Higher-Order Conditioning in the Spatial Domain

Abstract

Spatial learning and memory, the processes through which a wide range of living organisms encode, compute, and retrieve information from their environment to perform goal-directed navigation, has been systematically investigated since the early twentieth century to unravel behavioral and neural mechanisms of learning and memory. Early theories about learning to navigate space considered that animals learn through trial and error and develop responses to stimuli that guide them to a goal place. According to a trial-and error learning view, organisms can learn a sequence of motor actions that lead to a goal place, a strategy referred to as response learning, which contrasts with place learning where animals learn locations with respect to an allocentric framework. Place learning has been proposed to produce a mental representation of the environment and the cartesian relations between stimuli within it—which Tolman coined the cognitive map. We propose to revisit some of the best empirical evidence of spatial inference in animals, and then discuss recent attempts to account for spatial inferences within an associative framework as opposed to the traditional cognitive map framework. We will first show how higher-order conditioning can successfully account for inferential goal-directed navigation in a variety of situations and then how vectors derived from path integration can be integrated via higher-order conditioning, resulting in the generation of higher-order vectors that explain novel route taking. Finally, implications to cognitive map theories will be discussed.