Hasil untuk "Consciousness. Cognition"

R. Kanai, G. Rees

Florentin Koch

Recent work on artificial consciousness shifts evaluation from behaviour to internal architecture, deriving indicators from theories of consciousness and updating credences accordingly. This is progress beyond naive Turing-style tests. But the indicator-based programme remains epistemically under-calibrated: consciousness science is theoretically fragmented, indicators lack independent validation, and no ground truth of artificial phenomenality exists. Under these conditions, probabilistic consciousness attribution to current AI systems is premature. A more defensible near-term strategy is to redirect effort toward biologically grounded engineering -- biohybrid, neuromorphic, and connectome-scale systems -- that reduces the gap with the only domain where consciousness is empirically anchored: living systems.

Yifeng Chen, J. W. Sanders

A model of consciousness is proposed which, having a logical basis, lends itself to simulation using a simple mathematical model called Consciousness as Entropy Reduction (CER). The approach has been inspired by previous models such as GWT, IIT and an earlier less mainstream model called "Feature Map" in Psychology. CER considers the contents of consciousness and subconsciousness as \textit{scenarios}: a vector of patterns (or features) on various "channels" (or feature locations). In CER, a feature map itself is not consciousness but only the input \textit{scenario} into a world of possible subconscious \textit{scenarios} from which the conscious \textit{scenario} (i.e., conscious experience) is chosen. Essentially, it creates an internal simulation of the outside world. Solving problems in simulation internally as a "thought experiment" is obviously more economical than doing experiments in a real environment and lends itself to adaptability and hence is a major evolutionary advantage. CER also has connections with the Hopfield model in artificial neural networks.

Danko D. Georgiev

Functional theories of consciousness, based on emergence of conscious experiences from the execution of a particular function by an insentient brain, face the hard problem of consciousness of explaining why the insentient brain should produce any conscious experiences at all. This problem is exacerbated by the determinism characterizing the laws of classical physics, due to the resulting lack of causal potency of the emergent consciousness, which is not present already as a physical quantity in the deterministic equations of motion of the brain. Here, we present a quantum information theoretic approach to the hard problem of consciousness that avoids all of the drawbacks of emergence. This is achieved through reductive identification of first-person subjective conscious states with unobservable quantum state vectors in the brain, whereas the anatomically observable brain is viewed as a third-person objective construct created by classical bits of information obtained during the measurement of a subset of commuting quantum brain observables by the environment. Quantum resource theory further implies that the quantum features of consciousness granted by quantum no-go theorems cannot be replicated by any classical physical device.

Kaichen Ouyang

This paper develops a neural jamming phase diagram that interprets the emergence of consciousness in large language models as a critical phenomenon in high-dimensional disordered systems.By establishing analogies with jamming transitions in granular matter and other complex systems, we identify three fundamental control parameters governing the phase behavior of neural networks: temperature, volume fraction, and stress.The theory provides a unified physical explanation for empirical scaling laws in artificial intelligence, demonstrating how computational cooling, density optimization, and noise reduction collectively drive systems toward a critical jamming surface where generalized intelligence emerges. Remarkably, the same thermodynamic principles that describe conventional jamming transitions appear to underlie the emergence of consciousness in neural networks, evidenced by shared critical signatures including divergent correlation lengths and scaling exponents.Our work explains neural language models' critical scaling through jamming physics, suggesting consciousness is a jamming phase that intrinsically connects knowledge components via long-range correlations.

Lin Chen

What fundamental research questions are essential for advancing toward brain-inspired AI or AGI capable of performing any intellectual task a human can? We believe the key question today is the relationship between cognition and computation (RCC). For example, the widely discussed question "Will artificial intelligence replace the human mind?" is, in essence and in scientific terms, an issue concerning RCC. We have chosen to classify RCC into four categories: 1. The relationship between the primitives of cognition and the primitives of computation. 2. The relationship between the anatomical structure of neural representation of cognition and the computational architecture of artificial intelligence. 3. The relationship between emergents in cognition and emergents in computation. 4. The relationship between the mathematical foundations of cognition and computation. The cumulative empirical evidence and theoretical analyses led us to formulate the "Global-first" principle, which highlights the contrast between "Global-first" cognition and local-first computation in RCC, offering a specific and well-defined starting point for understanding RCC.

