‘Predictive Processing’ is an emerging paradigm in cognitive neuroscience that depicts the human mind as an uncertainty management system that constructs probabilistic predictions of sensory s...

In the last years, artificial intelligence (AI) safety gained international recognition in the light of heterogeneous safety-critical and ethical issues that risk overshadowing the broad beneficial impacts of AI. In this context, the implementation of AI observatory endeavors represents one key research direction. This paper motivates the need for an inherently _transdisciplinary_ AI observatory approach integrating diverse retrospective and counterfactual views. We delineate aims and limitations while providing hands-on-advice utilizing _concrete practical examples_. Distinguishing between unintentionally and intentionally triggered AI risks (...) with diverse socio-psycho-technological impacts, we exemplify a _retrospective descriptive analysis_ followed by a _retrospective counterfactual risk analysis_. Building on these AI observatory tools, we present near-term transdisciplinary guidelines for AI safety. As further contribution, we discuss _differentiated_ and tailored long-term directions through the lens of two disparate modern AI safety paradigms. For simplicity, we refer to these two different paradigms with the terms _artificial stupidity_ (AS) and _eternal creativity_ (EC) respectively. While both AS and EC acknowledge the need for a hybrid cognitive-affective approach to AI safety and overlap with regard to many short-term considerations, they differ fundamentally in the nature of multiple envisaged long-term solution patterns. By compiling relevant underlying _contradistinctions_, we aim to provide _future-oriented_ incentives for constructive dialectics in practical and theoretical AI safety research. (shrink)

In recent years, prevalent global societal issues related to fake news, fakery, misinformation, and disinformation were brought to the fore, leading to the construction of descriptive labels such as “post-truth” to refer to the supposedly new emerging era. Thereby, the (mis-)use of technologies such as AI and VR has been argued to potentially fuel this new loss of “ground-truth”, for instance, via the ethically relevant deepfakes phenomena and the creation of realistic fake worlds, presumably undermining experiential veracity. Indeed, _unethical_ and (...) malicious actors could harness tools at the intersection of AI and VR (AIVR) to craft what we call _immersive falsehood_, fake immersive reality landscapes deliberately constructed for malicious ends. This short paper analyzes the ethically relevant nature of the background against which such malicious designs in AIVR could exacerbate the intentional proliferation of deceptions and falsities. We offer a reappraisal expounding that while immersive falsehood could manipulate and severely jeopardize the inherently affective constructions of social reality and considerably complicate falsification processes, humans may neither inhabit a post-truth nor a post-falsification age. Finally, we provide incentives for future AIVR safety work, ideally contributing to a future era of _technology-augmented critical thinking_. (shrink)

We argue that the projective geometrical component of the Projective Consciousness Model can account for key aspects of pre-reflective self-consciousness and can relate PRSC intelligibly to another signal feature of subjectivity: perspectival character or point of view. We illustrate how the projective geometrical versions of the concepts of duality, reciprocity, polarity, closedness, closure, and unboundedness answer to salient aspects of the phenomenology of PRSC. We thus show that the same mathematics that accounts for the statics and dynamics of perspectival character (...) also accounts for PRSC. More generally, we argue that introducing higher-level geometrical concepts into the theory of PRSC, and into the theory of consciousness broadly, as the PCM does, promises to break longstanding theoretical impasses and dialectical stalemates. (shrink)

This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. Our approach can be described as _computational phenomenology_ because it applies methods originally developed in computational modelling to provide a formal model of the descriptions of lived experience in the phenomenological tradition of philosophy (e.g., the work of Edmund Husserl, Maurice Merleau-Ponty, etc.). The first section presents a brief review of the overall project to naturalize phenomenology. The second section presents and evaluates (...) philosophical objections to that project and situates our version of computational phenomenology with respect to these projects. The third section reviews the generative modelling framework. The final section presents our approach in detail. We conclude by discussing how our approach differs from previous attempts to use generative modelling to help understand consciousness. In summary, we describe a version of computational phenomenology which uses generative modelling to construct a computational model of the inferential or interpretive processes that best explain this or that kind of lived experience. (shrink)

In consciousness science, theories often differ not only in the account of consciousness they arrive at, but also with respect to how they understand their starting point. Some approaches begin with experimentally gathered data, whereas others begin with phenomenologically gathered data. In this paper, I analyse how the most influential phenomenology-first approach, namely the Integrated Information Theory (IIT) of consciousness, fits its phenomenologically gathered data with explanatory hypotheses. First, I show that experimentally driven approaches hit an explanatory roadblock, since we (...) cannot tell, at the present stage, which model of consciousness is best. Then, I show that IIT’s phenomenology-first approach implies a self-evidencing explanation according to which consciousness can be explained by starting from consciousness itself. I claim that IIT can take advantage of the virtuous circularity of this reasoning, but it also faces a data-fitting issue that is somehow similar to that faced by experiment-driven approaches: we are not given enough information to decide whether the explanatory hypotheses IIT employs to explain its phenomenological data are in fact best. I call this problem “the self-evidencing problem” for IIT, and after introducing it, I propose a possible way for IIT to solve it. (shrink)

