Beyond belongingness: Rethinking innate behavioral predispositions,learning constraints,and cognitive capacities

1. Genotype–environmental interactions revisited with a focus on learning and behavior

The nature versus nurture controversy has been around for several decades in fields such as genetics, computational neuroscience, ecology, comparative cognition, anthropology, developmental psychology, personality psychology, linguistics, psychiatry, and intelligence studies. In some of these fields, this matter is still hotly debated (e.g., Graves, 2015; Zador, 2019); advocates on both sides of the divide charge their opponents with reductionist biases (e.g., Buss, 2020; DiFrisco & Jaeger, 2020). Learning theory has its own version of the nature versus nurture debate in the issue of belongingness, because of the prevalent assumption that evidence of belongingness implies support for biological constraints on behavior. This is illustrated by the fact that findings on belongingness and related phenomena are often invoked to support the nativist side of the debate, even outside the field of associative learning (e.g., Pinker, 2002). Therefore, conceptual advances and better methodological tools for studying belongingness and related phenomena through the lens of refined associative learning theories might help other fields gain insight on this issue.

Nature and nurture positions can be stretched to such extremes that they disprove one another and end up on the verge of absurdity. On the one hand, nativists might say that without genes the organism would have never developed from the fertilized egg. On the other hand, advocates of the nurture position could argue that an organism cannot develop in a void; for genes to direct protein synthesis, exogenous sources and signals are required. Extreme propositions like these overlook the fact that both genes and environment are necessary conditions for building a phenotype (e.g., Narvaez et al., 2021). One way to respond to such statements is found in the misleading proposition that phenotypes are ∼50% due to genetics and ∼50% due to environmental factors. In a similar vein, claiming that the composition of phenotypic variance is simply intractable given current methodological hindrances may discourage further inquiry.

Despite representing more moderate views, both of these latter propositions overlook the fact that heritability, the canonical proxy for genetic contribution to phenotype, is a relational—rather than fixed—phenomenon (Bateson, 2001; Hinde, 1968). In short, the heritability (i.e., correlation between genotype and phenotype) of a given trait depends on the specific environment and population from which data are sampled (Rose, 2006; Sauce & Matzel, 2013). One common working example of this (although certainly not as clever as Sauce and Matzel’s case for scurvy) explains that the heritability of having two eyes in humans is close to zero. This is because genes rarely influence variation in this attribute given our typical environment; it is more likely to lose one or two eyes through environmental factors than to be born with none, one, or three eyes and then to transmit this distinctiveness to progeny. However, that is not to say that genes have no influence on having two eyes. In a similar vein, offspring often speak their parents’ language (i.e., high parental–offspring correlation), which is not to say that the language one speaks is genetically determined. The nature versus nurture debate often emerges whenever two factions do not agree on the relative contribution of genetic determinants to observed phenotypes under reasonably equivalent environmental conditions. This disagreement arises regarding studies comparing different species, subpopulations within a species, individuals within a population, stimulus modalities (like in Garcia and Koelling’s study), or response topographies (as in Breland and Breland’s paper).

The nurture position mainly objects to conclusions that are drawn when the reasonably equivalent-environmental-conditions assumption is suspected to be violated; that is, in cases in which internal validity is threatened. On the other hand, some nativists blame nurturists’ claims of “blank slatism” (e.g., Carl, 2018). The “blank slate” is used as a rhetorical weapon, a boogeyman based on John Locke’s radical empiricist doctrine, which current academics rarely (if ever) adopt. It could even be said in an a priori sense that blank slate assumptions cannot hold empirically, as they are based on a null hypothesis statement; that is, a proposition in which differences between two conditions are entirely absent. From a traditional statistical perspective, in the real world any null hypothesis can be rejected if the sample of observations is sufficiently large (McElreath, 2020). In this regard, one could declare the case closed in favor of alternative hypotheses that support nativist views, irrespective of the meaningfulness of the data. In a similar vein, any detectable alteration to behavior as a result of experience could be used to support the phenotypic plasticity thesis (i.e., the nurture position). But, again, how meaningful the effect is matters for characterizing the impact that plasticity has on actual organisms at every stage of life. Even when observing substantial behavioral alterations in the laboratory, some dispute whether those alterations can be extrapolated to real-world settings; that is, they express doubts concerning external validity.

Studying genes directly provides a powerful means to assess nativist assumptions in organic systems. In particular, combining correlational and experimental approaches has proved to be very productive (as argued in Sauce & Matzel, 2013). For example, recent studies have revealed that individuals diagnosed with idiopathic autistic spectrum disorder (ASD) exhibit differences involving genes that control differentiation, axon guidance, migration, and regional patterning in neural cells during early development (DeRosa et al., 2018). This is in line with prior reports of an overabundance of cortical neurons and excessive brain weight in individuals with ASD (Courchesne et al., 2011). Based on these findings, animal models were used to simulate the presumed neural development anomaly in idiopathic ASD. For instance, experimentally induced abnormal cell proliferation demonstrated to cause brain overgrowth and patterns of behavior judged as analogous to those of idiopathic ASD (Fang et al., 2014). These converging lines of evidence lead to the proposal of a meaningful genetic etiology for ASD (Courchesne et al., 2020).

