THE SIMULATION OF ORGANISMAL REALITY:
A Framework for Reconstructing Species-Specific Informational Worlds from Sensory Information, Environmental Structure, and Behavior
DOI: [to be assigned]
John Swygert
September 3, 2026
Abstract
All known life exists within a common physical universe, but no organism has access to that universe in its entirety. Organisms detect restricted subsets of environmental information according to their biological structures: electromagnetic radiation, chemicals, temperature, pressure, vibration, sound, fluid movement, electrical fields, magnetic information, gravity, moisture, mechanical stress, molecular signals, and numerous other variables. Those inputs are filtered by sensitivity thresholds, spatial and temporal resolution, physiological state, developmental history, memory where applicable, and biological information-processing mechanisms.
The consequence is profound: two organisms can occupy the same physical location while inhabiting radically different operational informational realities.
This paper proposes a scientific framework for reconstructing and simulating those organism-specific realities across known life. The proposal is not restricted to animals and does not require consciousness. Animals, plants, fungi, protists, bacteria, archaea, and other biological systems can all be examined according to the information they are capable of detecting, transmitting, integrating, and using in interaction with their environments.
The proposed framework separates five concepts that are frequently conflated: physical reality, biologically detectable reality, integrated informational state, operational reality, and subjective experience. The first four are potentially accessible to empirical measurement and computational modeling. The fifth cannot presently be inferred simply by reconstructing sensory or signaling information and must remain epistemically distinct.
A common simulated environment can therefore be passed through different organism-specific informational filters. The resulting models can be compared against observed movement, navigation, growth, predation, avoidance, feeding, reproduction, orientation, migration, communication, and other biological behavior. Sensory or signaling channels can then be removed, altered, amplified, delayed, or placed into conflict to determine which combinations are sufficient to reproduce observed behavior.
The ultimate objective is not to claim that science can presently reproduce what another organism subjectively experiences. It is to construct increasingly accurate simulations of what information exists for that organism, how that information relates to its biological state, and what behavior can emerge from the relationship.
1. Introduction
Humans ordinarily describe environments through human perception.
A forest is described in terms of visible trees, colors, terrain, sounds, odors, temperature, and movement. An ocean may appear comparatively featureless. Darkness appears to remove information from an environment because human visual performance deteriorates. Objects invisible to human eyes are casually described as invisible even when another organism detects them easily.
This language can obscure an important distinction.
Human perception is not physical reality.
It is a biological sampling of physical reality.
Other organisms sample different portions of the same environment.
A bee can obtain visual information from ultraviolet wavelengths unavailable to unaided human vision. Some animals obtain biologically useful information from Earth's magnetic field. Bats can extract spatial information from emitted sound and returning echoes. Pit-bearing snakes detect infrared radiation through specialized thermosensory structures. Sharks and rays possess electroreceptive systems capable of detecting weak electrical fields. Fish use mechanosensory lateral-line systems to detect water movement. Spiders can obtain detailed information from vibrations transmitted through webs and substrates.
The principle extends far beyond animals.
Plants detect light intensity, direction, spectral composition, gravity, temperature, moisture, mechanical disturbance, chemical signals, and numerous other environmental variables. Fungi respond to chemical, nutritional, thermal, mechanical, electrical, and other gradients. Motile microorganisms can follow chemical gradients. Bacteria can detect environmental conditions and alter movement, metabolism, gene expression, biofilm formation, and collective behavior accordingly.
These organisms occupy the same physical universe.
They do not possess the same biological access to it.
This paper proposes that modern sensing, environmental modeling, biological experimentation, telemetry, computational simulation, and artificial intelligence make it increasingly possible to reconstruct portions of these different informational worlds.
The central question is therefore not initially:
What does it feel like to be another organism?
It is:
What information exists for that organism to detect and use?
The distinction transforms a difficult philosophical question into a partially tractable scientific problem.
2. From Animal Perception to Organismal Reality
The concept of the Umwelt, developed principally through the work of Jakob von Uexküll, provides an important historical foundation for understanding organism-specific worlds.
An organism does not interact equally with every physical property surrounding it. Biological receptors, structures, signaling systems, and behavioral capacities determine which aspects of the environment become functionally significant.
This paper extends that principle into a computational framework.
The term organismal reality is used here cautiously. It does not mean that every organism necessarily possesses conscious experience.
Instead, organismal reality refers to the structured subset of physical and biological information that is accessible to an organism and capable of affecting its state or behavior.
