Comparative and Developmental Anthropology: Studying the Origins of Cultural Variability in Cognitive Function
Abstract and Keywords
This chapter examines the potency of combining cross-cultural, comparative, and developmental studies for understanding the dynamics of the interplay between cultural context and inherited predispositions during human development. This combined approach has contributed to our understanding of the emergence of cross-cultural variation in some of the most basic human cognitive functions such as spatial cognition, numerical cognition, visual perception, and social cognition. The same combined approach is also key to understanding human cultural variability in contrast to that found in other species: although there is now good evidence for population-level variation in apes and other animals, human’s vary in a wider repertoire of behaviors of which a larger proportion is socially acquired. By combining comparative, developmental, and cross-cultural studies, we may understand the species-typical psychological mechanisms that create, structure, and maintain uniquely human cultural variability.
Comparative and Developmental Cognitive Anthropology
When I first visited the ǂAkhoe Haiǁom, a Northern Namibian forager group, in 2005, I was packing my car to go to town for a 2-day break when I was approached by a man whom I had not met. Because we did not share a common language, much of the conversation that followed relied on facial expressions and gestures. I guessed that he was looking for a ride, which is a valuable opportunity for this remote population. Trying to make sure he wanted to go where I wanted to go, I pointed down the road toward Tsintsabis, the next town, just over 60 kilometers away, and said “Tsintsabis?” He laughed and pointed in the opposite direction, nodding. Then he pointed at the sky with a quizzing facial expression. I was lost. Recognizing this, he clarified. He pointed over his shoulder, then at the sky, and then reversed the point over his shoulder to go from back to front, and then pointed at the sky again. We had hit a dead end. It took the intervention of several other members of the community to make us understand each other. As it turns out, he was pointing over his shoulder to indicate the correct beeline direction toward Tsintsabis. He had laughed because the direction I had pointed in, down the road to Tsintsabis, was not at all the direction to Tsintsabis. He had pointed at the sky to ask about the estimated time of departure by indicating the position the sun would be at that time. Finally, he had attempted to enquire about the time of departure and the time of return. Although we both were under the impression that we communicated quite clearly, we had no idea what the other was talking about or how to make the other understand. This miscommunication was, of course, only the tip of the iceberg. It was also clear that, even now that I understood his communicative (p. 95) strategies, I could not respond in like manner. I did not know where the sun would be at 2 p.m., the time of intended departure, or how to communicate that I planned to return only in 2 days’ time. I also could not accurately point to any of the other towns I was planning to visit. In other words, he had a different set of information available to him. We were in the same place at the same time, solving the same task in different ways, and, at least speaking for myself, without the ability to simply switch to match the other’s communicative strategies. My conversation partner wielded a cognitive expertise that I did not. He did not ride with me in the end. I am not sure why.
Cross-Cultural Variability of Cognitive Function
Interactions such as the one just described drive home the fact that human cross-cultural variation is not restricted to behavioral strategies and traditions—eating either bread or rice for breakfast—but also manifests in the cognitive abilities and preferences of individuals—the way we can, tend to, or prefer to process information. Consequentially, two humans, in the very same place, at the very same time, might handle the very same task in very different ways. If such variation in cognitive ability and preference occurs not only from one individual to the next but on a population level, we refer to it as cross-cultural variability of cognitive function. Cross-cultural variability of cognitive function in adult humans has been investigated in different domains, showing that humans vary more than previously expected. For example, human adults from different cultural backgrounds might disagree about which of two lines is longer (Segall, Campbell, & Herskovits, 1963), whether green and blue are the same color (Roberson, Davies, & Davidoff, 2000), or whether a pile of seven coins is smaller than a pile of eight (Pica, Lemer, Izard, & Dehaene, 2004).
The cognitive sciences are again starting to embrace the challenge of cross-cultural variability (Henrich, Heine, & Norenzayan, 2010). A rising number of publications abstain from rushed claims of universality, but discuss—or ideally even investigate—cross-cultural variability of cognitive function. This includes the developmental sciences, with a growing set of studies investigating cross-cultural variation in children’s acquisition of social cognitive skill (Barrett et al., 2013; Callaghan et al., 2005; 2011; Cohen & Haun, 2013; House et al., 2013; Liszkowski, Brown, Callaghan, Takada, & de Vos, 2012), physical cognitive skill (Goldstein, Davidoff, & Roberson, 2009; Haun & Rapold, 2009; Haun, Rapold, Call, Janzen, & Levinson, 2006; Haun, Rapold, Janzen, & Levinson, 2011; Pica et al., 2004; Segall et al., 1963), and the differences in children’s environments that might cause some of the variation (Bolin, 2006; Gaskins, 2006; Keller et al., 2006; Lancy, Bock, & Gaskins, 2010; LeVine, 2007; Rogoff, 2003).
Levels of Explanation
According to Tinbergen (1963), to assess any behavior comprehensively one should address it on four different levels of explanation. Two proximate (here and now) levels explaining the behavior of an individual:
(a) In terms of its ontogeny—how does it emerge across the life span of individuals?
(b) In terms of causation—what are the mechanisms that enable it?
