Neural parasitism: could adaptive artificial intelligence systems incrementally reconfigure human neural plasticity and challenge the foundations of cognitive autonomy?

Cooperation, the process through which individuals work together to achieve common goals, is fundamental to human and animal societies and increasingly critical in artificial intelligence (AI). In this study, we investigated cooperation in mice and AI systems, examining how they learn to actively coordinate their actions to obtain shared rewards. We identified key social behavioral strategies and decision-making processes in mice that facilitate successful cooperation. These processes are represented in the anterior cingulate cortex (ACC), and ACC activity causally contributes to cooperative behavior. We extended our findings to AI systems by training artificial agents in a similar cooperation task. The agents developed behavioral strategies and neural representations reminiscent of those observed in the biological brain, revealing parallels between cooperative behavior in biological and artificial systems.

The question of expediency and the principal possibility of machine imitation of human intellect from the point of view of evaluating the perspectives of various directions of development of artificial intelligence systems is discussed. It is shown that even beyond this practical aspect, the solution to the question about the principal possibility of creating a machine equivalent of the human mind is of great importance for understanding the nature of human thinking, consciousness and mental in general. It is noted that the accumulated experience of creating various systems of artificial intelligence, as well as the currently available results of studies of human intelligence and human consciousness in philosophy and psychology allow us to give a preliminary assessment of the prospects of creating an algorithmic artificial system, equal in its capabilities to human intelligence. The analysis of the drawbacks revealed in the use of artificial intelligence systems by mass users and in scientific research is carried out. The key disadvantages of artificial intelligence systems are the inability to independently set goals, the inability to form a consolidated «opinion» when working with divergent data, the inability to objectively evaluate the results obtained and generate revolutionary new ideas and approaches. The disadvantages of the «second level» are the insufficiency of information accumulated by mankind for further training of artificial intelligence systems, the resulting training of models on the content partially synthesized by artificial intelligence systems themselves, which leads to «forgetting» part of the information obtained during training and increasing the cases of issuing unreliable information. This, in turn, makes it necessary to check the reliability of each answer given by the artificial intelligence system whenever critical information is processed, which, against the background of the plausibility of the data given by artificial intelligence systems and a comfortable form of their presentation, requires the user to have well-developed critical thinking. It is concluded that the main advantage of artificial intelligence systems is that they can significantly increase the efficiency of information retrieval and primary processing, especially when dealing with large data sets. The importance of the ethical component in artificial intelligence and the creation of a regulatory framework that introduces responsibility for the harm that may be caused by the use of artificial intelligence systems is substantiated, especially for multimodal artificial intelligence systems. The conclusion is made that the risks associated with the use of multimodal artificial intelligence systems consistently increase in the case of realization in them of such functions of human consciousness as will, emotions and following moral principles.

Video games have increasingly demonstrated a great deal of audiovisual realism, in par with the massive performance improvement of computer systems. At the same time, their Artificial Intelligence (AI) component falls short in terms of realism because it is usually based on non-adaptive methods. Adaptive AI mechanisms can help increase video game realism allowing the game to adapt in real-time to the game progress and the user behavior. Following a short overview of the progress of AI in video games in the past years, this paper highlights the creation of modern video games with basic and elementary adaptive game AI using the Unity game development framework. Particular emphasis is on the details of the AI component. First, a shooter game with basic AI is created. Finally, an action-adventure video game is created featuring elementary case-based adaptive AI. The objective in this game is to create enemies which are able to perceive changes in the environment and adapt their strategies accordingly. Proposed AI practices can migrate into relevant real world applications, such as video surveillance and intrusion detection systems, mission critical autonomous networked patrolling and/or save and rescue robots, vision and hearing assistive applications, intelligent video and behavioral analytics to detect and predict threats etc.

