Philosophical Transactions of the Royal Society B: Biological Sciences


    Archaeology has much to contribute to the study of cultural evolution. Empirical data at archaeological timescales are uniquely well suited to tracking rates of cultural change, detecting phylogenetic signals among groups of artefacts, and recognizing long-run effects of distinct cultural transmission mechanisms. Nonetheless, these are still relatively infrequent subjects of archaeological analysis and archaeology's potential to help advance our understanding of cultural evolution has thus far been largely unrealized. Cultural evolutionary models developed in other fields have been used to interpret patterns identified in archaeological records, which in turn provides independent tests of these models' predictions, as demonstrated here through a study of late Prehistoric stone projectile points from the US Southwest. These tests may not be straightforward, though, because archaeological data are complex, often representing events aggregated over many years (or centuries or millennia), while processes thought to drive cultural evolution (e.g. biased learning) operate on much shorter timescales. To fulfil archaeology's potential, we should continue to develop models specifically tailored to archaeological circumstances, and explore ways to incorporate the rich contextual data produced by archaeological research.

    This article is part of the theme issue ‘Bridging cultural gaps: interdisciplinary studies in human cultural evolution’.

    1. Introduction

    Archaeology generates vast amounts of empirical data related to material outcomes of human social learning. These data range in scale from individual artefact traits (e.g. decorations on pots) to global trait distributions (the spread of farming) and records of technological change spanning millions of years (stone tool ‘modes', sensu Clarke [1]). Archaeologists also routinely gather data that give environmental, demographic and social context to the evolution of material culture. These anthropic and contextual data are particularly well suited to studying long-run effects of distinct cultural transmission mechanisms on material cultural evolution; identifying phylogenetic relationships among artefacts; tracking long-term cultural stability, rates of change and diffusions of innovations; and exploring cultural extinctions, instances of convergent cultural evolution and other evolutionary outcomes that may not be predicted by current models. Nonetheless, many of these are still relatively infrequent (or entirely unexplored) subjects of archaeological analysis using Darwinian concepts and methods (cf. [2]). This is partly because archaeological data are complex and more often present a fragmentary record of aggregated events than a clear and detailed account of cultural evolutionary forces acting over long timespans; we perceive a gulf between the person-to-person exchanges that drive cultural transmission and the much coarser grain of the archaeological record. But, while the gulf is both real and consequential, a similar one exists between genetics and paleontology, yet their complementarity and mutual relevance to evolutionary biology are, today, undeniable. Indeed, relatively recent advances in archaeology—and examinations of archaeological data by scholars in other fields (e.g. [35])—show that there are a variety of ways archaeology might contribute to the development of cultural evolutionary theory. First, as we continue to identify archaeologically relevant units of observation and analysis, we increase the potential for archaeological data to provide independent tests of existing models' predictions, as demonstrated here through an original study of projectile points from the US Southwest. I argue, further, that we should pursue avenues of research that leverage archaeology's unique perspectives and rich—if complicated—records of social learning in real-world contexts to generate novel cultural evolutionary hypotheses.

    2. Current archaeological strengths: identifying patterns and processes

    Archaeological definitions of cultural evolution have varied through time and have only recently come to include concepts and methods informed by the modern evolutionary synthesis (sensu Huxley [6]; for archaeological histories see [2,7,8]). Three such approaches currently applied in archaeology are human behavioural ecology (HBE), phylogenetics and cultural transmission theory. To date, HBE has enjoyed the widest application in archaeology, particularly in the study of prehistoric hunter–gatherers' foraging decision. Reasons for this are both practical and historical: the bulk of material remains associated with prehistoric hunter–gatherers are subsistence related, and HBE's optimal foraging theory is in many ways compatible with archaeology's dominant (processualist) paradigm [7]. Nonetheless, many archaeologists are drawn to seminal texts by Cavalli-Sforza & Feldman [9] and Boyd & Richerson [10], which describe ways to analyse cultural data using techniques derived from evolutionary biology, genetics and population ecology, providing empirically testable predictions related to cultural change in social contexts. And, while expressly archaeological studies of gene–culture coevolution and cultural transmission theories remain few, a relatively small group of archaeologists is developing ways to interpret archaeological data in these terms [1119]. These developments include use of cladistics to map possible phylogenetic relationships among artefacts, and quantification of artefact variability to identify learning biases. Both approaches use established cultural evolutionary models to infer evolutionary processes from archaeological patterns, which, in turn, provide independent tests of the models' predictions.

