My research focuses on the evolution of culture through social learning. I am using computational models as well as animal experiments to investigate favourable conditions and cognitive mechanisms involved in social learning.
I am also passionate about facilitating discussions between researchers of different scientific fields, as well as between scientist and non-scientists.
Resource competition shapes the evolution of social learning
Animals gain information about their environment either through observation or interaction with other individuals (social learning) or by direct, personal exploration (individual learning). While socially acquired information can be a shortcut for often more costly individual learning, animals sometimes forgo available social information. In this project we use an agent-based model to analyse the effect of different levels of resource competition on the use of social information. We find that when individuals compete more, they use less social information and rely more on individual exploration. We also find that in competitive environments social learning is only adaptive where resources are highly, unevenly distributed but stable over time.
In an unreliable world, do bumblebee learn more socially?
In our current project, based on predictions from our previous project we want to test, whether animals are more likely to accept and use social information when resources are highly variable as compared to when they do not vary.
I am curating a website that collects recent papers, conference calls, and online talks on social learning and cultural evolution (Social Learning Digest). This is my attempt to create an information hub for young social learning researchers.
Together with Jamie Soul I am organizing regular meetings of the R-programming community (FLSRGroup) at our faculty. We want to encourage researchers to use R for their daily data analysis, but also for reproducible documentation of their work (see e.g. How to use knitr). We provide code examples from our meetings on github. One of our projects involved a sentiment analysis of christmas related tweets.
Currently, I started to organise meetings for the next generation of social learning and cultural evolution scientists. Knowing that this field is spread wide among diverse fields such as psychology, sociology, biology, but also physics, and archeology, I want to facilitate networking opportunities and hopefully also future co-operations. Together, with great support from my fellow researchers Alecia Carter (Cambridge), Matt Creasey (Exeter), Alex Lee (Oxford), Harry Marshall (Exeter), and Iza Romanowska (Southampton) I therefore founded the Young Social Learning Researchers community. Our first event will be at the Culture Conference in Birmingham. More soon.
Kranstauber B, Smolla M, Safi K. 2016 Similarity in spatial utilisation distributions measured by the Earth Mover’s Distance. Methods Ecol. Evol. (doi:10.1111/2041-210X.12649) – Full Text – Supplementary Materials – Data and R Code
Smolla M, Alem S, Chittka L, Shultz S. 2016 Copy-when-uncertain: bumblebees rely on social information when rewards are highly variable. Biol. Lett. 12:20160188. (doi:10.1098/rsbl.2016.0188) – Full Text – Press Release – Supplementary Materials – Data and R Code
Smolla, M., Gilman, R. T., Galla, T. & Shultz, S. 2015 Competition for resources can explain patterns of social and individual learning in nature. Proc. R. Soc. B Biol. Sci. 282, 20151405. (doi:10.1098/rspb.2015.1405) – Full Text – Press Release – Supplemental Materials – Conference Poster
Smolla, M., Ruchty, M., Nagel, M. & Kleineidam, C. J. 2014 Clearing pigmented insect cuticle to investigate small insects’ organs in situ using confocal laser-scanning microscopy (CLSM). Arthropod Struct. Dev. 43, 175–181. (doi:10.1016/j.asd.2013.12.006) – Full Text – Animation