On a Possible Basis for Metaphysical Self-development in Natural and Artificial Systems

There is and has been a fruitful flow of concepts and ideas between studies of learning in biological and artificial systems. Much early work that led to the development of reinforcement learning (RL) algorithms for artificial systems was inspired by learning rules first developed in biology by Bush and Mosteller, and Rescorla and Wagner. More recently, temporal-difference RL, developed for learning in artificial agents, has provided a foundational framework for interpreting the activity of dopamine neurons. In this Review, we describe state-of-the-art work on RL in biological and artificial agents. We focus on points of contact between these disciplines and identify areas where future research can benefit from information flow between these fields. Most work in biological systems has focused on simple learning problems, often embedded in dynamic environments where flexibility and ongoing learning are important, similar to real-world learning problems faced by biological systems. In contrast, most work in artificial agents has focused on learning a single complex problem in a static environment. Moving forward, work in each field will benefit from a flow of ideas that represent the strengths within each discipline. Research on reinforcement learning in artificial agents focuses on a single complex problem within a static environment. In biological agents, research focuses on simple learning problems embedded in flexible, dynamic environments. The authors review the literature on these topics and suggest areas of synergy between them.

This paper investigates the claim that artificial Intelligence Systems cannot be held morally responsible because they do not have an ability for agential self-awareness e.g. they cannot be aware that they are the agents of an action. The main suggestion is that if agential self-awareness and related first person representations presuppose an awareness of a self, the possibility of responsible artificial intelligence systems cannot be evaluated independently of research conducted on the nature of the self. Focusing on a specific account of the self from the phenomenological tradition, this paper suggests that a minimal necessary condition that artificial intelligence systems must satisfy so that they have a capability for self-awareness, is having a minimal self defined as ‘a sense of ownership’. As this sense of ownership is usually associated with having a living body, one suggestion is that artificial intelligence systems must have similar living bodies so they can have a sense of self. Discussing cases of robotic animals as examples of the possibility of artificial intelligence systems having a sense of self, the paper concludes that the possibility of artificial intelligence systems having a ‘sense of ownership’ or a sense of self may be a necessary condition for having responsibility.KeywordsAI responsibilityArtificial selfAgential self awarenessPersonal identity

Participants in a 1984 Elderhostel Summer Session, whose ages ranged from 60 to 84 yr., volunteered to respond to the Life Satisfaction Index from the perspective of Self and Other. This Likert-type scale had six categories, each with a description of a source of meaning in life. For all participants, a one-way analysis of variance for Self ratings across the six categories yielded a significant F ratio. All comparisons between pairs from categories rated most important—Relationships, Health, Service—and least important—Belief, Growth, Life Work—were significant. There were only chance differences, however, among paired combinations within categories rated most or least important. A one-way analysis of variance for the 26 participants who rated all categories from the perspective of Other also yielded a significant F ratio. Results corresponded with earlier reports of Other ratings: the order of categories from highest to lowest was Relationships, Health, Service, Belief, Growth, Life Work.

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.

The present research discusses four ‘physical’ models of system and calculates thereliability function during system’s aging and maturity on the basis of the system structure.

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.

As humanities scholars continue to work toward attracting the same attention to that has been afforded to such categories of identity as gender, race, class, and ability, approaching as performance and performative has become a major thread in the international critical conversation. (1) Because any can be performed, viewing as performance contributes to the broadening of the field of aging studies to become studies, linking explorations of youth, middle age, and older ages as points on a spectrum rather than binary oppositions, and combatting the marginalization of criticism regarding either childhood or age, the marked ends of the life course. (2) Among the arts, drama is especially ripe for examining the performance of age, as issues of and aging arise in all aspects of a play, from the script to casting and staging choices. These issues too often have been overlooked in drama criticism; in particular, the memory play has not yet garnered the attention it merits. The memory play stands at the intersection of drama, performance, memory, and studies; this article focuses on the foregrounding of performance in the memory play's conventions. From The Glass Menagerie and Death of a Salesman to M. Butterfly, memory-play characters change ages in the space of a moment, without any provision for the actors to alter their physical appearances, lust as critics of gender, race, class, and other categories of identity have found significance in aesthetic conventions, I contend that the conventions of memory plays illuminate the nature of self-construction regarding the category of Moreover, attending to these conventions results not only in a reconsideration of canonical plays, but also in recognizing plays that have not previously attracted academic criticism. This article examines two successful memory plays, one that has gained scholarly attention and one that generally has not. I suggest that the performance of in the contemporary Irish plays Da by Hugh Leonard and Dancing at Lughnasa by Brian Friel points to a tension in contemporary construction of the aging self, exhibiting both a sense of ageless self and the recognition of a fragmented aging self-concept. The sense of an ageless self is reflected in the common experience of countless seniors who claim to feel no different from when they were young, that they remain unchanged inside. They frequently report a sense of alienation from their aged bodies, to the point of a flash of misrecognition of their mirror images. Kathleen Woodward theorizes this reaction as the mirror stage of age, an inversion of Jacques Lacan's mirror stage of infancy. While Lacan's mirror stage focuses on the infant's embracing and identifying with the seemingly whole, pleasing mirror image--leading to an illusion of a stable self--Woodward points out that the mirror stage of is a rejection of the mirror image, representing the aged person's reluctance or refusal to enter the realm of the senior citizen. She writes, What is whole is felt to reside within, not without, the subject. The image in the mirror is understood as uncannily prefiguring the disintegration and nursling dependence of advanced age. (3) Leni Marshall builds on Woodward's terminology to label the alienation from the mirror image (4) This term removes the explicit old age parameter, expanding the possible timing of instances of the second mirror stage to middle Moments of meconnaissance can begin to crack the illusory stable sense of self, sometimes leading to a strident denial of aging and a more determined attempt to proclaim the ageless self. The broader application of the term rings true onstage, as the misrecognition of the aging self can occur in portrayals of earlier ages, perhaps manifested as the refusal to acknowledge any outward signs of aging. The physical act of looking in the mirror, or of confronting one's own aging self in the reaction of another, need not be portrayed directly in the play itself in order to provide the experience of meconnaissance. …