    (a) Cladistics

    Archaeologists first incorporated cladistics in the 1990s to explore possible phylogenetic relationships among artefacts and to ‘identify which character state changes are homologous—the result of inheritance—and which are analogous—the result of adaptation' [20, p. 728]. Since then, the approach has produced compelling evidence of ancestor–descendant relationships within classes of prehistoric technology including stone projectile points (e.g. arrowheads and spear points) and pottery [11,20,21]. It has also provoked stimulating discussions related to units and modes of cultural transmission, and the primacy of selective forces [2224]. Granting that many phylogenetic methods will produce lineage trees whether or not a true evolutionary relationship exists [25], and that cladistics is premised on vertical cultural transmission while much social learning follows other forms (e.g. horizontal, oblique; [20,22]), cladistics can be a powerful and principled means of identifying and quantifying the evolutionary relationships that are sometimes simply assumed in other approaches (see also [2,17]).

    (b) Artefact variability and cultural transmission

    Where cladistics uses similarity to establish historically meaningful artefact taxa, a second approach to cultural evolution uses measures of difference—patterns of artefact variation—to identify specific modes of cultural transmission. This approach leverages the fact that the archaeological record is, in a sense, simply a spatially and temporally expansive account of artefact variation, interpreted in terms of human behaviour. Inferring past behaviours from patterns of artefact variation requires a reliable measure of variability and a body of theory equipped to distinguish its potential sources. Bettinger & Eerkens [1215] have been particularly influential in this arena, exploring a variety of measures of within- and between-group variability (where groups are sets of artefact, sites, etc.) to ‘construct models that produce objective, explicit predictions about how variability should behave under different natural and cultural forces at various spatial and temporal scales' [15, p. 38].

    The natural and cultural forces to which Eerkens and Bettinger refer include things like raw material quality and abundance, artefact makers' proficiency, tool functional requirements, cultural attitudes towards variation and learning biases. In essence, within- and between-group variability reflects the strength of such forces; relatively low variation indicates tighter constraints on artefact form and relatively high variation, looser or no constraints. Distinguishing specific forces is an exercise in modelling, discussed below. Measuring variability is relatively simple, though, and the coefficient of variation (CV) is a robust and reliable means of quantifying variation to determine the degree to which artefacts of a kind were standardized [14]. Itself a standardized measure, CV can be used to make comparisons both within and between sets of artefacts, across space and through time to test predictions of cultural evolutionary theory, such as the strength of a particular learning bias under certain socioeconomic conditions, as explored in the Case study below (see §2c).

    Correlation is another simple measure that can be used to detect archaeological signals of cultural transmission. For example, Bettinger & Eerkens [13] argue that different learning biases—guided variation and indirect bias (sensu [10]) in this case—should produce distinct patterns of attribute correlation. Their study centres on stone projectile points whose attributes include length, width, thickness, weight and distances between particular landmarks. The authors hypothesize that guided variation, whereby individuals acquire cultural traits largely through trial and error, should be characterized by weaker attribute correlation than indirectly biased transmission—model-based biased transmission in this case, whereby social learners copy the behaviours of prestigious or successful individuals. When suites of traits are inherited together because they have been copied more or less faithfully from a single social model, any variation in their expression (differences in point length and width, for example) should be correlated; when individuals learn largely through trial and error, trait variation should be uncorrelated or only weakly so. The authors find support for these predictions among projectile points produced during the well-documented transition from atlatl-and-dart to bow-and-arrow technology in the US Great Basin (ca 1350 BP). Their analysis of a large database of projectile point attributes shows significant attribute correlation among points from central Nevada, whereas attributes vary largely independently among points from eastern California. Based on this result, the authors argue that bow-and-arrow technology ‘was maintained, and may have spread initially' by indirect bias in central Nevada and by guided variation in eastern California ([13], p. 235), a hypothesis later supported by behavioural experiments [26] and simulations [27].