Audit agencies such as the United States, the United Kingdom, the Netherlands, and Brazil conduct audits by artificial intelligence systems to ensure the effectiveness of the return of illegal supply and demand, illegal and unfair measures of duties, and the appropriateness of audits. In Korea, the Framework Act on Artificial Intelligence was enacted in 2025, and Article 20 of the General Act on Public Admiaiatration on Administration stipulates the automatic disposition of artificial intelligence for administrative disposition of binding acts. The Audit Office Act and the Public Audit Act require the establishment and operation of an audit-related information system, but it cannot be concluded as an artificial intelligence information system because it does not define the concept of the information system. Since there are no prestigious regulations on the scope and target of audit activities by audit agencies, standards, confidentiality, registration and certification of audit artificial intelligence systems, etc., securing predictability, transparency, and legality of artificial intelligence audits is not guaranteed. In addition, the concept of artificial intelligence audit should include not only the audit of the artificial intelligence system, but also all audit activities using the artificial intelligence system by auditors or audit agencies. In particular, it is necessary to establish a third-party auditor system as well as internal auditors and external auditors for audit by artificial intelligence systems. The U.S. California bill on artificial intelligence audit was proposed to the California Legislature in February 2025. It establishes an artificial intelligence system on the Internet of state audit and operation agencies to database information on audits, prepare procedures for registration, certification, and verification of artificial intelligence auditors and artificial intelligence systems, as well as disclosure and confidentiality of audit information, the subject of information to be provided to artificial intelligence systems, the explainability of artificial intelligence audits, and the obligation to store audit information for 10 years, but administrative or public institutions do not mandate the introduction of artificial intelligence systems. In this paper, after establishing the concept of artificial intelligence audit, the possibility of expanding artificial intelligence of audit in the intelligent information society and measures to prevent corruption are reviewed. In addition, since the category of artificial intelligence audit is unclear in Korea's artificial intelligence law and audit-related laws, the scope of artificial intelligence audit is clearly identified, and implications for Korea are sought by reviewing and analyzing artificial intelligence audit cases in the United States and the Netherlands in the public administration area. In addition, the legal task of artificial intelligence audit is ⅰ) the enactment of the Artificial Intelligence Audit Act, ⅱ) the acceptability of automatic decision-making of artificial intelligence audit, ⅲ) clarification of the standards and scope of artificial intelligence audit, ⅳ) legal task for the use of artificial intelligence to prevent fraud and illegal supply and demand, and ⅴ) the issue of strengthening new technology capabilities for future auditors and audit institutions are reviewed in five taps and suggested ways to improve them.

Audit agencies such as the United States, the United Kingdom, the Netherlands, and Brazil conduct audits by artificial intelligence systems to ensure the effectiveness of the return of illegal supply and demand, illegal and unfair measures of duties, and the appropriateness of audits. In Korea, the Framework Act on Artificial Intelligence was enacted in 2025, and Article 20 of the General Act on Public Admiaiatration on Administration stipulates the automatic disposition of artificial intelligence for administrative disposition of binding acts. The Audit Office Act and the Public Audit Act require the establishment and operation of an audit-related information system, but it cannot be concluded as an artificial intelligence information system because it does not define the concept of the information system. Since there are no prestigious regulations on the scope and target of audit activities by audit agencies, standards, confidentiality, registration and certification of audit artificial intelligence systems, etc., securing predictability, transparency, and legality of artificial intelligence audits is not guaranteed. In addition, the concept of artificial intelligence audit should include not only the audit of the artificial intelligence system, but also all audit activities using the artificial intelligence system by auditors or audit agencies. In particular, it is necessary to establish a third-party auditor system as well as internal auditors and external auditors for audit by artificial intelligence systems. The U.S. California bill on artificial intelligence audit was proposed to the California Legislature in February 2025. It establishes an artificial intelligence system on the Internet of state audit and operation agencies to database information on audits, prepare procedures for registration, certification, and verification of artificial intelligence auditors and artificial intelligence systems, as well as disclosure and confidentiality of audit information, the subject of information to be provided to artificial intelligence systems, the explainability of artificial intelligence audits, and the obligation to store audit information for 10 years, but administrative or public institutions do not mandate the introduction of artificial intelligence systems. In this paper, after establishing the concept of artificial intelligence audit, the possibility of expanding artificial intelligence of audit in the intelligent information society and measures to prevent corruption are reviewed. In addition, since the category of artificial intelligence audit is unclear in Korea's artificial intelligence law and audit-related laws, the scope of artificial intelligence audit is clearly identified, and implications for Korea are sought by reviewing and analyzing artificial intelligence audit cases in the United States and the Netherlands in the public administration area. In addition, the legal task of artificial intelligence audit is ⅰ) the enactment of the Artificial Intelligence Audit Act, ⅱ) the acceptability of automatic decision-making of artificial intelligence audit, ⅲ) clarification of the standards and scope of artificial intelligence audit, ⅳ) legal task for the use of artificial intelligence to prevent fraud and illegal supply and demand, and ⅴ) the issue of strengthening new technology capabilities for future auditors and audit institutions are reviewed in five taps and suggested ways to improve them.

