Minimal physicalism as a scale-free substrate for cognition and consciousness

Organisms record memories as messages to their future selves. Neither the mechanism that recorded a memory, the internal or environmental events that induced recording, nor the context in which the recording occurred are discoverable, however, when the memory is later retrieved. This fundamental uncertainty about the sources of memories can be seen as a consequence of a more general uncertainty about whether QRFs are shared, either by distinct observers at a single time, or by a single observer across time. This question of QRF sharing is provably finite Turing undecidable (Fields et al. 2021).

Prediction 8: Experiential complexity scales with internal compartmentalization. Evolution “discovered” the benefits of internal compartmentalization ca. 3.5 billion years ago with the development of microbial biofilms exhibiting differential exposure to the open environment and division of metabolic labor (Stal 2012). The organelles of eukaryotic cells, including internal membrane complexes such as the Golgi apparatus, are canonical intracellular compartments. Multicellularity is the most common form of macroscopic compartmentalization, but is not required; examples such as Acetabularia (Schweiger 1969), glass sponges (Leys 2016), and syncitial fungi (Roper et al. 2015) all illustrate complex functional compartmentalization within single giant cells.

This prediction is clearly in line with the expectations of IIT, and again provides a physical basis for these expectations.

Cellular compartments and intercellular boundaries are, from a biological perspective, dynamic structures that must be preserved through metabolic activity. This requirement can be stated abstractly:

Prediction 9: Memory stability scales with the frequency of read/write cycles. The stabilization of classical bit values by repeated cycles of preparation followed by observation is called the “quantum Zeno effect” (Misra and Sudarshan 1977); the probability that the state remains stable is proportional to the frequency of observations. Hence memory decay—“forgetting”—is predicted whenever memories are not routinely accessed. This is broadly observed across phylogeny. Repair systems for nucleic acids and degradation systems for proteins provide molecular-scale examples.

Rewriting classical information costs free energy, i.e. requires metabolism as discussed below. This requirement for read/write cycles suggests that compartmentalization plays a key role in the implementation of linear time QRFs:

Prediction 10: Any ordered sequence of separate memories together with a comparison function can serve as a linear time QRF. Trajectories, including looming, are the simplest linear time QRFs; in the limit, they may support only sequential comparisons and hence only distinguish “then” from “now.” Insects are capable of at least short-sequence linear time perception; spiders are capable of longer sequence perception (Japyassú and Laland 2017). Merely executing a fixed action pattern does not require experiencing linear time, although it clearly requires an internal clock. Molecular cell-cycle clocks are as old as life, and circadian clocks are as old as cyanobacteria (Johnson 2004); both are highly conserved across phylogeny (Doherty and Kay 2010). Possessing a molecular clock does not, by itself, enable time perception.

Prediction 11: Time and object/feature identity are duals. Perceiving a trajectory requires perceiving an object or feature executing that trajectory; conversely, motion perception is the basis of object identity (Fields 2011). Whether insects or relatively low-complexity vertebrates are aware of objects as such or only features of their environments is unclear; bees at least have robust spatial memory and feature perception, and may recognize objects as such (Chittka 2017), while fish appear to recognize conspecifics as distinct objects (Sovrano et al. 2018). Spiders are capable of robust object perception and object-directed planning (Japyassú and Laland 2017), as are cephalopods (Mather 2019), birds and mammals.

Environmental objects and features that are experienced as having stable identities are, to close the circle, the canonical bearers of stigmergic memories. Without perceptible object identity, time is not perceptible and hence memories cannot be recognized as such. Only an organism capable of experiencing linear time and objects or features with stable identities can experience memories. Such an organism inhabits a rich and meaningful umwelt, whether its ontology easily relates to our own or not.

With the understanding of QRFs developed above, we can return to the question of bioenergetics, see why living systems can have only limited awareness, cognitive capacity, and memory, and understand the tradeoffs between these.

The dynamics described by Equation (5) explicitly conserves energy: βATA = βBTB. Hence some fraction of the bits transferred from B to A must be “burned” by A to supply the free energy required to irreversibly encode classical information (Fields and Glazebrook 2020a). These free-energy supplying bits are not available as input to any QRF (Fields et al. 2021). As no QRF can read the values encoded by these bits, their values are irrelevant and indeed “invisible” to A. They can, therefore, be considered random for A, i.e. to constitute environmental “noise” or in thermodynamic terms, a heat bath. As noted above, however, the source of this noise is quantum, not classical; there is no third system injecting noise into the bipartite A-B interaction (see Fields et al. 2021, for further discussion).