    Development of these explicitly archaeological hypotheses has helped us identify and interpret patterns in material culture. However, even well-defined archaeological patterns of variation can sometimes be difficult to interpret in terms of cultural evolutionary processes. In some cases, for example, data are insufficient to draw comparisons between artefact types, sites or regions, and the significance of isolated CVs can be difficult to gauge. In their initial discussion of CV as a tool for scaling artefact variability, Eerkens & Bettinger [14] suggest independent standards to which archaeological CVs can be compared. To define the lower boundary of variation—the lowest CV we should expect in the absence of an external standard to which artefact makers compared their products (e.g. a ruler or template)—the authors cite a threshold of human perception, the ‘Weber fraction'. As Ernst Weber first observed in the 1800s, objects' linear measurements must differ by approximately 3% before the difference is perceptible to humans [2830]. Artefact makers limited only by this sensory threshold should produce artefacts that differ by an average of 3% in any dimension; the ratio of any attribute's standard deviation to its mean should be approximately 1.7% (CV ≈ 1.7, assuming a uniform distribution and a range of 6% around a sample's mean). Lower CVs would suggest use of an external standard. Conversely, ‘high' variation can be interpreted relative to the CV of a uniform distribution whose range is 200% of the mean (CV = 57.7), which is what we would expect if attribute values were chosen at random (see [14] for full discussion). Higher CVs may be indicative of deliberate attempts to make each object distinct (e.g. a social preference for self-expression or functional need for hunters' points to be differentiable).

    These independent standards provide guidelines for interpreting very high and very low CVs. Intermediate values are less readily explained, particularly in the absence of comparative collections—groups of artefacts whose production histories are well enough known to provide benchmark CVs for manufacture under specific conditions. Such comparative collections can be simulated, though, providing a control to which empirical data can be compared. The following case study uses simulations informed by rich contextual data to gauge the amount of standardization reflected in intermediate CVs associated with a collection of projectile points from a late prehistoric site in the US Southwest.

    (c) Case study

    Projectile points from the Henderson site (figures 1 and 2; N = 1029) are of two primary types: Washita (28%) and Fresno (27%; an additional 37% are indeterminate). Fresnos are excluded from the present study because it is unclear whether they are a distinct point type or simply Washita ‘preforms' (unfinished points) [31]. An analysis of Washita point variability shows that several attributes' CVs are in the intermediate range (table 1), but seemingly under relatively tight production constraints since they are much closer to 1.7 (the Weber fraction) than to 57.7 (random choice). Still, even the lowest CV (11%, maximum width) is difficult to interpret in isolation. The points' archaeological context, which indicates an increase in both the socioeconomic importance of bison and, perhaps, incentive to advertise group membership during the late prehistoric period, suggests a variety of plausible, testable hypotheses regarding cultural evolutionary mechanisms that might account for observed patterns of artefact variability. A discussion of these hypotheses follows this brief description of the site.

    Figure 1.

    Figure 1. Map of the US Southwest and westernmost southern High Plains (UT, Utah; CO, Colorado; AZ, Arizona; NM, New Mexico). The Henderson, Garnsey Bison Kill and Bloom Mound sites are located in close proximity to one another in the area indicated by the star. Base map modified from ‘North America second level political division 2 and Greenland.svg', by Alex Covarrubias [CC BY-SA 2.5 (], via Wikimedia Commons.

    Figure 2.

    Figure 2. (a) Archetypal Washita and Fresno projectile points. Average dimensions for Henderson site Washita points are provided in table 1. (b) Attributes considered in this study: mid, midline length; nw, neck width; ml, maximum length; bl, blade length; hl, haft length; w, maximum width; bw, base width. The illustrated point's maximum and base widths are the same; this is true of many, but not all, of the archaeological samples. (Point illustrations by Emily Wolfe.)

