  Counterfactual explanations (CFEs) highlight what changes to a model's input
would have changed its prediction in a particular way. CFEs have gained
considerable traction as a psychologically grounded solution for explainable
artificial intelligence (XAI). Recent innovations introduce the notion of
computational plausibility for automatically generated CFEs, enhancing their
robustness by exclusively creating plausible explanations. However, practical
benefits of such a constraint on user experience and behavior is yet unclear.
In this study, we evaluate objective and subjective usability of
computationally plausible CFEs in an iterative learning design targeting novice
users. We rely on a novel, game-like experimental design, revolving around an
abstract scenario. Our results show that novice users actually benefit less
from receiving computationally plausible rather than closest CFEs that produce
minimal changes leading to the desired outcome. Responses in a post-game survey
reveal no differences in terms of subjective user experience between both
groups. Following the view of psychological plausibility as comparative
similarity, this may be explained by the fact that users in the closest
condition experience their CFEs as more psychologically plausible than the
computationally plausible counterpart. In sum, our work highlights a
little-considered divergence of definitions of computational plausibility and
psychological plausibility, critically confirming the need to incorporate human
behavior, preferences and mental models already at the design stages of XAI
approaches. In the interest of reproducible research, all source code, acquired
user data, and evaluation scripts of the current study are available:
https://github.com/ukuhl/PlausibleAlienZoo
