How well do rudimentary plasticity rules predict adult visual object learning?
Fig 6
Further testing for potential differences between plasticity rules.
A. Possible scenarios, given similar benchmark scores. In the “behavioral measurement space” of this experiment, the MSEn metric is an estimate of the (squared) distance between a human and a model. There is a spectrum of possible scenarios consistent with the observation that a set of learning models have similar human MSEn scores. B. Rules generate highly similar predictions across all tested encoding stages. Across a range of representational models (random subset shown, drawn from 20 equally-sized bins across the observed MSEn range), the rule-to-rule distances are near zero, showing different rules consistently lead to highly similar predictions. The y-axis (MSEu) is a symmetric version of the MSEn metric that performs noise correction for both arguments (see Section 2.2 in S1 Appendix).
