How can physical systems generate subjective experiences? Can mental states function as causes, or are we mere automata? These perennial questions may finally be answerable with two unifying frameworks for understanding complex systems and minds: FEP-AI and IIT. These two meta-theoretical frameworks were developed in the context of understanding psychological and neurobiological phenomena, yet their implications are far more extensive. FEP-AI may be the first unified formalism and paradigm for the mind and life sciences, and IIT is one of the most widely known and technically detailed models of consciousness and informational synergy. FEP-AI describes what systems must be like in order to persist, and IIT describes what it means for systems to intrinsically exist as systems. Both FEP-AI and IIT constitute general systems theories with scopes transcending disciplinary boundaries, having relevance not only for the philosophy and science of mind but also for understanding all emergent complexity.

Here, I describe how these two frameworks complement each other as unified systems theories, and also show how FEP-AI allows IIT and GNWT to be combined into a synthetic framework for understanding consciousness: IWMT. This synthesis further attempts to characterize the nature of mental causation in terms of generalized Darwinism (Campbell, 2016) and thermodynamic work cycles, thus describing how conscious agency may be essential for understanding how flexible intelligence may be realized in biological (and potentially artificial) systems. Toward this end, I attempt to address consciousness and autonomy on functional, algorithmic, and implementational levels of analysis (Marr, 1983). Finally, I discuss implications of theories of consciousness for the enduring problems of artificial intelligence.

How could there be “something that it is like” to be a physical system (Nagel, 1974; Lycan, 1996)? In introducing the Hard problem, Chalmers (1997) contrasted this question with the “easy problem” of understanding how biological processes contribute to different psychological phenomena. Proponents of the Hard problem argue that we could have a complete cognitive science, and yet still not understand consciousness. Could cognition take place “in the dark” without generating any subjective experiences, or qualia? Could such philosophical zombies perform all the computations enabled by brains, yet lack subjectivity?

Intellectual positions on these matters range from the more inflationary claim that consciousness is a fundamental aspect of the universe, to the more deflationary claim that the Hard problem will be (dis-)solved by answering the easy problems of cognitive science (Dennett, 2018), with no “explanatory gap” remaining. Others have suggested that these metaphysical questions distract from the more productive endeavor of studying why particular experiences are associated with particular physical processes: i.e., the “real problem” of consciousness (Seth, 2016). Even disagreement about the generation of the Hard problem has become a topic of philosophical inquiry and has been named the “meta-problem” (Chalmers, 2018).

While numerous models have been suggested, none are generally considered to have solved the enduring problems of consciousness. Such a solution would require explanation spanning implementational, algorithmic, and functional levels of analysis, with rich connections to experience. Here, I suggest that this multi-level understanding can be obtained by using FEP-AI to ground and combine leading models of consciousness into a unified framework centered on integrated world modeling (IWMT). This article focuses on IIT and GNWT, and in forthcoming work, I will extend this synthesis to additional models—e.g., Higher-Order Thought theories (Brown et al., 2019; Graziano, 2019; Shea and Frith, 2019)—each of which emphasizes different aspects of the nature(s) of consciousness.

Yet another enduring problem can be found in that there is no clearly agreed upon definition of consciousness. Some theories focus on consciousness as phenomenal experience. Others emphasize consciousness as awareness of knowledge, or “access” (Block, 2008). IWMT's primary focus is explaining means by which biological systems may generate phenomenality, or experience as a subjective point of view (Williford et al., 2018; Feiten, 2020). However, IWMT suggests that a variety of higher-order and meta-cognitive capacities may be required in order to obtain coherent subjectivity—although not necessarily involving either access or explicit self-consciousness (Milliere and Metzinger, 2020)—and thereby an experienced world. More specifically, IWMT's primary claims are as follows:

In what follows, I attempt to justify these claims by integrating across leading theories of emergent causation and consciousness. This approach draws on the explanatory breadth and embodied cybernetic grounding of the FEP-AI, the focus on irreducible integrative complexity provided by IIT, and the functional and mechanistic details provided by GNWT. IWMT tries to make inroads into the enduring problems of consciousness by synergistically combining the relative strengths (and diverse perspectives) of these theories (Table 1).

This section provides an introduction to FEP-AI, IIT, and GNWT, as well as an initial account of how they may be combined within IWMT. Further details regarding FEP-AI and IIT are explored in subsequent sections, followed by a further integration with GNWT.

According to the 2nd law, systems should exhibit increasing disorder until they cease to exist. Yet some things do manage to (temporarily) persist, and so something about their configurations must organize environmental exchanges to avoid entropic accumulation (Schrodinger, 1944; Brillouin, 1951; Deacon, 2011; Ramstead et al., 2018). Persisting systems somehow generate dynamics that steer away from the maximally probable outcome of maximal disorder. In cybernetics and control theory, the requirements for such governing processes are expressed as the good regulator theorem and law of requisite variety: any effective controller must be able to (at least implicitly) model that system, and regulating models require sufficient complexity to represent the variety of states likely to be encountered (Conant and Ashby, 1970).

FEP-AI (Friston, 2019) views persisting systems as entailing generative models for the preconditions by which they persist. For a system to constitute a model, its composition must be able to either compress or predict information for that which is modeled. Persisting systems specifically generate mutual (probabilistic) information between past and future states based on their present compositions. These mappings between particular configurations and ensuing dynamics constitute likelihoods (as particular action tendencies), thus characterizing system compositions as generative models, which generate dynamics that maximize the probability of those particular compositions. If it were not the case that system configurations generate dynamics that maintain those configurations, then no persisting systems would exist. Thus, persisting systems can be viewed as generative models that generate evidence for themselves through their dynamics, and so engage in “self-evidencing” (Hohwy, 2016).

In this way, FEP-AI provides a formalization and generalization of autopoietic self-making as described by Maturana and Varela (1980):

To the degree systems persist, they possess attracting sets that define them as particular phase space densities—whose action constitutes trajectories through state space—with varying probabilities of occurrence. In autopoiesis, attractor dynamics produce the very mechanisms out of which they are generated. FEP-AI views these autopoietic attractor configurations and ensuing trajectories as self-predicting generative models (Palacios et al., 2020), where that which is generated is the very probabilistic densities that define the existence of particular systems.

