A General Theory of Meaning

A General Theory of Meaning (Empiric Informational Fundaments) Marcus Abundis Aarau, Switzerland 55mrcs@gmail.com B¨ on Informatics

Abstract. This essay examines a meaningful void in information theory noted by Claude Shannon and Warren Weaver. It details ‘scientific meaning’ (the standard model in physics, the periodic table, etc.) in relation to information and consciousness — where a scalable bridge is framed to join these disparate topics. That bridge conveys a ‘natural informatics’ or a general theory of meaning, via three empiric informational types: materially non-adaptive, discretely adaptive, and temporally adaptive.

Informational types thus recast conflicts seen in the above informational roles, using Bateson-like ‘differentiated differences’ to detail a dualisttriune informational continuum. This paper extends an earlier a priori study of the nature of information and intelligence, by detailing general empiric features (15 pages: 6,200 words, rev3/2018).

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1

INTRODUCTION

Humanity holds adaptive skills beyond those of other species. For example, as informational adaptations: the standard model in physics, the periodic table, genomics, and Darwinism detail scientific fundaments that have wide acceptance and utility. Those ‘scientific informational strategies’ are then enlarged via serial discovery and the adoption of new ideas. Humanity’s varied use of these-andother informational strategies drives a broad ‘cultural ecology’ as an adaptive trait that surpasses the adaptive capacities of other species.

The most successful of our informational strategies is science. But despite plain scientific gains, explanatory gaps remain, marking an incompleteness in our scientific views. Given the prior gains, understanding our scientific lapses is of interest as they imply a ‘latent potential’. To address those lapses this paper explores two such gaps: 1) questions of consciousness, and 2) the nature of information. Using informational analysis, this paper posits a Shannon-Weaver [26] theory of meaning to reframe those gaps. It presents information science or a ‘natural informatics’ as a way to jointly assess science and consciousness, detailed as general informational wherewithal.

2

MODEL DEVELOPMENT

This study starts with one evident aspect (science), and two rather vague roles (information and consciousness). While ‘fixed’ science is well known, less well known are its voids. For example, the standard model in physics is partial as it excludes gravity and dark matter/energy; moreover, new insights on epigenetic roles cloud prior notions of DNA. Such half-accomplished facets mean that science holds interim models to be improved upon. In line with interim scientific models, a similar ‘interim view’ of consciousness and information is needed to initiate this study. Those interim views will be refined later, as is typical to any scientific endeavor.

To initiate that interim view : consciousness is often labeled ‘personal experience’, but this is too vague to be useful [7]. Also, more precise terms are rare since a Hard Problem supposedly prohibits a more-exact view. But a true Hard Problem is doubtful [2], so an alternative framing of ‘consciousness as personal experience’ is suggested as: A schema for engaging in spontaneous energy-matter exchanges upon an evolutionary landscape.[1] This simple view of consciousness echoes the above-noted scientific models as similar terms can equally depict any of those models. While science has long surpassed such cursory views due to ‘serial discoveries’, these terms, by placing science and consciousness on roughly shared ground, enable a minimal comparative analysis.

Next, an interim view of information is more difficult to name. Information presumably holds all of science, consciousness, and more. It entails dance, body language, music, pheromones, and emotions; indeed, a long list arises if we consider all rational and irrational roles. If we look past purely human traits, the list of informational facets explodes. The innate vastness and diversity of informational roles makes a core notion of information difficult to name. An exception to this taxonomic difficulty is Shannon’s [25] objective ‘signal entropy’, but importantly, it excludes meaningful (subjective) informational detail or ‘content’.

Conversely, this study details subjective ‘informational consciousness’ in relation to science. As such, a narrowly-defined scientific view is first used here, to later support a ‘more-general and more-detailed’ informational vista.

Narrowly-defined information typifies the noted models: the standard model, the periodic table, DNA, and Darwinism each hold meaningful subject content, with exact-ness. They detail functional values and roles even as partial models.

In contrast, the functional point of art, dance, music, etc. is unclear. Further, scientific roles are materially direct, functionally reflexive: humans do not cause ‘science to occur’, as they do with art, dance, music, etc. Science merely depicts nature, interpreted-and-recorded to the best of our abilities.

Meaningful (scientific) information is often shown in detailed ‘tables’, as with the periodic table. Those tables hold discrete data, in an exact order, to mark functional roles. The tables are often called ‘metadata’ (knowledge about

data), a term used herein to typify any narrowly-defined informational role. As a next step, detail on ‘scientific-metadata logic’ is needed to place information and science on an equal footing, and to later afford a larger analysis. Once general metadata logic for science is named, it is applied to consciousness and more-vague informational roles, as a general bridge. The balance of this paper develops that empiric informational bridge, with a final aim of joining informational (‘science’ and ‘personal experience’) roles, and to later infer a general model of intelligence.