And on two ultimate (historical) levels explaining the behavior of population or species:
(c) In terms of its phylogeny—what is its evolutionary history?
(d) In terms of its adaptive advantage—why was it selected for?
The following sections assess the added value of a combined comparative, developmental, and cross-cultural approach to addressing the first three levels of explanation (a–c) concerning the phenomenon of cross-cultural variation of cognitive function. This multifaceted approach provides the potential to understand the dynamics of the ontogenetic emergence of cognitive variability by allowing the assessment of the ongoing and complex interaction between heritable predispositions of human cognition and the impact of the individual’s environment. Any discussion of the interaction of heritable predispositions and environmental influence, especially when observing behavior only, is a radical simplification of a highly complex process. The interaction of genetic, epigenetic, and environmental influences on human behavior is as of today largely unpredictable. The perspective presented in the next section of this chapter does not therefore aim to provide a description for this process, but merely to provide the developmental behavioral sciences with a set of didactic tools that will allow for a slightly more (p. 96) nuanced discussion of the interplay between cultural context and inherited predispositions during human development, replacing the still existing dichotomous examination of “nature versus nurture.” The same combined approach also allows for an assessment of the differences in human and nonhuman cross-cultural variability and, finally, the assessment of the mechanisms causing the species-unique structure of human cross-cultural variability. Several examples from the current literature from various areas of both physical and social cognition will serve to illustrate how these different methodological strands might be combined meaningfully.
The Ontogeny of Cultural Variation
Throughout development, children acquire cultural knowledge. They do so by means of and in interaction with universal biological predispositions. The acquired cultural knowledge entails the contents and the tools to master the challenges of particular environments. The term environment refers to both physical and social structures and processes. In this view, development is the construction and co-construction of cultural information based on informed hypotheses that are derived from the evolutionary heritage (Greenfield, Keller, Fuligni, & Maynard, 2003; Keller, Poortinga, & Schölmerich, 2002). To start to describe this interaction, we require a way of estimating both heritable predispositions and environmental impact.
Estimating Heritable Predispositions
The first question at stake here is whether any variance found in a human cognitive capacity is due to species-typical genetic variance (Mameli & Bateson, 2006). Although in some accounts, such as niche construction (Odling-Smee, Laland, & Feldman, 2003) or developmental systems theory (Oyama, Griffiths, & Gray, 2003), environmental features and even cultural systems can be used to transmit information across generations, here we solely focus on genetic heritability. Within this framework, “heritable” cognitive characteristics should be seen as part of the evolutionary endowment of the species; that is, as a set of reliably reoccurring developmental resources that is inherited from a last common ancestor (LCA) through descent with modification (Mameli & Bateson, 2006). One way to approach this question is to combine developmental and comparative studies.
In developmental science, the early onset of a cognitive capacity or preference is considered suggestive of a heritable cognitive trait (Carey & Spelke, 1996; Karmiloff-Smith, 2010; Spelke & Newport, 1998). Although the very early acquisition of a cognitive trait might support (not prove) claims of relevant heritable predispositions, a late acquisition does not prove the absence of such predispositions. Inherited cognitive capacities and preferences are not necessarily present at birth but might emerge only later in ontogeny. Children might be inherently prepared to acquire an ability or preference over time (Karmiloff-Smith, 2010). Although some sophisticated capacities are not present at birth, there is no a priori reason to exclude the possibility that heritable factors construct children’s abilities in these late-blooming cognitive domains. Given that they develop later in life, infant data alone are insufficient in determining the relevance of heritable cognitive predispositions in a given domain.
As defined earlier, “heritable” cognitive characteristics should be seen as part of the evolutionary endowment of the species, that is, inherited from an LCA (Mameli & Bateson, 2006). Conversely, any trait that is part of the evolutionary inheritance ever since an LCA becomes part of a shared repertoire between related species (Byrne, 1995). The reoccurrence of a certain cognitive trait across related species therefore suggests its heritability (Byrne, 1995; Haun, Jordan, Vallortigara, & Clayton, 2010). Following this comparative approach, humans have been compared to various primate species including pro-simians, monkeys, and the great apes (Tomasello & Call, 1997). And, indeed, continuities in cognitive function between humans and their phylogenetic cousins are striking even in more complex cognitive tasks such as perspective taking (Hare, Call, Agnetta, & Tomasello, 2000; Kaminski, Call, & Tomasello, 2008; Liebal, Call, Tomasello, & Pika, 2004) and cooperation (Melis, Hare, & Tomasello, 2006; Rekers, Haun, & Tomasello, 2011). These continuities, in principle, allow us to make inferences about extinct evolutionary ancestors of the selected set of species. The strength of such an inference, however, crucially depends on the selected sample of species. To make any reliable statement about an extinct organism’s cognitive architecture with some confidence, the comparisons need to be taxonomically informed: evolutionary taxonomy, or cladistics, (p. 97) is the classification of species in such a way that it correctly reflects evolutionary history. A valid grouping or “clade” within evolutionary taxonomy is a group of species with a common ancestor that is not ancestral to any other species (monophyletic group). The currently most common, but by far not the only, measure of similarity in cladistics is molecular structure and function (Enard & Pääbo, 2004). Figure 7.1 presents such an evolutionary taxonomy of our own clade, the great apes.