This distinction allows the framework to encompass known life without requiring unsupported claims concerning consciousness.
A bacterium can possess an operational informational relationship with a chemical gradient without assuming that the bacterium phenomenologically experiences that gradient.
A plant can respond differently to wavelengths of light without requiring the assertion that the plant visually experiences color.
A fungus can alter growth in response to environmental gradients without requiring a nervous system.
Therefore:
\[ \text{informational accessibility} \neq \text{conscious experience} \]
The framework concerns the former.
3. A Common Physical Reality
Let the physical environment surrounding an organism at position \(x\) and time \(t\) be represented as:
\[ E(x,t) \]
The environment can contain a very large set of physical and chemical variables:
\[ E=\{L,A,C,T,P,M,F,V,Q,G,H,\ldots\} \]
where, for example:
\(L\) = electromagnetic radiation,
\(A\) = acoustic energy,
\(C\) = chemical composition,
\(T\) = temperature,
\(P\) = pressure,
\(M\) = magnetic-field properties,
\(F\) = fluid movement,
\(V\) = mechanical vibration,
\(Q\) = electrical properties,
\(G\) = gravitational information,
\(H\) = humidity or water availability.
The complete physical state contains vastly more information than any individual organism detects.
An organism therefore does not receive \(E\) directly.
Instead, organism \(i\) possesses a biological transformation:
\[ \Phi_i \]
such that its biologically detectable environmental state is:
\[ D_i(x,t)=\Phi_i[E(x,t)] \]
Two organisms can consequently occupy identical coordinates:
\[ E_a(x,t)=E_b(x,t) \]
while:
\[ D_a(x,t)\neq D_b(x,t) \]
This is the central mathematical abstraction of the proposed framework.
Physical reality is shared. Detectable reality is organism-dependent.
4. Detectable Reality Is Not a Reduced Photograph
An organismal simulation should not simply reproduce an ordinary human visual scene and add special effects representing unusual senses.
That approach would remain fundamentally anthropocentric.
For many organisms, information that humans regard as secondary may be behaviorally dominant.
Chemical concentration may be more important than visual appearance.
Vibration may matter more than color.
Temperature may function as spatial information.
Pressure may indicate vertical position.
Magnetic-field parameters may contribute to navigation.
Fluid movement may reveal the presence and direction of another organism.
A simulation must therefore begin with the underlying environmental variables rather than with a human-rendered scene.
The question is not:
How would a human see this environment with an additional animal sense?
It is:
What biologically usable information is available to this organism in this environment?
That distinction is fundamental.
5. Sensory Information in the Broad Biological Sense
For animals, sensory systems commonly include photoreception, audition, olfaction, gustation, touch, thermoreception, proprioception, mechanoreception, electroreception, magnetoreception, vestibular information, and other specialized systems.
Across known life, however, the term sensory must be interpreted more broadly.
Biological detection can include responses to:
photons,
wavelength distributions,
chemical concentrations,
molecular identity,
temperature,
pressure,
gravity,
moisture,
electrical gradients,
magnetic conditions,
vibration,
mechanical stress,
nutrient availability,
toxins,
oxygen,
carbon dioxide,
pH,
salinity,
neighboring organisms,
signaling molecules,
pathogens,
and internal physiological variables.
Some organisms detect environmental information through specialized receptor organs.
Others detect it at cellular or molecular scales.
The underlying principle remains:
\[ \text{environmental difference} \rightarrow \text{biological detection} \rightarrow \text{state change} \]
The architecture of detection differs enormously.
The informational principle does not.
6. Internal Reality
Organisms do not respond only to their surroundings.
Their behavior or biological response depends upon internal state.
For an animal this may include:
hunger,
hydration,
body temperature,
oxygen status,
fatigue,
reproductive state,
circadian phase,
stress,
orientation,
energy reserves,
hormonal state,
and memory.
For plants, fungi, and microorganisms, internal variables may include:
water status,
nutrient status,
energy availability,
metabolic state,
developmental phase,
gene-expression state,
cellular damage,
chemical reserves,
reproductive condition,
and circadian or other biological timing.
Therefore, identical environmental information need not produce identical responses.
Let the internal state of organism \(i\) be:
\[ S_i(t) \]
Then the organism's operational state can be represented conceptually as:
\[ O_i(t)=\Psi_i[D_i(t),S_i(t),H_i(t)] \]
where \(H_i(t)\) represents biologically relevant history.
