Learning and Memory in Mimicry. I. Simulations of Laboratory Experiments

An understanding of the dynamics of mimicry requires the modelling of the behaviour of predators in the wild. Current knowledge about the behavioural expression of learning and the dynamics of forgetting is insufficient for the construction of a definitive model of such behaviour. In particular, there is insufficient information on the response of a vertebrate when subjected to the presentation of a single identical conditioned stimulus paired with two (or more) unconditioned stimuli of different intensity or even of opposite effect, which in the present situation can be regarded as ‘model’ and ‘mimic’. A general algorithm of learning and forgetting, based on the behavioural model of Bush and Mosteller, is proposed; it is applied as a linear operator in Monte Carlo simulations of the behaviour of a predator confronting a mixture of models and mimics. The algorithm is varied in detail: learning may be cumulative or instantaneous, constant or variable according to the strength of stimulus, and towards a continuously distributed or two-state (0 and 1) asymptote; forgetting may be cumulative or instantaneous, constant or variable according to the strength of stimulus, and dependent on time or on the occurrence of external events. Thirty different behaviour systems arise from rational combinations of the various learning and forgetting rules, including as special cases those behavioural models already proposed in the literature on mimicry. A standard experimental technique is the presentation to predators of a constant number of prey with varying proportions of models and mimics: we term this a reciprocal frequency experiment, and simulate its outcome for all thirty rational models. The results of such experiments, when appropriately transformed, will yield straight lines or curves according to the behavioural model employed. Models with all-or-none features (instantaneous learning or forgetting) tend to yield straight lines: curves tend to appear when the model assumes gradual or cumulative learning and forgetting. The result is dominated by the mode of learning; forgetting plays a secondary part. The method will therefore discriminate well between ‘switched’ and ‘gradual’ modes of learning and forgetting, but only if the experiments are carried out over a wide range of palatabilities for both the model and the mimic, and with other adequate design features. It is also necessary to design the experiment to distinguish between the dynamics of shortand long-term learning, which may well be different.

[1]  J. Huheey STUDIES IN WARNING COLORATION AND MIMICRY. VII. EVOLUTIONARY CONSEQUENCES OF A BATESIAN‐MÜLLERIAN SPECTRUM: A MODEL FOR MÜLLERIAN MIMICRY , 1976, Evolution; international journal of organic evolution.

[2]  J. Pearce,et al.  A model for Pavlovian learning: Variations in the effectiveness of conditioned but not of unconditioned stimuli. , 1980 .

[3]  G. Scarpelli,et al.  Mimicry in the burnet moth Zygaena ephialtes: population studies and evidence of a Batesian—Müllerian situation , 1979 .

[4]  R. Owen,et al.  Mathematical paradigms for mimicry: Recurrent sampling , 1984 .

[5]  J. Turner,et al.  Mimicry and the Monte Carlo predator: the palatability spectrum, and the origins of mimicry , 1984 .

[6]  M. Bouton Conditioning, remembering, and forgetting. , 1994 .

[7]  R. Rescorla,et al.  A theory of Pavlovian conditioning : Variations in the effectiveness of reinforcement and nonreinforcement , 1972 .

[8]  A I Houston,et al.  Memory and the efficient use of information. , 1987, Journal of theoretical biology.

[9]  Frederick Mosteller,et al.  Stochastic Models for Learning , 1956 .

[10]  Turner J.R.G. Butterfly mimicry: the genetical evolution of an adaptation. , 1977 .

[11]  N. Mackintosh,et al.  Conditioning And Associative Learning , 1983 .

[12]  J. Turner,et al.  The evolutionary dynamics of batesian and muellerian mimicry: similarities and differences , 1987 .

[13]  James E. Huheey,et al.  Mathematical Models of Mimicry , 1988, The American Naturalist.

[14]  P. Sheppard,et al.  THE EXISTENCE OF MÜLLERIAN MIMICRY , 1977, Evolution; international journal of organic evolution.

[15]  Studies in Waring Coloration and Mimicry VIII. Further Evidence for a Frequency-Dependent Model of Predation , 1980 .

[16]  G. Estabrook,et al.  Strategy for a Predator Encountering a Model-Mimic System , 1974, The American Naturalist.

[17]  G. Pasteur,et al.  A Classificatory Review of Mimicry Systems , 1982 .

[18]  M. Rothschild The mimicrats must move with the times , 1981 .

[19]  W. W. Benson ON THE SUPPOSED SPECTRUM BETWEEN BATESIAN AND MÜLLERIAN MIMICRY , 1977, Evolution; international journal of organic evolution.

[20]  J. Huheey BATESIAN AND MÜLLERIAN MIMICRY: SEMANTIC AND SUBSTANTIVE DIFFERENCES OF OPINION , 1980, Evolution; international journal of organic evolution.

[21]  H. Bates,et al.  XXXII. Contributions to an Insect Fauna of the Amazon Valley. Lepidoptera: Heliconidæ. , 1862 .

[22]  W. W. Benson,et al.  Adaptive Polymorphism Associated with Multiple Mullerian Mimicry in Heliconius numata (Lepid. Nymph.) , 1974 .

[23]  Predators Encountering a Model-Mimic System with Alternative Prey , 1981, The American Naturalist.

[24]  J. Huheey Studies of Warning Coloration and Mimicry. IV. A. Mathematical Model of Model‐Mimic Frequencies , 1964 .

[25]  L. Bobisud,et al.  One-Trial Versus Multi-Trial Learning for a Predator Encountering a Model- Mimic System , 1976, The American Naturalist.

[26]  M. Avery Application of mimicry theory to bird damage control , 1985 .

[27]  Speed Mp Mimicry and the psychology of predation. , 1990 .

[28]  H. Gleitman Chapter 1 – FORGETTING OF LONG-TERM MEMORIES IN ANIMALS1 , 1971 .

[29]  P. Chai Field observations and feeding experiments on the responses of rufous‐tailed jacamars (Galbula ruficauda) to free‐flying butterflies in a tropical rainforest , 1986 .

[30]  W. Wickler Mimicry in plants and animals , 1969 .

[31]  N. Mackintosh A Theory of Attention: Variations in the Associability of Stimuli with Reinforcement , 1975 .

[32]  R. Vane-Wright A unified classification of mimetic resemblances , 1976 .