This paper presents Integrated Information Theory of consciousness 3.0, which incorporates several advances over previous formulations. IIT starts from phenomenological axioms: information says that each experience is specific a sh it is what it is by how it differs from alternative experiences; integration says that it is unified a sh irreducible to non-interdependent components; exclusion says that it has unique borders and a particular spatio-temporal grain. These axioms are formalized into postulates that prescribe how physical mechanisms, such as neurons or (...) logic gates, must be configured to generate experience. The postulates are used to define intrinsic information as “differences that make a difference‘ within a system, and integrated information as information specified by a whole that cannot be reduced to that specified by its parts. By applying the postulates both at the level of individual mechanisms and at the level of systems of mechanisms, IIT arrives at an identity: an experience is a maximally irreducible conceptual structure, and the set of elements that generates it constitutes a complex. According to IIT, a MICS specifies the quality of an experience and integrated information Φ Max its quantity. From the theory follow several results, including: a system of mechanisms may condense into a major complex and non-overlapping minor complexes; the concepts that specify the quality of an experience are always about the complex itself and relate only indirectly to the external environment; anatomical connectivity influences complexes and associated MICS; a complex can generate a MICS even if its elements are inactive; simple systems can be minimally conscious; complicated systems can be unconscious; there can be true “zombies‘ a sh unconscious feed-forward systems that are functionally equivalent to conscious complexes. (shrink)

While life in general can be explained by the mechanisms of physics, chemistry and biology, to many scientists and philosophers it appears that when it comes to explaining consciousness, there is what the philosopher Joseph Levine called an “explanatory gap” between the physical brain and subjective experiences. Here we deduce the living and neural features behind primary consciousness within a naturalistic biological framework, identify which animal taxa have these features (the vertebrates, arthropods, and cephalopod molluscs), then reconstruct when consciousness first (...) evolved and consider its adaptive value. We theorize that consciousness is based on all the complex system features of life, plus the even more complex features of elaborate brains. We argue that the main reason why the explanatory gap between the brain and experience has been so refractory to scientific explanation is that it arises from both life and from varied and diverse brains and brain regions, so bridging the gap requires a complex, multifactorial account that includes the great diversity of consciousness, its personal nature that stems from embodied life, and the special neural features that make consciousness unique in nature. (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)

An overview and critical analysis of the study of consciousness, integrating findingsfrom philosophy, psychology, and cognitive neuroscience into a unified theoreticalframework.

A number of popular arguments for dualism start from a premise about an epistemic gap between physical truths about truths about consciousness, and infer an ontological gap between physical processes and consciousness. Arguments of this sort include the conceivability argument, the knowledge argument, the explanatory-gap argument, and the property dualism argument. Such arguments are often resisted on the grounds that epistemic premises do not entail ontological conclusion. My view is that one can legitimately infer ontological conclusions from epistemic premises, if (...) one is very careful about how one reasons. To do so, the best way is to reason first from epistemic premises to modal conclusions , and from there to ontological conclusions. Here, the crucial issue is the link between the epistemic and modal domains. How can one reason from theses about what is knowable or conceivable to theses about what is necessary or possible? To bridge the epistemic and modal domains, the framework of two-dimensional semantics can play a central role. I have used this framework in earlier work to mount an argument against materialism. Here, I want to revisit the argument, laying it out in a more explicit and careful form, and responding to a number of objections. In what follows I will concentrate mostly on the conceivability argument. I think that very similar considerations apply to the other arguments mentioned above, however. In the final section of the paper, I show how this analysis might yield a unified treatment of a number of anti-materialist arguments. (shrink)

Causal reductionism is the widespread assumption that there is no room for additional causes once we have accounted for all elementary mechanisms within a system. Due to its intuitive appeal, causal reductionism is prevalent in neuroscience: once all neurons have been caused to fire or not to fire, it seems that causally there is nothing left to be accounted for. Here, we argue that these reductionist intuitions are based on an implicit, unexamined notion of causation that conflates causation with prediction. (...) By means of a simple model organism, we demonstrate that causal reductionism cannot provide a complete and coherent account of ‘what caused what’. To that end, we outline an explicit, operational approach to analyzing causal structures. (shrink)

Uncovering the neural basis of consciousness is a major challenge to neuroscience. In this Perspective, Tononi and colleagues describe the integrated information theory of consciousness and how it might be used to answer outstanding questions about the nature of consciousness.

The goal of the paper is to review existing work on consciousness within the frameworks of Predictive Processing, Active Inference, and Free Energy Principle. The emphasis is put on the role played by the precision and complexity of the internal generative model. In the light of those proposals, these two properties appear to be the minimal necessary components for the emergence of conscious experience—a Minimal Unifying Model of consciousness.

The science of consciousness has made great strides by focusing on the behavioural and neuronal correlates of experience. However, while such correlates are important for progress to occur, they are not enough if we are to understand even basic facts, for example, why the cerebral cortex gives rise to consciousness but the cerebellum does not, though it has even more neurons and appears to be just as complicated. Moreover, correlates are of little help in many instances where we would like (...) to know if consciousness is present: patients with a few remaining islands of functioning cortex, preterm infants, non-mammalian species and machines that are rapidly outperforming people at driving, recognizing faces and objects, and answering difficult questions. To address these issues, we need not only more data but also a theory of consciousness a sh one that says what experience is and what type of physical systems can have it. Integrated information theory does so by starting from experience itself via five phenomenological axioms: intrinsic existence, composition, information, integration and exclusion. From these it derives five postulates about the properties required of physical mechanisms to support consciousness. The theory provides a principled account of both the quantity and the quality of an individual experience, and a calculus to evaluate whether or not a particular physical system is conscious and of what. Moreover, IIT can explain a range of clinical and laboratory findings, makes a number of testable predictions and extrapolates to a number of problematic conditions. The theory holds that consciousness is a fundamental property possessed by physical systems having specific causal properties. It predicts that consciousness is graded, is common among biological organisms and can occur in some very simple systems. Conversely, it predicts that feed-forward networks, even complex ones, are not conscious, nor are aggregates such as groups of individuals or heaps of sand. Also, in sharp contrast to widespread functionalist beliefs, IIT implies that digital computers, even if their behaviour were to be functionally equivalent to ours, and even if they were to run faithful simulations of the human brain, would experience next to nothing. (shrink)