The response becomes:
\[ B_i(t)=\Omega_i[O_i(t)] \]
Thus the complete conceptual sequence is:
\[ E \rightarrow D_i \rightarrow O_i \rightarrow B_i \]
with internal state and history continuously modifying the transformation.
7. Biological Equilibrium as an Organizing Principle
Many biological responses can be understood partly through regulation around preferred or survivable states.
Let:
\[ S^* \]
represent a preferred or viable state and:
\[ S(t) \]
the current state.
Then:
\[ \Delta S=S(t)-S^* \]
represents departure from that state.
An organism may respond to reduce some components of this difference.
A microorganism may move along a chemical gradient.
An animal may seek a preferred temperature.
A plant may alter growth toward light or redistribute physiological resources under water stress.
A fungus may extend growth toward a nutrient source.
The resulting behavior can appear strongly directional even when the organism does not possess a representation of a distant geographical objective.
This suggests:
\[ \text{disequilibrium} \rightarrow \text{detection} \rightarrow \text{response} \rightarrow \text{new environment} \rightarrow \text{reassessment} \]
Repeated local regulation can generate large-scale biological patterns.
However, equilibrium seeking should not be treated as a universal explanation.
Some organisms possess migratory programs, learned information, spatial memory, social information, or other mechanisms capable of producing behavior that temporarily moves away from immediate physiological optima in pursuit of future biological advantage.
The scientific objective is therefore to determine how multiple information systems interact.
8. Geographic Information and Environmental Equilibrium
Migration illustrates the importance of combining informational systems.
Consider a migratory marine organism.
It may simultaneously possess information concerning:
approximate geographical direction,
season,
temperature,
salinity,
water chemistry,
pressure,
current direction,
current velocity,
food availability,
predators,
reproductive condition,
and internal energy reserves.
Its movement may therefore be modeled as:
\[ B(t)=f(G,T,C,F,P,S,H) \]
where:
\(G\) = geographic information,
\(T\) = temperature,
\(C\) = chemical information,
\(F\) = fluid movement,
\(P\) = pressure or depth,
\(S\) = internal physiological state,
\(H\) = biological history.
The animal need not choose between geographic navigation and physiological regulation.
It can use both.
This produces a more realistic biological picture in which behavior represents a continuously changing compromise among multiple constraints.
9. The Moving-Highway Model
A particularly useful example arises in fluid environments.
Ocean currents are not a single horizontal flow.
Different water masses can move at different speeds and directions at different depths while possessing different temperatures, salinities, oxygen concentrations, nutrient conditions, and chemical properties.
Conceptually, the water column can sometimes be regarded as a changing system of vertically distributed moving highways.
An organism capable of relatively inexpensive vertical movement could potentially exploit horizontal transport by selecting among those layers.
The basic strategy would be:
\[ \text{sample conditions} \rightarrow \text{change depth} \rightarrow \text{sample current} \rightarrow \text{enter advantageous layer} \rightarrow \text{horizontal transport} \rightarrow \text{reassess} \]
This does not assert that a particular species necessarily performs this exact computation.
It defines a testable simulation hypothesis.
A simulated organism can be given experimentally supported sensory channels and realistic current fields. Researchers can then determine whether simple vertical-selection rules generate observed horizontal trajectories.
The organism might not need to expend energy traversing the entire horizontal distance.
It may instead expend energy finding the appropriate moving environment.
The environment itself then becomes part of the locomotor system.
10. Salmon as a Model System
Salmon provide an especially valuable example because their migrations involve different informational requirements at different geographical scales.
Substantial evidence supports olfactory imprinting and recognition in natal homing, while experimental work also supports geomagnetic information as an important component of large-scale navigation.
A simplified salmon information model might therefore include:
\[ D_{\text{salmon}}= \{M,C,T,F,P,L,\ldots\} \]
where:
\(M\) = magnetic information,
\(C\) = chemical information,
\(T\) = temperature,
\(F\) = hydrodynamic information,
\(P\) = pressure/depth,
\(L\) = light-related information.
The relative weighting of these channels could change during migration.
At oceanic scales, magnetic and environmental information may contribute to large-scale navigation.
Near a watershed, chemical information can become increasingly important.
Within tributaries, locally specific olfactory information may become decisive.
This suggests that organismal reality is not merely species-specific.
It can also be context-specific and scale-dependent.
11. The Same Ocean Is Not the Same Informational Ocean
To humans looking from a boat, an expanse of ocean may appear visually homogeneous.
