  In abstract argumentation, multiple argumentation semantics have been
proposed that allow to select sets of jointly acceptable arguments from a given
argumentation framework, i.e. based only on the attack relation between
arguments. The existence of multiple argumentation semantics raises the
question which of these semantics predicts best how humans evaluate arguments.
Previous empirical cognitive studies that have tested how humans evaluate sets
of arguments depending on the attack relation between them have been limited to
a small set of very simple argumentation frameworks, so that some semantics
studied in the literature could not be meaningfully distinguished by these
studies. In this paper we report on an empirical cognitive study that overcomes
these limitations by taking into consideration twelve argumentation frameworks
of three to eight arguments each. These argumentation frameworks were mostly
more complex than the argumentation frameworks considered in previous studies.
All twelve argumentation framework were systematically instantiated with
natural language arguments based on a certain fictional scenario, and
participants were shown both the natural language arguments and a graphical
depiction of the attack relation between them. Our data shows that grounded and
CF2 semantics were the best predictors of human argument evaluation. A detailed
analysis revealed that part of the participants chose a cognitively simpler
strategy that is predicted very well by grounded semantics, while another part
of the participants chose a cognitively more demanding strategy that is mostly
predicted well by CF2 semantics.
