Living systems have self-sustaining properties that distinguish them from inert physical systems (). They are typically composed of cells, maintain a stable internal state by converting food into usable energy, and grow and reproduce. Energy is acquired by the organism to keep its entropy low, defying for a time the inexorable march of the second law of thermodynamics. The morphology and physiology of living organisms facilitate their ability to acquire energy for sustenance. Constrained by morphology and physiology, another self-sustaining property of living systems is adaptive behavior. Behavioral strategies that improve an individual’s ability to acquire energy and convert it to produce successful offspring ultimately help its lineage proliferate over evolutionary time through the process of natural selection (). The latter is the ultimate goal of every animal (). This goal-directedness of animal behaviors is a feature that is absent in purely physical systems ().

In the following, we put forth three essential principles for biological behavior. For each one, we will first provide its theoretical articulation followed by illustrative examples in the domain of behavior. The three principles are a subset of features arguably unique to life. We claim their necessity to understanding living organisms and reformulate them as fundamental principles in behavior rather than as mere characteristics. The principles are materiality, agency, and historicity. Behaviorally, they account for the constitutive roles of (1) morphology and environment; (2) action-perception closed loops and purpose; and (3) individuality and historical contingencies. These factors make up “the life of behavior.” We hope that these considerations will shed light on our typical approach to understanding the mechanisms of behavior and why it is inadequate. We will elaborate upon each of these principles, but here is a summary of what to expect.

First, we often presume that the mechanisms of behavior will come more sharply into relief if the context in which the animal is placed is sterile (like a vat; e.g., fMRI scanner, Skinner box, empty Petri dish, or monkey chair), the stimuli presented to the animal were simplistic and passively delivered (e.g., oriented lines, tone-pips, or tastants), and the body is considered simply as a container for the brain or the passive interface for the brain to control (the body as another vat). The presumption is that by doing so, many variables are controlled for and thus factored out, so that one can focus on just the neural or other physiological data being acquired ( Figure 1 A). However, there is no avoiding the fact that body and brain are inseparable in their function () and that they operate in a world that is unique to the organism under study () ( Figures 1 B and 1C). Second, we often take up the formulation that perception and action are the beginning and end of a linear process, a stimulus-response arc or “sensorimotor transformation” ( Figure 1 D). The organism, however, has goals (energetics, survival, and reproduction) and agency (it initiates actions rather than just responds to “irritations”). Its behavior is more akin to a control loop with inputs modifying outputs that in turn modify the next set of inputs, etc., to achieve a life-sustaining goal ( Figures 1 E and 1F). Finally, behaviors are variable. This is inconvenient to the scientist, but averaging across trials and groups of animals eschews the individual (). Along the same lines, repeatedly presenting the same input signals in a temporally structured manner in order to repeatedly produce the same motor output obscures the fact that in the real world, no such thing could ever occur ( Figure 1 G). From the perspective of the animal, variable motor outputs are the means by which to achieve invariant perceptions that can satisfy the animal’s goals (). Specific goals are not static but vary in time, and each animal has a history on multiple timescales that determine its behavior ( Figures 1 H and 1I).

(G) Having de-contextualized space (A) and linearized time (D), the study of behavior falls prey to the “manipulate and measure” approach, where variability is deemed noise to be tamed with statistics.

(F) Servomechanisms fail to do justice to the fact that animals are proactive and have purposes in mind. Agency is intrinsic to life.

(E) Beyond linear causality, a feedback loop goes through the world and back. Behavior then is not simple production of output but rather control of input.

(D) Inertia is the default state of inert objects, whose motion is the result of reactive push-pull forces. Along with (A), brains are computers that transform stimuli into responses, which are equated with behavior.

(C) The brain and its body live in the real world, under the constraints of physical surroundings (umgebung) but also in the meaningful environment of the animal (umwelt). In sum, materiality matters.

(A–I) What “vats,” “arcs,” and “stats” miss (top part), and three fundamental principles (bottom part) for the behavior of living organisms: materiality, agency, and historicity. Images in (A)–(C) concern embodiment and meaningful space, (D)–(F) deal with causality and real time, and (G)–(I) depict concrete history and individuality.