Giulio Tononi's integrated information theory (IIT) proposes explaining consciousness by directly identifying it with integrated information. We examine the construct validity of IIT's measure of consciousness,phi(Φ), by analyzing its formal properties, its relation to key aspects of consciousness, and its co-variation with relevant empirical circumstances. Our analysis shows that IIT's identification of consciousness with the causal efficacy with which differentiated networks accomplish global information transfer (which is what Φ in fact measures) is mistaken. This misidentification has the consequence of requiring (...) the attribution of consciousness to a range of natural systems and artifacts that include, but are not limited to, large-scale electrical power grids, gene-regulation networks, some electronic circuit boards, and social networks. Instead of treating this consequence of the theory as a disconfirmation, IIT embraces it. By regarding these systems as bearers of consciousnessex hypothesi, IIT is led toward the orbit of panpsychist ideation. This departure from science as we know it can be avoided by recognizing the functional misattribution at the heart of IIT's identity claim. We show, for example, what function is actually performed, at least in the human case, by the cortical combination of differentiation with integration that IIT identifies with consciousness. Finally, we examine what lessons may be drawn from IIT's failure to provide a credible account of consciousness for progress in the very active field of research concerned with exploring the phenomenon from formal and neural points of view. (shrink)

The scientific study of consciousness or subjective experiencing is a rapidly expanding research program engaging philosophers of mind, psychologists, cognitive scientists, neurobiologists, evolutionary biologists and biosemioticians. Here we outline an evolutionary approach that we have developed over the last two decades, focusing on the evolutionary transition from non-conscious to minimally conscious, subjectively experiencing organisms. We propose that the evolution of subjective experiencing was driven by the evolution of learning and we identify an open-ended, representational, generative and recursive form of associative (...) learning, which we call Unlimited Associative Learning (UAL), as an evolutionary transition marker of minimal consciousness. This evolutionary marker provides evidence that the evolutionary transition to consciousness has gone to completion and allows reverse-engineering from this learning capacity to the system that enables it – making possible the construction of a toy model of UAL. The model allows us to identify some of the key processes and structures that constitute minimal consciousness, points its taxonomic distribution and the ecological context in which it first emerged, highlights its function and suggests a framework for exploring developmental and evolutionary modifications of consciousness. We point to ways of experimentally testing the relationship between UAL and consciousness in human and in non-human animals and discuss the theoretical and ethical implications of our approach. The framework we offer allows the exploration of the evolutionary changes in agency, value systems, selective processes and goals that were involved in the transition to subjective experiencing from a perspective that resonates with the approaches of bio-semioticians. (shrink)

Self-evidencing describes the purported predictive processing of all self-organising systems, whether conscious or not. Self-evidencing in itself is therefore not sufficient for consciousness. Different systems may however be capable of self-evidencing in different, specific and distinct ways. Some of these ways of self-evidencing can be matched up with, and explain, several properties of consciousness. This carves out a distinction in nature between those systems that are conscious, as described by these properties, and those that are not. This approach throws new (...) light on phenomenology, and suggests that some self-evidencing may be characteristic of consciousness. (shrink)

Several influential philosophers and scientists have advanced a framework, often called Neo-Cartesianism (NC), according to which animal suffering is merely apparent. Drawing upon contemporary neuroscience and philosophy of mind, Neo-Cartesians challenge the mainstream position we shall call Evolutionary Continuity (EC), the view that humans are on a nonhierarchical continuum with other species and are thus not likely to be unique in consciously experiencing negative pain affect. We argue that some Neo-Cartesians have misconstrued the underlying science or tendentiously appropriated controversial views (...) in the philosophy of mind. We discuss recent evidence that undermines the simple neuroanatomical structure-function correlation thesis that undergirds many Neo-Cartesian arguments, has an important bearing on the recent controversy over pain in fish, and places the underlying epistemology framing the debate between NC and EC in a new light that strengthens the EC position. (shrink)

The primary determinant of the self (S) is the conscious experience (CE) we have of it. Therefore, it does not come as a surprise that empirical research on S mainly resorts to the CE (or lack of CE) that subjects have of their S. What comes as a surprise is that empirical research on S does not tackle the problem of how CE contributes to building S. Empirical research investigates how S either biases the cognitive processing of stimuli or is (...) altered through a wide range of means (meditation, hypnosis, etc.). In either case, even for different reasons, considerations of how CE contributes to building S are left unspecified in empirical research. This article analyzes these reasons and proposes a theoretical model of how CE contributes to building S. According to the proposed model, the phenomenal aspect of consciousness is produced by the modulation—engendered by attentional activity—of the energy level of the neural substrate (that is, the organ of attention) that underpins attentional activity. The phenomenal aspect of consciousness supplies the agent with a sense of S and informs the agent on how its S is affected by the agent’s own operations. The phenomenal aspect of consciousness performs its functions through its five main dimensions: qualitative, quantitative, hedonic, temporal, and spatial. Each dimension of the phenomenal aspect of consciousness can be explained by a specific aspect of the modulation of the energy level of the organ of attention. Among other advantages, the model explains the various forms of S as outcomes resulting from the operations of a single mechanism and provides a unifying framework for empirical research on the neural underpinnings of S. (shrink)