FEP-AI goes on to quantify self-model evidence according to an information-theoretic functional of variational (or approximate) free energy (Dayan et al., 1995). Derived from statistical physics, this singular objective function is optimized by minimizing discrepancies between probabilistic beliefs and observations (i.e., prediction-error, or “surprisal”), penalized by model complexity. To the extent systems persist, they constitute existence proofs (Friston, 2018) that they were able to bound surprise (i.e., high-entropy configurations) relative to predictive models by which they perpetuate themselves. Systems must respond adaptively to a variety of situations in order to avoid entropy-increasing events, and so must entail models with sufficient complexity to predict likely outcomes, thus minimizing discrepancies between expectations and observations. However, these models must not have so much complexity that they waste energy or over-fit observations and fail to generalize their predictions (also, more complex models are more energetically costly to implement). Variational free energy provides an objective function that optimally balances these requirements for accuracy and simplicity.

The extreme generality of FEP-AI requires emphasis. Not only do nervous systems entail predictive models, but so do entire populations of organisms and their extended phenotypes (Dawkins, 1999) as teleonomical (Deacon, 2011; Dennett, 2017) predictions with respect to evolutionary fitness (Friston, 2018; Ramstead et al., 2018). By this account, nervous systems are merely a (very) special case of generative modeling, where all systems are models in their very existence, but where some systems also have sub-models that function as cybernetic controllers (Stepp and Turvey, 2010; Seth, 2015; Seth and Tsakiris, 2018). In these ways, FEP-AI provides a formalism where persisting dynamical systems can be understood as self-generating models, grounded in first principles regarding the necessary preconditions for existence in a world governed by the 2nd law.

This view of systems as self-predicting generative models has clear correspondences with IIT, since self-evidencing depends on capacity for generating self-cause–effect power. I suggest we should further expect model-evidence for system preservation to be related to a system's ability to function as a unified whole, and so integrated information maximization ought to accompany free energy minimization. Notably, IIT-based models of metabolic cycles and gene-regulatory networks—core processes for homeostasis and autopoiesis—suggest that adaptive capacities of biotic systems may require high-phi configurations (Marshall et al., 2017; Abrego and Zaikin, 2019). Systems with lower phi may be qualitatively different from systems with higher phi (Albantakis, 2017; Albantakis and Tononi, 2019), being less capable of state-dependent adaptation—and thereby learning—which may drastically limit their intelligence and agency. These IIT-informed studies are fully consistent with FEP-AI, wherein all persisting systems minimize free energy, but only evolved systems minimize expected free energy via generative models where causes can be modeled with temporal depth and counterfactual richness (Kirchhoff et al., 2018).

This formalization of autopoietic systems can also be derived with graphical modeling concepts, providing further bridges between FEP-AI and IIT. Graphical models represent systems as structured relationships among component variables and their connections. If these connected variables are associated with probabilities—whether due to uncertain observations or inherent stochasticity—then that representation is a probabilistic graphical model (PGM) (Koller and Friedman, 2009). PGMs specify probability distributions over variables, thus entailing probabilistic models of that which is represented. This mapping from connected graphs to probabilities allows PGMs to synergistically combine information from multiple sources. Integration into joint probability distributions affords inference of both likely beliefs from observations (i.e., discriminative models) and likely observations from beliefs (i.e., generative models). With importance for subsequent discussions of consciousness, these graphs not only enable the generation of probabilistic world models (i.e., inference) and refinements of these models with observations (i.e., learning), but PGMs also afford discrete estimates of the most likely values for variable combinations, as in maximum a posteriori (MAP) estimation.

For any PGM component, the set of surrounding nodes is referred to as a Markov blanket (MB) (Pearl, 1988), which establishes conditional independence between internal and external variables. All paths connecting internal and external states are mediated by MBs; thus, conditioning upon this blanketing set integrates all mutual information across this partition (i.e., marginalization). System MBs define epistemic relationships with the external world in providing the only source of information that internal states ever receive (Hohwy, 2017). Everything beyond MB boundaries is not directly observable, and so latent values of external states must be inferred.

Described as PGMs, the functional boundaries of systems are MBs (Kirchhoff et al., 2018), mediating all that can ever be known about or done to the outside world. Some examples: single-celled organism MBs are largely co-extensive with cellular membranes; nervous system MBs are composed of sensor and effector neurons by which they receive information from sensors and drive change with actuators; niche-constructing organism MBs constitute the boundaries of extended phenotypes, including bodies and external structures that regulate environmental interaction. Such functional boundaries are an essential source of adaptive constraints for biological systems (Rudrauf et al., 2003; Hordijk and Steel, 2015; Lane, 2016), both internally concentrating system-promoting complexity and limiting system-threatening exchanges with external environments. Thus, MBs are both epistemic and system-defining boundaries. With respect to IIT, the boundaries of maximal complexes (entailing maximally irreducible cause-effect structures) would also constitute MBs. Although each MICS represents a kind of world unto itself (Leibniz, 1714), FEP-AI's formalism of internal states as modeling external states (and vice versa) may provide a means of understanding how such inwardly directed phenomena can nonetheless come to “encode” meaningful information about the external world with which they co-evolve, potentially providing linkages between IIT's intrinsic integrated information and information theory more generally.

The dual epistemic and ontological roles of MBs help justify the extremely broad scope of both FEP-AI (and possibly IIT as well). Identifiable systems must have boundaries defining their extents relative to other systems. Persisting systems further require predictive models to maintain themselves and their MB boundaries as they interact with environments. Yet, because blanket states informationally shield internal states from the rest of the world, modeling external states and MB boundaries necessitates inference (Friston, 2017, 2018, 2019). In this way, the epistemic boundaries created by system-defining MBs require persisting dynamical systems to entail self-evidencing generative models.

The extreme generality of PGMs and the implicit modeling relationships prescribed by FEP-AI may be of an extremely simple variety, particularly if systems have limited dynamic character and restricted thermodynamic openness. To provide an intuition-stretching example, by virtue of persisting (and so generating model evidence for their existence), the configuration of rocks and resultant causal interactions could be viewed as instantiating an implicit “prediction” that intramolecular forces and limited exchanges will be sufficient to maintain their forms. On short timescales, rocks will be able to (non-adaptively) generate rock-like dynamics, which restrict thermodynamic exchanges, thus allowing rocks to temporarily avoid disintegration. However, in contrast to living systems, rocks lack functional closure (Joslyn, 2000; Pattee, 2001; Deacon, 2011; Gazzaniga, 2018) with the geological processes generating their forms. Without multi-level evolutionary optimization (Safron, 2019b), generative models will be of such simple varieties that they are incapable of predicting and responding to particular events (i.e., adaptation). In this way, rocks are “surprised” by every exchange with their environments capable of altering their structures, and so will steadily disintegrate as such exchanges accumulate over time. [Note: FEP-AI focuses on weakly mixing ergodic systems, and as such, this conceptual analysis of rocks lacks the kinds of formal treatments that have been—controversially (Biehl et al., 2020; Friston et al., 2020a)—applied to complex adaptive systems.]

