Abstract
We present a computational model of spatial navigation comprising different learning mechanisms in mammals, i.e., associative, cognitive mapping and parallel systems. This model is able to reproduce a large number of experimental results in different variants of the Morris water maze task, including standard associative phenomena (spatial generalization gradient and blocking), as well as navigation based on cognitive mapping. Furthermore, we show that competitive and cooperative patterns between different navigation strategies in the model allow to explain previous apparently contradictory results supporting either associative or cognitive mechanisms for spatial learning. The key computational mechanism to reconcile experimental results showing different influences of distal and proximal cues on the behavior, different learning times, and different abilities of individuals to alternatively perform spatial and response strategies, relies in the dynamic coordination of navigation strategies, whose performance is evaluated online with a common currency through a modular approach. We provide a set of concrete experimental predictions to further test the computational model. Overall, this computational work sheds new light on inter-individual differences in navigation learning, and provides a formal and mechanistic approach to test various theories of spatial cognition in mammals.
Highlights
The proposed computational model is composed of four main modules (Fig 1): an associative Direction strategy (D) which learns through model-free reinforcement learning to associate the perception of proximal cues within the environment with directions of movements; a cognitive mapping Planning strategy (P) which learns through model-based reinforcement learning a transition graph between different positions within the environment encoded in simulated
A multiple learning systems model for the interaction between navigation strategies place cells, and proposes directions of movement based on action plans towards the memorized goal position; and an Exploration strategy (E) which proposes random direction of movements
This results in more variability and plausibility of the simulated place fields compared to the the uniformly distributed Gaussian place cells that we used in the previous version of the model [14]
Summary
When dealing with instrumental conditioning experimental data, these dual systems models well explain animals’ tendency to alternate between initial flexible goal-oriented strategies, where the animal is hypothesized to use an internal model to plan and infer future consequences of action (model-based), and more automatic and habitual strategies at late stages of learning, where behavior is supposed not to rely on an internal model but rather on stimulus-response associations (model-free) (see e.g., [5, 15] for reviews). In the case of navigation paradigms, the model-based / model-free dichotomy has been found to better account for the diversity of navigation behaviors than the old classical distinctions between place and response strategies, or between allocentric / egocentric strategies [8] Such a distinction provides a possible explanation of the distinct roles of the hippocampus and different subparts of the striatum during navigation [8, 15]. System coordination has been proposed to depend on the uncertainty in the model-free system alone [11], relying on the strong assumption that the model-based system always has perfect information; Alternatively, some models
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