In evolutionary biology, cross-species comparisons and historical reconstruction use a set of statistical techniques called phylogenetic comparative methods (PCM). Among other possibilities (Nunn, 2011) these methods enable researchers to reconstruct probable ancestral states of shared but variable cognitive traits (Haun et al., 2010; MacLean et al., 2012) based on taxonomically informed selections of samples (Arnold & Nunn, 2010) and to place statistical measures of confidence on these reconstructions (Garland, Midford, & Ives, 1999; Pagel, Meade, & Barker, 2004). With the help of these statistical tools, we can reconstruct states of the common ancestor based on any reliable set of taxonomic relations of living species: if humans and all other great apes share features of a certain cognitive trait, the features are likely part of all species’ shared inheritance from their LCA and hence heritable in all species, including humans. One alternative interpretation is that (1) the same trait evolved independently several times within the same clade. This phenomenon is called convergent evolution or homoplasy. The other alternative is that (2) similar traits are acquired during ontogeny due to contextual constraints that are identical in all species. The likelihood of the former alternative decreases with an increase in sample size (the number of species) and the completeness of the tested family of species. For humans, a complete set of species with a single common ancestor that in turn is not ancestral to any other species is the great ape clade: orangutans (Pongo pygmaeus), gorillas (Gorilla gorilla), bonobos (Pan paniscus), chimpanzees (Pan troglodytes), and humans (Homo sapiens; see Figure 7.1). Hence, studies applying matched methods for comparing cognition across all great apes by means of PCM can estimate the likelihood that a certain cognitive trait develops by means of and in interaction with universal heritable predispositions.
The Interaction of Heritable Predispositions and Cultural Context
One conclusion often drawn from the presence of cross-cultural variation in a cognitive trait is that its acquisition is subject to environmental input only and not structured by heritable predispositions. One conclusion often drawn from the absence of cross-cultural variation in a cognitive trait is that it is impervious to environmental influence and emerges based on heritable predispositions. Both conclusions are grossly premature if based on the presence and absence of variation in adults alone. The inadequacy of these conclusions becomes apparent when we take a developmental perspective. Cross-cultural comparisons of cognitive abilities and preferences and the taxonomically informed comparisons of the same traits across the great ape clade provide us with estimates of both the variability of a trait across human populations and the heritable predispositions relevant to that trait. These two factors interact constantly throughout development. That’s why, based on adult variation alone, it is impossible to identify the (p. 98) relative contribution of either heritable predispositions or environmental impact to the developing trait. However, when additionally considering the developmental origins of a given trait, we can track the patterns of emergence of said trait. These ontogenetic patterns, as detailed later, often take characteristic shapes. These characteristic shapes indicate different relative contributions of heritable predispositions and contextual influence. The following examples demonstrate some of these recurrent developmental patterns:
• The presence of variation in adult cognition across cultures is largely driven by
(a) genetic variation on a population level;
(b) context-dependent cultural learning in the absence of heritable predispositions;
(c) context-dependent cultural learning overriding heritable predispositions.
• The absence of variation in adult cognition across cultures is largely due to
(d) heritable predispositions that are either impenetrable to learning or are under no pressure to change;
(e) learning in response to cross-culturally universal contextual features.
This categorical structure is only an approximation, ignoring the gradual and dynamic process of development. Its purpose is to serve as a didactic tool to sort evidence and exemplify the ways in which cross-cultural variation of cognition might emerge, taking a very first step toward a more nuanced description. It also should make clear that the presence and absence of variation in adult cognition does not enable us to infer its emergence. Here, I review findings from the cognitive sciences to illustrate and elaborate on each of the five categories (a–e).
The presence of variation in adult cognition across cultures is largely driven by genetic variation on a population level.
This rarely discussed account explaining how adult variation across cultures emerges states that the observed variation of adult cognition across cultures is the consequence of genetic variation on a population level. This is a politically controversial explanation for psychological differences between cultures, one with a dark history, and it should be examined with care (Gould, 1981). Researchers have identified some genes that vary systematically across populations, such as genes associated with skin color (Jablonski & Chaplin, 2000) or lactose intolerance (Beja-Pereira et al., 2003). One example for a cultural behavior that appears to be partly genetically determined on a population level is the use of linguistic tone, i.e. the use of voice pitch to convey lexical or grammatical distinctions. Dediu and Ladd (2007) showed that there is a relationship between the population frequency of two alleles (haplogroups of the brain growth and development-related genes ASPM and Microcephalin) and the presence of linguistic tone. They documented the relationship between the population frequency of these two alleles and the presence of linguistic tone, showing that it is not due to the usual explanatory factors represented by geography and history. In their account, differences across cultures in cognitive traits might be caused by certain alleles biasing information processing and thereby influencing the trajectory of individuals’ ontogenies, as well as of the trajectory of a cultural trait through iterated cultural transmission. Consequentially, a genetic base for cognitive variability across cultures cannot be entirely ruled out. Genetic variation should not, however, be considered a likely candidate for explaining the whole of cognitive variability across cultures. Human groups are genetically much more similar to one another than to any of the other great ape species (Figure 7.2; Kaessmann, Wiebe, Weiss, & Pääbo, 2001). In contrast, they vary much more in their behavior than the other great apes (Pagel & Mace, 2004). So, even if genetic variation plays a small role in accounting for cross-group variation, it remains reasonable to assume that most between-groups differences are overwhelmingly attributable to socially transmitted mechanisms (Heine & Norenzayan, 2006). Hence, the heritable predisposition discussed in the remaining sections of this chapter are assumed to be universal and no source of variation on a population level.