This consideration of rocks as (very) impoverished generative models provides a limit case for understanding what is and is not implied by FEP-AI: every ‘thing' can be viewed as having a basic kind of intelligence by virtue of existing at all, but neither rocks nor other similar inanimate objects are conscious (Friston, 2018, 2019). This limit case also shows major points of intersection between FEP-AI and IIT (Table 1), as both frameworks provide universal ontologies, and so must be applicable to every system, including rocks, and potentially even the processes giving rise to physical forces and their associated particles (Tegmark, 2014). However, according to IIT's exclusion axiom, rocks would not represent actual systems, in that maxima of integrated information would likely be found among separate components, and so neither (intrinsic) existence nor quasi-sentience would be ascribed. While the exclusion axiom may be essential for consciousness, relaxing this postulate in some cases may allow IIT to both (a) be fully compatible with FEP-AI and (b) better function as a general model of emergent causation. That is, for something to be said to exist, it may not be necessary for it to be a maximum of integrated information as irreducible cause–effect power. Rocks do indeed exist—while lacking consciousness—in that they possess emergent properties that are not present in their constituent elements (e.g., the intrinsic property of a boulder being able to maintain its form as it rolls (Bejan, 2016), or its extrinsic properties with respect to anything in the path of a large quickly moving object). Large-scale compositions may not represent maximal complexes, but may nonetheless play important roles with respect to internal functioning and interactions with other systems.

With respect to the exclusion principle, IIT theorists have suggested that advanced artificial intelligences could be unconscious “zombies” if deployed on von Neumann architectures (Tononi and Koch, 2015), which lack irreducible integration due to serial operation. However, alternative interpretations of IIT could extend phi analyses into temporally extended virtual processes, rather than solely focusing on “direct” realization by physical mechanisms. From an FEP-AI perspective, maximally explanatory models for computer programs may correspond to (MB-bounded) functional cycles on the software level. This proposal for updating IIT aligns with a recently-suggested theory of consciousness focusing on spatiotemporal scales at which functional closure is achieved (Chang et al., 2019), thus instantiating emergence and affording coarse-graining over lower levels of analysis. However, both Information Closure Theory and IIT purport that consciousness corresponds to any instance of emergent causation. IWMT, in contrast, argues that consciousness may be “what physics feels like from the inside” (Koch, 2012; Tegmark, 2014), if (and only if) physical processes support the generation of integrated system–world models with spatial, temporal, and causal coherence.

IWMT suggests that leading theories of consciousness can be synergistically combined within FEP-AI. FEP-AI and IIT both play dual roles in this synthesis, serving as both general systems theories and descriptions of the processes underlying subjective experience. FEP-AI and IIT intersect on multiple levels, with potential for understanding causal emergence on multiple scales. However, the nature of these explanations may vary across domains, including with respect to analytic assumptions. Integrated information may potentially be modeled in different (and differently valuable) ways in different contexts (Tegmark, 2016; Mediano et al., 2019a,b), which may range from the identification of natural kinds, to the nature of life, to perception, and even consciousness (Figure 1). Based on these considerations, I propose it may be productive to factorize IIT into two complementary versions:

In both cases, IIT would still correspond to an analysis of systems in terms of their irreducible cause–effect power. However, the relaxation of the exclusion axiom in IIT-Emergence could afford a more flexible handling of different kinds of emergent causation (e.g., relative cause–effect power from various coupling systems), as well as more thorough integration with FEP-AI. This broader version of IIT could also sidestep issues such as quasi-panpsychism, as integrated information would not necessarily represent a sufficient condition for generating conscious experiences. While this proposal may not resolve all debates between IIT and GNWT, it may provide further opportunities for integration and synergy between these two theories (e.g., applying—not necessarily consciousness-entailing—phi analyses to posterior and frontal cortices during different stages of cognitive cycles).

Broadly speaking, nervous systems can be straightforwardly understood as generative probabilistic graphical models (PGMs). The directed structure of neurons and their organization into networks of weighted connections generate patterns of effective connectivity (Friston, 1994), where flows of influence are physical instantiations of conditional probabilities. From this perspective, nervous systems can be viewed as modeling the world to the extent neural dynamics reflect patterns in the world. The Bayesian brain hypothesis (Friston, 2010) proposes this mutual information takes the form of probabilistic mappings from observations to likely causes, and that these inferences may approach bounded optimality with respect to ecological decision-theoretic objectives (Russell and Subramanian, 1995; Mark et al., 2010; Hoffman and Singh, 2012) over phylogenetic and ontogenetic timescales.

The Bayesian brain hypothesis is supported by evidence for a common cortical algorithm of hierarchical predictive processing (HPP)—a potential Rosetta stone for neuroscience (Mumford, 1991; Rao and Ballard, 1999; Hawkins and Blakeslee, 2004). In HPP, neuronal processes constitute hierarchically organized generative models, which attempt to predict likely (hierarchically organized) world states that could have caused actual sensory observations (Friston and Kiebel, 2009; Clark, 2013). Bottom-up sensory information is simultaneously predicted across levels by sending predictions—as Bayesian beliefs, or prior expectations—downwards in anticipation of sensory observations. Prediction-errors (i.e., discrepancies with predictions) are passed upwards toward higher levels, whose modifications update beliefs into posterior expectations, which then become new (empirical) predictions to be passed downwards. This coding scheme is Bayesian in implementing the kind of model selection involved in hierarchical hidden Markov models (George and Hawkins, 2009), or hierarchical Kalman filtering. HPP is also Bayesian in that hierarchical updates combine predictions and prediction-errors according to the relative (estimated) precision of these entailed probability distributions, with this precision-weighting constituting an inverse-temperature parameter by which attention is modulated (Friston et al., 2012b). Notably with respect to the present discussion—and as a source of empirical support for HPP—specific functional roles have been proposed for different frequency bands and cell types, with beta and gamma corresponding to respective predictions and prediction-errors from deep and superficial pyramidal neurons (Bastos et al., 2012; Chao et al., 2018; Scheeringa and Fries, 2019). To summarize, in HPP, each level models the level below it, extending down to sensor and effector systems, with all these models being integrated when they are combined into larger (MB-bounded) generative models (e.g., brains and organisms).

