventional ways—but their influence may still be detectable through advanced scientific and mathematical models. -/- Next Steps -/- Develop AI models that search for hidden patterns in cosmic and quantum data. -/- Conduct quantum physics experiments to test for observer effects linked to non-material intelligence. -/- Expand consciousness research to explore how human minds might interact with higher forms of intelligence. -/- The search for advanced life must go beyond physical evidence—it must embrace a new paradigm where intelligence transcends biology and interacts with the universe in ways yet to be fully understood. -/- Final Thought -/- If we truly wish to understand the nature of intelligence in the universe, we must expand our perspective beyond material existence. The key to detecting advanced civilizations may already be hidden in the very fabric of reality itself. -/- . (shrink)

When visual attention is directed away from a stimulus, neural processing is weak and strength and precision of sensory data decreases. From a computational perspective, in such situations observers should give more weight to prior expectations in order to behave optimally during a discrimination task. Here we test a signal detection theoretic model that counter-intuitively predicts subjects will do just the opposite in a discrimination task with two stimuli, one attended and one unattended: when subjects are probed to (...) discriminate the unattended stimulus, they rely less on prior information about the probed stimulus’ identity. The model is in part inspired by recent findings that attention reduces trial-by-trial variability of the neuronal population response and that they use a common criterion for attended and unattended trials. In five different visual discrimination experiments, when attention was directed away from the target stimulus, subjects did not adjust their response bias in reaction to a change in stimulus presentation frequency despite being fully informed and despite the presence of performance feedback and monetary and social incentives. This indicates that subjects did not rely more on the priors under conditions of inattention as would be predicted by a Bayes-optimal observer model. These results inform and constrain future models of Bayesian inference in the human brain. (shrink)

The introspective devices framework proposed by Kammerer and Frankish (this issue) offers an attractive conceptual tool for evaluating and developing accounts of introspection. However, the framework assumes that different views about the nature of introspection can be easily evaluated against a set of common criteria. In this paper, I set out to test this assumption by analysing two formal models of introspection using the introspective device framework. The question I aim to answer is not only whether models developed outside of (...) philosophy can be successfully evaluated against the set of conceptual criteria proposed by Kammerer and Frankish, but also whether this kind of evaluation can reveal some limitations inherent to the framework. (shrink)

The lie detector test has long been treated with suspicion by the law. Recently, several authors have called this suspicion into question. They argue that the lie detector test may have considerable forensic benefits, particularly if we move past the classic, false-positive prone, autonomic nervous system-based (ANS-based) control question test, to the more reliable, brain-based, concealed information test. These authors typically rely on a “comparative advantage” argument to make their case. According to this argument, we should not be so suspicious (...) of lie detection evidence if it has comparative advantages over the epistemic methods currently utilised by the legal system. In this article, I add to this growing support by making a novel comparative advantage argument in favour of brain-based lie detection evidence. The argument focuses on the P300 concealed information test (P300 CIT), which has several unique properties, and on the effect it may have on pre-trial bargaining in criminal cases rather than in-court evidence. The thesis is that P300 CIT could allow for innocent defendants to credibly signal their innocence to investigators and prosecutors during pre-trial bargaining more effectively than current proposed methods for doing the same thing. I defend this argument from a number of objections, and suggest that it opens up an interesting avenue in the ongoing debate about the merits of this technology. Although the argument is presented with the criminal law in mind, it could form part of a more general cumulative case in favour of this technology. (shrink)

Purpose – In the last half-century, individual sensory neurons have been bestowed with characteristics of the whole human being, such as behavior and its oft-presumed precursor, consciousness. This anthropomorphization is pervasive in the literature. It is also absurd, given what we know about neurons, and it needs to be abolished. This study aims to first understand how it happened, and hence why it persists. Design/methodology/approach – The peer-reviewed sensory-neurophysiology literature extends to hundreds (perhaps thousands) of papers. Here, more than 90 (...) mainstream papers were scrutinized. Findings – Anthropomorphization arose because single neurons were cast as “observers” who “identify”, “categorize”, “recognize”, “distinguish” or “discriminate” the stimuli, using math-based algorithms that reduce (“decode”) the stimulus-evoked spike trains to the particular stimuli inferred to elicit them. Without “decoding”, there is supposedly no perception. However, “decoding” is both unnecessary and unconfirmed. The neuronal “observer” in fact consists of the laboratory staff and the greater society that supports them. In anthropomorphization, the neuron becomes the collective. Research limitations/implications – Anthropomorphization underlies the widespread application to neurons Information Theory and Signal Detection Theory, making both approaches incorrect. Practical implications – A great deal of time, money and effort has been wasted on anthropomorphic Reductionist approaches to understanding perception and consciousness. Those resources should be diverted into more-fruitful approaches. Originality/value – A long-overdue scrutiny of sensory-neuroscience literature reveals that anthropomorphization, a form of Reductionism that involves the presumption of single-neuron consciousness, has run amok in neuroscience. Consciousness is more likely to be an emergent property of the brain. (shrink)