The presence of variation in adult cognition across cultures is largely driven by context-dependent cultural learning in the absence of heritable predispositions.
One example of a cognitive trait that is entirely learned pertains to visual illusions. The perception of many visual illusions varies across cultures (Segall et al., 1963). One particular illusion, the Ebbinghaus illusion (see Figure 7.3), appears to be a case of “context-dependent cultural learning in the absence of heritable predispositions.” In this illusion, a target circle surrounded by larger circles looks smaller, and one surrounded by smaller circles looks larger than they really are. The Himba, a remote pastorialist group in Northern Namibia, experience the (p. 99) Ebbinghaus illusion much less strongly than do English university students (de Fockert, Davidoff, Fagot, Parron, & Goldstein, 2007). But susceptibility to the illusion increased even after brief exposure to an urban environment among some Himba (Caparos et al., 2012). Recent developmental studies of susceptibility to the Ebbinghaus illusion found that at 4 years of age, English children do not perceive the illusion and hence are more accurate than adults at judging the relative size of the central circle. Even by the age of 10 years, children still performed better than adults (Doherty, Campbell, Tsuji, & Phillips, 2010). A comparison between US and Japanese children revealed that the two populations diverge over ontogenetic time. Whereas children from both cultures perform equally well at age 4–5, both perceive the Ebbinghaus illusion increasingly strongly as they grow older (6–7 and 8–9 years), but the effect is stronger in Japanese children, causing an increasing divergence of the two populations (Imada, Carlson &, Itakura, 2013).
Based on these developmental results, susceptibility to the Ebbinghaus illusion seems to develop late in some individuals and in some cultures, such as the Himba, hardly at all. A comparison between humans and baboons (Papio papio) demonstrated that only humans misjudged the central target (p. 100) size under the influence of the Ebbinghaus illusion, whereas baboons expressed a more veridical perception of target sizes (Parron & Fagot, 2007). As of yet, there are no data available from nonhuman great ape species, precluding any firm statements about the LCA. Based on the baboon data alone, one might propose that, for the case of the Ebbinghaus illusion, children appear to inherit no cognitive predisposition, and therefore adult variation emerges as a result of context-dependent experience.
The presence of variation in adult cognition across cultures is largely driven by context-dependent cultural learning overriding heritable predispositions.
One example for such a trait is the use of spatial frames of reference (FoRs) in memory. Relational language follows coordinate systems or FoRs, which serve to specify the directional relationships between objects in space in reference to a shared referential anchor (Levelt, 1996). Languages vary in the repertoire they code and also in the habitual usage of different FoRs (Levinson, 2003). Some languages mainly use egocentric FoR with terms like front, back, left, and right: “The ball is to the left of the tree” (from my point of view). Some languages, such as the ǂAkhoe Haiǁom mentioned in the introductory anecdote, mainly use a so-called allocentric FoR in which linguistic descriptions, for example, use cardinal direction type systems such as our North, South, East, and West: “The stick is north of the pebble.”
Spatial relational cognition can be categorized similarly in egocentric (view-dependent) and allocentric (view-independent) memory representations and varies similarly across cultures (Haun, 2011; Haun & Rapold, 2009; Haun et al., 2006; 2011; Levinson, 2003; Majid, Bowerman, Kita, Haun, & Levinson, 2004). While both egocentric and allocentric FoRs are common across cultures, children early on prefer allocentric over egocentric FoRs: As soon as they have become competent navigators (at 16 months of age; Acredolo, 1988), i.e. they successfully use both allocentric and egocentric cognitive strategies, English-speaking children, at least between 3 and 5 years of age, are better at allocentric strategies than at egocentric ones (Allen, 1999; Haun et al., 2006; Nardini, Burgess, Breckenridge, & Atkinson, 2006).
Finally, comparing across species, all great ape species, including 4-year-old German children, prefer to process spatial relations based on environmental cues and not self. Based on these results and the allocentric preference of young human navigators The inherited cognitive mode of operation in humans and the other great apes appears to be to process spatial relations using allocentric FoRs over view-dependent egocentric FoRs (Haun et al., 2006). In this case, cultural variation in spatial memory is no indicator of the absence of heritable predispositions. The nuanced picture that emerges instead is one in which the inherited bias toward the allocentric coding of spatial relations can be overridden by cultural learning.
The absence of variation in adult cognition across cultures is largely due to heritable predispositions that are either impenetrable to learning or are under no pressure to change.