Although evidence for HPP is strongest with respect to cortex, efficiency considerations (Harrison, 1952) provide reason to believe that this may be a more general phenomenon. Some evidence for extending HPP to non-cortical systems includes decoding of predictive information from retinal cells (Palmer et al., 2015), and also models of motor control involving spinal reflex arcs as predictions (Adams et al., 2013). HPP may further extend beyond nervous system functioning and into processes such as morphogenesis (Friston et al., 2015)—observed to exhibit near-optimal utilization of information (Krotov et al., 2014; Petkova et al., 2019)—and even phylogeny (Ramstead et al., 2018).

This leads to another surprising implication of FEP-AI: the broad applicability of the MB formalism suggests that any persisting adaptive system will enact some kind of HPP. More specifically, MB-bound systems contain MB-bound sub-systems, with nesting relations reflecting levels of hierarchical organization. More encompassing (hierarchically higher) models accumulate information from the sub-models they contain, with relative dynamics unfolding on either longer or shorter timescales, depending on relationships among nested MB-bounded systems. The epistemic boundaries instantiated by MBs mean that internal and external states are latent with respect to one another, and so must be inferred. Therefore, the communication of information regarding sub-system internal states (via MBs, definitionally) to the larger systems of which they are part is the propagation of a probabilistic belief—e.g., marginal message passing (Parr et al., 2019)—and so overall hierarchical organization of systems and sub-systems must instantiate HPP.

This generalized HPP may be supported by the near-ubiquitous phenomenon whereby coupling systems minimize free energy more effectively through forming larger systems via mutual entrainment (Jafri et al., 2016). From an FEP-AI perspective, this coupling relationship is one of mutual modeling and collaborative inference (Friston and Frith, 2015; Friston, 2017; Kirchhoff et al., 2018; Palacios et al., 2019). This generalized synchrony (Strogatz, 2012) has also been characterized in thermodynamic terms (Kachman et al., 2017; Friston, 2019), where systems spontaneously self-organize into resonant modes with the environments with which they couple—i.e., absorb work and minimize free energy according to Hamilton's principle of least action—where coordinated dynamics have been observed to contain mutually predictive information (Friston, 2013). Notably, coupled attractors have recently been found to adjust their dynamics beginning at sparsely frequented areas of phase space (Lahav et al., 2018). If these synchronizing manifolds begin to nucleate from improbable (and so surprising) alignments, this flow of (mutual-information maximizing) influence might be functionally understood as updating via “prediction-errors.” While admittedly speculative, these considerations suggest that generalized HPP (and selection for integrated information) could represent a universality class whose potential extensions are nearly as widespread as generalized synchrony itself. Generalized predictive synchrony may also have implications for IIT, potentially helping to explain how internally directed complexes of integrated information can come to resonate with the external world. Further, synchronization dynamics may provide a mechanistic basis for bridging FEP-AI, IIT, and GNWT, as described below with respect to integration via self-organizing harmonic modes (SOHMs).

Free energy may be most effectively minimized—and integrated information maximized (Marshall et al., 2016)—if synchronized couplings take the form of hierarchically organized modules, thus affording robustness, separable optimization, balanced integration and differentiation, evolvability via degeneracy, efficient communication via small-world connectivity, and flexible multi-scale responsivity via critical dynamics (Meunier et al., 2010; Wang et al., 2011; Ódor et al., 2015; Lin and Tegmark, 2017; Lin et al., 2017; Gazzaniga, 2018; Takagi, 2018; Badcock et al., 2019). Hierarchical organization, modularity, and self-organized criticality (SOC) may promote both integrated information maximization and free energy minimization (Friston et al., 2012a, 2014; Vázquez-Rodríguez et al., 2017; Hoffmann and Payton, 2018; Salehipour et al., 2018; Khajehabdollahi et al., 2019), potentially suggesting major points of intersection between FEP-AI and IIT across a wide range of systems.

For biological systems, cells integrate information unfolding at cellular scales, with tissues and organs integrating this information at organismic scales, with organisms and groups of organisms integrating this information at even broader scales. It is important to remember that FEP-AI can be viewed as a Bayesian interpretation of generalized Darwinism (Kaila and Annila, 2008; Harper, 2011; Frank, 2012; Campbell, 2016), and so these nested couplings can also be viewed in terms of natural selection and niche construction unfolding over multiple hierarchical scales (Constant et al., 2018; Ramstead et al., 2018; Badcock et al., 2019). More specifically, a hierarchy of MBs constitute a hierarchy of selective pressures (Safron, 2019b), with dynamics on one level being selected by the next level of organization. These informational shielding properties of MBs connect with debates regarding units of selection in evolutionary theory, in that only organismic phenotypes—and sometimes groups of organisms (Laland et al., 2015; Richerson et al., 2016)—are “visible” to natural selection with respect to phylogeny. However, specific phenotypes are determined by interactions between internal intrinsic dynamics (i.e., intra-system evolution) as well as external systems with which these dynamics couple via niche construction and phenotypic plasticity (Constant et al., 2018). To the (necessarily limited) extent these adaptively coupled nested scales are shaped by stable selective pressures, then the transmission of information across levels could approach Bayes-optimal (Kaila and Annila, 2008; Payne and Wagner, 2019) active inference by combining all relevant probabilistic influences via gradient ascent/descent over fitness/energy landscapes. That is, what is actively inferred by systems (as generative models) in FEP-AI is the inclusive fitness of the sum-total of all quasi-replicative (i.e., self-evidencing) dynamics capable of interacting on the spatial and temporal scales over which evolution (as inference) occurs.

While this discussion of Bayesian generalized Darwinism may seem needlessly abstract, this multi-level account is essential for understanding what we ought to expect to be generated by competing and cooperating quasi-replicative processes (i.e., evolution). It also provides another potential point of intersection with IIT, in that some dynamics will be more influential than others on the timescales at which interactions occur. Specifically, when considered as networks of relations, some sub-graphs will have more integrated information (i.e., intrinsic cause–effect power, or phi) than others, and phi associated with these subgraphs may parameterize capacity to shape overall directions of evolution.

Importantly, if evolution (as inference) applies not just on the level of phylogeny, but also to intra-organism dynamics, then this provides a means of understanding mental processes as both Bayesian model selection and a kind of (generalized) natural selection (Edelman, 1987). With respect to IIT, the irreducible internal cause-effect power for a particular subnetwork of effective connectivity may correlate with its degree of external cause-effect power in influencing the overall direction of evolution within a mind. If a subnetwork of effective connectivity entails a generative model for enacting particular (adaptive) system–world configurations, then a maximal complex of integrated information would also be a maximally explanatory model for overall system evolution, which may entail consciousness under certain conditions.

In this way, FEP-AI shows how mental causation may be isomorphic with evolutionary causation (i.e., action selection as generalized natural selection), where selective pressures constitute free energy gradients, thus providing formal connections with thermodynamic pressures and power-generation abilities. Power is force integrated over time, which may be more likely to be generated by systems capable of exerting cause–effect power over themselves, suggesting a potentially important role for integrated information in modeling evolutionary dynamics. In this way, by describing mental processes in terms of degrees of self-directed cause–effect power, IIT may help explain how particular processes—including those entailing beliefs and desires—possess varying capacities for contributing to informational and thermodynamic work cycles (Kauffman and Clayton, 2006; Deacon, 2011). Taken together, FEP-AI and IIT show how consciousness may not only represent a system's best guess of what is happening at any given moment, but a source of maximal control energy for system evolution, thus providing a means by which conscious intentions can have causal powers.

While HPP is an extremely broad framework, the difference between basic active inference and adaptive active inference is important to remember (Kirchhoff et al., 2018): while FEP-AI views all systems as models, only some of these models afford adaptivity, and only some systems also have models (Seth and Tsakiris, 2018). Living organisms possess specific sub-systems capable of supporting generative models with temporal depth and counterfactual richness (Friston et al., 2017c). These sub-systems are called brains, and they allow organisms to navigate exchanges with their environments by modeling not just present world configurations, but also possible world configurations predicted based on future (counterfactual) actions (i.e., expected free energy).

Brains acquire especially powerful predictive modeling abilities when they are organized according to multiple layers of hierarchical depth. This deep organization allows these systems to model not only transient events at lower levels, but also their organization into more temporally extended sequences at higher levels (Hawkins and Blakeslee, 2004; Baldassano et al., 2017; Friston et al., 2017c). Further, deep internal dynamics create a potential for functional decoupling between modeling and the unfolding of particular sensorimotor engagements (Tani, 2016; Sormaz et al., 2018; Corcoran et al., 2020), thus enabling counterfactual simulations (Kanai et al., 2019) with temporal “thickness”/“depth” (Humphrey, 2017; Friston, 2018), which when conscious enable imagination and explicit planning. These capacities afford the possibility of constructing rich causal world models (Hassabis and Maguire, 2009; Buchsbaum et al., 2012; Pearl and Mackenzie, 2018; MacKay, 2019), and as discussed below, preconditions for coherent conscious experience. In this way, while all brains may expand autonomous capacity by engaging in HPP, only some architectures may be capable of supporting flexible cognition. Thus, FEP-AI implies a near universality for generative modeling, but not necessarily for consciousness. We will now explore properties of nervous systems that may be particularly important for enabling conscious experiences via complexes of integrated information and global workspaces.

IWMT proposes a mechanism by which complexes of integrated information and global workspaces may emerge as metastable synchronous complexes of effective connectivity, or self-organizing harmonic modes (SOHMs). SOHMs are proposed to be attractors and eigenmodes (Friston et al., 2014)—or, solutions to harmonic functions—for phase space descriptions of system dynamics, with particular boundaries depending on network topologies over which synchronization occurs. This view of dynamical systems in terms of SOHMs can be understood as an extension of Atasoy et al.'s (2018) analytical framework wherein spectral decomposition is used to characterize brain activity as mixtures of “connectome harmonics.” When this method was first introduced, Atasoy et al. (2016) compellingly demonstrated how reaction-diffusion simulations of spreading activation could generate resting state networks as stable modes—or standing waves—so recapitulating well-known patterns of neuronal organization with minimal assumptions. Intriguingly, hallucinogenic compounds expanded the repertoire of these harmonic modes (Atasoy et al., 2017), increasing spectral diversity and shifting the distribution of modes toward power-law distributions, a putative—albeit controversial (Touboul and Destexhe, 2017)—hallmark of criticality (Fontenele et al., 2019). This finding is consistent with other studies of psychedelic compounds (Tagliazucchi et al., 2014; Schartner et al., 2017; Viol et al., 2017), supporting the hypothesis that brains may enhance dynamical reconfigurability by being “tuned” toward near-critical regimes (Pletzer et al., 2010; Haimovici et al., 2013; Carhart-Harris, 2018).

Atasoy et al. (2016) describe this modeling approach of identifying eigenfunctions (over a system's Laplacian) as having an extremely broad scope, with applications ranging from Turing's (1952) account of morphogenesis, to acoustic phenomena and other patterns observed with vibrating media (Ullmann, 2007), to solutions for electron orbitals in quantum mechanics (Schrödinger, 1926). Based on our previous discussion of probabilistic graphical models as a near-universal representational framework, the term “connectome harmonics” could be reasonably generalized to apply to all systems. However, IWMT introduces the new term of “SOHMs” to prevent confusion and to emphasize the dynamic self-organizing processes by which synchronous complexes may emerge, even when constituting local standing wave descriptions over dynamics (rather than constituting a Fourier basis for an entire connected system). That is, Atasoy's connectome harmonics constitute a more specific—and important for the sake of understanding consciousness—variety of SOHM.

SOHMs may act as systemic causes in selecting specific dynamics through synchronous signal amplification, with micro-dynamics having greater contributions to synchronizing macro-dynamics when phase-aligned. SOHMs could be viewed as either standing or traveling waves, depending on the level of granularity with which they are modeled (Friston et al., 2014; Mišić et al., 2015; Atasoy et al., 2018; Muller et al., 2018; Zhang et al., 2018). However, when viewed as harmonic modes, SOHMs would have specific boundaries and timescales of formation. In this way, resonant signal amplification within SOHMs could select patterns of effective connectivity based on the timescales at which maximal coherence is achieved. IWMT specifically proposes that these synchronous complexes promote “communication through coherence” (Hebb, 1949; Dehaene, 2014; Fries, 2015; Deco and Kringelbach, 2016; Hahn et al., 2019). From an FEP-AI perspective, this synchrony-enhanced communication would facilitate information sharing among (and marginalization over) coupled dynamics, thereby organizing message passing (or belief propagation) for inference (Parr and Friston, 2018a; Parr et al., 2019).

With respect to emergent causation, circular causal processes by which SOHMs form would constitute organization into renormalization groups and attracting flow paths along center manifolds (Haken, 1977, 1992; Bogolyubov and Shirkov, 1980; Li and Wang, 2018; Shine et al., 2019). This synchronization of micro-scale phenomena into larger groupings on meso- and macro-scales could be viewed as a kind of informational closure and coarse-graining (Hoel et al., 2016; Chang et al., 2019). Further, for self-evidencing generative models (Hohwy, 2016; Yufik and Friston, 2016; Kirchhoff et al., 2018), integrating processes underlying SOHM formation would calculate marginal joint posteriors based on specific (Bayesian) beliefs entailed by particular patterns of effective connectivity within and between various synchronous complexes.