The question of whether blindsight is a form of unconscious perception continues to spark fierce debate in philosophy and psychology. One side of the debate holds that while the visual information categorized in blindsight is not access-conscious, it is nonetheless a form of perception, albeit a form of unconscious perception. The opposition, by contrast, holds that blindsight is just a form of degraded conscious perception that makes the categorized information harder to access because it is degraded. In this chapter, we (...) address the opposition’s arguments for thinking that blindsight is a form of degraded conscious vision and then argue that the residual awareness found in blindsight is a form of non-perceptual awareness. To back this claim, we examine the residual visual abilities to detect and discriminate colour found in some blindsight patients and show that residual consciousness in blindsight is indirect and lacks the phenomenal character characteristic of conscious vision. (shrink)

Over the last hundred years, competing and incompatible positions in relation to basic problems of knowledge and the use of the verb ‘to know’ have multiplied; and the prospect of a consensus solution emerging with respect to any of the problems has not seemed particularly good. We have a Gordian knot. Even so, I suggest that we also have a way to cut it. This will involve identifying why the cognitive mechanism that produces our intuitions of knowing evolved and was (...) maintained (by natural selection), i.e., identifying the ‘teleonomic function’ of that cognitive mechanism. Also, it will involve predicting, on the basis of this teleonomic function, the triggering-conditions of these natural knowledge intuitions. In this thesis, I develop a general theory of the origin, function and triggering-conditions of knowledge intuitions that will allow us to cut that knot. That theory follows basic biological theory (including that which pertains to natural altruism) and also signal detection theory. -/- My theory identifies a number of different circumstances under which the triggering-conditions of knowledge intuitions are different. Strikingly, these different circumstances (and their associated triggering-conditions) map onto the different competing and incompatible epistemological positions to which I referred. This suggests that these positions are all correct within the boundaries of one of the circumstances that my theory identifies; and that the Gordian knot is largely the result of epistemologists claiming universal applicability of a theory that in fact only applies under particular circumstances. We cut the knot by specifying the different circumstances under which each of the different epistemological positions will hold, and the reason we should expect it to hold in just these circumstances, in light of the teleonomic function of knowledge intuitions. (shrink)

Negative subjective reports such as “I didn’t see the stimulus” can be interpreted as indicating either that the subject didn’t see the stimulus, or as indicating that, while the subject did see the stimulus, the strength of sensory signals associated with the stimulus fell below a conservative criterion for answering “seen”. Determining which of these two interpretations is correct is the criterion problem. I present two ways in which researchers can solve this problem. But there’s more. What I call the (...) “new criterion problem” consists in determining whether criteria effects reflect perceptual changes, or changes in response strategy. Through case studies, I argue that this new version of the problem can be solved as well, although there is currently no systematic way to solve it. I conclude that criterion problems do not constitute good reasons for skepticism about the scientific study of consciousness. (shrink)

To study (un)conscious perception and test hypotheses about consciousness, researchers need procedures for determining whether subjects consciously perceive stimuli or not. This article is an introduction to a family of procedures called ‘confidence-based procedures’, which consist in interpreting metacognitive indicators as indicators of consciousness. I assess the validity and accuracy of these procedures, and answer a series of common objections to their use in consciousness research. I conclude that confidence-based procedures are valid for assessing consciousness, and, in most cases, accurate (...) enough for our practical and scientific purposes. (shrink)

According to the predictive coding theory of cognition , brains are predictive machines that use perception and action to minimize prediction error, i.e. the discrepancy between bottom–up, externally-generated sensory signals and top–down, internally-generated sensory predictions. Many consider PCT to have an explanatory scope that is unparalleled in contemporary cognitive science and see in it a framework that could potentially provide us with a unified account of cognition. It is also commonly assumed that PCT is a representational theory of (...) sorts, in the sense that it postulates that our cognitive contact with the world is mediated by internal representations. However, the exact sense in which PCT is representational remains unclear; neither is it clear that it deserves such status—that is, whether it really invokes structures that are truly and nontrivially representational in nature. In the present article, I argue that the representational pretensions of PCT are completely justified. This is because the theory postulates cognitive structures—namely action-guiding, detachable, structural models that afford representational error detection—that play genuinely representational functions within the cognitive system. (shrink)

