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Thus far, computational psychiatry has heavily relied on decision-making paradigms and modeling of choice data (e.g. armed bandit tasks). This approach provides a rigorous way to quantify behaviors and related neural substrates in health and disease, but also leaving a rich repertoire of non-choice types of data unexamined. Filling this knowledge gap could potentially lead to new insights into alternative neurocomputational mechanisms underlying psychopathologies that have been traditionally ignored. For instance, depressed patients might be able to make reward-related choices in a similar way as healthy controls, but eye tracking data might reveal certain attention bias that cannot be modelled using choice data. In this special issue, we aim to start a conversation on how to better utilize such novel data types in computational psychiatry research. 

Topics include (but are not limited to):

• Modeling non-choice data during decision paradigms (e.g. reaction time, cursor/mouse tracking, eye-tracking, etc.);
• Modeling subjective data (e.g. ratings, self-reports); 
• Modeling of facial, voice, or movement data;
• Modeling of naturalistic paradigms and real-world data (e.g. GPS location);
• Linking non-choice data to neural measures.

We welcome a wide range of article types. Please see https://cpsyjournal.org/about/author-guidelines/#Article%20types for guidelines. 

Submissions are considered on a rolling basis. For presubmission inquiry, please email editor@computationalpsychiatry.org. Note: For full submissions, please indicate in your cover letter that the submission is for the special issue "Beyond Choices: Modeling Of Novel Data Types In Computational Psychiatry