Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-14T09:56:30.207Z Has data issue: false hasContentIssue false

Random isn't real: How the patchy distribution of ecological rewards may generate “incentive hope”

Published online by Cambridge University Press:  19 March 2019

Laurel Symes
Affiliation:
Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755. Laurel.symes@dartmouth.eduthalia.p.wheatley@dartmouth.eduwww.laurelsymes.com Bioacoustics Research Program, Lab of Ornithology, Cornell University, Ithaca, NY 14850.
Thalia Wheatley
Affiliation:
Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755. Laurel.symes@dartmouth.eduthalia.p.wheatley@dartmouth.eduwww.laurelsymes.com

Abstract

Anselme & Güntürkün generate exciting new insights by integrating two disparate fields to explain why uncertain rewards produce strong motivational effects. Their conclusions are developed in a framework that assumes a random distribution of resources, uncommon in the natural environment. We argue that, by considering a realistically clumped spatiotemporal distribution of resources, their conclusions will be stronger and more complete.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Arditi, R. & Dacorogna, B. (1988) Optimal foraging on arbitrary food distributions and the definition of habitat patches. The American Naturalist 131:837–46.Google Scholar
Fitzpatrick, C. L., Hobson, E. A., Mendelson, T. C., Rodríguez, R. L., Safran, R. J., Scordato, E. S. C., Servedio, M. R., Stern, C.A., Symes, L. B. & Kopp, M. (2018) Theory meets empiry: A citation network analysis. BioScience 68(10):805–12. https://doi.org/10.1093/biosci/biy083.Google Scholar
Harrigan, K. A., Collins, K., Dixon, M. J. & Fugelsang, J. (2010) Addictive gameplay: What casual game designers can learn from slot machine research. In: Futureplay ’10: Proceedings of the International Academic Conference on the Future of Game Design and Technology, Vancouver, BC, Canada, May 6–7, 2010, pp. 127–33. ACM. doi: 10.1145/1920778.1920796.Google Scholar
Iwasa, Y., Higashi, M. & Yamamura, N. (1981) Prey distribution as a factor determining the choice of optimal foraging strategy. The American Naturalist 117:710–23.Google Scholar
Jack, R., Crivelli, C. & Wheatley, T. (2018) Using data-driven methods to diversify knowledge of human psychology. Trends in Cognitive Sciences 22:15.Google Scholar
Krebs, J. R., Ryan, J. C. & Charnov, E. L. (1974) Hunting by expectation or optimal foraging? A study of patch use by chickadees. Animal Behaviour 22:953964.Google Scholar
McIntyre, N. E. & Wiens, J. A. (1999) Interactions between landscape structure and animal behavior: The roles of heterogeneously distributed resources and food deprivation on movement patterns. Landscape Ecology 14:437–47.Google Scholar
Pyke, G. H. (2015) Understanding movements of organisms: It's time to abandon the Lévy foraging hypothesis. Methods in Ecology and Evolution 6:116.Google Scholar
Racey, P. A. & Swift, S. M. (1985) Feeding ecology of Pipistrellus pipistrellus (Chiroptera: Vespertilionidae) during pregnancy and lactation: I. Foraging behaviour. Journal of Animal Ecology 54:205–15.Google Scholar
Scarf, D., Miles, K., Sloan, A., Goulter, N., Hegan, M., Seid-Fatemi, A., Harper, D. & Colombo, M. (2011) Brain cells in the avian “prefrontal cortex” code for features of slot-machine-like gambling. PLoS ONE 6:e14589.Google Scholar