2.1

Scientific Domains & Metadata Logic

First, it must be clear that this study uses science as a philosophic base, an outr´e view of information and consciousness is not posited. But a ‘scientific-philosophic base’ does not mean science as a singular object. Differences between scientific domains are plain and must be preserved. Still, a firm metadata bridge across the domains is needed to advance this study. That bridge must hold scientific domains apart, while giving a cohesive account of their differences.

The empiric metadata bridge framed herein consists namely of a domain’s ‘operative Fit’ and ‘referent base’ (together content), and marks a scale-able logic across scientific domains. These two roles were noted earlier [3] as subjective (S) operations and objective (O) referents, or dualist-triune S-O modeling. Next, the two roles (S-O: operative Fit and referent base) are detailed in four key scientific examples:

#1 The standard model in physics has an operative Fit (S) that employs weak nuclear and strong nuclear forces. Physics names four fundamental forces: weak nuclear, strong nuclear, gravity, and electromagnetic. The standard model references base particles (O) that join to form atomic nuclei via weak nuclear and strong nuclear forces – operative roles that bind reality (S) as we see it. Strong interactions work at two levels: 1) joining particles to create protons and neutrons, and 2) binding protons with neutrons as atomic nuclei. Next, weak interactions bring on ‘beta decay’ in protons and neutrons, where protons turn to neutrons and the reverse. The result of this reflexive proton-neutron binding and balancing is 118 uniquely ‘Fit-ted’ proton-neutron groups that serve as atomic nuclei. Thus, systemic novelty ensues as a set of objective-material variants (O0 : atomic elements) that, in turn, sustain a next operative role, as an innately scale-able base with growing complexity.

This short narrative on ‘atomic meaning’ omits many details. But exploring that level of detail is not the goal, which is, rather, to name an informational core across scientific domains as a meta-data logic. This example marks weak nuclear and strong nuclear forces driving a nuclear Fit (S) that defines atomic elements (O0 ). The exact-ness (precise Fit) of this ‘fine-tuned Universe’ is well known, and is detailed in the standard model of physics. With this necessarily simplistic scientific view [S-O Fit ⇒ (O0 )bjective result] in mind, I return to the analysis.

#2 The periodic table has an operative Fit (S) that uses electromagnetic force. Typical proton-neutron groups (one neutron per proton: referent base O0 nuclei) hold an equal number of electrons (S0 ). But variants within each of the 118 elements incite shifts; in contrast to an invariant ‘fine-tuned Universe’. For example, unequal numbers of electrons or neutrons (versus protons) in an atom mark an imbalance as: ‘+/- ions’ for electron variants, and ‘isotopes’ for neutron variants. Reflexive resolution of electromagnetic imbalances then yields three molecular bonds (S00 Fits): ionic, covalent, and metallic. This atomic binding and balancing yields diverse molecular groups, each with distinct operative traits. ‘Molecules’ thus mark a next operative role (O00 : a next objectifiedsubjective [O-S ] Fit) that again expands systemic novelty. For example, some 1060 – 10180 unique medium sized molecules are thought to be possible [5], versus the earlier 118 ‘fixed’ elements (a prior-Fit-ted Set).

Before going further, I offer a brief note on the non-adaptive or materiallydirect cases named above, versus the functionally-adaptive cases detailed below.

– In non-adaptive (direct) cases all facets are reflexive. For example, oxygen atoms are all alike, reacting equally — similarly, ‘balls thrown at a wall’ react in an embodied way. The atoms have no ‘different reactive roles’, from one atom to the next, no unique identity, as a de facto object (O) group. Next, object differences between (O) groups tell us if we confront an oxygen atom, an iron atom, a ‘golf ball’, or a [?], a fact so plain it barely merits noting. But this also means that stimulus ⇒ response (direct events, ‘blind functioning’) best typifies non-adaptive or reflexive material Fits (S), driven by objectlike ‘exact-ness’ that is innate to, or defines, the (O)bjects themselves.

– Conversely, adaptivity is quasi-reflexive, targeting ‘objective survival’. For example, a cat or bird thrown at a wall, unlike a ball, reacts with a like-butunique ‘survival logic’: all cats and birds are not equally Fit (S), unlike atoms and balls. Cat/bird agents react selectively, some in more-gainful ways than others. Hence, stimulus ⇒ processing ⇒ response best typifies adaptive logic (S), where each agent is subjectively-and -selectively different. Each agent’s ‘process’ (variably defined) helps determine ‘what it is like or means to be an [agent]’. Differential Fit-ness as adaptive logic (S) is detailed below.

Mechanistic Fits, as noted above, have no similar ‘logical risk of existential Fit-ness’ — they simply Fit or don’t Fit. Non-adaptive and adaptive cases mark two ‘logical types’ (base dualism: blind Fit versus adaptive Fit-ness) where both hold significant functional detail or meaningful content. With this ‘meaningful difference’ named as base differences in operative Fit, I next examine the character of adaptive logic or Fit-ness. #3 Genetic code has an operative Fit strongly implicating electromagnetic force, with a twist. Genomics mark life, but no theory of biology exists to explain life’s arrival. As such, we cannot easily jump from atomic or molecular roles to a (next) genomic operative role. An explanatory gap arises. This contrasts to the direct prompts and events that join the standard model and the periodic

table. This gap implies a ‘fifth fundamental force’ or some other as-yet-unseen logical role. Thus, we cannot call the interaction ‘electromagnetic’ as it entails more. I call this operative role base sentience (S), akin to Dawkins’s [10] selfish gene. Despite this imprecise framing some suppositions are still possible for this functional level.