For another organism, that same region could contain:
temperature boundaries,
salinity gradients,
chemical signatures,
current vectors,
pressure gradients,
magnetic variation,
acoustic information,
electrical signals,
polarization information,
prey-generated disturbances,
predator-generated disturbances,
and biological odors.
What appears to humans as empty water may therefore be an information-rich landscape.
The same principle applies to terrestrial environments.
A dark forest is not necessarily informationally dark to an organism relying heavily upon sound, smell, thermal information, vibration, or other channels.
The concept of environmental complexity must therefore be observer-relative.
12. Sharks and Electrical Reality
Sharks, rays, and related fishes provide another striking example.
Electroreception allows these animals to detect weak electrical fields associated with biological activity and environmental processes.
An organism hidden visually beneath sediment may therefore remain informationally detectable.
From the human perspective:
\[ \text{hidden} \]
may imply:
\[ \text{undetectable}. \]
From the electroreceptive organism's perspective, that inference can be false.
A scientifically constrained simulation could map measured or modeled electrical fields into a human-accessible representation.
The representation would not claim:
This is what electrical perception looks like to a shark.
Instead:
This visualization preserves selected informational relationships available through electroreception.
That wording is essential.
13. Bats and Acoustic Geometry
Echolocating bats demonstrate that spatial structure need not be derived primarily from ordinary vision.
An emitted signal interacts with the environment and returns information concerning:
distance,
direction,
movement,
surface properties,
object size,
and potentially other characteristics.
A simulation could therefore construct a dynamic spatial representation from acoustic returns.
Objects outside the relevant acoustic information space might become poorly defined even if they would be visually obvious to a human observer.
The player or artificial agent would consequently learn an environment whose geometry is produced through a different informational pathway.
Again, this does not reproduce bat phenomenology.
It reconstructs aspects of bat-accessible spatial information.
14. Bees and Spectral Reality
Flowers that appear one way to humans may contain ultraviolet patterns meaningful to pollinating insects.
Likewise, polarization information can provide orientation cues unavailable to unaided human perception.
A simulation of bee-accessible information should therefore not merely increase color saturation or apply an artistic filter.
It should transform environmental electromagnetic data according to experimentally measured receptor sensitivities and known behavioral responses.
This principle should govern the entire framework:
Translate biological information, not human stereotypes about animal senses.
15. Spiders and Extended Informational Structures
Spiders illustrate another important principle.
A sensory system need not terminate at the body's surface in any simple functional sense.
A web can transmit vibrations generated by prey, wind, potential mates, damage, or other disturbances.
The web therefore participates in information acquisition.
From a functional perspective:
\[ \text{organism}+\text{environmental structure} \]
can form an extended detection system.
Similar principles may apply to burrows, nests, water disturbances, chemical trails, microbial matrices, fungal networks, root systems, and other biologically modified environments.
This complicates the boundary between organism and informational apparatus.
The simulation framework should therefore permit environmental structures to become components of sensory acquisition.
16. Plants and the Informational Landscape
Including plants fundamentally expands the framework.
A plant does not navigate a landscape through animal locomotion, but it continuously detects and responds to environmental structure.
Relevant information can include:
light direction,
spectral composition,
photoperiod,
gravity,
temperature,
water availability,
mechanical contact,
wind,
nutrients,
neighboring vegetation,
herbivore damage,
pathogens,
and chemical signals.
Growth itself can function as a form of environmental exploration.
Roots enter heterogeneous soil environments.
Shoots encounter changing light fields.
Leaves alter orientation.
Stomatal behavior responds to environmental and internal conditions.
Development changes according to accumulated information.
Consequently, an organism does not need rapid locomotion for its relationship with reality to be dynamically informational.
A plant's operational reality unfolds on different temporal and spatial scales.
17. Fungal Reality
Fungi further challenge animal-centered concepts of perception.
Fungal hyphae grow through spatially heterogeneous environments while responding to nutrients, chemicals, moisture, temperature, physical barriers, interactions with other organisms, and internal resource conditions.
A mycelial network can occupy a distributed spatial domain.
Its informational architecture may therefore be fundamentally unlike that of a centralized animal nervous system.
A fungal simulation could model:
\[ \text{local detection} + \text{distributed growth} + \text{resource transport} + \text{network history} \]
to examine how large-scale adaptive patterns emerge from local interactions.
This does not require assigning humanlike cognition to fungi.