A central question of biology is: how can organisms maintain their phenomenal complexity with such small free energy budgets? The answer is alluded to above: quantum computational complexity, implemented by unitary Schrödinger evolution, is energetically free (Bennett 1982). Classical, i.e. observable or experienceable computational complexity is expensive, ln2 kBT per bit. Hence we have:

Prediction 12: Organisms only require the energy needed to maintain their classical states. As seen above, these are encoded on either exterior or intercompartmental boundaries. An organism’s energy budget must, therefore, supply the free energy needed to maintain, via Zeno-effect read/write cycles, the classical states of their compartment boundaries. Nonboundary states can remain quantum, evolve unitarily, and consume no free energy. Only the inputs and outputs of these quantum computations are classically encoded, all on boundaries.

Clearly not all boundary-localized processes are classical; electron-transport processes operating in the THz range could consume a cell’s entire energy budget if fully classical. The technical difficulty of observing nonclassical behavior in such systems (e.g. Cao et al. 2020) is not surprising. All of our observational outcomes are classical by definition; observing quantum coherence requires observing expectation violations in probability distributions over recorded classical events (e.g. Mermin 1993). This suggests that an indirect approach to quantitating nonclassicality in biological systems is required. From the above considerations, stable memories provide a quantitative lower limit on classicality, while the cellular energy budget provides an upper limit. Determining what states a cell or organism commits free energy to maintain, i.e. what the set points for homeostasis/allostasis are, may be the best approach to estimating the net classicality of biologically encoded information.

Biological encodings of classical information have lower limits of 1–2 nm in radius, e.g. the size of a typical protein active site (Liang et al. 1998) or a gap-junction channel (Sosinsky and Nicholson 2005), and about 200 fs in time, e.g. the response time of rhodopsin to photons (Wang et al. 1994). Cellular response times, even for bioelectric responses, are orders of magnitude larger and involve much larger areas. This loss in resolution is a consequence of bioenergetics:

Prediction 13: Actionable classical encodings are coarse-grained. Actionability requires irreversible encoding as in Fig. 3. The free energy to support this encoding must come from B, and hence must consume some of the bits encoded on B as fuel. The information encoded by these bits is irreversibly lost to A; hence all of A’s irreversible encodings are coarse-grained.

Any system A that encodes information irreversibly is, therefore, faced with a choice that its computational architecture must resolve: the tradeoff between preserving information via memory and losing information due to coarse-graining. A flexible solution to this tradeoff is to devote memory resources to only some input information, i.e. to the results computed by only some QRFs, allowing these selected results to be recorded at higher resolution while recording others either at low resolution or not at all. Hence we have:

Prediction 14: Living systems in complex, dynamic environments will evolve attention-switching systems. Attention has long been associated with consciousness (Engel and Singer 2001) and attention allocation is one of the main functions of competition for access to the workspace in GNW models (Dehaene and Naccache 2001), whether formulated in terms of the “rich club” (Sporns 2013), “connective core” (Shanahan 2012) or “giant component” (Wallace 2005) of the larger network. Indeed the “self” has been described as a working model of attention allocation (Graziano and Webb 2014; see also below). In humans, attention can drive entirely illusory object perception (Ongchoco and Scholl 2019), consistent with active-inference models of Bayesian-precision allocation (Kanai et al. 2015). Here, we see a requirement for active attention as a consequence of the thermodynamic requirements of classically encoding information.

The approach/avoid switching in simple chemotactic systems such as E. coli provides a basal model of the switch between active exploration and expectation revision at the heart of active inference theory (Friston 2010, 2013). Such models suggest that every such switch is governed by a reference value set by some QRF. Dorsal/ventral attention system switching in humans (Vossel et al. 2014) is sensitive to a vast array of expectations, the reference values of which are unknown and in at least some cases subject to considerable individual variation. Salience assignments driving both exploratory and reactive behavior are, in particular, highly dependent on individual experience, strongly coupled to the core self-representation and the reward system, and subject to variation in both prosocial and pathological directions (Uddin 2015). Much of ethology can be viewed as the comparative study of salience. Understanding how QRFs that regulate salience vary across phylogeny will be a major step toward answering the “what is it like?” question in a systematic way.

The considerations above reinforce the obvious point that acquiring and monitoring the usage of free-energy resources is one of the core functions of any living system. A second core function, equally necessary for the maintenance of homeostasis/allostasis, is damage control. Within the MP framework, these core functions are supported by a QRF that encodes the set points that serve as the overall homeostatic attractor. This QRF defines the “self” in MP.

Prediction 15: The “self” comprises three core monitoring functions, for free-energy availability, physiological status, and organismal integrity, and three core response functions, free-energy acquisition, physiological damage control, and defense against parasites and other invaders. These will be found in every organism. Indeed they are found even in E. coli, which has inducible metabolite acquisition and digestion systems (Jacob and Monod 1961), the generalized “heat shock” stress response system (Burdon 1986), and restriction enzymes that detect and destroy foreign, e.g. viral DNA (Horiuchi and Zinder 1972). All of these responses act to restore an overall homeostatic setpoint, i.e. an expected nonequilibrium state; hence they can all be viewed as acting to minimize environmental variational free energy or Bayesian expectation violation (Friston 2010; 2013).