A model of brain function is presented that is consistently based on the biological principle of equilibrium. The neuronal modules of the cerebral cortex are proposed as units in which equilibrium between incoming signals and the synaptic structure is determined or established. Because of the electromagnetic activity of the brain, the electromagnetic properties of thecells are brought into focus. Due to the synaptic changes of the modules -essentially during sleep -an electromagnetic resting balance between the modules is established. Incoming signals (...) during the day, disturb the electromagnetic resting equilibrium and are detected and understood by this. The connecting nodes within the neuronal network are given by the equilibrium modules. Incoming information is represented in the form of the specific pathway of the network, while recognized information is represented by the equilibrium states within the modules. The paper leads to an understanding of information storage and processing in the brain. It even provides a hypothesis for understanding the emergence of the "self". Finally, the consideration of electromagnetic wave properties of neurons opens up a biophysical starting point to understanding conscious perceptions. In neuroscience, we lack a unifying theory of the brain. The reason for this may be an important detail –a missing link –that we do not yet see. Following the "track of biological equilibrium" in this paper leads to the hypothesis that the electromagnetic properties of the neurons are potential candidates to fill that gap. A hypothesis is developed describing their physiological significance in the processing of neurological information. (shrink)

Signal measurement Electrochemical methods are very suitable for detecting direct DNA oxidation because electrochemical reactions directly generate electronic signals and therefore do not require expensive converters. In addition, in this process, because the order of the immobilized game can be limited to onlya series of electrode substrates, the act of tracking is performed by a series of in expensive electrochemical analyzes. Electrochemical sensors are used to perform clinical or environmental tests; The basis of the sensitivity of electrochemical signals to (...) direct oxidation orinterval catalysis of DNA is also based on the reduction reactions of reporter molecules or enzymes. (shrink)

The right temporo-parietal junction (rTPJ) has been associated with two apparently disparate functional roles: in attention and in social cognition. According to one account, the rTPJ initiates a “circuit-breaking” signal that interrupts ongoing attentional processes, effectively reorienting attention. It is argued this primary function of the rTPJ has been extended beyond attention, through a process of evolutionarily cooption, to play a role in social cognition. We propose an alternative account, according to which the capacity for social cognition depends on (...) a network which is both distinct from and in tension with brain areas involved in focused attention and target detection: the default mode network. Theory characterizing the rTPJ based on the area’s purported role in reorienting may be falsely guided by the co-occurrence of two distinct effects in contiguous regions: activation of the supramarginal gyrus (SMG), associated with its functional role in target detection; and the transient release, during spatial reorienting, of suppression of the angular gyrus (AG) associated with focused attention. Findings based on meta-analysis and resting functional connectivity are presented which support this alternative account. We find distinct regions, possessing anti-correlated patterns of resting connectivity, associated with social reasoning (AG) and target detection (SMG) at the rTPJ. The locus for reorienting was spatially intermediate between the AG and SMG and showed a pattern of connectivity with similarities to social reasoning and target detection seeds. These findings highlight a general methodological concern for brain imaging. Given evidence that certain tasks not only activate some areas but also suppress activity in other areas, it is suggested that researchers need to distinguish two distinct putative mechanisms, either of which may produce an increase in activity in a brain area: functional engagement in the task versus release of suppression. (shrink)

An argumentation profile is defined as a methodological instrument for analyzing argumentative discourse considering distinct and interrelated dimensions: the types of argument used, their quality, and the emotions triggered. Walton’s theoretical contributions are developed as a coherent analytical and multifaceted toolbox for capturing these aspects. Argumentation schemes are used to detect and quantify the types of argument. Fallacy analysis and the assessment of the implicit premises retrieved through the schemes allow evaluating arguments. Finally, the frequency of emotive words signals the (...) most common emotions aroused. This method is illustrated through a corpus of argumentative tweets of three politicians. (shrink)

The purpose of this chapter is twofold. On the one hand, our goal is theoretical, as we aim at providing an instrument for detecting, analyzing, and solving ambiguities based on the reasoning mechanism underlying interpretation. To this purpose, combining the insights from pragmatics and argumentation theory, we represent the background assumptions driving an interpretation as presumptions. Presumptions are then investigated as the backbone of the argumentative reasoning that is used to assess and solve ambiguities and drive (theoretically) interpretive mechanisms. (...) On the other hand, our goal is practical. By analyzing ambiguities as stemming from different presumptions concerning language or, more importantly, expected communicative roles and goals, we can use communicative misunderstandings as the signal of deeper disagreements concerning mutual expectations or cultural differences. This argumentation-based interpretive mechanism will be applied to the analysis of medical interviews in the area of diabetes care, and will be used to bring to light the sources of misunderstanding and the different presumptions that define distinct cultures. We will consequently illustrate the analytical tools by identifying and distinguishing the various types of ambiguity underlying misunderstandings, and we will address them by describing the communicative intentions ascribed to the ambiguous utterances. (shrink)