Remarkably, research endeavors like the one just alluded are not always as successful in providing clear-cut evidence for specific genetic influences on behavior. Hereditary mechanisms, such as genetic control of neuronal migration, have also been hypothesized as causal factors for developmental dyslexia, given numerous reports of parent–offspring correlations (e.g., Wolff & Melngailis, 1994). However, researchers in this field have recently expressed concerns about the strength of the evidence in favor of this view. Guidi et al. (2018), for example, questioned how relevant the contribution of this etiology is to the phenotype of developmental dyslexia, given that genetic studies have failed to replicate key findings. Hence, caution must be exercised when drawing conclusions from family patterns in psychiatric conditions; parent–offspring correlations are not always indicative of clear-cut genetic transmission of maladaptive traits.

Nurturist claims also benefit from combining analysis of experimental and correlational evidence. Any systematic demonstration of phenotypical plasticity could be regarded as evidence in favor of the nurturist position. Such sources of evidence often come from studies in which randomized samples receive different experimental treatments and display differentiated expression of some attribute (morphological, functional, etc.). In the case of the learning literature examples abound, such as habituation, sensitization, and simple Pavlovian conditioning phenomena. While these examples are well known, even outside the field of learning theory, more impressive cases involving two-phase learning phenomena (see Byrom & Murphy, 2018) are rarely acknowledged. Some examples of these phenomena include latent inhibition, perceptual learning, overshadowing, blocking, sensory preconditioning, second-order conditioning, conditioned reinforcement, conditioned inhibition, Pavlovian-to-instrumental transfer, and partial reinforcement extinction effects. These phenomena demonstrate that environmental regularities can shape behavior not only in situ, but also that they in turn can influence the way in which organisms engage with new environmental conditions. An important implication is that genetic predispositions in learning rates or maximum capacity may give rise to mediated genetic–environment interactions (Byrom & Murphy, 2018). This follows from the assumption that the influence of the environment (behavior after the second stage of learning) on the phenotype (behavioral state after the first stage of learning) depends on the genotype (predisposed rate or upper bound of learning). According to Byrom and Murphy (2018), this brings about a complex, multicausal web of effects, as genes also depend on the environment (first stage of learning) to influence phenotypic expression (behavior after the second stage of learning) in the first place.

As suggested above, canonical learning phenomena demonstrate the plausibility of principled mechanisms for phenotypic plasticity in the domain of behavior. These demonstrations beg inquiry into how these mechanisms are implemented by organic systems and their implications for adaptation in concrete ecological niches. However, assessing how these learning phenomena interact with other factors is fairly difficult since they sometimes require multiple control conditions to be indisputably demonstrated (e.g., Papini & Bitterman, 1993; Rescorla, 1967). Numerous control conditions require increased sample sizes or else lessen the statistical power of a study; in turn, adding biological manipulations (ablation, drug, nutritional, etc.) to classical learning protocols requires further controls that multiply the existing ones. One increasingly common research practice involves assessing correlations between performance in learning tasks and real-life outcomes, such as health-related traits. However, low or null correlations have been routinely reported (e.g., He et al., 2013). This may be explained by poor psychometric properties associated with behavioral measures (Hedge et al. 2018). Specifically, these measures are well suited to detect the effects of experimental manipulations or situational factors but are often not sensitive enough to account for spontaneous inter-individual variability. In addition, real-life outcomes are often measured via self-report scales, which intend to capture behavioral patterns in a variety of unstructured situations; however, these measures risk involving biased subjective judgments (Dang et al., 2020) and a mismatch between the reference population and the study sample. For these reasons, correlations between performance on learning tasks and real-life outcomes are usually noisy and hard to ascertain. Thus, behavioral markers, while theoretically valuable (Byrom & Murphy, 2018) have been deemed unsuitable for diagnosis or prognosis (Dang et al., 2020).

One alternative path to studying the plasticity of behavior or the preparedness of complex behavioral patterns can be found in the use of simulated artificial agents. Such an approach employs rationally discoverable computation principles to achieve specific functions or on which to perform controllable interventions (Bongard & Levin, 2021). Unlike organic systems, artificial agents allow for expeditious exploration of the boundaries of nativist and nurturist assumptions that is virtually free of ethical conflicts. For example, one might ask whether complex forms of adaptive behavior can arise from a neural network with random connection weights (which is arguably tantamount to a blank slate). A study from Najarro and Risi (2020) found that this may be the case, reporting that artificial agents with just that feature were able to solve a two-dimensional car racing task and quadruped locomotion using Hebbian learning rules ¹ . However, possibility does not imply feasibility; in this sense, Zador (2019) has argued that training such networks requires enormous numbers of trials, which starkly contrasts with how organic systems learn. This riddle instantiates the challenges for external validity associated with nurturist appeals mentioned above.

