Understanding human culture : theoretical and experimental studies of cumulative culture

There is something extraordinary about human culture. The striking complexity of our technologies, institutions, beliefs, and norms has allowed us to colonise the entire planet. One aspect in which human culture is unique relates to its cumulative nature – we accumulate and build on knowledge from the previous generations, leading to incremental improvement in skill, which allows us to produce technologies no one individual could have invented on their own. Understanding the drivers and dynamics of this type of cumulative culture is essential for understanding how human culture has interacted with human evolution. This thesis is concerned with precisely that, and uses a mixture of theoretical and experimental approaches linking individual-level decisions to population-level processes in cumulative culture contexts. Chapter 1 provides some essential background information. In Chapter 2 I used an agent-based simulation model to show that refinement, or incremental improvement in cultural traits, can lead to a drastic decrease of cultural diversity at the population level. This pattern was confirmed using experimental data from a collaborative programming competition in Chapter 3, where I showed that in a cumulative setting, the differential riskiness of copying and innovation drives participants to converge on very similar solutions, leading to a loss of cultural diversity. In Chapter 4 I explored individual differences in social learning strategies, finding considerable variation in how individuals rely on copying, with more successful individuals being more exploratory. I found that successful individuals had more influence on subsequent entries, which is consistent with a prestige bias. Finally, Chapter 5 addressed the link between group structure, diversity, and cumulative improvement. I found that larger groups accumulate more improvement than smaller groups, but smaller groups can also inhibit the convergence patterns we witnessed in larger groups, suggesting an optimal level of connectivity responsible for cumulative improvement

The Importance of Noise Colour in Simulations of Evolutionary Systems.

Simulations of evolutionary dynamics often employ white noise as a model of stochastic environmental variation. Whilst white noise has the advantages of being simply generated and analytically tractable, empirical analyses demonstrate that most real environmental time series have power spectral densities consistent with pink or red noise, in which lower frequencies contribute proportionally greater amplitudes than higher frequencies. Simulated white noise environments may therefore fail to capture key components of real environmental time series, leading to erroneous results. To explore the effects of different noise colours on evolving populations, a simple evolutionary model of the interaction between life-history and the specialism-generalism axis was developed. Simulations were conducted using a range of noise colours as the environments to which agents adapted. Results demonstrate complex interactions between noise colour, reproductive rate, and the degree of evolved generalism; importantly, contradictory conclusions arise from simulations using white as opposed to red noise, suggesting that noise colour plays a fundamental role in generating adaptive responses. These results are discussed in the context of previous research on evolutionary responses to fluctuating environments, and it is suggested that Artificial Life as a field should embrace a wider spectrum of coloured noise models to ensure that results are truly representative of environmental and evolutionary dynamics

The Importance of Noise Colour in Simulations of Evolutionary Systems

Simulations of evolutionary dynamics often employ white noise as a model of stochastic environmental variation. Whilst white noise has the advantages of being simply generated and analytically tractable, empirical analyses demonstrate that most real environmental time series have power spectral densities consistent with pink or red noise, in which lower frequencies contribute proportionally greater amplitudes than higher frequencies. Simulated white noise environments may therefore fail to capture key components of real environmental time series, leading to erroneous results. To explore the effects of different noise colours on evolving populations, a simple evolutionary model of the interaction between life-history and the specialism-generalism axis was developed. Simulations were conducted using a range of noise colours as the environments to which agents adapted. Results demonstrate complex interactions between noise colour, reproductive rate, and the degree of evolved generalism; importantly, contradictory conclusions arise from simulations using white as opposed to red noise, suggesting that noise colour plays a fundamental role in generating adaptive responses. These results are discussed in the context of previous research on evolutionary responses to fluctuating environments, and it is suggested that Artificial Life as a field should embrace a wider spectrum of coloured noise models to ensure that results are truly representative of environmental and evolutionary dynamics

The influence of model-based biases and observer prior experience on social learning mechanisms and strategies

This thesis investigated social learning mechanisms and strategies relating to the characteristics of a model (the individual transmitting the information) and the prior experience of an observer (the individual acquiring the social information) in children and chimpanzees. Experimental designs that mirrored naturalistic settings enabled an investigation of how social learning mechanisms and strategies were affected by: (1) the characteristics of a model, (2) the prior experience of an observer, (3) continued model demonstrations and (4) repeated observer interactions with the task. If models provided viable novel solutions then their characteristics seemed ineffectual upon children’s copying of these solutions. Yet the characteristics of the model did influence children’s copying of irrelevant actions; children who observed an adult reproduced more causally irrelevant actions than those who observed a child. Furthermore, when a known peer with higher, rather than lower, past-proficiency matched a child’s original solution the child was more likely to continue using this solution. Chimpanzees were biased towards touching the tool seeded by a known conspecific with higher, rather than lower, past proficiency but this bias did not affect which tool a chimpanzee successfully used. Both species showed an ability to learn multiple demonstrated methods of success within their corresponding tasks and to explore beyond demonstrated methods. It is argued that both species show more task-behavioural flexibility than previously thought and the implications for this in terms of cultural evolution are discussed

