Thesis (Ph. D.)--University of California, Santa Cruz, 1985. / Typescript. Includes bibliographical references (leaves 480-539).

Many human behaviours are thought to depend upon cognitive capacities enriched with innate domain-specific knowledge. Underpinning this view is the hypothesis that evolution can shape cognition to include strong innate inductive biases. In this thesis, I re-examine that hypothesis with respect to a broad class of behaviours: those that we learn from other individuals. Taking human language as a test case, I present an analysis of the co-evolutionary process that underpins the formation of innate constraints on cognition for behaviours that are culturally transmitted through inductive inference. I derive a series of mathematical models of this process, built around Bayesian models of cognition and cultural transmission, and ask how they can inform our expectations about cognition in a cultural species. I argue that the traditional marriage of nativism and evolutionary reasoning is undermined by this process, as is the suggestion that cognitive adaptation to cultural behaviours is outright implausible. I explore the co-evolutionary dynamics induced by cultural transmission, and conclude that they can radically manipulate the evolution of cognition: culture can intervene in the formation of hard-wired knowledge, but nevertheless facilitate rapid cognitive adaptation. The analyses I report make strong, testable predictions about the nature of inductive biases for cultural behaviours, and offer solutions to a number of long-standing conundrums in the evolution of language.

This thesis investigates the emergence in human evolution of communication through symbols, or conventional, arbitrary signs. Previous work has argued that symbolic speech was preceded by communication through nonarbitrary signs, but how vocal symbolic communication arose out of this has not been extensively studied. Thus far, past research has emphasized the advantages of vocal symbols and pointed to communicative and evolutionary pressures that would have spurred their development. Based on semiotic principles, I examine emergence in terms of two factors underlying symbols: interpretation and conventionalization. I address the question with a consideration of embodied human experience – that is, accounting for the particular features that characterize human communication. This involves simultaneous expression through vocal and gestural modalities, each of which has distinct semiotic properties and serves distinct functions in language today. I examine research on emerging sign systems together with research on properties of human communication to address the question of symbol emergence in terms of the specific context of human evolution. I argue that, instead of in response to pressures for improved communication, symbolic vocalizations could have emerged through blind cultural processes out of the conditions of multimodal nonarbitrary communication in place prior to modern language. Vocalizations would have been interpreted as arbitrary by virtue of their semiotic profile relative to that of gesture, and arbitrary vocalizations could have become conventionalized via the communicative support of nonarbitrary gestures. This scenario avoids appealing to improbable evolutionary and psychological processes and provides a comprehensive and evolutionarily sound explanation for symbol emergence. I present experiments that test hypotheses stemming from this claim. I show that novel arbitrary vocal forms are interpreted and adopted as symbols even when these are uninformative and gesture is the primary mode of communication. I also present computational models that simulate multi-channel, heterosemiotic communication like that of arbitrary speech and nonarbitrary gesture. These demonstrate that information like that provided by gesture can enable the conventionalization of symbols across a population. The results from experiments and simulations together support the claim that symbolic communication could arise naturally from multimodal nonarbitrary communication, offering an explanation for symbol emergence more consistent with evolutionary principles than existing proposals.

Human languages are not just tools for transmitting cultural ideas, they are themselves culturally transmitted. This single observation has major implications for our understanding of how and why languages around the world are structured the way they are, and also for how scientists should be studying them. Accounting for the origins of what turns out to be such a uniquely human ability is, and should be, a priority for anyone interested in what makes us different from every other lifeform on Earth. The way the scientific community thinks about language has seen considerable changes over the years. In particular, we have witnessed movements away from a purely descriptive science of language, towards a more explanatory framework that is willing to embrace the difficult questions of not just how individual languages are currently structured and used, but also how and why they got to be that way in the first place. Seeing languages as historical entities is, of course, nothing new in linguistics. Seeing languages as complex adaptive systems, undergoing processes of evolution at multiple levels of interaction however, is. Broadly speaking, this thesis explores some of the implications that this perspective on language has, and argues that in addition to furthering our understanding of the processes of biological evolution and the mechanisms of individual learning required specifically for language, we also need to be mindful of the less well-understood cultural processes that mediate between the two. Human communication systems are not just direct expressions of our genes. Neither are they independently acquired by learners anew at every generation. Instead, languages are transmitted culturally from one generation to another, creating an opportunity for a different kind of evolutionary channel to exist. It is a central aim of this thesis to explore some of the adaptive dynamics that such a cultural channel has, and investigate the extent to which certain structural and statistical properties of language can be directly explained as adaptations to the transmission process and the learning biases of speakers. In order to address this aim, this thesis takes an experimental approach. Building on a rich set of empirical results from various computational simulations and mathematical models, it presents a novel methodological framework for exploring one type of cultural transmission mechanism, iterated learning, in the laboratory using human participants. In these experiments, we observe the evolution of artificial languages as they are acquired and then transmitted to new learners. Although there is no communication involved in these studies, and participants are unaware that their learning efforts are being propagated to future learners, we find that many functional features of language emerge naturally from the different constraints imposed upon them during transmission. These constraints can take a variety of forms, both internal and external to the learner. Taken collectively, the data presented here suggest several points: (i) that iterated language learning experiments can provide us with new insights about the emergence and evolution of language; (ii) that language-like structure can emerge as a result of cultural transmission alone; and (iii) that whilst structure in these systems has the appearance of design, and is in some sense ‘created’ by intentional beings, its emergence is in fact wholly the result of non-intentional processes. Put simply, cultural evolution plays a vital role in language. This work extends our framework for understanding it, and offers a new method for investigating it.