Genetic code also exists reflexively but as part of a ‘life program’. That code is thus partly-reflexive as compared to the prior direct roles. Genetic code often occurs as chromatid pairs in cells. Chromosome and code differences define ‘agents’: a discretely differentiable coded referent base (O) for each agent’s (individual) and species’s (group) anatomy and physiology. Some code produces proteins and other code drives epigenetic (switchable cellular) roles. DNA appears as lateral-base pairs (double helix) and laddered-triplet codons: a 2-3 Fit (dualist-triune). Triplet codons enable the assembly of molecules into 20 standard amino acids, that then join as proteins. Molecular binding and balancing of amino acids into proteins is the result. Systemic novelty thus expands again, due to discretely differentiable genomic ‘code objects’ (O) recombined or reFit-ted (S) via: parental mixing, random mutation, and codon activation (3-2 Fit). These adaptive processes afford a logical ‘survive’ reaction, in response to variable-diverse environs. Conversely, adaptive failure marks extinction.

Some 8.74-million eukaryote (genomic) species are thought to exist, excluding viruses and microbes [28]. This also implies myriad subtly-unique individuals in each species’s group. Further, this number likely represents less than 1% of all species that have existed over Earth’s history [22]. As such, a large number of diverse interacting/competing species, composed of numerous discrete (subtlyvaried) individual agents, across logistically and temporally diffuse environs, drive a next operative role of ‘Darwinism’.

As before, this genomic narrative omits many details. But again, that detail is not the goal. Here, we merely note discretely coded objects (O: DNA) in a mechanistic role that initiates adaptive Fit-ness (S). Countless subtly-varied agents at individual-and-group levels arise, versus the above 118 ‘fixed’ atomic elements, and a fine-tuned Universe. Thus, the nature of S-O Fit shifts again, to where subtly-varied diversity and adaptive Fit-ness come to the fore.

Before continuing this analysis, I highlight some key evolving facets: – First, a dualist-triune Fit is noted in this genomic model. Dualist-triunes also occur in prior roles as a 3-2: atomic proton-neutron + electron, and molecular bonds as ionic + covalent-metallic, and more. A dualist-triune continuum (3-2 – 3-2) was named earlier [3] as implicating a general ‘subjective Fit’ or core pattern/innate structure, within a larger computational universe.

– Second, where-and-how ‘information occurs’ changes. The prior cases hold information as inherent to the objects that define ‘functional roles’: directly embodied. But, DNA is a discretely differentiable code or working memory that guides larger ‘functional systems’. Embodied-versus-encoded roles and systems shift the ‘informational locus’ away from embodied (O)bjects, toward informational abstracts: coded referents (O) ‘interpreted’ (S) to drive sys-

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temic functions, as working memory. This interpretative role requires some manner of base sentience (a cursory BIOS, ‘life process’).

Third, encoded roles also typify metadata and imply further information scale-ability, and growing complexity (intelligence?); also seen in Shannon’s signal entropy [3].

Fourth, a non-adaptive direct ‘mechanical Fit’ is now used for adaptive ‘mechanical interpretation’ of code (in a system), as a basic mechanical advance.

This ‘mechanical interpretive advance’ conveys a second-type of (S) Fit-ness, versus the earlier direct (O)bject-like (S) Fits (Figure 1).

Fifth, this informational shift asserts subtly-varied individual adaptation over object-like group Fits: ‘select-able Fit-ness’ in evolution by natural selection (EvNS). Without this shift to (S) adaptivity, EvNS is not possible, no selectable options would exist to allow EvNS to occur.

Sixth, subtly-varied adaptivity (S) entails functional redundancy (biological degeneracy [12]) as ‘more than one way to survive’; first evident with genomic code. This redundancy further amplifies the importance of (S)ubjective roles.

With discrete genomic code detailed here as an evolving ‘first adaptive type’, beyond the earlier non-adaptive (O)bject-like Fits, we next explore Darwinism as a second adaptive type.

