Bayes optimality of human perception, action and learning: behavioural and neural evidence

Ulrik R. Beierholm

doi:10.1007/978-3-319-12084-3_10

Bayes optimality of human perception, action and learning: behavioural and neural evidence

Ulrik R. Beierholm^*

^*Corresponding author for this work

Psychology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

369 Downloads (Pure)

Abstract

The primary role of any biological nervous system (including the human) is to process incoming information in a way that allows motor choices to be made that increases the subjective utility of the organism. Or put slightly differently, “to make sure good things happen”. There are a number of ways that such a process can be done, but one possible hypothesis is that the human nervous system has been optimized to maximize the use of available resources, thus approximating optimal computations. In the following I will discuss the possibility of the nervous system performing such computations in perception, action and learning, and the behavioural and neural evidence supporting such ideas.

Original language	English
Title of host publication	Brain-Inspired Computing
Subtitle of host publication	Lecture Notes in Computer Science
Publisher	Springer
Pages	117-129
Number of pages	13
Volume	8603
ISBN (Print)	9783319120836
DOIs	https://doi.org/10.1007/978-3-319-12084-3_10
Publication status	Published - 2014
Event	International Workshop on Brain-Inspired Computing - Cetraro, Italy Duration: 8 Jul 2013 → 11 Jul 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8603
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	International Workshop on Brain-Inspired Computing
Country/Territory	Italy
City	Cetraro
Period	8/07/13 → 11/07/13

Bibliographical note

Brain-Inspired Computing: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Keywords

Bayesian inference
Behaviour
fMRI
Neural recordings
Optimality
Reinforcement learning

ASJC Scopus subject areas

General Computer Science
Theoretical Computer Science

Access to Document

10.1007/978-3-319-12084-3_10

Cite this

Beierholm, U. R. (2014). Bayes optimality of human perception, action and learning: behavioural and neural evidence. In Brain-Inspired Computing: Lecture Notes in Computer Science (Vol. 8603, pp. 117-129). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8603). Springer. https://doi.org/10.1007/978-3-319-12084-3_10

@inproceedings{8b3f3f51f82d461b9f6d9c8dca60fb79,

title = "Bayes optimality of human perception, action and learning: behavioural and neural evidence",

abstract = "The primary role of any biological nervous system (including the human) is to process incoming information in a way that allows motor choices to be made that increases the subjective utility of the organism. Or put slightly differently, “to make sure good things happen”. There are a number of ways that such a process can be done, but one possible hypothesis is that the human nervous system has been optimized to maximize the use of available resources, thus approximating optimal computations. In the following I will discuss the possibility of the nervous system performing such computations in perception, action and learning, and the behavioural and neural evidence supporting such ideas.",

keywords = "Bayesian inference, Behaviour, fMRI, Neural recordings, Optimality, Reinforcement learning",

author = "Beierholm, {Ulrik R.}",

note = "Brain-Inspired Computing: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); International Workshop on Brain-Inspired Computing ; Conference date: 08-07-2013 Through 11-07-2013",

year = "2014",

doi = "10.1007/978-3-319-12084-3_10",

language = "English",

isbn = "9783319120836",

volume = "8603",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "117--129",

booktitle = "Brain-Inspired Computing",

}

Beierholm, UR 2014, Bayes optimality of human perception, action and learning: behavioural and neural evidence. in Brain-Inspired Computing: Lecture Notes in Computer Science. vol. 8603, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8603, Springer, pp. 117-129, International Workshop on Brain-Inspired Computing, Cetraro, Italy, 8/07/13. https://doi.org/10.1007/978-3-319-12084-3_10

Bayes optimality of human perception, action and learning: behavioural and neural evidence. / Beierholm, Ulrik R.
Brain-Inspired Computing: Lecture Notes in Computer Science. Vol. 8603 Springer, 2014. p. 117-129 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8603).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Bayes optimality of human perception, action and learning

T2 - International Workshop on Brain-Inspired Computing

AU - Beierholm, Ulrik R.

N1 - Brain-Inspired Computing: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PY - 2014

Y1 - 2014

N2 - The primary role of any biological nervous system (including the human) is to process incoming information in a way that allows motor choices to be made that increases the subjective utility of the organism. Or put slightly differently, “to make sure good things happen”. There are a number of ways that such a process can be done, but one possible hypothesis is that the human nervous system has been optimized to maximize the use of available resources, thus approximating optimal computations. In the following I will discuss the possibility of the nervous system performing such computations in perception, action and learning, and the behavioural and neural evidence supporting such ideas.

AB - The primary role of any biological nervous system (including the human) is to process incoming information in a way that allows motor choices to be made that increases the subjective utility of the organism. Or put slightly differently, “to make sure good things happen”. There are a number of ways that such a process can be done, but one possible hypothesis is that the human nervous system has been optimized to maximize the use of available resources, thus approximating optimal computations. In the following I will discuss the possibility of the nervous system performing such computations in perception, action and learning, and the behavioural and neural evidence supporting such ideas.

KW - Bayesian inference

KW - Behaviour

KW - fMRI

KW - Neural recordings

KW - Optimality

KW - Reinforcement learning

UR - http://www.scopus.com/inward/record.url?scp=84909590360&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-12084-3_10

DO - 10.1007/978-3-319-12084-3_10

M3 - Conference contribution

AN - SCOPUS:84909590360

SN - 9783319120836

VL - 8603

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 117

EP - 129

BT - Brain-Inspired Computing

PB - Springer

Y2 - 8 July 2013 through 11 July 2013

ER -

Beierholm UR. Bayes optimality of human perception, action and learning: behavioural and neural evidence. In Brain-Inspired Computing: Lecture Notes in Computer Science. Vol. 8603. Springer. 2014. p. 117-129. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-12084-3_10

Bayes optimality of human perception, action and learning: behavioural and neural evidence

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this