Stephen Fitz

The Machine Consciousness Hypothesis states that consciousness is a substrate-free functional property of computational systems capable of second-order perception. I propose a research program to investigate this idea in silico by studying how collective self-models (coherent, self-referential representations) emerge from distributed learning systems embedded within universal self-organizing environments. The theory outlined here starts from the supposition that consciousness is an emergent property of collective intelligence systems undergoing synchronization of prediction through communication. It is not an epiphenomenon of individual modeling but a property of the language that a system evolves to internally describe itself. For a model of base reality, I begin with a minimal but general computational world: a cellular automaton, which exhibits both computational irreducibility and local reducibility. On top of this computational substrate, I introduce a network of local, predictive, representational (neural) models capable of communication and adaptation. I use this layered model to study how collective intelligence gives rise to self-representation as a direct consequence of inter-agent alignment. I suggest that consciousness does not emerge from modeling per se, but from communication. It arises from the noisy, lossy exchange of predictive messages between groups of local observers describing persistent patterns in the underlying computational substrate (base reality). It is through this representational dialogue that a shared model arises, aligning many partial views of the world. The broader goal is to develop empirically testable theories of machine consciousness, by studying how internal self-models may form in distributed systems without centralized control.

Florence Baills, Stefan Baumann

Prosody and gesture are two known cues for expressing information structure by emphasising new or important elements in spoken discourse while attenuating given information. Applying this potentially multimodal form-meaning mapping to a foreign language may be difficult for learners. This study investigates how native speakers and language learners use prosodic prominence and head gestures to differentiate levels of givenness.

Ana Maria F. F. de Oliveira, Daniel Mendes Lobato, Luiz Gabriel Signorelli et al.

Nur Aizaan Anwar, Cosmin Badea

As artificially intelligent systems become more anthropomorphic and pervasive, and their potential impact on humanity more urgent, discussions about the possibility of machine consciousness have significantly intensified, and it is sometimes seen as 'the holy grail'. Many concerns have been voiced about the ramifications of creating an artificial conscious entity. This is compounded by a marked lack of consensus around what constitutes consciousness and by an absence of a universal set of criteria for determining consciousness. By going into depth on the foundations and characteristics of consciousness, we propose five criteria for determining whether a machine is conscious, which can also be applied more generally to any entity. This paper aims to serve as a primer and stepping stone for researchers of consciousness, be they in philosophy, computer science, medicine, or any other field, to further pursue this holy grail of philosophy, neuroscience and artificial intelligence.

Robin W. Spencer

Consciousness is presented not as a unified and uniquely human characteristic, but rather as an emergent property of several building blocks, most of which are demonstrably present in other species. Each block has its own rationale under natural selection and could have arisen independently, and the jumps between blocks -- which culminate in consciousness -- are small enough to be evolutionarily plausible. One underappreciated block involves unconscious engram playback and discrimination, and plays a major role in brain storage optimization. This function is present in birds and nearly all mammals and is recognized by its side-effect: dreams.

Jules Dorschner, Laura Teresa Hernandez Salazar, Matthias Laska

Abstract Recent research suggests that socio-ecological factors such as dietary specialization and social complexity may be drivers of advanced cognitive skills among primates. Therefore, we assessed the ability of 12 black-handed spider monkeys (Ateles geoffroyi), a highly frugivorous platyrrhine primate with strong fission-fusion dynamics, to succeed in a serial visual reversal learning task. Using a two-alternative choice paradigm we first trained the animals to reliably choose a rewarded visual stimulus over a non-rewarded one. Upon reaching a pre-set learning criterion we then switched the reward values of the two stimuli and assessed if and how quickly the animals learned to reverse their choices, again to a pre-set learning criterion. This stimulus reversal procedure was then continued for a total of 80 sessions of 10 trials each. We found that the spider monkeys quickly learned to reliably discriminate between two simultaneously presented visual stimuli, that they succeeded in a visual reversal learning task, and that they displayed an increase in learning speed across consecutive reversals, suggesting that they are capable of serial reversal learning-set formation with visual cues. The fastest-learning individual completed five reversals within the 80 sessions. The spider monkeys outperformed most other primate and nonprimate mammal species tested so far on this type of cognitive task, including chimpanzees, with regard to their learning speed in both the initial learning task and in the first reversal task, suggesting a high degree of behavioral flexibility and inhibitory control. Our findings support the notion that socio-ecological factors such as dietary specialization and social complexity foster advanced cognitive skills in primates.