Fig. 1. Exact-Fit and Fit-ness: simple-to-complex shifts. A Niche (second line) shows Agents X, Y, and Z (left) with Fit-ness (across). The Niche has eight roles (a-h, top) of equal import (0.125) with Agent traits (A−H ) functionally scored 0-9. RoleT rait pairs may be: role a) number of fingers needed as traitA , b) needed hand size as traitB , c) arm length as C , and so on. As a most-simple case consider aA alone, just two results arise: ‘Fit’ (Agent Z) and ‘no-Fit’ (Agents X and Y). Next, Total Scores (4.125, right) imply all Agents Fit equally. All agents Fit ‘to a degree’, but Agent Z best Fits a, d, and f, and fails e; Agent Y Fits e; and Agent X has no roleT rait exact-ness at all. The mildly-complex case (all eight roles) shows ambiguous Fit-ness arises with complexity, as ‘more than one way to survive’. Complex Niches afford/demand more functional ambiguity (-ness) in Agents. Complexity grows if dynamic weighting of roles-traits arises, as in open systems/environs. This map of vari-able Fit/Fit-ness shows simpleto-complex shifts key to modeling information and intelligence, and also applies to Darwinism. Lastly, functional ambiguity marks a need for ‘hands-on’ trial-and-error conduct (functional reduction), which some see as ‘computationally irreducible’ [31].

#4 Darwinism holds a test-of-time as the next operative level (S), where ‘time has value’ as another type of Fit. Survival-or-extinction conveys systemic temporality and again echoes the prior Fits: no-Fit reduces an agent to moredirect (O)-like roles, but Fit-ness implies ‘strong-Fits’ and ‘weak-Fits’ in agents.

Strong-to-weak Fits have a temporal quality (dur-ability), as compared to the earlier ‘fixed’ roles. Fit-ness is selectively framed by the happenstance innate to EvNS (open systems), and proven by the ‘passage of survived time’.

But if durability is the key trait, is it precisely defined or located? What are its (O) referents? How do durable acts (S) arise? Does a fifth force drive Fit behavior (S), or is it more (O)-like, driven by genomic code/instinct? Is ‘free will’ likely, and what does it mean? Environs themselves are often reflexively chaotic, making any firm Fit unlikely. These issues all point to questions of Fitness, consciousness, and a bounded-yet-ambiguous solution space. I label this unclear operative role Selection Dynamics, and assert that suppositions are again possible for this also-uncertain case.

Predator-prey roles reflexively frame durable niches via discrete acts. That Fit-behavior is best typified by energy/food chains or pyramids as a referent base. The chains, pyramids, and webs map causal relations (O-survival) on an evolutionary landscape. They name diverse energy-matter exchanges (weather, food, rivals, mates, disease, etc.) ‘over time’. The full chain/web is thus the most apt reference: each discrete T rait (O), in a logistically-and-temporally diffuse chain of direct prompts/events, frames each agent’s (S) role in O-survival. Here, many subtly-varied agent paths arise as: variable-diverse agents (T raits) contra variable-diverse environs (roles) ≈ dynamic niche Fit-ness (Figure 1).

Temporality asserts ‘time marking’ as another interpretive abstract, beyond coded roles. Time as now is materially direct, but ‘past’ as last now and ‘future’ as next now (a 3-2 form) implicate an imaginal working memory. For example, an agent ‘re-calls’ eating fruit in a field and walks to that field to sate its hunger (it ‘imagines’). It is not enough the agent ‘eats fruit’ in a direct (O) way, it must visualize-and-act on a Fit-behavior (S: driven re-call ), with the fruit ‘in season’ and before ‘starving to death’, in a synchronous way. Spatial-temporal Fit-ness marks a well-known fundament of ‘commonsense’ (as BIOS) [14,15].

As such, an agent’s depth, breadth, and recombination of re-called Fit (S) as temporal facets, drives success or failure in the task. Experienced recall is used in a specific way: select-able imagined Fits as a prepositional logic (natural [spacetime] language), with logistical/temporal intent — Agents walk to a field, for fruit, by lunch-time (marking place and time [29]). If not so used, agents must perpetually re-discover (versus re-call ) metabolic Fits as each homeostatic needs arises ‘over time’. Also, prepositional logic per force preceeds [true/false] propositional logic. Temporal Darwinism selects for ‘more-effective and more-efficient’ Fit-ness, with re-call as a ‘knowledge base’ (S content) of Fit-opportunities that are tried and tested, and hence, validated or negated via EvNS. ‘Recall behavior’ and spatial-temporality are as one. For example, Shannon information theory also uses temporality but as signal-and-noise, signal entropy, and message recall. Some see (temporal) Darwinism more as a genomic role, but

the value of any genomic role/trait is proven (functionally reconciled) within space-time niches. Temporal recall thus marks a ‘cognitive trait’ central to effective agency. Also, more temporality (↑O-survival ≈ ↑durable signals) enlarges ‘working memory’, via more-numerous abstract (imagined Fit) references. Growing abstract Fit-ness underpins all human ‘cultural advances’. The depth-andbreadth-and-rate (temporal acceleration) of our cultural ecology is seen, by some, to even ‘out-compete’ EvNS (resource control) — as an Anthropocene is thought to incite an extinction event. Despite ongoing debates on extinction events, human temporal accrual and re-call of information as ‘knowledge’ is plain, and with known Darwinian value (scientific informational strategies).

Temporality thus marks a ‘second evolving adaptive type’, beyond discrete adaptive code (DNA), and beyond non-adaptive direct material events (atoms, protons, etc.). Each has functional significance (meaningful content/detail) of different types, and also underpin necessarily-entwined diverse functional roles, traits, and systems.

