Journal of Theoretical Biology 347 (2014) 74–83
Contents lists available at ScienceDirect
Journal of Theoretical Biology journal homepage: www.elsevier.com/locate/yjtbi
Cumulative cultural evolution: The role of teaching Laureano Castro a, Miguel A. Toro b,n a b
Centro Asociado de Madrid, UNED, Centro Andrés Manjón, C/Francos Rodríguez 77, 28039 Madrid, Spain Departamento de Producción Animal, ETS Ingenieros Agrónomos, UPM, Ciudad Universitaria, 28040 Madrid, Spain
H I G H L I G H T S
Imitation is essential for human cumulative culture but it is not enough. Errors in imitation impede cumulative culture as the culture becomes more complex. Complex cumulative culture requires teaching to ensure accurate transmission. Teaching is adaptive whenever the gain on fitness compensates the cost of teaching.
art ic l e i nf o
a b s t r a c t
Article history: Received 27 June 2013 Received in revised form 31 December 2013 Accepted 6 January 2014 Available online 14 January 2014
In humans, cultural transmission occurs usually by cumulative inheritance, generating complex adaptive behavioral features. Cumulative culture requires key psychological processes (fundamentally imitation and teaching) that are absent or impoverished in non-human primates. In this paper we analyze the role that teaching has played in human cumulative cultural evolution. We assume that a system of cumulative culture generates increasingly adaptive behaviors, that are also more complex and difficult to imitate. Our thesis is that, as cultural traits become more complex, cumulative cultural transmission requires teaching to ensure accurate transmission from one generation to the next. In an increasingly complex cultural environment, we consider that individuals commit errors in imitation. We develop a model of cumulative cultural evolution in a changing environment and show that these errors hamper the process of cultural accumulation. We also show that a system of teaching between parents and offspring that increases the fidelity of imitation unblocks the accumulation and becomes adaptive whenever the gain in fitness compensates the cost of teaching. & 2014 Elsevier Ltd. All rights reserved.
Keywords: Imitation Assessor Innovation Social learning strategies
1. Introduction Culture, defined as variation acquired and maintained by social learning, is common in nature, but it is nowhere as important in any species as in humans, where it has led to a process of cumulative cultural evolution with great adaptive value (Boyd and Richerson, 1985; Tomasello, 1999; Richerson and Boyd, 2005; Enquist and Ghirlanda, 2007). This cumulative process is of enormous importance because it transforms culture into a second evolutionary inheritance system (Boyd and Richerson, 1985) that can generate complex cultural adaptations, allowing our species to rapidly and successfully colonize numerous environments (Richerson and Boyd, 2005; Hill et al., 2009; Mesoudi, 2011). Despite this significance, it is unclear how cumulative cultural evolution arose, especially when other primates with well developed
n
Corresponding author. Tel.: þ 34 914524900x1645. E-mail addresses: [email protected] (L. Castro), [email protected] (M.A. Toro). 0022-5193/$ - see front matter & 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jtbi.2014.01.006
social learning abilities show comparably restricted ranges (Tennie et al., 2009; Whiten, 2011). This has led to considerable debate. For example, Boyd and Richerson (1996), and Tomasello and his colleagues (Tomasello, 1999; Tennie et al., 2009; Tomasello et al., 1993) have suggested that cumulative cultural evolution may depend on specific social learning mechanisms, in particular imitation and/or teaching. For Boyd and Richerson (1996), cumulative cultural evolution is not present in chimpanzee culture because chimpanzees unfold their imitative learning abilities in a less consistent manner than humans. That is, although a given individual may learn someone else0 s innovation by imitation, there will be no other individual that could later imitate it in a precise manner. Consequently, this innovation cannot be propagated, and becomes lost until someone else re-invents it. For Tomasello and his colleagues (Tomasello, 1999; Tennie et al., 2009, 2012) chimpanzee cultural traditions represent behavioral biases of different populations that are generated by founder effects, individual learning and mostly product-oriented (rather than process-oriented) copying. However, human culture has the distinctive characteristic that it accumulates modifications over time (the
L. Castro, M.A. Toro / Journal of Theoretical Biology 347 (2014) 74–83
‘ratchet effect’). That is because human social learning is more oriented towards process than product and, secondly, because some potentially species-unique processes of cooperation, social imitation, pedagogy and social norms of conformity might plausibly be linked to the human cultural ratchet. Dean et al. (2012), in a study with capuchin monkeys, chimpanzees, and children, found strong support for the view of Tomasello and his colleagues that cumulative culture requires a package of key psychological processes (i.e., teaching through verbal instruction, imitation, and prosocial tendencies), that are present in humans but are absent or impoverished in chimpanzees and capuchins. Since true imitation and teaching have traditionally proven notoriously difficult to identify in animals (Caro and Hauser, 1992), their arguments have provided a conveniently neat explanation for the apparent absence of cumulative cultural evolution. However, their reasoning is has not gone undisputed (Laland and Hoppitt, 2003; Whiten, 2005). It seems clear that human beings have the cognitive ability to engage in high-fidelity information transmission and that the latter is a key driver of human cumulative culture (Lewis and Laland, 2012). We have defended (Lewis and Laland, 2012; Castro and Toro, 2002, 2004; Castro et al., 2004, 2010) that a key factor enabling the evolution of a human cumulative cultural system of inheritance is that some of our hominid ancestors, which we call assessors or Homo suadens (derived from Latin suadeo: advice, evaluate, counsel, approve), developed both the capacity for true imitation and the capacity to approve or disapprove of offspring0 s learned behavior. The capacity of approving or disapproving children0 s learned behavior may be considered an elementary form of teaching that increases the reliability of imitation, making social learning more accurate when behavior is complex. We have suggested that approval and disapproval of offspring0 s learned behavior was the cognitive mechanism that allowed our ancestors to keep and accumulate the complex behavioral findings of a generation and pass them on to the next. In a similar way, Gergely and Csibra (Csibra and Gergely, 2006; Gergely and Csibra, 2011) propose that human communication is specifically adapted to allow the transmission of generic knowledge between individuals. Such a communication system, which they call ‘natural pedagogy’, enables fast and efficient social learning of cognitively opaque cultural knowledge that would be hard to acquire relying on observational learning mechanisms alone. Therefore, it seems reasonable to assume that human cumulative culture does not only depend on imitation (Lewis, 2007; MacDonald, 2007), but also on teaching (Tehrani and Riede, 2008; Hewlett et al., 2011). A recent review on the ethnographic evidence of teaching, incorporating nonverbal costly behaviors of potential teachers that facilitate behavioral acquisition by observers, indicates that the gradual scaffolding of skills in a novice through demonstration, intervention and collaboration has played an essential role in securing the accurate transmission of skills across generations (Tehrani and Riede, 2008). In this paper we analyze the role that teaching has played in human cumulative cultural evolution. Recently, Fogarty et al. (2011) have proposed that teaching evolved in humans because cumulative culture allows to teach valuable information that is otherwise difficult-to-acquire. Their analysis suggests that teaching and cumulative culture reinforce each other, and may have coevolved, because teaching is more advantageous in a cumulative culture setting, whereas cumulative knowledge gain is frequently reliant on teaching. Following a similar reasoning we defend that human cumulative cultural transmission needs, as behavior becomes more complex, a teaching mechanism to ensure the accurate transmission from one generation to the next. Otherwise, there is no accumulation. To explore this hypothesis we developed a model of cumulative culture evolution in a changing environment inspired by both a
75
social learning model developed by Rendell et al. Rendell et al. (2010) to implement a tournament of social learning strategies and our previous model to analyze competition between unselective strategies that differ only in the degree of social versus asocial learning (Castro and Toro, 2012). We consider mixed social learning strategies that first learn by imitating parents and subsequently by imitation or innovation chosen at random. We assume that cumulative cultural transmission is possible and produces more adaptive complex behaviors by accumulating successive insights on the same conduct (Ehn and Laland, 2012; Mesoudi, 2011; Enquist et al., 2011). We also assume that this progressive complexity hinders the correct imitation of behaviors. Then we show that both conditions, imitation and innovation, are sufficient for the emergence of cumulative cultural transmission. Next, we show that the process of cumulative cultural evolution stops if imitation is less efficient as the behavior becomes more complex. And finally, we also show that this impediment can be overcome if parents are able to facilitate the correct imitation of behavior by their children, with a certain cost, e.g., if individuals are assessors (Castro and Toro, 2004). In other words, we show that assessor transmission, or other similar ways of teaching, can unblock accumulation when behavior becomes so complex that imitation is not enough to replicate it properly and can be adaptive whenever the gain on fitness compensates for the cost of teaching. The development of imitative abilities does not necessarily lead to a gradual development of innovative capacity (Aoki, 2001). It is difficult to predict whether innovation and imitation can lead to cultural accumulation that is adaptive enough to compensate for the costs of developing and maintaining teaching mechanisms. For that reason, as a final point, we suggest that elementary forms of teaching may have evolved in the hominid line for causes other than achieving accurate cultural transmission. Specifically, we propose that teaching may greatly contribute to avoiding the cost of learning wrong behaviors, and to save time when choosing among different behavioral alternatives that are difficult to evaluate.
