Cognitive training comprises a class of relatively new therapeutic interventions that target mechanisms implicated across different mental health conditions. Cognitive training programs are designed to enhance emotional functioning, either directly by cultivating different strategies for emotional processing and/or responses or indirectly by bolstering cognitive control processes in a non-emotional context, which in turn should improve emotional functioning.
Although many recent studies indicate that cognitive training shows merit, others fail to demonstrate its efficacy. These inconsistent findings may at least partly result from differences in individuals’ ability to benefit from cognitive training in general, and from specific training types in particular. Consistent with the move toward personalized medicine, we propose using machine learning approaches to help optimize cognitive training gains. More specifically, machine learning algorithms are incorporated to identify which individuals will be most responsive to cognitive training in general and to discern which methods may be a better fit for certain individuals.
Relevant Literature:
Shani, R., Tal, S., Zilcha-Mano, S., & Okon-Singer, H. (2019). Can machine learning approaches lead toward personalized cognitive training?. Frontiers in behavioral neuroscience, 64.
Shani, R., Tal, S., Derakshan, N., Cohen, N., Enock, P. M., McNally, R. J., ... & Okon-Singer, H. (2021). Personalized cognitive training: Protocol for individual-level meta-analysis implementing machine learning methods. Journal of psychiatric research, 138, 342-348.