arXiv subjects

Giulio Tononi

Publications and source records attributed to Giulio Tononi.

At least 19 recordsLinked to original sources

arXiv2026

Can extrinsic methods reveal intrinsic structure? Complementing IIT with QStr

Integrated Information Theory (IIT) proposes that all quality is structure: the quality of every experience can be characterized as a phenomenal structure and accounted for as a causal structure. The IIT method can be called intrinsic in that, first, it starts by characterizing the intrinsic structure of a single experience in an absolute sense (not relative to other experiences) and, second, it preserves this intrinsic perspective when accounting for experience in physical terms. By contrast, the method of the Qualia Structure paradigm (QStr) can be called extrinsic in that its main tool is relational characterization, used to investigate both the quality of experience and the corresponding neural or information structures. This chapter gives conceptual clarity to IIT's intrinsic method, assesses the theoretical compatibility between QStr and IIT, and sketches concrete ways QStr can complement IIT. We argue that QStr and IIT share experience and its structure as their explanandum, while their methodological starting points and explanans are distinct yet complementary. QStr can bolster IIT's current research program by offering novel ways to approach narrow qualia (e.g., color), which are notoriously difficult to decompose through introspection. QStr can also supply IIT with new mathematical tools-for example, from category theory and metric geometry. In turn, IIT may help ground QStr's relational, extrinsic structures in absolute, intrinsic structures.

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arXiv2026

The Rosetta Stone and Levels of Principled Inference to the Experience of Another Mind

The classical problem of Other Minds has dogged philosophers for millennia; asking if we have any way to truly understand the experience of another mind. We know our own intrinsic experience by acquaintance, but can only ever hope to possess an extrinsic description of another's, with the two separated by an acquaintance gap. Structural approaches aim to characterise experience in terms of a mathematical structure, and promise a 'Rosetta Stone'; that is, a principled method to translate the contents of experience into a mathematical structure. In this chapter, we examine two such approaches - the Qualia Structure Paradigm (Qstr) and Integrated Information Theory (IIT) - to ask what, if anything, they allow us to infer about another mind should they possess the Rosetta Stone they seek. Qstr proceeds inter-phenomenally; aiming to exhaustively characterise an experience by its relations to all other experiences and providing a necessary condition on the sameness of experience. IIT proceeds intra-phenomenally; conjecturing that a single experience is accounted for by the cause-effect structure unfolded from a substrate in a state, an explanatory identity that is both necessary and sufficient for the sameness of experience. While neither approach can cross the acquaintance gap to another's mind, they provide a common formal medium through which minds may be compared and in turn reduce the size of this gap. We identify the strength of inference with levels of structural correspondence in Category Theory. These descend from isomorphism through strong and weak adjunction, to a principled limit where inference runs out. We conclude that these structural approaches and the existence of their respective Rosetta Stones would not solve the problem of Other Minds, but instead provide a system of principled constraints on what we may infer, which is far more than what has been justifiable before.

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arXiv2026

Consciousness as Intrinsic Structure: Towards a Chemistry of Experience

To be conscious is to have an experience - not a collection of phenomenal atoms, but a structured whole composed of distinctions and the relations that bind them. Integrated Information Theory (IIT) identifies the essential properties of every experience (axioms), formulates them operationally as postulates that a substrate must satisfy, and unfolds the cause-effect power of the resulting complex into a $\Phi$-structure. Accounting for a content of experience is then a matter of identifying the phenomenal distinctions and relations that compose it, and showing them reflected one-to-one in the causal distinctions and relations of the corresponding $\Phi$-structure. We apply that method to three pervasive contents whose structure is partly open to introspection. Spatial extendedness is composed of spots, whose elemental signature is reflexivity, bound by reflexive inclusion, connection, and fusion; temporal flow is composed of moments, whose signature is directedness, bound by directed inclusion, connection, and fusion; objects bind a particular configuration of features to a general concept through relations bearing the signature of hierarchy. The endeavor is akin to chemistry, which accounts for an endless variety of compounds from a limited set of elements and the ways they bond. We assess these accounts against seven criteria of a good explanation - scope, synthesis, specificity, self-consistency, system consistency, simplicity, and scientific validation - and sketch the prediction that follows: altering the structure specified by a substrate should alter the corresponding content, even when activity and behavior are held comparable. The same method may reach narrow qualia, which resist introspection, and the compound contents that bind many qualia together, though there it remains a proof of concept and an open research program.

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arXiv2025

Integrated Information Theory: A Consciousness-First Approach to What Exists

This overview of integrated information theory (IIT) emphasizes IIT's "consciousness-first" approach to what exists. Consciousness demonstrates to each of us that something exists--experience--and reveals its essential properties--the axioms of phenomenal existence. IIT formulates these properties operationally, yielding the postulates of physical existence. To exist intrinsically or absolutely, an entity must have cause-effect power upon itself, in a specific, unitary, definite and structured manner. IIT's explanatory identity claims that an entity's cause-effect structure accounts for all properties of an experience--essential and accidental--with no additional ingredients. These include the feeling of spatial extendedness, temporal flow, of objects binding general concepts with particular configurations of features, and of qualia such as colors and sounds. IIT's intrinsic ontology has implications for understanding meaning, perception, and free will, for assessing consciousness in patients, infants, other species, and artifacts, and for reassessing our place in nature.

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arXiv2025

Intrinsic cause-effect power: the tradeoff between differentiation and specification

Integrated information theory (IIT) starts from the existence of consciousness and characterizes its essential properties: every experience is intrinsic, specific, unitary, definite, and structured. IIT then formulates existence and its essential properties operationally in terms of cause-effect power of a substrate of units. Here we address IIT's operational requirements for existence by considering that, to have cause-effect power, to have it intrinsically, and to have it specifically, substrate units in their actual state must both (i) ensure the intrinsic availability of a repertoire of cause-effect states, and (ii) increase the probability of a specific cause-effect state. We showed previously that requirement (ii) can be assessed by the intrinsic difference of a state's probability from maximal differentiation. Here we show that requirement (i) can be assessed by the intrinsic difference from maximal specification. These points and their consequences for integrated information are illustrated using simple systems of micro units. When applied to macro units and systems of macro units such as neural systems, a tradeoff between differentiation and specification is a necessary condition for intrinsic existence, i.e., for consciousness.

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arXiv2025

Integrated information and predictive processing theories of consciousness: An adversarial collaborative review

As neuroscientific theories of consciousness continue to proliferate, the need to assess their similarities and differences - as well as their predictive and explanatory power - becomes ever more pressing. Recently, a number of structured adversarial collaborations have been devised to test the competing predictions of several candidate theories of consciousness. In this review, we compare and contrast three theories being investigated in one such adversarial collaboration: Integrated Information Theory, Neurorepresentationalism, and Active Inference. We begin by presenting the core claims of each theory, before comparing them in terms of the phenomena they seek to explain, the sorts of explanations they avail, and the methodological strategies they endorse. We then consider some of the inherent challenges of theory-testing, and how adversarial collaboration addresses some of these difficulties. The stage is then set for the empirical work to come: first, we outline the key hypotheses to be tested across a series of multi-site experiments; second, we discuss the kinds of observations that would support or challenge each theory; third, we consider how these theories might assimilate or accommodate such observations. Finally, we show how data harvested across disparate experiments (and their replicates) may be formally integrated to provide a quantitative measure of the evidential support accrued under each theory. Besides orienting the reader to the theoretical foundations of our collaboration, this review aims to provide valuable meta-scientific insights into the mechanics of adversarial collaboration and theory-testing in general - including the way theories may be evaluated in terms of the scientific progress they deliver.