    Table 1.Summary statistics for Washita points from the Henderson site. The rows labelled ‘CV = 10% (5%, 3%)' list percentages of simulated variances greater than the archaeological variance when simulated using the corresponding CV. Weight is measured in grams; all linear measurements are in millimetres. Differences in attribute sample size owe to the fact that several of the points in the Henderson collection are broken. To maximize the data available for study, any point complete in a particular dimension (e.g. width) was included in analysis of that dimension. For example, points with broken tips were not included in analyses of maximum length but, if their bases were intact, their widths were included.

    weight thickness max. length midline length max. width base width blade length haft length neck width
    N samples 87 259 136 136 162 157 135 227 249
     mean 0.61 3.04 21.29 20.15 11.86 11.53 14.86 6.91 6.58
     s.d. 0.20 0.58 4.11 3.93 1.33 1.52 3.81 1.35 1.18
    CV 0.33 0.19 0.19 0.20 0.11 0.13 0.26 0.19 0.18
    CV = 10% 59 79 95 94 100 100 79 96 99
    CV = 5% 44 43 65 65 89 81 55 65 70
    CV = 3% 41 38 54 55 64 60 49 52 55

    The Henderson site represents the remains of a modestly sized residential complex occupied between AD 1250 and AD 1350 by a relatively small group of hunter–farmers. Like the Puebloan groups to the west, Henderson's occupants grew and ate corn, but bison hunting appears to have been more important, both economically and socially [32]. For example, projectile points are abundant while milling equipment, used to process corn and other agricultural products, is relatively scarce and crudely made, and often incorporated into domestic architecture before its useable life was exhausted. Dental caries are infrequent among Henderson's human skeletal remains and isotopic signatures on bones indicate modest reliance on C4 plants, both in contrast to patterns seen among committed farmers [33,34]. Moreover, a virtual absence of bison ribs and vertebrae from the Henderson assemblage as well as that of a nearby, peri-contemporaneous bison kill site (Garnsey; figure 1) suggests that dried bison meat and hides were traded, likely with Puebloan groups to the west. Beyond their economic value, bison may also have been socially important: bison bones are found almost exclusively in roasting features located in public plazas, while bones of other species are primarily found in hearths associated with individual households [32].

    Bison's centrality to economic and social life at Henderson may have translated to hunters' prestige, which, in turn, may have biased the transmission of information related to projectile point production. As mentioned above, the Washita points found at the Henderson site are more standardized than we might expect if point production were under loose or no constraints (e.g. a deliberately individualistic enterprise). Hunters, especially consistently successful ones, may have been preferentially copied, a bias perhaps facilitated by public ‘feasting' events where people may have had greater access to hunters and their gear. A reasonable hypothesis, then, is that a restricted pool of social models and a strong learning bias would lead to relatively high projectile point standardization (i.e. attribute CVs approaching 1.7).

    Alternatively, point production might have been influenced by group-affiliative norms, perhaps even an incentive to advertise group membership. Southeastern New Mexico, where Henderson is located, appears to have been a boundary zone between the farming Pueblos to the west and mobile bison hunters of the southern High Plains and Edwards Plateau to the east (figure 1). McElreath and colleagues [3537] have argued that ecological boundaries promote the evolution of ethnic markers—characteristics that readily identify members of a group—a phenomenon that may be amplified at tense boundaries where social differentiation can have even greater fitness implications. Archaeological evidence from the Bloom Mound, a contemporaneous site roughly 1 mile from Henderson (figure 1), suggests that Henderson area groups may have been at odds with Plains groups over access to bison and/or trade partnerships with Puebloans [38]. Accordingly, a second hypothesis regarding point production in the Henderson area is that standardization was a form of ethnic marking. Points were almost certainly not a primary means of advertising group membership, but ethnographic studies suggest that functional classes of artefacts including projectile points do sometimes serve this purpose [39].

