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Web Topic 1.2
Information and Communication


Throughout this book, the term information is used repeatedly. In recent years, a number of authors have questioned the utility and even the propriety of invoking information concepts in studies of animal communication. Here we summarize some of these concerns, explain why we feel they are unnecessary or unsupported by recent studies, and indicate the specific steps during communication where we feel information concepts play an essential role.

The concerns

Below we list the problems that are most often raised about the use of the term “information” in studies of animal communication, ranging from doubts that the term has been sufficiently defined and consistently applied to proposals for models of animal communication in which the provision of information is irrelevant.

The duality problem

Following a lead by Cherry (Cherry 1966), W. John Smith proposed a distinction between the message and the meaning of animal signals (Smith 1968, 1977). He defined the message as “each kind of information that a display makes available about a referent.” The provision of information was dependent on the assignment of specific signals to specific referents using a code that was shared by the sender and the receiver. He then proposed two aspects of meaning. One arose from the consequences to receivers of adjusting subsequent actions on the basis of received signals, and another focused on the consequences to senders of these changes in receiver actions. In this view, without a message, there could be no meaningful change in consequences for either party, and without meaning, there was no point for either party to communicate.

While this is intuitively appealing, given the parallels with human speech, the application of these two terms (message and meaning) has proved challenging. The codes mapping animal signals on referents are invariably imperfect (e.g., different referents may elicit the same signal, and different signals may be given for the same referent), which makes identification of the message by both receivers and researchers a quantitative rather than a qualitative task. Assuming receivers accurately identify an incoming signal, subsequent authors have claimed the meaning to be the inferred referent given the code, the appropriate receiver action when that referent is present, or the likely fitness effects of taking the appropriate action. There is thus considerable confusion in the literature as to whether one of these stages or some combination is the appropriate sense of receiver meaning. There is the further complication that different receivers often respond differently and experience different consequences for the same signal, and different senders of the same signal might experience different consequences of whatever actions receivers perform. The seemingly indeterminate links between signals and referents, and between messages and meanings have undermined support for the utility of these terms, at least as originally defined.

Rather than discard the entire duality, some authors have championed one of the original components while minimizing emphasis on the other. The development of information theory (Shannon and Weaver 1949) provided new tools for measuring the amount of information provided by a signal relative to that needed to completely resolve some prior uncertainty. These computations explicitly incorporated the coding system of the sender. The emphasis here was thus the message; consequences were not considered. For over a decade, these tools were enthusiastically applied to a wide variety of animal signal systems (Quastler 1958; Attneave 1959; Johnson 1970; Dingle 1972; Wilson 1975; Hailman 1977; Bell and Gorton 1978; Losey 1978). However, the same concerns that had arisen over the initial duality were raised again: if different receivers had reasons to invoke different prior uncertainties, each would receive different amounts of information for the same signal. And even if most receivers obtained the same amount of information from a signal, the fitness consequences could differ markedly between them. The indeterminate relationships between a given coding system and the amount of information (due to variable priors), and the fact that fitness consequences, not information, are the ultimate focus of evolution, led other authors to adopt the opposite extreme and argue that information provision should not be a part of any definition of communication (Rendall et al. 2009; Owren et al. 2010). The philosopher Scott-Phillips (2008, 2010) even argued that the provision of information was at best “incidental” to the basic communication process.

Other philosophers and economists have taken a totally opposite view. D. K. Lewis (1969) used classical game theory to examine signaling games in which coding was totally conventional and both signaling and receiving were costless (often called “cheap talk” in the philosophical and economic literature). He found that perfect codes shared by senders and receivers were most likely to lead to a stable outcome. Concerns about how a population might arrive at such an equilibrium were resolved by invoking evolutionary game theory and adaptive dynamics models (Skyrms 1996, 2002; Huttegger 2007a). These analyses again found that perfect or, at worst, moderately imperfect signaling was the only likely ESS. The Lewis model has been extended to a wide variety of contexts, including coding with more than binary alternatives, finite populations, senders in one species signaling to receivers in another, presence or absence of mutation, and biologically relevant signaling (e.g., the Sir Phillip Sydney game) (Huttegger 2007a, b; Pawlowitsch 2007; Hofbauer and Huttegger 2008; Skyrms 2009; Huttegger et al. 2010; Huttegger and Zollman 2010; Skyrms 2010a, b). The outcome of these models is nearly always the same: reliable coding is not only essential to communication, it is the only stable outcome.