2. The model We designed our simulation model along the lines described by Rendell et al. (2010) and Castro and Toro (2012). We consider a changing environment. This simulated environment is a “multiarmed bandit”. In our model the bandit has 40 arms, each representing a different behavior and each with a distinct payoff. Each behavior has 10 increasing complexity levels that represent a progressive accumulation of insights in that behavior. Thus, we assume that each level of refinement is directly dependent on the previous one and is associated with an improvement in fitness of its user. The payoff pi1 for an act i in level 1 was an integer drawn at random from an exponential distribution (k ¼1; values were squared, then doubled, and finally rounded to give integers mostly falling within the range 0–50). Payoffs changed between rounds with independent probability pc¼ 0.001, with new payoffs drawn at random from the same distribution. Payoffs for the different levels of each behavior were constructed so that the payoff pij of level j of behavior i was equal to pij ¼pi1 þ(j 1) α pi1, where α is a factor that allowed to scale the intensity of the rising payoff as the level changed. Therefore, when the payoff of a behavior changes, the payoff for each level of that behavior is modified. We consider α¼0.4 in standard conditions. Each strategy had to specify how individual agents in a finite population of 100 individuals chose between three possible moves in each round, namely Innovate, Observe, and Exploit. Innovate represents asocial learning, for example, trial and error learning or insight. The agent chooses a random behavior and learns the next higher level of that behavior stored in its repertoire. If it knows all
76
L. Castro, M.A. Toro / Journal of Theoretical Biology 347 (2014) 74–83
levels of that conduct, it chooses a different behavior. An Innovate move always returns accurate information about the payoff of the randomly selected behavior. Observe represents any form of imitation through which an agent can acquire a behavior performed by another individual in the last round. Playing Observe did not necessarily result in a new behavior being learned. If the observer has the observed behavior in his repertoire or has a higher level of the same observation, then it does not take advantage and lost the round for practical purposes. Moreover, if no other agents played Exploit in the last round, then nothing was learned. The number of agents sampled when playing Observe was one. We consider that asocial information (play Innovate) was no more costly than social information (play Observe). Lastly, Exploit represents the performance of a behavior from the agent0 s repertoire, equivalent to pulling one of the multiarmed bandit0 s levers. Each simulation contained a population of 100 agents. Each agent possessed a behavioral repertoire, which was empty at the start of the agent0 s life. An agent0 s repertoire could subsequently only contain acts through some form of learning. Agents could only obtain a payoff by playing Exploit and always chose to perform the best strategy from their repertoire. When an individual chose Exploit, it received the current payoff specified in the environment. Observe is error prone with regard to both payoff and level of observed behavior. The returned payoff estimate was subject to normally distributed random error (rounded to the nearest integer) with mean 0 and standard deviation spayoffError ¼ 1 (with the returned payoff estimate lower bounded at 0). Independently, an Observe move can occur without error with probability h, and return the same level of behavior to that performed by the observed agent, or with error with probability 1 h, and return only the next highest level of the behavior that the observer has in his repertoire. In both cases, the payoff learned was still that of the observed agent. In our model we consider two values for the probability h of imitating a behavior without error. First h¼1, where the imitation is always perfect. Second, h¼ (1/(l l’)), where l and l’ are the levels that have the behavior in the observed individual and in the individual who observes with l 4l’, because if l rl’ the observation is lost. Thus, if the difference between levels is 1, the imitation is always perfect and as the difference between levels l–l’ increases, the difficulty of replicate the behavior faithfully also increases. We consider two types of strategies: x-imitators (xIMI) and x-assessors (xASS). In the first round of the game, all strategies acquire a behavior by innovative learning. After that, all randomly play Exploit for 80% of the rounds and learn (Observe or Innovate) the remaining 20%. We chose 80% of exploitation value because it is close to the optimal strategy that a pure innovator strategy (i.e., a strategy that when learning always plays Innovate including the first movement) should use to obtain the highest fitness (Castro and Toro, 2012). When learning the strategies, xIMI and xASS play Observe x % of the time and play Innovate the rest of the time (1 x). When a new individual is born he always plays Observe as the first movement and learns the best parent0 s behavior. The only difference between xIMI and xASS is that imitators learn the best parent0 s behavior with probability h or level 1 of that behavior with probability 1 h, while assessor individuals always learn the best parent0 s behavior. Moreover, assessor parents have a cost due to the time t (number of rounds without learning or exploiting) that they spend teaching their children, with t¼ 1 in the standard conditions. We analyzed the dynamics of each strategy when occurs alone and when compete with the others in pairwise round-robin contest. Each pairwise contest consisted of 40 simulations in which agents performing strategy A and agents performing strategy B were introduced at equal frequencies (0.5). To analyze
the dynamics of a population consisting of a single strategy we put this strategy competing against itself under the same conditions. Agents died with a constant probability of 1/50 per round and were replaced by the offspring of another agent. The probability that an agent was chosen to reproduce was proportional to its mean lifetime payoff, calculated as its summed payoff from playing Exploit, divided by the number of simulation rounds that it had lived. Offspring inherited their parent0 s strategy unless there was a mutation, in which case the offspring was given the other strategy playing in that simulation. The mutation rate used was 0.001. This high rate does not qualitatively affect our outcomes but decreases drift and offers significant computational advantages in terms of time to equilibrium. We recorded the average frequency of each strategy in the population over the last 10,000 rounds of each 40,000-round simulation and gave each strategy a score that was the mean of these values over the simulations in which it participated. Strategies were ranked on their average contest score once they had played against every other strategy. We also recorded the average individual fitness over the last 10,000 rounds, measured as mean lifetime payoff, the average level of behaviors, and the average number of different behaviors that the individuals exploit. 3. Results 3.1. Non-cumulative culture In this scenario we suppose that there are only behaviors of level 1, so cumulative cultural evolution is not possible. Because there are no levels of increasing complexity, individuals always imitate with maximum efficacy (h¼ 1). We studied homogeneous populations made up of 100 individual x-imitators (xIMI) or
Fig. 1. Average fitness of x-imitator (xIMI) or x-assessor (xASS) strategies in pure populations of 100 individuals without and with (accurate and inaccurate) cumulative culture.
L. Castro, M.A. Toro / Journal of Theoretical Biology 347 (2014) 74–83
77
x-assessors (xASS), where all individuals use the same mixed social learning strategy. The fitness that these populations attain is given in Fig. 1. Social learning strategies that, when learning, imitate with a probability between 40% (40IMI and 40ASS) and 80% (80IMI and 80ASS), are those that achieve more fitness both among x-imitators and among x-assessors. However, the fitness of mixed strategies 0IMI and 0ASS that when learning imitate the parents for the first movement and afterwards always innovate is very close to the fitness of the best strategies. This value of fitness contrasts with the value of 2.6 that is obtained by the pure imitator strategies 100IMI and 100ASS, that always imitate and never innovate. We have also calculated the fitness of a pure innovator strategy, that always learns by innovation, even the first time when he does not imitate his parents. The average fitness of this pure innovator strategy is 7.2, much smaller than the fitness of mixed strategies 0IMI and 0ASS, that take advantage of what their parents have learned, but greater than 2.6, the fitness of pure imitator strategies 100IMI and 100ASS. The success of mixed strategies compared to pure innovator and pure imitator (100IMI and 100ASS) strategies arises from some peculiarities in our model. First, with 40 alternatives there is a low probability that pure innovators develop optimal conduct in an average life of 50 rounds. Second, each individual always exploits the best behavior he knows and, therefore, learning by imitation is a successful strategy because the exploited behavior patterns available to copy constitute a select subset that has already been chosen for their high payoff (Rendell et al., 2010; Castro and Toro, 2012). Finally, the populations in the present simulations have a finite number of individuals (100) and each one dedicates a similar and constant amount of time to learning. Social learning tends to homogenize the populations and, as a result, individuals can
devote part of their learning period to innovate, increasing their behavioral repertoire, without suffering a fitness cost with respect to the individuals who learn only socially. We can measure the homogeneity of a population by the average number n of different behaviors that individuals exploit in each round. This number decreases with the degree of imitation of studied strategies and is n ¼1 for the pure imitator strategies 100IMI and 100ASS (Fig. 2). This explains why the 100IMI and 100ASS strategies have lower fitness, because the individuals only have one behavior stored in their repertoire and are not able to modify their behavior in an adaptive way when the environment changes. The populations with xASS strategies are slightly less homogeneous than the equivalent populations of xIMI strategies (Fig. 2). This is because the cost of teaching in xASS populations affects the individuals selected as parents that, on average, have greater fitness, so their advantage compared to the unselected individuals is smaller than with xIMI strategies. Social learning strategies that imitate approximately 80% of the time they learn (80IMI y 80ASS) can compete better in pairwise round-robin contests and attain an equilibrium with the other strategies where they are predominant (Fig. 4). These optimal strategies compete better than other more innovative strategies, such as 0IMI and 0ASS, because they discover the best behavior in the population more quickly by imitation. They also compete better than other imitator strategies such as 100IMI and 100ASS, because the population is homogeneous enough for the individuals to acquire the optimal conduct directly from their parents or from the first times they play Observer (Fig. 4). The time they dedicate to innovate allows individuals with the optimal strategy to adapt back when there is a change in the payoff of the conduct that was optimal.
Fig. 2. Average number of different behaviors of x-imitator (xIMI) or x-assessor (xASS) strategies in pure populations of 100 individuals without and with (accurate and inaccurate) cumulative culture.
Fig. 3. Average level of x-imitator (xIMI) or x-assessor (xASS) strategies in pure populations of 100 individuals with accurate and inaccurate cumulative culture.
78
L. Castro, M.A. Toro / Journal of Theoretical Biology 347 (2014) 74–83
Fig. 4. Average individual fitness and average frequency for both strategies A and B competing in a pairwise contest (non-cumulative culture, accurate imitation h ¼ 1).
