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4 - Experimental Evolution and Mechanisms for Prepared Learning

from Part I - Evolution of Learning Processes

Published online by Cambridge University Press:  26 May 2022

Mark A. Krause
Affiliation:
Southern Oregon University
Karen L. Hollis
Affiliation:
Mount Holyoke College, Massachusetts
Mauricio R. Papini
Affiliation:
Texas Christian University
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Summary

Decades of research contend with the notion that animals come prepared by evolution to learn about some stimuli and responses better than others. Biological preparedness – and contrapreparedness – can influence how potential information is acquired, processed, and used in decision-making. Theory predicts that preparedness is the result of patterns of reliability of stimuli in predicting reward across the evolutionary history of the lineage. The evolution of preparedness can be tested experimentally, and also by considering the natural history and the pattern of reliability of stimuli and rewards for a given species. We present predictions as well as explanations for how evolution can prepare animals to make choices about their environment. Why animals learn some things better than others is at the heart of what makes behavior adaptive and by working from relatively simple theory it is possible to directly test these hypotheses and analyze traits both underlying and evolving with prepared learning.

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Publisher: Cambridge University Press
Print publication year: 2022

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References

Adami, C., Ofria, C., & Collier, T. C. (2000). Evolution of biological complexity. Proceedings of the National Academy of Sciences, 97(9), 44634468. https://doi.org/10.1073/pnas.97.9.4463CrossRefGoogle ScholarPubMed
Brand, P., & Ramírez, S. R. (2017). The evolutionary dynamics of the odorant receptor gene family in corbiculate bees. Genome Biology and Evolution, 9(8), 20232036. https://doi.org/10.1093/gbe/evx149Google Scholar
Burger, J. M. S., Kolss, M., Pont, J., & Kawecki, T. J. (2008). Learning ability and longevity: A symmetrical evolutionary trade-off in Drosophila. Evolution, 62(6), 12941304. https://doi.org/10.1111/j.1558-5646.2008.00376.xGoogle Scholar
Burnham, T. C., Dunlap, A. S., & Stephens, D. W. (2015). Experimental evolution and economics. Sage OPEN (October–December) 1–17. https://doi.org/10.1177/2158244015612524Google Scholar
Dall, S., Giraldeau, L., Olsson, O., McNamara, J., & Stephens, D. W. (2005). Information and its use by animals in evolutionary ecology. Trends in Ecology & Evolution, 20(4), 187193. https://doi.org/10.1016/j.tree.2005.01.010Google Scholar
Davis, R. L., & Zhong, Y. (2017). The biology of forgetting – A perspective. Neuron, 95(3), 490503. https://doi.org/10.1016/j.neuron.2017.05.039Google Scholar
Domjan, M., Cusato, B., & Krause, M. (2004). Learning with arbitrary versus ecological conditioned stimuli: Evidence from sexual conditioning. Psychonomic Bulletin & Review, 11(2), 232246. https://doi.org/10.3758/bf03196565Google Scholar
Dunlap, A. S., McLinn, C. M., MacCormick, H. A., Scott, M. E., & Kerr, B. (2009). Why some memories do not last a lifetime: Dynamic long-term retrieval in changing environments. Behavioral Ecology, 20(5), 10961105. https://doi.org/10.1093/beheco/arp102CrossRefGoogle Scholar