Now, we will elaborate upon each of the three principles of life as they relate to behavior.

In the analysis of tool-use behavior, the body and the environment cannot be added later. They may be literally peripheral, but not conceptually. The behavior only emerges because of the organism’s worldly and bodily activities. In terms of the world, for example, chimpanzee populations exhibit differences in ant foraging, as tree branches usable as tools for gathering ants are present at some sites, but not in others (). Even the species of ants can influence tool use (); more aggressive ant species, for instance, necessitate longer tools by chimpanzees to avert biting of their hands (). In light of all this exquisitely rich evidence about the myriad of spatial and temporal dimensions that constitute tool use by chimps, one may wonder to what extent a purely neural explanation of such behavior—one that would successfully map its circuitry completely and exhaustively dissect all the so-called necessary and sufficient neurons involved (something not too far, it seems, from the neuroscientist’s dream;)—could become an explanation of the behavior ().

Chimps use sticks to gather honey, termites, and ants from the ground or trees in order to eat them without getting stung, bit, or pinched (). We can use this behavior to illustrate how materiality, agency, and historicity contribute to an explanation. The goal of tool-using in this case is to acquire energy. This goal belongs to a hierarchy of goals: a higher level than having enough energy is the goal of not dying; a level below acquiring energy is the goal of finding a tool and then using it properly, which in turn entails the goal of approaching the potential food source. Levels above answer why; the levels below answer how. Depending on the task (breaking, prodding, or collecting), the stick can be held in various ways, including the precision grip (that is, between any two fingers but without the use of the palm). This illustrates an equifinality; chimps will diverge in the means but converge in the goal. To perform a precision grip requires specific hand biomechanics (e.g., thumbs that rotate around a joint;). All Old World primates (and one New World monkey) can perform a precision grip; other animals cannot. Tool use also requires specialized neural circuitry operating in conjunction with those biomechanics in a goal-directed manner. The precision grip is correlated with extensive cortico-motoneuronal terminations in the ventral horn of the spinal cord (), and motor planning and coordination are associated with neocortical areas 2 and 5, which are enlarged in tool-using primates (). Finally, tool use is also a learned behavior; young chimpanzees learn by watching older chimpanzees combined with trial and error (). Thus, tool use is a behavior bound to the body and brain circuits, and that emerges on evolutionary and developmental timescales.

Note that like the world itself, the individual body changes over time and, as a result, so does an individual’s umwelt and the behavior exhibited. In human infancy, for example, there are changes in the ways the body moves in, and interacts with, the environment (e.g., sitting up to crawling to walking). These changes, in turn, impact the development of skills and experiences that play a role in the emergence of other behaviors such communication (). Learning to sit upright without support allows deeper breathing that increases the power for producing longer, less noisy vocalizations like babbling (consonant-vowel combinations) (). The production of babbling results in more frequent contingent responses from caregivers (), which acts as a ratchet for further vocal learning by the infant (), thus leading to new forms of communication with increasing complexity. An infant that can sit up has also changed the way she observes the world and engages with caregivers (e.g., joint attention), as the infant can now freely rotate the head and trunk. Moreover, the hands are more able to grasp and manipulate objects, providing greater opportunities to share with caregivers who, in turn, facilitate further communicative development (e.g., by naming objects held by infants) (). In these scenarios, caregivers (part of the umwelt) adjust to the infant’s changing behavior. The developing body and motor skill acquisition results in an ever-changing meaningful environment.