As we can see in this section, important developments have boosted the study of genetic and environmental contributions to behavior. However, both sides of the coin endure their own biases and limitations. While they are difficult to overcome directly, being mindful of the diversity of factors that determine and underpin behavior can contribute to a nuanced understanding of this matter.

2. Typical environment and non-obvious experiences

Behavioral taxonomies frequently distinguish between learned and unlearned behaviors; the former can be defined as those that crucially depend on experiences and the latter as those emerging regardless of any experience. In traditional associative learning studies, the experimentally induced experiences affecting subsequent behavior are often well structured and share multiple aspects with the testing situation. However, in real life, experiences impacting a particular behavior do not need to be very similar to those in which a new behavior pattern is expressed. Turvey and Sheya (2017) assert that cases in which experiences affect behavior in a non-obvious manner often led to the misclassification of behavior as unlearned (or innate). A remarkable example is a study conducted by Masataka (1993). This author reported that fear of snakes is expressed in squirrel monkey specimens which ate freely moving insects but not in those that based their diet on fruit. Another example is provided by a study conducted by Moore (1992); the main finding in this study was that sexual behavior in adult rats was determined by differential interactions between males and females with their mother during infancy.

These findings provide evidence that experiences that occur during development in natural environments may be sufficient to determine behavioral traits that are typically exhibited by adults. Therefore, overlooking key features in the species-typical environment determining these traits may lead to incorrect conclusions. Importantly, crucial non-obvious environmental factors that determine phenotypes in general (Anlas & Trivedi, 2021)—and behavior in particular (see Kuo, 1976)—could be traced back as far as the gestational or pre-hatching stages. Note that, given this possibility, “unlearned” (not relying in experience) and “innate” (present shortly after birth) are not equivalent terms because innate behavior could be influenced by prenatal experiences.

Observation of shared phenotypic features within a taxa or populations can lead to the erroneous conclusion that they are genetically determined. Aspects of the environment that are taken for granted (non-obvious) may play an important role in influencing behavior and cognitive capacities. Some of them may be so ubiquitous in the species-typical environment that might preclude the emergence of alternative phenotypes. Another hallmark example of this possibility is the classic experiment conducted by Held and Hein (1963). In this study, light-deprived kittens failed to pass visual perception tests unless they were exposed to a condition involving visual feedback dependent on self-displacement. Importantly, the kittens exposed to equivalent displacement-contingent visual stimulation but in a fashion that did not depend on their own movements failed the visual perception tests. This finding indicates that an environmental aspect that is present during typical development, such as the non-obvious self-motion dependent visual experience, plays an important role in perceptual abilities in later life.

Pointing at a set of behavioral phenotypes that is characteristic of all members of a species is surely of descriptive value; however, comparative cognition researchers should inquire into what would have happened if those subjects were removed from their typical environment (at least, in a purely counterfactual thought exercise). At the whole-organism level of analysis, the typical environment could either be a species’ natural niche or artificial laboratory housing (see DiFrisco & Jaeger, 2020 for other levels of analysis; e.g., cellular, tissular). Importantly, key aspects may be present in both natural and artificial settings. This is the case of displacement-dependent visual stimulation for cats, which is (almost) inevitably present both in natural and laboratory settings. Another, perhaps more controverted example, is language development in humans. At a certain age, all human beings exhibit language skills unless they are severely impaired. This might lead some to conclude that language is an unlearned or innate faculty. However, to survive to that age, humans must be nurtured by other humans. There are no examples of humans surviving from birth to maturity away from a cultural environment so that we can unequivocally assess the contribution of nurture to language competence. The unavailability of this counterfactual condition (i.e., what would have happened holding all else constant) prods researchers to consider possibilities beyond the available data.

