A key question individuals face in any social learning environment is when to innovate alone and when to imitate others. Previous simulation results have found that the best performing groups exhibit an intermediate balance, yet it is still largely unknown how individuals collectively negotiate this balance. We use an immersive collective foraging experiment, implemented in the Minecraft game engine, facilitating unprecedented access to spatial trajectories and visual field data. The virtual environment imposes a limited field of view, creating a natural trade-off between allocating visual attention towards individual innovation or to look towards peers for social imitation. By analyzing foraging patterns, social interactions (visual and spatial), and social influence, we shine new light on how groups collectively adapt to the fluctuating demands of the environment through specialization and selective imitation, rather than homogeneity and indiscriminate copying of others.

Humans are uniquely capable social learners. Our capacity to learn from others across short and long timescales is a driving force behind the success of our species. Yet there are seemingly maladaptive patterns of human social learning, characterized by both overreliance and underreliance on social information. Recent advances in animal research have incorporated rich visual and spatial dynamics to study social learning in ecological contexts, showing how simple mechanisms can give rise to intelligent group dynamics. However, similar techniques have yet to be translated into human research, which additionally requires integrating the sophistication of human individual and social learning mechanisms. Thus, it is still largely unknown how humans dynamically adapt social learning strategies to different environments and how group dynamics emerge under realistic conditions. Here, we use a collective foraging experiment in an immersive Minecraft environment to provide unique insights into how visual-spatial interactions give rise to adaptive, specialized, and selective social learning. Our analyses show how groups adapt to the demands of the environment through specialization rather than homogeneity and through the adaptive deployment of selective imitation rather than indiscriminate copying. We test these mechanisms using computational modeling, providing a deeper understanding of the cognitive mechanisms that dynamically influence social decision-making in ecological contexts. Specialization of learning strategies and selective attention thus offer insights into the adaptive foundations of human social learning.

The ability of chemically amplified resists to transfer an aerial image at increasingly smaller dimensions is critical to extreme ultraviolet (EUV) lithography success at increasingly smaller process nodes. Stochastic inhomogeneities in resist exposure and patterning have been studied, which include photon shot noise and resist surface roughness. However, previous work has indicated that inhomogeneities and defectivity are present in multicomponent resists beyond those predicted by random statistics. This is thought to be due to self-segregation of components in the multi-component chemically amplified resist (CAR). The results in this paper show that the most critical part of the resist chemical segregation occurs during the spin coating process after a significant amount of the solvent has evaporated, but while there is still enough solvent to enable molecular mobility within the resist.