    As a preliminary test of these hypotheses, and to gauge the significance of Henderson points' intermediate CVs, I simulated point attribute data for a variety of learning scenarios. To test the first hypothesis, where all point makers model led their points on those produced by the most successful hunters, I assume that the target value for each attribute is reflected in the archaeological sample's mean value for that attribute. If such a bias were strong, attribute values should vary only according to knapper skill, raw material quality and human perception (the Weber fraction); attribute CVs should be quite low due to a small model pool and strong learning bias. Under the second hypothesis, where point variability at Henderson was constrained by group-affiliative norms, the incentive to standardize may have been stronger than under the first hypothesis but, depending on how learning individuals acquired information, we might expect higher point variability. That is, if the relevant information and skills were acquired within households, attribute CVs would likely be higher than if knappers copied a small number of successful hunters, even if knapper skill and material quality were invariant within the community. I modelled the second hypothesis assuming within-household learning at three levels of transmission fidelity: (i) CV = 10%, which is twice the hypothesized ‘limit of human ability to standardize manually produced artifacts' [30, p. 667] (see next item); (ii) CV = 5%, the hypothesized ‘limit of human ability to standardize manually produced artifacts' [30, p. 667] or the ratio of standard deviation to mean expected assuming the minimum error introduced by limitations of perception (the Weber fraction), motor skill and memory when artefacts are produced without the aid of external measures like rulers or scales; and (iii) CV = 3%, the threshold of human visual perception (Weber fraction, assuming a normal rather than a uniform distribution).

    Speth [32] estimates that the Henderson site has approximately 100 ‘room blocks'— rectangular dwellings thought to have housed single nuclear or small extended families. Calibrated radiocarbon data indicate that the site was occupied for approximately 100 years (ca AD 1250 to AD 1350), assumed here to represent four learning generations. As a preliminary test of the hypotheses described above, and to gauge the significance of Henderson points' intermediate CVs, I simulated point attribute data for a variety of learning scenarios and generated CVs to which I then compared the real data. For the first simulation, I assigned each room block (house) a starting target value for each point attribute (e.g. maximum length) by taking a single random draw from that attribute's archaeological distribution. In this simulation, each house then ‘produced' four points per generation, a sample equivalent to the average archaeological density of Washita points per room block at Henderson. The points' attribute values were drawn from normal distributions with standard deviations equal to 10% of the corresponding mean (CV = 10%). Each distribution's first-generation mean was determined by a single random draw as described above; subsequent generation means were the average of the preceding generation's four point attributes. For each attribute, I ran 1000 simulations of within-house point production across four generations in each of 100 houses, each run of the simulation begun with new, randomly drawn within-house starting target values. I recorded each run's variance and then repeated the routine using CVs of 5% and 3%. (The point data and R code for this simulation are available as electronic supplementary material.)

    Distributions of the simulated samples' attribute variances provide a framework for interpreting archaeological attribute variances (figure 3a). Henderson site Washita attribute variances most closely resemble those of samples simulated assuming within-house (vertical) transmission and a CV of 3% (figure 3b). Preliminarily, this can be interpreted as extremely high-fidelity copying, limited only by the makers' ability to perceive differences between their points and those they copied (the Weber fraction). Considering the points' broader archaeological context, it is plausible that such high-fidelity copying was motivated by a strong group-affiliative norm or incentive to advertise group membership in light of tensions between Henderson area groups and Plains groups to the east.

    Figure 3.

    Figure 3. (a) Comparison of simulated (distributions) and archaeological (vertical black lines) Henderson site Washita point attributes variances. Each distribution describes 1000 simulations. The best-fitting model (CV of 3%, 5% or 10%) for each attribute is indicated by close alignment of the mean simulation variance and empirical variance. Each model's fit is summarized as a density plot in (b), which shows the standardized distances of attributes' simulated mean variances from the same attributes' archaeological variances. Henderson site Washita attribute variances most closely resemble those of samples simulated assuming within-house (vertical) transmission and a CV of 3%, tentatively interpreted as extremely high-fidelity copying, limited only by the makers' ability to perceive differences between their points and those they copied (the Weber fraction).