Dunlap, A. S., Nielsen, M. E., Dornhaus, A., & Papaj, D. R. (2016). Foraging bumble bees weigh the reliability of personal and social information. Current Biology, 26(9), 11951199. https://doi.org/10.1016/j.cub.2016.03.009CrossRefGoogle ScholarPubMed
Dunlap, A. S., & Stephens, D. W. (2009). Components of change in the evolution of learning and unlearned preference. Proceedings of the Royal Society B: Biological Sciences, 276(1670), 32013208. https://doi.org/10.1098/rspb.2009.0602Google Scholar
Dunlap, A. S., & Stephens, D. W. (2012). Tracking a changing environment: optimal sampling, adaptive memory and overnight effects. Behavioural Processes, 89(2), 8694. https://doi.org/10.1016/j.beproc.2011.10.005CrossRefGoogle ScholarPubMed
Dunlap, A. S., & Stephens, D. W. (2014). Experimental evolution of prepared learning. Proceedings of the National Academy of Sciences, 111(32), 1175011755. https://doi.org/10.1073/pnas.1404176111Google Scholar
Dunlap, A. S., & Stephens, D. W. (2016). Reliability, uncertainty, and costs in the evolution of animal learning. Current Opinion in Behavioral Sciences, 12, 7379. https://doi.org/10.1016/j.cobeha.2016.09.010CrossRefGoogle Scholar
Dwyer, D. M. (2015). Experimental evolution of sensitivity to a stimulus domain alone is not an example of prepared learning. Proceedings of the National Academy of Sciences, 112(5), E385. https://doi.org/10.1073/pnas.1420871112Google Scholar
Farris, S. M., & Schulmeister, S. (2011). Parasitoidism, not sociality, is associated with the evolution of elaborate mushroom bodies in the brains of hymenopteran insects. Proceedings of the Royal Society B: Biological Sciences, 278(1707), 940951. https://doi.org/10.1098/rspb.2010.2161CrossRefGoogle Scholar
Fawcett, T. W., Fallenstein, B., Higginson, A. D., Houston, A. I., Mallpress, D. E. W., Trimmer, P. C., & McNamara, J. M. (2014). The evolution of decision rules in complex environments. Trends in Cognitive Sciences, 18(3), 153161. https://doi.org/10.1016/j.tics.2013.12.012CrossRefGoogle ScholarPubMed
Ferrari, M. C. O., Vrtělová, J., Brown, G. E., & Chivers, D. P. (2012). Understanding the role of uncertainty on learning and retention of predator information. Animal Cognition, 15(5), 807813. https://doi.org/10.1007/s10071-012-0505-yCrossRefGoogle ScholarPubMed
Garcia, J., & Koelling, R. A. (1966). Relation of cue to consequence in avoidance learning. Psychonomic Science, 4(1), 123124. https://doi.org/10.3758/bf03342209Google Scholar
Garland, T., & Rose, M.R. (2009). Experimental evolution: Concepts, methods, and applications of selection experiments (1st ed.). University of California Press.CrossRefGoogle Scholar
Hauser, F. E., & Chang, B. S. W. (2017). Insights into visual pigment adaptation and diversity from model ecological and evolutionary systems. Current Opinion in Genetics & Development, 47, 110120. https://doi.org/10.1016/j.gde.2017.09.005CrossRefGoogle ScholarPubMed
Kikuchi, D. W., & Pfennig, D. W. (2013). Imperfect mimicry and the limits of natural selection. The Quarterly Review of Biology, 88(4), 297315. https://doi.org/10.1086/673758CrossRefGoogle ScholarPubMed
Knudsen, E. I. (2007). Fundamental components of attention. Annual Review of Neuroscience, 30(1), 5778. https://doi.org/10.1146/annurev.neuro.30.051606.094256Google Scholar
Köksal, F., Domjan, M., & Weisman, G. (1994). Blocking of the sexual conditioning of differentially effective conditioned stimulus objects. Animal Learning & Behavior, 22, 103111.Google Scholar
Koops, M. A. (2004). Reliability and the value of information. Animal Behaviour, 67(1), 103111. https://doi.org/10.1016/j.anbehav.2003.02.008CrossRefGoogle Scholar
