Bavelier, D. | Search bioRxiv

bioRxiv2024

Global patterns of genetic admixture reveal effects of language contact

When speakers of different languages are in contact, they often borrow features like sounds, words, or syntactic patterns from one language to the other, but the lack of historical data has hampered estimation of this effect at a global scale. We break out of this impasse by using genetic admixture as a proxy for population contact. We find that language pairs whose speaker populations underwent genetic admixture or that are located in the same geo-historical area share more features than others, suggesting borrowing. The effect varies strongly across features, partly following expectations from differences in lifelong learnability, partly responding to differences in social imbalances during contact. Additionally, we find that for some features, admixture decreases sharing. This likely reflects signals of divergence (schismogenesis) under contact.

evolutionary biology↗

bioRxiv2021

A mixture of generative models strategy helps humans generalize across tasks

What role do generative models play in generalization of learning in humans? Our novel multi-task prediction paradigm--where participants complete four sequence learning tasks, each being a different instance of a common generative family--allows the separate study of within-task learning (i.e., finding the solution to each of the tasks), and across-task learning (i.e., learning a task differently because of past experiences). The very first responses participants make in each task are not yet affected by within-task learning and thus reflect their priors. Our results show that these priors change across successive tasks, increasingly resembling the underlying generative family. We conceptualize multi-task learning as arising from a mixture-of-generative-models learning strategy, whereby participants simultaneously entertain multiple candidate models which compete against each other to explain the experienced sequences. This framework predicts specific error patterns, as well as a gating mechanism for learning, both of which are observed in the data.

neuroscience↗