Skip to main content Accessibility help
Hostname: page-component-594f858ff7-jtv8x Total loading time: 0 Render date: 2023-06-07T10:36:18.372Z Has data issue: false Feature Flags: { "corePageComponentGetUserInfoFromSharedSession": true, "coreDisableEcommerce": false, "corePageComponentUseShareaholicInsteadOfAddThis": true, "coreDisableSocialShare": false, "useRatesEcommerce": true } hasContentIssue false

1 - Evolution of attention in learning

Published online by Cambridge University Press:  10 January 2011

Nestor Schmajuk
Duke University Medical Center, Durham
Get access



A variety of phenomena in associative learning suggest that people and some animals are able to learn how to allocate attention across cues. Models of attentional learning are motivated by the need to account for these phenomena. We start with a different, more general motivation for learners, namely, the need to learn quickly. Using simulated evolution, with adaptive fitness measured as overall accuracy during a lifetime of learning, we show that evolution converges to architectures that incorporate attentional learning. We describe the specific training environments that encourage this evolutionary trajectory and we describe how we assess attentional learning in the evolved learners.

Birds do it, bees do it; maybe ordinary fleas do it. They all learn from experience. But why is learning so ubiquitous? Why not just be born already knowing how to behave? That would save a lot of time and a lot of error. Presumably, we are born ignorant either because evolution is unfinished or because what we need to know is too complex to be fully coded in the genome. Either way, it seems that evolution has cleverly found a mechanism for dealing with the birth of ignorance; a mechanism that we call learning.

Of course, it may be that learning is merely something that organisms do for fun in their spare time. Perhaps there is not much adaptive value in learning, and little cost, and therefore no selective pressure on the mechanisms of learning.

Publisher: Cambridge University Press
Print publication year: 2010

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.)