It requires only acknowledging that biological information can be acquired, propagated, and acted upon through architectures unlike our own.
18. Microbial Reality
At microbial scales, the concept becomes even more revealing.
For a bacterium, a chemical gradient can constitute meaningful spatial structure.
Changes in nutrient concentration, oxygen, pH, temperature, toxins, signaling molecules, and other variables can dramatically alter behavior.
The environment can therefore be represented not primarily as visible objects but as multidimensional fields.
A microbial informational world might be modeled as:
\[ O_{\text{microbe}} = f(C_1,C_2,\ldots,C_n,T,pH,O_2,S_i,\ldots) \]
where chemical gradients dominate spatial organization.
To humans, a microscopic droplet may appear nearly uniform.
To a microorganism, it can contain mountains and valleys of biological opportunity expressed chemically rather than visually.
19. Communication Creates Additional Reality
Organisms do not merely detect environmental information.
They also modify the informational environment.
Animals vocalize, display, scent-mark, vibrate substrates, produce electrical signals, and alter their surroundings.
Plants release chemical compounds and interact through numerous signaling pathways.
Microorganisms produce quorum-sensing molecules.
Fungi and other organisms modify chemical and physical environments.
Therefore:
\[ E(t+1) \]
is partly a product of organismal activity at:
\[ E(t). \]
Organisms are simultaneously receivers and producers of biologically meaningful information.
The simulation must eventually represent this feedback.
20. Building an Organismal Reality Model
A standardized simulation methodology can be constructed in stages.
Stage 1: Construct the Physical Environment
Represent measurable environmental variables independently of any organism.
Stage 2: Construct the Biological Detection Profile
Determine which environmental variables the organism can detect and at what thresholds, ranges, resolutions, and timescales.
Stage 3: Model Internal State
Represent relevant physiological and developmental variables.
Stage 4: Construct Information Integration
Model how multiple inputs influence one another and how their weighting changes with context.
Stage 5: Generate Behavioral or Biological Responses
Allow the organism to move, grow, orient, communicate, feed, avoid, reproduce, or otherwise respond.
Stage 6: Compare Against Real Organisms
Test simulated outputs against empirical observations.
Stage 7: Manipulate Information
Remove or alter individual information channels and determine how behavior changes.
This creates an iterative scientific framework rather than a static visualization.
21. Sensory Ablation in Simulation
One of the most powerful experimental methods would be computational sensory ablation.
Suppose the complete model is:
\[ O_i=f(A,B,C,D,E). \]
Researchers can test:
\[ O_i^{(-A)} \] \[ O_i^{(-B)} \]
and combinations such as:
\[ O_i^{(-A,-C)}. \]
If removal of a channel produces behavior resembling biological sensory-disruption experiments, confidence in the model increases.
If removal produces no meaningful effect despite strong biological evidence that the sense is important, the model is incomplete.
This makes the simulation falsifiable.
22. Cue-Conflict Experiments
Information channels can also be deliberately placed into conflict.
For example:
magnetic information indicates one direction,
chemical information another,
temperature favors a third,
current transportation favors a fourth.
The organism's response reveals how information may be weighted.
If biological experiments produce similar cue conflicts, simulation results can be directly compared with empirical behavior.
This allows researchers to investigate not merely what an organism detects, but:
Which information wins when reality presents conflicting instructions?
That may be one of the most informative questions in comparative behavior.
23. Artificial Agents Inside Organismal Realities
Artificial intelligence provides another experimental method.
An artificial agent can be placed inside the simulation but denied access to the complete environmental state.
Instead of receiving:
\[ E(t), \]
it receives only:
\[ D_i(t). \]
The agent therefore operates under approximately the same informational constraints modeled for the organism.
Researchers can then ask whether survival, navigation, migration, foraging, avoidance, or other behaviors emerge from learning.
If a relatively simple information architecture produces behavior resembling that of the organism, this may identify sufficient computational mechanisms.
If it does not, additional information or architecture may be required.
The artificial agent becomes a tool for testing hypotheses about informational sufficiency.
24. Superimposing Organismal Realities
One of the most informative visualization methods would be to superimpose multiple organismal realities upon the same physical environment.
Suppose a salmon, shark, microorganism, human, and planktonic organism occupy overlapping water.
The physical simulation remains constant:
\[ E. \]
Each receives:
\[ D_1,D_2,D_3,D_4,D_5. \]
Researchers can compare these layers individually or simultaneously.