An alternative approach replaced the original notion of message with signal reliability (Maynard Smith and Harper 2003; Searcy and Nowicki 2005) and the original notion of meaning with the value of information (Gould 1974; Stephens 1989; Bradbury and Vehrencamp 2000; Koops 2004; Dall et al. 2005; McLinn and Stephens 2006, 2010). The value of information integrates signal reliability with receiver decoding, decision making, and fitness consequences into one number that is subject to selection. We outline an example of its use in more detail at the end of this document. This and related arguments have led to a chorus of support for the continued inclusion of information concepts in studies of animal communication (Hasson 2000; Dall et al. 2005; Stegmann 2005, 2009; Castellano 2009; Carazo and Font 2010; Font and Carazo 2010; Seyfarth et al. 2010). In fact, information sharing is now recognized as one of the key adaptations that has led to major evolutionary changes throughout organismal history (Maynard Smith and Szathmàry 1995; Maynard Smith 1999; Lachmann et al. 2000; Maynard Smith 2000; Jablonka 2002).

The arms race problem

Early ethologists largely ignored the possibility of deceit in animal communication either because they assumed that neither party was capable of actions outside the norm (e.g., they were locked into fixed action patterns) or because they assumed communication was a cooperative venture. These views were challenged by Krebs and Dawkins who argued that senders should, and usually do, try to manipulate receivers to the sender’s advantage, and receivers should, and usually do, try to “read the minds” of senders to the receiver’s advantage (Dawkins and Krebs 1978; Krebs and Dawkins 1984). There is now little disagreement that senders and receivers can have conflicts of interest: the optimal interaction for one party may not be identical with that of the other. The question is then where the subsequent evolutionary trajectories will lead. One possibility is that the arms race between the two parties is unending over evolutionary time—each adaptation that gives one party an advantage will eventually be overcome by a counter-adaptation in the other party. This does not seem to be the case. As outlined in the text, most (but not all) communication systems in animals appear to be at some sort of equilibrium. There are three possible types of equilibria: one in which the sender is able to keep the system at its optimum at a net cost to the receiver; one in which the receiver is at its optimum at the expense of the sender; and one (or more) in which both parties have a net benefit, but neither does as well as it would at its own optimum.

Some authors have asserted that arms races are only likely to end when the sender acquires a strategy that the receiver cannot counter (Rendall et al. 2009; Owren et al. 2010). This might occur if the sender can mimic some stimulus that the receiver attends to for other reasons (sensory exploitation); any attempt by the receiver to escape the exploitation would undermine some other necessary adaptation. The equilibrium in these authors’ view is thus a sender dominant one. Since receivers cannot help but respond, the issue of information provision is irrelevant and these authors have argued that term be dropped from discussions of signal evolution.

The weight of evidence does not support these propositions. A number of the studies originally proposed in support of persistent sensory exploitation now appears better explained by recurrent loss and recovery of an early adaptation that benefitted both parties (see Ron 2008 for túngara frog; see Chapter 10 for other examples). In addition, there are numerous evolutionary models identifying realizable conditions under which receivers can eventually escape persistent exploitation. These models usually lead to an intermediate equilibrium in which both parties obtain an average net benefit that is contingent upon the provision of minimally reliable information to the receiver by the sender. The last few decades have seen a major effort to test these models in a wide variety of taxa and signaling contexts. As reviewed in the text, the conditions favoring intermediate equilibria are much more often found to be present than absent, and where fitnesses can be measured, both parties gain an average net benefit by communicating. While sender exploitation of receiver sensory biases remains one of several likely starting points for signal evolution, it appears that most systems subsequently move on to intermediate equilibria.

The coding problem

Traditional models of animal communication assume that senders and receivers have at least reasonably concordant, if not identical, coding schemes. Signals given randomly cannot provide information to receivers. Signals given selectively can provide information, but senders must show some consistency in which signal they emit for a given referent, and receivers must have some previously acquired expectations about likely sender assignments.

Several authors have questioned the use of the coding concept in animal communication. Some argue that the terms “encoding” and “code” are never explicitly defined, and are indiscriminately applied to very different signaling phenomena (Rendall et al. 2009; Owren et al. 2010). Other authors challenge the utility of the coding concept because the contexts in which a given signal is emitted may completely change the assigned referent implied: examples include human sarcasm (Scott-Phillips 2010) and context-dependent signaling in birds (Smith 1977).

