Specialized hybrid learners resolve Rogers' paradox about the adaptive value of social learning. (7th February 2017)

• Record Type: Journal Article
• Authors: Kharratzadeh, Milad; Montrey, Marcel; Metz, Alex; Shultz, Thomas R.
• Is Part Of: Journal of theoretical biology. Volume 414(2017)
• Page Start: 8
• Page End: 16
• Publication Date: 2017-02-07
• Subjects: Rogers' paradox -- Evolution -- Social learning -- Invasion analysis -- Agent-based simulation; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2016.11.017
• Languages: English
• ISSNs: 0022-5193

Abstract

Abstract: Culture is considered an evolutionary adaptation that enhances reproductive fitness. A common explanation is that social learning, the learning mechanism underlying cultural transmission, enhances mean fitness by avoiding the costs of individual learning. This explanation was famously contradicted byRogers (1988), who used a simple mathematical model to show that cheap social learning can invade a population without raising its mean fitness. He concluded that some crucial factor remained unaccounted for, which would reverse this surprising result. Here we extend this model to include a more complex environment and limited resources, where individuals cannot reliably learn everything about the environment on their own. Under such conditions, cheap social learning evolves and enhances mean fitness, via hybrid learners capable of specializing their individual learning. We then show that while spatial or social constraints hinder the evolution of hybrid learners, a novel social learning strategy, complementary copying, can mitigate these effects. Abstract : Graphical abstract: Abstract : Highlights: Extending Rogers' model by adding a second task and an attention parameter. Introducing hybrid learners who learn one task individually and the other socially. Invasion analysis identifies parameter regions where hybrid learners evolve and are stable. Verifying the analytical results with agent-based simulations. Simulation results extend beyond the analytical results to settings with spatial constraints on learning and reproduction.