Other things being equal, xIMI individuals are more fit than xASS because they transmit the behavior from parents to offspring with the same efficacy, but without the time cost that xASS parents need to control their children. For this reason an xIMI strategy always displaces the equivalent xASS. Fig. 4 includes an example of how the best imitator strategy 80IMI displaces the best assessor strategy 80ASS. 3.2. Cumulative culture Now we consider that each behavior has 10 levels of increasing complexity. Individuals that innovate choose a behavior at random and can learn the level above the one stored in their repertoire. Thus, the cumulative cultural evolution process can emerge. Imitation can occur without error with probability h, or with error with 1 h. We study two values of h: h¼1, in which the imitation is always accurate, and h ¼(1/(l l’)), in which the probability to imitate without error depends on the difference between the levels of complexity of both the observed individual (l) and the individual that observes (l’) with l4 l’. 3.2.1. Accurate imitation When imitation occurs without error (h¼1), the strategies that accumulate more include those that innovate a lot and imitate little, between 0% and 20% (0IMI, 20IMI, 0ASS, and 20ASS) (Fig. 3). In fact, the maximum level of cultural accumulation (l¼5.9) is obtained by imitating the parent only the first time and then innovating to learn (0IMI and 0ASS strategies). Obviously, if we decrease the number of different behavioral alternatives or the probability that a behavior changes fitness, higher levels can be attained, closer to the maximum
(l¼10). The homogeneity of populations, measured as the average number n of different behaviors exploited in each round, decreases as the degree of imitation of strategies xIMI and xASS increases (Fig. 2). For the same degree of imitation, populations are more variable with than without cultural accumulation because the levels of complexity multiply the potential number of variants: from 40 alternatives to 400. With accumulation populations using xASS strategies are also slightly less homogeneous than the equivalent populations using xIMI (Fig. 2). In a pairwise contest among xIMI strategies, the 20IMI strategy is the best and almost optimal when alone. That strategy displaces or attains an equilibrium where it gains the majority compared to any other strategy (Fig. 5). Something similar occurs with the 20ASS strategy, which is the best both alone and when competing in pairwise contests (Fig. 5). The 20IMI and 20ASS strategies are more competitive than other strategies that imitate less (such as 0IMI or 0ASS) or others that imitate more (such as 100IMI or 100ASS). These strategies reach an optimal balance between a degree of imitation that allows them to profit from the best behaviors present in the population and a degree of innovation that augments the probability of increasing the level of complexity of the behaviors stored in their repertoire. Other things being equal, the xIMI strategies are better than the equivalent xASS strategies, since the latter have the cost teaching. In Fig. 5 we show an example how the best imitator strategy 20IMI displaces the best assessor strategy 20ASS.
3.2.2. Inaccurate imitation If we consider that imitation loses efficacy as behaviors become more complex (h¼1/(l l’)), xIMI and xASS strategies behave very
L. Castro, M.A. Toro / Journal of Theoretical Biology 347 (2014) 74–83
79
Fig. 5. Average individual fitness and average frequency for both strategies A and B competing in a pairwise contest (cumulative culture, accurate imitation h ¼ 1).
differently. The xIMI strategies in populations formed by a single strategy obtain lower average fitness and lower levels of accumulation than when imitation is accurate (Figs. 1 and 3). The maximum values for xIMI strategies are obtained when they imitate roughly half of the time they learn. In particular, the 40IMI strategy reaches the maximum with 40.1 average fitness and an average level of complexity of 2.6. If xIMI strategies compete in pairwise contests, the optimum strategy is 60IMI (Fig. 6). It is better than strategies that imitate less, as strategy 40IMI, optimal in populations formed by a single strategy, and than strategies that imitate more like 100IMI, because it reaches an optimal balance between imitation and innovation. Choosing 60% imitation allows to exploit, despite errors in imitating, the achievements of other individuals and 40% innovation allows to increase the level of complexity of the behaviors stored in their repertoire. A different case would be if the same xIMI strategies with and without imitation errors compete between themselves. Under the same conditions, better imitation is always favored because fitness is higher (Fig. 1). Meanwhile, xASS strategies can achieve a similar level of fitness and cultural accumulation to when imitation is completely effective (Figs. 1 and 3). The optimal strategy in populations formed by a single strategy is between 0ASS and 20ASS that innovate a lot and imitate very little (Fig. 1). This is because children inherit their parent0 s strategy without error and, as levels of accumulation increase, the possibility that an individual changes strategy is smaller, as the majority of observations inaccurately reproduce the behavior imitated. If xASS strategies compete in pairwise contests, the best strategy is 40ASS. That strategy is better than other strategies, like 0ASS and 20ASS, that achieve greater fitness in
populations formed by a single strategy, and than others that imitate more, like 100ASS (Fig. 6). The optimal balance requires imitating 40% of the time you learn and innovating the rest of this time. Note that with inaccurate imitation xASS strategies have similar or even higher fitness and cultural accumulation levels than those obtained when imitation is accurate. However, this gives them no advantage when competing with each other, on equal conditions. If the same xASS strategy competes with and without accurate imitation, the winning strategy is the one that imitates more accurately, because it takes better advantage of the accomplishments of others. The errors in imitation greatly increase population variability between xIMI strategies. Not so with xASS strategies because new generations are formed from very few parents: those who have achieved high levels of accumulation and high fitness that accurately transmit to their children. The xASS strategies are now much more competitive than xIMI (Fig. 6), since their fitness and accumulation levels are much higher and offset the cost of teaching between parents and children. Thus now the best strategy 40ASS moves completely to the imitator strategies 60IMI and 40IMI (Fig. 6).
3.2.3. Impact of the different parameters Table 1 shows the standard conditions in which simulations were run. Changes of ion of these parameters introduce quantitative modifications in the results, without modifying the main conclusion: cultural accumulation is blocked among imitator individuals as complexity increases if imitation is not accurate. Obviously, if we increase the average life of individual or if we
80
L. Castro, M.A. Toro / Journal of Theoretical Biology 347 (2014) 74–83
Fig. 6. Average individual fitness and average frequency for both strategies A and B competing in pairwise contest (cumulative culture, inaccurate imitation h ¼ 1/(l l’)).
Table 1 Standard conditions of the simulation. Population Number of Number of Probability Probability Probability Probability
size behavioral alternatives cumulative cultural levels of change of the payoff of a behavior of individual death of play Exploit of imitation without error
The increase of fitness for level Probability of mutation Cost of teaching
100 40 10 0.001 0.02 0.8 Accurate h ¼1; inaccurate h ¼(1/l l’) 0.4 0.001 1 round
decrease the number of cultural variants, the levels of accumulation attained will increase but, sooner or later the accumulation process is stopped. An especial situation refers to the cost of teaching. If we increase the number of rounds that individuals invest in teaching its offspring from one round to five or ten (10% or 20% of the average life) the level of complexity and the fitness that attain the individuals decrease slightly. Fig. 7 shows how it varies for the best strategy 40ASS when imitation is inaccurate. Increasing the cost does not prevent that this strategy 40ASS displace any imitator strategy because fitness differences are still great. In other words, if individuals attain high levels of cultural complexity the transmission between parents and offspring can cope with an intense dedication to teaching by parents. A different case occurs if the
cognitive capacity of individuals allows lower levels of complexity for each variant. Fig. 8 shows the competition between strategies 40ASS and 40IMI for different costs of teaching with 10 and 2 levels of cultural complexity. In the last case it can be seen how the strategy 40ASS lost competitiveness as the cost of teaching increases and can be beaten by the strategy 40IMI if the cost is enough high. Other interesting aspect refers to the population size. Events of cumulative culture should be more frequent in a large population (Henrich, 2004). Recently, using a dual-task computer game, Derex et al. (2013) demonstrate how changes in group size can generate both adaptive cultural evolution and maladaptive losses of culturally acquired skills. In our model of cumulative culture, if we change the size of the population when imitation is inaccurate, we obtain desired results similar to those authors (Fig. 9). But also now the increase of cultural complexity depends to a greater degree of effectiveness of the transmission of the ASS strategy than of the population size.
4. Discussion Theoretical analyses on the evolution of social learning in a changing environment have shown that, as long as the only benefit of imitation is that imitators avoid the cost of individual learning, imitation does not increase the ability of species to adapt, and is insufficient to explain the adaptive success of human culture, a counterintuitive conclusion known as Rogers’ paradox
L. Castro, M.A. Toro / Journal of Theoretical Biology 347 (2014) 74–83
81
Fig. 7. Average fitness and average level of the strategy 40ASS for different costs of teaching (1, 5 or 10 rounds) in pure populations of 100 individuals with cumulative culture and inaccurate imitation h ¼1/(l l’).
Fig. 8. Average frequency and average fitness for both the strategy 40ASS (A and C) and the strategy 40IMI (B and D) competing in pairwise contests with accumulation and inaccurate imitation h ¼1/(l l’) and for different costs of teaching (1, 5 and 10 rounds): (a) 10 levels of cumulative culture and (b) 2 levels.
82
L. Castro, M.A. Toro / Journal of Theoretical Biology 347 (2014) 74–83
Fig. 9. Average fitness and average level of strategies
40IMI
(Rogers, 1988). To overcome Rogers’ paradox, Boyd and Richerson (1995) have shown that imitation can be adaptive and increase the average fitness of imitators if it makes individual learning less costly or more accurate. The first condition (i.e., imitation makes individual learning less costly), predicts the emergence of smart mixed strategies that rely on some selective combination of the two types of learning (Feldman et al., 1996; Kameda and Nakanishi, 2003; Enquist et al., 2007; Franz and Nunn, 2009; Rendell et al., 2009). The second condition (i.e., imitation makes individual learning more accurate) is satisfied if imitation allows cumulative cultural evolution (Boyd and Richerson, 1995). Those predictions fit well with the results obtained in our model. Without cumulative culture, the fitness of mixed strategies that imitate their parent first and then innovate or imitate at random is higher than the fitness of populations with only innovators or imitators, overcoming Rogers’ paradox, both in pure populations (Fig. 1) and when they compete with each other (Fig. 4). Although in pure populations the fitness is very similar between the different strategies (Fig. 1), social learning strategies that imitate approximately 80% of the time they learn compete better in pairwise round-robin contests (Fig. 4). With cumulative culture and accurate transmission, the fitness of the strategies is further increased (Fig. 1). Cumulative culture favors mixed strategies that spend more time innovating. In fact, 0IMI and 0ASS strategies that imitate parental behavior without error and innovate the rest of the time obtain maximum levels of cultural complexity (Fig. 3) and fitness (Fig. 1) in populations formed by a single strategy. However, in pairwise contests they cannot beat others that imitate more (20IMI and 20ASS) and that therefore make better use of the accomplishments of others (Fig. 5). In any case, cumulative culture also favors mixed strategies that innovate and imitate. One consequence of the cumulative process is the emergence of increasingly complex behaviors that an individual alone cannot develop and that, in time, are more difficult to replicate through imitation (Ehn and Laland, 2012; Mesoudi, 2011; Enquist et al., 2011). It seems reasonable to assume that sooner or later the growing complexity of cultural variations also introduces frequent errors in imitation and, therefore, the process of cumulative cultural evolution would be affected. The hypothesis we propose is that the development of a system of cumulative cultural transmission (as probably occurred in several hominid species and reached its zenith in our species) required the development of a system of high fidelity transmission based on teaching between parents and children. The results obtained are consistent with this proposal. If there is
and
40ASS
in populations of 200, 100 and 20 individuals.