One example for a cognitive trait that is not learned but heavily structured by heritable predispositions is the ability to discriminate large quantities by means of approximation (approximate arithmetic). This ability is common in human adults across cultures such as the United States, the Munduruku (Pica et al., 2004), and the Piraha (Gordon, 2004; Frank, Everett, Fedorenko, & Gibson, 2008), across which there is otherwise strong variation in numerical abilities (Frank et al., 2008; Gordon, 2004; Pica et al., 2004). Infants are able to solve quite similar tasks from a very early age. Six-month-old infants, for example, can discriminate 8 versus 16 dots on a screen (Xu & Spelke, 2000). After seeing repeated presentations of either 16 (or 8) until they habituate, they will now look longer at an array of 8 (or 16) dots. Hence, infants are able to discriminate the two arrays. Controls for surface area and other characteristics ensure that infants can discriminate by number alone (Xu & Spelke, 2000). Furthermore, a large set of studies has shown that the approximate arithmetic abilities in infants are imprecise and ratio-dependent. Six-month-old infants successfully discriminate 8 versus 16 and 16 versus 32 dots, but fail with 8 versus 12 and 16 versus 24 (Xu & Spelke, 2000). In other words, 6-month-olds can discriminate numerosities with a 1:2 but not a 2:3 ratio (Feigenson, Dehaene, & Spelke, 2004). The ability to discriminate these more difficult ratios, however, increases quickly with age. At 10 months, infants are already able to tell apart arrays with a 2:3 ratio, and adults can discriminate ratios as small as 7:8. In summary, approximate arithmetic abilities emerge early and become more proficient across age but do not change in structure (Feigenson et al., 2004).
(p. 101) For the domain of approximate arithmetic, basic performance characteristics in quantity discrimination tasks are shared across animal taxa (Cantlon, Platt, & Brannon, 2009), including all nonhuman great apes (Hanus & Call, 2007). All tested great apes can select the larger of two quantities by approximate arithmetic, both when presented simultaneously and in sequence, even when the quantities are large and the numerical distance between them is small (Hanus & Call, 2007). Similar performance levels have been reported for human children from roughly 6 years of age onward (Halberda & Feigenson, 2008), indicating a common heritage of the proximate number system (Feigenson et al., 2004). Given the heritable predisposition, early onset, and cross-cultural stability of approximate arithmetic abilities, the absence of variation across cultures is likely due to a strong heritable predisposition that is either impervious to learning or under no contextual pressure to change.
The absence of variation in adult cognition across cultures is largely due to learning in response to cross-culturally universal contextual features.
One example that is at least suggestive of universal learning is declarative, referential gestures. The gestures exchanged among chimpanzees in their natural habitat are largely imperative and non-referential (Tomasello, 2008). In contrast, among humans, declarative referential gestures, such as pointing, emerge already around the first birthday (Bates, Benigni, Bretherton, Camaioni, & Volterra, 1979; Liszkowski, Carpenter, Henning, Striano, & Tomasello, 2004).
Recent studies have shown remarkable cross-cultural stability in the existence as well as the structure of infant pointing across cultures. Children in Kyoto (Japan), Montaro Valley (Peru), Nova Scotia (Canada), Rossel Island (Papua New Guinea), Srikakulam (India), and Tzeltal and Yukatec Mayans (Mexico) start pointing around their first birthday, as reported previously for European and American samples. In all settings, the majority of infants uses the specific form of index-finger pointing (Callaghan et al., 2011; Liszkowski et al., 2012). Evidence that declarative referential communication is to some extent learned in the context of social interaction (Werner & Kaplan, 1963) comes from the study of individual differences within a cultural setting (Gaffan, Martins, Healy, & Murray, 2010; Liszkowski & Tomasello, 2011) and population-level variation in some limited features, such as the frequency of points (Salomo & Liszkowski, 2012). On an individual level, infants’ frequency of pointing is related to social interactional practices of looking together at objects and to social-cognitive skills for comprehending reference (Liszkowski & Tomasello, 2011). Cross-cultural variability in the amount of joint action and declarative gestures to which infants were exposed across three different cultures—Yucatec-Mayans (Mexico), Dutch (Netherlands), and Shanghai-Chinese (China)—caused variation in the frequency with which infants pointed. Infants gestured somewhat more depending on the amount of joint action and gestures they were exposed to (Salomo & Liszkowski, 2012). Hence, whereas declarative referential pointing emerges most likely universally across cultures, the frequency with which infants point varies both interindividually as well as cross-culturally depending on the amount of infant–caregiver social interaction.
Interestingly, some scientists have argued that, given the right environmental structure, chimpanzees will produce humanlike referential declarative gestures when communicating with humans (Leavens, Ely, Hopkins, & Bard, 2012). This would indicate that species differences in this behavior are not directly determined by differences in heritable predispositions to gesture referentially with a declarative motivation. Another indicator is that pointing is learned by both species, just much more naturally by humans. Hence, it appears that human infants acquire referential declarative gestures universally in the absence of a shared heritable predisposition.