<p><b>Artificial intelligence systems have become proficient at linking environmental features to targets to describe simple patterns in data. However, these systems can struggle with many real-world problems that entail hierarchical patterns within patterns, for example, in recognizing object ontologies where one object is made-up of other objects. Although it is possible to capture such complex structures by utilizing state-of-the-art deep networks, the knowledge is often stored in layers that do not take advantage of the potential benefits provided by reusing patterns within a layer of the system.</b></p> <p>Biological nervous systems can learn knowledge from simple and small-scale problems and then apply it to resolve more complex and large-scale problems in similar and related domains. However, rudimentary attempts to apply this transfer learning in artificial intelligence systems have struggled. This may be due to the homogeneous nature of their knowledge representation. The current understanding of the learning mechanisms in the brains of human and non-human animals can be used as inspiration to improve learning in artificial agents. Research into lateral asymmetry of the brain shows that it enables modular learning at different levels of abstraction that facilitate transfer between tasks.</p> <p>The proposed thesis is that an artificial intelligence system that enables lateralization and modular learning at different levels of abstraction has the ability to solve complex hierarchical problems that a similar homogeneous system can not. The comprehensive goal of this thesis is to accomplish lateralized learning, inspired by the principles of biological intelligence, in artificial intelligence systems. The objectives are to show that lateralization and modular learning assist the novel systems to encapsulate the underlying knowledge patterns in the form of building blocks of knowledge. These building blocks of knowledge are to be tested on analyzable Boolean tasks as well as practical computer vision and navigation tasks. Academic contributions are related to the novel methods of the linking, transfer, and sharing of learned knowledge which are based on the analogous strategies of the brain.</p> <p>This thesis proposes a general framework for lateralized artificial intelligence systems. The novel lateralized framework spans key aspects of knowledge perception, knowledge representation and utilization, and patterns of connectivity. It determines the essential functionality, critical methods, and associated parameters that are required to be incorporated into an artificial intelligence system to behave as a lateralized artificial intelligence system.</p> <p>This thesis creates a novel evolutionary machine learning system, by adapting the lateralized framework, to obtain a proof-of-concept of the lateralized approach. Considering the same problem at different levels of abstraction enables the novel system to reframe a complex problem as a simple problem and efficiently resolve it. The results on analyzable Boolean tasks show that the problems that contain a natural hierarchy of patterns are solved to a scale that exceeds previous work (i.e. 18-bit hierarchical multiplexer problem), and reusing learned general patterns as constituents for future problems advances transfer learning (e.g. n-bit parity problem effectively becomes a sequence of 2-bit parity problems). </p> <p>This thesis creates a novel lateralized artificial intelligence system, by adapting the lateralized framework, that shows robustness in a real-world domain that includes uncertainty, noise, and irrelevant and redundant data. The results of image classification tasks show that the lateralized system efficiently learns hierarchical distributions of knowledge, demonstrating performance that is similar to (or better than) other state-of-the-art deep systems as it reasons using multiple representations. Crucially, the novel system outperformed all the state-of-the-art deep models for the classification (binary classes) of normal and adversarial images by 0.43%-2.56% and 2.15%-25.84%, respectively. This thesis creates another novel multi-class lateralized system for computer vision problems to show that the lateralized approach can be scaled and not limited to learning classifier systems.</p> <p>Both the Boolean and computer vision problems are single step problems in the spatial domain. However, most biological tasks, which exhibit heterogeneity, are temporal in nature. This thesis creates a novel frame-of-reference based artificial intelligence system, by adapting the lateralized framework, to address perceptual aliasing in multi-step decision making tasks. Considering aliased states at a constituent level enables the novel system to place them appropriately in holistic level policies. Consequently, the novel system transforms a non-Markov environment into a deterministic environment and efficiently resolves it. Experimental results show that the novel system effectively solves complex aliasing patterns in non-Markov environments that have been challenging to artificial agents. For example, the novel system utilizes only 6.5, 3.71, and 3.22 steps to resolve Maze10, Littman57, and Woods102, respectively.</p> <p>A final contribution of this work is to obtain evidence of the benefits/costs of lateralization from artificial intelligence in order to inform cognitive neuroscience. Given that lateralization is ubiquitous in brains, evolutionary benefits can be assumed, at least in some domains. But that does not mean those benefits extend to all domains. The cognitive neuroscience research community has been struggling to determine the trade-off between the benefits and costs of lateralization. It has been hypothesized that lateralization has benefits that may counterbalance its costs. Lateralization has been associated with both poor and good performance. This thesis demonstrates the value of viable artificial systems for testing the costs and benefits of lateralization in biological systems.</p>

