Decision fatigue leads to reduced efficiency in the rate and quality of decisions. Thus, examining the reasons behind decision fatigue and comprehending its influence on decision-making in various professional domains is pivotal to improving the decision-making process. The aim of this integrative review is to investigate the causes and effects of decision fatigue from the existing literature and develop a framework that can be applied across different domains. A comprehensive literature search in three databases identified 1,027 articles on decision fatigue. After screening the articles using Integrative reviews and Meta-Analysis (PRISMA) and Joanna Briggs Institute (JBI) appraisal methods, 23 articles investigating decision fatigue across various domains were selected. The selected articles were investigated through root cause analysis and thematic synthesis. Findings revealed ten causes of decision fatigue, which were classified as individual, organizational, and external causes, and four primary effects and seven secondary effects of decision fatigue. Using these findings, a conceptual framework that offers a comprehensive understanding of decision fatigue across diverse domains was developed. Knowledge of what causes decision fatigue can help optimize the decision-making process for decision makers. This study contributes to the concept of decision fatigue within organizational settings to enhance organizational behavior, psychology, and offers implications for improving decision-making processes in diverse professional domains. The findings can help develop interventions to mitigate decision fatigue and improve overall decision-making.
Marie Hirel, Michele Marziliano, Hélène Meunier
et al.
Abstract For optimal decision-making, social animals can benefit from evaluating others’ behaviours. Some species seemingly consider the skills of others when deciding who to interact with in different contexts. Yet, whether and how nonhuman animals form impressions about others’ competence is still unclear. In this study, we investigated whether Tonkean macaques (Macaca tonkeana) and brown capuchins (Sapajus apella) can evaluate the skilfulness of others. Subjects observed two human actors (one skilful, one unskilled) trying to open several food containers. Only the skilful actor successfully opened the containers and released food so the experimenter could give it to the subjects. Our results revealed that subjects did not choose the skilful actor significantly more frequently than the unskilled one. Their choices for the skilful actor did not increase through trials nor were they based on the outcomes experienced in previous trials. However, when we considered their initial preferences for the human actors, we observed a significant shift in preference for the skilful actor. Our subjects also looked preferentially at the skilful over the unskilled actor when both simultaneously manipulated a container. While the underlying cognitive mechanisms (impression formation vs. outcome-based process) are still unclear, our findings indicate that Tonkean macaques and brown capuchins may have used social information about the actors’ skills to inform their decisions and raise questions about which behavioural measures best capture social evaluation in nonhuman species.
Brian Zaboski, Sarah Fineberg, Patrick Skosnik
et al.
Objective: Identifying obsessive-compulsive disorder (OCD) using brain data remains challenging. Resting-state electroencephalography (EEG) offers an affordable and noninvasive approach, but identifying predictive signals in EEG data has met with little success, even with the application of traditional machine learning methods. We explored whether convolutional neural networks (CNNs) applied to EEG time-frequency representations can distinguish individuals with OCD from healthy controls. Method: We collected resting-state EEG data from 20 unmedicated participants (10 with OCD, 10 healthy controls). Four-second EEG segments were transformed into time-frequency representations. We then trained a 2D CNN using a leave-one-subject-out cross-validation framework to perform subject-level classification and compared its performance to a more traditional support vector machine (SVM) approach. Next, using multimodal fusion, we examined whether adding clinical and demographic information improved classification. Results: The CNN classifier achieved high subject-level performance, distinguishing individuals with an accuracy of 85.0% and an area under the curve (AUC) of 0.88. This significantly outperformed the SVM baseline, which performed no better than chance (45.0% accuracy, AUC: 0.47). A subsequent multimodal analysis revealed that clinical and demographic variables did not contribute any additional independent information. Conclusion: CNNs applied to resting-state EEG show promise for identifying OCD, outperforming traditional machine learning methods. These findings highlight the potential of deep learning to uncover complex, diagnostically relevant patterns in neural data. While limited by sample size, this work supports further investigation into multimodal models for psychiatric classification, warranting replication in larger, more diverse samples.
