title: Dissociation of visual associative and motor learning in Drosophila at the flight simulator.
abstract: Ever since operant conditioning was studied experimentally, the relationship between associative learning and possible motor learning has become controversial. Although motor learning and its underlying neural substrates have been extensively studied in mammals, it is still poorly understood in invertebrates. The visual discriminative avoidance paradigm of Drosophila at the flight simulator has been widely used to study the flies' visual associative learning and related functions, but it has not been used to study the motor learning process. In this study, newly-designed data analysis was employed to examine the flies' solitary behavioural variable that was recorded at the flight simulator-yaw torque. Analysis was conducted to explore torque distributions of both wild-type and mutant flies in conditioning, with the following results: (1) Wild-type Canton-S flies had motor learning performance in conditioning, which was proved by modifications of the animal's behavioural mode in conditioning. (2) Repetition of training improved the motor learning performance of wild-type Canton-S flies. (3) Although mutant dunce(1) flies were defective in visual associative learning, they showed essentially normal motor learning performance in terms of yaw torque distribution in conditioning. Finally, we tentatively proposed that both visual associative learning and motor learning were involved in the visual operant conditioning of Drosophila at the flight simulator, that the two learning forms could be dissociated and they might have different neural bases.
authors.affiliation: Laboratory of Visual Information Processing, Center for Brain and Cognitive Sciences, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, PR China
authors.forename: Shunpeng
authors.initials: S
authors.lastname: Wang
authors.forename: Yan
authors.initials: Y
authors.lastname: Li
authors.forename: Chunhua
authors.initials: C
authors.lastname: Feng
authors.forename: Aike
authors.initials: A
authors.lastname: Guo