A Machine Learning Approach for Detecting Vicarious Trial and Error Behaviors

Vicarious trial and error behaviors (VTEs) indicate periods of indecision during decision-making, and have been proposed as a behavioral marker of deliberation. In order to understand the neural underpinnings of these putative bridges between behavior and neural dynamics, researchers need the ability to readily distinguish VTEs from non-VTEs. Here we utilize a small set of trajectory-based features and standard machine learning classifiers to identify VTEs from non-VTEs for rats performing a spatial delayed alternation task (SDA) on an elevated plus maze. We also show that previously reported features of the hippocampal field potential oscillation can be used in the same types of classifiers to separate VTEs from non-VTEs with above chance performance. However, we caution that the modest classifier success using hippocampal population dynamics does not identify many trials where VTEs occur, and show that combining oscillation-based features with trajectory-based features does not improve classifier performance compared to trajectory-based features alone. Overall, we propose a standard set of features useful for trajectory-based VTE classification in binary decision tasks, and support previous suggestions that VTEs are supported by a network including, but likely extending beyond, the hippocampus.

Automated summary

The study uses trajectory-based features and machine learning classifiers to distinguish VTEs from non-VTEs in rats, showing that hippocampal field potential oscillation features can also be used in classification but with modest success. Combining oscillation-based features with trajectory-based features does not improve classifier performance compared to using trajectory-based features alone.