This last example provides a good opportunity to highlight the complexity of the interaction between heritable predispositions and environmental impact in development. As stated earlier, in humans, the synchronous emergence of pointing gestures and the similar frequency of use are likely due to universal learning. The characteristic hand shape of the index-finger point, however, might be promoted by a heritable predisposition to slightly raise the index finger relative to its neighbor when relaxing the hand (Povinelli & Davis, 1994). Amongst great apes, this is only true for humans. Hence, species differences in the hand shape might be due to heritable predispositions, even if differences in the likelihood with which the general behavior is acquired are not. Or, to give another example, the likelihood with which human children acquire declarative referential gestures might be higher than it is for chimpanzees because of (p. 102) heritable predispositions, causing human children to interact socially with others in species-unique ways and promoting the motivations and skills that benefit the acquisition of pointing (see section on Uniquely Human Social Cognition). As stated earlier, the examples given here are not meant to be an adequate description of the complex interplay of heritable predispositions and environmental impact, but they are a first step in the right direction. In this spirit, the universal features of human declarative referential communication seem to be largely due to learning in response to cross-culturally universal features of human social interaction.
By documenting the dynamics of the emergence of cross-cultural variation—or its absence—through combining cross-cultural with developmental and comparative data, we learn about the structure of the cognitive ability itself and, maybe more importantly, the dynamics of the interplay between cultural context and inherited predispositions during the emergence of adult cognition. This emergence is structured by learning mechanisms and biases that guide the transmission of cultural knowledge and mediate the impact of the environment on the developing child. It is in these mechanisms and biases where we must seek the explanation for the extensive cultural variability that identifies the human species.
The Phylogeny of Cultural Variation
For a long time, it was thought that no other animal species showed anything resembling human culture. But detailed and long-term studies of various animal species in their natural habitats have established that they, too, form distinct social, or perhaps cultural, groups with multiple behavioral differences (Allen, Weinrich, Hoppitt, & Rendell, 2013; Boughman & Wilkinson, 1998; Hunt & Gray, 2003; van Schaik et al., 2003; Whiten et al., 1999). Arguably, some of the most detailed and convincing recent evidence for nonhuman culture—defined as socially acquired, population-specific behavior (Perry, 2006)—comes from the study of two of our closest phylogenetic relatives: chimpanzees (Luncz, Mundry, & Boesch, 2012; van Leeuwen, Cronin, Haun, Mundry, & Bodamer, 2012) and orangutans (Krützen, Willems, & van Schaik, 2011). Chimpanzee populations, for example, vary in their relative preferences for stone (Figure 7.4 A1) and wooden tools (Figure 7.4 A2) for cracking nuts (Luncz et al., 2012) and in the propensity and the style with which they engage in a particular kind of mutual grooming: the grooming handclasp (Figure 7.4 B1-2) (van Leeuwen et al., 2012). Hence, chimpanzees display cultural variation in both physical tasks and social behaviors. All together, 39 different behaviors have been documented to vary across a set of six wild chimpanzee populations. The behaviors might be present in one and absent in another population or vary in their specific local instantiation (Whiten et al., 1999).
In comparison to all these cases, however, human cross-cultural variation still appears to be unique in both extent and structure. Humans appear to display a wider repertoire of behaviors that vary more distinctly across communities and of which a larger subsection are socially acquired (Pagel & Mace, 2004). This difference between human and nonhuman behavioral and cognitive variability demands an explanation. The human capacity for cultural variability must root in a set of characteristics that enable, structure, and stabilize cultural variation beyond what can be observed in other primates. This foundation of cultural behavior must be universal across all humans. Again, a developmental perspective is of great benefit here: childhood is when individuals acquire many of the most important culture-specific behaviors and abilities. Species differences in the structure of early development (see section on The Phylogeny of Cognitive Development) and differences in the capacities, strategies, and biases relevant for social learning in this period (see section on Uniquely Human Social Cognition) are at the core of most accounts explaining uniquely human cultural variability. By the same methodological triangulation of cross-cultural, developmental, and comparative approaches, we can identify those characteristics of early development that are both unique to humans among the great apes, but universal across all humans and might therefore serve as defining features of the human species.
The Phylogeny of Cognitive Development
One important example of a unique and universal human characteristic is the prolonged cranial ontogeny in human infants relative to the other African great apes (Hublin, 2005): in human evolution, the evolutionary trend toward increasing brain size favored wide pelvises in females to secure the mother’s and infant’s safety during birth. At the same time, bipedalism required increasingly narrow pelvises for stability during locomotion. Likely as a result of these (p. 103) two developments, human children are born at an early stage of brain development relative to other African great ape species (Hublin, 2005). Thus, the infant’s head still fits through the mother’s narrower pelvis without compromising the trend toward bigger brains. Whereas chimpanzee brains are already 45% of the average adult size at birth and 85% of the adult size at 1 year of age, the human brain is only 25% of the adult size at birth, increasing to around 55% 1 year later. It takes humans up to 6 years to reach 85% of the average adult brain size (Hublin, 2005). So the major growth period of the human brain—the time when it is maximally plastic—takes place outside the womb, where children are much more exposed to their physical and social environment. Therefore, human children may be expected to adapt to the local circumstance much more easily for a longer period than, say, young chimpanzees do (Bruner, 1972).