    Alternative explanations are possible, but not well supported by available data. Despite the potential for a strong prestige bias given the socioeconomic importance of bison, it does not appear that reverence for successful hunters was the primary driver of point standardization (see above). However, the pattern at Henderson could be explained by strong functional constraints on artefact form—perhaps points whose attributes differed significantly from the mean negatively affected hunting success—or a division of labour that delegated point production to a group of skilled artisans. Other evidence from the site contradicts these alternative hypotheses, however. For example, several points in the Roswell assemblage cannot be assigned to a named type, nor do they show any standardization among them, suggesting that functional constraints did not preclude the production and use of unstandardized points. Such constraints, if they existed, appear not to have been sufficiently strong to produce the observed level of standardization among Washita points. Moreover, the prevalence and distribution of knapping debris at the site suggest that at least some point production was done at home whereas a group of skilled artisans might have camped near the stone source instead, to reduce transport-related production costs [40,41].

    While certainly not conclusive, the preliminary finding that point standardization at Henderson most closely resembles faithful within-house vertical transmission, possibly motivated by a strong group-affiliative norm, is both a compelling alternative to more traditional ecological explanations and a hypothesis that could be tested using other lines of evidence. For example, assessment of a region-wide spatial distribution of point variability relative to social and ecological boundaries would be instructive. If projectile points were, in essence, a form of ethnic marking, the incentive to standardize should have relaxed with increased distance from boundaries and I would predict clinal variation in CVs, with the lowest near boundaries and the highest towards the centre of a group's territory.

    This case study shows how simulation, informed by cultural evolutionary models, available archaeological data and relevant contextual information, can generate ‘comparative collections' for use in the interpretation of artefact standardization and the assessment of cultural transmission in archaeological contexts. In turn, archaeological analyses can provide independent tests of established models' predictions. Moreover, the measures of artefact variability used in this and other case studies [1315,26,27] could be used in novel ways to explore evolutionary outcomes that may not be predicted by existing models. Following a brief discussion of persistent challenges associated with archaeological data, I suggest potential lines of inquiry that integrate simulation, measures of diversity and archaeology's rich—if complex—record of real-world social learning to generate new cultural evolutionary hypotheses.

    3. Obstacles to archaeology's contribution: time averaging, preservation biases and low resolution

    As we pursue a greater role for archaeology in the continued development of cultural evolutionary theory, it is worth bearing certain limitations in mind. There is some concern, for instance, that archaeology's coarse-grained, typically aggregated record of human behaviour is inadequate for evaluating cultural evolutionary models that are based on person-to-person transmission of cultural information [42]. Two common phenomena affecting the archaeological record, ‘time averaging' and preservation biases, are particularly problematic in this regard.

    Archaeological tests of cultural evolutionary theory can be complicated by time averaging, whereby artefacts produced at different times become spatially associated, giving the false impression of contemporaneity [43,44]. This is problematic because it can artificially inflate measures of variability or diversity: variants produced at one time come to rest beside distinct variants produced at other times, decreasing the likelihood that observed archaeological patterns accurately reflect human behaviours and evolutionary processes [44]. The magnitude of these effects can scale with the duration of site occupation because, as more time elapses, there is both more opportunity for the record to be affected and, potentially, more variants to be admixed. However, time averaging is more directly related to environmental factors—primarily soil erosion, which conflates distinct deposits, but also agents that promote vertical mixing (e.g. burrowing animals); time averaging is a form of preservation bias.

    Preservation biases affect the likelihood that material remains (e.g. artefacts) and their original spatial relationships are preserved in the archaeological record. Biasing factors include environmental variables such as those mentioned above, as well as physical properties of the remains themselves (e.g. organic materials are less likely to preserve than inorganic ones) and the original location of a deposit (e.g. riverside sites are generally more susceptible to destruction than sites on less dynamic landforms [45,46]). Like time averaging, differential preservation reduces the integrity of the archaeological record, disproportionately affecting certain materials and geographic regions, potentially biasing our understanding of how and why material culture associated with different groups—or particular demographics within groups (e.g. gender- or age-specific artefacts)—evolve at different rates or by different means.

