Higher-Order Conditioning in the Spatial Domain

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 mech...

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Autores principales:	Youcef Bouchekioua, Yutaka Kosaki, Shigeru Watanabe, Aaron P. Blaisdell
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	higher-order conditioning cognitive map spatial memory associative learning inference spatial integration Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/7e6fc701d1ba4e8380d47d6e2a481fb7
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id	oai:doaj.org-article:7e6fc701d1ba4e8380d47d6e2a481fb7
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spelling	oai:doaj.org-article:7e6fc701d1ba4e8380d47d6e2a481fb72021-11-30T12:30:57ZHigher-Order Conditioning in the Spatial Domain1662-515310.3389/fnbeh.2021.766767https://doaj.org/article/7e6fc701d1ba4e8380d47d6e2a481fb72021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnbeh.2021.766767/fullhttps://doaj.org/toc/1662-5153Spatial 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.Youcef BouchekiouaYutaka KosakiShigeru WatanabeAaron P. BlaisdellFrontiers Media S.A.articlehigher-order conditioningcognitive mapspatial memoryassociative learninginferencespatial integrationNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Behavioral Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	higher-order conditioning cognitive map spatial memory associative learning inference spatial integration Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	higher-order conditioning cognitive map spatial memory associative learning inference spatial integration Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Youcef Bouchekioua Yutaka Kosaki Shigeru Watanabe Aaron P. Blaisdell Higher-Order Conditioning in the Spatial Domain
description	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.
format	article
author	Youcef Bouchekioua Yutaka Kosaki Shigeru Watanabe Aaron P. Blaisdell
author_facet	Youcef Bouchekioua Yutaka Kosaki Shigeru Watanabe Aaron P. Blaisdell
author_sort	Youcef Bouchekioua
title	Higher-Order Conditioning in the Spatial Domain
title_short	Higher-Order Conditioning in the Spatial Domain
title_full	Higher-Order Conditioning in the Spatial Domain
title_fullStr	Higher-Order Conditioning in the Spatial Domain
title_full_unstemmed	Higher-Order Conditioning in the Spatial Domain
title_sort	higher-order conditioning in the spatial domain
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/7e6fc701d1ba4e8380d47d6e2a481fb7
work_keys_str_mv	AT youcefbouchekioua higherorderconditioninginthespatialdomain AT yutakakosaki higherorderconditioninginthespatialdomain AT shigeruwatanabe higherorderconditioninginthespatialdomain AT aaronpblaisdell higherorderconditioninginthespatialdomain
_version_	1718406631482458112

Higher-Order Conditioning in the Spatial Domain

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