Umwelts are unique not only to species but also to individuals. How you engage with the world is different from how others do, and this difference, of course, can be related to an individual’s body in nontrivial ways. For example, in both the visual and auditory domains, objects with a looming motion toward an observer (i.e., are rapidly approaching versus receding) have a perceptual priority. When observers are asked to estimate the arrival of looming sound sources, for instance, they consistently respond too early, perceiving them as closer than they actually are (). In a natural environment, this underestimation results in more time to act—to evade or engage the source—and thus affords a “margin of safety” that may provide a selective advantage (). This idea is consistent with comparative work on the perceptual biases toward looming sounds () and with neuroimaging data showing that looming sounds preferentially activate motor planning areas (). However, perceiving and acting in response to looming sounds depend not only on perceptual abilities and the neural activity it elicits but also on the motor capabilities of the listener. Listeners with less physical strength and lower aerobic fitness respond sooner to looming sounds and with a larger margin of safety than listeners in better health (). This shows that, within a species, the umwelt and the individual body form a system that enables adaptive behavior.

dubbed these assumptions about what an animal may or may not care about as the “umwelt gamble.” By gambling, one ignores the fact that animals are biased toward certain modalities and signals within them and may interpret such signals in a way an experimenter cannot guess at. Even the manner in which we mark individuals for purely identification purposes (e.g., collars, tags, dyes, bands) can influence behavioral patterns that we may think are isolated only to the experimental variables of our own choose. For example, a study in which male zebra finch were individually identified through the use of colored leg bands (a widely used practice) unexpectedly found that those leg bands influenced female choice (). Similar unexpected influences of colors occur in humans as well. Across a range of sports, wearing red is consistently associated with a higher probability of winning, indicating that we respond to different colors differently without even realizing it (). It is thus more appropriate to conceive the world as a forum for action than as a place of things.

The biology of animals thus poses a conundrum to the object-subject separation that allowed so much progress in physics in particular and science in general. The underlying mission of physics is to establish laws between objects that are valid regardless of the point of reference, which implies and necessitates the absence of any absolute center. Yet, biology is a historical science whose objects of study are individuals. To be objective would then entail to decenter oneself (the scientist) while keeping the organism at the center. We neuroscientists, rightly concerned with anthropomorphism in our interpretations, should revisit the notion of objectivity in favor of zoomorphism: to study animal behavior from the perspective of the animal. This, of course, has immediate practical consequences for the design of our experiments. The use of artificial, “simplified” stimuli in behavioral and neural experimentation is commonplace (oriented lines, tones, moving dots, etc.) (), but every choice made by us with our own goals in mind is making assumptions about the perception and goals of the animal under study. A paradigmatic example is when a scientist says that no stimuli were presented to the animal (an approach that seeks to control or limit sensory experience), whereas any human prisoner in isolation knows that there can hardly be a bigger perceptual experience than that.

As scientists, no doubt, we can act on an organism. Umwelts (or umwelten, in proper German) are not just parallel universes, they intersect; cut a tree, and the birds must leave. Yet, the physical excitation we present to animals as part of our standard experimental designs not only needs to occur but also has to be noticed. What the organism cares about is what it will attend to, thus dictating its perceptions (and the actions, by virtue of which it will manage those perceptions). In other words, the umwelt is “an elective extraction from the Umgebung” (, p. 112). Paradoxically, in the typical behavioral or neuroscience experiment, the umgebung (which is alien to the animal) is in turn nothing other than the umwelt of the scientist (scientists are humans, and humans are also animals), who are operating in the symbolic universe of abstractions (coding, entropy, statistical significance, etc.). The umwelt thus reveals a strong and concealed “conflict of interests” in biology. If behavior is a functional loop that is enacted in a meaningful context, then animals (the objects of study of the scientist) are also subjects (studied by other subjects: us). This creates what could be called a “clash of umwelts.” The almost uncountable variety of worlds—all seemingly as far away from each other that they become almost nonoverlapping, even incommunicable ()—are actually in conflict with one another. What is meaningful from the point of view of the organism need not be from the point of view of the scientist studying it, and vice versa. Asputs it, “Hedgehogs as such do not cross roads….On the contrary, it is man-made roads that cross the hedgehog’s milieu”(p. 22).