accumulation but imitation is inaccurate, errors in imitating hinder the process of accumulation and xASS strategies can displace xIMI (Fig. 6), provided that the cost of teaching is less than the benefits obtained by learning without error paternal behavior (Fig. 8). The problem is to explain the origin of complex forms of cumulative culture that permit the evolution of teaching. Cumulative culture requires innovation and accurate cultural transmission. Although individual learning can favor the development of social learning abilities, the development of imitative abilities does not necessarily generate a positive feedback process to favor higher levels of innovation (Aoki, 2001; Henrich and McElreath, 2003). Those individuals who imitate get access to behavioral innovations from individuals with a higher intellectual capacity, not necessarily their parents, and therefore, imitation may restrain the adaptive advantage of a greater investment on intellectual capacity – i.e., on innovation. Without higher innovation capacity it is not easy that cumulative culture arises and can compensate for the costs of developing and maintaining teaching mechanisms. A possible explanation for cumulative cultural evolution is that the transition from a non-cumulative scenario to a cumulative culture arose as a result of a previous development of elementary forms of parent– child education, which in turn evolved for reasons other than to achieve high fidelity transmission culture. In particular, we suggest that teacher parents can provide offspring with what they learn, including things they can or cannot do (Castro and Toro, 2004; Castro et al., 2004, 2010). The former can be imitated by other individuals that are not the parents, but the latter is transmissible only by teaching, for example by disapproval of behavior between parents and offspring. Moreover, teacher parents can favor the implementation of behavior, which has no immediate positive evaluation by the children. Once emerged, teaching can foster greater innovation capacity because the behaviors are preferentially transmitted from innovator parents to their children. Moreover, adaptive complex behavior can also improve the imitation ability to take advantage of the lessons learned by other individuals, different from the parents. All these combined processes innovation, imitation, and teaching may favor the development of adaptive behaviors that are more complex and difficult to imitate, and those in turn favor more efficient teaching, leading to an autocatalytic process of coevolution that probably occurred in our species. In summary, our proposal defends that innovation and imitation are not enough to achieve a progressive cultural accumulation: the evolution of teaching is also necessary.
L. Castro, M.A. Toro / Journal of Theoretical Biology 347 (2014) 74–83
References Aoki, K., 2001. Theoretical and empirical aspects of gene-culture coevolution. Theor. Popul. Biol. 59, 253–261. Boyd, R., Richerson, P.J., 1985. Culture and the Evolutionary Process. University of Chicago Press, Chicago, IL Boyd, R., Richerson, P.J., 1995. Why does culture increase human adaptability? Ethol. Sociobiol. 16, 125–143. Boyd, R., Richerson, P.J., 1996. Why culture is common but cultural evolution is rare? Proc. Br. Acad. 88, 77–93. Caro, T.M., Hauser, M.D., 1992. Is there teaching in nonhuman animals? Q. Rev. Biol. 67, 151–174. Castro L., Toro M.A., 2002. Cultural Transmission and the Capacity to Approve or Disapprove of Offspring0 s Behaviour. Jom-emit 6 (〈http://jom-emit.cfpm.org/ 2002/vol6/castro_l&toro_ma.html〉). Castro, L., Toro, M.A., 2004. The evolution of culture: from primate social learning to human culture. Proc. Natl. Acad. Sci. USA 101, 10235–10240. Castro, L., Toro, M.A., 2012. Imitation or innovation? Unselective mixed strategies can provide a better solution. Theory Biosci. 131, 273–279. Castro, L., Medina, A., Toro, M.A., 2004. Hominid cultural transmission and the evolution of language. Biol. Philos. 19, 721–737. Castro, L., Castro-Nogueira, L., Castro-Nogueira, M.A., Toro, M.A., 2010. Cultural transmission and social control of human behavior. Biol. Philos. 25, 347–360. Csibra, G., Gergely, Gy., 2006. Social learning and social cognition: the case for pedagogy. In: Munakata, Y., Johnson, M.H. (Eds.), Processes of Change in Brain and Cognitive Development: Attention and Performance XXI, 2006. Oxford University Press, Oxford, pp. 249–274. Dean, L.G., Kendal, R.L., Schapiro, S.J., Thierry, B., Laland, K.N., 2012. Identification of the social and cognitive processes underlying human cumulative culture. Science 335, 1114–1118. Derex, M., Beugin, M.P., Godelle, B., Raymond, M., 2013. Experimental evidence for the influence of group size on cultural complexity. Nature 503, 389–391, http://dx.doi.org/10.1038/nature12774. Ehn, M., Laland, K.N., 2012. Adaptive strategies for cumulative cultural learning. J. Theor. Biol. 301, 103–111. Enquist, M., Ghirlanda, S., 2007. Evolution of social learning does not explain the origin of human cumulative culture. J. Theor. Biol. 246, 123–135. Enquist, M., Eriksson, K., Ghirlanda, S., 2007. Critical social learning: a solution to Rogers0 s paradox of nonadaptive culture. Am. Anthropol. 109, 727–734. Enquist, M., Ghirlanda, S., Eriksson, K., 2011. Modelling the evolution and diversity of cumulative culture. Philos. Trans. R. Soc. Lond. B: Biol. Sci 366, 412–423. Feldman, M.W., Aoki, K., Kumm, J., 1996. Individual versus social learning: evolutionary analysis in a fluctuating environment. Anthropol. Sci. 104, 209–231. Fogarty, L., Strimling, P., Laland, K.N., 2011. The evolution of teaching. Evolution 65, 2760–2770. Franz, M., Nunn, C.L., 2009. Rapid evolution of social learning. J. Evol. Biol. 22, 1914–1922.
83
Gergely, Gy., Csibra, G., 2011. Natural pedagogy as evolutionary adaptation. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 366, 1149–1157. Henrich, J., 2004. Demography and cultural evolution: how adaptive cultural processes can produce maladaptive losses: the Tasmanian case. Am. Antiq. 69, 197–214. Henrich, J., McElreath, R., 2003. The evolution of cultural evolution. Evol. Anthropol. 12, 123–135. Hewlett, B.S., Fouts, H.N., Boyette, A.H., Hewlett, B.L., 2011. Social learning among Congo Basin hunter–gatherers. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 366, 1168–1178. Hill, K., Barton, M., Hurtado, A.M., 2009. The emergence of human uniqueness. Evol. Anthropol. 18, 187–200. Kameda, T., Nakanishi, D., 2003. Does social/cultural learning increase human adaptability? Rogers0 s question revisited. Evol. Hum. Behav. 24, 242–260. Laland, K.N., Hoppitt, W.J.E., 2003. Do animals have culture? Evol. Anthropol. 12, 150–159. Lewis, H.M., Laland, K.N., 2012. Transmission fidelity is the key to the build-up of cumulative culture. Philos. Trans. R. Soc. Lond. B: Biol. Sci 367, 2171–2180. Lewis, J., 2007. Ekila: blood, bodies, and egalitarian societies. J. R. Anthropol. 14, 297–335. MacDonald, K., 2007. Cross-cultural comparison of learning in human hunting. Hum. Nat. 18, 386–402. Mesoudi, A., 2011. Cultural Evolution: How Darwinian Theory Can Explain Human Culture and Synthesize the Social Sciences. University of Chicago Press, Chicago, IL Mesoudi, A., 2011. Variable cultural acquisition costs constrain cumulative cultural evolution. PLoS ONE 6, e18239. Rendell, L., Fogarty, L., Laland, K.N., 2009. Rogers’ paradox recast and resolved: population structure and the evolution of social learning strategies. Evolution 64, 534–548. Rendell, L., Boyd, R., Cownden, D., Enquist, M., Eriksson, K., Feldman, M.W., Fogarty, L., Ghirlanda, S., Lillicrap, T., Laland, K.N., 2010. Why copy others? Insights from the social learning strategies tournament. Science 328, 208–213. Richerson, P.J., Boyd, R., 2005. Not by Genes Alone. University of Chicago Press, Chicago, IL Rogers, A.R., 1988. Does biology constrain culture? Am. Anthropol. 90, 819–831. Tehrani, J.J., Riede, F., 2008. Towards an archaeology of pedagogy: learning, teaching and the generation of material culture traditions. World Archaeol. 40, 316–331. Tennie, C., Call, J., Tomasello, M., 2009. Ratcheting up the ratchet: on the evolution of cumulative culture. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 364, 2405–2415. Tennie, C, Call, J., Tomasello, M., 2012. Untrained chimpanzees (Pan troglodytes schweinfurthii) fail to imitate novel actions. PLoS ONE 7, e41548. Tomasello, M., 1999. The Cultural Origins of Human Cognition. Harvard University Press, Cambridge, MA Tomasello, M., Kruger, A.C., Ratner, H.H., 1993. Cultural learning. Behav. Brain Sci. 16, 495–552. Whiten, A., 2005. The second inheritance system of chimpanzees and humans. Nature 437, 52–55. Whiten, A., 2011. The scope of culture in chimpanzees, humans and ancestral apes. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 366, 997–1007.