[Note: More details on potential mechanisms for SOHM formation and functional consequences can be found in IWMT Revisited (Safron, 2019a) in the sections: “Phenomenal binding via ESMs (Embodied Self-Models)” and “Mechanisms for integration and workspace dynamics.”]

With respect to conscious perception, the resonant signal amplification by which SOHMs emerge could potentially contribute to the calculation of highly precise—albeit not necessarily accurate (Hohwy, 2012; Vul et al., 2014)—joint distributions (or maximal a posteriori (MAP) estimates derived thereof). The ability of synchronous complexes to select phase-aligned patterns has clear correspondences with theories of consciousness emphasizing re-entrant signaling (Singer, 2001; Varela et al., 2001; Crick and Koch, 2003; Edelman et al., 2011; Shanahan, 2012; Dehaene, 2014; Grossberg, 2017) and in terms of Bayesian model selection (Hohwy, 2012, 2013), could be understood as promoting winner-take-all dynamics among competing and cooperating inferential flows. SOHMs may also help provide mechanistic bases for “ignition” events accompanying phase transitions in which perception becomes conscious (Dehaene and Changeux, 2011; Friston et al., 2012a; Arese Lucini et al., 2019). IWMT specifically proposes that conscious ignition corresponds to surpassing critical thresholds for SOHM formation via self-synchronized neural activity, thus forming meta-stable complexes as dynamic cores of integrated information, functioning as neuronal global workspaces.

The ability of SOHMs to select aligned patterns may help explain how seemingly definite experiences could emerge from probabilistic world models (Wiese, 2017; Block, 2018; Clark, 2018; Gross, 2018), as opposed to generating a “Bayesian blur,” or superposition of possibilities. This hypothesis is consistent with Clark's (2018) suggestion that coherent and precise inference stems from requirements for engaging with environments via sensorimotor couplings (Clark, 2016). Along these lines, by enabling the generation of inferences with rapidity and reliability, SOHMs could afford approximate models capable of guiding action–perception cycles and decision-making (von Uexküll, 1957; Fuster, 2009; Madl et al., 2011; Vul et al., 2014; Linson et al., 2018; Parr and Friston, 2018b). Further, these sensorimotor engagements may promote SOHM formation by providing coherent sources of correlated information, thus affording the possibility of learning even more sophisticated models (Pfeifer and Bongard, 2006; Safron, 2019a,c). IWMT proposes that this continual shaping of behavior based on rich causal world models may be both a major adaptive function of consciousness and a precondition for developing coherent conscious experience. [Note: If consciousness requires semiotic closure Chang et al., 2019 via action–perception cycles, then this cybernetic grounding suggests that systems like plants and insect colonies are unlikely to be conscious, even if capable of sophisticated (but limited) levels of intelligence.]

SOHM dynamics may help to explain many kinds of rhythmic phenomena, such as the fact that oscillations tend to occur at faster rates in organisms with smaller brains (Buzsáki and Watson, 2012); all else being equal, smaller systems are likely to arrive at synchronous equilibria more quickly, with larger systems requiring relatively more time for synchronizing their micro-dynamics. SOHMs may also help to explain why different rhythms (Table 3) would be associated with different processes in hierarchical predictive processing (HPP) (Bastos et al., 2015; Sedley et al., 2016; Chao et al., 2018), where faster gamma oscillations communicate bottom-up prediction-errors ‘calculated' by local microcircuits, and where slower beta oscillations generate top-down predictions via integrating information (i.e., accumulating model evidence) from more spatially-extended sources. These beta complexes may potentially be organized via nesting within even larger and slower-forming SOHMs, such as those generated at alpha, theta, and delta frequencies. This cross-frequency phase coupling (Canolty and Knight, 2010) could allow for the stabilization of multi-scale dynamics within HPP, with increasing levels of hierarchical depth affording modeling of complex and temporally extended causes (Friston et al., 2017c). Hierarchical nesting of SOHMs could allow modeling to simultaneously (and synergistically) occur at multiple levels of granularity, thus affording both global stability (Humphrey, 2017) and fine-grained adaptive control as overall systems couple with their environments.

If SOHMs integrate information in the ways suggested here—marginalizing over synchronized components—then the largest SOHM of a system would generate a joint posterior (or estimate derived thereof) over all smaller SOHMs contained within its scope. These encompassing SOHMs would integrate information across heterogeneous processes, as well as affording unified sources of control energy for system evolution. These maximal SOHMs could generate estimates of overall organismic states, thus forming dynamic cores of integration for perception and action, potentially enabling autonomous control by integrated self-processes. Further, privileged positions of maximal SOHMs with respect to network centrality (Aadithya et al., 2010) and modeling capacity could promote directional entrainment of smaller complexes, thus promoting coherent agentic action selection.

For biological systems, the dynamics within maximal SOHMs may have the clearest correspondences with events unfolding at organismic scales. For organisms such as C. elegans, these dynamics might unfold at the frequencies of locomotory eigenmodes, potentially concentrated in a core of richly connected nodes (Towlson et al., 2013), thus allowing enslavement of a worm's peripheral pattern generators by predictive models coordinating the enaction of coherent movement vectors. For organisms such as Homo sapiens, these dynamics might unfold at the frequencies of real and imagined sensorimotor contingencies (Elton, 2000; O'Regan and Noë, 2001; Tani, 2016; Chen et al., 2017; Prinz, 2017; Zadbood et al., 2017; Baldassano et al., 2018; Chang et al., 2019), potentially concentrated along deep portions of cortical generative models, thus allowing enslavement of an individual's sensorium and effectors by rich causal models of self and world. Whether in worms or humans, SOHMs would entail joint posteriors (or associated maximal estimates) from probabilistic models for embodied agents and the environments with which they couple. In these ways, Maximal SOHMs may be coextensive with both maxima of integrated information (i.e., MICS) and global workspaces. However, while SOHMs with the greatest amount of irreducible integrated information may correspond to basic phenomenal consciousness (e.g., complexes centered on posterior cortices), organization into an even larger (albeit possibly less irreducibly integrated) synchronous complex involving the frontal lobes may be required for access consciousness and agentic control.