All animals share a common world, but not all animals have a world in common. Each organism has its own umwelt (meaningful environment) and not just an umgebung (physical surroundings) (). The umwelt is a foundational concept in zoology, with theoretical implications for neuroscience’s anthropomorphism (). Every animal species (and, within it, every individual) experiences the world differently. The world, considered as a physical fact, is the objective space in which we observe animals behave. As a biological fact, however, the relation between organism and environment is such that the former not only submits to the latter but actually carves it out. For psychologists, the Gibsonian translation is “affordance.” It means, for example, that a stone is not simply a stone but a-stone-for-a-snail as an opportunity to climb or a-stone-for-a-human as an opportunity to throw and hit something far away.

Along similar lines, it would typically (and reasonably) be presumed that changes in vocal production over the course of development are the results of learning and, thus, changes in the nervous system. In growing marmoset monkeys, however, computational modeling and experiments placing infants in helium-oxygen environments revealed that, as lungs grow bigger, their changing sensory feedback onto vocal central pattern generators results in the disappearance of the production of context-inappropriate vocalizations without any need for concomitant changes in CNS structure (). The developing body can create distinct behavioral changes by itself and reduce the computational and energetic burden on the nervous system (a strategy that is often exploited by roboticists;).

The developing body also shapes motor output. Human newborns, for instance, are able to make well-coordinated stepping movements when held upright, but these movements disappear by the time they reach ∼2 months of age (). While it was assumed by many that the change in stepping behavior was due solely to the developing nervous system (e.g., the behavior disappeared because there was too much inhibition that had not been “pruned” back yet;),hypothesized that the loss of stepping behavior was due to body growth; the infants’ legs typically fatten up postnatally, and they do not yet have the strength to move heavier legs. To test this hypothesis, they submerged the infants’ legs in water, effectively decreasing their mass. This resulted in the reappearance of stepping and thus falsified the alternative hypothesis that neural change was necessary. The change in behavior was due to changes in the body.

The constitutive role of our bodies’ physical conformation to behavior and experience is reflected in how it changes and guides the nervous system during development. Continuing with the ear example, we localize sounds well at a very young age, but since our ears are still growing and changing shape, the developing brain must recalibrate itself to account for these bodily changes (). Contrary to what one would assume, the neural circuits of the auditory system are dependent upon the shape of the ears to guide their function. Auditory cortical neurons in very young ferrets encode spatial location poorly (). The standard presumption to explain this poor tuning would be that the neural circuits are still developing (e.g., perhaps lacking refined inhibitory inputs) and/or need to time to be molded by experience. However, the coarse spatial tuning is actually because the shape of the ears (the body) is still developing and not yet adult-like. Experimentally providing the same young ferrets the ears of an adult (via virtual acoustics) can immediately drive those auditory cortical neurons to encode sound location with adult-like accuracy (). Thus, the developing body is guiding the sensory functions of the nervous system in this case, not the other way around.

Every part of a species’ anatomy potentially exhibits both species-specific specializations and individually specific variation. The outer ear, for instance, is extremely variable in size, shape, and mobility, even among primates, and these factors determine how one hears (). In nocturnal primates that rely primarily on hearing to catch prey, the ears are very large (relative to head size) and mobile. Mobility is conferred through a special set of muscles. In humans, the ear is small and does not move very much. The shape of the ear—its ridges and valleys—filter sounds before they hit the eardrum (). Critically, which parts of a given sound get louder or softer according to this filtering also depend on whether the sound is hitting the outer ear from above or below. Through learning, we learn to associate those acoustic differences with the vertical location of the sound source.

We typically treat the body as if it is simply the carrier of the brain, with the brain being the central computing device for processing unfiltered inputs from the outside to generate platonic forms of motor outputs. What we forget is that the body, and its species-typical structure, is constitutive in this process. Different parts of the body act as filters for both incoming and outgoing signals (). Thus, the way we interact with the environment—the way we acquire sensorimotor knowledge—is in inextricably dependent upon the shape of our bodies (). Animals with differently shaped bodies interact with the world differently and thus acquire behavioral capacities in different ways. This is not just about differences, however; without a body, behavior and cognition are impossible ().

