In this paper, we take a meta-theoretical stance and aim to compare and assess two conceptual frameworks that endeavor to explain phenomenal experience. In particular, we compare Feinberg & Mallatt’s Neurobiological Naturalism (NN) and Tononi’s and colleagues' Integrated Information Theory (IIT), given that the former pointed out some similarities between the two theories (Feinberg & Mallatt 2016c-d). To probe their similarity, we first give a general introduction to both frameworks. Next, we expound a ground plan for carrying out our analysis. (...) We move on to articulate a philosophical profile of NN and IIT, addressing their ontological commitments and epistemological foundations. Finally, we compare the two point-by-point, also discussing how they stand on the issue of artificial consciousness. (shrink)

1. 1.1. Suffering is bad. This is why, ceteris paribus, there are strong moral reasons to prevent suffering. Moreover, typically, those moral reasons are stronger when the amount of suffering at st...

Illusionism about consciousness entails that phenomenal consciousness doesn’t exist. The distribution question concerns the distribution of consciousness in the animal kingdom. Skepticism of animal consciousness is the view that few or no kinds of animals possess consciousness. Thus, illusionism seems to imply a skeptical view on the distribution question. However, I argue that illusionism and skepticism of animal consciousness are actually orthogonal to each other. If illusionism is true, then phenomenal consciousness does not ground intrinsic value so that the non-existence (...) of phenomenal consciousness would not have ethical implications. In this case, the adequate reaction is to reformulate the distribution question in terms of the quasi-phenomenal functional features associated with the illusion of phenomenal consciousness which do ground intrinsic value. Hence, either illusionism is false or the distribution question does not concern phenomenal consciousness. In any case, illusionism does not directly tell us anything about the distribution question. However, since illusionism forces us to revise our traditional conception of consciousness, it indirectly affects research on animal consciousness by questioning that consciousness is a unified and an all-or-nothing property. (shrink)

In this paper we provide a philosophical analysis of the Hard Problem of consciousness and the implications of conceivability scenarios for current neuroscientific research. In particular, we focus on one of the most prominent neuroscientific theories of consciousness, integrated information theory (IIT). After a brief introduction on IIT, we present Chalmers’ original formulation and propose our own layered view of the hard problem, showing how 2 separate issues can be distinguished. More specifically, we argue that it’s possible to disentangle a (...) core problem of consciousness from a layered hard problem, the latter being essentially connected to Chalmers’ conceivability argument. We then assess the relation between the Hard Problem and IIT, showing how the theory resists conceivability scenarios, and how it is equipped to face up to the hard problem in its broadest acceptation. (shrink)

In this paper we provide a philosophical analysis of the Hard Problem of consciousness and the implications of conceivability scenarios for current neuroscientific research. In particular, we focus on one of the most prominent neuroscientific theories of consciousness, Integrated Information Theory. After a brief introduction on IIT, we present Chalmers’ original formulation and propose our own Layered View of the Hard Problem, showing how two separate issues can be distinguished. More specifically, we argue that it’s possible to disentangle a Core (...) Problem of Consciousness from a Layered Hard Problem, the latter being essentially connected to Chalmers’ conceivability argument. We then assess the relation between the Hard Problem and IIT, showing how the theory resists conceivability scenarios, and how it is equipped to face up to the hard problem in its broadest acceptation. (shrink)

In today’s society, it becomes increasingly important to assess which non-human and non-verbal beings possess consciousness. This review article aims to delineate criteria for consciousness especially in animals, while also taking into account intelligent artifacts. First, we circumscribe what we mean with “consciousness” and describe key features of subjective experience: qualitative richness, situatedness, intentionality and interpretation, integration and the combination of dynamic and stabilizing properties. We argue that consciousness has a biological function, which is to present the subject with a (...) multimodal, situational survey of the surrounding world and body, subserving complex decision-making and goal-directed behavior. This survey reflects the brain’s capacity for internal modeling of external events underlying changes in sensory state. Next, we follow an inside-out approach: how can the features of conscious experience, correlating to mechanisms inside the brain, be logically coupled to externally observable properties? Instead of proposing criteria that would each define a “hard” threshold for consciousness, we outline six indicators: goal-directed behavior and model-based learning; anatomic and physiological substrates for generating integrative multimodal representations; psychometrics and meta-cognition; episodic memory; susceptibility to illusions and multistable perception; and specific visuospatial behaviors. Rather than emphasizing a particular indicator as being decisive, we propose that the consistency amongst these indicators can serve to assess consciousness in particular species. The integration of scores on the various indicators yields an overall, graded criterion for consciousness, somewhat comparable to the Glasgow Coma Scale for unresponsive patients. When considering theoretically derived measures of consciousness, it is argued that their validity should not be assessed on the basis of a single quantifiable measure, but requires cross-examination across multiple pieces of evidence, including the indicators proposed here. Current intelligent machines, including deep learning neural networks and agile robots, are not indicated to be conscious yet. Instead of assessing machine consciousness by a brief Turing-type of test, evidence for it may gradually accumulate when we study machines ethologically and across time, considering multiple behaviors that require flexibility, improvisation, spontaneous problem-solving and the situational conspectus typically associated with conscious experience. (shrink)