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Increasingly, researchers are finding that important details of behavior involve subtle actions that are hard to quantify, such as changes in the prevalence of grooming in models of anxiety (Peça et al., 2011), Binary Options licking a limb in models of pain (Browne, 2017), and manipulation of food objects for fine sensorimotor control (Neubarth, 2020; Sauerbrei et al., 2020). To quantify these observations, the most commonly used approach, to our knowledge, is for researchers to manually watch videos with a stopwatch to count the time each behavior of interest is exhibited (Figure 1A). In some cases, behavioral tests allow quantification of behavior Binary Options through tracking an animal’s location in space, such as in the three-chamber assay, open-field arena, Morris water maze, and elevated plus maze (EPM) (Pennington, 2019). The analysis of animal behavior is a common approach in a wide range of biomedical research fields, including basic neuroscience research (Krakauer et al., 2017), translational analysis of disease models, and development of therapeutics. In these cases, researchers often closely observe videos of animals and then develop a list of behaviors they want to measure. For example, researchers study behavioral patterns of animals to investigate the effect of a gene mutation, understand the efficacy of potential pharmacological therapies, or uncover the neural underpinnings of behavior. Therefore, it would be a significant advance if a researcher could define a list of behaviors of interest, such as face grooming, tail grooming, limb licking, locomoting, rearing, and so on, and then use automated software to identify when and how frequently each of the behaviors occurred in a video. Furthermore, scoring of behaviors can vary greatly between researchers especially as new researchers are trained (Segalin, 2020) and can be subject to bias. Also, because this approach requires manual viewing, often only one or a small number of behaviors are studied at a time. This approach takes immense amounts of researcher time, often equal to or greater than the duration of the video per individual subject. In addition, researchers often do not label the video frames when specific behaviors occur, precluding subsequent analysis and review of behavior bouts, such as bout durations and the transition probability between behaviors.
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Recall is the fraction of frames actually having a given behavior that are correctly labeled as that behavior by the model (true positives/(true positives + false negatives)). Precision is the fraction of frames labeled by the model as a given behavior that are actually that behavior (true positives/(true positives + false positives)). We report the F1 score in the main figures and show precision and recall performance in the figure supplements. Because the accuracy and F1 score depend on our choice of a threshold to turn the probability of a given behavior on a given frame into a binary prediction about the presence of that behavior, we also computed the area under the receiver operating curve (AUROC), which summarizes performance as a function of the threshold. 98% accuracy simply by guessing that the behavior was absent on all frames. Therefore, we also computed the F1 score, a metric ranging from 0 (bad) to 1 (perfect) that takes into account the rates of false positives and false negatives. The F1 score is the geometric mean of the precision and recall of the model.