Darwinism marks a complex informational vista where the referent base shifts again, laying across a vast logistical-temporal (space-time) landscape. As such, ‘information’ entails no one Fit (S) or code (O). Instead, meaningful content accrues with time, as temporal Fit-ness. Also, ‘four fundamental forces’ do not suitably cover Selection Dynamics. Instead, ‘16 forms of energy’ (kinetic, wave, potential, etc.) imply many energetic roles drive EvNS. Systemic EvNS has no one energetic role regularly surpassing another, as seen in direct roles. High-order energetic variety is evident as a missing ‘unified field theory’, that would otherwise join quantum-level and cosmic-level events, and marks another explanatory gap.

Darwinism departs from the prior three cases in its subtly-variant quality and chaotic events, versus more-direct roles. That spatial, temporal, and energetic vari-ability inhibits ‘computational (predictive) clarity’ — one cannot reliably assert when, where, and how events will occur (Figure 1). Chaos theory arose, in part, to address that lack of clarity. Similarly, Stephen Wolfram [31] notes ‘computational irreducibility’ in some roles, where only unsure estimates are likely (e.g., weather forecasts). Nassim Taleb [27] points to anomalous ‘black swan’ events. This bounded-yet-ambiguous solution space marks a forever-shifting landscape that mostly affords Fit-ness, with few ‘fixed’ Fit roles. Instead, myriad subtly-varied, functionally redundant (re biological degeneracy) agents contend with EvNS. If one of those agents fails in its task, myriad kindred agents ‘soldier on’ in enduring pursuit of adaptive O-survival.

In the end, we accept Darwinism, not as an exact account of all agents in every referential detail, but as a compelling meta-landscape. It holds a high intuitive-descriptive appeal, but where much is inexactly detailed. Still, Darwin’s success marks the value of a good intuitive-descriptive Fit. Also, in ‘modeling as a discipline’ some intuitive Fit is needed, before attempting more-precise mathematical proofs or the like. With no ‘initial Fit’, there is nothing to test, prove, or improve upon, as with any interim scientific model.

3

INTERIM SUMMARY: A General Theory of Meaning

Next, I summarize parts of this analysis to posit a theory of meaning. After that, I explore some implications of having such a theory. From the prior scientific cases we see that: – Types: three types of meaning (S) exist: materially direct, discrete adaptive code, and temporal adaptivity (a 3-2 form). Each type has a different ‘locus of meaning’, with specific (O)bjective content and (S) Fit-ness.

– The types are respectively seen in: 1) the standard model of physics and the periodic table, 2) genomics, and 3) Darwinism.

– Together, the types exhaust all logical meta-data possibilities, there is no fourth fundamental (a priori ) meta-data type. This does not exclude higherorder (more complex) meta-data roles and types.

– Type Reduction (lower-order): each instance of a type maintains a material Fit. If not, it becomes more (O)-like, it ‘dies’. This means Fit-ness is dynamic or vari-able, as seen in open systems/environs: ↑↓entropy, Generic Entropy.

– The types hold a dualist triune form, even in reducing to more (O)-like roles. If that 2-3/3-2 form dissipates, informational value fades and it too ‘dies’, becoming Pure Noise: ↓entropy (Dissipative Force toward physical symmetry) [3].

– Type Expansion (↑entropy, higher-order creation): types inter-act where a ‘selected-Fit’ (with a degree of exact-ness) drives new operative roles (O0 ), thus framing a basic functional continuum (↑↓scale-able S-O signaling).

– As such, systemic novelty ‘expands and emerges’ (diversifies and bifurcates), with currently unknown upward limits: Generative Entropy.

– Also, the types take a more-subjective (S) bent as adaptativity enumerates (complexity). Fit-ness becomes harder to define exactly, with myriad similarbut-distinct agents, operating jointly within dynamic environs (ambiguity).

– Pattern: as ‘types in a functional continuum’, an empiric dualist-triune ‘meta-form’ arises, as a 2-3 and 3-2 trait: a . . . 3-2-3-2-3. . . continuum.

– That continuum implies a fourth discover-able (meta-meta) informational type, past the originally-targeted metadata bridge with three basic types.

This ‘fourth type’ remains to be detailed.

Despite lingering logical gaps (life and energy), as with all interim scientific models, this theory still holds some useful implications.

First, it frames meaningful information past stock ideas: ‘naive materialism’ [11] sees everything as (O)bject-like, and ‘naive animism’ [7] sees all as adaptive (S), equally errant views. This arises again as nature-nurture debates between AI (artificial intelligence) leaders, pondering the ‘innate structure’ needed for artificial general intelligence (AGI) [19,20]. Missing in that exchange is careful consideration of meaningful content. Exceptionally, Andrew Ng [23] notes the importance of future ‘strategic data acquisitions’ and content as a competitive barrier. But engineers enamored with algorithms (blind to content?) often typify the industry. Apple’s meager introduction of a Maps program is one example [9,24]. IBM’s Watson equally suffers a lack of content and seeks to repair that

lapse by offering outside parties ‘free run-time’ in exchange for domain specific content. Conversely, this study considers meaningful content at length.