Specialized molecular pathways for the core functions of the self are supplemented by specialized intercellular communication pathways in multicellular organisms and by inter-individual communication pathways in social organisms, e.g. eusocial insects (Robinson 1992) or humans. Networks of neurons support feeding, locomotion (a primary stress response) and defense already in Cnidarians (Bosch et al. 2017) and at least feeding and locomotion in Ctenophores (Moroz 2015); these functions become far more complex in bilaterian animals, particularly in active animals including arthropods, cephalopods, and vertebrates (Liebeskind et al. 2017), and form the basis of “core consciousness” in Damasio’s (2000) framework.

Considerable evidence now indicates that interoception in humans, and so presumably in mammals generally, employs a predictive coding mechanism (Hohwy 2013; Seth 2013) and is strongly coupled to the core self-representation and the salience network via the insula—cingulate—orbitofrontal loop (Craig 2010; Uddin 2015; Seth and Tsakiris 2018). This predictive coding system manages homeostasis/allostasis across the scale hierarchy from cells to organ systems (Corcoran and Hohwy 2019) and couples interoception to exteroception and proprioception (Barrett and Simmons 2015; Seth 2013; Barrett 2017; Seth and Tsakiris 2018). Intriguingly, emotional and stress responses, core components of the self, are highly sensitive to gut microbiome activity in humans and other animals (Vuong et al. 2017; Sarkar et al. 2018). As all multicellular eukaryotes have obligate symbiotic microbiomes (hence are “holobionts”; Gilbert 2014), one can expect that microbial contributions to stress detection and response are universal.

The thermodynamic costs of memory impose a particular restriction on the self, which predatory eukaryotic unicells such as Paramecium, animals, and quite possibly plants solve by weighting the less-certain future lower in resource priority than the (by default assumed to be) more-certain past:

Prediction 16: Organisms bias their “cognitive light cones” (Levin 2019), their representational and computational boundaries of concern or goal-directedness, toward the past. Memory, in other words, takes precedence over planning. The extent to which food caching, cooperative hunt organization, and other future-directed activities by nonhuman animals provides evidence of deliberative, explicit planning remains controversial (Bayne et al. 2019), with some arguing that all forms of imaginative “mental time travel” are human-specific (Suddendorf and Corballis 2007). It is, however, clear that explicit planning requires classically encoded representations of future events and so competes for resources with memory. As planning also requires explicit memory, it cannot win this competition.

The free-energy costs of mental time travel in either direction constrain it to an “off-line” activity when organisms are faced with rapid change that requires high-resolution perception and action. Such constraints also apply to the real-time representational costs of the self. Hence we can predict:

Prediction 17: Increasing real-time response requirements will disrupt encoding of the self-representation. This is observed in humans in “flow” states (Csikszentmihályi 1990), in highly automated, expertise-dependent activities including most social interaction (Bargh and Ferguson 2000; Bargh et al. 2012), and in experimental manipulations in which fatigue in various modalities affects cognitive performance (Massar et al. 2018). The extent to which nonhuman animals are able to apply “theory of mind” reasoning to themselves, and hence maintain a metacognitive self-representation, remains controversial (Martin and Santos 2016).

The picture that emerges from these energetic considerations is of a self-representation with critical functions and deep evolutionary roots (Levin 2020), but with only limited and transient exposure to awareness via explicit encoding. This is broadly consistent with the constructive views of the self outlined by Blackmore (2002), Chater (2018), Graziano and Webb (2014) among others.

Psychology in the 20th century was consumed with controversies pitting evolution (“Nature”) against learning (“Nurture”), with developmental processes straddling the conceptual gap in between. The development of robust machine learning algorithms, the advent of evo–devo (Müller 2007; Carroll 2008), the recognition that evolution itself can be viewed as a learning process (Power et al. 2015; Watson and Szathmáry 2016; Kouvaris et al. 2017), and in particular, the scale-free applicability of active inference models (Friston 2013; Friston et al. 2015; Fields and Levin 2020b,c; Kuchling et al. 2020) have done much to deconstruct these distinctions. As a scale-free approach in which any lineage can also be considered an individual (Fields and Levin 2020c), MP treats evolution, development, and learning as instances of a common mechanism. It distinguishes two nonoverlapping cases: gradual alterations in the relative contributions of QRFs or components of QRFs to outcomes, and saltatory changes in QRFs themselves.

Prediction 18: Context changes drive QRF changes. Here, we define context changes strictly: For a fixed set {oi} of observables, a context change x → y has occurred if the probability distributions Prob(oi|x) and Prob (oi|y) are well defined but the joint distribution Prob(oi|x∨y) is not (Kochen and Specker 1967; Dzhafarov et al. 2017). Here, “observables” are by definition classifiers, each of whose components consist of event/(condition, context)/valuation triples (Fields and Glazebrook 2020c; Fields et al. 2021). Under these conditions, QRF change in response to context change is required to maintain Bayesian coherence (Fields and Glazebrook 2020c).