3. The environment from the perspective of the subject

Held and Hein’s (1963) findings mentioned in the section above demonstrate that the environment is more than a static arrangement of stimuli surrounding subjects. Aspects that are immediately and continuously contingent (i.e., statistically dependent) upon subjects’ actions can be considered part of the subjective environment (or unwelt; see Gomez-Marin & Ghazanfar, 2019) and have meaningful and lasting effects on behavior. An account proposed by Powers (1973) and taken up recently by others (Gomez-Marin & Ghazanfar, 2019; Yin, 2020) states that behaving agents can be characterized as closed-loop negative feedback control systems. Yin (2020) asserts that behavior should not be conceived of as a unidirectional input → output process; instead, behavior is composed of inputs guiding actions and actions guiding inputs in a closed feedback loop. The input → output sequence is just a part of this whole interaction, while the output → input unit is equally crucial but is frequently disregarded and must be addressed as well (see also Brembs, 2020). Thus, a minimal model of behavior (or else cognition; see Brancazio et al. 2020) should consider it an emergent phenomenon composed of a collection of closed loops. This rationale has been assumed to hold for single-celled organisms (e.g., Bich & Bechtel, 2022) all the way to multicellular organisms with complex nervous systems (e.g., Sosa & Alcalá, 2022). Previous emphasis on the unidirectional input → output component may, in part, be explained by the relative ease with which researchers are able to manipulate “what happens to the subject” (Yin, 2020). However, actual behavior is likely more complex than often assumed.

This account faces the challenge of being rather counterintuitive. When presenting a stimulus in a laboratory setting (or elsewhere), researchers often assume that it remains invariant throughout its duration. However, from the subject’s perspective, the stimulus may vary substantially before, during, and after an action has been performed in response to it. For a behaving agent, an action that occurs at a given moment, t _i , will modify the ensemble of sensory inputs at a subsequent moment, t _i+1 . This occurs because motor systems are coupled with proprioceptive receptors or can indirectly alter the signals that other sensory modalities provide. Such output-dependent input variation has meaningful implications whenever sensory inputs systematically trigger further actions or possess system-disturbing properties, which in practice is often the case. Researchers may have some control over what happens to the subject at t _i , but they will know little of what happens thereafter from the subject’s perspective. In short, a manipulated stimulus event cannot be equated with an actual input (Yin, 2020). Therefore, it is best to assume that a stimulus function or stimulus gradient that depends on the subject’s action exists from the onset of the stimulus event. This would be a particularly fertile framework for comparative cognition and behavioral neuroscience. Such research programs often aim to disentangle or even integrate cognitive processes and associative learning to account for seemingly analog behavioral patterns in different taxa (Zentall, 2013).

As a working example, we can consider a counterintuitive finding in a recent cognitive neuroscience study conducted by Basile et al. (2020). These authors reported that bilateral lesions in the hippocampus leave macaque monkeys’ performance virtually intact in several memory tasks. This finding controverts the dominant notion that the hippocampus plays a role as a “memory storing” module. However, Basile et al. (2020) attempted to conciliate their results with previous findings by taking into account their subjects’ perspective. Lesion studies in rodents and clinical reports in humans have indisputably related the hippocampus with memory performance. Nevertheless, tasks for assessing memory in rats and the way in which humans learn from their natural environment (e.g., Capaldi & Neath, 1995) may differ in a crucial way from the conditions in Basile et al.‘s study.

Importantly, in this study all the tasks for assessing memory capacity were programmed on a touchscreen interface. Basile et al. suggested that the hippocampus plays a major role in spatial allocentric memory used for navigation (see Robinson et al., 2020), rather than in general associative memory. Given that interacting with a touchscreen does not require displacement, which presumably would have required substantial recruitment of the hippocampus, performance was not disrupted by the lesions. The authors underscored that most paradigms used to assess memory in rodents involve displacement between different locations; even if such a feature of the task is irrelevant from the perspective of the researcher, it inevitably impacts performance and, thus, the interpretation of data. Spatial navigation, a variable that is dependent on the subjects’ actions, may act as a confounding factor in canonical studies on general memory.

Altogether, Basile et al. (2020)’s findings illustrate that anchoring cognitive capacities on intuitive taxonomies detached from careful study of behavior is problematic (e.g., Cisek, 2021; Pessoa et el., 2022). Rather than regarding brain structures as modules that perform certain computations, considering the unique way in which organisms experience and act upon their environment has been proposed as an upgraded approach to make sense of the neurobiological basis of behavior (e.g., Krakauer et al. 2017). This perspective highlights the dangers of taking for granted that the nominal task requirements are mere triggers for the capacities that the brain possesses. Viewing the brain as a biological container of cognitive capacities lends to the assumption that specific genetic programs directly lead to the development of and thus cause said capacities. Therefore, modeling behavior as an emergent complex of action–perception loops help prevent unwarranted claims for belongingness and similar biological predispositions.

