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Several species of Polistes paper wasp are well known for their social hierarchies and the ability for individual wasps to modulate their social behaviors based on recognizable facial features of other wasps. For example, wasps that observe an aggressive social interaction between two other wasps will later behave differently toward the winner and loser of that interaction. Being able to alter the physical appearance of wasps~(e.g., with paint) has allowed for testing hypothetical roles of individual recognition in hierarchy formation, which is how researchers know that wasps are attending to faces specifically. However, these physical methods are limited in their scope. Social insects who respond to visual stimuli from other insects have been shown to give the same responses to playbacks of video recordings of those stimuli, which suggests that there may be a role for generative methods in social-insect research. Being able to computationally change the faces of individual wasps in a video recording of wasp social interactions would greatly expand the experimental toolbox of the behavioral researcher. Toward this end, we evaluate the use of an existing annotation-free model for image animation by motion transfer, the thin-plate spline motion model, for creating realistic videos that depict the face of a paper wasp performing behaviors recorded by another. Not needing to pre-define important landmarks is a strength of this method for this application space, but we find that "deep faking wasps" poses unique and non-trivial problems that still need to be solved before off-the-shelf motion transfer models can be used in the insect behavioral laboratory. 

Vulnerable populations (e.g., older adults) can be hard to reach online. During a pandemic like COVID-19 when much research data collection must be conducted online only, these populations risk being further underrepresented. This paper explores methodological strategies for rigorous, efficient survey research with a large number of older adults online, focusing on (1) the design of a survey instrument both comprehensible and usable by older adults, (2) rapid collection (within hours) of data from a large number of older adults, and (3) validation of data using attention checks, independent validation of age, and detection of careless responses to ensure data quality. These methodological strategies have important implications for the inclusion of older adults in online research.