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Model organisms (MO) are widely used in neuroscience to study brain processes, behavior, and the biological foundation of human diseases. However, the use of MO has also been criticized for low reliability and insufficient success rate in the development of therapeutic approaches, because the success of MO use also led to overoptimistic and simplistic applications, which sometimes resulted in wrong conclusions. Here, we develop a conceptual framework of MO to support scientists in their practical work and to foster discussions about their power and limitations. For this purpose, we take advantage of concepts developed in the philosophy of science and adjust them for practical application by neuroscientists. We suggest that MO can be best understood as tools that are used to gain information about a group of species or a phenomenon in a species of interest. These learning processes are made possible by some properties of MO, which facilitate the process of acquisition of understanding or provide practical advantages, and the possibility to transfer information between species. However, residual uncertainty in the reliability of information transfer remains, and incorrect generalizations can be side-effects of epistemic benefits, which we consider as representational and epistemic risks. This suggests that to use MO most effectively, scientists should analyze the similarity relation between the involved species, weigh advantages and risks of certain epistemic benefits, and invest in carefully designed validation experiments. Altogether, our analysis illustrates how scientists can benefit from philosophical concepts for their research practice.
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