Proceedings of the Annual Meeting of the Cognitive Science Society

A common assumption in the cognitive sciences is thatartificial and natural language learning rely on sharedmechanisms. However, attempts to bridge the two haveyielded ambiguous results. We suggest that an empiricaldisconnect between the computations employed duringlearning and the methods employed at test may explain thesemixed results. Further, we propose statistically-basedchunking as a potential computational link between artificialand natural language learning. We compare the acquisition ofnon-adjacent dependencies to that of natural languagestructure using two types of tasks: reflection-based 2AFCmeasures, and processing-based recall measures, the latterbeing more computationally analogous to the processes usedduring language acquisition. Our results demonstrate thattask-type significantly influences the correlations observedbetween artificial and natural language acquisition, withreflection-based and processing-based measures correlatingwithin – but not across – task-type. These findings havefundamental implications for artificial-to-natural languagecomparisons, both methodologically and theoretically.