A multi-level understanding of SOHMs in terms of neuronal dynamics and probabilistic inference suggests that we should expect these complexes to form over subnetworks with coherent mutual information, which is more likely if patterns of effective connectivity entail coherent and well-evidenced world models. With respect to loopy message passing for approximate inference (Koller and Friedman, 2009; Friston et al., 2017b), these coherent models may have a (circular) causal significance in that they would be more likely to provide consistent inferential flows, and so be more likely to first converge upon stable posteriors, and so be more likely to dominate rounds of Bayesian model selection. Notably, this kind of convergence is more likely for Bayesian networks that balance integration and differentiation—associated with high phi (Marshall et al., 2016)—and this is precisely what is observed for “rich club” connectivity cores (Sporns, 2013; Mišić et al., 2015; Cohen and D'Esposito, 2016; Mohr et al., 2016). Further, high degrees of re-entrant connectivity and potential for recurrent dynamics suggests that these richly connected networks are particularly likely to serve as loci of “ignition” events in global workspace models (Dehaene and Changeux, 2011; Shanahan, 2012). Finally, considering that integrated information reflects a system's ability to exert cause–effect power over itself, SOHMs may be particularly likely to form along high phi networks.

A maximal SOHM—as a MICS and MICE repertoires—within a brain may center on posterior cortices, and in particular the temporoparietal junction (Graziano, 2019) and posteromedial cortices (PMCs) (O'Reilly et al., 2017), with synchronizing complexes forming at alpha frequencies generating basic phenomenal consciousness. Nesting of these alpha rhythms within theta frequencies may further allow for coupling with the frontal lobes and hippocampal complex, thus affording goal-directed and access consciousness from global workspace dynamics. IWMT's focus on PMCs and alpha frequencies (as synchronizing manifolds) is based on both the types of information available to these systems/processes (Papez, 1937; Jann et al., 2009; Gramann et al., 2010; Knyazev et al., 2011; Damasio, 2012), as well as empirical associations with attention and working memory (Palva and Palva, 2011; Kerr et al., 2013; Michalareas et al., 2016; Sato et al., 2018; Bagherzadeh et al., 2019). PMCs receive information from upper levels of each sensory hierarchy, as well as the position of an organism in space, including head-direction information. This information is likely a prerequisite for organizing perception into egocentric reference frames (Brewer et al., 2011, 2013; Guterstam et al., 2015; Li et al., 2018; Smigielski et al., 2019). In line with models in which consciousness depends on projective geometry (Rudrauf et al., 2017; Williford et al., 2018), a stable source of egocentric perspective may represent a practically necessary precondition for there to be “something that it is like:” i.e., the ability to generate models with spatial, temporal, and causal coherence for system and world.

IWMT focuses on space, time (i.e., relative dynamics in space), and cause (i.e., predictable regularities in these dynamics), but wholistic self-processes (Damasio, 2012; Humphrey, 2017) may also be essential for developing world models capable of generating coherent subjectivity. Self-processes may be practically necessary for consciousness because the integration of large-scale brain activity may be required for the coherent regulation of action–perception cycles, and thereby cybernetic sense-making. Self-processes could allow for selection of specific models on the basis of relevance (Shanahan and Baars, 2005; Davey and Harrison, 2018; Linson et al., 2018; Hattori et al., 2019), with stable self-models extending this organization across time (Dennett, 1992; Hirsh et al., 2013; Buonomano, 2017), thereby enabling the learning required to construct experienceable world models. In brief, IWMT proposes that Kant's preconditions for judgment are also necessary preconditions for consciousness (Northoff, 2012; De Kock, 2016). While PMCs may be sufficient for basic phenomenal consciousness, larger complexes may be required for certain kinds of higher-order cognition, including access and autonoetic consciousness (Brown et al., 2019; LeDoux, 2019; Shea and Frith, 2019). This integration of action with perception is likely crucial for agentic planning and the counterfactual simulations upon which it is based (Hassabis and Maguire, 2009; MacKay, 2019), without which the development of coherent world models may be impossible (De Kock, 2016; Friston, 2017).

To summarize (Table 4), in systems where synchrony both emerges from and facilitates coherent message passing, SOHMs enable both workspace dynamics and high degrees of meaningful informational integration, where meaning is a difference that makes a difference to the ability of a system to survive and achieve its goals. However, integrated information and workspaces only entail consciousness when applied to systems that can also be understood as Bayesian belief networks, where beliefs have coherence because they have actual semantic content by virtue of evolving through interactions with a coherently structured (and so semi-predictable) world. Without those meaningful external connections, systems could have arbitrarily large amounts of integrative potential, but there still may be nothing that it is like to be such systems.

[Note: For some testable hypotheses related to these ideas, please refer to Supplementary Material.]

Helmholtz (1878) is often viewed as providing the first clear description of perception as inference:

Dayan, Hinton, Neal, and Zemel (Dayan et al., 1995) constructed machine learning systems based on these principles, trained using cost functions based on Helmholtz free energy. These kinds of architectures can be trained to handle noisy inputs or infer missing data, with more recent versions being able to generate completely novel combinations of features. These are all aspects of conscious (and unconscious) perception and have many commonalities with HPP within FEP-AI.

Variational autoencoders (Kingma and Welling, 2014) are composed of encoders and generative decoders connected by low-dimensional bottlenecks, where encoders learn to compress input data into reduced-dimensionality feature spaces, and where decoders learn to use these latent features to infer likely details of higher-dimensional data. HPP models of sensory cortices (Figure 2; Table 5) may be approximated as disentangled variational autoencoders, where encoders and decoders are constituted by respective hierarchies of superficial and deep pyramidal neurons (Kanai et al., 2019). However, rather than training solely based on divergences between respective input and output layers of encoder and decoder networks, prediction-error is minimized at all levels simultaneously based on comparisons between time-varying sensory observations and internally-generated predictions. HPP in brains further involves multiple interacting autoencoding hierarchies, with connections being particularly strong in deeper association cortices—corresponding to reduced dimensionality latent spaces—thus affording synergistic inferential power with shared priors from multi-modal sensory integration and world modeling.

IWMT proposes that connections between the low-dimensionality bottlenecks from various modalities may form an auto-associative network supporting loopy belief propagation—or message passing—thus constituting a turbo code (Berrou and Glavieux, 1996), and hence approaching the Shannon limit with respect to optimality in communicating information over noisy channels (Figure 3; Table 6). [Note: While any instantiation of loopy belief propagation may be understood as realizing a turbo code, IWMT specifically suggests that a broad network of cross-modal effective connectivity is required for coherent integrated world modeling.] This framing of HPP in terms of autoencoders and turbo codes could provide a computational analog for neural systems underlying consciousness: a reduced-dimensionality representational bottleneck that extracts the most important details from sensory data, and which affords inferential synergy by providing a workspace where specialist models can be combined, integrated, and then rebroadcast. [Note: HPP dimensionality-reduction may have relevance to the sketch-like nature of awareness proposed in Graziano's Attention Schema Theory (Graziano, 2013, 2019).] According to IWMT, coherent self-world modeling likely also requires organizing this information into spatiotemporal trajectories, as afforded by the hippocampal system and machine learning architectures that attempt to reproduce its functioning (Fraccaro et al., 2017; Ha and Schmidhuber, 2018; Whittington et al., 2018; Wu et al., 2018), and as suggested by impaired counterfactual modeling with medial temporal lobe damage (Hassabis and Maguire, 2009; MacKay, 2019).