Natural behaviors are inescapably contingent on context (space) and history (time). The “space” for any animal is its body and environment. This context is explanatorily inseparable from the content; it is always constitutive of the behavioral phenomenon itself. Attempting to control the space by placing animal (Skinner box, restraint chair, or using an “anesthetized” preparation) or human (e.g., fMRI scanner) in an “impoverished” arena while delivering isolated, reduced, and/or arbitrary sensory signals to elicit stereotyped bodily actions is effectively attempting to create a vat in which it is presumed that greater understanding of the brain and behavior will emerge. On the contrary, it is more like attempting to do the brain’s job for it. The brain has a body that evolved and developed together as a unit embedded in the world ( Figure 1 A).

It is important to note that just closed loops are not sufficient. For example,vehicles are passive-reactive machines with simple circuits in a closed loop with signals in the environment that, via our reflexive anthropomorphism, seem like they have goals and desires but obviously do not. Exquisite mimesis does not qualify as living (). Real biological organisms, from bacteria to giraffes, need to move forward in the world. Moving, in turn, implies decision making: to move there and not here. Perception can be conceptualized as closed-loop convergence processes (). One needs to decide what to look at, and to see, one needs to look. The study of behavior must then confer proactivity and subjectivity to their objects of study as well as real purpose. This is possible by erecting a principle extending the physical principle of inertia: agency. The inertial view of nature applied to living beings reads that things do not change by themselves; one must push them so that they push back. This assumes (whether intentionally or not) that behavior operates via a passive mechanism. But animals are agents capable of producing actions, not just responses. They are proactive, not reactive.

Consistent with the idea that turn-taking is driven by the goal of social contact, humans adjust the amplitude of their voices as a function of distance from a listener; we do so in a manner that would compensate for such distance (). Again, this ability is presumed to be the product of high-level sociocognitive skills, like theory of mind. Non-human primates like marmoset monkeys were thought to lack such socially related flexibility in vocal production. However, based on the predictions from a simple model whereby vocal feedback from a conspecific modulates the drive to produce a vocalization (), it was hypothesized that they should readily be able to exhibits this type of cooperative vocal control. A playback experiment revealed that marmoset monkeys, like humans, increased the amplitude of their contact calls—and produced such calls with shorter response latencies—toward more distant conspecifics (). Cooperative vocal control appears to be a simple system property with the goal of social contact that does not necessitate any particularly advanced socio-cognitive computations.

An alternative hypothesis is that vocal turn-taking (particularly, in nonhuman primates) is a closed-loop behavior with the goal of maintaining social contact (a form of “grooming at a distance”;). A study of the vocal exchanges of a small New World primate, the marmoset monkey (Callithrix jacchus), showed that they will participate in contact call exchanges with any conspecific and that these exchanges have a temporal structure that is similar to the turn-taking rules that humans use (): rare interruptions and a consistent silent interval between utterances. However, there is no evidence that marmosets have any mind-reading skills like humans. Evidence that humans engaged in turn-taking are in a closed loop includes that there is (1) periodic coupling in the timing of utterances across two interacting individuals and (2) entrainment, where if the timing of one individual’s vocal output quickens or slows, the other’s follows suit (). The vocal exchanges of marmoset monkeys share both of these features ().

Here is another example where seemingly complex behaviors are accounted for by simple closed-loop heuristics. One way to enhance signal quality during communication is to prevent interference through taking turns. By pausing after transmission, a sender allows signals from other individuals to transpire and be heard before another signal is emitted. The elimination of overlap via turn-taking increases the likelihood of the signal being heard accurately. As a consequence, an exchange of signals between two or more individuals has a structure. A successful instance of human vocal turn-taking, for example, would involve person 1 speaking while person 2 attends, followed by a response from person 2, be it a statement or an indication for person 1 to continue speaking. These rules are universal for human conversations (). It has been argued that this human cooperative vocal communication is unique and requires complex cognitive traits like mind reading (shared intentionality) not observed in other primate species ().

