PubMed · 1207164
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A D Carlson, B Soucek. 1975. Computer simulation of firefly flash sequences.. https://doi.org/10.1016/s0022-5193(75)80085-3
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In a model, conflicts of interest between communicating individuals are shown to have an important influence on the cost and form of signals that evolve. Two types of conflict are considered: competition between senders to obtain a response from the receiver, and conflict between the sender and the receiver. The receiver system is modelled as an artificial neural network whose 'resistance' to signals is represented as a motivational factor that varies independently of the signal. Biases in the receiver system act as the selective force on signals, causing them to become more costly and conspicuous as the intensity of conflict increases. There is some evidence that competition between senders and sender-receiver conflict may have qualitatively different outcomes. We give examples of some situations to which the model might be applied and point out some predictions that could be tested empirically.
Recently, simple neural network models have been used to explain the evolution of important phenomena in animal signalling, such as extravagant ornamentation and symmetrical signals, as responses to inevitable 'hidden preferences' of recognition systems. We argue that these very simple models may be misleading because they may not behave in important ways like the recognition systems of real animals and so cannot justify their claim to demonstrate general principles of perception in a signalling context. We show that the way in which these simple models respond to exaggerated signals may not be, as is claimed, a close parallel to the phenomena of peak shift or supernormal responses. We also argue that the preference for symmetrical patterns shown by the models is unlikely to reflect the way computationally that real animals solve problems of pattern invariance and may be an artefact of the particular way the models have been set up. Whereas more sophisticated neural net models do capture known properties of real visual systems and are consequently of great use in understanding perception, the same cannot be said of very simple one-dimensional models with small numbers of units and connections. Given the far reaching explanatory claims made of these simpler models their limitations should be more widely recognized.
Conflicts of interest arise between signaller and receiver in most kinds of biological communication. Some authors have argued that this conflict is likely to give rise to deceit and exploitation, as receivers lag behind in the coevolutionary 'arms race' with signallers. Others have argued that such manipulation is likely to be short-lived and that receivers can avoid being deceived by paying attention to signals that are costly and hence 'unfakeable.' These two views have been hard to reconcile. Here, we present results from simulations of signal evolution using artificial neural networks, which demonstrate that honesty can coexist with a degree of exploitation. Signal cost ensures that receivers are able to obtain some honest information, but is unable to prevent exploitative signalling strategies from gaining short-term benefits. Although any one receiver bias that is open to exploitation will subsist for only a short period of time once signallers begin to take advantage of it, new preferences of this kind are constantly regenerated through selection and random drift. Hidden preferences and sensory exploitation are thus likely to have an enduring influence on the evolution of honest, costly signals. At the same time, honesty and cost are prerequisites for the evolution of exploitation. When signalling is cost-free, selection cannot act to maintain honesty, and receivers rapidly evolve to ignore signals. This leads to a reduction in the extent of hidden preference, and a consequent loss of potential for exploitation.