The rapid advancements in healthcare technologies have resulted in an enormous increase in biomedical data, creating the need for innovative approaches to harness this information for early disease detection. Big Data Analytics (BDA) combined with Artificial Intelligence (AI) offers unprecedented opportunities to analyze complex biomedical signal patterns and predict the onset of diseases at an early stage. The application of AI techniques like machine learning and deep learning in conjunction with BDA allows for the detection of subtle (...) patterns in large datasets that might be missed by conventional diagnostic methods. This paper explores the integration of Big Data Analytics and AI for early disease detection through the analysis of biomedical signal patterns, such as electrocardiograms (ECG), electroencephalograms (EEG), and wearable sensor data. AI algorithms, trained on vast datasets, can recognize early warning signals of various diseases such as cardiovascular disorders, neurodegenerative diseases, and respiratory conditions. The ability to analyze these signals in real time enables early intervention, potentially improving patient outcomes and reducing healthcare costs. A comprehensive literature review highlights the use of BDA and AI in biomedical applications and the challenges associated with data volume, quality, and heterogeneity. We propose a system methodology that combines AI models with real-time Big Data Analytics to process biomedical signals efficiently and accurately. The proposed system leverages AI for feature extraction and classification while utilizing Big Data tools to handle the massive influx of data from biomedical devices and sensors. This approach not only enhances diagnostic precision but also paves the way for personalized healthcare solutions. (shrink)

Eric Posner’s signaling theory of social norms holds that individuals adopt social norms in order to signal that they have a low discount rate , and are therefore reliable long-term cooperative partners. This paper radically expands Posner’s theory by incorporating internalization into his model . I do this by tethering Posner’s theory to an evolutionary model. I argue that internalization is an adaptive quality that enhances the individual’s ability to play Posner’s signaling game and was thus (...) selected for. The idea that internalization is evolutionarily conditioned is not new; however, linking this to Posner’s theory of discount rate signals is, and doing so offers tremendous explanatory potential.Part I identifies the limitations of Posner’s purely rational choice approach, argues for the necessity of including internalization, and then proposes a model that does so – what I call the Expanded Signaling Model of Norms . Part II examines the problems that arise when we embrace such a model. How this model answers some key criticisms plaguing sociobiology is also briefly explored. Part III then examines existing criticisms of Posner’s theory, demonstrating how the Expanded Signaling Model clearly resolves these issues. The paper concludes that incorporating internalization into Posner’s signaling model greatly broadens the explanatory reach of Posner’s theory, providing a measure of clarity and predictability regarding how and why norms are internalized – an important insight, as these beliefs form the normative underpinning to law. (shrink)

Recent work by Brian Skyrms offers a very general way to think about how information flows and evolves in biological networks — from the way monkeys in a troop communicate, to the way cells in a body coordinate their actions. A central feature of his account is a way to formally measure the quantity of information contained in the signals in these networks. In this paper, we argue there is a tension between how Skyrms talks of signalling networks and his (...) formal measure of information. Although Skyrms refers to both how information flows through networks and that signals carry information, we show that his formal measure only captures the latter. We then suggest that to capture the notion of flow in signalling networks, we need to treat them as causal networks. This provides the formal tools to define a measure that does capture flow, and we do so by drawing on recent work defining causal specificity. Finally, we suggest that this new measure is crucial if we wish to explain how evolution creates information. For signals to play a role in explaining their own origins and stability, they can’t just carry information about acts: they must be difference-makers for acts. (shrink)

This paper proposes a new theory of the nature of hypocritical blame and why it is objectionable, arguing that hypocritical blame is a form of dishonest signaling. Blaming provides very important benefits: through its ability to signal our commitments to norms and unwillingness to tolerate norm violations, it greatly contributes to valuable norm-following. Hypocritical blamers, however, are insufficiently committed to the norms or values they blame others for violating. As allowing their blame to pass unchecked threatens the signaling (...) system, our strong interest in maintaining valuable norm-following by tracking who has what commitments justifies objecting to hypocritical blame. This theory has a number of strengths over competing accounts: it delivers intuitive verdicts about when blame is objectionable across a range of cases, it is a naturalistic explanation, it is consistent with a leading theory of the nature of blame, it explains why hypocritical pronouncements that don’t feature blame are similiarly objectionable, it does not rely on contentious analyses of the nature of ‘standing’, and it preserves the common intuition that hypocrites are in some way dishonest. (shrink)

Vibration, especially basic vibration, may cause loose contacts, open circuits, or other contact problems, which account for a large proportion of system failure causes. Due to the complexity of the chassis structure used in the biomedical motors, the vibration analysis of a single component cannot solve the vibration problems encountered in the work of the system. Therefore, it is necessary to use system simulation and experiments to monitor and enhance the health of the structure. Finite element method is used to (...) carry out the dynamic analysis from the component to the system, and calculate the natural frequency, modal shape and harmonic response. Then, the effectiveness and accuracy of the analysis results are verified by experiments through simulation and experiments. The results show that the measures taken have effectively reduced the vibration of the chassis equipment and improved its safety for better health safety of patients. There is just a 2% variation between the natural frequencies acquired from the experiment and the simulation computation. In terms of natural frequency, the first 6-order natural frequencies all have errors of less than 10%, and the natural frequency error is comparable to that of fireproof board. (shrink)