    The record's limitations are a perennial archaeological concern but, while time averaging and biased preservation complicate interpretations, they need not paralyse cultural evolutionary research. Archaeological studies that assess variability among continuous data (e.g. projectile point lengths) can readily incorporate simple tests to detect potential biases. For example, if preferences for different point lengths changed through time, this may present as multiple modes in a time-averaged assemblage's length distribution. Of course, attribute variability can itself vary through time if preferences, people's tolerance of variation, or learning biases change. This would be much more difficult to detect archaeologically. Nonetheless, in many cases enough is known about a region and its record that independent evidence can help identify and minimize the effects of these confounding factors. Additionally, in some instances modelling and simulation can be used to approximate the effects of data lost to time-averaging or preservation biases to estimate how pronounced an archaeological pattern would have to be before relevant biasing factors are identifiable by available means.

    Even when not affected by time-averaging or preservation biases, the archaeological record's resolution is often mis-aligned with cultural evolutionary questions posed in other fields. Most cultural deposits are aggregated samples of multiple years at best, and more often of centuries or millennia, while cultural information is transmitted on much shorter timescales. Likewise, while cultural transmission theory centres on individuals' learning biases, the archaeological record most often represents group-level products of these biases. Rather than projecting predictions derived from existing models, simulations and laboratory experiments directly onto the archaeological record, we should continue efforts to identify archaeologically relevant units of observation and analysis, as described in the previous section, and find new ways to capitalize on the record's greatest strengths: large-scale and long-term perspectives of both cultural change and the social and ecological contexts in which it occurred, as discussed below.

    4. Archaeology and the continued development of cultural evolutionary theory

    The metrics and methods described in the Current archaeological strengths section (§2) and Case study (§2c above) can be used to explore a range of topics that build on existing cultural evolutionary theory and use archaeology's unique perspective to full advantage. For example, simply mapping spatial distributions of trait variation (CVs, correlations) is likely to reveal patterns that suggest social and ecological barriers to (or conduits for) transmission, which can then be used to formulate new hypotheses. Similarly, observing how trait variation tracks with other social and ecological phenomena (e.g. increased environmental productivity and high CVs; population contraction and patterns of variation consistent with conformism) can inform our understanding of long-term patterns of material cultural evolution. This kind of exploratory analysis has the potential to both reveal and mitigate issues associated with low-resolution archaeological records described in the previous section, particularly when the analyses are performed at large spatial and temporal scales. At smaller scales, studies of trait variation can address whether different classes of artefacts (e.g. projectile weaponry versus grinding stones used to process food) evolved at different rates, perhaps as a function of their visibility (e.g. household equipment may evolve more slowly because it is less publicly visible). Differential rates of change among artefact classes might, in turn, suggest different evolutionary mechanisms. Lastly, measures of variation can be incorporated into more complex evolutionary models, as described below.

    Modelling and simulation form the foundation of modern approaches to cultural evolution. These methods have obvious advantages including their potential for exploring causal relationships through isolation and manipulation of variables, and their capacity for replication and repetition. Laboratory experiments designed to gauge humans' adherence to modelled expectations under controlled conditions [4749] are a complement to modelling and simulation, offering some of the same benefits (reproducibility, repeatability) while exploring the effects of humanity on cultural evolution. Archaeology has the potential to further enhance our understanding through observations of cultural evolution ‘in the wild' [50]. That is, archaeological records capture real-world, high-stakes, long-run outcomes of evolutionary processes, which can be very different from short-run outcomes and model predictions [51,52]. This is partly because cultural change is a complex process involving interactions among social, ecological and demographic variables. Archaeological projects routinely generate data that provide direct or approximate measures of these variables, which can be used to develop models that incorporate archaeology's large-scale and long-term perspectives.