Ilenia Ceccarelli, Arianna Bagnis, Cristina Ottaviani et al.

Abstract The study explores whether racial identity and appearance-based trustworthiness judgments can affect recognition of pain in medical students differing in levels of resting heart rate variability (HRV), a measure of parasympathetic control of the heart. After undergoing HRV assessment, 68 medical students (37 females) participated in a dynamic pain recognition task, viewing video clips of White and Black faces, which differed in perceived trustworthiness based on facial appearance, transitioning from neutral to intense pain expressions. Response time, pain intensity attribution and treatment recommendations were analyzed. Pain was recognized slower and estimated as less intense in Black compared to White faces, leading to a lower likelihood of recommending therapy. Pain recognition was faster for untrustworthy-looking White faces compared to trustworthy ones, while perceived trustworthiness had a minimal impact on the speed of pain recognition in Black faces. However, untrustworthy-looking faces were estimated to express more pain, particularly for Black faces. Notably, these biases were more pronounced in individuals with low, rather than high, resting HRV. Considering that therapeutic decisions mirrored pain intensity attribution, it would be important to increase awareness of these biases during medical training in order to promote equity in future pain assessment and treatment.

F. Berlinghieri, G. Rizzuto, L. Kruizinga et al.

Abstract Cognitive bias is defined as the influence of emotions on cognitive processes. The concept of the cognitive judgement bias has its origins in human psychology but has been applied to animals over the past 2 decades. In this study we were interested in determining if laterality and personality traits, which are known to influence learning style, might also be correlated with a cognitive bias in the three-spined sticklebacks (Gasterosteus aculeatus). We used the judgement bias test with the go/no-go procedure where fish were first trained to discriminate between a black and white card and, after reaching a minimum learning criterion, tested their response to an ambiguous card (grey). Optimistic subjects were expected to have a high expectation of reward associated with an ambiguous stimulus, whereas pessimistic subjects a high expectation of non-reward. We used an emergence and a mirror test to quantify boldness and laterality, respectively. We hypothesised that male, bolder and more strongly lateralized fish would be more optimistic than female, shy and less strongly lateralised fish. We found that males and more strongly lateralized fish were more optimistic than females and less strongly lateralized fish. In addition, bold males were more optimistic than shy males as we predicted, but females showed the opposite pattern. Finally, fish trained on the black colour card learned the training task faster than those trained on a white card. Our results indicate that both laterality and personality traits are linked to animals’ internal states (pessimistic or optimistic outlooks) which likely has broad implications for understanding animal behaviour particularly in a welfare context.

Peter Coppola

How subjective experience (i.e., consciousness) arises out of objective material processes has been called the hard problem. The neuroscience of consciousness has set out to find the sufficient conditions for consciousness and theoretical and empirical endeavours have placed a particular focus on the cortex and subcortex, whilst discounting the cerebellum. However, when looking at neuroimaging research, it becomes clear there is substantial evidence that cerebellar, cortical and subcortical functions are correlated with consciousness. Neurostimulation evidence suggests that alterations in any part of the brain may provoke alterations in experience, but the most extreme changes are provoked via the subcortex. I then evaluate neuropsychological evidence and find abnormality in any part of the brain may provoke changes in experience; but only damage to the oldest regions seem to completely obliterate experience. Finally, I review congenital and experimental decorticate cases, and find that behavioral evidence of experience is largely compatible with the absence of the cortex. The evidence, taken together, indicates that the body, subcortex and environment are sufficient for behaviours that suggest bastic experiences. I then emphasise both the importance of the individual's developmental trajectory and the interdependencies between different neural systems.

Junyi Chu, Laura E. Schulz

Tsvetomira Tsoneva, Tsvetomira Tsoneva, Gary Garcia-Molina et al.