Artificial Intelligence (AI) has evolved from a specialised field of computing into a vital part of how we generate knowledge, make decisions, and address ethical issues (OpenAI 2025). As these developments occur rapidly, progress in self-reflective and adaptive AI has amplified debates about whether machines can have consciousness in business, science, and academia. To provide clarity on navigating this complex area, this review looks at ways to detect signs of consciousness in artificial systems. Specifically, from 2020 to 2025, three primary trends have influenced research on artificial consciousness, establishing the context for this review.

The complexity of human and artificial systems plays a crucial role in Design for Social Innovation (DfSI), an approach that aims to solve social problems through the co-creation of innovative solutions. In this context, design must take into account the dynamic interactions between individuals, communities, and advanced technologies. Indeed, the concept of systemic complexity is fundamental to understanding how heterogeneous elements can interact, influence each other, and generate nonlinear and often unexpected outcomes. In the field of DfSI, the main challenge is to integrate human systems, characterized by diverse behaviors, needs, and values, with artificial systems, such as artificial intelligence (AI) and cybernetic systems, which operate according to algorithmic logic. This integration requires a thorough understanding of socio-technical dynamics, including the analysis of social networks, collective decision-making processes, and technological mediation. Human systems, inherently complex, are defined by a network of social, cultural, and economic relationships. In DfSI, these systems must be viewed not only as recipients of innovations but also as active co-creators. Indeed, human participation is essential to ensure that design solutions are sustainable, accepted, and adapted to local contexts. The systems approach to DfSI, therefore, requires interdisciplinary collaboration that integrates expertise in design, social sciences, technology, and ethics. The goal is to develop design methodologies that are capable of managing complexity and promoting inclusive, sustainable, and adaptive social innovation. Internationally, there are interesting case studies demonstrating the potential of integrating artificial systems into complex social problems in a wide variety of contexts. For example: i) Smart Cities and Social Innovation - Case Study: the city of Barcelona; ii) Healthcare Innovation with AI - Case Study: Babyl in Rwanda; iii) Educational Innovation and AI - Case Study: Adaptive Learning Platforms; iv) Sustainable Agriculture and AI - Case Study: Precision Agriculture in Kenya. In Italy, too, the application of advanced digital technologies in the social context is now widespread. Significant examples include: i) "Educational Robotics" project - Stripes Cooperative; ii) "AI & Welfare" project - Idee in Rete National Consortium; iii) Artificial Intelligence for Social Good project - ABN Consortium; iv) "Care for Carers" project - ASAD Social Cooperative. This research has systematized DfSI initiatives that integrate artificial systems into complex social problems currently present in the Umbria Region, with the aim of addressing the challenge of scalability, i.e., the ability to adapt design solutions to different contexts and communities. This process aims to create a map of criticalities and potentials, facilitating interactions, as well as the development of operational guidelines that can stimulate the emergence of new creative and innovative opportunities. The goal is to develop design methodologies that can manage complexity and promote inclusive, sustainable, and adaptive social innovation. In conclusion, the complexity of human and artificial systems is a key challenge in DfSI, but it also offers significant opportunities to develop more effective and resilient solutions. The interaction between these systems must be carefully managed to ensure that social innovation is driven by people's needs and supported by technologies in an ethical and responsible manner. 1. Manzini, E. (2015). Design, When Everybody Designs: An Introduction to Design for Social Innovation. MIT Press. 2. Mulgan, G. (2019). Social Innovation: How Societies Find the Power to Change. Policy Press. 3. Latour, B. (2005). Reassembling the Social: An Introduction to Actor-Network-Theory. Oxford University Press. 4. Smith, A., & Stirling, A. (2018). Grassroots Innovation and Innovation Democracy. Science and Technology Studies, 31(2), 35-52. 5. Ratti, C., & Claudel, M. (2016). The City of Tomorrow: Sensors, Networks, Hackers, and the Future of Urban Life. Yale University Press. 6. Fu, Z., & Zhou, Y. (2020). Research on human–AI co-creation based on reflective design practice. CCF Transactions on Pervasive Computing and Interaction, 2(1), 33-41. 7. Dionisio, M. et al., (2023) The role of digital social innovations to address SDGs: A systematic review Environmental Management and Sustainable Development” (23 February 2023), Springer 8. Cila, N., Giaccardi, E., Trotto, A., & Bogers, S. (2017). Products as agents: Metaphors for designing the products of the IoT age.