Computer applications to medicine. Medical informatics, Psychiatry
The science of consciousness has been successful over the last decades. Yet, it seems that some of the key questions remain unanswered. Perhaps, as a science of consciousness, we cannot move forward using the same theoretical commitments that brought us here. It might be necessary to revise some assumptions we have made along the way. In this piece, I offer no answers, but I will question some of these fundamental assumptions. We will try to take a fresh look at the classical question about the neural and explanatory correlates of consciousness. A key assumption is that neural correlates are to be found at the level of spiking responses. However, perhaps we should not simply take it for granted that this assumption holds true. Another common assumption is that we are close to understanding the computations underlying consciousness. I will try to show that computations related to consciousness might be far more complex than our current theories envision. There is little reason to think that consciousness is an abstract computation, as traditionally believed. Furthermore, I will try to demonstrate that consciousness research could benefit from investigating internal changes of consciousness, such as aha-moments. Finally, I will ask which theories the science of consciousness really needs.
Francois Stockart, Alexis Robin, Hal Blumenfeld
et al.
Despite many years of research, the quest to identify neural correlates of perceptual consciousness (NCC) remains unresolved. One major obstacle lies in methodological limitations: most studies rely on non-invasive neural measures with limited spatial or temporal resolution making it difficult to disentangle proper NCCs from concurrent cognitive processes. Additionally, the relatively low sensitivity of non-invasive neural measures limits the interpretation of null findings in studies targeting proper NCCs. In this review, we discuss how human intracranial recordings can advance the search for NCCs, by offering high spatiotemporal resolution, improved signal sensitivity, and broad cortical and subcortical coverage. We review studies that have examined NCCs at the level of single neurons and populations of neurons, and evaluate their implications on the debates between cognitive and sensory theories of consciousness. Finally, we highlight the limits of current intracranial human recordings and propose future directions based on emerging technologies and novel experimental paradigms.
This paper presents a novel paradigm of the local percept-perceiver phenomenon to formalize certain observations in neuroscientific theories of consciousness. Using this model, a set-theoretic formalism is developed for artificial systems, and the existence of machine consciousness is proved by invoking Zermelo-Fraenkel set theory. The article argues for the possibility of a reductionist form of epistemic consciousness within machines.
Human consciousness is still a concept hard to define with current scientific understanding. Although Large Language Models (LLMs) have recently demonstrated significant advancements across various domains including translation and summarization, human consciousness is not something to imitate with current upfront technology owing to so-called hallucination. This study, therefore, proposes a novel approach to address these challenges by integrating psychoanalysis and the Myers-Briggs Type Indicator (MBTI) into constructing consciousness and personality modules. We developed three artificial consciousnesses (self-awareness, unconsciousness, and preconsciousness) based on the principles of psychoanalysis. Additionally, we designed 16 characters with different personalities representing the sixteen MBTI types, with several attributes such as needs, status, and memories. To determine if our model's artificial consciousness exhibits human-like cognition, we created ten distinct situations considering seven attributes such as emotional understanding and logical thinking. The decision-making process of artificial consciousness and the final action were evaluated in three ways: survey evaluation, three-tier classification via ChatGPT, and qualitative review. Both quantitative and qualitative analyses indicated a high likelihood of well-simulated consciousness, although the difference in response between different characters and consciousnesses was not very significant. This implies that the developed models incorporating elements of psychoanalysis and personality theory can lead to building a more intuitive and adaptable AI system with humanoid consciousness. Therefore, this study contributes to opening up new avenues for improving AI interactions in complex cognitive contexts.
