title: Accounting for negative automaintenance in pigeons: a dual learning systems approach and factored representations.
abstract: Animals, including Humans, are prone to develop persistent maladaptive and suboptimal behaviours. Some of these behaviours have been suggested to arise from interactions between brain systems of Pavlovian conditioning, the acquisition of responses to initially neutral stimuli previously paired with rewards, and instrumental conditioning, the acquisition of active behaviours leading to rewards. However the mechanics of these systems and their interactions are still unclear. While extensively studied independently, few models have been developed to account for these interactions. On some experiment, pigeons have been observed to display a maladaptive behaviour that some suggest to involve conflicts between Pavlovian and instrumental conditioning. In a procedure referred as negative automaintenance, a key light is paired with the subsequent delivery of food, however any peck towards the key light results in the omission of the reward. Studies showed that in such procedure some pigeons persisted in pecking to a substantial level despite its negative consequence, while others learned to refrain from pecking and maximized their cumulative rewards. Furthermore, the pigeons that were unable to refrain from pecking could nevertheless shift their pecks towards a harmless alternative key light. We confronted a computational model that combines dual-learning systems and factored representations, recently developed to account for sign-tracking and goal-tracking behaviours in rats, to these negative automaintenance experimental data. We show that it can explain the variability of the observed behaviours and the capacity of alternative key lights to distract pigeons from their detrimental behaviours. These results confirm the proposed model as an interesting tool to reproduce experiments that could involve interactions between Pavlovian and instrumental conditioning. The model allows us to draw predictions that may be experimentally verified, which could help further investigate the neural mechanisms underlying theses interactions.
authors.affiliation: Sorbonne Universits, UPMC Univ Paris 06, UMR 7222, Institut des Systèmes Intelligents et de Robotique, Paris, France; CNRS, UMR 7222, Institut des Systèmes Intelligents et de Robotique, Paris, France.
authors.forename: Florian
authors.initials: F
authors.lastname: Lesaint
authors.affiliation: Sorbonne Universits, UPMC Univ Paris 06, UMR 7222, Institut des Systèmes Intelligents et de Robotique, Paris, France; CNRS, UMR 7222, Institut des Systèmes Intelligents et de Robotique, Paris, France.
authors.forename: Olivier
authors.initials: O
authors.lastname: Sigaud
authors.affiliation: Sorbonne Universits, UPMC Univ Paris 06, UMR 7222, Institut des Systèmes Intelligents et de Robotique, Paris, France; CNRS, UMR 7222, Institut des Systèmes Intelligents et de Robotique, Paris, France.
authors.forename: Mehdi
authors.initials: M
authors.lastname: Khamassi
mesh_terms: D000465:Algorithms; D000818:Animals; D001522:Behavior, Animal; D010856:Columbidae; D007858:Learning; D008962:Models, Theoretical