In response, it is instructive to note that a reliance on coding schemes is not unique to communication, but instead is a condition for most sensory processing. The primary function of sensory organs is to detect changes in ambient conditions. Animals must be able to categorize conditions or there would be no benefit to sampling the environment. This is true even for the simplest case in which an animal only monitors the presence or absence of a single stimulus; it is even more relevant to the many organisms that routinely discriminate between multiple stimuli. The sampling and sorting of ambient stimuli, particularly those emitted by other organisms, is ubiquitous in animals (Danchin et al. 2004; Wagner and Danchin 2010). Stimuli vary in how well their emission is correlated with specific and unique conditions. Few stimuli of  interest to animals will be “natural” in the sense that they are guaranteed to be available if and only if a given condition is true (Scarantino 2010). Many will be “normative,” in that the correlation between a given stimulus and a given condition is sufficiently high that it is worth attending to them (Millikan 1989, 2004; Stegmann 2009). Stimuli that are moderately reliable indicators of the presence of particular conditions are called cues. Whether acquired through genetic inheritance, learning, or some combination of the two, nearly all animals rely on interpretive codes to make sense of the many cues that they might encounter. They can then generalize these codes as necessary to deal with novel stimuli (Ghirlanda and Enquist 2003). The reception and classification of a cue stimulus, when combined with the interpretive code, provides information that can then be used to influence decisions about whether and how to change current actions and physiological states.

Seen in this light, signal codes are just a special case of a more general sensory strategy used by all animals. In fact, many receivers appear to rely on a combination of cues and signals to make decisions. The relative weighting of signals and contextual cues can be quite variable: some signals elicit the same responses regardless of contexts, whereas others elicit responses that are very sensitive to ambient conditions (Smith 1977; Marler et al. 1992). Reliable signals that are more heavily weighted than ambient cues are often singled out as “referential.” However, there appears to be a continuum rather than a dichotomy in most taxa, and it is not surprising that relative weightings should vary with the relative reliabilities of the stimuli and the fitness consequences of alternative actions.

Secondly, the claim that coding schemes are never explicitly defined in animal signal literature (Rendall et al. 2009; Owren et al. 2010) is inaccurate. Explicit and quantitative definitions of animal signal coding schemes are provided in the prior and current editions of this text and in countless other publications (including a whole book, Hailman 2008) ignored by these authors. The critics may not agree with these definitions, but claiming they don’t exist is spurious. While the same authors who claim coding schemes are never defined acknowledge that receiver actions may be sensitive to both cues and signals, they see the observed variability in relative weightings as undermining any formal definition of signal coding schemes. We would argue that once you acknowledge the overlap of cue and signal coding schemes, variable weighting is an adaptation that natural selection is sure to favor.

Finally, it seems appropriate to ask just how reliable animal signal codes really are. If the codes do not enhance decision making above random choice, any invocation of coding is moot. As discussed in the text, signal reliability depends on the consistency with which senders assign signals to referents, the level of signal distortion during propagation, and the degree to which receivers share the sender’s coding scheme and can correctly assign incoming stimuli to expected templates. Because the minimal reliability that justifies communication depends significantly on the fitness consequences of receiver actions (Bradbury and Vehrencamp 2000; Koops 2004), one might expect observed values to be highly variable among taxa, modalities, and contexts. In fact, measures of signal reliability obtained in recent decades find most animals using intermediate levels of reliability: signals provide much better information than relying on prior probabilities alone, but signal coding is almost never perfect (see Chapter 8; Maynard Smith and Harper 2003; Dall et al. 2005; Searcy and Nowicki 2005; Seyfarth et al. 2010). In exceptional cases where signal reliability is found to be surprisingly low, the contexts are such that the value of information for those signals remains positive for all parties (Gyger and Marler 1988; Møller 1988).

The black box problem

The use of metaphorical models for animal and human behaviors has been a long tradition in psychology. It was also an early tool of ethologists who tried to explain phenomena such as vacuum and displacement behaviors (see Chapter 10) by postulating hypothetical “drives” whose dynamics and interactions could be adjusted to replicate the observed patterns. Perhaps the most famous of these was the hydraulic model proposed by Lorenz and Leyhausen (1973). However, as neurobiology became more sophisticated, one after another of these hypothetical constructs was found wanting (see Web Topic 10.4; Berridge 2004). In parallel, the enthusiasm for applications of information theory to animal behavior in the 1970’ waned as it became clear that receiver actions after receipt of a signal were not a good guide to underlying decision processes: did a receiver fail to respond to a stimulus because it could not discriminate it from some alternative (an amount of information issue), or because it did not pay to change its current behavior (a fitness consequences issue)? As a result, most ethologists, and those in the descendent field of behavioral ecology, began to eschew speculations about brain mechanisms and instead focus on the economics of animal behavior: what ecological factors caused one species to be polygynous but a related species to be monogamous, what payoffs justified being territorial in a given habitat, and what were the costs to a male of directing carotenoids into coloration instead of into immune function? Reviewers often chastised authors who treated the brain as anything except a black box and suppressed any speculations about the mechanisms behind assessment and decision making.

