A Machine Learning Approach for Detecting Vicarious Trial and Error Behaviors

Abstract

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.

Flexible decision-making is related to strategy learning, vicarious trial and error, and medial prefrontal rhythms during spatial set-shifting

“…As an example, two of the strongest relationships we see are higher mPFC theta and beta on VTE trials compared to non-VTE trials ( Figure 8 , 2 nd row, left and middle columns). This is in line with results showing hippocampal activity changes during VTE (Amemiya & Redish, 2018; Johnson & Redish, 2007; Miles et al ., 2021; Papale et al ., 2016; Schmidt et al ., 2019) and the evidence for hippocampal-prefrontal interactions during VTE (Hasz & Redish, 2020b; Schmidt et al ., 2019; Stout et al ., 2022). The beta rhythm, specifically, has recently been shown to synchronize the mPFC and hippocampus via brief activity bursts in the nucleus reuniens during an odor sequence memory task (Jayachandran et al ., 2023), and our result provides further evidence that beta-rhythmic activity in the mPFC component of this tri-partite circuit is crucial for memory-guided decision-making.…”

“…To mitigate the errors, we reassigned certain trajectories initially not identified as VTE but with x-positions that crossed a certain threshold into the VTE category, and used the combination of a low z-ln(idphi) measure (Bett et al ., 2012; Blumenthal et al ., 2011; McLaughlin & Redish, 2023; Papale et al ., 2012; Schmidt et al ., 2013, 2019; Stout et al ., 2022) and a failure to cross lower x-position boundary to reassign any VTEs that may have been mistakenly identified. Informal inspections of randomly sampled data subsets after classification suggest that this method is between 80% and 90% accurate, which is in-line with supervised classification methods and near the threshold for interrater agreement (Miles et al ., 2021).…”

“…A subset of the rats (n = 3) had neural recordings from the mPFC. Recordings were collected from custom-built tetrode micro-drives with Intan headstages and Open-Ephys acquisition systems, as described in (Kidder et al ., 2021; Miles et al ., 2021). All mPFC recordings were localized to the prelimbic cortex ( Figure 7A ), between approximately 3.7 and 4.3 mm anterior to bregma.…”