Watson requires many months of laborious training, as experts must feed vast quantities of well-organized data into the platform for. . . any useful conclusions. . . The ‘well-organized’ requirement is especially challenging for Watson, as unprepared data sets are typically insufficient.[6] Second, Claude Shannon and Warren Weaver’s [26] work offers a ‘mother for all models’ [17]. But Shannon and Weaver [26] also saw signal entropy as ‘disappointing and bizarre’: 1) disappointing without a ‘theory of meaning’ for a common-sense view of information; 2) bizarre in using a term seen as ‘disorder’ (entropy) to convey ‘informational order’. This study directly covers the issue of a missing theory of meaning. Also, apparent contradictory/ambiguous roles for disorder (‘noise’) and order (‘signal’) are shown as ↑↓entropy, with innate bi-directionality. Hence, a bi-directional informational continuum (unified empiric view) is shown here, rather than seeing noise-and-signal as disparate or contradictory roles. They are de facto complementary roles [3].

Third, the continuum holds a general dualist-triune ‘fractal Fit’ evident in open systems and closed systems, now shown adjacent on a continuum. This ‘scale-ably Fit-ted’ view thus maps simple-to-complex information and adaptive ‘intelligent’ roles. A general-empiric fractal Fit also appears in an earlier a prior conceptual dualist-triune view [3]. With further research (re fourth informational type), the map may also extend to ‘emergent properties’. Lastly, that fractal Fit may complement ‘chaos theory’ and Wolfram’s ‘computational rules’, as a way of refining computational approaches to EvNS. Finally, the model’s incompleteness should not be alarming. Interim scientific models are the norm, where one reasonably expects future gains if the model usefully augments our grasp of present circumstances.

4

COMPARATIVE STUDY: Fit-ness & ‘Consciousness’

Next, we see if a theory of meaning helps to answer questions of consciousness.

Toward that end: I first assert consciousness is reflexive, as is science. We do not cause our own ‘means of thinking’ (or experiencing) to arise, even if we often refine/expand our thoughts and feelings. Similarly, we do not cause ‘science to occur’, even if we often refine/expand our scientific models. Consciousness and science thus share a reflexive base — humans are born into ‘these things’ that lie beyond our immediate control. Next, to expand on that reflexive consciousness requires a sense of the force or energy that underpins consciousness: 4.1

Energetic Roles (re operative Fit)

If we take a cursory view of human consciousness from above as: A schema for spontaneous energy-matter exchanges on an evolutionary landscape,

Fig. 2. Trans-Disciplinary Informational Core. Scientific domains (left, Operative Level) are shown in a simple-to-complex explanatory order (bottom-to-top). The boxes mark three system types, which then implicate three types of meaning/Fits. This informational tree entails the emergence of systemic novelty (bifurcations, branching events, and so on), as reflected by an expanding Referent Base (right).

nothing here names an operative force. Still, most studies infer an electromagnetic role in human brains as cellular action potentials or ion transfers. This idea is fortified by estimates that: while the brain entails only two percent of human body mass, it uses 20% of our caloric input and 15% of cardiac output, all of which implicates some localized energetic (cerebral?) activity.

But little is proven here beyond that ‘something happens’ in the brain. Still, this may mark a type of local-coding role, as seen with DNA. But our best maps of the brain’s energetic activity (via fMRI) show only regional activity.

A local force causing named functions is not seen, as meaningful S-O models require. In terms of Shannon information theory, signal-and-noise variants are too indistinct to convey clear insights. Also, an exact study of the brain’s discrete energy signals is unlikely. Roughly 100,000 neurons fill one cubic millimeter, the smallest fMRI view currently possible [18]. This offers poor resolution of brain activity. If we use invasive tools for better measurement, we risk altering what we hope to assess. Mechanically probing minute neural facets incites noise and can injure a subject. Lastly, fMRIs and similar tools offer no insight into a ‘fifth (or other) force’ as needed to resolve a larger explanatory gap: underlying causes of life. This difficulty in assessing: life, signals, signal process, and noise, within the human brain, leads some to assert that ‘Engineers will be first to solve the question of consciousness’, via new measurement tools (personal exchange with Abi Behar Montefiore, 29 April 2016).

Alternatively, higher-order energy inputs point to the practical metabolite: creation, elimination, and sentience that drive biological homeostasis. Direct

forces causing discrete functions (S) are now evident: meaningful entropic signal-and-noise foci, via sensors/organs, differentiate diffuse Darwinian events.

