  A central problem in cognitive science and behavioural neuroscience as well
as in machine learning and artificial intelligence research is to ascertain
whether two or more decision makers (be they brains or algorithms) use the same
strategy. Accuracy alone cannot distinguish between strategies: two systems may
achieve similar accuracy with very different strategies. The need to
differentiate beyond accuracy is particularly pressing if two systems are near
ceiling performance, like Convolutional Neural Networks (CNNs) and humans on
visual object recognition. Here we introduce trial-by-trial error consistency,
a quantitative analysis for measuring whether two decision making systems
systematically make errors on the same inputs. Making consistent errors on a
trial-by-trial basis is a necessary condition for similar processing strategies
between decision makers. Our analysis is applicable to compare algorithms with
algorithms, humans with humans, and algorithms with humans. When applying error
consistency to object recognition we obtain three main findings: (1.)
Irrespective of architecture, CNNs are remarkably consistent with one another.
(2.) The consistency between CNNs and human observers, however, is little above
what can be expected by chance alone -- indicating that humans and CNNs are
likely implementing very different strategies. (3.) CORnet-S, a recurrent model
termed the "current best model of the primate ventral visual stream", fails to
capture essential characteristics of human behavioural data and behaves
essentially like a standard purely feedforward ResNet-50 in our analysis. Taken
together, error consistency analysis suggests that the strategies used by human
and machine vision are still very different -- but we envision our
general-purpose error consistency analysis to serve as a fruitful tool for
quantifying future progress.
