The evolution of functionally referential meaning in a structured world

Human Consciousness: Where Is It From and What Is It for

Consciousness is not a process in the brain but a kind of behavior that, of course, is controlled by the brain like any other behavior. Human consciousness emerges on the interface between three components of animal behavior: communication, play, and the use of tools. These three components interact on the basis of anticipatory behavioral control, which is common for all complex forms of animal life. All three do not exclusively distinguish our close relatives, i.e., primates, but are broadly presented among various species of mammals, birds, and even cephalopods; however, their particular combination in humans is unique. The interaction between communication and play yields symbolic games, most importantly language; the interaction between symbols and tools results in human praxis. Taken together, this gives rise to a mechanism that allows a creature, instead of performing controlling actions overtly, to play forward the corresponding behavioral options in a “second reality” of objectively (by means of tools) grounded symbolic systems. The theory possesses the following properties: (1) It is anti-reductionist and anti-eliminativist, and yet, human consciousness is considered as a purely natural (biological) phenomenon. (2) It avoids epiphenomenalism and indicates in which conditions human consciousness has evolutionary advantages, and in which it may even be disadvantageous. (3) It allows to easily explain the most typical features of consciousness, such as objectivity, seriality and limited resources, the relationship between consciousness and explicit memory, the feeling of conscious agency, etc.

Why is combinatorial communication rare in the natural world, and why is language an exception to this trend?

In a combinatorial communication system, some signals consist of the combinations of other signals. Such systems are more efficient than equivalent, non-combinatorial systems, yet despite this they are rare in nature. Why? Previous explanations have focused on the adaptive limits of combinatorial communication, or on its purported cognitive difficulties, but neither of these explains the full distribution of combinatorial communication in the natural world. Here, we present a nonlinear dynamical model of the emergence of combinatorial communication that, unlike previous models, considers how initially non-communicative behaviour evolves to take on a communicative function. We derive three basic principles about the emergence of combinatorial communication. We hence show that the interdependence of signals and responses places significant constraints on the historical pathways by which combinatorial signals might emerge, to the extent that anything other than the most simple form of combinatorial communication is extremely unlikely. We also argue that these constraints can be bypassed if individuals have the socio-cognitive capacity to engage in ostensive communication. Humans, but probably no other species, have this ability. This may explain why language, which is massively combinatorial, is such an extreme exception to nature's general trend for non-combinatorial communication.

Nodes having a major influence to break cooperation define a novel centrality measure: game centrality

Cooperation played a significant role in the self-organization and evolution of living organisms. Both network topology and the initial position of cooperators heavily affect the cooperation of social dilemma games. We developed a novel simulation program package, called ‘NetworGame’, which is able to simulate any type of social dilemma games on any model, or real world networks with any assignment of initial cooperation or defection strategies to network nodes. The ability of initially defecting single nodes to break overall cooperation was called as ‘game centrality’. The efficiency of this measure was verified on well-known social networks, and was extended to ‘protein games’, i.e. the simulation of cooperation between proteins, or their amino acids. Hubs and in particular, party hubs of yeast protein-protein interaction networks had a large influence to convert the cooperation of other nodes to defection. Simulations on methionyl-tRNA synthetase protein structure network indicated an increased influence of nodes belonging to intra-protein signaling pathways on breaking cooperation. The efficiency of single, initially defecting nodes to convert the cooperation of other nodes to defection in social dilemma games may be an important measure to predict the importance of nodes in the integration and regulation of complex systems. Game centrality may help to design more efficient interventions to cellular networks (in forms of drugs), to ecosystems and social networks.

How do communication systems emerge?

Communication involves a pair of behaviours--a signal and a response--that are functionally interdependent. Consequently, the emergence of communication involves a chicken-and-egg problem: if signals and responses are dependent on one another, then how does such a relationship emerge in the first place? The empirical literature suggests two solutions to this problem: ritualization and sensory manipulation; and instances of ritualization appear to be more common. However, it is not clear from a theoretical perspective why this should be the case, nor if there are any other routes to communication. Here, we develop an analytical model to examine how communication can emerge. We show that: (i) a state of non-interaction is evolutionarily stable, and so communication will not necessarily emerge even when it is in both parties' interest; (ii) the conditions for sensory manipulation are more stringent than for ritualization, and hence ritualization is likely to be more common; and (iii) communication can arise by a third route, when the intention to communicate can itself be communicated, but this may be limited to humans. More generally, our results demonstrate the utility of a functional approach to communication.