Humans’ cognitive development differs from that of other African great apes also in other ways. Research has shown that on physical cognition tasks, humans, chimpanzees, and bonobos perform at the same level at 2 years of age, but by 4 years children have advanced whereas chimpanzees and bonobos remain at their 2-year-old performance levels. On social cognition tasks, in contrast, children outperform chimpanzees and bonobos already at 2 years, and this difference is even bigger by 4 years. In other words, pattern and pace of cognitive development differ between humans and other (p. 104) apes, particularly in the social domain (Wobber, Herrmann, Hare, Wrangham, & Tomasello, 2013). Hence, both the prolonged timing and the focus on the social domain in human development might benefit the individuals’ internalization of the surrounding social environment and result in increased cross-cultural variation.
Uniquely Human Social Cognition
Recent studies have identified several human social behaviors that appear to have no corresponding match in other primates. Examples are pretend play or fictional games that children invent together with a play partner (Rakoczy, Warneken, & Tomasello, 2008); joint attention, featuring mutually recognized shared attention to an external object (Bruner, 1998; Tomasello, Carpenter, Call, Behne, & Moll, 2005); shared intentionality, featuring collaborative interactions in which participants share psychological states with one another (Searle, 1995; Tomasello et al., 2005); false belief reasoning or understanding that others might hold beliefs about the world that are untrue (Call & Tomasello, 2008); social preference, preferring to do things together over doing the same thing alone (Rekers et al., 2011); overimitation, the tendency to copy actions faithfully, including un- or counterproductive aspects (Horner & Whiten, 2005); normative conformity, the tendency to change ones behavior to others to avoid negative social consequences (Haun & Tomasello, 2011; Haun, van Leeuwen, & Edelson, 2013); natural pedagogy, the sensitivity to a restricted set of communicative signals as indicative of a teaching context (Csibra & Gergely, 2009); and norm psychology, the tendency to adhere to, enforce, and redress violations of the shared behavioral standards of one’s community (Chudek & Henrich, 2011; Rakoczy & Schmidt, 2013). Because these behaviors are proposed to be nonexistent in the other great apes, PCMs cannot reconstruct LCA states given the lack of similarities across species. Nevertheless, some of these behaviors emerge early in ontogeny and have been shown to vary little across cultures. Overimitation, false belief reasoning, and normative conformity are three such examples.
If chimpanzees copy the particular actions of their conspecifics (i.e., imitate), they appear to do so infrequently and with relatively low fidelity (Tennie, Call, & Tomasello, 2009). Children, in contrast, tend to copy actions faithfully, even those aspects that are superfluous or disadvantageous (Horner & Whiten, 2005; Nielsen, 2006). This phenomenon has come to be called overimitation (Lyons, Young, & Keil, 2007). It emerges in the second year of life (Nielsen, 2006) and becomes increasingly pervasive through the preschool period (McGuigan, Whiten, Flynn, & Horner, 2007). One recent study has replicated these findings in Bushman communities of the South African Kalahari Desert (Nielsen & Tomaselli, 2010), reporting striking similarities in performance to prior samples. Kalahari San children’s performance, similar to that of children from US and UK/European samples, was unaffected by age, the cultural background of the model, or the children’s opportunity to learn on their own how to operate the apparatuses (Nielsen & Tomaselli, 2010).
False Belief Reasoning
Maybe the most frequently discussed ability that distinguishes humans from other great apes is the ability to take the mental perspective of others (Call & Tomasello, 2008). Some of the many abilities summarized under the term “theory of mind” have homologues in other great apes, but humans show an extraordinary facility with making inferences about the beliefs of others and, in particular, false beliefs (Call & Tomasello, 2008). Understanding that others have a false belief requires the individual to understand that others’ representations contradict not only one’s own, but also reality. Because being able to do so requires a true representation of another individual’s mental state, false belief understanding is seen as sign for a fully fledged theory of mind (Wellman, Cross, & Watson, 2001).
In recent years, several studies have compared understanding of others’ false beliefs across different human cultures (Avis & Harris, 1991; Callaghan et al., 2005; Knight, Sousa, Barrett, & Atran, 2004; Liu, Wellman, Tardif, & Sabbagh, 2008; Vinden, 1996; 1999). All of these studies relied on a highly language-dependent narrative task—the so-called Sally-Anne Task (SAT; Wimmer & Perner, 1983). In this task, children are presented with a story in which Sally hides her marble in her box and then leaves the room. While Sally is gone, Anne moves the marble from Sally’s box to her own basket. When Sally returns shortly after, children are asked where they expect she might look for her marble. If children understand that Sally holds a false belief about the location of her marble, they will predict Sally will search in her box. Many, however, see the usage of story-based tasks in a cross-cultural setting as highly problematic (Lillard, 1998). It (p. 105) can be expected that the problems of translating stories into foreign languages, including not only mistakes, but also the required adaptations to cultural sensitivities, will render results unreliable (Segall, Dasen, Berry, & Poortinga, 1990). Furthermore, children across cultures might well vary in how readily they are able and willing to answer questions about others mental states and likely behavior, but not in their understanding of others’ false beliefs per se.