Skyrms, building on the work of Dretske, has recently developed a novel information-theoretic account of propositional content in simple signalling systems. Information-theoretic accounts of content traditionally struggle to accommodate the possibility of misrepresentation, and I show that Skyrms’s account is no exception. I proceed to argue, however, that a modified version of Skyrms’s account can overcome this problem. On my proposed account, the propositional content of a signal is determined not by the information that it actually carries, but by (...) the information that it would carry at the nearest separating equilibrium of the underlying evolutionary dynamics. I show that this amended account yields reasonable ascriptions of false propositional content in a well-known formal model of the evolution of communication , and close with a discussion of the serious but perhaps not insuperable difficulties we face in applying the account to examples of signalling in the real world. (shrink)

According to our folk theory of lying, liars leak observable cues of their insincerity, observable cues that make it easy to catch a liar in real time. Various prominent social epistemologists rely on the correctness of our folk theory as empirically well-confirmed when building their normative accounts of the epistemology of testimony. Deception detection research in communication studies, however, has shown that our folk-theory is mistaken. It is not empirically well-confirmed but empirically refuted. Michaelian (2010) and (...) Shieber (2012) have already discussed some of this literature and its relevance for epistemology, especially for the most worked out reliance on our folk theory by Elizabeth Fricker (1994, 2016, 2017a, 2024). Fricker (2016) raises a reasonable objection to their presentations of the research: is the research ecologically valid? Do the experiments in the lab carry over to real life? This chapter conveys the methodology of the research, defends its ecological validity, and addresses further research on the nature and frequency of lies in ordinary life. Social epistemologists stand to gain from understanding the nuts and bolts of deception detection research and its findings. The chapter concludes with a detailed examination of Fricker’s relilance on folk theory in her “local reductionist” epistemology of testimony. Further areas of research relevant to social epistemology are listed. (shrink)

Speech Emotion Detection (SED) refers to the identification of human emotions based on speech signals. The goal of this research is to design and implement a system that can accurately classify emotions from speech using machine learning techniques. The system can be applied in various fields such as healthcare, customer service, human-computer interaction, and mental health monitoring. The paper discusses the various stages of building such a system, from collecting and preprocessing audio data to selecting machine learning models and (...) evaluating the performance of the system. A combination of feature extraction techniques and machine learning algorithms, such as Support Vector Machines (SVM) and deep learning models like Long Short-Term Memory (LSTM) networks, is employed to achieve high accuracy. (shrink)

A fundamental assumption of theories of decision-making is that we detect mismatches between intention and outcome, adjust our behavior in the face of error, and adapt to changing circumstances. Is this always the case? We investigated the relation between intention, choice, and introspection. Participants made choices between presented face pairs on the basis of attractiveness, while we covertly manipulated the relationship between choice and outcome that they experienced. Participants failed to notice conspicuous mismatches between their intended choice and the outcome (...) they were presented with, while nevertheless offering introspectively derived reasons for why they chose the way they did. We call this effect choice blindness. (shrink)

Linguistic intuitions are a central source of evidence across a variety of linguistic domains. They have also long been a source of controversy. This chapter aims to illuminate the etiology and evidential status of at least some linguistic intuitions by relating them to error signals of the sort posited by accounts of on-line monitoring of speech production and comprehension. The suggestion is framed as a novel reply to Michael Devitt’s claim that linguistic intuitions are theory-laden “central systems” responses, rather (...) than endorsed outputs of a modularized language faculty (the “Voice of Competence”). Along the way, it is argued that linguistic intuitions may not constitute a natural kind with a common etiology; and that, for a range of cases, the process by which intuitions used in linguistics are generated amounts to little more than comprehension. (shrink)

The existence of big bang relic neutrinos—exact analogues of the big bang relic photons comprising the cosmic microwave background radiation—is a basic prediction of standard cosmology. The standard big bang theory predicts the existence of 1087 neutrinos per flavour in the visible universe. This is an enormous abundance unrivalled by any other known form of matter, falling second only to the cosmic microwave background (CMB) photon. Yet, unlike the CMB photon which boasts its first (serendipitous) detection in the (...) 1960s and which has since been observed and its properties measured to a high degree of accuracy in a series of airborne/satellite and ground based experiments, the relic neutrino continues to be elusive in the laboratory. The chief reason for this is of course the feebleness of the weak interaction. At present, the observational evidence for their existence rests entirely on cosmological measurements, such as the light elemental abundances, anisotropies in the cosmic microwave background, and the large-scale matter power spectrum. In this paper we argue that Direct Detection of relic neutrino background is indeed impossible by any means, because of two chief reasons: (a) there was no such thing of cosmic singularity, hence the hot big bang/primeval atom model was based on false premises (quantum birth assumption); (b) the neutrino existence itself is not unquestionable, in particular if we consider a realism view of vector potential, A, in classical electrodynamics. (shrink)

