Standardized and reproducible measurement of decision-making in mice

Abstract

Progress in neuroscience is hindered by poor reproducibility of mouse behavior. Here we show that in a visual decision making task, reproducibility can be achieved by automating the training protocol and by standardizing experimental hardware, software, and procedures. We trained 101 mice in this task across seven laboratories at six different research institutions in three countries, and obtained 3 million mouse choices. In trained mice, variability in behavior between labs was indistinguishable from variability within labs. Psychometric curves showed no significant differences in visual threshold, bias, or lapse rates across labs. Moreover, mice across laboratories adopted similar strategies when stimulus location had asymmetrical probability that changed over time. We provide detailed instructions and open-source tools to set up and implement our method in other laboratories. These results establish a new standard for reproducibility of rodent behavior and provide accessible tools for the study of decision making in mice.

Extracting the Dynamics of Behavior in Decision-Making Experiments

“…Although our method is applicable to any binary decision-making task, for concreteness we intro-80 duce our method in the context of the task used by the International Brain Lab (IBL) (illustrated in Figure 1A) (IBL et al, 2020). In this visual detection task, a mouse is positioned in front of a screen and a wheel.…”

“…We now turn to real data, and show how our method can be used to characterize the detailed trajectories of learning in a large cohort of animals. We examined a dataset from the International Brain Lab (IBL) containing behavioral data from >100 mice on a standardized sensory decisionmaking task (see task schematic in Figure 1A) (IBL et al, 2020). 190 We began by analyzing choice data from the earliest sessions of training.…”

“…All research dependent upon animals consistently making accurate decisions must endure the poorly understood and resource-hungry bottleneck of animal training. 30 Conventional tools for characterizing choice behavior include the psychometric curve and logistic regression, in combination with coarse performance statistics (see, for example, Akrami et al (2018) and IBL et al (2020), the sources of the mouse, rat, and human behavior analyzed below) (Green and Swets, 1966). These approaches aggregate data from a range of trials, often spanning many sessions.…”

“…We apply our model to behavioral data collected from throughout the training process of two experimental paradigms, spanning three species (mouse, rat, and human). After validating on simulated data, we leverage our model to analyze an example mouse that learns to synchronize its choice behavior to the block structure of a visual detection task (IBL et al, 2020). We then uncover the dominance of trial history information in rats, but not humans, during early training 60 on an auditory parametric working memory task (Akrami et al, 2018).…”

“…On each trial, a sinusoidal grating (with contrast values between 0 and 100%) appears on either the left or right side of the screen. Mice must report the side of the grating by turning a wheel (left or right) in order to receive a water reward (see Methods for details) (IBL et al, 2020). (B) An example table of the task variables X assumed to govern behavior for a subset of trials {t − 2, .…”