As Bengio (2017) has suggested with his work on the “consciousness prior,” the reduced dimensionality of these (disentangled) features may be well-suited for identifying major axes of meaningful variations in the world, such as those involved in the kinds of causal processes we can manipulate and perceive, and which can also be mapped onto linguistic systems. This later affordance has relevance to Higher-Order Theories of consciousness, including those emphasizing agentic modeling and social communication (Metzinger, 2010; Graziano, 2013; Rudrauf et al., 2017; Brown et al., 2019; Shea and Frith, 2019).

The thalamocortical system enabling dynamic cores of integration and conscious workspaces first evolved hundreds of millions of years before these higher-order processes existed (Edelman, 2004). These richly connected subnetworks enable high-bandwidth message passing—as likely required for realizing turbo codes in biological systems—but are also metabolically expensive, consuming nearly 50% of cortical metabolism in humans (Heuvel et al., 2012). However, part of the way these energetic costs may be justified is by (a) reducing the number of (noisy) neuronal signal transactions required to achieve adequately reliable perceptual inference, (b) enhancing the speed of model selection for the sake of fine-grained control, and (c) allowing for imagination-based planning and causal reasoning (Pearl and Mackenzie, 2018). Rich-club connected subnetworks can even be found in C. elegans with their 302 neurons (Towlson et al., 2013). This could be taken to imply that consciousness is nearly a billion years old, but IWMT suggests that this is likely a mistaken inference, as deep hierarchies may be required for generating coherent experience.

IWMT does not suggest that consciousness corresponds to either the output layers of generative models as currently used in machine learning or the processes calculating those outputs. Although architectures with self-attention mechanisms have been implemented with great success (Kovaleva et al., 2019), the outputs of such systems tend to be functionally disconnected from each other, as well as the processes by which they are generated. This is not the case for brains, for which IWMT proposes that joint posteriors and estimates (and samples derived thereof) are calculated via spreading neuronal activity, where message-passing/belief-propagation is promoted (or scheduled) via synchronous dynamics (i.e., SOHMs). As opposed to current generations of generative models, the functioning of these synchronized subnetworks (and the calculations they entail) span multiple levels of hierarchical depth, with bidirectional linkages to generative processes involving models with spatial, temporal, and causal coherence for system and world.

Further, the anatomical and functional directedness of neuronal connections at any point in time contain information that will bias future dynamics, so influencing likelihoods with which meta-stable regimes are subsequently produced. If these networks are altered according to principles of spike-timing dependent plasticity, and if systems develop in the context of embodied agents interacting with their environments, then these state transitions are likely to contain coherent information reflecting causal world structures (Hayek, 1952; Markram et al., 2011; Lakoff, 2014). In these ways and more (e.g. recurrent dynamics persisting across SOHM-formation events), each quale state would functionally connect and constrain future quale states based on past quale states. Further, biological neural networks do not generate feature maps as isolated vectors over stimulus dimensions, but as vectors coupled over multiple levels of hierarchical depth, via neuronal dynamics. Thus, consciousness may be entailed by the functioning of a probabilistic model that generates tensors in neuronal (and representational) phase space, specifying joint posteriors (or estimates derived thereof), where that which is being modeled/estimated is the causes of sensation. If this is the type of mathematical object that corresponds to subjective experience, then substantial progress may have been made toward solving the Hard problem of consciousness.

How could there be “something that it is like” to be a physical system or entailed mathematical object? IWMT suggests that this question may be satisfyingly answered if such a system can calculate—or probabilistically infer—sequences of sensorimotor states. Perhaps intuitively, such a sequential unfolding would have more of a resemblance to the flowing of the stream of consciousness for the kinds of embodied–embedded agents that we are. If models can generate particular combinations of information present within and between sensory modalities, then we may finally begin to have prima facie reasons to believe that such processes could generate subjective experience.

Global workspaces have been analogized as functioning as (non-Cartesian) theaters (Dehaene, 2014) in which information is rendered visible to otherwise isolated modules, with attention acting as a “spotlight” prioritizing some contents over others. Similar metaphors for awareness have been used by Crick and Koch (2003) with their neuronal coalitions model and also by Hobson and Friston (2016) in suggesting that frontal lobe ensembles produce awareness when they “look” at posterior sensory information. While the implication of some sort of little person in the brain, or homunculus, is nearly universally reviled, this dismissal may be a significant part of the Hard problem's intractability. That is, in attempting to do away with homunculi, cognitive science may have lost track of the importance of both embodiment and centralized control structures. If “cognition” is primarily discussed in the abstract, apart from its embodied–embedded character, then it is only natural that explanatory gaps between brain and mind should seem unbridgeable. IWMT, in contrast, suggests that many quasi-Cartesian intuitions may be partially justified. As discussed in Safron (2019a,c), brains may not only infer mental spaces, but they may further populate these spaces with body-centric representations of sensations and actions at various degrees of detail and abstraction. From this view, not only are experiences re-presented to inner experiencers, but these experiencers may take the form of a variety of embodied self-models with degrees of agency. In these ways, IWMT situates embodiment at the core of both consciousness and agency, so vindicating many (but not all) folk psychological intuitions.

With respect to the meta-problem, one could imagine postulating a “Hard problem” of generative models in machine learning, for which an unbridgeable explanatory gap may be perceived between the remarkable ability of these architectures to generate rich and novel stimuli (e.g., an “imagined” face), contrasted with the determinism of their underlying computations. Yet this seemingly intractable problem could then be solved via deeper technical understanding. IWMT proposes that this epistemic situation may be analogous to the one we face with consciousness. Rather than the “Hard problem” being reduced to many “easy problems”—and so being (dis-)solved as we discover we were asking the wrong question—it may be the case of this most challenging and profound problem actually being solved through the discovery of sufficiently powerful bridging principles. IWMT suggests such principles may be finally available by using FEP-AI to integrate leading theories of consciousness.