The emergence of signaling systems has been observed in numerous experimental and real‐world contexts, but there is no consensus on which (if any) shared mechanisms underlie such phenomena. A number of explanatory mechanisms have been proposed within several disciplines, all of which have been instantiated as credible working models. However, they are usually framed as being mutually incompatible. Using an exemplar‐based framework, we replicate these models in a minimal configuration which allows us to directly compare them. This reveals that the development of optimal signaling is driven by similar mechanisms in each model, which leads us to propose three requirements for the emergence of conventional signaling. These are the creation and transmission of referential information, a systemic bias against ambiguity, and finally some form of information loss. Considering this, we then discuss some implications for theoretical and experimental approaches to the emergence of learned communication.
Language is one of the most complex of human traits. There are many hypotheses about how it originated, what factors shaped its diversity, and what ongoing processes drive how it changes. We present the Causal Hypotheses in Evolutionary Linguistics Database (CHIELD, https://chield.excd.org/), a tool for expressing, exploring, and evaluating hypotheses. It allows researchers to integrate multiple theories into a coherent narrative, helping to design future research. We present design goals, a formal specification, and an implementation for this database. Source code is freely available for other fields to take advantage of this tool. Some initial results are presented, including identifying conflicts in theories about gossip and ritual, comparing hypotheses relating population size and morphological complexity, and an author relation network.
It is hard to define structural categories of language (e.g. noun, verb, adjective) in a way which accounts for linguistic variation. This leads Haspelmath to make the following claims: i) unlike in biology and chemistry, there are no natural kinds in language; ii) there is a fundamental distinction between descriptive and comparative linguistic categories, and; iii) generalisations based on comparisons between languages can in principle tell us nothing about specific languages. The implication is that cross-linguistic categories cannot support scientific induction. I disagree: generalisations on the basis of linguistic comparison should inform the language sciences. Haspelmath is not alone in identifying a connection between the nature of the categories we use and the kind of inferences we can make (e.g. Goodman’s ‘new riddle of induction’), but he is both overly pessimistic about categories in language and overly optimistic about categories in other sciences: biology and even chemistry work with categories which are indeterminate to some degree. Linguistic categories are clusters of co-occurring properties with variable instantiations, but this does not mean that we should dispense with them: if linguistic generalisations reliably lead to predictions about individual languages, and if we can integrate them into more sophisticated causal explanations, then there is no a priori requirement for a fundamental descriptive/comparative distinction. Instead, we should appreciate linguistic variation as a key component of our explanations rather than a problem to be dealt with.
Computational model simulations have been very fruitful for gaining insight into how the systematic structure we observe in the world’s natural languages could have emerged through cultural evolution. However, these model simulations operate on a toy scale compared to the size of actual human vocabularies, due to the prohibitive computational resource demands that simulations with larger lexicons would pose. Using computational complexity analysis, we show that this is not an implementational artifact, but instead it reflects a deeper theoretical issue: these models are (in their current formulation) computationally intractable. This has important theoretical implications, because it means that there is no way of knowing whether or not the properties and regularities observed for the toy models would scale up. All is not lost however, because awareness of intractability allows us to face the issue of scaling head-on, and can guide the development of our theories.
The emergence of signaling systems has been observed in numerous experimental and realworld contexts, but there is no consensus on which (if any) shared mechanisms underlie such phenomena. A number of explanatory mechanisms have been proposed within several disciplines, all of which have been instantiated as credible working models. However, they are usually framed as being mutually incompatible. Using an exemplar-based framework, we replicate these models in a minimal configuration which allows us to directly compare them. This reveals that the development of optimal signaling is driven by similar mechanisms in each model, which leads us to propose three requirements for the emergence of conventional signaling. These are the creation and transmission of referential information, a systemic bias against ambiguity, and finally some form of information loss. Considering this, we then discuss some implications for theoretical and experimental approaches to the emergence of learned communication.
Interceptive eavesdropping on the alarm calls of heterospecifics provides crucial information about predators. Previous research suggests predator discrimination, call relevance, reliability, and reception explain when eavesdropping will evolve. However, there has been no quantitative analysis to scrutinize these principles, or how they interact. We develop a mathematical framework that formalizes the study of the key principles thought to select for eavesdropping. Interceptive eavesdropping appears to be greatly affected by the threat faced by caller and eavesdropper, as well as presence of informational noise affecting the detection of calls and predators. Accordingly, our model uses signal detection theory to examine when selection will favor alarm calling by a sender species and fleeing by an eavesdropping receiver species. We find eavesdropping is most strongly selected when (1) the receiver faces substantial threats, (2) species are ecologically similar, (3) senders often correctly discriminate threats, (4) receivers often correctly perceive calls, and (5) the receiver's personal discrimination of threats is poor. Furthermore, we find (6) that very high predation levels can select against eavesdropping because prey cannot continuously flee and must conserve energy. Reliability of heterospecific calls for identifying threats is thought to be important in selecting for eavesdropping. Consequently, we formally define reliability, showing its connection to specificity and sensitivity, clarifying how these quantities can be measured. We find that high call relevance, due to similar vulnerability to predators between species, strongly favors eavesdropping. This is because senders trade-off false alarms and missed predator detections in a way that is also favorable for the eavesdropper, by producing less of the costlier error.Unexpectedly, highly relevant calls increase the total number of combined errors and so have lower reliability. Expectedly, when noise greatly affects personally gathered cues to threats, but not heterospecific calls or detection of predators, eavesdropping is favored.
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