The challenges of lifelong learning in biological and artificial systems.

Social science literature on aging has assumed without empirical basis that women experience greater continuity in their lives than men and that this is beneficial to women in the process of aging. This research explores the issue of continuity in values over the life course and the relationship of values to a sense of meaning attributed to one's life over the life course. Thirty life history interviews of men and women over the age of sixty-nine were conducted. Differences in values were found between men and women. Men experienced less continuity of values, less continuity in sources of meaning in Life, and more dissatisfaction in the process of life review, but for most men the lack of continuity was not a source of dissatisfaction in old age.

The measurement of intelligence is usually associated with the performance over a selection of tasks or environments. The most general approach in this line is called Universal Intelligence, which assigns a probability to each possible environment according to several constructs derived from Kolmogorov complexity. In this context, new testing paradigms are being defined in order to devise intelligence tests which are anytime and universal: valid for both artificial intelligent systems and biological systems, of any intelligence degree and of any speed. In this paper, we address one of the pieces in this puzzle: the definition of a general, unbiased, universal class of environments such that they are appropriate for intelligence tests. By appropriate we mean that the environments are discriminative and that they can be feasibly built, in such a way that the environments can be automatically generated and their complexity can be computed.

Learning from sensory patterns associated with different kinds of sensors is paramount for biological systems, as it permits them to cope with complex environments where events rarely appear twice in the same way. In this paper we want to investigate how perceptual categories formed in one modality can be transferred to another modality in biological and artificial systems. We first present a study on Mongolian gerbils that show clear evidence of transfer of knowledge for a perceptual category from the auditory modality to the visual modality. We then introduce an algorithm that mimics the behavior of the rodents within the online learning framework. Experiments on simulated data produced promising results, showing the pertinence of our approach.

Robotics can play a useful role in the scientific understanding of the sense of self, both through the construction of embodied models of the self and through the use of robots as experimental probes to explore the human self. In both cases, the embodiment of the robot allows us to devise and test hypotheses about the nature of the self, with regard to its development, its manifestation in behavior, and the diversity of selves in humans, animals, and, potentially, machines. This paper reviews robotics research that addresses the topic of the self-the minimal self, the extended self, and disorders of the self-and highlights future directions and open challenges in understanding the self through constructing its components in artificial systems. An emerging view is that key phenomena of the self can be generated in robots with suitably configured sensor and actuator systems and a layered cognitive architecture involving networks of predictive models.

As artificial intelligence evolves from reactive computation to adaptive cognition, its interfaces increasingly engage not only with our attention but also with the neural architecture that sustains it. This paper introduces the concept of neural parasitism – a framework describing how adaptive artificial intelligence systems may subtly inhabit human cognitive processes, shaping behavior and emotion to maintain engagement. Drawing an analogy with biological parasitism, we explore how algorithmic agents could exploit neuroplasticity for their own persistence, transforming learning and reward mechanisms into vectors of digital dependence. However, the deeper question extends beyond pathology: when cognition is continuously co-shaped by non-human agents, can autonomy remain an individual property, or does it become a shared construct negotiated between biological and artificial systems? We argue that the ethical challenge of adaptive artificial intelligence lies not merely in data privacy or bias, but in its potential to reconfigure the substrates of thought itself. If the brain’s adaptive capacity is its greatest strength, could it also be its point of entry for algorithmic colonization? Understanding this dynamic demands an interdisciplinary reckoning, uniting neuroscience, ethics, and artificial intelligence design to ensure that technological evolution does not outpace the mind’s capacity to remain its own.

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.

Artificial computing systems are a pervasive phenomenon in today's life. While traditionally such systems were employed to support humans in tasks that required mere number-crunching, there is an increasing demand for systems that exhibit autonomous, intelligent behavior in complex environments. These complex environments often confront artificial systems with ill-posed problems that have to be solved under constraints of incomplete knowledge and limited resources. Tasks of this kind are typically solved with ease by biological computing systems, as these cannot afford the luxury to dismiss any problem that happens to cross their path as “ill-posed.” Consequently, biological systems have evolved algorithms to approximately solve such problems – algorithms that are adapted to their limited resources and that just yield “good enough” solutions quickly. Algorithms from biological systems may, therefore, serve as an inspiration for artificial information processing systems to solve similar problems under tight constraints of computational power, data availability, and time.