Burgos, J. E. (2007). Evolving artificial neural networks in Pavlovian environments. In Donahoe, J. W. and Packard-Dorsel, V., eds., Neural-Network Models of Cognition: Biobehavioral Foundations. The Netherlands: North-Holland Elsevier, pp. 58–80.Google Scholar
Chun, M. M. (2000). Contextual cueing of visual attention. Trends in Cognitive Sciences, 4(5), 170–178.CrossRefGoogle ScholarPubMed
Clark, C. W. & Dukas, R. (2003). The behavioral ecology of a cognitive constraint: limited attention. Behavioral Ecology, 14(2), 151–156.CrossRefGoogle Scholar
Dibbets, P., Maes, J. H. R., Boermans, K.&Vossen, J. M. H. (2001). Contextual dependencies in predictive learning. Memory, 9(1), 29–38.CrossRefGoogle ScholarPubMed
Dukas, R. (2004). Causes and consequences of limited attention. Brain, Behavior and Evolution, 63, 197–210.CrossRefGoogle ScholarPubMed
Edmonds, B. & Norling, E. (2007). Integrating learning and inference in multi-agent systems using cognitive context. Lecture Notes in Computer Science, 4442, 142.CrossRefGoogle Scholar
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison–Wesley.
Goodman, N. D., Tenenbaum, J. B., Feldman, J. & Griffiths, T. L. (2008). A rational analysis of rule-based concept learning. Cognitive Science, 32(1), 108–154.CrossRefGoogle ScholarPubMed
Hall, G. & Channell, S. (1985). A comparison of intradimensional and extradimensional shift learning in pigeons. Behavioural Processes, 10, 285–295.CrossRefGoogle ScholarPubMed
Hinton, G. E. & Plaut, D. C. (1987). Using fast weights to deblur old memories. In Proceedings of the 9th Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Erlbaum, pp. 177–186.Google Scholar
Johnston, T. D. (1982). Selective costs and benefits in the evolution of learning. Advances in the Study of Behavior, 12, 65–106.CrossRefGoogle Scholar
Kamin, L. J. (1969). Predictability, surprise, attention, and conditioning. In Campbell, B. A. and Church, R. M., eds., Punishment. New York: Appleton–Century–Crofts, pp. 279–296.Google Scholar
Kruschke, J. K. (1992). ALCOVE: an exemplar-based connectionist model of category learning. Psychological Review, 99, 22–44.CrossRefGoogle ScholarPubMed
Kruschke, J. K. (1996a). Base rates in category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 3–26.Google ScholarPubMed
Kruschke, J. K. (1996b). Dimensional relevance shifts in category learning. Connection Science, 8, 201–223.CrossRefGoogle Scholar
Kruschke, J. K. (2001). Toward a unified model of attention in associative learning. Journal of Mathematical Psychology, 45, 812–863.CrossRefGoogle Scholar
Kruschke, J. K. (2003). Attentional theory is a viable explanation of the inverse base rate effect: a reply to Winman, Wennerholm, and Juslin (2003). Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 1396–1400.Google Scholar
Kruschke, J. K. (2009). Highlighting: a canonical experiment. In Ross, B., ed., The Psychology of Learning and Motivation, vol. 51. Illinois: Elsevier, pp. 153–185.Google Scholar
Kruschke, J. K. & Blair, N. J. (2000). Blocking and backward blocking involve learned inattention. Psychonomic Bulletin and Review, 7, 636–645.CrossRefGoogle ScholarPubMed
Kruschke, J. K., Kappenman, E. S. & Hetrick, W. P. (2005). Eye gaze and individual differences consistent with learned attention in associative blocking and highlighting. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 830–845.Google ScholarPubMed
Little, D. R. & Lewandowsky, S. (2009). Beyond non-utilization: irrelevant cues can gate learning in probabilistic categorization. Journal of Experimental Psychology: Human Perception and Performance, 35(2), 530–550.Google Scholar
Lubow, R. E. (1989). Latent Inhibition and Conditioned Attention Theory. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Mackintosh, N. J. (1975). A theory of attention: variations in the associability of stimuli with reinforcement. Psychological Review, 82, 276–298.CrossRefGoogle Scholar
McCloskey, M. & Cohen, N. J. (1989). Catastrophic interference in connectionist networks: the sequential learning problem. In Bower, G., ed., The Psychology of Learning and Motivation, vol. 24. New York: Academic Press, pp. 109–165.Google Scholar
Medin, D. L. & Edelson, S. M. (1988). Problem structure and the use of base-rate information from experience. Journal of Experimental Psychology: General, 117, 68–85.CrossRefGoogle ScholarPubMed
Mery, F. & Kawecki, T. J. (2003). A fitness cost of learning ability in Drosophila melanogaster. Proceedings of the Royal Society B: Biological Sciences, 270(1532), 2465–2469.CrossRefGoogle ScholarPubMed
Miller, G. F. & Todd, P. M. (1990). Exploring adaptive agency i: theory and methods for simulating evolution of learning. In Touretzky, D. S., Elman, J. L., Sejnowski, T. J., and Hinton, G. E., eds., Proceedings of the 1990 Connectionist Models Summer School. San Mateo, CA: Morgan Kaufmann, pp. 65–80.Google Scholar
Miller, R. R. & Matzel, L. D. (1988). The comparator hypothesis: a response rule for the expression of associations. In Bower, G. H., ed., The Psychology of Learning and Motivation: Advances in Research and Theory, vol. 22. San Diego, CA: Academic Press, pp. 51–92.Google Scholar
Moore, B. R. (2004). The evolution of learning. Biological Review, 79, 301–335.CrossRefGoogle ScholarPubMed
Nelson, J. B. & Sanjuan, M. (2006). A context-specific latent inhibition effect in a human conditioned suppression task. The Quarterly Journal of Experimental Psychology, 59(6), 1003–1020.CrossRefGoogle Scholar
Nosofsky, R. M., Gluck, M. A., Palmeri, T. J., McKinley, S. C. & Glauthier, P. (1994). Comparing models of rule-based classification learning: a replication of Shepard, Hovland, and Jenkins (1961). Memory and Cognition, 22, 352–369.CrossRefGoogle Scholar
Nosofsky, R. M., Palmeri, T. J.&McKinley, S. C. (1994). Rule-plus-exception model of classification learning. Psychological Review, 101, 53–79.CrossRefGoogle ScholarPubMed
Raine, N. E. & Chittka, L. (2008). The correlation of learning speed and natural foraging success in bumble-bees. Proceedings of the Royal Society B: Biological Sciences, 275(1636), 803–808.CrossRefGoogle ScholarPubMed
Rescorla, R. A. & Wagner, A. R. (1972). A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and non-reinforcement. In Black, A. H. and Prokasy, W. F., eds., Classical Conditioning ii: Current Research and Theory. New York: Appleton–Century–Crofts, pp. 64–99.Google Scholar
Rosas, J. M. & Callejas-Aguilera, J. E. (2006). Context switch effects on acquisition and extinction in human predictive learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(3), 461–474.Google ScholarPubMed
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning internal representations by back-propagating errors. In Rumelhart, D. E. and McClelland, J. L., eds., Parallel Distributed Processing, vol. 1. Cambridge, MA: MIT Press.Google Scholar
Schmajuk, N. A. (2002). Latent Inhibition and Its Neural Substrates: From Animal Experiments to Schizophrenia. Norwell, MA: Kluwer Academic.CrossRef
Schmajuk, N. A., Lam, Y. W. & Gray, J. A. (1996). Latent inhibition: a neural network approach. Journal of Experimental Psychology: Animal Behavior Processes, 22, 321–349.Google ScholarPubMed
Shepard, R. N., Hovland, C. L. & Jenkins, H. M. (1961). Learning and memorization of classifications. Psychological Monographs, 75(13). (Whole No. 517.)CrossRefGoogle Scholar
Slamecka, N. J. (1968). A methodological analysis of shift paradigms in human discrimination learning. Psychological Bulletin, 69, 423–438.CrossRefGoogle ScholarPubMed
Todd, P. M. & Miller, G. F. (1991). Exploring adaptive agency ii: simulating the evolution of adaptive learning. In Meyer, J.-A. and Wilson, S. W., eds., From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior. Cambridge, MA: MIT Press, pp. 306–315.Google Scholar
Yang, L.-X. & Lewandowsky, S. (2003). Context-gated knowledge partitioning in categorization. Learning and Memory, 29(4), 663–679.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the or variations. ‘’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats