The proliferation of the capability for producing and distributing deepfake videos threatens the integrity of systems of justice, democratic processes, and the general ability to critically assess evidence. This study sought to identify individual differences that predict one’s ability to detect these forgeries. It was hypothesized that measures of affect detection and political orientation would correlate with performance on a deepfake detection task. Within a sample (N = 173) of college undergraduates and participants from Amazon’s M-turk, affect detection ability was shown to correlate with deepfake detection ability, r(171) = .73, p < .001, and general orientation to the political left was shown to correlate with deepfake detection ability, r(171) = .42, p < .001. The results of this study serve to identify populations who are particularly susceptible to deception via deepfake video and to inform the development of interventions that may help defend against attempts to influence them.

Sequential learning in artificial neural networks is known to trigger catastrophic interference (CI), where previously learned skills are forgotten after learning new skills. This is in direct contrast to humans’ ability to learn increasingly complex skills across the lifespan without major instances of CI. The present work builds on techniques for mitigating CI that have been proposed in prior work. Anderson and Schooler (1991) first documented that the memory environment has a lawful structure. Following from their observation, we constructed a training environment where previously mastered tasks (Boolean functions) decrease in frequency over time according to a power law. It was predicted that training in this environment would (1) mitigate CI, (2) replicate human performance in learning curves following a power law of practice, and (3) promote positive transfer of training to new skills, all without the need to posit additional mechanisms. The present results support all three predictions.

As techniques for generating deepfake videos (in which a generative adversarial network is used to alter the human subject of a video) improve, so does the potential to use them for nefarious ends. It is therefore important that we identify those among us who are most susceptible to being fooled. Tidler and Catrambone (2021) presented results which suggest that one’s political orientation and affect detection ability are strong predictors of one’s ability to detect deepfakes. The current work is a re-analysis of their data, intended to reveal a more highly-resolved profile of a susceptible individual. The data (N = 169) were submitted to a k-means clustering algorithm which revealed a 2-cluster solution. Younger, more progressive thinking individuals, with agnostic or atheistic religious beliefs, and greater adeptness at spotting deepfakes formed Cluster 1; and individuals with more conservative political leanings, greater religiosity, and less skill at detecting deepfakes formed Cluster 2.