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arXiv2024

Intrinsic meaning, perception, and matching

Integrated information theory (IIT) argues that the substrate of consciousness is a maximally irreducible complex of units. Together, subsets of the complex specify a cause-effect structure, composed of distinctions and their relations, which accounts in full for the quality of experience. The feeling of a specific experience is also its meaning for the subject, which is thus defined intrinsically, regardless of whether the experience occurs in a dream or is triggered by processes in the environment. Here we extend IIT's framework to characterize the relationship between intrinsic meaning, extrinsic stimuli, and causal processes in the environment, illustrated using a simple model of a sensory hierarchy. We argue that perception should be considered as a structured interpretation, where a stimulus from the environment acts merely as a trigger for the complex's state and the structure is provided by the complex's intrinsic connectivity. We also propose that perceptual differentiation - the richness and diversity of structures triggered by representative sequences of stimuli - quantifies the meaningfulness of different environments to a complex. In adaptive systems, this reflects the "matching" between intrinsic meanings and causal processes in an environment.

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arXiv2024

Shannon information and integrated information: message and meaning

Information theory, introduced by Shannon, has been extremely successful and influential as a mathematical theory of communication. Shannon's notion of information does not consider the meaning of the messages being communicated but only their probability. Even so, computational approaches regularly appeal to "information processing" to study how meaning is encoded and decoded in natural and artificial systems. Here, we contrast Shannon information theory with integrated information theory (IIT), which was developed to account for the presence and properties of consciousness. IIT considers meaning as integrated information and characterizes it as a structure, rather than as a message or code. In principle, IIT's axioms and postulates allow one to "unfold" a cause-effect structure from a substrate in a state, a structure that fully defines the intrinsic meaning of an experience and its contents. It follows that, for the communication of meaning, the cause-effect structures of sender and receiver must be similar.

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arXiv2024

Dissociating Artificial Intelligence from Artificial Consciousness

Developments in machine learning and computing power suggest that artificial general intelligence is within reach. This raises the question of artificial consciousness: if a computer were to be functionally equivalent to a human, being able to do all we do, would it experience sights, sounds, and thoughts, as we do when we are conscious? Answering this question in a principled manner can only be done on the basis of a theory of consciousness that is grounded in phenomenology and that states the necessary and sufficient conditions for any system, evolved or engineered, to support subjective experience. Here we employ Integrated Information Theory (IIT), which provides principled tools to determine whether a system is conscious, to what degree, and the content of its experience. We consider pairs of systems constituted of simple Boolean units, one of which -- a basic stored-program computer -- simulates the other with full functional equivalence. By applying the principles of IIT, we demonstrate that (i) two systems can be functionally equivalent without being phenomenally equivalent, and (ii) that this conclusion is not dependent on the simulated system's function. We further demonstrate that, according to IIT, it is possible for a digital computer to simulate our behavior, possibly even by simulating the neurons in our brain, without replicating our experience. This contrasts sharply with computational functionalism, the thesis that performing computations of the right kind is necessary and sufficient for consciousness.

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arXiv2024

Why does time feel the way it does? Towards a principled account of temporal experience

Time flows, or at least the time of our experience does. Can we provide an objective account of why experience, confined to the short window of the conscious present, encompasses a succession of moments that slip away from now to then--an account of why time feels flowing? Integrated Information Theory (IIT) aims to account for both the presence and quality of consciousness in objective, physical terms. Given a substrate's architecture and current state, the formalism of IIT allows one to unfold the cause-effect power of the substrate, yielding a cause-effect structure. According to IIT, this accounts in full for the presence and quality of experience, without any additional ingredients. In previous work, we showed how unfolding the cause-effect structure of non-directed grids, like those found in many posterior cortical areas, can account for the way space feels--namely, extended. Here we show that unfolding the cause-effect structure of directed grids can account for how time feels--namely, flowing. First, we argue that the conscious present is experienced as flowing because it is composed of phenomenal distinctions (moments) that are directed, and these distinctions are related in a way that satisfies directed inclusion, connection, and fusion. We then show that directed grids, which we conjecture constitute the substrate of temporal experience, yield a cause-effect structure that accounts for these and other properties of temporal experience. In this account, the experienced present does not correspond to a process unrolling in "clock time," but to a cause-effect structure specified by a system in its current state: time is a structure, not a process. We conclude by outlining similarities and differences between the experience of time and space, and some implications for the neuroscience, psychophysics, and philosophy of time.