Contents lists available at ScienceDirect
Journal of Theoretical Biology journal homepage: www.elsevier.com/locate/yjtbi
Cumulative cultural evolution: The role of teaching Laureano Castro a, Miguel A. Toro b,n a b
Centro Asociado de Madrid, UNED, Centro Andrés Manjón, C/Francos Rodríguez 77, 28039 Madrid, Spain Departamento de Producción Animal, ETS Ingenieros Agrónomos, UPM, Ciudad Universitaria, 28040 Madrid, Spain
H I G H L I G H T S
Imitation is essential for human cumulative culture but it is not enough. Errors in imitation impede cumulative culture as the culture becomes more complex. Complex cumulative culture requires teaching to ensure accurate transmission. Teaching is adaptive whenever the gain on fitness compensates the cost of teaching.
art ic l e i nf o
a b s t r a c t
Article history: Received 27 June 2013 Received in revised form 31 December 2013 Accepted 6 January 2014 Available online 14 January 2014
In humans, cultural transmission occurs usually by cumulative inheritance, generating complex adaptive behavioral features. Cumulative culture requires key psychological processes (fundamentally imitation and teaching) that are absent or impoverished in non-human primates. In this paper we analyze the role that teaching has played in human cumulative cultural evolution. We assume that a system of cumulative culture generates increasingly adaptive behaviors, that are also more complex and difficult to imitate. Our thesis is that, as cultural traits become more complex, cumulative cultural transmission requires teaching to ensure accurate transmission from one generation to the next. In an increasingly complex cultural environment, we consider that individuals commit errors in imitation. We develop a model of cumulative cultural evolution in a changing environment and show that these errors hamper the process of cultural accumulation. We also show that a system of teaching between parents and offspring that increases the fidelity of imitation unblocks the accumulation and becomes adaptive whenever the gain in fitness compensates the cost of teaching. & 2014 Elsevier Ltd. All rights reserved.
Keywords: Imitation Assessor Innovation Social learning strategies
1. Introduction Culture, defined as variation acquired and maintained by social learning, is common in nature, but it is nowhere as important in any species as in humans, where it has led to a process of cumulative cultural evolution with great adaptive value (Boyd and Richerson, 1985; Tomasello, 1999; Richerson and Boyd, 2005; Enquist and Ghirlanda, 2007). This cumulative process is of enormous importance because it transforms culture into a second evolutionary inheritance system (Boyd and Richerson, 1985) that can generate complex cultural adaptations, allowing our species to rapidly and successfully colonize numerous environments (Richerson and Boyd, 2005; Hill et al., 2009; Mesoudi, 2011). Despite this significance, it is unclear how cumulative cultural evolution arose, especially when other primates with well developed
n
Corresponding author. Tel.: þ 34 914524900x1645. E-mail addresses: [email protected] (L. Castro), [email protected] (M.A. Toro). 0022-5193/$ - see front matter & 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jtbi.2014.01.006
social learning abilities show comparably restricted ranges (Tennie et al., 2009; Whiten, 2011). This has led to considerable debate. For example, Boyd and Richerson (1996), and Tomasello and his colleagues (Tomasello, 1999; Tennie et al., 2009; Tomasello et al., 1993) have suggested that cumulative cultural evolution may depend on specific social learning mechanisms, in particular imitation and/or teaching. For Boyd and Richerson (1996), cumulative cultural evolution is not present in chimpanzee culture because chimpanzees unfold their imitative learning abilities in a less consistent manner than humans. That is, although a given individual may learn someone else0 s innovation by imitation, there will be no other individual that could later imitate it in a precise manner. Consequently, this innovation cannot be propagated, and becomes lost until someone else re-invents it. For Tomasello and his colleagues (Tomasello, 1999; Tennie et al., 2009, 2012) chimpanzee cultural traditions represent behavioral biases of different populations that are generated by founder effects, individual learning and mostly product-oriented (rather than process-oriented) copying. However, human culture has the distinctive characteristic that it accumulates modifications over time (the
L. Castro, M.A. Toro / Journal of Theoretical Biology 347 (2014) 74–83
‘ratchet effect’). That is because human social learning is more oriented towards process than product and, secondly, because some potentially species-unique processes of cooperation, social imitation, pedagogy and social norms of conformity might plausibly be linked to the human cultural ratchet. Dean et al. (2012), in a study with capuchin monkeys, chimpanzees, and children, found strong support for the view of Tomasello and his colleagues that cumulative culture requires a package of key psychological processes (i.e., teaching through verbal instruction, imitation, and prosocial tendencies), that are present in humans but are absent or impoverished in chimpanzees and capuchins. Since true imitation and teaching have traditionally proven notoriously difficult to identify in animals (Caro and Hauser, 1992), their arguments have provided a conveniently neat explanation for the apparent absence of cumulative cultural evolution. However, their reasoning is has not gone undisputed (Laland and Hoppitt, 2003; Whiten, 2005). It seems clear that human beings have the cognitive ability to engage in high-fidelity information transmission and that the latter is a key driver of human cumulative culture (Lewis and Laland, 2012). We have defended (Lewis and Laland, 2012; Castro and Toro, 2002, 2004; Castro et al., 2004, 2010) that a key factor enabling the evolution of a human cumulative cultural system of inheritance is that some of our hominid ancestors, which we call assessors or Homo suadens (derived from Latin suadeo: advice, evaluate, counsel, approve), developed both the capacity for true imitation and the capacity to approve or disapprove of offspring0 s learned behavior. The capacity of approving or disapproving children0 s learned behavior may be considered an elementary form of teaching that increases the reliability of imitation, making social learning more accurate when behavior is complex. We have suggested that approval and disapproval of offspring0 s learned behavior was the cognitive mechanism that allowed our ancestors to keep and accumulate the complex behavioral findings of a generation and pass them on to the next. In a similar way, Gergely and Csibra (Csibra and Gergely, 2006; Gergely and Csibra, 2011) propose that human communication is specifically adapted to allow the transmission of generic knowledge between individuals. Such a communication system, which they call ‘natural pedagogy’, enables fast and efficient social learning of cognitively opaque cultural knowledge that would be hard to acquire relying on observational learning mechanisms alone. Therefore, it seems reasonable to assume that human cumulative culture does not only depend on imitation (Lewis, 2007; MacDonald, 2007), but also on teaching (Tehrani and Riede, 2008; Hewlett et al., 2011). A recent review on the ethnographic evidence of teaching, incorporating nonverbal costly behaviors of potential teachers that facilitate behavioral acquisition by observers, indicates that the gradual scaffolding of skills in a novice through demonstration, intervention and collaboration has played an essential role in securing the accurate transmission of skills across generations (Tehrani and Riede, 2008). In this paper we analyze the role that teaching has played in human cumulative cultural evolution. Recently, Fogarty et al. (2011) have proposed that teaching evolved in humans because cumulative culture allows to teach valuable information that is otherwise difficult-to-acquire. Their analysis suggests that teaching and cumulative culture reinforce each other, and may have coevolved, because teaching is more advantageous in a cumulative culture setting, whereas cumulative knowledge gain is frequently reliant on teaching. Following a similar reasoning we defend that human cumulative cultural transmission needs, as behavior becomes more complex, a teaching mechanism to ensure the accurate transmission from one generation to the next. Otherwise, there is no accumulation. To explore this hypothesis we developed a model of cumulative culture evolution in a changing environment inspired by both a
75
social learning model developed by Rendell et al. Rendell et al. (2010) to implement a tournament of social learning strategies and our previous model to analyze competition between unselective strategies that differ only in the degree of social versus asocial learning (Castro and Toro, 2012). We consider mixed social learning strategies that first learn by imitating parents and subsequently by imitation or innovation chosen at random. We assume that cumulative cultural transmission is possible and produces more adaptive complex behaviors by accumulating successive insights on the same conduct (Ehn and Laland, 2012; Mesoudi, 2011; Enquist et al., 2011). We also assume that this progressive complexity hinders the correct imitation of behaviors. Then we show that both conditions, imitation and innovation, are sufficient for the emergence of cumulative cultural transmission. Next, we show that the process of cumulative cultural evolution stops if imitation is less efficient as the behavior becomes more complex. And finally, we also show that this impediment can be overcome if parents are able to facilitate the correct imitation of behavior by their children, with a certain cost, e.g., if individuals are assessors (Castro and Toro, 2004). In other words, we show that assessor transmission, or other similar ways of teaching, can unblock accumulation when behavior becomes so complex that imitation is not enough to replicate it properly and can be adaptive whenever the gain on fitness compensates for the cost of teaching. The development of imitative abilities does not necessarily lead to a gradual development of innovative capacity (Aoki, 2001). It is difficult to predict whether innovation and imitation can lead to cultural accumulation that is adaptive enough to compensate for the costs of developing and maintaining teaching mechanisms. For that reason, as a final point, we suggest that elementary forms of teaching may have evolved in the hominid line for causes other than achieving accurate cultural transmission. Specifically, we propose that teaching may greatly contribute to avoiding the cost of learning wrong behaviors, and to save time when choosing among different behavioral alternatives that are difficult to evaluate.