The technological revolutionary achievements of the modern world inevitably pose a number of issues to humanity that require legal reflection. The most breakthrough achievements of the last few years are artificial intelligence systems. These systems are very successfully integrated into many spheres of life of the world community. To date, many countries have no systematic legal norms regulating the scope of artificial intelligence. The aim of this work is to formulate specific proposals for the legislative regulation of the field of artificial intelligence. To reach this aim, the authors analyzed legislative acts and law enforcement practice in the Russian Federation and in other modern technologically developed countries. Special attention was paid to determining the possibility of considering artificial intelligence as a subject of law. As part of the work, the authors also examined the doctrinal points of view of both the domestic and foreign scientific community. Based on the results of a comprehensive study, the authors propose to consider the possibility of attributing limited legal personality to some artificial intelligence systems: not all artificial intelligence systems should be given certain rights and responsibilities, but only those that have signs of strong artificial intelligence. In this regard, the authors propose to classify all artificial intelligence systems depending on how significant legal facts and legal consequences they are able to generate. It is advisable to structure the list of “advanced” artificial intelligence systems into one group – “strong intelligent systems”. The list of less developed artificial intelligence systems should be included in another group – “weak intelligent systems”. It is advisable classify artificial intelligence systems not on the principle of a generalized enumeration of their functionality, but on the principle of their specific literal enumeration. A specific list of “strong intelligent systems” will be formed and approved by the Government of the Russian Federation. Further, in connection with the proposed classification, comes the idea of attributing legal personality to “strong intelligent systems”. By analogy with the institution of legal entities, it is possible to provide a procedure for delegating certain rights and obligations to strong artificial intelligence, thereby bringing out a new subject in certain legal relations. Thus, the results of the study can outline certain boundaries of the work of artificial intelligence, contributing to the creation of specific constituent documents or protocols of functioning.

In both artificial and biological systems, the centered kernel alignment (CKA) has become a widely used tool for quantifying neural representation similarity. While current CKA estimators typically correct for the effects of finite stimuli sampling, the effects of sampling a subset of neurons are overlooked, introducing notable bias in standard experimental scenarios. Here, we provide a theoretical analysis showing how this bias is affected by the representation geometry. We then introduce a novel estimator that corrects the bias for both input and feature sampling. We use our method for evaluating both brain-to-brain and model-to-brain alignments and show that it delivers reliable comparisons even with very sparsely sampled neurons. We perform within-animal and across-animal comparisons on electrophysiological data from visual cortical areas V1, V4, and IT, and use these as benchmarks to evaluate model-to-brain alignment. We also apply our method to reveal how object representations become progressively disentangled across layers in both biological and artificial systems. These findings underscore the importance of correcting feature-sampling biases in CKA and demonstrate that our bias-corrected estimator provides a more faithful measure of representation alignment. The improved estimates increase our understanding of how neural activity is structured across both biological and artificial systems.

This study was conducted to answer the question of how artificial intelligence-based pricing systems affect customer satisfaction and the financial performance of Vietnamese travel agencies. The study was conducted to explore factors that enhance customer satisfaction and financial performance in travel agencies using artificial intelligence-based pricing strategies, with a specific focus on understanding the interaction between the effectiveness of perceived artificial intelligence systems, the extent to which artificial intelligence is applied, the trust of regulators, and perceptions of price fairness based on artificial intelligence. Using a linear structure model, we collected and analyzed survey data from 372 people to test five research hypotheses. The findings show that the perceived effectiveness of artificial intelligence systems, the level of application of artificial intelligence and the trust of managers significantly increase customer satisfaction, thereby positively impacting the financial performance of travel companies in Vietnam. However, the study did not find a statistically significant effect of perceptions of price fairness based on artificial intelligence on customer satisfaction. These results are important because they show that travel companies need to focus on the application of effective, adaptive and reliable artificial intelligence systems while addressing fairness concerns through transparency and effective communication with customers. The implications underscore how travel companies can improve the customer experience and achieve financial efficiencies by leveraging artificial intelligence technologies in their dynamic pricing strategies.

