Binding the Person-Specific Approach to Modern AI in the Human Screenome Project: Moving past Generalizability to Transferability

Nilam Ram, Nick Haber, Thomas N. Robinson, Byron Reeves

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Advances in ability to comprehensively record individuals’ digital lives and in AI modeling of those data facilitate new possibilities for describing, predicting, and generating a wide variety of behavioral processes. In this paper, we consider these advances from a person-specific perspective, including whether the pervasive concerns about generalizability of results might be productively reframed with respect to transferability of models, and how self-supervision and new deep neural network architectures that facilitate transfer learning can be applied in a person-specific way to the super-intensive longitudinal data arriving in the Human Screenome Project. In developing the possibilities, we suggest Molenaar add a statement to the person-specific Manifesto–“In short, the concerns about generalizability commonly leveled at the person-specific paradigm are unfounded and can be fully and completely replaced with discussion and demonstrations of transferability.”.

Original languageEnglish (US)
JournalMultivariate Behavioral Research
StateAccepted/In press - 2023

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)

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