Behavioral conservatism is linked to complexity of behavior in chimpanzees (<i>Pan troglodytes</i>):implications for cognition and cumulative culture

Cumulative culture is rare, if not altogether absent in nonhuman species. At the foundation of cumulative learning is the ability to modify, relinquish, or build upon previous behaviors flexibly to make them more productive or efficient. Within the primate literature, a failure to optimize solutions in this way is often proposed to derive from low-fidelity copying of witnessed behaviors, suboptimal social learning heuristics, or a lack of relevant sociocognitive adaptations. However, humans can also be markedly inflexible in their behaviors, perseverating with, or becoming fixated on, outdated or inappropriate responses. Humans show differential patterns of flexibility as a function of cognitive load, exhibiting difficulties with inhibiting suboptimal behaviors when there are high demands on working memory. We present a series of studies on captive chimpanzees that indicate that behavioral conservatism in apes may be underlain by similar constraints: Chimpanzees showed relatively little conservatism when behavioral optimization involved the inhibition of a well-established but simple solution, or the addition of a simple modification to a well-established but complex solution. In contrast, when behavioral optimization involved the inhibition of a well-established but complex solution, chimpanzees showed evidence of conservatism. We propose that conservatism is linked to behavioral complexity, potentially mediated by cognitive resource availability, and may be an important factor in the evolution of cumulative culture.</p

Comment concerning cumulative cultural evolution, on M. O'Brien and K.N. Laland 'Genes, culture and agriculture: an example of human niche construction'

O’Brien and Laland point out that human culture is exceptional in its cumulative nature. This is often characterized by the ratchet effect, highlighting that high-fidelity social transmission can underpin the accumulation of trait modifications. They also note that the developmental niche-construction processes underlying cultural evolution are understudied. I agree that the evolutionary consequences of culturally constructed learning environments are indeed understudied and that attention to this area may provide a fresh assessment of cumulative cultural evolution. An important focus of cumulative cultural evolution research is in assessing individual cognitive prerequisites that facilitate high-fidelity cultural transmission and the adoption of adaptive innovations (Ehn and Laland 2012). However, it is also important to consider the role of developmental niche construction and the ecological inheritance of learning environments, including forms of symbolic representation and material culture, on cumulative cultural evolution (Cole 1995; Sterelny 2012; Wheeler and Clark 2009). Culturally derived scaffolding for learning can have a direct effect on the differential adoption and retention of cultural traits (cultural selection). For instance, pedagogical traditions in apprenticeships, including traditional patterns of intervention, correction, and collaboration may influence the fidelity of transmission and the potential for cumulative cultural evolution (Gergely and Csibra 2006; Tehrani and Reide 2008; Tennie, Call, and Tomasello 2009). There is also the potential for cumulative cultural evolutionary dynamics to be shaped by forms of symbolic representation. Mathematical history provides particularly obvious examples, where invention of new notation systems, for instance Hindu-Arabic in place of Roman numerals or Feynman diagrams in quantum mechanics, dramatically altered the evolvability of research fields (Gauvain 1998). Thus, for the cumulative cultural evolution of many traits, high-fidelity social transmission and the potential for invention may be critically affected by culturally constructed learning environments (Tennie, Call, and Tomasello 2009). Furthermore, a complete account of cognition required for cumulative cultural evolution may often be reliant on its extension beyond the mind of the individual and on its distributed nature across people and artefacts (Donald 2000; Hutchins 1995, 2008). Without accounting explicitly for the role of developmental niche construction and the ecological inheritance of learning environments, there can be an over- or misattribution of cognitive facility to the mind in order to explain the cumulative cultural evolution of skills such as computational tasks (Hutchins 1995). O’Brien and Laland provide a detailed account of potential gene-culture coevolutionary pathways affecting the cumulative cultural evolution of farming technologies and medicinal practices. A key process in these dynamics is likely to be the niche construction of inherited learning environments, which themselves can be subject to cultural selection and affected by ecological and genetic evolutionary dynamics of human, crop, livestock, and pathogen populations. Thus, the simple ratchet analogy hides complex mechanisms that can result in cumulative cultural evolution of knowledge and beliefs (Tennie, Call, and Tomasello 2009)

From the social learning theory to a social learning algorithm for global optimization

Traditionally, the Evolutionary Computation (EC) paradigm is inspired by Darwinian evolution or the swarm intelligence of animals. Bandura's Social Learning Theory pointed out that the social learning behavior of humans indicates a high level of intelligence in nature. We found that such intelligence of human society can be implemented by numerical computing and be utilized in computational algorithms for solving optimization problems. In this paper, we design a novel and generic optimization approach that mimics the social learning process of humans. Emulating the observational learning and reinforcement behaviors, a virtual society deployed in the algorithm seeks the strongest behavioral patterns with the best outcome. This corresponds to searching for the best solution in solving optimization problems. Experimental studies in this paper showed the appealing search behavior of this human intelligence-inspired approach, which can reach the global optimum even in ill conditions. The effectiveness and high efficiency of the proposed algorithm has further been verified by comparing to some representative EC algorithms and variants on a set of benchmarks

Adaptive intelligence applied to numerical optimisation

The article presents modification strategies theoretical comparison and experimental results achieved by adaptive heuristics applied to numerical optimisation of several non-constraint test functions. The aims of the study are to identify and compare how adaptive search heuristics behave within heterogeneous search space without retuning of the search parameters. The achieved results are summarised and analysed, which could be used for comparison to other methods and further investigation