2. The model We designed our simulation model along the lines described by Rendell et al. (2010) and Castro and Toro (2012). We consider a changing environment. This simulated environment is a “multiarmed bandit”. In our model the bandit has 40 arms, each representing a different behavior and each with a distinct payoff. Each behavior has 10 increasing complexity levels that represent a progressive accumulation of insights in that behavior. Thus, we assume that each level of refinement is directly dependent on the previous one and is associated with an improvement in fitness of its user. The payoff pi1 for an act i in level 1 was an integer drawn at random from an exponential distribution (k ¼1; values were squared, then doubled, and finally rounded to give integers mostly falling within the range 0–50). Payoffs changed between rounds with independent probability pc¼ 0.001, with new payoffs drawn at random from the same distribution. Payoffs for the different levels of each behavior were constructed so that the payoff pij of level j of behavior i was equal to pij ¼pi1 þ(j 1) α pi1, where α is a factor that allowed to scale the intensity of the rising payoff as the level changed. Therefore, when the payoff of a behavior changes, the payoff for each level of that behavior is modified. We consider α¼0.4 in standard conditions. Each strategy had to specify how individual agents in a finite population of 100 individuals chose between three possible moves in each round, namely Innovate, Observe, and Exploit. Innovate represents asocial learning, for example, trial and error learning or insight. The agent chooses a random behavior and learns the next higher level of that behavior stored in its repertoire. If it knows all
76
L. Castro, M.A. Toro / Journal of Theoretical Biology 347 (2014) 74–83
levels of that conduct, it chooses a different behavior. An Innovate move always returns accurate information about the payoff of the randomly selected behavior. Observe represents any form of imitation through which an agent can acquire a behavior performed by another individual in the last round. Playing Observe did not necessarily result in a new behavior being learned. If the observer has the observed behavior in his repertoire or has a higher level of the same observation, then it does not take advantage and lost the round for practical purposes. Moreover, if no other agents played Exploit in the last round, then nothing was learned. The number of agents sampled when playing Observe was one. We consider that asocial information (play Innovate) was no more costly than social information (play Observe). Lastly, Exploit represents the performance of a behavior from the agent0 s repertoire, equivalent to pulling one of the multiarmed bandit0 s levers. Each simulation contained a population of 100 agents. Each agent possessed a behavioral repertoire, which was empty at the start of the agent0 s life. An agent0 s repertoire could subsequently only contain acts through some form of learning. Agents could only obtain a payoff by playing Exploit and always chose to perform the best strategy from their repertoire. When an individual chose Exploit, it received the current payoff specified in the environment. Observe is error prone with regard to both payoff and level of observed behavior. The returned payoff estimate was subject to normally distributed random error (rounded to the nearest integer) with mean 0 and standard deviation spayoffError ¼ 1 (with the returned payoff estimate lower bounded at 0). Independently, an Observe move can occur without error with probability h, and return the same level of behavior to that performed by the observed agent, or with error with probability 1 h, and return only the next highest level of the behavior that the observer has in his repertoire. In both cases, the payoff learned was still that of the observed agent. In our model we consider two values for the probability h of imitating a behavior without error. First h¼1, where the imitation is always perfect. Second, h¼ (1/(l l’)), where l and l’ are the levels that have the behavior in the observed individual and in the individual who observes with l 4l’, because if l rl’ the observation is lost. Thus, if the difference between levels is 1, the imitation is always perfect and as the difference between levels l–l’ increases, the difficulty of replicate the behavior faithfully also increases. We consider two types of strategies: x-imitators (xIMI) and x-assessors (xASS). In the first round of the game, all strategies acquire a behavior by innovative learning. After that, all randomly play Exploit for 80% of the rounds and learn (Observe or Innovate) the remaining 20%. We chose 80% of exploitation value because it is close to the optimal strategy that a pure innovator strategy (i.e., a strategy that when learning always plays Innovate including the first movement) should use to obtain the highest fitness (Castro and Toro, 2012). When learning the strategies, xIMI and xASS play Observe x % of the time and play Innovate the rest of the time (1 x). When a new individual is born he always plays Observe as the first movement and learns the best parent0 s behavior. The only difference between xIMI and xASS is that imitators learn the best parent0 s behavior with probability h or level 1 of that behavior with probability 1 h, while assessor individuals always learn the best parent0 s behavior. Moreover, assessor parents have a cost due to the time t (number of rounds without learning or exploiting) that they spend teaching their children, with t¼ 1 in the standard conditions. We analyzed the dynamics of each strategy when occurs alone and when compete with the others in pairwise round-robin contest. Each pairwise contest consisted of 40 simulations in which agents performing strategy A and agents performing strategy B were introduced at equal frequencies (0.5). To analyze
the dynamics of a population consisting of a single strategy we put this strategy competing against itself under the same conditions. Agents died with a constant probability of 1/50 per round and were replaced by the offspring of another agent. The probability that an agent was chosen to reproduce was proportional to its mean lifetime payoff, calculated as its summed payoff from playing Exploit, divided by the number of simulation rounds that it had lived. Offspring inherited their parent0 s strategy unless there was a mutation, in which case the offspring was given the other strategy playing in that simulation. The mutation rate used was 0.001. This high rate does not qualitatively affect our outcomes but decreases drift and offers significant computational advantages in terms of time to equilibrium. We recorded the average frequency of each strategy in the population over the last 10,000 rounds of each 40,000-round simulation and gave each strategy a score that was the mean of these values over the simulations in which it participated. Strategies were ranked on their average contest score once they had played against every other strategy. We also recorded the average individual fitness over the last 10,000 rounds, measured as mean lifetime payoff, the average level of behaviors, and the average number of different behaviors that the individuals exploit. 3. Results 3.1. Non-cumulative culture In this scenario we suppose that there are only behaviors of level 1, so cumulative cultural evolution is not possible. Because there are no levels of increasing complexity, individuals always imitate with maximum efficacy (h¼ 1). We studied homogeneous populations made up of 100 individual x-imitators (xIMI) or
Fig. 1. Average fitness of x-imitator (xIMI) or x-assessor (xASS) strategies in pure populations of 100 individuals without and with (accurate and inaccurate) cumulative culture.
L. Castro, M.A. Toro / Journal of Theoretical Biology 347 (2014) 74–83
77
x-assessors (xASS), where all individuals use the same mixed social learning strategy. The fitness that these populations attain is given in Fig. 1. Social learning strategies that, when learning, imitate with a probability between 40% (40IMI and 40ASS) and 80% (80IMI and 80ASS), are those that achieve more fitness both among x-imitators and among x-assessors. However, the fitness of mixed strategies 0IMI and 0ASS that when learning imitate the parents for the first movement and afterwards always innovate is very close to the fitness of the best strategies. This value of fitness contrasts with the value of 2.6 that is obtained by the pure imitator strategies 100IMI and 100ASS, that always imitate and never innovate. We have also calculated the fitness of a pure innovator strategy, that always learns by innovation, even the first time when he does not imitate his parents. The average fitness of this pure innovator strategy is 7.2, much smaller than the fitness of mixed strategies 0IMI and 0ASS, that take advantage of what their parents have learned, but greater than 2.6, the fitness of pure imitator strategies 100IMI and 100ASS. The success of mixed strategies compared to pure innovator and pure imitator (100IMI and 100ASS) strategies arises from some peculiarities in our model. First, with 40 alternatives there is a low probability that pure innovators develop optimal conduct in an average life of 50 rounds. Second, each individual always exploits the best behavior he knows and, therefore, learning by imitation is a successful strategy because the exploited behavior patterns available to copy constitute a select subset that has already been chosen for their high payoff (Rendell et al., 2010; Castro and Toro, 2012). Finally, the populations in the present simulations have a finite number of individuals (100) and each one dedicates a similar and constant amount of time to learning. Social learning tends to homogenize the populations and, as a result, individuals can
devote part of their learning period to innovate, increasing their behavioral repertoire, without suffering a fitness cost with respect to the individuals who learn only socially. We can measure the homogeneity of a population by the average number n of different behaviors that individuals exploit in each round. This number decreases with the degree of imitation of studied strategies and is n ¼1 for the pure imitator strategies 100IMI and 100ASS (Fig. 2). This explains why the 100IMI and 100ASS strategies have lower fitness, because the individuals only have one behavior stored in their repertoire and are not able to modify their behavior in an adaptive way when the environment changes. The populations with xASS strategies are slightly less homogeneous than the equivalent populations of xIMI strategies (Fig. 2). This is because the cost of teaching in xASS populations affects the individuals selected as parents that, on average, have greater fitness, so their advantage compared to the unselected individuals is smaller than with xIMI strategies. Social learning strategies that imitate approximately 80% of the time they learn (80IMI y 80ASS) can compete better in pairwise round-robin contests and attain an equilibrium with the other strategies where they are predominant (Fig. 4). These optimal strategies compete better than other more innovative strategies, such as 0IMI and 0ASS, because they discover the best behavior in the population more quickly by imitation. They also compete better than other imitator strategies such as 100IMI and 100ASS, because the population is homogeneous enough for the individuals to acquire the optimal conduct directly from their parents or from the first times they play Observer (Fig. 4). The time they dedicate to innovate allows individuals with the optimal strategy to adapt back when there is a change in the payoff of the conduct that was optimal.
Fig. 2. Average number of different behaviors of x-imitator (xIMI) or x-assessor (xASS) strategies in pure populations of 100 individuals without and with (accurate and inaccurate) cumulative culture.
Fig. 3. Average level of x-imitator (xIMI) or x-assessor (xASS) strategies in pure populations of 100 individuals with accurate and inaccurate cumulative culture.
78
L. Castro, M.A. Toro / Journal of Theoretical Biology 347 (2014) 74–83
Fig. 4. Average individual fitness and average frequency for both strategies A and B competing in a pairwise contest (non-cumulative culture, accurate imitation h ¼ 1).