This creates a comparative informational map of an ecosystem.
The method could reveal which environmental variables are shared among species, which are unique, and where interactions occur between informational worlds.
Predator and prey may detect one another through different channels.
Pollinator and flower may participate in mutually structured signaling environments.
Host and pathogen may inhabit overlapping chemical realities.
The ecosystem becomes not only a network of energy and matter.
It becomes a network of partially overlapping informational realities.
25. Human Translation of Nonhuman Information
A practical challenge remains.
Humans cannot directly experience many biological information channels.
Therefore the simulation requires a translation layer:
\[ D_i \rightarrow H_i \]
where \(H_i\) is a human-accessible representation preserving important relationships within \(D_i\).
Magnetic intensity might become spatial texture.
Electrical fields might become dynamic contours.
Chemical gradients might become visible density fields or sound.
Vibration might become geometry or auditory structure.
Ultraviolet information can be mapped into visible colors.
Echolocation can be translated into transient spatial forms.
No translation should be presented as literal phenomenology.
The scientifically correct statement is:
The representation is an analogy preserving selected informational structure.
This prevents an aesthetically convincing simulation from becoming an epistemological overclaim.
26. Validation
A scientifically useful organismal-reality simulation must be validated against independent evidence.
Potential validation data include:
migration trajectories,
telemetry tracks,
orientation experiments,
laboratory sensory experiments,
sensory thresholds,
receptor physiology,
spectral sensitivities,
electrophysiology,
genetic disruption studies,
growth responses,
chemical-gradient experiments,
predator-prey interactions,
feeding behavior,
circadian behavior,
environmental measurements,
current profiles,
magnetic-field measurements,
temperature profiles,
and field observations.
A model should not be considered successful merely because it produces plausible behavior.
It should reproduce measurable biological patterns not used solely to construct it.
27. Evidence Classification
Every component should carry an explicit evidence status.
A practical classification would be:
Established — supported by extensive, replicated evidence.
Supported — substantial evidence exists, although important details remain unresolved.
Provisional — plausible and evidence-informed but incompletely demonstrated.
Experimental — introduced as a hypothesis for computational testing.
Representational — a human-interface decision rather than a claim about biological phenomenology.
This system would make uncertainty visible.
Scientific uncertainty becomes metadata.
28. Falsifiability
The framework becomes scientifically valuable only if it can fail.
A model should make predictions.
If a proposed salmon navigation architecture predicts trajectories inconsistent with real salmon, the architecture must be revised.
If a simulated plant response fails under experimentally measured conditions, the model is inadequate.
If a microbial model predicts movement opposite to observed chemotactic behavior, it is wrong or incomplete.
Therefore:
\[ \text{simulation agreement} \]
provides support, while:
\[ \text{simulation disagreement} \]
provides information.
Simulation should not be used to illustrate conclusions already assumed.
It should be used to expose assumptions to testing.
29. The Boundary of Consciousness
The framework has obvious implications for consciousness research, but those implications require restraint.
Reconstructing everything detectable by an organism would still not establish what that organism experiences subjectively.
Therefore:
\[ \text{Detectable Reality} \neq \text{Subjective Reality}. \]
Likewise:
\[ \text{Operational Reality} \neq \text{Consciousness}. \]
Nevertheless, understanding the information available to an organism establishes an important boundary condition for theories of experience.
Before asking:
What is it like to be a bat?
science can ask:
What information is available to a bat, how is it structured, and what behavior does it support?
Before asking whether a plant or microorganism possesses any form of experience, science can characterize the information it detects and integrates without prejudging the consciousness question.
This separates empirical investigation from philosophical inference.
30. Comparative Reality as a Scientific Discipline
The framework suggests the possibility of a broader field of comparative organismal reality.
Instead of cataloging senses independently, researchers could characterize organisms according to multidimensional informational profiles.
For each organism:
\[ R_i= \{r_1,r_2,\ldots,r_n\} \]
where each \(r\) represents a measurable information channel and its properties.
Species could then be compared according to:
information type,
detection threshold,
spatial range,
temporal resolution,
directional sensitivity,
integration,
persistence,
behavioral relevance,
energetic cost,
and environmental dependence.
The result would be something analogous to a coordinate system of biological information access.
Two organisms taxonomically distant from one another might occupy surprisingly similar informational regions.
Closely related organisms might differ substantially.
This would permit comparison based not only on anatomy or genetics but on how organisms access physical reality.