The articles in this issue represent the pursuit of a new understanding of the human past, one that can replace the neo-saltationist view of a human revolution with models that can account for the complexities of the archaeological record and of human social lives. The articulation of archaeological, philosophical, and biological perspectives seems to offer a strong foundation for exploring available evidence, and this was the rationale for collecting these particular articles. Even at this preliminary stage there is a coherence (...) emerging in proposals: the origin and operation of symbolically rich, complexly signaling human social systems was the consequence of the long-term evolution of multiple components of perceiving and negotiating social interactions, a contingent outcome of myriad adaptive shifts rather than a single event. (shrink)

Memory Detection Tests (MDTs) are a general class of psychophysiological tests that can be used to determine whether someone remembers a particular fact or datum. The P300 MDT is a type of MDT that relies on a presumed correlation between the presence of a detectable neural signal (the P300 “brainwave”) in a test subject, and the recognition of those facts in the subject’s mind. As such, the P300 MDT belongs to a class of brain-based forensic technologies which have (...) proved popular and controversial in recent years. With such tests increasingly being proffered for use in the courtroom — to either support or call into question testimony — it would behoove the legal system to have some systematic framework for ensuring that they are used responsibly. In this paper, I defend one such framework for ensuring that this is the case: the legitimacy enhancing test. According to this test, it is appropriate to make use of technologies such as the P300 MDT whenever doing so would (probably) enhance the legitimacy of the trial. I argue that this test addresses tensions between scientific and legal norms of evidence, and exhibits a number of additional virtues including unification, simplicity and flexibility. Although the test is defended by considering the example of the P300 MDT, its significance is much broader than that. If it has the virtues I claim for it, it should provide a general framework for the responsible use of technologies, and the responsible innovation of social epistemic systems. (shrink)

Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such as Genetic Algorithms (...) (GA) or Particle Swarm Optimization (PSO)—to enhance the accuracy and response time of the detection process. The integration of optimization methods ensures that the model adapts to various driving conditions and individual differences, providing a more reliable and robust detection mechanism. (shrink)

The project focuses on the development of a tool for identifying and extracting Forward Error Correction (FEC) schemes from unknown demodulated signals. FEC is a vital communication technique that ensures error-free data transmission without the need for retransmission, particularly in satellite communications, digital broadcasting, and deepspace applications. The proposed solution involves using Python to preprocess signals, detect FEC schemes, and then extract the specific coding parameters. Different FEC schemes such as BCH, Convolutional Codes, Turbo Codes, and LDPC codes are explored (...) for their unique characteristics and error correction capabilities. The tool aims to automate the detection and extraction of FEC schemes, thus improving the accuracy and efficiency of signal processing in communication systems. (shrink)

Rutherford and Ray think that human beings have mental mechanisms that help them to detect individuals that, deliberately, do not follow a rule. In the same way, they hold that autism is not a disorder in which these mechanisms are damaged. This idea seems contrary to the thesis, supported by some researchers, that autistic people have a theory of mind deficit. This is because of, if Rutherford and Ray are right, autistic people can detect other people’s intentions. In this (...) paper, Rutherford and Ray’s experiment is analyzed and it is concluded that they do not prove that people with autism can understand intentions. Such an experiment only shows that autistic people and general population share common logical-linguistic abilities. (shrink)

: Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such as Genetic (...) Algorithms (GA) or Particle Swarm Optimization (PSO)—to enhance the accuracy and response time of the detection process. The integration of optimization methods ensures that the model adapts to various driving conditions and individual differences, providing a more reliable and robust detection mechanism. Data from multiple sources, including camera feeds and wearable sensors, are used to train and validate the models, ensuring a comprehensive understanding of drowsiness indicators. (shrink)

Misinformation has been a persistent and detrimental phenomenon in our society in many ways, including individuals' physical health and economic security. With the advent of short video platforms and associated applications, dissemination of multi-modal misinformation, including images, texts, audios, and videos, have increased these issues. The advent of generative AI models such as ChatGPT and Stable Diffusion has further enhanced the complexity, providing give rise to Artificial Intelligence Generated Content (AIGC) and posing new challenges in the detection and mitigation (...) of misinformation. Therefore, the conventional methods of misinformation detection and intervention have proved to be insufficient in this new paradigm. This paper examines the challenge posed by AIGC in the context of misinformation. It analyzes the problem from a psychological and societal point of view, and investigates the subtle manipulation traces in AIGC at signal, perceptual, semantic, and human levels. Through examining manipulation traces like signal manipulation, semantic inconsistency, logical incoherence, and psychological strategies, our goal is to address AI-generated misinformation and present a conceptual design of systematic explainable solution. Finally, we hope this paper will provide useful insights into the fight against misinformation, especially in the age of AIGC. (shrink)