Other things being equal, xIMI individuals are more fit than xASS because they transmit the behavior from parents to offspring with the same efficacy, but without the time cost that xASS parents need to control their children. For this reason an xIMI strategy always displaces the equivalent xASS. Fig. 4 includes an example of how the best imitator strategy 80IMI displaces the best assessor strategy 80ASS. 3.2. Cumulative culture Now we consider that each behavior has 10 levels of increasing complexity. Individuals that innovate choose a behavior at random and can learn the level above the one stored in their repertoire. Thus, the cumulative cultural evolution process can emerge. Imitation can occur without error with probability h, or with error with 1 h. We study two values of h: h¼1, in which the imitation is always accurate, and h ¼(1/(l l’)), in which the probability to imitate without error depends on the difference between the levels of complexity of both the observed individual (l) and the individual that observes (l’) with l4 l’. 3.2.1. Accurate imitation When imitation occurs without error (h¼1), the strategies that accumulate more include those that innovate a lot and imitate little, between 0% and 20% (0IMI, 20IMI, 0ASS, and 20ASS) (Fig. 3). In fact, the maximum level of cultural accumulation (l¼5.9) is obtained by imitating the parent only the first time and then innovating to learn (0IMI and 0ASS strategies). Obviously, if we decrease the number of different behavioral alternatives or the probability that a behavior changes fitness, higher levels can be attained, closer to the maximum
(l¼10). The homogeneity of populations, measured as the average number n of different behaviors exploited in each round, decreases as the degree of imitation of strategies xIMI and xASS increases (Fig. 2). For the same degree of imitation, populations are more variable with than without cultural accumulation because the levels of complexity multiply the potential number of variants: from 40 alternatives to 400. With accumulation populations using xASS strategies are also slightly less homogeneous than the equivalent populations using xIMI (Fig. 2). In a pairwise contest among xIMI strategies, the 20IMI strategy is the best and almost optimal when alone. That strategy displaces or attains an equilibrium where it gains the majority compared to any other strategy (Fig. 5). Something similar occurs with the 20ASS strategy, which is the best both alone and when competing in pairwise contests (Fig. 5). The 20IMI and 20ASS strategies are more competitive than other strategies that imitate less (such as 0IMI or 0ASS) or others that imitate more (such as 100IMI or 100ASS). These strategies reach an optimal balance between a degree of imitation that allows them to profit from the best behaviors present in the population and a degree of innovation that augments the probability of increasing the level of complexity of the behaviors stored in their repertoire. Other things being equal, the xIMI strategies are better than the equivalent xASS strategies, since the latter have the cost teaching. In Fig. 5 we show an example how the best imitator strategy 20IMI displaces the best assessor strategy 20ASS.
3.2.2. Inaccurate imitation If we consider that imitation loses efficacy as behaviors become more complex (h¼1/(l l’)), xIMI and xASS strategies behave very
L. Castro, M.A. Toro / Journal of Theoretical Biology 347 (2014) 74–83
79
Fig. 5. Average individual fitness and average frequency for both strategies A and B competing in a pairwise contest (cumulative culture, accurate imitation h ¼ 1).
differently. The xIMI strategies in populations formed by a single strategy obtain lower average fitness and lower levels of accumulation than when imitation is accurate (Figs. 1 and 3). The maximum values for xIMI strategies are obtained when they imitate roughly half of the time they learn. In particular, the 40IMI strategy reaches the maximum with 40.1 average fitness and an average level of complexity of 2.6. If xIMI strategies compete in pairwise contests, the optimum strategy is 60IMI (Fig. 6). It is better than strategies that imitate less, as strategy 40IMI, optimal in populations formed by a single strategy, and than strategies that imitate more like 100IMI, because it reaches an optimal balance between imitation and innovation. Choosing 60% imitation allows to exploit, despite errors in imitating, the achievements of other individuals and 40% innovation allows to increase the level of complexity of the behaviors stored in their repertoire. A different case would be if the same xIMI strategies with and without imitation errors compete between themselves. Under the same conditions, better imitation is always favored because fitness is higher (Fig. 1). Meanwhile, xASS strategies can achieve a similar level of fitness and cultural accumulation to when imitation is completely effective (Figs. 1 and 3). The optimal strategy in populations formed by a single strategy is between 0ASS and 20ASS that innovate a lot and imitate very little (Fig. 1). This is because children inherit their parent0 s strategy without error and, as levels of accumulation increase, the possibility that an individual changes strategy is smaller, as the majority of observations inaccurately reproduce the behavior imitated. If xASS strategies compete in pairwise contests, the best strategy is 40ASS. That strategy is better than other strategies, like 0ASS and 20ASS, that achieve greater fitness in
populations formed by a single strategy, and than others that imitate more, like 100ASS (Fig. 6). The optimal balance requires imitating 40% of the time you learn and innovating the rest of this time. Note that with inaccurate imitation xASS strategies have similar or even higher fitness and cultural accumulation levels than those obtained when imitation is accurate. However, this gives them no advantage when competing with each other, on equal conditions. If the same xASS strategy competes with and without accurate imitation, the winning strategy is the one that imitates more accurately, because it takes better advantage of the accomplishments of others. The errors in imitation greatly increase population variability between xIMI strategies. Not so with xASS strategies because new generations are formed from very few parents: those who have achieved high levels of accumulation and high fitness that accurately transmit to their children. The xASS strategies are now much more competitive than xIMI (Fig. 6), since their fitness and accumulation levels are much higher and offset the cost of teaching between parents and children. Thus now the best strategy 40ASS moves completely to the imitator strategies 60IMI and 40IMI (Fig. 6).
3.2.3. Impact of the different parameters Table 1 shows the standard conditions in which simulations were run. Changes of ion of these parameters introduce quantitative modifications in the results, without modifying the main conclusion: cultural accumulation is blocked among imitator individuals as complexity increases if imitation is not accurate. Obviously, if we increase the average life of individual or if we
80
L. Castro, M.A. Toro / Journal of Theoretical Biology 347 (2014) 74–83
Fig. 6. Average individual fitness and average frequency for both strategies A and B competing in pairwise contest (cumulative culture, inaccurate imitation h ¼ 1/(l l’)).
Table 1 Standard conditions of the simulation. Population Number of Number of Probability Probability Probability Probability
size behavioral alternatives cumulative cultural levels of change of the payoff of a behavior of individual death of play Exploit of imitation without error
The increase of fitness for level Probability of mutation Cost of teaching
100 40 10 0.001 0.02 0.8 Accurate h ¼1; inaccurate h ¼(1/l l’) 0.4 0.001 1 round
decrease the number of cultural variants, the levels of accumulation attained will increase but, sooner or later the accumulation process is stopped. An especial situation refers to the cost of teaching. If we increase the number of rounds that individuals invest in teaching its offspring from one round to five or ten (10% or 20% of the average life) the level of complexity and the fitness that attain the individuals decrease slightly. Fig. 7 shows how it varies for the best strategy 40ASS when imitation is inaccurate. Increasing the cost does not prevent that this strategy 40ASS displace any imitator strategy because fitness differences are still great. In other words, if individuals attain high levels of cultural complexity the transmission between parents and offspring can cope with an intense dedication to teaching by parents. A different case occurs if the
cognitive capacity of individuals allows lower levels of complexity for each variant. Fig. 8 shows the competition between strategies 40ASS and 40IMI for different costs of teaching with 10 and 2 levels of cultural complexity. In the last case it can be seen how the strategy 40ASS lost competitiveness as the cost of teaching increases and can be beaten by the strategy 40IMI if the cost is enough high. Other interesting aspect refers to the population size. Events of cumulative culture should be more frequent in a large population (Henrich, 2004). Recently, using a dual-task computer game, Derex et al. (2013) demonstrate how changes in group size can generate both adaptive cultural evolution and maladaptive losses of culturally acquired skills. In our model of cumulative culture, if we change the size of the population when imitation is inaccurate, we obtain desired results similar to those authors (Fig. 9). But also now the increase of cultural complexity depends to a greater degree of effectiveness of the transmission of the ASS strategy than of the population size.
4. Discussion Theoretical analyses on the evolution of social learning in a changing environment have shown that, as long as the only benefit of imitation is that imitators avoid the cost of individual learning, imitation does not increase the ability of species to adapt, and is insufficient to explain the adaptive success of human culture, a counterintuitive conclusion known as Rogers’ paradox
L. Castro, M.A. Toro / Journal of Theoretical Biology 347 (2014) 74–83
81
Fig. 7. Average fitness and average level of the strategy 40ASS for different costs of teaching (1, 5 or 10 rounds) in pure populations of 100 individuals with cumulative culture and inaccurate imitation h ¼1/(l l’).
Fig. 8. Average frequency and average fitness for both the strategy 40ASS (A and C) and the strategy 40IMI (B and D) competing in pairwise contests with accumulation and inaccurate imitation h ¼1/(l l’) and for different costs of teaching (1, 5 and 10 rounds): (a) 10 levels of cumulative culture and (b) 2 levels.
82
L. Castro, M.A. Toro / Journal of Theoretical Biology 347 (2014) 74–83
Fig. 9. Average fitness and average level of strategies
40IMI
(Rogers, 1988). To overcome Rogers’ paradox, Boyd and Richerson (1995) have shown that imitation can be adaptive and increase the average fitness of imitators if it makes individual learning less costly or more accurate. The first condition (i.e., imitation makes individual learning less costly), predicts the emergence of smart mixed strategies that rely on some selective combination of the two types of learning (Feldman et al., 1996; Kameda and Nakanishi, 2003; Enquist et al., 2007; Franz and Nunn, 2009; Rendell et al., 2009). The second condition (i.e., imitation makes individual learning more accurate) is satisfied if imitation allows cumulative cultural evolution (Boyd and Richerson, 1995). Those predictions fit well with the results obtained in our model. Without cumulative culture, the fitness of mixed strategies that imitate their parent first and then innovate or imitate at random is higher than the fitness of populations with only innovators or imitators, overcoming Rogers’ paradox, both in pure populations (Fig. 1) and when they compete with each other (Fig. 4). Although in pure populations the fitness is very similar between the different strategies (Fig. 1), social learning strategies that imitate approximately 80% of the time they learn compete better in pairwise round-robin contests (Fig. 4). With cumulative culture and accurate transmission, the fitness of the strategies is further increased (Fig. 1). Cumulative culture favors mixed strategies that spend more time innovating. In fact, 0IMI and 0ASS strategies that imitate parental behavior without error and innovate the rest of the time obtain maximum levels of cultural complexity (Fig. 3) and fitness (Fig. 1) in populations formed by a single strategy. However, in pairwise contests they cannot beat others that imitate more (20IMI and 20ASS) and that therefore make better use of the accomplishments of others (Fig. 5). In any case, cumulative culture also favors mixed strategies that innovate and imitate. One consequence of the cumulative process is the emergence of increasingly complex behaviors that an individual alone cannot develop and that, in time, are more difficult to replicate through imitation (Ehn and Laland, 2012; Mesoudi, 2011; Enquist et al., 2011). It seems reasonable to assume that sooner or later the growing complexity of cultural variations also introduces frequent errors in imitation and, therefore, the process of cumulative cultural evolution would be affected. The hypothesis we propose is that the development of a system of cumulative cultural transmission (as probably occurred in several hominid species and reached its zenith in our species) required the development of a system of high fidelity transmission based on teaching between parents and children. The results obtained are consistent with this proposal. If there is
and
40ASS
in populations of 200, 100 and 20 individuals.