Abstract One of the executive functions, inhibitory control, enables animals to suppress ineffective behaviors and facilitate flexible behavior. Seabirds, particularly those of the family Laridae, exploit diverse food resources across various environments. This suggests a possible link between their foraging behavior and inhibitory control. However, to date, inhibitory control in seabirds has not been assessed. We used a cylinder task to assess inhibitory control in wild black-tailed gulls, which are highly omnivorous seabirds. The task required gulls to suppress the dominant response of pecking at food inside a transparent cylinder, detour to the side openings, and retrieve the food without pecking the cylinder. The trial was considered successful if the gull retrieved the food without pecking the cylinder. Ten of the 12 individuals succeeded in the task within 10 trials, with their success rates improving across trials. These findings suggest that black-tailed gulls exhibit moderate levels of inhibitory control among birds and may learn detouring behavior through repetition.
In this paper, the Buddhist view on language and its implications for perception and cognition will be analyzed. The aim of this paper is to demonstrate that archaic Buddhism, as documented in the suttas of the Pāli Canon, already presents a well-articulated theory of knowledge, and that Buddhist considerations on the problem of language are comparable to Saussure’s early linguistic theories, as well as to fundamental issues in the philosophy of perception and theories of cognition. This comparison with Buddhist thought seeks to provide a technical approach to the problem of consciousness in order to structure a systematic dialogue between the philosophy of mind and language, cognitive sciences, and linguistics, offering an original perspective on these topics through Buddhist thought.
INTRODUCTION. In the process of adapting to the changing realities of geopolitics and international law, collective legitimation has become one of the main political functions of the UN as a means of politically significant approval (or disapproval) of the behaviour and positions of states as consistent with international law and at the same time meeting the needs of the present, a means of recognising them as legitimate (or illegitimate). Awareness of the dynamic nature of such a phenomenon of contemporary international law as “international legal legitimation”, competent mastery of international legal argumentation skills, their constant improvement and updating, innovative approach to the formulation of international legal positions that could really claim to be dominant and progressive, development of an optimal mechanism for their articulation and legitimation will allow to ensure the competitiveness of Russia’s international legal policy among such rigid, wilful, but, it should be admitted, calculating and shrewd strategies of legitimation, which are applied primarily by the United States of America. Against the background of Washington’s generally successful and effective international legal legitimation of its interventions, as a result of which not a single resolution was adopted either by the UN Security Council or the UN General Assembly (UNGA) that would have qualified the American invasions of Iraq, Libya or Syria as “aggression”, the international legal support for the Russian Federation’s special military operation in the Ukraine (SVO) needs to be improved, since precisely this qualification was given to it by the UNGA. The relevance and significance of the issue increases in view of the need to enhance the quality of the international legal justification of the SVO, which should be aimed at the recognition, approval and acceptance by the majority of members of the international community of Russia’s international legal position on this issue, i. e. at its international legal legitimation, capable of reversing the currently prevailing pro-Western international legal consciousness. Therefore, this phenomenon encapsulates a tremendous applied value in the context of the increasing number of hybrid threats to the national security of our country and the emergence of new sources of such threats.MATERIALS AND METHODS. The theoretical and empirical basis of the research is constituted by domestic and mainly foreign international legal literature with an emphasis on the latest scientific developments and also with reference to the relevant to the topic law enforcement practice (practice of the International Court of Justice) and material of specialised dictionaries. On the basis of integrative (multidimensional) approach to scientific legal research, taking into account such principles of scientific cognition as theoretical novelty and scientific relevance, the study was guided, in particular, by hermeneutic, formal-legal, formallogical, structural-functional and systematic methods, as well as methods of analysis and synthesis, legal modelling, legal construction and strategic planning.RESEARCH RESULTS. The article reviews relevant studies on the term “legitimation” in international law, outlines various approaches to the definition and interpretation of this notion, reveals the content and structural elements of the process of international legal legitimation aimed at achieving, recognising or confirming the legitimacy of an international legal position of a state. Forming her own vision of the notion of “international legal legitimation”, the author has devised a number of classifications (structural schemes): a typology of approaches to the definition of the term “legitimation” (nihilistic, idealistic and compromise), the structure of the process of international legal legitimation, the types of international legal legitimation according to the time and will criteria.DISCUSSION AND CONCLUSIONS. Amid the large-scale and powerful “legal aggression” of the West against Russia, the phenomenon of international legal legitimation of the state’s actions often raises the question of the need to be bold, inventive and even creative in formulating international legal positions in a particular area. Our hypothesis is that in the real world, in which international law is a “product of a game of powers and interests”, the direct and active involvement of the Russian Federation in the process of constructing a common model of international legal legitimation of states’ positions would help to increase the effectiveness of the protection of Russian interests. The first step on this path could be the development and approbation of an international legal mechanism of legitimation of Russia’s international legal position on the SVO. Besides, it is important to analyse the international legal legitimation practices of particular states not only in order to study and potentially absorb their experience, but also to learn how to predict and anticipate further international legal manoeuvres of a certain country so as to be able to respond to them in a timely and appropriate manner.