The present study aims to connect the psychophysical research on the human visual perception of flicker with the neurophysiological research on steady-state visual evoked potentials (SSVEPs) in the context of their application needs and current technological developments. In four experiments, we investigated whether a temporal contrast sensitivity model could be established based on the electrophysiological responses to repetitive visual stimulation and, if so, how this model compares to the psychophysical models of flicker visibility. We used data from 62 observers viewing periodic flicker at a range of frequencies and modulation depths sampled around the perceptual visibility thresholds. The resulting temporal contrast sensitivity curve (TCSC) was similar in shape to its psychophysical counterpart, confirming that the human visual system is most sensitive to repetitive visual stimulation at frequencies between 10 and 20 Hz. The electrophysiological TCSC, however, was below the psychophysical TCSC measured in our experiments for lower frequencies (1–50 Hz), crossed it when the frequency was 50 Hz, and stayed above while decreasing at a slower rate for frequencies in the gamma range (40–60 Hz). This finding provides evidence that SSVEPs could be measured even without the conscious perception of flicker, particularly at frequencies above 50 Hz. The cortical and perceptual mechanisms that apply at higher temporal frequencies, however, do not seem to directly translate to lower frequencies. The presence of harmonics, which show better response for many frequencies, suggests non-linear processing in the visual system. These findings are important for the potential applications of SSVEPs in studying, assisting, or augmenting human cognitive and sensorimotor functions.

Nicolas Coucke, Nicolas Coucke, Mary Katherine Heinrich et al.

Self-organized groups of robots have generally coordinated their behaviors using quite simple social interactions. Although simple interactions are sufficient for some group behaviors, future research needs to investigate more elaborate forms of coordination, such as social cognition, to progress towards real deployments. In this perspective, we define social cognition among robots as the combination of social inference, social learning, social influence, and knowledge transfer, and propose that these abilities can be established in robots by building underlying mechanisms based on behaviors observed in humans. We review key social processes observed in humans that could inspire valuable capabilities in robots and propose that relevant insights from human social cognition can be obtained by studying human-controlled avatars in virtual environments that have the correct balance of embodiment and constraints. Such environments need to allow participants to engage in embodied social behaviors, for instance through situatedness and bodily involvement, but, at the same time, need to artificially constrain humans to the operational conditions of robots, for instance in terms of perception and communication. We illustrate our proposed experimental method with example setups in a multi-user virtual environment.

Henk Barendregt, Antonino Raffone

Consciousness will be introduced axiomatically, inspired by Buddhist insight meditation and psychology, logic in computer science, and cognitive neuroscience, as consisting of a stream of $configurations$ that is $compound$, $discrete$, and (non-deterministically) $computable$. Within this context the notions of self, concentration, mindfulness, and various forms of suffering can be defined. As an application of this set up, it will be shown how a combined development of concentration and mindfulness can attenuate and eventually eradicate some of the forms of suffering.

Eduardo C. Garrido Merchán, Sara Lumbreras

Consciousness and intelligence are properties commonly understood as dependent by folk psychology and society in general. The term artificial intelligence and the kind of problems that it managed to solve in the recent years has been shown as an argument to establish that machines experience some sort of consciousness. Following the analogy of Russell, if a machine is able to do what a conscious human being does, the likelihood that the machine is conscious increases. However, the social implications of this analogy are catastrophic. Concretely, if rights are given to entities that can solve the kind of problems that a neurotypical person can, does the machine have potentially more rights that a person that has a disability? For example, the autistic syndrome disorder spectrum can make a person unable to solve the kind of problems that a machine solves. We believe that the obvious answer is no, as problem solving does not imply consciousness. Consequently, we will argue in this paper how phenomenal consciousness and, at least, computational intelligence are independent and why machines do not possess phenomenal consciousness, although they can potentially develop a higher computational intelligence that human beings. In order to do so, we try to formulate an objective measure of computational intelligence and study how it presents in human beings, animals and machines. Analogously, we study phenomenal consciousness as a dichotomous variable and how it is distributed in humans, animals and machines. As phenomenal consciousness and computational intelligence are independent, this fact has critical implications for society that we also analyze in this work.