An alternative hypothesis—a closed-loop hypothesis—is that the dodger’s goal in such a context is simply to maintain a constant distance from the robber (). Any behavioral change with regard to food type or eating time could be accomplished by simply decreasing or increasing the inter-animal distance. Such a rule would eliminate the need to perform complex and time-consuming computations by only considering the maintenance of a constant distance between two animals, as opposed to the calculation of a dodge angle based on more elaborate information. If this closed-loop hypothesis is correct, then dodger angle should be a compensatory action in order to maintain the controlled variable (inter-animal distance). In light of this hypothesis, a new analysis of food-protection behavior revealed that the dodger moved to maintain a set distance from the robber (regardless of the robber’s movements) and that the distance was far less variable than dodger angle (). Moreover, context cues such as food type and the identity of the robber had the simple effect of increasing or decreasing the inter-animal distance that needed to be achieved by the dodging rat. (For a highly quantitative study illuminating how motor variability serves perceptual constancy in the domain of touch, see.)

Social interactions are often considered as complex behaviors requiring complex mechanisms, and as social group size increases, it is typically believed that greater amounts of neural resources must be dedicated to such behaviors () (though the validity of these ideas has been increasing called in to question; see). Cybernetic approaches to social interactions reveal that seemingly complex behaviors can arise through simple rules and thus do not require increasing amounts of computational power (). For example, rats are social animals, and one consequence of that is that they must protect their food from other rats that want to steal it. Robbing and dodging in rats involves one animal (the dodger) possessing a small piece of food and another animal (the robber) attempting to acquire the food (). The robber approaches the head of the dodger, and the dodger evades by swerving laterally away. Given the apparent correlation between the angle swept through by the dodger to evade the robber and both the type of food being consumed and the identity of the robber, this food-protection behavior seems to be an excellent system in which to study a complex, ethologically relevant aspect of rat social cognition. In this view, the dodging rat must calculate the angular displacement of their swerve away from the robber, calculate the time it takes to eat a piece of food, and take into account the robber’s identity, distance, and speed. This seems like exceedingly complex cognition given that rats make dodging decisions and movements in fractions of a second and most often do so successfully. Because we are fascinated (as one should be) by the complexity of the brain, we have no difficulties in ascribing really difficult computations to it.

The essence of the problem thus stems from a simple but subtle confusion, a conflation of space with time. What comes in (which certainly must be called input) is not what comes first (the so-called stimulus), nor does what comes out (output) constitute what is last (response). The notion of stimulus implicitly postulates the beginning of the whole situation, which is only so from the experimenter’s point of view. Here are examples of behaviors cast in the light of behavior loops instead of arcs.

There is a second related conceptual caveat to the behavioral arc: once the animal is engaged, the supposedly independent variable (sensory input) is not independent anymore; the output feeds back and, together with the stimulus, constitutes the input to the organism, producing the output again. Thus, behavior is not linearly causal as the sensory-motor transformation idea implies. The motor response influences the subsequent sensory input, and the sensory input determines the motor response. Behavior is a loop, not an arc. Or aswrote more than 120 years ago, “What we have is a circuit, not an arc or broken segment of a circle.” The so-called readout, conceived as the result of the operations of the brain matter upon something we experimenters “write in,” reflects a covert anthropomorphism; what the animal does is what we see, and what it sees is what we do. Behavior is something more than this linear sensory-motor transformation inscribed in a stimulus-response phenomenon. It is circularly causal, consistent with the cybernetics idea put forth byand later advocated by others (e.g.,). Behavior is, to a great extent, the control of perception (). Namely, living beings have life-sustaining intentions (fleeing, fighting, feeding, and reproducing), and they behave in order to satisfy them. Perceiving the consequences of their actions is the only way they can know, and so it is actually the only thing animals care about. In other words, for an animal, its output means little if it does not control, in some way, its input.