Luckily, recent advances in cognitive science and neurobiology have changed the situation completely. Clever signal detection theory paradigms now allow one to measure the amount of information and the value of information separately and non-invasively (see Web Topic 8.10). A multitude of neurobiological efforts now focus explicitly on elucidating how the brains of animals and people accomplish the tasks associated with communication. On the sender’s side, the neurobiology of Drosophila displays, frog and cricket calls, and passerine song acquisition and production are largely worked out. On the receiver’s side, significant advances have been made in our understanding of sensory processing, stimulus categorization, encoding and decoding, the storage of perceived valence, and decision making in a wide variety of taxa. In growing numbers, the relevant genes have been identified.

It is thus no longer taboo to ask whether animal receivers can use receipt of a given signal to perform a Bayesian update on stored probability estimates or instead invoke some sort of heuristic shortcut. One can now identify specific parts of a vertebrate or invertebrate brain that carry out individual stages in decision making (see Web Topic 8.7). The many efforts to understand this type of process in humans, where self-reporting can be used to confirm neurobiological models, are now being applied and tested in animals (see Chapter 8). Results so far confirm the basic model of receiver updating and decision making outlined in this book. The steps outlined in this and other models can increasingly be tested at both the proximal (mechanistic) and ultimate (fitness consequence) levels. So far, results are supportive. The initial success of the basic Bayesian design has spawned second-generation models that can explain hierarchical processing (e.g., Yang and Shadlen 2007; Tennenbaum et al. 2011). Clearly, the black box has come a long way since the early days of hydraulics.

The math problem

The most effective way to view the interaction between the amount of information in a signaling system and the fitness consequences involves algebraic formulations (see below). It is a curious fact that nearly all of the publications arguing against the incorporation of information in definitions of communication rely on entirely verbal arguments, excluding algebra. Most do not even cite the many models defining the quantitative conditions that favor stable signaling equilibria, and, even if these appear in the reference list, the models themselves receive no serious attention in the associated texts (Scott-Phillips 2008; Rendall et al. 2009; Owren et al. 2010; Scott-Phillips 2010). While some of these authors are philosophers, for whom a persuasive verbal argument is the gold standard, the avoidance of any mathematics seems odd when so much effort has gone into deriving rigorous evolutionary models for communication.

While algebraic formulation does not guarantee that all terms will be included or clearly defined, it often makes deletions conspicuous and badly defined terms clearly unmeasurable. In contrast, it is very easy, as can be seen in several of the cited papers, to construct a plausible verbal argument that hides the omission of contrary citations and data. Verbal arguments also make it easy to claim that a critical term (such as information) is “poorly defined” or to recast an opposing argument with such hyperbole that it becomes an easily disproved straw man.

Perhaps the most pernicious aspect of verbal arguments is the perceived need to partition quantitatively varying phenomena into discrete categories. Much of the dissent over definitions of communication arises from one group finding a case that cannot be assigned to available discrete categories or is inappropriately assigned by another’s definition. The problem is that many of the phenomena associated with animal communication do not fit into tidy, discrete categories. Behaviors performed during physical conflicts can both provide information to an opponent and set up a tactical advantage. The relative importance of the two can vary continuously between fights, and even shift during the same fight by the same two animals. Is such a behavior a signal or a fighting tactic? Discrete categories simply cannot handle these cases. Many biological parameters of interest vary continuously—forcing them into discrete categories, though intellectually convenient, is thus artificial. Many discrete definitions for biological phenomena end up having multiple criteria. What should one do with cases that meet all but one of these criteria? Such cases are often the most instructive, and ignoring them is foolish. Slavish obedience to discrete definitions is a recurrent problem; it is much better to accept the existence of continua and mixtures. Since this is often hard to do verbally, it is best left to algebraic expressions.