4. Uses and abuses of twin studies as natural experiments and the animal model solution

One of the most powerful methodological tools available for inquiring into the contribution of genes to behavior (or any phenotypic trait) is the comparison of monozygotic twins, dizygotic twins, fraternal twins, and unrelated individuals. Studies of monozygotic twins reared together are assumed to abstract the contribution of the unique environment from within-twin variation. The discrepancy in the similarity of traits between monozygotic and dizygotic twins is usually invoked to estimate the contribution of genetic factors. The discrepancy in the similarity of traits between pairs of twins (either monozygotic or dizygotic) reared together and reared apart is used as a proxy for the contribution of the shared environment. For example, in a recent study, Baxi et al. (2020) scanned the brains of a large sample of monozygotic and dizygotic twins. The main findings were that the thickness of the gray matter showed a greater genetic contribution, while the microstructure of the cortex (e.g., the cytoarchitecture and gyrification) exhibited a relatively greater unique environment contribution. Although this study did not use an experimental design, the authors suggest that the unique environment may play an important role in differences in the personality traits and mental health of monozygotic twins. It is worth recalling that the estimates for the contribution of genetic and environmental factors are crucially dependent on the surroundings in which the population develops (Sauce & Matzel, 2013). Therefore, these estimates may at least partly be constrained by the geographical and cultural circumstances from which the sample was extracted.

Despite these sorts of studies being deemed as “natural experiments,” their interpretation presents some difficulties, which arise from a lack of control over several relevant factors. For example, we might consider whether and to what extent twins who are reared apart develop in meaningfully different environments (see Moore & Shenk, 2017). In addition, most twin studies are blind to complex genotype–environment interactions (Jaffee & Price, 2007). For example, one’s physical appearance is influenced by genes, but could also determine how others treat us (a non-obvious environmental feature), which in turn might shape our behavior (Scarr & McCartney, 1983; but see Conley et al., 2013). Furthermore, giving birth to twins in humans is not random (Bhalotra & Clarke, 2019), bringing into question the validity of twin studies as well-controlled natural experiments. Yet another problem is that twin studies often rely on the assumption of random mating between parents, for which there is evidence that it is not met (Yengo et al., 2018). This later issue could impact estimates of genetic contributions to a nontrivial degree (Vinkhuyzen et al. 2012).

In light of the problems listed above, some authors have called for studying the relative contributions of genetic and environmental factors to behavior by embracing a true experimental approach. This can be reached using randomized interventions in human twins (Burt et al. 2019) or in animal models of twinship (Briley et al., 2019; Byrom & Murphy, 2018) to test the boundaries of phenotypic plasticity holding genetic factors constant. The nine-branded armadillo (Dasypus novencintus) can help to circumvent some of the problems with human twin studies. One relevant peculiarity of this species is that females give birth to monozygotic quadruplets; this potentially allows for a more powerful and controlled assessment of the relative contribution of genes, shared environments, and unique environments to phenotypes. For example, Ballouz et al. (2019) compared genetic variability (allelic expression imbalance) in armadillo quadruplet litters living in captivity starting from the gestation period with that reported in human twins. This study’s main finding points to substantially higher epigenetic variation in humans when compared to captive armadillos. The authors suggest that such a degree of variation may arise because human twins can accumulate sufficiently unique lifestyle experiences given the non-controlled features of the environment in which they typically develop. Although the main dependent variable in this study was allelic expression at a nucleotide level, the authors hypothesized that said factor may have key phenotypic effects downstream. For instance, the authors incidentally reported that variability in allelic expression was correlated with weight variability within the armadillo litters.

5. Unique environment and stochasticity

Researchers from different groups employ different terms for conceptualizing the source giving rise to traits that are neither related to genes nor to known variations in the environment. Some researchers use the term “unique environment,” and others prefer “stochastic variation” (e.g., Mitchell, 2021). While both terms usually refer to the same portion of variance in different studies, they seem to denote different underlying assumptions. On the one hand, the term “unique environment” seems to imply a set of experiences that are not controlled for in the study design. This factor would influence behavior (or any other phenotype) via an equivalent mechanism as that through which the shared environment does; the crucial difference is the fact that one of these environmental factors is actually traced, while the other is not. The term “stochastic variation,” on the other hand, implies that there is a third—qualitatively different (see Mitchell, 2021)—factor in addition to genes and environment for determining behavior (or any phenotype).

Honegger and de Bivort (2018) suggest that when genes and the environment are matched and phenotypic differences are still observed it is likely due to stochastic influences. The stochastic factors that they appealed to are not unknown quantum operations (see Bell, 2014) but rather phenomena that can be understood within Newtonian physical rules. These authors argue that most of the stochastic processes that give rise to individual behavioral differences come from chaotic molecular (e.g., gene transcription) and cellular (e.g., axonal guidance) events. Specifically, small differences in initial conditions could lead to relatively large effects that can be further amplified by positive feedback mechanisms at higher levels. Honegger and de Bivort (2018) list a number of sound adaptive advantages of a built-in (or endogenous) susceptibility to stochastic variation. Furthermore, they provide examples of brain mechanisms presumed to generate behavioral variability in non-vertebrate species. The authors recognize the possibility that other factors may play a role in individual differences, such as phenotypic plasticity. This factor would involve adjusting some traits of the biological agent in response to the sensed environment, ideally in order to optimize some resource-exploitation process. However, Honegger and de Bivort (2018) discard this factor as a relevant influence on individual differences in behavior because, according to them, embodied morphing rules as such would be costly to evolve. Therefore, the authors chose stochasticity-generating genetic mechanisms to account for most within-species individual differences, as they represent a solution that is readily attainable by evolution.