    Interactions and feedback among cultural, ecological and demographic systems can dilute (or amplify) ‘pure' effects of change in one system on another. For example, culture (e.g. technologies, behaviours and institutions) can mitigate environmental pressures, raise local carrying capacities and improve survivorship and fertility, stimulating population growth, which can then feed back and effect subsequent cultural change [53]. Attempts to understand material cultural evolution as simply an adaptive response to changed conditions or a product of biased cultural transmission may fail to account for important variables. Nonetheless, archeological explanations have historically centred on prime movers or singular causes whose relationship to cultural change is assumed to be direct and unambiguous. To maximize archaeological records' potential, we should augment traditional approaches with multi-system models of cultural evolution that incorporate rich contextual evidence related to the social and ecological contexts of evolution, as in the following example.

    Toolkit richness, or the number of different kinds of tools in an archaeological assemblage, has been used as a proxy for cultural complexity in recent debates surrounding the evolutionary role of demographics [5460]. To model the relative effects of ecological, demographic and cultural variables on the evolution of toolkit complexity, we might first identify all relevant variables: e.g. ecological: diet breadth, food density (high- and low-ranked food patches per km2), food dispersion (an index of resource clumping), food richness (number of food ‘types' sensu Bettinger and colleagues [7]) and food availability (number of available calories per km2); demographic: population size, density and connectivity among groups (e.g. number of shared cultural elements); cultural: artefact variability, as described in the previous section. A number of plausible hypotheses can then be identified (e.g. toolkit richness is a factor of (H1) population size; (H2) population size + diet breadth; … (Hi) population size + diet breadth + population density + between-group connectivity + food density + food dispersion + etc.) and models fit to available archaeological and paleoenvironmental data can then be compared using formal information criteria to identify those with the best predictive power. This exercise can be repeated for multiple types of data from within a single site to understand rates of change among different artefact classes (e.g. projectile weaponry versus grinding stones, as mentioned above), or at a global scale to understand conditions that promote material cultural diversification and the trend of increasing technological and social complexity that began during the late Pleistocene. By reframing archaeological approaches to include questions that pertain directly to evolutionary context, we might provide a more nuanced understanding of cultural evolution.

    5. Conclusion

    Archaeological data are not only well suited to examining the complex dynamics of cultural evolution, they are essential: they are often our only means of empirically testing the long-run effects of distinct evolutionary mechanisms. Nonetheless, archaeology's potential to help advance our understanding of cultural evolution has been largely unrealized to this point. Our contribution likely lies in the vast amounts of anthropological and contextual data we generate, which can be used to develop evolutionary models specifically tailored to archaeological circumstances and that account for real-world messiness including interactions among cultural, ecological and demographic variables. Ultimately, archaeology's better integration with the broader field of cultural evolution (sensu Cavali-Sforza & Feldman [9], Boyd & Richerson [10]) is critical for assessing the effects of evolutionary mechanisms on long time scales.

    Data accessibility

    The primary data and all R code associated with the simulation and analysis are available as electronic supplementary material.

    Competing interests

    I declare that I have no competing interests.


    Research funding was provided by the Regents of the University of Michigan and UM Department of Anthropology. Participation in the ‘New Perspectives in Cultural Evolution' workshop was funded by the John Templeton Foundation.


    The author thanks Marcus Feldman, Nicole Creanza, and Oren Kolodny for the invitation to participate in the ‘New perspectives in cultural evolution' workshop and to contribute to this special issue; John Speth for access to the Henderson site collections, and enlightening and engaging discussions of the region's prehistory; Edward Potchen, Laura Kochlefl, Gordon Beeman and Theodore Stern for collecting the projectile point data; Emily Wolfe for the projectile point illustrations in figure 2; Andrew Marshall for assistance with the simulation and figure 3; and two anonymous reviewers for their feedback on a draft of this paper.


    One contribution of 16 to a theme issue ‘Bridging cultural gaps: interdisciplinary studies in human cultural evolution’.

    Electronic supplementary material is available online at

    Published by the Royal Society. All rights reserved.


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