But, as noted, Darwinian temporality is complex, making concise analysis difficult. Also, the brains presumed central role cannot be left out. Hence, all facets: direct (Darwinian diffuse) forces, discrete sentience, and a cerebral core must somehow energetically cohere for a unified view to arise. ‘Energetic co-ordination’ — a type of Fit — presumably occurs via some executive neural code in the brain (a DNA/computer analogy), but we are unsure. Any effort to detail that code returns us to the earlier problem of trying to read minute subtle energy signals in the brain, which current technology precludes.

Issues of energetic coordination typify a ‘binding problem’. One way to solve a binding problem is to name a functional data table (metadata, code, logic, signals, etc.). For example, four prior cases mark ‘scientific solutions’: diffusediscrete-direct roles are modeled via coded tables. Three causal types are ‘bound together’ (Fit-ted) and explanatory gaps are closed. But no such table is possible for consciousness, again, due to current technical limits.

4.2

Material Role (re operative Fit)

Next, in assessing a referent base (O) for consciousness a cerebral core is noted above. As such, issues akin to the above again arise. If the brain is a neurally coded base for consciousness, we must see how its (O) referents are situated.

Gross cerebral anatomy and functions seem plain, but referent values or code that mark meaningful roles are not clear. If we study neurons as a referent base we see over 100 neuron types, totaling some 86 billion neurons, and thousands of links per neuron. But if we cannot detail minute energy signals in neuronal systems, naming a material code is equally unlikely (How can it be measured?).

Finally, some researchers wonder if such neural code even exists [18], or if humans can comprehend such material code [21].

Alternatively, a higher-order referent base for consciousness is working memory, where aggregate experience drives survival. As such, if we fail to ‘recall’ to eat or drink, or to feel pain, in a timely manner, we die. Homeostasis also uses direct memory (body temperature ≈ 98.6◦ F ). Similarly, computer HDD memory holds vast amounts of data in operating systems, while the financial value of other data on that HDD can exceed the computer’s total cost (re Apple Maps and IBM Watson ‘content examples’). Memory or identity as ‘durably encoded roles’ also marks all manner of metadata and content. Lastly, if memory ever de-coheres, its meaningful value is lost. Hence, memory as ‘informational content’ is the essence of practical-consciousness and, in turn, indispensable in the conduct of daily life.

But classifying memory is an unsettled area. Science defines memory as the material: encoding, storage, and retrieval of data (3-2 form). Psychology frames memory in two parts: implicit and explicit (base dualism). Implicit memory has procedural, priming, and perceptual facets; explicit memory has semantic, episodic, and autobiographic roles [30]. For comparison, this dualistic-triune view of memory mirrors DNA’s material base-pairs and triplet-codons (also a type of

working memory). While we have a sense of DNA’s material referents that mark life, we have no idea of ‘a code’ that conveys mind. This ‘coding gap’ echoes the prior binding problem and limits what we can say about the ensuing roles of data storage and retrieval, or ‘informational consciousness’ as personal experience.

Despite binding problems and referent-coding gaps, we have a real sense of how memory works. Computer HDD memory is a basic example, with encoded material in a generative recombinant role. Also, we plainly ‘feel’ memory, as an intuitive-descriptive case for memory. But does that practical sentience suitably describe or explain consciousness? Is the adaptive proof afforded by ‘personal identity’ equal to, for example, the proof we use to support Darwinism? More analysis is needed to answer this question.

4.3

Informational Recombination & End Results (re operative Fit)

So far this study offers few details on consciousness. We instead see clues one cannot verify due to technical limits. Now, we must see if working memory supports Darwinian consciousness. Earlier, this study asserts that human adaptivity surpasses that of other species, with ‘scientific models’ as proof. Scientific logic, as a cognitive Fit-behavior, endures a ‘test of time’ and thus marks an already-verified proof of human temporal (Darwinian) adaptation. Hence, we can say ‘recombinant science’ (n´ee empiric memory, experience, or identity) presents the needed ‘Darwinian proof’. But there is more to say about memory in temporally adaptive roles to fortify this proof.

Nature is deeply creative, generatively reductive-and -recombinant: dynamic.

Agents endure that dynamic via effective-and -efficient adaptation: S-O models.

In turn, to create models agents need a suitably vari-able working memory to map dynamic environs, where ‘better mapping ≈ better survival’. For example, recombinant DNA (parental mixing, code activation, and mutation) marks ‘variable (adaptive) roles’, as a type of working memory. As such, we may ask: ‘How does DNA/working memory vari-ability drive Darwinian durability?’

For temporal vari-ability: DNA mixing and mutation is the least-timely of recombinant roles, requiring agent reproduction and maturation: generational life-cycles. Each adaptation must be ‘proven’ via EvNS, with outcomes framed by agent mortality. But epigenetic activation works on cellular levels, avoiding agent life-cycles. Thus, epigenetic roles are more-timely, with targeted cellular traits (via codons), framed by cell mortality. Epigenetics also hold a logical past, present, and future: discrete codons as past events, presently ‘stored’ for future needs. But beyond epigenetics, behavioral recall (personal memory) is even more-timely and more-widely targeted. Memory based roles have long-term and immediate effects (via cultural ecology). Agent and cell mortality are surpassed by abstract time marking, with outcomes now framed by the ‘mortality’ of ideas and concepts. That ‘temporal cognition’ is proven via innovative behaviors on the evolutionary landscape. Lastly, as joint operative roles, more vari-ability in temporal agency implies more innate flexibility (plasticity, adaptivity) in mapping one’s environs.

