Are Vision Transformers More Data Hungry Than Newborn Visual Systems?

Vision transformers (ViTs) are top performing models on many computer vision benchmarks and can accurately predict human behavior on object recognition tasks. However, researchers question the value of using ViTs as models of biological learning because ViTs are thought to be more data hungry than brains, with ViTs requiring more training data to reach similar levels of performance. To test this assumption, we directly compared the learning abilities of ViTs and animals, by performing parallel controlled rearing experiments on ViTs and newborn chicks. We first raised chicks in impoverished visual environments containing a single object, then simulated the training data available in those environments by building virtual animal chambers in a video game engine. We recorded the first-person images acquired by agents moving through the virtual chambers and used those images to train self supervised ViTs that leverage time as a teaching signal, akin to biological visual systems. When ViTs were trained through the eyes of newborn chicks, the ViTs solved the same view invariant object recognition tasks as the chicks. Thus, ViTs were not more data hungry than newborn visual systems: both learned view invariant object representations in impoverished visual environments. The flexible and generic attention based learning mechanism in ViTs combined with the embodied data streams available to newborn animals appears sufficient to drive the development of animal-like object recognition.Comment: Accepted in Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023

Gene flow and genetic structure of two of Arkansas’s rarest darter species (Teleostei: Percidae), the Arkansas Darter, Etheostoma cragini, and the Least Darter, E. microperca

Distinguishing the effects of naturally caused historical fragmentation from those of contemporary landscape modification is critically important to understanding the consequences of human influences on patterns of gene flow and population dynamics. Nonetheless, relatively few recent studies focusing on this issue have dealt with species that showed evidence of historical fragmentation. In the current study, we disentangled the effects of fragmentation operating over separate timescales on two darter species, Etheostoma cragini and E. microperca, from the Ozark Highlands. Formerly more wide-spread within this region in Arkansas, these species now occur only in highly isolated habitats (i.e., spring-runs). We separated fragmentation effects at distinct spatial and temporal scales by using several molecular loci (i.e., mtDNA/nuclear DNA/nuclear microsatellite DNA), as well as a variety of analytical approaches. Sequence divergence among Ozark and northern populations of E. microperca indicate long-standing isolation resulting from vicariant events. Both species were further isolated in unique ‘island’ habitats, sometimes at fine spatial scales, as shown by sequence divergence among Ozark Highland populations of E. cragini. Microsatellite data also revealed additional subdivision among Arkansas populations with E. cragini divided into three distinct populations and E. microperca into two. Overall, migration rates were similar among contemporary and historical time periods although patterns of asymmetric migration were inverted for E. cragini. Estimates of contemporary effective population size (Ne) were substantially lower for both species than past population sizes. Overall, historical processes involving natural fragmentation have had long-lasting effects on these species, potentially making them more susceptible to current anthropogenic impacts