Kotrschal, A., Corral-Lopez, A., Amcoff, M., & Kolm, N. (2014). A larger brain confers a benefit in a spatial mate search learning task in male guppies. Behavioral Ecology, 26(2), 527532. https://doi.org/10.1093/beheco/aru227CrossRefGoogle Scholar
Kotrschal, A., Rogell, B., Bundsen, A., Svensson, B., Zajitschek, S., Brännström, I., Immler, S., Maklakov, A. A., & Kolm, N. (2013). Artificial selection on relative brain size in the guppy reveals costs and benefits of evolving a larger brain. Current Biology, 23(2), 168171. https://doi.org/10.1016/j.cub.2012.11.058CrossRefGoogle ScholarPubMed
Kraaijeveld, K., Oostra, V., Liefting, M., Wertheim, B., de Meijer, E., & Ellers, J. (2018). Regulatory and sequence evolution in response to selection for improved associative learning ability in Nasonia vitripennis. BMC Genomics, 19(1), 115. https://doi.org/10.1186/s12864-018-5310-9Google Scholar
Kraemer, P. J., & Golding, J. M. (1997). Adaptive forgetting in animals. Psychonomic Bulletin & Review, 4(4), 480491. https://doi.org/10.3758/bf03214337Google Scholar
Krause, M. A., Cusato, B., & Domjan, M. (2003). Extinction of conditioned sexual responses in male Japanese quail (Coturnix japonica): Role of species typical cues. Journal of Comparative Psychology, 117, 7686.Google Scholar
Leadbeater, E., & Dawson, E. H. (2017). A social insect perspective on the evolution of social learning mechanisms. Proceedings of the National Academy of Sciences, 114(30), 78387845. https://doi.org/10.1073/pnas.1620744114CrossRefGoogle ScholarPubMed
Liefting, M., Hoedjes, K. M., Le Lann, C., Smid, H. M., & Ellers, J. (2018). Selection for associative learning of color stimuli reveals correlated evolution of this learning ability across multiple stimuli and rewards. Evolution, 72(7), 14491459. https://doi.org/10.1111/evo.13498Google Scholar
Linwick, D., Patterson, J., & Overmier, J. B. (1981). On inferring selective association: Methodological considerations. Animal Learning & Behavior, 9(4), 508512. https://doi.org/10.3758/bf03209782CrossRefGoogle Scholar
LoLordo, V. M. (1979). Selective associations. In Dickinson, A. and Boakes, R. A. (Eds.), Mechanisms of learning and motivation: A memorial volume to Jerzy Konorski (pp. 367398). Lawrence Erlbaum..Google Scholar
Mackintosh, N. J. (1974). The psychology of animal learning. Academic Press.Google Scholar
Maharaj, G., Horack, P., Yoder, M., & Dunlap, A. S. (2018). Influence of preexisting preference for color on sampling and tracking behavior in bumble bees. Behavioral Ecology, 30(1), 150158. https://doi.org/10.1093/beheco/ary140Google Scholar
Marcus, M., Burnham, T. C., Stephens, D. W., & Dunlap, A. S. (2017). Experimental evolution of color preference for oviposition in Drosophila melanogaster. Journal of Bioeconomics, 20(1), 125140. https://doi.org/10.1007/s10818-017-9261-zCrossRefGoogle Scholar
McNamara, J. M., & Houston, A. I. (1987). Memory and the efficient use of information. Journal of Theoretical Biology, 125(4), 385395. https://doi.org/10.1016/s0022-5193(87)80209-6Google Scholar
Mery, F., & Kawecki, T. J. (2002). Experimental evolution of learning ability in fruit flies. Proceedings of the National Academy of Sciences, 99(22), 1427414279. https://doi.org/10.1073/pnas.222371199Google Scholar
Mery, F., & Kawecki, T. J. (2003). A fitness cost of learning ability in Drosophila melanogaster. Proceedings of the Royal Society of London B Biological Sciences, 270, 24652469. https://doi.org/10.1098/rspb.2003.2548Google Scholar