In material vari-ability: the ‘recombinant scale’ is equally vital. For example, energetic self-regulation (homeostasis) supports genomic and cellular roles. But ‘stress’ can ruin those roles and threaten agent survival (e.g., proteins fail near 115◦ F). But genomic mixing and mutation can yield new adaptive materials.

Hence, new or improved cells, sensors, organs, and the like, alter homeostasis or otherwise fortify agents against stress. This re-Fit-ting of (O) codons is tested via EvNS. But epigenetic activation offers no new materials, using codons that are already ‘in place’, versus re-Fit-ted codons. Genomic activation is thus mildly adaptive, as compared to genomic mixing and mutation. But again, surpassing (O) codon genomics, recall behavior in a recombinant role (working memory) yields more-numerous and more-diverse material inventions and discoveries. Driven material-and-abstract ideals (will: ‘material survival cognition’) compels agents to infer novel materials from memory. As noted above, our ‘doing’ of engineering, art, science, trade, etc. points to ‘Darwinian proof’ as a material-cultural ecology often seen as vital to human survival.

Thus, recombinant genomics and material-experience as ‘working memory’ drive a broad adaptive vista: 1) of long and short timing, 2) with general and targeted material aims, 3) as recurrent and novel events, and 4) in an effectiveand-efficient (select-able) manner. This adaptive continuum departs from naturenurture debates, of many type, and from ‘discrete units and levels’ of selection.

It instead asserts continuous hierarchal (scale-able) evolution [13] in an acutely creative role.

Conversely, some readers may claim all creative roles and traits are basically genomic, implying inventions like a Boeing 747, or even the location of fruited fields, reside in genetic code. But we know already the practical depth, breadth, and recombination of memory — space-time recall in minimal agency — is what underpins human creativity and innovation.

But, detail on human memory is elusive. Foremost, a weak grasp of signals associated with that informational memory is already noted. Also, the raw inputs that fill one’s memory (perceptions and context) vary widely; not just among humans and other species, but also from one human to another. Further, recombinant habits (or ‘types of intelligence’) between humans and other species, and among humans, varies greatly [8]. The innate subjectivity of what is and is not tool use, or art, useful inventions, discoveries, and research is broad. Lastly, moral and political debates often cloud creativity. For example, infamously, humans can behave like a self-predatory species (wars, peer review, etc.). Rivalry over who does and does not merit resources underpins/drives many of our social and creative roles. In fact, self-predation in humans suggests a formidable style of ‘pro-active versus natural’ select-ability that may further accelerate human Fit-ness, beyond that of other species.

Inventive psychological roles demand recombinant working memory, where science, trade, engineering, and more, rely heavily on abstract logic. But memory as detailed earlier (implicit and explicit; or encoding, storage, and retrieval) is silent on recombination. Memory without recombination has no adaptive value, logic, or capacity; it cannot describe or explain Darwinian consciousness. Detail-

ing recombinant memory as an ‘adaptive psychology’ exceeds this paper’s focus.

Instead, I broach psycho-logical roles elsewhere [4]. For now I only assert that to model conscious Darwinian Fit-ness, an empiric ‘adaptive creativity’ must be detailed, where few models now exist — except as noted herein.

5

CONCLUSION

One final task remains to conclude this paper: detailing consciousness in an informational or a quasi-scientific role.

– First, no firm proof of consciousness exists (energetic or referent base). Still, situating consciousness beside scientific roles is straightforward. Its reflexive existence firmly places it alongside scientific models, in a non-supernatural role, even if we lack desired details. Nothing (empiric) denies its natural origin, and nature is itself reflexive, as is science.

– Second, consciousness as recombinant memory in a causal role (temporal adaptation) is hard to miss. EvNS demands a creative informatics of us, and no other empiric role explains a material-cultural ecology and its role in human survival.

– Third, a ‘feedback loop’ must mediate between DNA and EvNS, best called ‘conscious survival’. Genomics alone cannot explain or verify adaptive (survival) outcomes, genomics only offer possibly-useful adaptive options.

– Fourth, while a referent-coding gap currently exists for consciousness and memory, our grasp of metadata/signal coding makes future discovery of ‘coded models’ for consciousness and memory quite likely.

– Sixth, seemingly odd emotional or irrational human acts may imply a type of recombinant rehearsal, in advance of needs for greater cognitive/behavioral plasticity, conjoint with neural plasticity. An inverse case for inflexible roles is also warranted, where ‘fixed’ efficiency carries more import [16].

As a final note, it now seems possible to improve on the earlier definition of consciousness, to read as follows: A schema for energy-matter exchanges upon an evolutionary landscape, using recombinant memory as a Self-referential survival device.

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