Neutral stability, drift, and the diversification of languages

The diversification of languages is one of the most interesting facts about language that seek explanation from an evolutionary point of view. Conceptually the question is related to explaining mechanisms of speciation. An argument that prominently figures in evolutionary accounts of language diversification is that it serves the formation of group markers which help to enhance in-group cooperation. In this paper we use the theory of evolutionary games to show that language diversification on the level of the meaning of lexical items can come about in a perfectly cooperative world solely as a result of the effects of frequency-dependent selection. Importantly, our argument does not rely on some stipulated function of language diversification in some co-evolutionary process, but comes about as an endogenous feature of the model. The model that we propose is an evolutionary language game in the style of Nowak et al. (1999) [The evolutionary language game. J. Theor. Biol. 200, 147-162], which has been used to explain the rise of a signaling system or protolanguage from a prelinguistic environment. Our analysis focuses on the existence of neutrally stable polymorphisms in this model, where, on the level of the population, a signal can be used for more than one concept or a concept can be inferred by more than one signal. Specifically, such states cannot be invaded by a mutation for bidirectionality, that is, a mutation that tries to resolve the existing ambiguity by linking each concept to exactly one signal in a bijective way. However, such states are not resistant against drift between the selectively neutral variants that are present in such a state. Neutral drift can be a pathway for a mutation for bidirectionality that was blocked before but that finally will take over the population. Different directions of neutral drift open the door for a mutation for bidirectionality to appear on different resident types. This mechanism-which can be seen as a form of shifting balance-can explain why a word can acquire a different meaning in two languages that go back to the same common ancestral language, thereby contributing to the splitting of these two languages. Examples from currently spoken languages, for instance, English clean and its German cognate klein with the meaning of "small," are provided.

Primate vocal communication: a useful tool for understanding human speech and language evolution?

Language is a uniquely human trait, and questions of how and why it evolved have been intriguing scientists for years. Nonhuman primates (primates) are our closest living relatives, and their behavior can be used to estimate the capacities of our extinct ancestors. As humans and many primate species rely on vocalizations as their primary mode of communication, the vocal behavior of primates has been an obvious target for studies investigating the evolutionary roots of human speech and language. By studying the similarities and differences between human and primate vocalizations, comparative research has the potential to clarify the evolutionary processes that shaped human speech and language. This review examines some of the seminal and recent studies that contribute to our knowledge regarding the link between primate calls and human language and speech. We focus on three main aspects of primate vocal behavior: functional reference, call combinations, and vocal learning. Studies in these areas indicate that despite important differences, primate vocal communication exhibits some key features characterizing human language. They also indicate, however, that some critical aspects of speech, such as vocal plasticity, are not shared with our primate cousins. We conclude that comparative research on primate vocal behavior is a very promising tool for deepening our understanding of the evolution of human speech and language, but much is still to be done as many aspects of monkey and ape vocalizations remain largely unexplored.

13 Networks II: Teamwork

This chapter shows that for many tasks the use of signals is crucial in establishing the coordination needed for effective teamwork. Teamwork may in some circumstances be achieved by a simple exchange of signals between equals. In other situations a good team may need a leader.

3 Information

This chapter shows that information is carried by signals. It flows through signaling networks that not only transmit it, but also filter, combine, and process it in various ways. We can investigate the flow of information using a framework of generalized signaling games. The dynamics of evolution and learning in these games illuminate the creation and flow of information.

7 Learning

This chapter argues that investigation of reinforcement learning is a complement to the study of belief learning, rather than being a ‘dangerous antagonist’. It begins at the low end of the scale, to see how far simple reinforcement learning can get us, and then move up. Exactly how does degree of reinforcement affect the strengthening of the bond between stimulus and response? Different answers are possible, and these yield alternative theories of the law of effect.