Inductive evolution: cognition, culture, and regularity in language

Cultural artifacts, such as language, survive and replicate by passing from mind to mind. Cultural evolution always proceeds by an inductive process, where behaviors are never directly copied, but reverse engineered by the cognitive mechanisms involved in learning and production. I will refer to this type of evolutionary change as inductive evolution and explain how this represents a broader class of evolutionary processes that can include both neutral and selective evolution. This thesis takes a mechanistic approach to understanding the forces of evolution underlying change in culture over time, where the mechanisms of change are sought within human cognition. I define culture as anything that replicates by passing through a cognitive system and take language as a premier example of culture, because of the wealth of knowledge about linguistic behaviors (external language) and its cognitive processing mechanisms (internal language). Mainstream cultural evolution theories related to social learning and social transmission of information define culture ideationally, as the subset of socially-acquired information in cognition that affects behaviors. Their goal is to explain behaviors with culture and avoid circularity by defining behaviors as markedly not part of culture. I take a reductionistic approach and argue that all there is to culture is brain states and behaviors, and further, that a complete explanation of the forces of cultural change can not be explained by a subset of cognition related to social learning, but necessarily involves domain-general mechanisms, because cognition is an integrated system. Such an approach should decompose culture into its constituent parts and explore 1) how brains states effect behavior, 2) how behavior effects brain states, and 3) how brain states and behaviors change over time when they are linked up in a process of cultural transmission, where one person's behavior is the input to another. I conduct several psychological experiments on frequency learning with adult learners and describe the behavioral biases that alter the frequencies of linguistic variants over time. I also fit probabilistic models of cognition to participant data to understand the inductive biases at play during linguistic frequency learning. Using these inductive and behavioral biases, I infer a Markov model over my empirical data to extrapolate participants' behavior forward in cultural evolutionary time and determine equivalences (and divergences) between inductive evolution and standard models from population genetics. As a key divergence point, I introduce the concept of non-binomial cultural drift, argue that this is a rampant form of neutral evolution in culture, and empirically demonstrate that probability matching is one such inductive mechanism that results in non-binomial cultural drift. I argue further that all inductive problems involving representativeness are potential drivers of neutral evolution unique to cultural systems. I also explore deviations from probability matching and describe non-neutral evolution due to inductive regularization biases in a linguistic and non-linguistic domain. Here, I offer a new take on an old debate about the domain-specificity vs -generality of the cognitive mechanisms involved in language processing, and show that the evolution of regularity in language cannot be predicted in isolation from the general cognitive mechanisms involved in frequency learning. Using my empirical data on regularization vs probability matching, I demonstrate how the use of appropriate non-binomial null hypotheses offers us greater precision in determining the strength of selective forces in cultural evolution

Human Culture and Cognition

Human behaviour is largely influenced by culture. Culture evolves cumulatively over time. The origins of culture in our lineage necessitated the evolution of psychological biases so humans could tractably navigate the emerging information environment. I examine the nature of these biases and conclude that they are unlikely to be genetically coded to any significant degree. This is because of the flexibility such biases needed to possess in the face of fluid cultural environments and because of the developmental mechanisms of the brain. I further outline three possible views on what the nature of the information these biases act upon might be. First there is the view that cultural information is constructed and held in individual minds but does not flow in any meaningful replicative fashion between minds. Second is the view that culture is information distributed in a population and cultural evolution is the temporal change of this populationlevel information as a result of low fidelity individual copying events. Finally, I argue that meme theory, which asserts that culture is usefully seen as bits of information that replicate in transmission, is a fruitful model of cultural evolution. Keywords Cognition, cultural evolution, culture, evolutionary psychology, memes, neuroconstructivism, psychological biases

Four Essays in Microeconomics: Social Norms and Social Preferences

The four essays deal with social motivators for human behavior in economics, namely social norms and social preferences. The first three essays present and analyze a particular social preference model, socially attentive preferences. The fourth essay gives a review of the theoretical economic literature on social norms

Statistical physics of vaccination

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination–one of the most important preventive measures of modern times–is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research