Law of nations, Comparative law. International uniform law
Computational modeling is a critical tool for understanding consciousness, but is it enough on its own? This paper discusses the necessity for an ontological basis of consciousness, and introduces a formal framework for grounding computational descriptions into an ontological substrate. Utilizing this technique, a method is demonstrated for estimating the difference in qualitative experience between two systems. This framework has wide applicability to computational theories of consciousness.
The article develops a generative model of the human translating mind, grounded in empirical translation process data. It posits that three embedded processing layers unfold concurrently in the human mind, and their traces are detectable in behavioral data: sequences of routinized/automated processes are observable in fluent translation production, cognitive/reflective thoughts lead to longer keystroke pauses, while affective/emotional states may be identified through characteristic typing and gazing patterns. Utilizing data from the CRITT Translation Process Research Database (TPR-DB), the article illustrates how the temporal structure of keystroke and gaze data can be related to the three assumed hidden mental processing strata. The article relates this embedded generative model to various theoretical frameworks, dual-process theories and Robinson's (2023) ideosomatic theory of translation, opening exciting new theoretical horizons for Cognitive Translation Studies, grounded in empirical data and evaluation.
Mathis Immertreu, Achim Schilling, Andreas Maier
et al.
This study explores the potential for artificial agents to develop core consciousness, as proposed by Antonio Damasio's theory of consciousness. According to Damasio, the emergence of core consciousness relies on the integration of a self model, informed by representations of emotions and feelings, and a world model. We hypothesize that an artificial agent, trained via reinforcement learning (RL) in a virtual environment, can develop preliminary forms of these models as a byproduct of its primary task. The agent's main objective is to learn to play a video game and explore the environment. To evaluate the emergence of world and self models, we employ probes-feedforward classifiers that use the activations of the trained agent's neural networks to predict the spatial positions of the agent itself. Our results demonstrate that the agent can form rudimentary world and self models, suggesting a pathway toward developing machine consciousness. This research provides foundational insights into the capabilities of artificial agents in mirroring aspects of human consciousness, with implications for future advancements in artificial intelligence.
Consciousness is notoriously hard to define with objective terms. An objective definition of consciousness is critically needed so that we might accurately understand how consciousness and resultant choice behaviour may arise in biological or artificial systems. Many theories have integrated neurobiological and psychological research to explain how consciousness might arise, but few, if any, outline what is fundamentally required to generate consciousness. To identify such requirements, I examine current theories of consciousness and corresponding scientific research to generate a new definition of consciousness from first principles. Critically, consciousness is the apparatus that provides the ability to make decisions, but it is not defined by the decision itself. As such, a definition of consciousness does not require choice behaviour or an explicit awareness of temporality despite both being well-characterised outcomes of conscious thought. Rather, requirements for consciousness include: at least some capability for perception, a memory for the storage of such perceptual information which in turn provides a framework for an imagination with which a sense of self can be capable of making decisions based on possible and desired futures. Thought experiments and observable neurological phenomena demonstrate that these components are fundamentally required of consciousness, whereby the loss of any one component removes the capability for conscious thought. Identifying these requirements provides a new definition for consciousness by which we can objectively determine consciousness in any conceivable agent, such as non-human animals and artificially intelligent systems.
