Operant Procedures for Assessing Behavioral Flexibility in Rats

Executive functions consist of multiple high-level cognitive processes that drive rule generation and behavioral selection. An emergent property of these processes is the ability to adjust behavior in response to changes in one’s environment (i.e., behavioral flexibility). These processes are essential to normal human behavior, and may be disrupted in diverse neuropsychiatric conditions, including schizophrenia, alcoholism, depression, stroke, and Alzheimer’s disease. Understanding of the neurobiology of executive functions has been greatly advanced by the availability of animal tasks for assessing discrete components of behavioral flexibility, particularly strategy shifting and reversal learning. While several types of tasks have been developed, most are non-automated, labor intensive, and allow testing of only one animal at a time. The recent development of automated, operant-based tasks for assessing behavioral flexibility streamlines testing, standardizes stimulus presentation and data recording, and dramatically improves throughput. Here, we describe automated strategy shifting and reversal tasks, using operant chambers controlled by custom written software programs. Using these tasks, we have shown that the medial prefrontal cortex governs strategy shifting but not reversal learning in the rat, similar to the dissociation observed in humans. Moreover, animals with a neonatal hippocampal lesion, a neurodevelopmental model of schizophrenia, are selectively impaired on the strategy shifting task but not the reversal task. The strategy shifting task also allows the identification of separate types of performance errors, each of which is attributable to distinct neural substrates. The availability of these automated tasks, and the evidence supporting the dissociable contributions of separate prefrontal areas, makes them particularly well-suited assays for the investigation of basic neurobiological processes as well as drug discovery and screening in disease models.