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3D Printable Device for Automated Operant Conditioning in the Mouse.

ABSTRACT: Operant conditioning (OC) is a classical paradigm and a standard technique used in experimental psychology in which animals learn to perform an action to achieve a reward. By using this paradigm, it is possible to extract learning curves and measure accurately reaction times (RTs). Both these measurements are proxy of cognitive capabilities and can be used to evaluate the effectiveness of therapeutic interventions in mouse models of disease. Here, we describe a fully 3D printable device that is able to perform OC on freely moving mice, while performing real-time tracking of the animal position. We successfully trained six mice, showing stereotyped learning curves that are highly reproducible across mice and reaching >70% of accuracy after 2 d of conditioning. Different products for OC are commercially available, though most of them do not provide customizable features and are relatively expensive. This data demonstrate that this system is a valuable alternative to available state-of-the-art commercial devices, representing a good balance between performance, cost, and versatility in its use.

SUBMITTER: Mazziotti R

PROVIDER: S-EPMC7218003 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Operant conditioning of neural activation has been researched for decades in humans and animals. Many theories suggest two parallel learning processes, implicit and explicit. The degree to which feedback affects these processes individually remains to be fully understood and may contribute to a large percentage of non-learners. Our goal is to determine the explicit decision-making processes in response to feedback representing an operant conditioning environment. We developed a simulated operant conditioning environment based on a feedback model of spinal reflex excitability, one of the simplest forms of neural operant conditioning. We isolated the perception of the feedback signal from self-regulation of an explicit unskilled visuomotor task, enabling us to quantitatively examine feedback strategy. Our hypothesis was that feedback type, biological variability, and reward threshold affect operant conditioning performance and operant strategy. Healthy individuals (N = 41) were instructed to play a web application game using keyboard inputs to rotate a virtual knob representative of an operant strategy. The goal was to align the knob with a hidden target. Participants were asked to "down-condition" the amplitude of the virtual feedback signal, which was achieved by placing the knob as close as possible to the hidden target. We varied feedback type (knowledge of performance, knowledge of results), biological variability (low, high), and reward threshold (easy, moderate, difficult) in a factorial design. Parameters were extracted from real operant conditioning data. Our main outcomes were the feedback signal amplitude (performance) and the mean change in dial position (operant strategy). We observed that performance was modulated by variability, while operant strategy was modulated by feedback type. These results show complex relations between fundamental feedback parameters and provide the principles for optimizing neural operant conditioning for non-responders.

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3D Printable Device for Automated Operant Conditioning in the Mouse.

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