The concept of neural correlates of consciousness (NCC), which suggests that specific neural activities are linked to conscious experiences, has gained widespread acceptance. This acceptance is based on a wealth of evidence from experimental studies, brain imaging techniques such as fMRI and EEG, and theoretical frameworks like integrated information theory (IIT) within neuroscience and the philosophy of mind. This paper explores the potential for artificial consciousness by merging neuromorphic design and architecture with brain simulations. It proposes the Neuromorphic Correlates of Artificial Consciousness (NCAC) as a theoretical framework. While the debate on artificial consciousness remains contentious due to our incomplete grasp of consciousness, this work may raise eyebrows and invite criticism. Nevertheless, this optimistic and forward-thinking approach is fueled by insights from the Human Brain Project, advancements in brain imaging like EEG and fMRI, and recent strides in AI and computing, including quantum and neuromorphic designs. Additionally, this paper outlines how machine learning can play a role in crafting artificial consciousness, aiming to realise machine consciousness and awareness in the future.
Neural theories of consciousness face three difficulties: (1) The selection problem: how are those neurons which cause consciousness selected, from all the other neurons which do not? (2) the precision problem: how do neurons hold a detailed internal model of 3D space, as the origin of our spatial conscious experience? and (3) the decoding problem: how are the many distorted neural representations of space in the brain decoded, to give our largely undistorted conscious experience of space? These problems can all be addressed if the brains internal model of local 3D space is held not in neurons, but in a wave excitation (holding a projective transform of Euclidean space), and if the wave is the source of spatial consciousness. Such a wave has not yet been detected in the brain, but there are good reasons why it has not been detected; and there is indirect evidence for a wave, in the mammalian thalamus, and in the central body of the insect brain. The resulting projective wave theory of consciousness gives good agreement with the spatial form of our consciousness. It has a positive Bayesian balance between the complexity of its assumptions and the data it accounts for; this gives a basis to believe it.
Could an AI have conscious experiences? Any answer to this question should conform to Evidentialism - that is, it should be based not on intuition, dogma or speculation but on solid scientific evidence. I argue that such evidence is hard to come by and that the only justifiable stance on the prospects of artificial consciousness is agnosticism. In the current debate, the main division is between biological views that are sceptical of artificial consciousness and functional views that are sympathetic to it. I argue that both camps make the same mistake of over-estimating what the evidence tells us. Scientific insights into consciousness have been achieved through the study of conscious organisms. Although this has enabled cautious assessments of consciousness in various creatures, extending this to AI faces serious obstacles. AI thus presents consciousness researchers with a dilemma: either reach a verdict on artificial consciousness but violate Evidentialism; or respect Evidentialism but offer no verdict on the prospects of artificial consciousness. The dominant trend in the literature has been to take the first option while purporting to follow the scientific evidence. I argue that if we truly follow the evidence, we must take the second option and adopt agnosticism.
Christopher J. Whyte, Andrew W. Corcoran, Jonathan Robinson
et al.
The multifaceted nature of subjective experience poses a challenge to the study of consciousness. Traditional neuroscientific approaches often concentrate on isolated facets, such as perceptual awareness or the global state of consciousness and construct a theory around the relevant empirical paradigms and findings. Theories of consciousness are, therefore, often difficult to compare; indeed, there might be little overlap in the phenomena such theories aim to explain. Here, we take a different approach: starting with active inference, a first principles framework for modelling behaviour as (approximate) Bayesian inference, and building up to a minimal theory of consciousness, which emerges from the shared features of computational models derived under active inference. We review a body of work applying active inference models to the study of consciousness and argue that there is implicit in all these models a small set of theoretical commitments that point to a minimal (and testable) theory of consciousness.