The rationale advanced by Honegger and de Bivort (2018) requires, at least, some nuance. The evidence and arguments they provide in support of the plausibility of genetic-based mechanisms to give rise to stochasticity are compelling. However, this kind of mechanism’s contribution to individual differences may vary from one taxon to another. Some animal lineages have evolved particularly malleable nervous systems for implementing morphing rules to adjust their behavior in response to environmental regularities (Fanselow & Wassum, 2016). According to Honegger and de Bivort (2018), this is especially complex and costly; therefore, it may not apply to species with relatively simple nervous systems that are more bound to stereotypical behavioral routines throughout their lifecycles. In addition, the notion of a chaotic flow from equivalent states to individually differentiated paths could also be applied considering learning theory. Initial experience-mediated behavioral changes would lead organisms to engage differently with their environment, further shaping the behavioral phenotype in a given direction (Byrom & Murphy, 2018). Such a process can be distinguished from purely biological stochastic events in that the former involves the environment’s impact on receptors in the nervous system as proximal causes. In short, stochastic noise in the environment may indeed shape individuality; if so, it would have to be via (known and yet to be known) principled rules of behavior change as well as by other means.

Some researchers have already highlighted the need to identify the mechanisms through which behavioral traits are acquired. For example, Zietsch and Sidari (2019) have asserted that the mechanisms that lead genetic variation to give rise to behavioral traits (or phenotypes in general; see Jaeger et al., 2012; Uller et al., 2020) are, in the best case, underspecified. Herein lies an opportunity for the field of learning theory. As mentioned above, the literature on learning is full of studies that show how structured experiences lead behavioral changes in situ as well as in transference situations. In a given situation, behavior is determined by the configuration of the nervous system, such that (all other things being equal) changes in behavior require a disruption of such a configuration.

Unfortunately, documenting specific plastic changes in the nervous system that arise from particular experiences is logistically difficult. However, there is increasingly compelling evidence that changes in the composition of certain nervous system sites can emerge from typical learning protocols and how those changes are disrupted by adverse early experiences. One example can be found in the literature on latent inhibition. This phenomenon consists in learning to ignore repeatedly presented stimuli, which is considered an adaptive behavior because of the importance of disregarding uninformative events in our surroundings (Costa et al., 2021). Moreover, deficits in this process have been associated with psychotic proneness (Lubow, 2005) revealing its impact on health. Recently, Jacob et al. (2021) found that stimulus pre-exposure lead to particular activity patterns in certain dopamine sites. In turn, activation of those sites was required to learn from pre-exposure experiences. Complementarily, Han et al. (2012) showed that peri-adolescent social isolation led to deficits in learning from stimulus pre-exposure and that this disruptive effect was mediated by the expression of certain dopamine receptors. Then again, physical appearance (arguably, in part, genetically determined) is associated with peer acceptance and thus with varying degrees of social withdrawal (Vannatta et al. 2009). Altogether, these sources of evidence reveal the intricate causal webs in which genetic and environmental factors can interact to give rise to behavioral variation. Accounting for learning principles informed by neurobiological mechanisms, in addition to genes and genuinely stochastic processes, would provide a better principled understanding of individual differences in behavior.

6. Neural computation versus biomechanics

As described in the previous section, behaviors that are peculiar to an individual, a species, or a developmental stage are constrained by neural architecture (either genetically determined or acquired). However, caution must be exercised when proposing this kind of mediation, at least in the absence of reasonable evidence for the specific neural underpinnings involved. Gomez-Marin and Ghazanfar (2019) provided two great examples of how overlooked interactions of biomechanistic and environmental factors could dictate, in some cases, peculiar behavioral unfolding. First, Zhang and Ghazanfar (2018) placed adult marmoset monkeys in an artificial gaseous environment lighter than air and reported that they produced vocalizations typical of young marmosets. Such a finding led these authors to hypothesize that the transition from infant to adult vocalization is influenced by the mechanical properties of their lungs, rather than solely by neural maturation. Second, Thelen (1984) showed that human infants around 2 months of age also regress to behaviors that are typical of a prior developmental stage under appropriate environmental conditions. She decreased the gravitational force through buoyancy by partly submerging infants in water. As a result, infants performed motor patterns that are typical to the previous stage of development. This finding is remarkable because changes in motoric patterns at early stages are often attributed solely to mechanisms of neural development. In some fields of developmental science, a common interpretation of behavioral changes across the life span is that a dormant genetic program is unpackaged in the brain at certain ages, perhaps with the help of certain trigger signals. Both these examples challenge explanations inclined to claim neural computational differences whenever distinctive behavioral patterns are observed.