Humans readily infer the meanings of novel symbols in communicative contexts of varying complexity, and several researchers in the field of language evolution have explicitly acknowledged that inference plays a key role in accounting for the evolution of symbolic communication. However, in this field at least, there has been very little investigation into the nature of inference in this regard. That is, evolutionary linguists have yet to address the following questions if we are to have a fuller picture of how humans came to communicate symbolically: 1. What kinds of inference are there? Specifically, i Diachronically, what forms of inference are comparatively simpler in evolutionary terms, and thus shared with a wider range of species? What forms of inference are more complex, and limited to humans or to us and our closest relatives? ii Synchronically, if humans are capable of several kinds of complex inference, how do we know which particular kind of inference is being applied in solving a given problem? 2. How do symbol-learning problems vary? Specifically, i What makes a particular symbol-learning problem more or less complex in terms of the kind of inference needed to solve it? ii How would the communicative context of our pre-linguistic ancestors have been different from that of a human child learning words from its linguistic parent? This dissertation takes a step towards answering these questions by investigating a little-known form of inference called `abduction' (or insightful hypothesis generation), which has thus far been wholly overshadowed in language evolution by a much better understood form called `induction' (or probabilistic hypothesis evaluation). I will argue that abduction and induction are both comparatively complex in the diachronic terms expressed above in 1.i, and while induction is useful in accounting for how modern children learn words from linguistic adults, abduction is more important in situations like those that would have faced our pre-lingistic ancestors as they first began to use symbols. That is, I will argue on both theoretical and empirical grounds that abductive inference was an evolutionary milestone as our ancestors crossed what Deacon (1997) calls the symbolic threshold.

Software developers adapt to the fast-moving nature of software systems with agile development techniques. However, database developers lack the tools and concepts to keep pace. Data, already existing in a running product, needs to be evolved accordingly, usually by manually written SQL scripts. A promising approach in database research is to use a declarative database evolution language, which couples both schema and data evolution into intuitive operations. Existing database evolution languages focus on usability but did not aim for completeness. However, this is an inevitable prerequisite for reasonable database evolution to avoid complex and error-prone workarounds. We argue that relational completeness is the feasible expressiveness for a database evolution language. Building upon an existing language, we introduce CoDEL. We define its semantic using relational algebra, propose a syntax, and show its relational completeness.

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.

Software developers adapt to the fast-moving nature of software systems with agile development techniques. However, database developers lack the tools and concepts to keep pace. Data, already existing in a running product, needs to be evolved accordingly, usually by manually written SQL scripts. A promising approach in database research is to use a declarative database evolution language, which couples both schema and data evolution into intuitive operations. Existing database evolution languages focus on usability but did not aim for completeness. However, this is an inevitable prerequisite for reasonable database evolution to avoid complex and error-prone workarounds. We argue that relational completeness is the feasible expressiveness for a database evolution language. Building upon an existing language, we introduce CoDEL. We define its semantic using relational algebra, propose a syntax, and show its relational completeness.

Software developers adapt to the fast-moving nature of software systems with agile development techniques. However, database developers lack the tools and concepts to keep the pace. Whenever the current database schema is evolved, the already existing data needs to be evolved as well. This is usually realized with manually written SQL scripts, which is error-prone and explains significant costs in software projects. A promising solution are declarative database evolution languages, which couple both schema and data evolution into intuitive operations. Existing database evolution languages focus on usability but do not strive for completeness. However, this is an inevitable prerequisite to avoid complex and error-prone workarounds. We present CODEL which is based on an existing language but is relationally complete. We precisely define its semantic using relational algebra, propose a syntax, and formally validate its relational completeness. Having a complete and comprehensive database evolution language facilitates valuable support throughout the whole evolution of a database. As an instance, we present VACO, a tool supporting developers with variant co-evolution. Given a variant schema derived from a core schema, VACO uses the richer semantics of CODEL to semi-automatically co-evolve this variant with the core.

Despite having being discovered nearly 80 years ago, bonobos (Pan paniscus) are still one of the least well understood of the great apes, largely remaining in the shadow of their better known cousins, the chimpanzees (Pan troglodytes). This is especially evident in the domain of communication, with bonobo vocal behaviour still a neglected field of study, especially compared to that of chimpanzees. In this thesis, I address this issue by exploring the natural vocal communication of bonobos and its underlying cognition, focusing on the role that vocalisations play during two key contexts, food discovery and sex. In the context of food-discovery, I combine observational and experimental techniques to examine whether bonobos produce and understand vocalisations that convey meaningful information about the quality of food encountered by the caller. Results indicate that bonobos produce an array of vocalisations when finding food, and combine different food-associated calls together into sequences in a way that relates to perceived food quality. In a subsequent playback study, it was demonstrated that receivers are able to extract meaning about perceived food quality by attending to these calls and integrating information across call sequences. In the context of sexual interactions, I examine the acoustic structure of female copulation calls, as well as patterns in call usage, to explore how these signals are used by individuals. My results show that females emit copulation calls in similar ways with both male and female partners, suggesting that these signals have become partly divorced from a function in reproduction, to assume a greater social role. Overall, my results highlight the relevance of studying primate vocalisations to investigate the underlying cognition and suggest that vocalisations are important behavioural tools for bonobos to navigate their social and physical worlds.