Two recent studies applied nonverbal tests either adapted from a design previously used with nonhuman great apes (Haun, Girndt, Liebal, & Kaminski, in preparation) or with preverbal infants (Barrett et al., 2013). Both studies report very little cross-cultural variability in performance. In the first study, children played a competitive game against an experimenter in which they could maximize their winnings if they correctly predicted their opponents behavior based on their understanding of the other’s mental states. Four- to seven-year-old children from three communities were compared—Leipzig (Germany), ǂAkhoe Haiǁom (Northern Namibia), and Safotu (Western Samoa). Several studies have previously noted a delay in Samoan children’s acquisition of false belief reasoning (Callaghan et al., 2005; Mayer & Träuble, 2013) using narrative-based tests. However, children from all three cultures showed identical levels of performance when applying false-belief reasoning in a competitive social game (Haun et al., in preparation). The second study used a set of so-called spontaneous-response tasks (Baillargeon, Scott, & He, 2010). These include preferential looking tasks, anticipatory looking tasks, and violation of expectation tasks. Again, this study found identical performance in all tasks across children aged 2–4 years from three traditional societies: the Salar community in China, a Shuar/Colono community in Ecuador, and a Yasawan community in Fiji (Barrett et al., 2013). The two studies seem to indicate that children across highly varied cultures become proficient interpreters of others’ beliefs.
Humans do not just learn from others as all great apes do—they conform to them. Humans will alter their opinions or behavior to match that of their peers, even if those peers are conspicuously wrong (Asch, 1956; Bond & Smith, 1996), an effect dubbed “conformity” (Asch, 1956) or sometimes even “strong conformity” (Haun & Tomasello, 2011). Such conformity moves groups toward behavioral homogeneity while at the same time stabilizing between-group heterogeneity (Henrich & Boyd, 1998). Adults across a wide variety of cultures (Bond & Smith, 1996), as well children as young as 4 years of age (Haun & Tomasello, 2011), adjust their responses to a conspicuously erroneous group of peers. Furthermore, humans appear to conform based on two different motivations: informational and normative motivations (Deutsch & Gerard, 1955). Informational conformers conform to maximize performance (“the majority must be correct”). Normative conformers conform because of the social benefits of conforming relative to dissenting. Although it has been shown that chimpanzees will learn from the majority when acquiring a novel task (Haun, Rekers, & Tomasello, 2012), chimpanzees, in contrast to human children as young as 2 years of age, do not adjust their behaviour to the majority, if such a change is not accompanied by an increase in payoffs (Haun, Rekers & Tomasello, 2014; Van Leeuwen, Cronin, Schütte, Call & Haun 2013). Although some studies have even claimed humanlike conformity in nonhuman primates (Hopper, Schapiro, Lambeth, & Brosnan, 2011; van de Waal, Borgeaud, & Whiten, 2013; Whiten, Horner, & de Waal, 2005; but see Haun et al., 2013; van Leeuwen & Haun, 2013 and van Leeuwen & Haun, 2014, for a critical perspective), no study to date has convincingly shown a nonhuman primate conform based on normative motivations. Because normative conformity has been shown in humans early in development and in adults across cultures, species differences in the motivations underlying conformity might be a contributing factor to species differences in cross-cultural variation.
Human cognition varies substantially on a population level. It is important to consider however, that the presence or absence of population-level variation in any given cognitive skill alone is not indicative of the developmental dynamics of said skill. Despite recurrent appearances of such arguments in the literature, the presence of cross-cultural variation in a cognitive trait is no indicator that the acquisition of that trait is not structured by heritable predispositions. Equally, the absence of cross-cultural variation in a cognitive trait is no indicator that its acquisition is impervious to environmental influence and that it emerges based on heritable predispositions alone. To understand the relative contributions of heritable predispositions and the environment, a combined approach using cross-cultural, comparative, and developmental studies is helpful because it makes visible certain recurrent dynamics of the (p. 106) developmental process. Although being far way from a realistic account of the complex emergence of an individual’s cognitive structures, the resulting categories can serve as thinking tools to sort evidence and exemplify the ways in which cross-cultural variation of cognition might emerge.
One fact about human cross-cultural variation that is largely agreed upon is that human behavior varies more across populations than that of other closely related animal species, such as the great apes. Although there is now good evidence for population-level variation in apes and other animals, humans vary in a wider repertoire of behaviors, of which a larger proportion is socially acquired. The same combined comparative, developmental, and cross-cultural approach is also key to understanding the species-typical psychological mechanisms that create, structure, and maintain uniquely human cultural variability.
Many scientists have called for a psychological and developmental science for a cultural species (Heine & Norenzayan, 2006; Henrich et al., 2010; Jensen, 2012). The research summarized in this chapter shows that this call is being answered. This emerging research perspective addresses cross-cultural variation of psychological processes, not only in their final manifestations but also in their ontogenetic dynamics. The emerging psychological science emphasizes the combination of cross-cultural, developmental, and comparative studies to gain a more nuanced understanding of how human cognition enables, structures, and responds to human culture.
The author owes special thanks to Cristine Legare, Heidi Keller, Michael Tomasello, and Katja Liebal for vital comments on earlier drafts of the chapter and to Ronja Büchner for editorial assistance. This work is supported by the Max Planck Society for the Advancement of Science.
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