This site uses cookies

By using this site, you consent to our use of cookies. You can view our terms and conditions for more information.

Sampling heuristics for active function learning

Rebekah Gelpi
University of Toronto ~ Department of Psychology
Nayan Saxena
University of Toronto ~ Department of Statistical Sciences
George Lifchits
Daphna Buchsbaum
Christopher Lucas

People are capable of learning diverse functional relationships from data; nevertheless, they are most accurate when learning linear relationships, and deviate further from estimating the true relationship when presented with non-linear functions. We investigate whether, when given the opportunity to learn actively, people choose samples in an efficient fashion, and whether better sampling policies improve their ability to learn linear and non-linear functions. We find that, across multiple different function families, people make informative sampling choices consistent with a simple, low-effort policy that minimizes uncertainty at extreme values without requiring adaptation to evidence. While participants were most accurate at learning linear functions, those who more closely adhered to the simple sampling strategy also made better predictions across all non-linear functions. We discuss how the use of this heuristic might reflect rational allocation of limited cognitive resources.



function learning
active learning

There is nothing here yet. Be the first to create a thread.

Cite this as:

Gelpi, R., Saxena, N., Lifchits, G., Buchsbaum, D., & Lucas, C. G. (2021, July). Sampling heuristics for active function learning. Paper presented at Virtual MathPsych/ICCM 2021. Via