11 Networks I: Logic and Information Processing

This chapter discusses the combination of simple signals to form complex signals. When multiple senders convey different information to a receiver (or to multiple receivers) the receiver is confronted with a problem of information processing. How does one take all these inputs and fix on what to output — what to do? Logical inference is only part of this bigger problem of information processing. It is a problem routinely solved every second by our nervous system as floods of sensory information are filtered, integrated, and used to control conscious and unconscious actions.

12 Complex Signals and Compositionality

This chapter focuses on an earlier point in the evolution of signaling. It considers how one might come to have — in the most primitive way — a complex signal composed of simple signals. This is done with the smallest departure possible from signaling models that have been previously examined in this book.

5 Evolution in Lewis Signaling Games

Signaling systems had been shown to be the only evolutionarily stable strategies in n-state, n-signal, and n-act signaling games. They were the only attractors in the replicator dynamics. In simple cases, it was clear why almost every possible starting point was carried to a signaling system. This chapter considers how far these positive results generalize.

2 Signals in Nature

This chapter surveys some of the signaling systems in nature. Darwin sees some kind of natural salience operating at the origin of language. At that point signals are not conventional, but rather the signal is somehow naturally suited to convey its content. Signaling is then gradually modified by evolution.

10 Inventing New Signals

This chapter presents a simple, tractable model for the invention of new signals. It can be easily studied by simulation, and connections with well-studied processes from population genetics suggest that analytic results are not completely out of reach.

6 Deception

This chapter shows that in all kinds of signaling systems in nature there is information transmission which is sufficient to maintain signaling, but there is also misinformation and even deception. Misinformation is straightforward. If receipt of a signal moves probabilities of states it contains information about the state. If it moves the probability of a state in the wrong direction — either by diminishing the probability of the state in which it is sent, or raising the probability of a state other than the one in which it is sent — then it is misleading information, or misinformation. If misinformation is sent systematically and benefits the sender at the expense of the receiver, then it is deception.

8 Learning in Lewis Signaling Games

This chapter argues that we can and do learn to signal. We are not the only species able to do this, although others may not do it so well. The real question is what is required to be able to learn to signal. Or, better, what kind of learning is capable of spontaneously generating signaling? If the learning somehow has the signaling system preprogramed in, then learning to signal is not very interesting. If the learning mechanism is general purpose and low level, learning to signal is quite interesting.

14 Learning to Network

This chapter introduces a low-rationality probe and adjust dynamics to approximate higher rationality learning in the basic Bala–Goyal models. Both best response dynamics and probe and adjust learned networks that reinforcement learning did not. In general, probe and adjust learns a network structure if best response with inertia does.

9 Generalizing Signaling Games: Synonyms, Bottlenecks, Category Formation

This chapter presents a model of signaling with invention of new signals. It maintains the assumption that in all contingencies sender and receiver get the same payoff. But even where sender and receiver continue to have pure common interest, relaxing the strict assumptions on payoffs imposed so far may lead to new phenomena.

1 Signals

Whatever one thinks of human signals, it must be acknowledged that information is transmitted by signaling systems at all levels of biological organization. Monkeys, birds, bees, and even bacteria have signaling systems. Multicellular organisms are only possible because internal signals coordinate the actions of their constituents. This chapter addresses two main questions: How can interacting individuals spontaneously learn to signal? How can species spontaneously evolve signalling systems? It discusses how we can bring contemporary theoretical tools to bear on these questions.

4 Evolution

Darwin was right about the broad outlines of the theory of evolution. Traits are inherited by some unknown mechanism. There is some process that produces natural variation in these traits. The traits may affect the ability of the organism to reproduce, and thus the average number of individuals bearing the traits in the next generation. Therefore, those traits that enhance reproductive success increase in frequency in the population, and those that lead to reproductive success below the average decrease in frequency. This chapter discusses the three essential factors in Darwin's account: (i) natural variation, (ii) differential reproduction, and (iii) inheritance.

Introduction

This introductory chapter begins with a discussion of signalling. It then argues that the relation of signalling theory to philosophy is epistemology, because it deals with selection, transmission, and processing of information. It is philosophy of (proto)-language. It addresses cooperation and collective action — issues that usually reside in social and political philosophy.