A first caveat with this methodological “manipulate and measure” ideology is the inapplicability of the assumption of ceteris paribus (“all else being equal”). We set up our experiments and interpret the data under the scruple that the results are valid under that assumption. But there is no ceteris paribus in biology ( Figure 2 ). This means we cannot copy and paste the conceptual presuppositions and experimental approaches we use in physics ( Figure 2 A). Experimentation in behavior needs to account for (1) the specificity of different animal forms across species ( Figure 2 B); (2) the diversity of individuals within species ( Figure 2 C); (3) the totality of the organism and its environment ( Figure 2 D); and (4) the irreversibility of lived experience ( Figure 2 E). We will come back to some of these points later.

(E) During development, animals undergo major changes not only in their behavior but also in their bodies. Despite our sequential measurements, behavior is generated serially. History matters.

(D) Behavior studied in animals whose bodies and worlds have been truncated can be misleading and not generalizable (the behavior of a paralytic cat in an empty arena is far from cat behavior).

(C) Laboratory wild-type animals are often anything but wild or exact controls for transgenic animals. Average group behavior may not coincide with any of the behavior of the individuals in that group. Individuality is real and relevant.

(B) Despite the existence of common biological mechanisms and shared principles of behavior, humans are not a collage of a handful of laboratory animals. Organisms with fewer neurons are not necessarily “simpler” organisms either, and “organism models” are not “model organisms” ().

It may seem like a truism to claim that behavior is the ultimate output of the nervous system. Although it is difficult to define what constitutes behavior (), most would argue that it is some sort of output. Having access to the neural level does not modify this position (). Even, one of the founders of ethology, defined behaviors as the “total movements made by the intact animal.” Typically, this end product is considered the result of a sensory-motor transformation. Behavior in the form of this arc then allows the experimenter to vary the sensory inputs (manipulate) and observe the motor output (measure) to estimate a “transfer function” of behavior, followed by an investigation of the neural mechanisms that may be the implementation this computation ().

Part III - Historicity: Living Organisms Are Individuals

Longo and Montévil, 2012 Longo G.

Montévil M. The inert vs. the living state of matter: extended criticality, time geometry, anti-entropy–an overview. Behavioral variability is always deemed as noise to a first approximation ( Figure 1 G). This reflects our drive to go from concrete instances to universal claims. The standard is to reproduce and replicate. Yet, the relatively neat assumption that “everything else is being kept equal” becomes particularly problematic for behavior, because every moment of behavior is the result of a unique history, a history that has unfolded on many different timescales. In physics, we often seek atemporal relationships among universals (rather than particulars) and perceive that what is of real importance is to be found in general laws and normative explanations of behavior. But what if behavioral variability is not an inescapable “bug” of biology that scientists must contend with but an adaptive feature? Variability is perhaps the ability to vary rather than the “nausea” (noise, etymologically) of the scientist not being able to make sense of the system. Earlier, we argued that motor variability serves perceptual constancy (and not the other way around). A strong corollary of the principle of historicity is twofold: the lack of (1) genericity in biological objects (two electrons are interchangeable, but two homozygotic twins are not) and (2) specificity of biological trajectories (). Like the different spatial scales of the umwelt, biological historicity plays out on different nested timescales, and the variability that is generated from an individual’s history writ large seems more functional than noisy.

Loos et al., 2015 Loos M.

Koopmans B.

Aarts E.

Maroteaux G.

van der Sluis S.

Verhage M.

Smit A.B. Neuro-BSIK Mouse Phenomics Consortium

Within-strain variation in behavior differs consistently between common inbred strains of mice. Honegger and de Bivort, 2018 Honegger K.

de Bivort B. Stochasticity, individuality and behavior. Ayroles et al., 2015 Ayroles J.F.

Buchanan S.M.

O’Leary C.

Skutt-Kakaria K.

Grenier J.K.

Clark A.G.

Hartl D.L.

de Bivort B.L. Behavioral idiosyncrasy reveals genetic control of phenotypic variability. Pantoja et al., 2016 Pantoja C.

Hoagland A.

Carroll E.C.

Karalis V.

Conner A.

Isacoff E.Y. Neuromodulatory regulation of behavioral individuality in zebrafish. Schuett et al., 2011 Schuett W.