I will provide one more example to supplement those by Gomez-Marin and Ghazanfar (2019). Early reports featured evidence that a specific gene mutation in Drosophila flies, making them yellow in color instead of the typical gray, showed a reduced fertilization rate for males (Sturtevant, 1915). Observation thereof led researchers to suggest that an alteration in the neural circuitry controlling courtship behavior contributed to lessen mating success (e.g., Bastock,1956). ² However, Massey et al. (2019) found that for intact individuals, the so-called yellow gene determined a morphological feature in males’ legs that helped with clutching females for copulation, thus, bolstering reproduction. Therefore, males with the mutation had difficulty mating due to mechanical properties associated with their surface anatomy, rather than due to alterations in neural functioning. While there are some compelling examples of genes affecting courtship behaviors through neural means (see Yamamoto & Koganezawa, 2013), Massey et al. (2019) urged abandonment of the idea that all so-called instincts are somehow “hardwired” into the nervous system (e.g., Stockinger et al. 2005). Instead, these authors argued that the mechanical properties of the anatomy must be considered in order to fully understand behavior.

7. Belongingness in the light of phylogenetic refinement of behavior

Cisek (2019) suggested that phylogenetic refinement may help to build strong theories of behavior to inform research on comparative cognition. The basic idea is that every behavioral mechanism is an extension of an ancestral one, which appeals to the principle of functional continuity. According to Cisek (2019), this framework might be an alternative to cognitive modules (e.g., attention, memory, executive function) as a framework for comparing animal behavior across species. Cisek (2019) argues that the phylogenetic refinement approach will lead to the development of conceptual taxonomies that more readily reflect the nature of biological systems that give rise to complex animal behavior (e.g., nervous systems). Within this framework, history begins with a unified function. This author suggests that behavior is a specialized closed feedback loop like the approaches described in previous sections. This emergent feature is thought to have aided metabolism in the beginning to maintain a relatively stable, self-perpetuating homeostatic state. The nervous system function underlying this ancestral form of behavior evolved to take over interactions with the external environment. This process endowed some organisms with increased control over the external environment, which required key changes in their neurobiological organization. In short, metabolism is an archaic control mechanism in which behavior is embedded, and the latter has further developed increasingly complex nested features in select species. This could lead to characterizing animal behavior as an assembly of parcellated sensory–motor loops varying in sophistication. Interestingly, according to Yin (2020), advanced neuroscience techniques would help to elucidate the pathways and nodes in neural networks that underpin this hierarchical organization of behavior.

Some of the elements that constitute the behavioral repertoire in an organism could be recruited in a parallel or alternate fashion. This implies mutually exclusive systems competing at times to obtain or maintain control according to a hierarchical scheme. The performance of these systems is determined by the architecture of the nervous system as well as by transitory states informed by internal and external environments. The activity associated with the elements in charge of a behavioral function is tuned to respond to a particular class of trigger events and is abolished by another class of events. However, in complex systems, sensory–motor loops could become less fixed and gain “autonomy” by anticipating future outcomes from different cues via certain decoupling (learning) mechanisms. From the perspective of associative learning, experience with environmental statistical regularities is required for anticipation to happen. In addition, functional decoupling can be understood as changes in the degree to which certain environmental features affect (increase or decrease, in real time or prospectively; see Sosa & Alcalá, 2022) the activity of a given effector system.

Environmental pressures, such as conspecific competition, predation, scarcity, and unpredictability, may have fueled the evolution of this capacity for learning in some species. Hebb (1949) was a pioneer in suggesting that the internuncial portions of nervous systems (such as the associative cortex in mammals) are pivotal for learning and performance of complex behavioral patterns. He argued that animals with rigid behavioral repertoires have relatively large portions of primary sensory and motor nervous tissues compared to internuncial tissue. In contrast, a smaller ratio of primary to internuncial tissue leads to a capacity for finer discriminations to anticipate imminent outcomes, and thus to actions that exhibit less control from the apparent immediate environment. Relatively large internuncial regions enable organisms to display more seemingly spontaneous (decoupled) activity, but at the cost of slower initial learning rates. That is presumably one of the reasons for the atypically long period it takes human beings to develop proficiently sophisticated skills compared to other species.

Recent studies have captured the hierarchical organization that Hebb (1949) hypothesized. For example, Fox et al. (2020) found that the elicitation rate (i.e., the proportion of times that brain electrode activation reliably led to detectable responses in awake persons) of different regions in the human cortex correlates with its functional anatomical hierarchy. Specifically, stimulation of primary (unimodal sensory or motor) areas showed higher elicitation rates, and this measure decreased as the distance from these primary areas increased. In addition, the elicited effects became more heterogeneous, ascending the cortical hierarchy; that is, stimulating primary areas mainly elicited effects of a single type between participants, while stimulating areas connectionally distant from primary areas exhibited a wider variety of complex effects. Interestingly, a similar gradient of functional connectivity has been reported in other species. Huntenburg et al. (2021) identified several different gradients from primary sensory areas to more transmodal regions in the mouse cortex; importantly, gradients corresponded with sensory specialization in this species. For instance, the authors reported a gradient separating audiovisual from somatomotor areas and another separating the snout from the lower limb somatosensory regions.