An integrated model of animal communication

Below, we briefly summarize two similar algebraic treatments of animal communication that explicitly integrate information provision with fitness consequences. Several more recent formulations are also available, but these two set the scene and will suffice to make our point. One model was published by Bradbury and Vehrencamp (1998, 2000) and the other by Koops (2004). The two models share the following assumptions and components:

The way in which these components can be combined into the value of information is most easily seen in the Bradbury and Vehrencamp model. Here is a brief outline of that approach:

Relevance to earlier concerns

What is information?

Information in these models is the change in a receiver’s estimated probabilities that a given condition is currently true. It is not a substance so it cannot “flow” from sender to receiver. The Δϕi can be used as measures of the amount of information provided by signals. Note that the change will depend on the prior probabilities: receipt of a signal can create big change if the initial expectation was chance, but a small change if the signal only confirms the receiver’s strong prior bias. An average for a signal set can be obtained by discounting each Δϕi by the probability that it will be used,

Δϕ = pΔϕ1 + (1 – p) Δϕ2.

This can be scaled in various ways to make it more useful. The typical approach is to scale it relative to the maximum reliability. Since ratios can get very small or very large, log scales (bits) are often used.

Restoring the duality

While none of the terms in the computation of VI fit the original definitions of message and meaning, it should be clear from the algebraic model that an improvement in either signal reliability or payoffs can trigger a shift from a default strategy to reliance on signals. While it is true that it is the net fitness payoff (value of information) that is the focus of selection, this payoff is equally dependent on how much information is provided and how much getting it right versus getting it wrong affects fitness. Quantifying reliabilities and fitness payoffs are thus equally important tasks when examining signal economics.

Where is the code?

Reliability is a measure of the probability that a receiver correctly identifies the current condition given available cues and signals. To do that, it must combine receipt of a particular cue or signal, consultation of the coding scheme, and its prior probability estimates to generate an updated estimate of the probability that a condition is true. The protocols by which the receiver processes and categorizes the signal, retrieves correlations from a stored coding scheme, generates an update, and makes a decision are all parts of increasingly well-understood brain functions. It no longer pays to ignore this formerly “black box.” Sensory processing and classification have been dissected in detail in many species. Many animals appear to use Bayesian updating or nearly Bayesian heuristics to generate updates. How this is achieved neurobiologically is currently a subject of intense research but considerable progress has already been made. We do not specify how a given reliability is achieved in the model presented above, but likely scenarios are discussed in Chapter 8 and its associated Web Topics.

Receiver variability

The problem that different receivers might invoke different priors and have different values of ΔWi can be accommodated for by computing a different VI for each type of receiver. While critics might argue that this simply puts numbers on the problem of indeterminacy noted earlier, the fact is that variation among individuals in fitness consequences for a given strategy is a normal part of evolutionary dynamics. The selective advantage of using signals must be based not on a few individual cases but on the population-wide average value of information. One expects that this average will be positive in populations where the use of signals is the norm.

Arms races

The problem that senders and receivers might have different optima is best handled with one of the many models of signal evolution using evolutionary game theory. If the optima for both parties are intermediate, (due to accelerating costs or decelerating improvements in reliability), the interesting question is whether or not the equilibria predicted by game theory are above the minimum reliabilities for both parties. Most existing models do not predict a net fitness loss (negative value of information) for either party at an equilibrium (ESS).

Verbal versus algebraic descriptions

The problem that receivers may rely on variable weightings of cues and signals for decisions remains intractable given an insistence on discrete verbal classifications, but is easily accommodated by these algebraic models.


The amount of information provided by a set of signals, the differences in fitness payoffs for correct versus wrong decisions, and the costs of participating in communication all play parallel roles in determining whether selection will favor signaling over alternative strategies. Because cue monitoring grades into signaling, and most receivers base decisions on both cues and signals, discrete categories separating what is a signal and what is not can be very misleading. Conflicts of interest are common in signaling dyads, and these can interact in complicated ways to determine the equilibrium levels of reliability. However, most animal signals appear to have an intermediate level of reliability. This may reflect the opposing forces exerted by the two parties on the system, or it may be more a result of the fact that increased investments (costs) in communication likely result in asymptotic benefits for both parties, and thus the optima for both have intermediate values. Any conflict is then over which optimum is closer to the equilibrium.

Not everyone accepts this viewpoint, and a recent compendium edited by Ulrich Stemann (2013) shows that the arguments, with new examples of each of the “problems” listed above, continue unabated. We have tried to make our case in this textbook and the accompanying online units, and will let history decide who, if anyone, got it right.

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