31. Ecosystems as Overlapping Informational Worlds
Ecology traditionally examines relationships involving energy, matter, populations, competition, predation, symbiosis, and environmental conditions.
An informational perspective adds another layer.
Every ecosystem contains overlapping detection and signaling networks.
A flower reflects particular wavelengths and releases chemicals.
A pollinator detects some of them.
A predator detects the pollinator.
A plant detects herbivore damage.
Microorganisms detect chemicals released by roots.
Fungi encounter chemical and physical information within soil.
None of these organisms necessarily possesses access to the same environmental representation.
The ecosystem can therefore be conceptualized as:
\[ \mathcal{E} = \bigcup_{i=1}^{n}O_i \]
where each \(O_i\) is an organism-specific operational informational reality embedded within a shared physical environment.
The overlaps are biologically consequential.
The non-overlaps may be equally consequential.
32. Scientific Applications
A mature simulation framework could contribute to numerous fields.
In movement ecology, it could test navigational hypotheses.
In sensory ecology, it could integrate multiple sensory channels within realistic environments.
In behavioral ecology, it could examine information-dependent decision making.
In plant biology, it could model environmental detection across changing conditions.
In mycology, it could investigate distributed environmental response.
In microbiology, it could visualize chemical and physical landscapes at organismally relevant scales.
In neuroscience, it could provide realistic sensory input environments.
In artificial intelligence, it could test agents operating under biologically constrained information.
In conservation biology, it could investigate how human environmental modification alters informational environments.
In education, it could allow students to explore biologically meaningful realities radically different from their own.
And in consciousness research, it could establish empirically constrained descriptions of the information potentially available for experience without confusing those descriptions with experience itself.
33. Open Scientific Infrastructure
The framework would benefit strongly from open-source development.
No single research group possesses complete expertise concerning every sensory system and every organism.
A modular architecture could allow specialists to construct and validate individual components.
A species or organism module could contain:
taxonomy,
environment,
biological detection systems,
receptor characteristics,
detection thresholds,
temporal resolution,
spatial resolution,
internal-state variables,
behavioral rules,
known uncertainties,
experimental evidence,
validation datasets,
and references.
Version control would permit models to evolve as scientific knowledge changes.
Competing models could coexist.
A researcher could select:
\[ M_{i,1} \]
or:
\[ M_{i,2} \]
for alternative hypotheses concerning the same organism.
The simulation therefore becomes not a declaration of biological truth but an evolving scientific laboratory.
34. Relationship to Interactive Simulation
The scientific framework described here also provides the foundation for interactive systems in which humans can enter translated approximations of organism-specific informational worlds.
Such systems can function as research tools, educational environments, or games.
The scientific framework and the entertainment application should remain distinguishable.
The simulation asks:
What information is biologically available?
The interactive representation asks:
How can that information be translated into a form a human can explore?
A scientifically grounded game can therefore become a public interface to a serious comparative-biology infrastructure without turning visualization choices into biological claims.
35. The Fundamental Experiment
The most revealing experiment may ultimately be remarkably simple.
Construct one physical environment.
Place multiple organisms within it.
Measure the same underlying reality.
Then calculate what portions of that reality are accessible to each organism.
For organism \(A\):
\[ D_A=\Phi_A(E) \]
For organism \(B\):
\[ D_B=\Phi_B(E) \]
For organism \(C\):
\[ D_C=\Phi_C(E) \]
Then compare:
\[ D_A \cap D_B, \] \[ D_A \cap D_C, \]
and:
\[ D_A-D_B. \]
The intersections represent shared information.
The differences represent portions of reality available to one organism but unavailable to another.
That comparison could be extended across entire ecosystems.
For the first time, researchers could attempt to visualize not merely where organisms live, but which dimensions of the environment exist biologically for each of them.
36. Conclusion
Life occupies one physical universe.
It does not follow that all life occupies one informational universe.
Every organism encounters physical reality through biological constraints.
Photoreceptors admit some electromagnetic information and exclude other information.
Chemical receptors transform molecular distributions into biological signals.
Mechanoreceptors make movement and vibration meaningful.
Electroreception exposes fields inaccessible to ordinary human perception.
Magnetoreception can make planetary magnetic structure biologically relevant.
Plants detect environmental patterns without animal sensory organs.
Fungi explore heterogeneous environments through distributed growth.
Microorganisms inhabit chemical landscapes whose important structures may be effectively invisible to us.
The consequence is not that every organism necessarily possesses a conscious private universe.
