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.
Highlights
Persistent maladaptive and suboptimal behaviours are commonly observed in animals, including Humans, and supposed to result from possible constraints solved by the interaction of neural mechanisms not clearly identified yet
Classical negative automaintenance The central phenomenon that we intend to replicate with the present computational model is the greater or lesser persistence in pigeons to peck a key light that, while predictive of reward delivery, leads to its omission in case of contact
This model provides a plausible explanation, maybe partial, for the conflictual observations between the studies of Williams and Williams [8] and Sanabria et al [19]. It suggests that negative automaintenance arises from the competition of two reinforcement learning systems, one of which relies on factored representations to use values over features rather than states
Summary
Persistent maladaptive and suboptimal behaviours are commonly observed in animals, including Humans, and supposed to result from possible constraints (e.g. energy versus efficiency tradeoff) solved by the interaction of neural mechanisms not clearly identified yet. Breland and Breland [1] studied animals that learned to retrieve rewards given some action (e.g. drop an object to get food). Guitart-Masip et al [3] showed that many humans have difficulties to learn to withhold from acting to get rewarded in a go/no-go task. These maladaptive behaviours have been suggested to arise from the interactions between multiple decision systems in the brain [4,5,6,7], namely Pavlovian and instrumental systems
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