LAW IN DIGITAL REALITY

Artificial sensory systems mimic the five human senses to facilitate data interaction between the real and virtual worlds. Accurate data analysis is crucial for converting external stimuli from each artificial sense into user-relevant information, yet conventional signal processing methods struggle with the massive scale, noise, and artificial sensory systems characteristics of data generated by artificial sensory devices. Integrating artificial intelligence (AI) is essential for addressing these challenges and enhancing the performance of artificial sensory systems, making it a rapidly growing area of research in recent years. However, no studies have systematically categorized the output functions of these systems or analyzed the associated AI algorithms and data processing methods. In this review, we present a systematic overview of the latest AI techniques aimed at enhancing the cognitive capabilities of artificial sensory systems replicating the five human senses: touch, taste, vision, smell, and hearing. We categorize the AI-enabled capabilities of artificial sensory systems into four key areas: cognitive simulation, perceptual enhancement, adaptive adjustment, and early warning. We introduce specialized AI algorithms and raw data processing methods for each function, designed to enhance and optimize sensing performance. Finally, we offer a perspective on the future of AI-integrated artificial sensory systems, highlighting technical challenges and potential real-world application scenarios for further innovation. Integration of AI with artificial sensory systems will enable advanced multimodal perception, real-time learning, and predictive capabilities. This will drive precise environmental adaptation and personalized feedback, ultimately positioning these systems as foundational technologies in smart healthcare, agriculture, and automation.

The paper explores Time Dependent Optimization (Tdo) as a measure of adaptiveness in artificial systems. We first discuss this choice and review classical Tdo models to propose a canonic benchmark. Then we underline the central role of diversity in adaptive dynamics for biological and cybernetic systems and illustrate by a state of the art of evolutionary Tdo (Etdo). A Simple Artificial Immune System (Sais) is then proposed and experimentally compared to Etdo. Encouraging results are explained by strong analogies between Sais and GAs as well as Sais's ability to manage stable heterogeneous populations as a model of Idiotypic Networks. We conclude by discussing the relevance of artificial immune systems as genuinely adaptive artificial systems.

This paper addresses the formation of discrete representations from continuous processes by intelligent systems of varying natures. It explores a range of philosophical approaches—such as monism, sensationalism, dualism, physicalism, behaviorism, cybernetics, and semiotics—that present different interpretations of how intelligence and consciousness transform continuous phenomena into discrete forms. The limitations of existing theories in articulating the mechanisms underlying this transformation are critically examined. By incorporating the concept of discretization, which is traditionally associated with digital signal processing, into interdisciplinary research on artificial intelligence (AI) and cognitive sciences, the paper provides a novel perspective on the functions of both natural and artificial intelligence. Discretization emerges as a pivotal function of AI, enabling the conversion of continuous processes into discrete representations. An analogy is drawn between the discretization occurring in human consciousness and the mechanisms employed in artificial intelligence, underscoring the significance of forming discrete representations in the realms of perception and intellectual information processing. A discussion of the Heisenberg uncertainty principle within the context of discretization reveals how this process can lead to information loss and uncertainty within artificial intelligence systems. The concept of discretization has the potential to immensely enrich our understanding of information processing in human consciousness, elucidating how unique sensations and qualities of the perceived world are constructed, and how these processes relate to notions of self-organization and strange loops, as proposed by Douglas Hofstadter. Furthermore, the exploration of qualia contributes to the argument that qualia—which represent a collection of conscious, subjective experiences—must inherently include a discretization function as a fundamental aspect of organizing subjective perceptions. If this proposition holds true, then the philosophical zombie presented in Chalmers's thought experiment becomes conceptually untenable, rendering his criticisms of physicalism and materialism less convincing. This research offers new avenues for exploring the interaction between continuous and discrete phenomena, thereby proposing directions for future investigations in the philosophy of consciousness and the cognitive sciences.