In a world that relies increasingly on large amounts of data
and on powerful Machine Learning (ML) models, the veracity
of decisions made by these systems is essential. Adversarial
samples are inputs that have been perturbed to mislead the in-
terpretation of the ML and are a dangerous vulnerability. Our
research takes a first step into what can be an important innova-
tion in cognitive science: we analyzed human’s judgments and
decisions when confronted with targeted (inputs constructed
to make a ML model purposely misclassify an input as some-
thing else) and non-targeted (a noisy perturbed input that tries
to trick the ML model) adversarial samples. Our findings sug-
gest that although ML models that produce non-targeted adver-
sarial samples can be more efficient than targeted samples they
result in more incorrect human classifications than those of tar-
geted samples. In other words, non-targeted samples interfered
more with human perception and categorization decisions than
targeted samples.