accumulation but imitation is inaccurate, errors in imitating hinder the process of accumulation and xASS strategies can displace xIMI (Fig. 6), provided that the cost of teaching is less than the benefits obtained by learning without error paternal behavior (Fig. 8). The problem is to explain the origin of complex forms of cumulative culture that permit the evolution of teaching. Cumulative culture requires innovation and accurate cultural transmission. Although individual learning can favor the development of social learning abilities, the development of imitative abilities does not necessarily generate a positive feedback process to favor higher levels of innovation (Aoki, 2001; Henrich and McElreath, 2003). Those individuals who imitate get access to behavioral innovations from individuals with a higher intellectual capacity, not necessarily their parents, and therefore, imitation may restrain the adaptive advantage of a greater investment on intellectual capacity – i.e., on innovation. Without higher innovation capacity it is not easy that cumulative culture arises and can compensate for the costs of developing and maintaining teaching mechanisms. A possible explanation for cumulative cultural evolution is that the transition from a non-cumulative scenario to a cumulative culture arose as a result of a previous development of elementary forms of parent– child education, which in turn evolved for reasons other than to achieve high fidelity transmission culture. In particular, we suggest that teacher parents can provide offspring with what they learn, including things they can or cannot do (Castro and Toro, 2004; Castro et al., 2004, 2010). The former can be imitated by other individuals that are not the parents, but the latter is transmissible only by teaching, for example by disapproval of behavior between parents and offspring. Moreover, teacher parents can favor the implementation of behavior, which has no immediate positive evaluation by the children. Once emerged, teaching can foster greater innovation capacity because the behaviors are preferentially transmitted from innovator parents to their children. Moreover, adaptive complex behavior can also improve the imitation ability to take advantage of the lessons learned by other individuals, different from the parents. All these combined processes innovation, imitation, and teaching may favor the development of adaptive behaviors that are more complex and difficult to imitate, and those in turn favor more efficient teaching, leading to an autocatalytic process of coevolution that probably occurred in our species. In summary, our proposal defends that innovation and imitation are not enough to achieve a progressive cultural accumulation: the evolution of teaching is also necessary.
L. Castro, M.A. Toro / Journal of Theoretical Biology 347 (2014) 74–83
References Aoki, K., 2001. Theoretical and empirical aspects of gene-culture coevolution. Theor. Popul. Biol. 59, 253–261. Boyd, R., Richerson, P.J., 1985. Culture and the Evolutionary Process. University of Chicago Press, Chicago, IL Boyd, R., Richerson, P.J., 1995. Why does culture increase human adaptability? Ethol. Sociobiol. 16, 125–143. Boyd, R., Richerson, P.J., 1996. Why culture is common but cultural evolution is rare? Proc. Br. Acad. 88, 77–93. Caro, T.M., Hauser, M.D., 1992. Is there teaching in nonhuman animals? Q. Rev. Biol. 67, 151–174. Castro L., Toro M.A., 2002. Cultural Transmission and the Capacity to Approve or Disapprove of Offspring0 s Behaviour. Jom-emit 6 (〈http://jom-emit.cfpm.org/ 2002/vol6/castro_l&toro_ma.html〉). Castro, L., Toro, M.A., 2004. The evolution of culture: from primate social learning to human culture. Proc. Natl. Acad. Sci. USA 101, 10235–10240. Castro, L., Toro, M.A., 2012. Imitation or innovation? Unselective mixed strategies can provide a better solution. Theory Biosci. 131, 273–279. Castro, L., Medina, A., Toro, M.A., 2004. Hominid cultural transmission and the evolution of language. Biol. Philos. 19, 721–737. Castro, L., Castro-Nogueira, L., Castro-Nogueira, M.A., Toro, M.A., 2010. Cultural transmission and social control of human behavior. Biol. Philos. 25, 347–360. Csibra, G., Gergely, Gy., 2006. Social learning and social cognition: the case for pedagogy. In: Munakata, Y., Johnson, M.H. (Eds.), Processes of Change in Brain and Cognitive Development: Attention and Performance XXI, 2006. Oxford University Press, Oxford, pp. 249–274. Dean, L.G., Kendal, R.L., Schapiro, S.J., Thierry, B., Laland, K.N., 2012. Identification of the social and cognitive processes underlying human cumulative culture. Science 335, 1114–1118. Derex, M., Beugin, M.P., Godelle, B., Raymond, M., 2013. Experimental evidence for the influence of group size on cultural complexity. Nature 503, 389–391, http://dx.doi.org/10.1038/nature12774. Ehn, M., Laland, K.N., 2012. Adaptive strategies for cumulative cultural learning. J. Theor. Biol. 301, 103–111. Enquist, M., Ghirlanda, S., 2007. Evolution of social learning does not explain the origin of human cumulative culture. J. Theor. Biol. 246, 123–135. Enquist, M., Eriksson, K., Ghirlanda, S., 2007. Critical social learning: a solution to Rogers0 s paradox of nonadaptive culture. Am. Anthropol. 109, 727–734. Enquist, M., Ghirlanda, S., Eriksson, K., 2011. Modelling the evolution and diversity of cumulative culture. Philos. Trans. R. Soc. Lond. B: Biol. Sci 366, 412–423. Feldman, M.W., Aoki, K., Kumm, J., 1996. Individual versus social learning: evolutionary analysis in a fluctuating environment. Anthropol. Sci. 104, 209–231. Fogarty, L., Strimling, P., Laland, K.N., 2011. The evolution of teaching. Evolution 65, 2760–2770. Franz, M., Nunn, C.L., 2009. Rapid evolution of social learning. J. Evol. Biol. 22, 1914–1922.
83
Gergely, Gy., Csibra, G., 2011. Natural pedagogy as evolutionary adaptation. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 366, 1149–1157. Henrich, J., 2004. Demography and cultural evolution: how adaptive cultural processes can produce maladaptive losses: the Tasmanian case. Am. Antiq. 69, 197–214. Henrich, J., McElreath, R., 2003. The evolution of cultural evolution. Evol. Anthropol. 12, 123–135. Hewlett, B.S., Fouts, H.N., Boyette, A.H., Hewlett, B.L., 2011. Social learning among Congo Basin hunter–gatherers. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 366, 1168–1178. Hill, K., Barton, M., Hurtado, A.M., 2009. The emergence of human uniqueness. Evol. Anthropol. 18, 187–200. Kameda, T., Nakanishi, D., 2003. Does social/cultural learning increase human adaptability? Rogers0 s question revisited. Evol. Hum. Behav. 24, 242–260. Laland, K.N., Hoppitt, W.J.E., 2003. Do animals have culture? Evol. Anthropol. 12, 150–159. Lewis, H.M., Laland, K.N., 2012. Transmission fidelity is the key to the build-up of cumulative culture. Philos. Trans. R. Soc. Lond. B: Biol. Sci 367, 2171–2180. Lewis, J., 2007. Ekila: blood, bodies, and egalitarian societies. J. R. Anthropol. 14, 297–335. MacDonald, K., 2007. Cross-cultural comparison of learning in human hunting. Hum. Nat. 18, 386–402. Mesoudi, A., 2011. Cultural Evolution: How Darwinian Theory Can Explain Human Culture and Synthesize the Social Sciences. University of Chicago Press, Chicago, IL Mesoudi, A., 2011. Variable cultural acquisition costs constrain cumulative cultural evolution. PLoS ONE 6, e18239. Rendell, L., Fogarty, L., Laland, K.N., 2009. Rogers’ paradox recast and resolved: population structure and the evolution of social learning strategies. Evolution 64, 534–548. Rendell, L., Boyd, R., Cownden, D., Enquist, M., Eriksson, K., Feldman, M.W., Fogarty, L., Ghirlanda, S., Lillicrap, T., Laland, K.N., 2010. Why copy others? Insights from the social learning strategies tournament. Science 328, 208–213. Richerson, P.J., Boyd, R., 2005. Not by Genes Alone. University of Chicago Press, Chicago, IL Rogers, A.R., 1988. Does biology constrain culture? Am. Anthropol. 90, 819–831. Tehrani, J.J., Riede, F., 2008. Towards an archaeology of pedagogy: learning, teaching and the generation of material culture traditions. World Archaeol. 40, 316–331. Tennie, C., Call, J., Tomasello, M., 2009. Ratcheting up the ratchet: on the evolution of cumulative culture. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 364, 2405–2415. Tennie, C, Call, J., Tomasello, M., 2012. Untrained chimpanzees (Pan troglodytes schweinfurthii) fail to imitate novel actions. PLoS ONE 7, e41548. Tomasello, M., 1999. The Cultural Origins of Human Cognition. Harvard University Press, Cambridge, MA Tomasello, M., Kruger, A.C., Ratner, H.H., 1993. Cultural learning. Behav. Brain Sci. 16, 495–552. Whiten, A., 2005. The second inheritance system of chimpanzees and humans. Nature 437, 52–55. Whiten, A., 2011. The scope of culture in chimpanzees, humans and ancestral apes. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 366, 997–1007.