Dall S.R.

Baeumer J.

Kloesener M.H.

Nakagawa S.

Beinlich F.

Eggers T. Personality variation in a clonal insect: the pea aphid, Acyrthosiphon pisum. In fact, methodological approaches that specifically try to reduce or eliminate phenotypic variability are disappointing when considered carefully. For example, much of neuroscience uses inbred strains of mice housed in uniform environments. The motivation behind this is based on the notion that there are genetic mechanisms that should yield the persistence of certain phenotypes in the face of environmental factors that cannot be controlled for (revisit Figure 2 ). However, this turns out to be impossible. When the spontaneous homecage behavior of commonly used inbred mice, raised under identical conditions, was measured using automated procedures, there was substantial variability (). Moreover, across different strains of inbred mice, there were different levels of variability; some strains were much higher in their behavior variability than others. This is important, because one might have suggested that the within-strain variability was just unaccounted-for environmental factors or related to the stochastic effects of physical laws at the molecular level (e.g., intermolecular interactions subjected to thermodynamic instability) (), but this would not explain systematic across-strain differences. In fact, it suggests that behavioral variability is built into the system. In support of this, a study of inbred lines of Drosophila was used to address the variability of locomotor “handedness” (the tendency to turn left or right in a y-maze) (). Like the mice strains, there was considerable variability in the trait, and the amount of variability was linked to different genotypes. By crossing flies with measured levels of handedness variability, the degree of variability was shown to be heritable. Even behaviors that appear as reflexes show heritable variability, such as the startle response in zebrafish () or in the parthenogenetic pea aphid whose clonal daughters show variation in leaping-from versus clinging-to vegetation ().

Honegger and de Bivort, 2018 Honegger K.

de Bivort B. Stochasticity, individuality and behavior. These data show that evolution has not selected out the mechanisms that generate behavioral variability (and thus, individuality); rather, it has selected for them. Behavioral variability is advantageous, because the environment is unpredictable, and thus an individual’s umwelt may not be exactly the same as its parents’ umwelt. Built-in behavior variability ensures that at least some individuals may be able to thrive under a novel set of conditions—a form of bet hedging ().

Gould, 1977 Gould S.J. Ontogeny and Phylogeny. Baldwin, 1896 Baldwin J.M. A new factor in evolution. Laland et al., 2010 Laland K.N.

Odling-Smee J.

Myles S. How culture shaped the human genome: bringing genetics and the human sciences together. Behavior and its neurobiology are the product of not only evolutionary processes but also developmental ones as well (). Evolution acts on developmental processes to produce adult phenotypes. Changing developmental trajectories is the only way to evolve phenotypic changes. More pointedly, adaptive behaviors that are learned initially can drive the evolution of developmental processes that include the modification of genes. For example, if the goals of any organism (reproduction and survival) were enhanced by a new behavior acquired through learning, then the differential survival of those individuals might be enhanced by skipping the time it takes for the learning to occur by assimilating the process during development. This is known as the Baldwin effect (). For example, human populations with a tradition of raising domesticated animals for milk production have acquired lactose tolerance via the evolution of developmental changes (including genetic changes) that keep the lactase enzyme active during adulthood (). Dairy culture increases the selective advantage for this trait.

Thelen and Smith, 1998 Thelen E.

Smith L.B. Dynamic systems theories. Bergson, 1907 Bergson H. L’Évolution Créatrice. Individual behaviors carry with them a history and build momentum; these accumulated histories constitute the stuff of learning and development change. Learning and developmental change can lead to differential engagement with the environment, a new umwelt that in turn affects individual behaviors. “Habit, memory, learning, adaptation, and development form one seamless web built on process over time—activities in the real world” (). The organism (brain and body) changes over time (evolution, development, and single event), which changes its umwelt (niche and affordances), which then modifies the organism (brain and body). It is perpetual change in a closed loop, rolling toward a goal (survival and reproduction) while also unfolding, rather than unfurling, creatively ().

Every individual mouse, fly, monkey, and human has a history that forges its behavior.