Iterated learning and the evolution of compositional structure
For our penultimate paper we’ll be reading Beckner et al. (2017), which provides a reanalysis and online replication of Experiment 2 of Kirby, Cornish & Smith (2008) - the 2008 paper started out as a Masters dissertation by Hannah Cornish (Dr Hannah Cornish these days), supervised by me and Simon Kirby. Beckner et al. start with a reanalysis of data from our paper, but the more relevant part here is their experiment, a large online iterated artificial language learning experiment. Participants are asked to learn an artificial language which provides labels/descriptions for groups of novel objects, where those objects come in 3 shapes, 3 colours, and appear in groups of 1, 2 or 3. After training on a language, participants are tested, producing a new set of labels/descriptions. Those descriptions are then used as training for another participant: the language is passed from participant to participant down a chain of transmission, potentially changing as it goes as a result of accumulated errors and innovations made by participants. Beckner et al. impose a bottleneck on transmission (participants are trained on labels/descriptions for a subset of the objects, but required to generalise to the entire object set at test time), and filter out ambiguous labels (to discourage the language from collapsing down to a single label, which was a result we got in Experiment 1 of our 2008 paper). They find, as in the original experiment, that compositional structure develops through this iterated learning process: the first participant in each chain is trained on a language where each label/description is random and completely idiosyncratic, but the languages gradually evolve regularities, e.g. where shape and number are consistently encoded separate morphemes which are combined to form a complex (well, 2-part) signal.
As usual, in this week’s practical you’ll get a chance to look at a similar experiment in jsPsych, which will involve code from our week 4 word learning experiment again but also the infrastructure to run an iterated learning design, which involves manipulating CSV files on the server.
A couple of things to note:
Kirby, S., Cornish, H., & Smith, K. (2008). Cumulative cultural evolution in the laboratory:An experimental approach to the origins of structure in human language. Proceedings of the National Academy of Sciences, USA, 105, 10681-10686.
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