As other components start implementing the feature (e.g. more hardware supporting some feature), a common interface will naturally emerge, and then a new abstraction can be created to provide the new feature through that interface.
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We recruited 557 participants from Prolific (277 male, 271 female, 9 self-identify; MageM_{\text{age}} = 42.92 years, SDSD = 13.83, range: 18-82). The sample was 63% White, 13% Black, 11% Latin American, 6% Multi-Racial, 4% East Asian, and 3% other ethnicities. Of these, 504 participants (90.5%) provided a final hypothesis and were included in discovery rate analyses, while 512 participants (91.9%) provided a final likelihood rating and were included in confidence change analyses. Participants who exited the chatbot interface after providing their hypothesis but before rating their final confidence were excluded from confidence analyses only. All participants who completed the study provided informed consent and were paid $1.10. The study took the median participant 5.4 minutes. The study was approved by an Institutional Review Board.