In conclusion, from an evolutionary point of view, sensory and motor systems that are coupled and involve meaningful feedback loops benefit from efficient transmission (involving “hardwired” spinal or brainstem circuitries in chordates) and usually implement relatively stereotyped forms of behaviors, such as reflexes. On the other hand, sophisticated behavioral patterns that are not universally required for survival or reproduction can be learned. Their implementation demands neural tissue that is flexible enough (e.g., higher order association cortices) to couple sensory and motor assemblages that are not already linked, and extensive experience would be needed. In the middle of this spectrum lies the case of behaviors that are underpinned by cortical loops that are least separated by internuncial tissues and thus learned more readily. The latter would constitute a putative neural basis for so-called belongingness.

8. Miscellaneous examples for analysis

The phylogenetic refinement approach to behavior that Cisek (2019) proposed is quite compelling and promising as a conceptual tool for understanding belongingness, learning constraints, and differential cognitive capacities. However, in its current state, it does not commit to any particular characterization of behavioral change. Integrating Cisek’s (2019) approach and the notions described in previous sections with key findings from the fields of associative learning, comparative cognition, and behavioral neuroscience would be of great value. In the following subsections, I will attempt to do just that and will revisit the notions presented above to approach representative findings from these research domains. First, it is important to characterize one of the most elemental forms of behavioral change, reinforcement learning, and a possible mechanism for its pervasiveness.

9. Closing remarks

Behavior can vary from one species to another, from one population to another, from one individual to another, and within the same individual given different environmental constraints. When differences are robust and reliable, scientists are challenged to further study the mechanisms that drive variation. In so doing, they would do well to acknowledge the diversity of potential environmental arrangements, neurobiological substrates, and ontogenetic pathways by which a particular behavior pattern comes into being. For some species, learning can be regarded as a major mechanism driving behavioral change and studying it from an associative perspective has provided formal rules and principles by which behavior varies as a result of fluctuations in environmental regularities. Accordingly, organisms engage with their environment in new ways, leading to similarities or differences in their phenotypes, depending on whether said regularities are shared. However, the capacity to learn is hardly ever equipotent, which complicates the matter for both theory building and attempts at intervention. Although biases in learning potential have typically been attributed to genetic, neurobiological, and evolutionary factors, there are a number of other potential sources that can influence and underlie such tendencies.

This paper aimed to offer researchers from the field of associative learning insights (whether well-established or emerging) from other disciplines on mechanisms that influence and underpin behavioral phenotypes. Such insights can help to inspire novel research questions, as well as new ways of inquiring into and making sense the enduring debate on nature versus nurture. Special emphasis has been laid on strengthening ties among fields and adopting a cautious approach to inconclusive findings. Being mindful of the variety of possible factors through which behavioral differences can arise is key to reveal our knowledge gaps. Primarily aiming to reappraise the role of environmental factors that are often overlooked, this paper supplemented, reframed, contested, but most of all added nuance to certain conventional interpretations of key findings across several domains. Some of the claims herein are novel, while others echo and buttress ideas already found in the literature with varying degrees of acceptance. Certainly, while there is room for further refinement and improvement, at minimum, these insights can promote constructive discussion. I discussed in depth phenomena that touch on the nature–nurture issue as it relates to associative learning as well as of behavioral neuroscience and comparative cognition. This kind of inquiry, however, might as well be relevant to other fields like behavioral genetics, computational psychiatry, artificial intelligence, evolutionary biology, and developmental science.

The concerns discussed herein may be considered as a part of a growing effort across fields—such as behavioral ecology, systems neuroscience, and evolutionary developmental biology—to carefully reinterpret data in consideration of factors that are subtle, complex, or difficult to control for. For example, evolutionary biologists are increasingly interested in building mechanistic theories of phenotypic plasticity. However, even when acknowledging the important role of environmental factors, researchers from these fields do not always appeal to the literature on associative learning theory. This might be because developmental dynamics determine phenotypic pathways at multiple scales, and biologists have mainly focused on mechanisms on a molecular scale. The principles of learning are a powerful tool when approaching the plasticity of behavior at the whole-individual level, one fundamental share of animal phenotypes. Here, I outlined possible avenues for more quickly bridging the schism across boundary scales. It seems that learning is among the links missing between genetics and behavioral development. Bringing these fields into dialogue with associative learning theory has the potential to yield fruitful collaboration and advancement.