Mery, F., & Kawecki, T. J. (2004). The effect of learning on experimental evolution of resource preference in Drosophila melanogaster. Evolution, 58(4), 757. https://doi.org/10.1554/03-540Google Scholar
Mery, F., Pont, J., Preat, T., & Kawecki, T. J. (2007). Experimental evolution of olfactory memory in Drosophila melanogaster. Physiological and Biochemical Zoology, 80(4), 399405. https://doi.org/10.1086/518014CrossRefGoogle ScholarPubMed
Miller, S. E., Legan, A. W., Henshaw, M. T., Ostevik, K. L., Samuk, K., Uy, F. M., & Sheehan, M. J. (2020). Evolutionary dynamics of recent selection on cognitive abilities. Proceedings of the National Academy of Sciences, 117(6), 30453052. https://doi.org/10.1073/pnas.1918592117CrossRefGoogle ScholarPubMed
Morand-Ferron, J. (2017). Why learn? The adaptive value of associative learning in wild populations. Current Opinion in Behavioral Sciences, 16, 7379.CrossRefGoogle Scholar
Oberling, P., Bristol, A. S., Matute, H., & Miller, R. R. (2000). Biological significance attenuates overshadowing, relative validity, and degraded contingency effects. Animal Learning & Behavior, 28, 172186.CrossRefGoogle Scholar
Pavlov, I. P. (1927). Conditioned reflexes. Oxford University Press.Google Scholar
Pontes, A. C., Mobley, R. B., Ofria, C., Adami, C., & Dyer, F. C. (2020). The evolutionary origin of associative learning. The American Naturalist, 195(1), E1E19. https://doi.org/10.1086/706252Google Scholar
Reader, S. M. (2016). Animal social learning: Associations and adaptations. F1000Research, 5, 2120. https://doi.org/10.12688/f1000research.7922.1Google Scholar
Rescorla, R. A. & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In Black, A. H. & Prokasy, W. F. (Eds.), Classical conditioning II: Current research and theory (pp. 6499). Appleton-Century-Crofts.Google Scholar
Riffell, J. (2020). The neuroecology of insect-plant interactions: The importance of physiological state and sensory integration. Current Opinion in Insect Science, 42, 118124. https://doi.org/10.1016/j.cois.2020.10.007CrossRefGoogle ScholarPubMed
Rubi, T. L., & Stephens, D. W. (2015). Should receivers follow multiple signal components? An economic perspective. Behavioral Ecology, 27(1), 3644. https://doi.org/10.1093/beheco/arv121Google Scholar
Rubi, T. L., & Stephens, D. W. (2016). Why complex signals matter, sometimes. In Bee, M. & Miller, C. (Eds.), Psychological mechanisms in animal communication. Animal signals and communication (Vol. 5, pp. 119136). Springer. https://doi.org/10.1007/978-3-319-48690-1_5Google Scholar
Seligman, M. E. (1970). On the generality of the laws of learning. Psychological Review, 77(5), 406418. https://doi.org/10.1037/h0029790CrossRefGoogle Scholar
Silva, F. J. (2018). The puzzling persistence of “neutral” conditioned stimuli. Behavioural Processes, 157, 8090. https://doi.org/10.1016/j.beproc.2018.07.004CrossRefGoogle ScholarPubMed
Snell-Rood, E. C., & Steck, M. (2015). Experience drives the development of movement-cognition correlations in a butterfly. Frontiers in Ecology and Evolution, 3, 6373. https://doi.org/10.3389/fevo.2015.00021CrossRefGoogle Scholar
Stevens, M. (2013). Sensory ecology, behaviour, and evolution (Illustrated ed.). Oxford University Press.CrossRefGoogle Scholar
Van Damme, S., De Fruyt, N., Watteyne, J., Kenis, S., Peumen, K., Schoofs, L., & Beets, I. (2021). Neuromodulatory pathways in learning and memory: Lessons from invertebrates. Journal of Neuroendocrinology, 33(1), e1291. https://doi.org/10.1111/jne.12911CrossRefGoogle ScholarPubMed

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