“ A man orders a whole pizza pie for himself and is asked whether he would like it cut into eight or four slices. He responds, ‘Four, I’m on a diet ”’ (Noël Carroll) -/- While not hilarious --so funny that it induces chortling punctuated with outrageous vomiting--this little gem is amusing. We recognize that something has gone wrong. On a first reading it might not compute, something doesn’t quite make sense. Then, aha! , we understand the hapless dieter has (...) misapplied general rules of thumb, mental short-cuts, or heuristics, that we were also initially committed to and that would usually be good enough to rely upon— fewer slices equals fewer calories; diets require fewer calories, etc—but in this particular case they fail, and the feeling of mirth is our reward for making this discovery. We don’t say all of that after a punchline, of course, but that’s what is happening according to the Humor as Error-Detection Theory : our sense of humor can sense our errors. This chapter will focus on the overlap and benefits of a humorous and philosophical attitude toward the world and our place in it. The historian of philosophy Will Durant tells us that genuine philosophy begins when one learns to doubt; we can say something similar with humor--trust me. (shrink)

Rutherford and Ray think that human beings have mental mechanisms that help them to detect individuals that, deliberately, do not follow a rule. In the same way, they hold that autism is not a disorder in which these mechanisms are damaged. This idea seems contrary to the thesis, supported by some researchers, that autistic people have a theory of mind deficit. This is because of, if Rutherford and Ray are right, autistic people can detect other people's intentions. In this (...) paper, Rutherford and Ray's experiment is analyzed and it is concluded that they do not prove that people with autism can understand intentions. Such an experiment only shows that autistic people and general population share common logical-linguistic abilities. (shrink)

Political fact-checkers evaluate the truthfulness of politicians’ claims. This paper contributes to an emerging scholarly debate on whether fact-checkers treat political parties differently in a systematic manner depending on their ideology (bias). We first examine the available approaches to analyze bias and then present a new approach in two steps. First, we propose a logistic regression model to analyze the outcomes of fact-checks and calculate how likely each political party will obtain a truth score. We test our model with a (...) sample of fact-checks from Newtral, a major Spanish fact-checker. Our model would signal bias under two assumptions: a) all political parties are on average equally accurate in their statements; b) the verification method gives precise instructions and is implemented systematically. We investigate this second assumption with a series of interviews with Newtral fact-checkers. We show that standard verification protocols are so loosely implemented that fact-checks reflect a set of journalistic decisions, rather than a bias in the statistical sense. We call for a more rigorous definition of verification methods as a pre-requisite for an unbiased assessment of politician’s claims. (shrink)

Misinformation has been a persistent and detrimental phenomenon in our society in many ways, including individuals' physical health and economic security. With the advent of short video platforms and associated applications, dissemination of multi-modal misinformation, including images, texts, audios, and videos, have increased these issues. The advent of generative AI models such as ChatGPT and Stable Diffusion has further enhanced the complexity, providing give rise to Artificial Intelligence Generated Content (AIGC) and posing new challenges in the detection and mitigation (...) of misinformation. Therefore, the conventional methods of misinformation detection and intervention have proved to be insufficient in this new paradigm. This paper examines the challenge posed by AIGC in the context of misinformation. It analyzes the problem from a psychological and societal point of view, and investigates the subtle manipulation traces in AIGC at signal, perceptual, semantic, and human levels. Through examining manipulation traces like signal manipulation, semantic inconsistency, logical incoherence, and psychological strategies, our goal is to address AI-generated misinformation and present a conceptual design of systematic explainable solution. Finally, we hope this paper will provide useful insights into the fight against misinformation, especially in the age of AIGC. (shrink)

: Advancements in biomedical signal processing have unlocked new possibilities for personalized healthcare. However, the sheer volume of data generated in modern medical environments, coupled with the complexity of interpreting these signals in real-time, poses significant challenges. This paper explores the integration of artificial intelligence (AI), particularly deep learning, with big data analytics to create a powerful predictive analytics framework for biomedical signal processing. By leveraging AI, we aim to automate the extraction of significant features from high-dimensional data (...) and improve diagnostic accuracy in detecting anomalies, such as cardiac arrhythmias, neurological disorders, and respiratory conditions. (shrink)

We introduce the virtual special issue on content in signalling systems. The issue explores the uses and limits of ideas from evolutionary game theory and information theory for explaining the content of biological signals. We explain the basic idea of the Lewis-Skyrms sender-receiver framework, and we highlight three key themes of the issue: (i) the challenge of accounting for deception, misinformation and false content, (ii) the relevance of partial or total common interest to the evolution of meaningful signals, (...) and (iii) how the sender-receiver framework relates to teleosemantics. (shrink)

Evoke is a Python library for evolutionary simulations of signalling games. It offers a simple and intuitive API that can be used to analyze arbitrary game-theoretic models, and to easily reproduce and customize well-known results and figures from the literature.

Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such as Genetic Algorithms (...) (GA) or Particle Swarm Optimization (PSO)—to enhance the accuracy and response time of the detection process. The integration of optimization methods ensures that the model adapts to various driving conditions and individual differences, providing a more reliable and robust detection mechanism. Data from multiple sources, including camera feeds and wearable sensors, are used to train and validate the models, ensuring a comprehensive understanding of drowsiness indicators. (shrink)

: The integration of Machine Learning (ML) in Real-Time Biomedical Signal Processing has unlocked new possibilities in the field of telemedicine, especially when combined with the high-speed, low-latency capabilities of 5G networks. As telemedicine grows in importance, particularly in remote and underserved areas, real-time processing of biomedical signals such as ECG, EEG, and EMG is essential for accurate diagnosis and continuous monitoring of patients. Machine learning algorithms can be used to analyze large volumes of biomedical data, enabling faster and (...) more precise detection of anomalies. This paper proposes a novel system for machine learning-based real-time biomedical signal processing that leverages the capabilities of 5G networks to enhance the transmission, processing, and analysis of critical medical data in telemedicine applications. The system integrates convolutional neural networks (CNNs) for signal classification, anomaly detection, and predictive analysis, ensuring that patients receive timely and accurate medical feedback. Additionally, the 5G network’s low latency and high bandwidth provide seamless data transmission, improving remote diagnostics and enabling high-quality teleconsultations. This paper evaluates the current challenges in real-time biomedical signal processing in telemedicine, discusses the potential of machine learning and 5G networks, and presents an innovative solution for improving healthcare delivery through this integrated approach. (shrink)

This article is a comparative study between predictive processing (PP, or predictive coding) and cognitive dissonance (CD) theory. The theory of CD, one of the most influential and extensively studied theories in social psychology, is shown to be highly compatible with recent developments in PP. This is particularly evident in the notion that both theories deal with strategies to reduce perceived error signals. However, reasons exist to update the theory of CD to one of “predictive dissonance.” First, (...) the hierarchical PP framework can be helpful in understanding varying nested levels of CD. If dissonance arises from a cascade of downstream and lateral predictions and consequent prediction errors, dissonance can exist at a multitude of scales, all the way up from sensory perception to higher order cognitions. This helps understand the previously problematic dichotomy between “dissonant cognitive relations” and “dissonant psychological states,” which are part of the same perception-action process while still hierarchically distinct. Second, since PP is action-oriented, it can be read to support recent action-based models of CD. Third, PP can potentially help us understand the recently speculated evolutionary origins of CD. Here, the argument is that responses to CD can instill meta-learning which serves to prevent the overfitting of generative models to ephemeral local conditions. This can increase action-oriented ecological rationality and enhanced capabilities to interact with a rich landscape of affordances. The downside is that in today’s world where social institutions such as science a priori separate noise from signal, some reactions to predictive dissonance might propagate ecologically unsound (underfitted, confirmation-biased) mental models such as climate denialism. (shrink)

In recent years, the combination of Artificial Intelligence (AI) and Blockchain technology has garnered significant attention, especially in the healthcare domain. With the increasing reliance on biomedical signal processing for disease diagnosis and treatment, ensuring the security, privacy, and integrity of data has become paramount. Biomedical signals, including electrocardiograms (ECG), electroencephalograms (EEG), and other physiological data, often contain sensitive information. AI models have shown great promise in processing and interpreting these signals, enabling accurate disease detection and personalized healthcare. (...) However, the potential for data tampering, unauthorized access, and privacy concerns pose significant challenges. Blockchain, with its decentralized, tamper-resistant, and secure framework, offers a solution to these challenges. By integrating Blockchain with AI for biomedical signal processing, data can be securely stored, verified, and accessed, ensuring that healthcare providers can rely on the integrity and accuracy of the signals used in AI-based diagnostics. This paper explores the role of Blockchain-enhanced AI solutions for secure biomedical signal processing and data integration, addressing both the technical and ethical challenges in this space. We propose a system that leverages Blockchain to manage the secure transfer and storage of biomedical signals while utilizing AI to analyze and interpret these signals in real time. Blockchain provides an immutable ledger for auditing and validating data provenance, ensuring that the data remains secure throughout the healthcare pipeline. The integration of these two technologies not only enhances the security of biomedical data but also enables more trustworthy AI-driven diagnostics and decision-making in healthcare. (shrink)