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):
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 firstname.lastname@example.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
Posted on 21 Jun 2021