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arXiv2023

Upper bounds for integrated information

Originally developed as a theory of consciousness, integrated information theory provides a mathematical framework to quantify the causal irreducibility of systems and subsets of units in the system. Specifically, mechanism integrated information quantifies how much of the causal powers of a subset of units in a state, also referred to as a mechanism, cannot be accounted for by its parts. If the causal powers of the mechanism can be fully explained by its parts, it is reducible and its integrated information is zero. Here, we study the upper bound of this measure and how it is achieved. We study mechanisms in isolation, groups of mechanisms, and groups of causal relations among mechanisms. We put forward new theoretical results that show mechanisms that share parts with each other cannot all achieve their maximum. We also introduce techniques to design systems that can maximize the integrated information of a subset of their mechanisms or relations. Our results can potentially be used to exploit the symmetries and constraints to reduce the computations significantly and to compare different connectivity profiles in terms of their maximal achievable integrated information.

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arXiv2022

Integrated information theory (IIT) 4.0: Formulating the properties of phenomenal existence in physical terms

This paper presents Integrated Information Theory (IIT) 4.0. IIT aims to account for the properties of experience in physical (operational) terms. It identifies the essential properties of experience (axioms), infers the necessary and sufficient properties that its substrate must satisfy (postulates), and expresses them in mathematical terms. In principle, the postulates can be applied to any system of units in a state to determine whether it is conscious, to what degree, and in what way. IIT offers a parsimonious explanation of empirical evidence, makes testable predictions, and permits inferences and extrapolations. IIT 4.0 incorporates several developments of the past ten years, including a more accurate translation of axioms into postulates and mathematical expressions, the introduction of a unique measure of intrinsic information that is consistent with the postulates, and an explicit assessment of causal relations. By fully unfolding a system's irreducible cause-effect power, the distinctions and relations specified by a substrate can account for the quality of experience.

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arXiv2022

System Integrated Information

Integrated information theory (IIT) starts from consciousness itself and identifies a set of properties (axioms) that are true of every conceivable experience. The axioms are translated into a set of postulates about the substrate of consciousness (called a complex), which are then used to formulate a mathematical framework for assessing both the quality and quantity of experience. The explanatory identity proposed by IIT is that an experience is identical to the cause-effect structure unfolded from a maximally irreducible substrate (a $\Phi$-structure). In this work we introduce a definition for the integrated information of a system ($\varphi_s$) that is based on the existence, intrinsicality, information, and integration postulates of IIT. We explore how notions of determinism, degeneracy, and fault lines in the connectivity impact system integrated information. We then demonstrate how the proposed measure identifies complexes as systems whose $\varphi_s$ is greater than the $\varphi_s$ of any overlapping candidate systems.

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arXiv2022

Only what exists can cause: An intrinsic powers view of free will

This essay addresses the implications of integrated information theory (IIT) for free will. IIT is a theory of what consciousness is and of how its presence and quality can be accounted for in physical terms. According to IIT, the presence of consciousness is accounted for by a maximum of cause-effect power in the brain. Moreover, the way an experience feels is accounted for by how that cause-effect power is structured. If IIT is right, we do have free will in a genuine sense: we have alternatives, reasons, and values, we make decisions, and we-not our neurons or atoms-are the cause of our willed actions and bear responsibility for them. IIT's argument for genuine free will hinges on the proper understanding of consciousness as intrinsic existence, captured by its intrinsic powers ontology: what exists absolutely, in physical terms, are intrinsic entities, and only what exists can cause.

