Forward and backward inference in spatial cognition.

This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of 'lower-level' computations involving forward and backward inference over time. For exam...

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Autores principales:	Will D Penny, Peter Zeidman, Neil Burgess
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/0dd7dba21bef428a996557ca3acd778e
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spelling	oai:doaj.org-article:0dd7dba21bef428a996557ca3acd778e2021-11-18T05:53:18ZForward and backward inference in spatial cognition.1553-734X1553-735810.1371/journal.pcbi.1003383https://doaj.org/article/0dd7dba21bef428a996557ca3acd778e2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24348230/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of 'lower-level' computations involving forward and backward inference over time. For example, to estimate where you are in a known environment, forward inference is used to optimally combine location estimates from path integration with those from sensory input. To decide which way to turn to reach a goal, forward inference is used to compute the likelihood of reaching that goal under each option. To work out which environment you are in, forward inference is used to compute the likelihood of sensory observations under the different hypotheses. For reaching sensory goals that require a chaining together of decisions, forward inference can be used to compute a state trajectory that will lead to that goal, and backward inference to refine the route and estimate control signals that produce the required trajectory. We propose that these computations are reflected in recent findings of pattern replay in the mammalian brain. Specifically, that theta sequences reflect decision making, theta flickering reflects model selection, and remote replay reflects route and motor planning. We also propose a mapping of the above computational processes onto lateral and medial entorhinal cortex and hippocampus.Will D PennyPeter ZeidmanNeil BurgessPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 12, p e1003383 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Will D Penny Peter Zeidman Neil Burgess Forward and backward inference in spatial cognition.
description	This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of 'lower-level' computations involving forward and backward inference over time. For example, to estimate where you are in a known environment, forward inference is used to optimally combine location estimates from path integration with those from sensory input. To decide which way to turn to reach a goal, forward inference is used to compute the likelihood of reaching that goal under each option. To work out which environment you are in, forward inference is used to compute the likelihood of sensory observations under the different hypotheses. For reaching sensory goals that require a chaining together of decisions, forward inference can be used to compute a state trajectory that will lead to that goal, and backward inference to refine the route and estimate control signals that produce the required trajectory. We propose that these computations are reflected in recent findings of pattern replay in the mammalian brain. Specifically, that theta sequences reflect decision making, theta flickering reflects model selection, and remote replay reflects route and motor planning. We also propose a mapping of the above computational processes onto lateral and medial entorhinal cortex and hippocampus.
format	article
author	Will D Penny Peter Zeidman Neil Burgess
author_facet	Will D Penny Peter Zeidman Neil Burgess
author_sort	Will D Penny
title	Forward and backward inference in spatial cognition.
title_short	Forward and backward inference in spatial cognition.
title_full	Forward and backward inference in spatial cognition.
title_fullStr	Forward and backward inference in spatial cognition.
title_full_unstemmed	Forward and backward inference in spatial cognition.
title_sort	forward and backward inference in spatial cognition.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/0dd7dba21bef428a996557ca3acd778e
work_keys_str_mv	AT willdpenny forwardandbackwardinferenceinspatialcognition AT peterzeidman forwardandbackwardinferenceinspatialcognition AT neilburgess forwardandbackwardinferenceinspatialcognition
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Forward and backward inference in spatial cognition.

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