The consequence is more fundamental and more scientifically defensible:
Every organism has a biologically bounded relationship with physical reality.
Modern science can increasingly measure both sides of that relationship.
We can measure environments.
We can characterize receptors and response systems.
We can track organisms.
We can measure internal states.
We can manipulate sensory information.
We can reconstruct environmental fields.
We can build computational models.
And we can compare the resulting behavior against life itself.
This makes possible a new class of simulation:
\[ \text{Physical Reality} \rightarrow \text{Biological Detection} \rightarrow \text{Information Integration} \rightarrow \text{Operational Reality} \rightarrow \text{Biological Response}. \]
The objective is not to pretend that humans can simply look through the eyes—or sensory apparatus—of another organism.
Many organisms do not have eyes.
Many do not have nervous systems.
Some detect aspects of reality for which humans possess no corresponding sense.
The objective is instead to reconstruct the informational relationships through which organisms encounter the world.
Once those relationships are reconstructed, they can be compared.
They can be superimposed.
They can be experimentally manipulated.
They can be given to artificial agents.
They can be translated for human exploration.
And they can be tested against actual biological behavior.
The same forest can therefore become many informational forests.
The same ocean can become many informational oceans.
The same soil can become many informational landscapes.
The same droplet can become many biological worlds.
There is no need to propose multiple physical realities.
There is one physical environment containing information that different organisms access differently.
Understanding those differences may provide an extraordinary new perspective on behavior, ecology, evolution, biological information processing, and eventually the scientific study of consciousness.
To understand how another form of life behaves, we may first need to understand which reality is available for it to behave within.
References
Bennett, A. T. D., & Cuthill, I. C. (1994). Ultraviolet vision in birds: What is its function? Vision Research, 34(11), 1471–1478. DOI: 10.1016/0042-6989(94)90149-X.
Bradbury, J. W., & Vehrencamp, S. L. (2011). Principles of Animal Communication. Sinauer Associates.
Darwin, C., & Darwin, F. (1880). The Power of Movement in Plants. John Murray.
Duszyński, J., Świeżawska, B., Szmidt-Jaworska, A., & Jaworski, K. (2025). Plants and electrical signaling: A long and continuing history of investigation. International Journal of Molecular Sciences.
Foster, K. W., & Smyth, R. D. (1980). Light antennas in phototactic algae. Microbiological Reviews, 44(4), 572–630.
Holland, R. A. (2007). Orientation and navigation in bats: known unknowns or unknown unknowns? Behavioral Ecology and Sociobiology, 61, 653–660.
Lohmann, K. J., Goforth, K. M., Mackiewicz, A. G., Lim, D. S., & Lohmann, C. M. F. (2022). Magnetic maps in animal navigation. Journal of Comparative Physiology A, 208, 41–67. DOI: 10.1007/s00359-021-01529-8.
Lohmann, K. J., & Lohmann, C. M. F. (2019). There and back again: natal homing by magnetic navigation in sea turtles and salmon. Journal of Experimental Biology, 222, jeb184077. DOI: 10.1242/jeb.184077.
Lohmann, K. J., Lohmann, C. M. F., & Endres, C. S. (2008). The sensory ecology of ocean navigation. Journal of Experimental Biology, 211, 1719–1728. DOI: 10.1242/jeb.015792.
Mora, C. V., Davison, M., Wild, J. M., & Walker, M. M. (2004). Magnetoreception and its trigeminal mediation in the homing pigeon. Nature, 432, 508–511. DOI: 10.1038/nature03077.
Quinn, T. P. (1997). Homing in Pacific salmon: mechanisms and ecological basis. Journal of Experimental Biology, 200, 2287–2294.
Stevens, M. (2013). Sensory Ecology, Behaviour, and Evolution. Oxford University Press.
Uexküll, J. von. (2010). A Foray into the Worlds of Animals and Humans: With a Theory of Meaning. University of Minnesota Press. Original work published 1934.
Walker, M. M., Dennis, T. E., & Kirschvink, J. L. (2002). The magnetic sense and its use in long-distance navigation by animals. Current Opinion in Neurobiology, 12(6), 735–744. DOI: 10.1016/S0959-4388(02)00389-6.
Wiltschko, R., & Wiltschko, W. (2021). The discovery of the use of magnetic navigational information. Journal of Comparative Physiology A, 207, 35–46.
John Swygert
September 3, 2026
Copyright © John Swygert 2026
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