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arXiv2021

What we are is more than what we do

If we take the subjective character of consciousness seriously, consciousness becomes a matter of "being" rather than "doing". Because "doing" can be dissociated from "being", functional criteria alone are insufficient to decide whether a system possesses the necessary requirements for being a physical substrate of consciousness. The dissociation between "being" and "doing" is most salient in artificial general intelligence, which may soon replicate any human capacity: computers can perform complex functions (in the limit resembling human behavior) in the absence of consciousness. Complex behavior becomes meaningless if it is not performed by a conscious being.

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arXiv2020

A macro agent and its actions

In science, macro level descriptions of the causal interactions within complex, dynamical systems are typically deemed convenient, but ultimately reducible to a complete causal account of the underlying micro constituents. Yet, such a reductionist perspective is hard to square with several issues related to autonomy and agency: (1) agents require (causal) borders that separate them from the environment, (2) at least in a biological context, agents are associated with macroscopic systems, and (3) agents are supposed to act upon their environment. Integrated information theory (IIT) (Oizumi et al., 2014) offers a quantitative account of causation based on a set of causal principles, including notions such as causal specificity, composition, and irreducibility, that challenges the reductionist perspective in multiple ways. First, the IIT formalism provides a complete account of a system's causal structure, including irreducible higher-order mechanisms constituted of multiple system elements. Second, a system's amount of integrated information ($Φ$) measures the causal constraints a system exerts onto itself and can peak at a macro level of description (Hoel et al., 2016; Marshall et al., 2018). Finally, the causal principles of IIT can also be employed to identify and quantify the actual causes of events ("what caused what"), such as an agent's actions (Albantakis et al., 2019). Here, we demonstrate this framework by example of a simulated agent, equipped with a small neural network, that forms a maximum of $Φ$ at a macro scale.

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arXiv2019

When is an action caused from within? Quantifying the causal chain leading to actions in simulated agents

An agent's actions can be influenced by external factors through the inputs it receives from the environment, as well as internal factors, such as memories or intrinsic preferences. The extent to which an agent's actions are "caused from within", as opposed to being externally driven, should depend on its sensor capacity as well as environmental demands for memory and context-dependent behavior. Here, we test this hypothesis using simulated agents ("animats"), equipped with small adaptive Markov Brains (MB) that evolve to solve a perceptual-categorization task under conditions varied with regards to the agents' sensor capacity and task difficulty. Using a novel formalism developed to identify and quantify the actual causes of occurrences ("what caused what?") in complex networks, we evaluate the direct causes of the animats' actions. In addition, we extend this framework to trace the causal chain ("causes of causes") leading to an animat's actions back in time, and compare the obtained spatio-temporal causal history across task conditions. We found that measures quantifying the extent to which an animat's actions are caused by internal factors (as opposed to being driven by the environment through its sensors) varied consistently with defining aspects of the task conditions they evolved to thrive in.

q-bio.QM↗

arXiv2019

Black-boxing and cause-effect power

Reductionism assumes that causation in the physical world occurs at the micro level, excluding the emergence of macro-level causation. We challenge this reductionist assumption by employing a principled, well-defined measure of intrinsic cause-effect power - integrated information (Φ), and showing that, according to this measure, it is possible for a macro level to "beat" the micro level. Simple systems were evaluated for Φ across different spatial and temporal scales by systematically considering all possible black boxes. These are macro elements that consist of one or more micro elements over one or more micro updates. Cause-effect power was evaluated based on the inputs and outputs of the black boxes, ignoring the internal micro elements that support their input-output function. We show how black-box elements can have more common inputs and outputs than the corresponding micro elements, revealing the emergence of high-order mechanisms and joint constraints that are not apparent at the micro level. As a consequence, a macro, black-box system can have higher Φ than its micro constituents by having more mechanisms (higher composition) that are more interconnected (higher integration). We also show that, for a given micro system, one can identify local maxima of Φ across several spatiotemporal scales. The framework is demonstrated on a simple biological system, the Boolean network model of the fission-yeast cell-cycle, for which we identify stable local maxima during the course of its simulated biological function. These local maxima correspond to macro levels of organization at which emergent cause-effect properties of physical systems come into focus, and provide a natural vantage point for scientific inquiries.

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